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calcXsec.py
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calcXsec.py
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import json
import ROOT
from collections import OrderedDict
import math
from CMSPLOTS.myFunction import DrawHistos
from modules.postFitScripts import GetPOIValue
from modules.Utils import roundToError, findPrecision
from copy import deepcopy
from data.FLAG import doPAS
import sys
import argparse
parser = argparse.ArgumentParser(description="Make postfit plots and tables")
parser.add_argument("--doInclusive", action="store_true", dest="doInclusive",help="Run on inclusive results; false runs on fiducial results.")
args = parser.parse_args()
doFiducial = not args.doInclusive
doElectron = False
doMuon = False
syncPrecision = False
f_xsecs_MC = "data/xsecs_fid.json" if doFiducial else "data/xsecs_inc.json"
with open(f_xsecs_MC) as f:
xsecs_MC = json.load(f)
#print(xsecs_MC)
maps = {
"lepplus": "wmp",
"lepminus": "wmm",
"leplep": "zmm",
}
lumiUncs = {
"13TeV": 0.023,
"5TeV": 0.019,
"sqrtS": math.sqrt(0.022**2 + 0.017**2)
}
if doFiducial:
#f_fit = "forCombine_WithVpT/Fiducial/test0/commands/scripts/card_combined.root"
f_fit = "forCombine_default/Fiducial/test0/commands/scripts/card_combined.root"
if doElectron:
f_fit = "forCombine_default/Fiducial/test0/commands/scripts/card_e.root"
elif doMuon:
f_fit = "forCombine_default/Fiducial/test0/commands/scripts/card_mu.root"
else:
f_fit = "forCombine_default/Inclusive/test0/commands/scripts/card_combined.root"
f_fit_fid = "forCombine_default/Fiducial/test0/commands/scripts/card_combined.root"
f_root = ROOT.TFile(f_fit)
tree = f_root.Get("fitresults")
tree.GetEntry(0)
f_root_fid = ROOT.TFile(f_fit_fid)
tree_fid = f_root.Get("fitresults")
tree_fid.GetEntry(0)
xsecs = OrderedDict()
xsecs_ratios = OrderedDict()
xsecs_ratios_sqrtS = OrderedDict()
xsecs_ratios_sqrtS_ratios = OrderedDict()
def getRelError(ifilename, poiname):
val_poi, err_poi = GetPOIValue(ifilename, poiname)
return err_poi / val_poi
def getRelStatSystError(ifilename, poiname, hname):
ifile = ROOT.TFile(ifilename)
himpact_grouped = ifile.Get(hname)
# find the POI bin for poiname
ibinX = -1
for binX in range(1, himpact_grouped.GetNbinsX()+1):
poi = himpact_grouped.GetXaxis().GetBinLabel(binX)
if poi == poiname:
ibinX = binX
break
assert ibinX >=0, "Can not find the POI {} in the postfit file {}. Please check.".format(poiname, ifilename)
ibinY = -1
for binY in range(1, himpact_grouped.GetNbinsY()+1):
nuis = himpact_grouped.GetYaxis().GetBinLabel(binY)
if nuis == "stat":
ibinY = binY
break
assert ibinY >=0, "Can not find the stat nuisance in the postfit file {}. Please check.".format(ifilename)
err_stat = himpact_grouped.GetBinContent(ibinX, ibinY)
val_poi, err_poi = GetPOIValue(ifilename, poiname)
#print(f"err_stat: {err_stat} for POI {poiname} in {ifilename}; val_poi {val_poi}; err_stat / val_poi: {err_stat / val_poi}")
err_syst = math.sqrt(err_poi**2 - err_stat**2)
return (err_stat / val_poi, err_syst / val_poi)
def GetTotError(xsecs):
return math.sqrt(xsecs[1]**2 + xsecs[2]**2 + xsecs[3]**2)
def GetTotRelError(xsecs):
return GetTotError(xsecs) / xsecs[0]
for sqrtS in ["13TeV", "5TeV"]:
xsecs[sqrtS] = OrderedDict()
xsecs_ratios[sqrtS] = OrderedDict()
for ch in ["lepplus", "lepminus", "leplep"]:
mu, mu_err = GetPOIValue(f_fit, f"{ch}_{sqrtS}_sig_mu")
#print("mu value for", ch, sqrtS, " is ", mu, " +/- ", mu_err)
if not doFiducial:
# use the fiducial central val as the inclusive central value
mu, _ = GetPOIValue(f_fit_fid, f"{ch}_{sqrtS}_sig_mu")
xsec_MC = xsecs_MC[sqrtS]['born'][maps[ch]]
#print("xsec value for", ch, sqrtS, "is", xsec_MC)
#xsecs[sqrtS][ch] = (mu * xsec_MC, math.sqrt(mu_err**2 + lumiUncs[sqrtS]**2) * xsec_MC)
mu_stat_err, mu_syst_err = getRelStatSystError(f_fit, f"{ch}_{sqrtS}_sig_mu", "nuisance_group_impact_mu")
xsecs[sqrtS][ch] = (mu * xsec_MC, mu_stat_err * xsec_MC, mu_syst_err * xsec_MC, lumiUncs[sqrtS] * xsec_MC)
#print("xsec fit value for", ch, sqrtS, "is", xsecs[sqrtS][ch])
#print("stat, syst unc ", mu_stat_err, mu_syst_err)
#sys.exit(0)
mu_err = getRelError(f_fit, f"Winc_{sqrtS}_sig_sumxsec")
mu_stat_err, mu_syst_err = getRelStatSystError(f_fit, f"Winc_{sqrtS}_sig_sumxsec", "nuisance_group_impact_sumpois")
xsec_tot_W = xsecs[sqrtS]["lepplus"][0] + xsecs[sqrtS]["lepminus"][0]
xsecs[sqrtS]["winc"] = (xsec_tot_W, mu_stat_err * xsec_tot_W, mu_syst_err * xsec_tot_W, lumiUncs[sqrtS] * xsec_tot_W)
print("Winc result", xsecs[sqrtS]["winc"])
#xsecs[sqrtS]["Winc"] = (xsec_tot_W, math.sqrt(mu_err**2 + lumiUncs[sqrtS]**2) * xsec_tot_W)
# cross section ratios
# W+ / W-
mu_err = getRelError(f_fit, f"WchgRatio_{sqrtS}_ratiometaratio")
mu_stat_err, mu_syst_err = getRelStatSystError(f_fit, f"WchgRatio_{sqrtS}_ratiometaratio", "nuisance_group_impact_ratiometapois")
xsec_ratio_Wchg = xsecs[sqrtS]["lepplus"][0] / xsecs[sqrtS]["lepminus"][0]
#xsecs_ratios[sqrtS]["WchgRatio"] = (xsec_ratio_Wchg, mu_err * xsec_ratio_Wchg, 0.)
xsecs_ratios[sqrtS]["WchgRatio"] = (xsec_ratio_Wchg, mu_stat_err * xsec_ratio_Wchg, mu_syst_err * xsec_ratio_Wchg, 0.0 * xsec_ratio_Wchg)
# W / Z
mu_err = getRelError(f_fit, f"WZRatio_{sqrtS}_ratiometaratio")
mu_stat_err, mu_syst_err = getRelStatSystError(f_fit, f"WZRatio_{sqrtS}_ratiometaratio", "nuisance_group_impact_ratiometapois")
xsec_ratio_WZ = xsecs[sqrtS]["winc"][0] / xsecs[sqrtS]["leplep"][0]
#xsecs_ratios[sqrtS]["WZRatio"] = (xsec_ratio_WZ, mu_err * xsec_ratio_WZ, 0.)
