def makeEfficiency(passed, total, title, lineColor):
if TEfficiency.CheckConsistency(passed, total):
efficiency = TEfficiency(passed, total)
#title = std::regex_replace(title, std::regex("\\muCandGenEtaMuons"), "tagging efficiency");
efficiency.SetTitle(title)
efficiency.SetStatisticOption(6)
#TEfficiency.EStatOption.kBUniform
efficiency.SetPosteriorMode()
efficiency.SetLineColor(lineColor)
return efficiency
else:
print(
"makeEfficiency TEfficiency::CheckConsistency(*ptGenPtTTMuonNom, *ptGenPtTTMuonDenom) failed"
)
exit(1)
#
# DOES NOT SEEM TO WORK FOR WEIGHTED HISTOGRAMS --> IT REDUCES TO THE NORMAL APPROX
#
#t_efficiency.SetStatisticOption(TEfficiency.kFCP)
#
# Use TEfficiency, with the Bayesian uniform prior at XX% CL (set before)
# (This is the same as the TGraphAsymmErrors below)
#
# In order to get the same value for efficiency as in a frequentist approach (as from TH1::Divide("B")),
# the MODE should be used as an estimator. This works as long as a uniform prior is chosen.
#
# Please refer to the TEfficiency class docs for details:
# https://root.cern.ch/doc/master/classTEfficiency.html
#
t_efficiency.SetStatisticOption(TEfficiency.kBUniform)
t_efficiency.SetPosteriorMode()
# 3.
#
# Calculate efficiency using TGraphAsymmErrors
# (uses a Bayesian uniform prior beta(1,1) for the efficiency with 68% CL. It handles the errors in case eff is 0 or 1)
#
g_efficiency = TGraphAsymmErrors(hist_eff)
g_efficiency.Divide(hist_pass,hist_tot,"cl=0.683 b(1,1) mode")
# Save the efficiencies in the proper dictionaries
#
hists[histname + "_Efficiency_" + append_str] = hist_eff
tefficiencies[histname + "_Efficiency_" + append_str] = t_efficiency
graphs[histname + "_Efficiency_" + append_str + "_graph"] = g_efficiency
