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)
# Open file
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()
# (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:
# 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
n_particles = cur.execute(
"SELECT nParticles FROM particleSourceMessenger;").fetchone()[0]
name, pde, pct, papl, paps, n_cell, v_ov = cur.execute(
"SELECT name, pdeAt400nm, crosstalkProbability, apProbLong, apProbShort, numberOfCells, overVoltage FROM sipmModel;"
).fetchone()
con.close()
# Read cached results from tsv file
e, n = np.loadtxt(path, delimiter=" ").transpose() # MeV, #
# Determine bin width
wvl = 4.135667516e-15 * 299792458.0 / e * 1e3
wvl_min = min(wvl)
wvl_max = max(wvl)
nbins = int((wvl_max - wvl_min) / args.bin_width)
eff = TEfficiency("%d-pass" % i, "%d-eff" % i, nbins, wvl_min, wvl_max)
eff.SetUseWeightedEvents()
eff.SetStatisticOption(TEfficiency.kFNormal)
for wvli, ni in zip(wvl, n):
eff.FillWeighted(True, ni, wvli)
eff.FillWeighted(False, n_particles - ni, wvli)
x = np.array(
[eff.GetTotalHistogram().GetBinCenter(i) for i in xrange(nbins)])
y = np.array([eff.GetEfficiency(i) for i in xrange(nbins)]) * 100.0
yerr_low = np.array([
eff.ClopperPearson(int(n_particles),
int(eff.GetEfficiency(i) * n_particles), 0.68,
False) for i in xrange(nbins)
]) * 100.0
yerr_up = np.array([
eff.ClopperPearson(int(n_particles),
int(eff.GetEfficiency(i) * n_particles), 0.68, True)
for i in xrange(nbins)
import sys
from ROOT import TEfficiency
if (len(sys.argv) != 3):
print
print 'USAGE : python FCstats.py Ntotal Npass'
print
exit()
tot = int(sys.argv[-2])
passing = int(sys.argv[-1])
k = TEfficiency()
k.SetStatisticOption(TEfficiency.kFFC)
# get +/- 1 sigma interval from Feldman Cousins statistics
eff = passing / float(tot)
eff_upper = k.FeldmanCousins(tot, passing, 0.6827, True)
eff_lower = k.FeldmanCousins(tot, passing, 0.6827, False)
print 'Efficiency : %.03f.' % (eff)
print '1-sigma interval : [%.03f, %.03f]' % (eff_lower, eff_upper)