xsecs_ratios[sqrtS]["WZRatio"] = (xsec_ratio_WZ, mu_stat_err * xsec_ratio_WZ, mu_syst_err * xsec_ratio_WZ, 0.0 * xsec_ratio_WZ)
# measure the ratios between sqrtS
mu_err = getRelError(f_fit, "sqrtS_Wplus_ratio_ratiometaratio")
mu_stat_err, mu_syst_err = getRelStatSystError(f_fit, "sqrtS_Wplus_ratio_ratiometaratio", "nuisance_group_impact_ratiometapois")
xsec_ratio_sqrtS_Wplus = xsecs["13TeV"]["lepplus"][0] / xsecs["5TeV"]["lepplus"][0]
xsecs_ratios_sqrtS["lepplus"] = (xsec_ratio_sqrtS_Wplus, mu_stat_err * xsec_ratio_sqrtS_Wplus, mu_syst_err * xsec_ratio_sqrtS_Wplus,lumiUncs["sqrtS"] * xsec_ratio_sqrtS_Wplus)
#xsecs_ratios_sqrtS["lepplus"] = (xsec_ratio_sqrtS_Wplus, math.sqrt(mu_err**2 + lumiUncs["sqrtS"]**2) * xsec_ratio_sqrtS_Wplus)
mu_err = getRelError(f_fit, "sqrtS_Wminus_ratio_ratiometaratio")
mu_stat_err, mu_syst_err = getRelStatSystError(f_fit, "sqrtS_Wminus_ratio_ratiometaratio", "nuisance_group_impact_ratiometapois")
xsec_ratio_sqrtS_Wminus = xsecs["13TeV"]["lepminus"][0] / xsecs["5TeV"]["lepminus"][0]
xsecs_ratios_sqrtS["lepminus"] = (xsec_ratio_sqrtS_Wminus, mu_stat_err * xsec_ratio_sqrtS_Wminus, mu_syst_err * xsec_ratio_sqrtS_Wminus, lumiUncs["sqrtS"] * xsec_ratio_sqrtS_Wminus)
#xsecs_ratios_sqrtS["lepminus"] = (xsec_ratio_sqrtS_Wminus, math.sqrt(mu_err**2 + lumiUncs["sqrtS"]**2) * xsec_ratio_sqrtS_Wminus)
mu_err = getRelError(f_fit, "sqrtS_Zinc_ratio_ratiometaratio")
mu_stat_err, mu_syst_err = getRelStatSystError(f_fit, "sqrtS_Zinc_ratio_ratiometaratio", "nuisance_group_impact_ratiometapois")
xsec_ratio_sqrtS_Z = xsecs["13TeV"]["leplep"][0] / xsecs["5TeV"]["leplep"][0]
xsecs_ratios_sqrtS["leplep"] = (xsec_ratio_sqrtS_Z, mu_stat_err * xsec_ratio_sqrtS_Z, mu_syst_err * xsec_ratio_sqrtS_Z, lumiUncs["sqrtS"] * xsec_ratio_sqrtS_Z)
#xsecs_ratios_sqrtS["leplep"] = (xsec_ratio_sqrtS_Z, math.sqrt(mu_err**2 + lumiUncs["sqrtS"]**2) * xsec_ratio_sqrtS_Z)
mu_err = getRelError(f_fit, "sqrtS_Winc_ratio_ratiometaratio")
mu_stat_err, mu_syst_err = getRelStatSystError(f_fit, "sqrtS_Winc_ratio_ratiometaratio", "nuisance_group_impact_ratiometapois")
xsec_ratio_sqrtS_Winc = xsecs["13TeV"]["winc"][0] / xsecs["5TeV"]["winc"][0]
xsecs_ratios_sqrtS["winc"] = (xsec_ratio_sqrtS_Winc, mu_stat_err * xsec_ratio_sqrtS_Winc, mu_syst_err * xsec_ratio_sqrtS_Winc, lumiUncs["sqrtS"] * xsec_ratio_sqrtS_Winc)
#xsecs_ratios_sqrtS["winc"] = (xsec_ratio_sqrtS_Winc, math.sqrt(mu_err**2 + lumiUncs["sqrtS"]**2) * xsec_ratio_sqrtS_Winc)
# double ratios between sqrtS
mu_err = getRelError(f_fit, "sqrtS_WchgRatio_ratio_doubleratiometaratio")
mu_stat_err, mu_syst_err = getRelStatSystError(f_fit, "sqrtS_WchgRatio_ratio_doubleratiometaratio", "nuisance_group_impact_doubleratiometapois")
xsec_ratio_sqrtS_Wchg = xsecs_ratios["13TeV"]["WchgRatio"][0] / xsecs_ratios["5TeV"]["WchgRatio"][0]
xsecs_ratios_sqrtS_ratios["WchgRatio"] = (xsec_ratio_sqrtS_Wchg, mu_stat_err * xsec_ratio_sqrtS_Wchg, mu_syst_err * xsec_ratio_sqrtS_Wchg, 0.)
#xsecs_ratios_sqrtS_ratios["WchgRatio"] = (xsec_ratio_sqrtS_Wchg, mu_err * xsec_ratio_sqrtS_Wchg)
mu_err = getRelError(f_fit, "sqrtS_WZRatio_ratio_doubleratiometaratio")
mu_stat_err, mu_syst_err = getRelStatSystError(f_fit, "sqrtS_WZRatio_ratio_doubleratiometaratio", "nuisance_group_impact_doubleratiometapois")
xsec_ratio_sqrtS_WZ = xsecs_ratios["13TeV"]["WZRatio"][0] / xsecs_ratios["5TeV"]["WZRatio"][0]
xsecs_ratios_sqrtS_ratios["WZRatio"] = (xsec_ratio_sqrtS_WZ, mu_stat_err * xsec_ratio_sqrtS_WZ, mu_syst_err * xsec_ratio_sqrtS_WZ, 0.)
