tfile = TFile.Open(infile)
# ____________________________________________________________________________
# emtf_eff_vs_genpt_l1pt20
hname = "emtf_eff_vs_genpt_l1pt20"
h1a_denom = tfile.Get(hname + "_denom")
h1a_numer = tfile.Get(hname + "_numer")
h1b_denom = tfile.Get(hname2026_f(hname) + "_denom")
h1b_numer = tfile.Get(hname2026_f(hname) + "_numer")
print h1a_denom.GetEntries(), h1a_numer.GetEntries()
print h1b_denom.GetEntries(), h1b_denom.GetEntries()
h1a_eff = TEfficiency(h1a_numer, h1a_denom)
h1a_eff.SetStatisticOption(0) # kFCP
h1a_eff.SetConfidenceLevel(0.682689492137) # one sigma
h1a_eff.SetMarkerColor(632) # kRed
h1a_eff.SetLineColor(632) # kRed
h1a_eff.SetLineWidth(2)
h1b_eff = TEfficiency(h1b_numer, h1b_denom)
h1b_eff.SetStatisticOption(0) # kFCP
h1b_eff.SetConfidenceLevel(0.682689492137) # one sigma
h1b_eff.SetMarkerColor(600) # kBlue
h1b_eff.SetLineColor(600) # kBlue
h1b_eff.SetLineWidth(2)
gr = h1a_eff.CreateGraph()
gr.Draw("ap")
frame = gr.GetHistogram()
frame = frame.Clone(hname + "_frame")
hist_eff_fake_scaled.SetName(eff_fake_scaled_name)
hists[eff_QMisID_name] = hist_eff_QMisID
hists[eff_fake_scaled_name] = hist_eff_fake_scaled
# *********************************************************************************************************************************
# 2.
# The TEfficiency class handles the special cases not covered by TH1::Divide
#
t_efficiency = None
if TEfficiency.CheckConsistency(hist_pass, hist_tot,"w"):
t_efficiency = TEfficiency(hist_pass, hist_tot)
t_efficiency.SetName(histname + "_TEfficiency_" + append_str)
t_efficiency.SetConfidenceLevel(0.683)
# Use TEfficiency, with the frequentist Clopper-Pearson confidence interval at XX% CL (set before)
# (this handles the eff = 0, 1 case)
#
# 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:
