Merge pull request #64 from MET-OM/dev_examples

Dev examples

README.md

@@ -6,7 +6,7 @@
 [![Documentation Status](https://readthedocs.org/projects/metocean-stats/badge/?version=latest)](https://metocean-stats.readthedocs.io/en/latest/?badge=latest)
 
 ## Purpose 🎯
-Metocean-stats is a tool for comprehensive statistics and visualization of metocean data (wind, waves, ocean currents, tide levels, air and water temperature, sea ice, and more). The tool is also compatible with WEkEO Jupyter Lab, allowing seamless integration and use.
+Metocean-stats is a tool for comprehensive statistics and visualization of metocean data (wind, waves, ocean currents, tide levels, air and water temperature, sea ice, and more). The tool is also compatible with WEkEO Jupyter Lab, allowing seamless integration and use. For instructions on how to install metocean-stats in WEkEO, please refer [here](https://docs.google.com/document/d/1uEvG_YZ43eu3vE33Qv9t5Bzd0QAmspKjqjzFLHEkJys/edit?usp=sharing).
 
 ![metocean-stats](https://github.com/MET-OM/metocean-stats/blob/main/docs/files/readme_plots.png)
 

examples/example_NORA_wind_waves.py

@@ -0,0 +1,81 @@
+from metocean_stats import plots, tables, maps
+from metocean_stats.stats.aux_funcs import *
+from pathlib import Path
+import warnings
+warnings.filterwarnings("ignore")
+
+##############PROVIDE INFO BY THE USER#################
+# Impot data (e.g., use metocean-api to download metocean data)
+## For NORA10 data use:
+df = readNora10File('../tests/data/NORA_test.txt') 
+## For NORA3 data use:
+#import pandas as pd
+#df = pd.read_csv('../path/to/NORA3.csv', comment="#", index_col=0, parse_dates=True)
+
+# Define names for each variable in the dataframe (df):
+var_wind_dir = 'D10' # for wind direction
+var_wind = 'W10' # for wind speed
+var_hs = 'HS' # for significant wave height
+var_wave_dir= 'DIRM' # Mean wave direction
+var_tp = 'TP'  # Peak Wave Period
+output_folder = 'output_wind_waves' # folder where output figures and tables will be saved 
+######################################################
+
+
+# Check if the output directory exists, if not, create it
+folder = Path(__file__).parent / output_folder 
+if not folder.exists():
+    folder.mkdir(parents=True)
+
+# The following code is used to generate various plots and tables for wind and wave data analysis.
+
+# Wind:
+plots.var_rose(df,var_dir=var_wind_dir,var=var_wind,method='overall',max_perc=40,decimal_places=1, units='m/s',output_file=folder /  'wind_omni.png')
+plots.var_rose(df,var_dir=var_wind_dir,var=var_wind,method='monthly',max_perc=40,decimal_places=1, units='m/s',output_file=folder /  'wind_monthly.png')
+plots.plot_directional_stats(df,var=var_wind,step_var=0.1,var_dir='D10',title = 'W10[m/s]', output_file=folder /  'directional_wind_stats.png')
+plots.table_directional_non_exceedance(df,var=var_wind,step_var=2,var_dir='D10',output_file=folder /  'table_wind_directional_non_exceedance.csv')
+plots.plot_monthly_stats(df,var=var_wind,title = 'Wind Speed at 10 m [m/s]', output_file=folder /  'monthly_wind_stats.png')