#xsecs_ratios_sqrtS_ratios["WZRatio"] = (xsec_ratio_sqrtS_WZ, mu_err * xsec_ratio_sqrtS_WZ)
print("results xsecs: ", xsecs)
print("results xsecs ratios: ", xsecs_ratios)
print("results xs ratios sqrtS: ", xsecs_ratios_sqrtS)
print("results xs ratios sqrtS ratios: ", xsecs_ratios_sqrtS_ratios)
#pdfsets = ["nnpdf4.0", "nnpdf3.1", "ct18", "msht20"]
pdfsets = ["nnpdf3.1", "nnpdf4.0", "ct18", "msht20"]
# compare the theory predictions with the measured values
from data.theoryResults import *
if doFiducial:
xsec_th = xsec_fid
else:
xsec_th = xsec_inc
results = OrderedDict()
for sqrtS in ["13TeV", "5TeV"]:
results[sqrtS] = OrderedDict()
for ch in ["lepplus", "lepminus", "winc", "leplep"]:
results[sqrtS][ch] = OrderedDict()
results[sqrtS][ch]['Measured'] = xsecs[sqrtS][ch]
for pdf in pdfsets:
results[sqrtS][ch][pdf.upper()] = xsec_th[pdf].GetXsec(ch, sqrtS)
results[sqrtS][f"{ch}_diff"] = OrderedDict()
#results[sqrtS][f"{ch}_diff"]['Measured'] = (1., xsecs[sqrtS][ch][1] / xsecs[sqrtS][ch][0])
results[sqrtS][f"{ch}_diff"]["Measured"] = (1., GetTotRelError(xsecs[sqrtS][ch]))
for pdf in pdfsets:
results[sqrtS][f"{ch}_diff"][pdf.upper()] = ((xsec_th[pdf].GetXsec(ch, sqrtS)[0] / xsecs[sqrtS][ch][0]),(xsec_th[pdf].GetXsec(ch, sqrtS)[1] / xsecs[sqrtS][ch][0]), (xsec_th[pdf].GetXsec(ch, sqrtS)[2] / xsecs[sqrtS][ch][0]))
# measure the V/V ratios at the same sqrtS
results[f"{sqrtS}_ratios"] = OrderedDict()
for ch in ["WchgRatio", "WZRatio"]:
results[f"{sqrtS}_ratios"][ch] = OrderedDict()
results[f"{sqrtS}_ratios"][ch]['Measured'] = xsecs_ratios[sqrtS][ch]
for pdf in pdfsets:
results[f"{sqrtS}_ratios"][ch][pdf.upper()] = getattr(xsec_th[pdf], f"Get{ch}")(sqrtS == "13TeV")
results[f"{sqrtS}_ratios"][f"{ch}_diff"] = OrderedDict()
#results[f"{sqrtS}_ratios"][f"{ch}_diff"]['Measured'] = (1.0, xsecs_ratios[sqrtS][ch][1] / xsecs_ratios[sqrtS][ch][0])
results[f"{sqrtS}_ratios"][f"{ch}_diff"]["Measured"] = (1.0, GetTotRelError(xsecs_ratios[sqrtS][ch]))
for pdf in pdfsets:
results[f"{sqrtS}_ratios"][f"{ch}_diff"][pdf.upper()] = ((getattr(xsec_th[pdf], f"Get{ch}")(sqrtS == "13TeV")[0] / xsecs_ratios[sqrtS][ch][0] ), (getattr(xsec_th[pdf], f"Get{ch}")(sqrtS == "13TeV")[1] / xsecs_ratios[sqrtS][ch][0]), (getattr(xsec_th[pdf], f"Get{ch}")(sqrtS == "13TeV")[2] / xsecs_ratios[sqrtS][ch][0]))
# measure the V xsec ratios between sqrtS
results["sqrtS_ratios"] = OrderedDict()
for ch in ["lepplus", "lepminus", "winc", "leplep"]:
results["sqrtS_ratios"][ch] = OrderedDict()
results['sqrtS_ratios'][ch]['Measured'] = xsecs_ratios_sqrtS[ch]
for pdf in pdfsets:
results['sqrtS_ratios'][ch][pdf.upper()] = xsec_th[pdf].GetSqrtSRatio(ch)
#print("sqrtS ratios", results['sqrtS_ratios'][ch][pdf.upper()])
results['sqrtS_ratios'][f"{ch}_diff"] = OrderedDict()
#results['sqrtS_ratios'][f'{ch}_diff']['Measured'] = (1., xsecs_ratios_sqrtS[ch][1] / xsecs_ratios_sqrtS[ch][0])
results['sqrtS_ratios'][f'{ch}_diff']['Measured'] = (1., GetTotRelError(xsecs_ratios_sqrtS[ch]))
for pdf in pdfsets:
results['sqrtS_ratios'][f"{ch}_diff"][pdf.upper()] = ((xsec_th[pdf].GetSqrtSRatio(ch)[0] / xsecs_ratios_sqrtS[ch][0] ), (xsec_th[pdf].GetSqrtSRatio(ch)[1] / xsecs_ratios_sqrtS[ch][0] ), (xsec_th[pdf].GetSqrtSRatio(ch)[2] / xsecs_ratios_sqrtS[ch][0] ))
# measure the V/V ratios between sqrtS
results["sqrtS_double_ratios"] = OrderedDict()
for ch in ["WchgRatio", "WZRatio"]:
results["sqrtS_double_ratios"][ch] = OrderedDict()
results['sqrtS_double_ratios'][ch]['Measured'] = xsecs_ratios_sqrtS_ratios[ch]
for pdf in pdfsets:
results['sqrtS_double_ratios'][ch][pdf.upper()] = xsec_th[pdf].GetSqrtSDoubleRatio(ch)
results['sqrtS_double_ratios'][f"{ch}_diff"] = OrderedDict()
#results['sqrtS_double_ratios'][f"{ch}_diff"]['Measured'] = (1., xsecs_ratios_sqrtS_ratios[ch][1] / xsecs_ratios_sqrtS_ratios[ch][0])
results['sqrtS_double_ratios'][f"{ch}_diff"]['Measured'] = (1., GetTotRelError(xsecs_ratios_sqrtS_ratios[ch]))
for pdf in pdfsets:
results['sqrtS_double_ratios'][f"{ch}_diff"][pdf.upper()] = ((xsec_th[pdf].GetSqrtSDoubleRatio(ch)[0] / xsecs_ratios_sqrtS_ratios[ch][0]), (xsec_th[pdf].GetSqrtSDoubleRatio(ch)[1] / xsecs_ratios_sqrtS_ratios[ch][0]), (xsec_th[pdf].GetSqrtSDoubleRatio(ch)[2] / xsecs_ratios_sqrtS_ratios[ch][0]))
outdir = "plots/xsecResults_fid" if doFiducial else "plots/xsecResults_inc"
if doMuon:
outdir += "_mu"
elif doElectron:
outdir += "_e"
from modules.Utils import FormatTable, FormatROOTInput, FormatText
from modules.postFitScripts import WriteOutputToText
def dict2Table(list_results):
results_new = OrderedDict()
for results in list_results:
for key, val in results.items():
if "diff" in key:
continue
results_new[key] = val
return results_new
print(results['13TeV'])
outputs = FormatTable(dict2Table([results['13TeV'],results['13TeV_ratios']]), doTranspose=True)
print(outputs)
WriteOutputToText(outputs, f"{outdir}/tables/results_13TeV_all.tex")
outputs = FormatText(dict2Table([results['13TeV'],results['13TeV_ratios']]), doTranspose=True)
print(outputs)
WriteOutputToText(outputs, f"{outdir}/tables/results_13TeV_all.txt")
outputs = FormatTable(dict2Table([results['5TeV'],results['5TeV_ratios']]), doTranspose=True)
print(outputs)
WriteOutputToText(outputs, f"{outdir}/tables/results_5TeV_all.tex")
outputs = FormatText(dict2Table([results['5TeV'],results['5TeV_ratios']]), doTranspose=True)
print(outputs)
WriteOutputToText(outputs, f"{outdir}/tables/results_5TeV_all.txt")
outputs = FormatTable(dict2Table([results['sqrtS_ratios'], results['sqrtS_double_ratios']]), doTranspose=True)
print(outputs)
WriteOutputToText(outputs, f"{outdir}/tables/results_sqrtS_all.tex")
outputs = FormatText(dict2Table([results['sqrtS_ratios'], results['sqrtS_double_ratios']]), doTranspose=True)
print(outputs)
WriteOutputToText(outputs, f"{outdir}/tables/results_sqrtS_all.txt")
def DrawHorizontalCompGraph(xsecs_diffs, outputname, xmin = 0.95, xmax = 1.05, is5TeV = False, doCombineYear = False, canH = 600, canW=800, showXsecs = True, pdfsets_plot = ["NNPDF4.0"], poiname = "#sigma^{tot}"):
markers = [21, 22, 23, 24, 25, 26, 27]
colors = [4, 3, 7, 6, 9, 46]
tgraphs = OrderedDict()