+tables.table_monthly_non_exceedance(df,var=var_wind,step_var=2,output_file=folder /  'table_monthly_non_exceedance.csv')
+plots.plot_prob_non_exceedance_fitted_3p_weibull(df,var=var_wind,output_file=folder /  'prob_non_exceedance_fitted_3p_weibull_wind.png')
+plots.plot_monthly_return_periods(df,var=var_wind,periods=[1, 10, 100],distribution='Weibull3P_MOM', units='m/s',output_file=folder /  'W10_monthly_extremes.png')
+plots.plot_directional_return_periods(df,var=var_wind,var_dir='D10',periods=[1, 10, 100],distribution='Weibull3P_MOM', units='m/s',adjustment='NORSOK',output_file=folder /  'W10_dir_extremes_Weibull_norsok.png')
+plots.plot_monthly_weather_window(df,var=var_wind,threshold=10, window_size=12,output_file=folder /  'NORA10_monthly_weather_window_wind_10_12_plot.png')
+plots.plot_multi_diagnostic_return_levels(df, var=var_wind, dist_list = ['GP', 'Weibull_2P', 'EXP'], periods=np.arange(0.1, 1000, 0.1),threshold=None,output_file=folder /  'plot_wind_diagnostic_return_levels.png')
+
+
+# Waves:
+plots.plot_multi_diagnostic_return_levels(df, var=var_hs, dist_list = ['GP', 'Weibull_2P', 'EXP'], periods=np.arange(0.1, 1000, 0.1),threshold=None,output_file=folder /  'plot_waves_diagnostic_return_levels.png')
+plots.plot_prob_non_exceedance_fitted_3p_weibull(df,var=var_hs,output_file=folder /  'prob_non_exceedance_fitted_3p_weibull_hs.png')
+tables.scatter_diagram(df, var1=var_hs, step_var1=1, var2=var_tp, step_var2=1, output_file= folder /  'Hs_Tp_scatter.csv')
+tables.table_var_sorted_by_hs(df, var=var_tp, var_hs=var_hs, output_file=folder /  'Tp_sorted_by_Hs.csv')
+tables.table_monthly_non_exceedance(df,var=var_hs,step_var=0.5,output_file=folder /  'Hs_table_monthly_non_exceedance.csv')
+plots.plot_monthly_stats(df,var=var_hs,show=['Maximum','P99','Mean'], title = 'Hs[m]', output_file=folder /  'Hs_monthly_stats.png')
+tables.table_directional_non_exceedance(df,var=var_hs,step_var=0.5,var_dir=var_wave_dir,output_file=folder /  'Hs_table_directional_non_exceedance.csv')
+plots.plot_directional_stats(df,var=var_hs,step_var=0.5, var_dir=var_wave_dir, title = '$H_s$[m]', output_file=folder /  'directional_waves_stats.png')
+plots.plot_joint_distribution_Hs_Tp(df,var_hs=var_hs,var_tp=var_tp,periods=[1,10,100,1000], title='Hs-Tp joint distribution',output_file=folder /  'Hs.Tp.joint.distribution.png',density_plot=True)
+tables.table_monthly_joint_distribution_Hs_Tp_param(df,var_hs=var_hs,var_tp=var_tp,periods=[1,10,100,10000],output_file=folder /  'monthly_Hs_Tp_joint_param.csv')
+tables.table_directional_joint_distribution_Hs_Tp_param(df,var_hs=var_hs,var_tp=var_tp,var_dir=var_wave_dir,periods=[1,10,100],output_file=folder /  'dir_Hs_Tp_joint_param.csv')
+plots.plot_monthly_weather_window(df,var=var_hs,threshold=4, window_size=12,output_file=folder /  '_monthly_weather_window_Hs_4_12_plot.png')
+plots.plot_nb_hours_below_threshold(df,var=var_hs,thr_arr=(np.arange(0.05,20.05,0.05)).tolist(),output_file=folder /  'Hs_number_hours_per_year.png')
+tables.table_monthly_return_periods(df,var=var_hs,periods=[1, 10, 100, 10000],distribution='Weibull3P_MOM', units='m',output_file=folder /  'HS_monthly_extremes_Weibull.csv')
+tables.table_directional_return_periods(df,var=var_hs,periods=[1, 10, 100, 10000], units='m',var_dir = var_wave_dir,distribution='Weibull3P_MOM', adjustment='NORSOK' ,output_file=folder /  'directional_wave_extremes_weibull.csv')
+plots.plot_monthly_return_periods(df,var=var_hs,periods=[1, 10, 100],distribution='Weibull3P_MOM', units='m',output_file=folder /  'HS_monthly_extremes.png')
+plots.plot_directional_return_periods(df,var=var_hs,var_dir=var_wave_dir,periods=[1, 10, 100, 10000 ],distribution='GUM', units='m',output_file=folder /  'dir_extremes_GUM.png')
+plots.plot_directional_return_periods(df,var=var_hs,var_dir=var_wave_dir,periods=[1, 10, 100, 10000],distribution='Weibull3P_MOM', units='m',adjustment='NORSOK',output_file=folder /  'dir_extremes_wave_Weibull_norsok.png')
+tables.table_monthly_joint_distribution_Hs_Tp_return_values(df,var_hs=var_hs,var_tp=var_tp,periods=[1,10,100,10000],output_file=folder /  'monthly_Hs_Tp_joint_return_values.csv')
+tables.table_directional_joint_distribution_Hs_Tp_return_values(df,var_hs=var_hs,var_tp=var_tp,var_dir=var_wave_dir,periods=[1,10,100,1000],adjustment='NORSOK',output_file=folder /  'directional_Hs_Tp_joint_return_values.csv')
+tables.table_Hs_Tpl_Tph_return_values(df,var_hs=var_hs,var_tp=var_tp,periods=[1,10,100,10000],output_file=folder /  'hs_tpl_tph_return_values.csv')
+plots.plot_tp_for_given_hs(df, var_hs, var_tp,output_file=folder /  'tp_for_given_hs.png')
+tables.table_tp_for_given_hs(df, var_hs, var_tp,max_hs=20,output_file=folder /  'tp_for_given_hs.csv')
+tables.table_tp_for_rv_hs(df, var_hs=var_hs, var_tp=var_tp,periods=[1,10,100,10000],output_file=folder /  'tp_for_rv_hs.csv')
+tables.table_wave_induced_current(df, var_hs=var_hs,var_tp=var_tp,depth=200,ref_depth=200, spectrum = 'JONSWAP',output_file=folder /  'JONSWAP_wave_induced_current_depth200.csv')
+tables.table_wave_induced_current(df, var_hs=var_hs,var_tp=var_tp,depth=200,ref_depth=200, spectrum = 'TORSEHAUGEN',output_file=folder /  'TORSEHAUGEN_wave_induced_current_depth200.csv')
+tables.table_hs_for_given_wind(df, var_hs,var_wind, bin_width=2, max_wind=42, output_file= folder /  'table_perc_hs_for_wind.csv')
+plots.plot_hs_for_given_wind(df, var_hs, var_wind,output_file=folder /  'hs_for_given_wind.png')
+tables.table_hs_for_rv_wind(df, var_wind=var_wind, var_hs=var_hs,periods=[1,10,100,10000],output_file=folder /  'hs_for_rv_wind.csv')
+tables.table_Hmax_crest_return_periods(df,var_hs=var_hs, var_tp=var_tp, depth=200, periods=[1, 10, 100,10000],sea_state='long-crested',output_file=folder /  'table_Hmax_crest_rp.csv')
+tables.table_directional_Hmax_return_periods(df,var_hs=var_hs, var_tp = var_tp,var_dir=var_wave_dir, periods=[10, 100,10000],adjustment='NORSOK', output_file=folder /  'table_dir_Hmax_return_values.csv')
+plots.plot_multi_joint_distribution_Hs_Tp_var3(df,var_hs=var_hs,var_tp=var_tp,var3=var_wind,var3_units='m/s',periods=[100],var3_bin=5,threshold_min=100,output_file=folder /  'Hs.Tp.joint.distribution.multi.binned.var3.png')
+
+
+