counts = 0
for ch in xsecs_diffs:
if "diff" not in ch:
continue
counts += 1
tgraphs["Measured"] = ROOT.TGraphErrors()
tgraphs["Measured"].SetFillColorAlpha(15, 0.6)
for j,pdf in enumerate(pdfsets_plot):
pdf = pdf.upper()
tgraphs[pdf] = ROOT.TGraphAsymmErrors()
tgraphs[pdf].SetMarkerStyle(markers[j])
tgraphs[pdf].SetMarkerSize(1.0)
tgraphs[pdf].SetMarkerColor(colors[j])
tgraphs[pdf].SetLineColor(colors[j])
tgraphs[pdf].SetLineWidth(2)
i = 0
procNames = []
splitLines = []
valMeasureds = []
valTheorys = []
splitLine = ROOT.TLine(xmin, counts - i + 0.5, xmax, counts - i + 0.5)
splitLine.SetLineColor(ROOT.kBlack)
splitLine.SetLineStyle(1)
splitLine.SetLineWidth(1)
splitLines.append(splitLine)
if showXsecs:
#splitline = ROOT.TLine(0.58, counts - i + 0.5, 0.58, 0.96)
#splitline.SetLineColor(ROOT.kBlack)
#splitline.SetLineStyle(1)
#splitline.SetLineWidth(1)
#splitLines.append(splitline)
# draw the labels of cross sections and theory predictions
valMeasured = ROOT.TPaveText(0.58, (counts + 1 ) / (counts + 1.5) * 0.81 + .10, 0.94, (counts +1 +0.48) / (counts+1.5)*0.81+0.10, "NDC")
valMeasured.SetFillStyle(0)
valMeasured.SetBorderSize(0)
valMeasured.SetTextAlign(12)
valMeasured.SetTextFont(42)
valMeasured.AddText("Measured#pm unc")
valMeasured.SetTextColor(ROOT.kBlack)
valMeasured.SetTextSize(0.03)
valMeasureds.append(valMeasured)
valTheory = ROOT.TPaveText(0.58, (counts +1 - 0.48) / (counts + 1.5) * 0.81 + .10, 0.94, (counts +1 ) / (counts+1.5)*0.81+0.10, "NDC")
valTheory.SetFillStyle(0)
valTheory.SetBorderSize(0)
valTheory.SetTextAlign(12)
valTheory.SetTextFont(42)
valTheory.AddText("Theory#pm unc (NNPDF3.1)")
valTheory.SetTextColor(colors[0])
valTheory.SetTextSize(0.03)
valTheorys.append(valTheory)
for ch in xsecs_diffs:
if "diff" not in ch:
continue
tgraphs["Measured"].SetPoint(i, xsecs_diffs[ch]["Measured"][0],counts - i)
tgraphs["Measured"].SetPointError(i, xsecs_diffs[ch]["Measured"][1], 0.5)
for j, pdf in enumerate(pdfsets_plot):
pdf = pdf.upper()
tgraphs[pdf].SetPoint(i, xsecs_diffs[ch][pdf][0], counts - i - j * 0.15 + 0.2 )
tgraphs[pdf].SetPointEXlow(i, xsecs_diffs[ch][pdf][1])
tgraphs[pdf].SetPointEXhigh(i, xsecs_diffs[ch][pdf][2])
tgraphs[pdf].SetPointEYlow(i, 0.0)
tgraphs[pdf].SetPointEYhigh(i, 0.0)
splitLine = ROOT.TLine(xmin, counts - i - 0.5, xmax, counts - i - 0.5)
splitLine.SetLineColor(ROOT.kBlack)
splitLine.SetLineStyle(2)
splitLine.SetLineWidth(1)
splitLines.append(splitLine)
procName = ROOT.TPaveText(0.09, (counts - i - 0.18) / (counts + 1.5) * 0.81 + .08, 0.29, (counts - i + 0.18) / (counts+1.5)*0.81+0.08, "NDC")
#print("y1, y2", (counts - i - 0.2) / (counts + 1) - .05, (counts - i + 0.2) / (counts + 1) - 0.05)
procName.SetFillStyle(0)
procName.SetBorderSize(0)
procName.SetTextAlign(12)
procName.SetTextFont(42)
procName.AddText(FormatROOTInput(ch.replace("_diff", "")))
procName.SetTextColor(ROOT.kBlack)
procName.SetTextSize(0.03)
procNames.append(procName)
if showXsecs:
unit = " pb" if (not ("Over" in ch or "Ratio" in ch) and not doCombineYear) else ""
if syncPrecision:
# find the precision of the measured and theory values
# not optimal.
# ideally should directly use the table result instead
# but these are changed to strings for formatting...
valM = xsecs_diffs[ch.replace("_diff","")]["Measured"]
precisionM, isIntM = findPrecision(valM)
valT = xsecs_diffs[ch.replace("_diff","")][pdfsets_plot[0]]
precisionT, isIntT = findPrecision(valT)
precision = max(precisionM, precisionT)
isInt = isIntM or isIntT
valMeasured = ROOT.TPaveText(0.58, (counts - i ) / (counts + 1.5) * 0.81 + .07, 0.94, (counts - i +0.38) / (counts+1.5)*0.81+0.07, "NDC")
valMeasured.SetFillStyle(0)
valMeasured.SetBorderSize(0)
valMeasured.SetTextAlign(12)
valMeasured.SetTextFont(42)
val = xsecs_diffs[ch.replace("_diff","")]["Measured"]
if not syncPrecision:
precision, isInt = findPrecision(val)
rval = roundToError(val, precision, isInt)
#print("measured : ", val)
strMeasured = f"{rval[0]}"
if float(rval[1]) != 0.:
strMeasured += f"#pm {rval[1]}_{{stat}}"
if float(rval[2]) != 0.:
strMeasured += f"#pm {rval[2]}_{{syst}}"
if not "Ratio" in ch and "Over" not in ch:
strMeasured += f"#pm {rval[3]}_{{lumi}}"
strMeasured += unit
valMeasured.AddText(strMeasured)
valMeasured.SetTextColor(ROOT.kBlack)
valMeasured.SetTextSize(0.03)
valMeasureds.append(valMeasured)
valTheory = ROOT.TPaveText(0.58, (counts - i - 0.38) / (counts + 1.5) * 0.81 + .07, 0.94, (counts - i ) / (counts+1.5)*0.81+0.07, "NDC")
valTheory.SetFillStyle(0)
valTheory.SetBorderSize(0)
valTheory.SetTextAlign(12)
valTheory.SetTextFont(42)
val = xsecs_diffs[ch.replace("_diff","")][pdfsets_plot[0]]
if not syncPrecision:
precision, isInt = findPrecision(val)
rval = roundToError(val, precision, isInt)
#print("theory ", val)
if rval[2] == rval[1]:
# symmetric error
valstring = f"{rval[0]}\pm {rval[1]}"
else:
valstring = f"{rval[0]}^{{\plus {rval[2]}}}_{{\minus {rval[1]}}}"
valstring += unit
valTheory.AddText(valstring)
#valTheory.AddText(f"{rval[0]}^{{+{rval[2]}}}_{{-{rval[1]}}}{unit}")
valTheory.SetTextColor(colors[0])
valTheory.SetTextSize(0.03)
valTheorys.append(valTheory)
i += 1
theoLine = ROOT.TLine(1., 0.5, 1., counts + 0.5)
theoLine.SetLineColor(ROOT.kRed)
theoLine.SetLineStyle(1)
theoLine.SetLineWidth(3)
#headerText = ROOT.TPaveText(0.15, (counts +0.9 ) / (counts + 1.5) * 0.81 + .08, 0.30, (counts + 1.4) / (counts+1.5)*0.81+0.08, "NDC")
#headerText.SetFillStyle(0)
#headerText.SetBorderSize(0)
#headerText.SetTextAlign(12)
#headerText.AddText(header)
#headerText.SetTextColor(ROOT.kBlack)
#headerText.SetTextSize(0.035)
# customized legends
legends = []
legends_draw_options = []
# data
grLeg_Measured_0 = ROOT.TGraphErrors()
grLeg_Measured_0.SetFillColorAlpha(15, 0.6)
grLeg_Measured_0.SetFillStyle(1001)
grLeg_Measured_0.SetMarkerColor(15)
grLeg_Measured_0.SetMarkerStyle(1)
grLeg_Measured_0.SetPoint(0, xmin + 0.02, len(procNames) + 1.24)
grLeg_Measured_0.SetPointError(0, 0.005, 0.12)
grLeg_Measured_1 = ROOT.TGraphErrors()
grLeg_Measured_1.SetMarkerColor(2)
grLeg_Measured_1.SetMarkerStyle(1)
grLeg_Measured_1.SetLineColor(2)
grLeg_Measured_1.SetLineWidth(2)
grLeg_Measured_1.SetPoint(0, xmin + 0.02, len(procNames) + 1.24)
grLeg_Measured_1.SetPointError(0, 0, 0.12)
legends.append(grLeg_Measured_0)
legends.append(grLeg_Measured_1)
legends_draw_options.append("E2")
legends_draw_options.append("LZ")
text_leg = OrderedDict()
text_leg["Measured"] = ROOT.TPaveText(0.125, 0.85, 0.15, 0.87, "NDC")
text_leg["Measured"].SetFillStyle(0)
text_leg["Measured"].SetBorderSize(0)
text_leg["Measured"].SetTextAlign(12)
text_leg["Measured"].SetTextFont(42)
text_leg["Measured"].AddText("Measured")
text_leg["Measured"].SetTextColor(ROOT.kBlack)
text_leg["Measured"].SetTextSize(0.03)
DrawHistos(list(tgraphs.values()) + legends, list(tgraphs.keys())[1:], xmin, xmax, "Theory / Measured Ratio of " + poiname, 0.5, len(procNames) + 0.5 + 1.5, "", outputname, dology=False, drawoptions = ["E2"] + ["1PE"] * len(pdfsets_plot) + legends_draw_options, legendoptions = ["F"] + ["PLF"] * len(pdfsets_plot), legendPos = [0.25, 0.81, 0.57, 0.91], is5TeV = is5TeV, doCombineYear = doCombineYear, canH = canH, canW = canW, yndivisions=0, additionalToDraw=[theoLine]+splitLines+procNames+valMeasureds+valTheorys+list(text_leg.values()), legendNCols = 2, leftmargin=0.05, tickx=0, doPAS = doPAS, legOffset = -1, legendTextSize=0.03)
#DrawHistos(list(tgraphs.values()), list(tgraphs.keys()), xmin, xmax, "Ratio (Theory / Measured) of " + poiname, 0.5, len(procNames) + 0.5 + 1.5, "", outputname, dology=False, drawoptions = ["E2"] + ["1PE"] * len(pdfsets_plot), legendoptions = ["F"] + ["PLF"] * len(pdfsets_plot), legendPos = [0.06, 0.80, 0.57, 0.9], is5TeV = is5TeV, doCombineYear = doCombineYear, canH = canH, canW = canW, yndivisions=0, additionalToDraw=[theoLine]+splitLines+procNames+valMeasureds+valTheorys, legendNCols = 3, leftmargin=0.05, tickx=0, doPAS =True)
if doFiducial:
poiname = "#sigma^{fid}"
else:
poiname = "#sigma^{tot}"
poiname_13TeV = f"{poiname}_{{13TeV}}"
toplot = deepcopy(results['13TeV'])
toplot.update(results['13TeV_ratios'])
#DrawHorizontalCompGraph(toplot, f"{outdir}/plots/results_13TeV", 0.85, 1.15)
DrawHorizontalCompGraph(toplot, f"{outdir}/plots/results_13TeV", 0.85, 1.18, pdfsets_plot=["NNPDF3.1", "NNPDF4.0", "CT18", "MSHT20"], showXsecs = True, poiname = poiname_13TeV)
poiname_5TeV = f"{poiname}_{{5.02TeV}}"
toplot = deepcopy(results['5TeV'])
toplot.update(results['5TeV_ratios'])
DrawHorizontalCompGraph(toplot, f"{outdir}/plots/results_5TeV", 0.85, 1.18, is5TeV = True, pdfsets_plot=["NNPDF3.1", "NNPDF4.0", "CT18", "MSHT20"], showXsecs = True, poiname = poiname_5TeV)
poiname_13vs5 = f"{poiname}_{{13TeV}} / {poiname}_{{5.02TeV}}"
toplot = deepcopy(results['sqrtS_ratios'])
toplot.update(results['sqrtS_double_ratios'])
DrawHorizontalCompGraph(toplot, f"{outdir}/plots/results_sqrtS", 0.85, 1.18, doCombineYear = True, pdfsets_plot=["NNPDF3.1", "NNPDF4.0", "CT18", "MSHT20"], showXsecs = True, poiname = poiname_13vs5)