def getEfficiency(datasets, numerator="Numerator", denominator="Denominator"):
# statOption = ROOT.TEfficiency.kFNormal
statOption = ROOT.TEfficiency.kFCP # Clopper-Pearson
# statOption = ROOT.TEfficiency.kFFC # Feldman-Cousins
first = True
isData = False
teff = ROOT.TEfficiency()
for dataset in datasets:
n = dataset.getDatasetRootHisto(numerator).getHistogram()
d = dataset.getDatasetRootHisto(denominator).getHistogram()
if d.GetEntries() == 0:
continue
checkNegatives(n, d)
# removeNegatives(n)
# removeNegatives(d)
print dataset.getName(), "entries", n.GetEntries(), d.GetEntries()
print " bins", n.GetNbinsX(), d.GetNbinsX()
print " lowedge", n.GetBinLowEdge(1), d.GetBinLowEdge(1)
eff = ROOT.TEfficiency(n, d)
eff.SetStatisticOption(statOption)
weight = 1
if dataset.isMC():
weight = dataset.getCrossSection() / d.GetEntries()
for i in range(1, d.GetNbinsX() + 1):
print " bin", i, d.GetBinLowEdge(i), n.GetBinContent(
i), d.GetBinContent(i)
eff.SetWeight(weight)
if first:
teff = eff
if dataset.isData():
tn = n
td = d
first = False
else:
teff.Add(eff)
if dataset.isData():
tn.Add(n)
td.Add(d)
if isData:
teff = ROOT.TEfficiency(tn, td)
teff.SetStatisticOption(self.statOption)
return teff
def getEfficiency(datasets,numerator="Numerator",denominator="Denominator"):
# statOption = ROOT.TEfficiency.kFNormal
statOption = ROOT.TEfficiency.kFCP # Clopper-Pearson
# statOption = ROOT.TEfficiency.kFFC # Feldman-Cousins
first = True
isData = False
teff = ROOT.TEfficiency()
for dataset in datasets:
n = dataset.getDatasetRootHisto(numerator).getHistogram()
d = dataset.getDatasetRootHisto(denominator).getHistogram()
if d.GetEntries() == 0:
continue
checkNegatives(n,d)
# removeNegatives(n)
# removeNegatives(d)
print dataset.getName(),"entries",n.GetEntries(),d.GetEntries()
print " bins",n.GetNbinsX(),d.GetNbinsX()
print " lowedge",n.GetBinLowEdge(1),d.GetBinLowEdge(1)
eff = ROOT.TEfficiency(n,d)
eff.SetStatisticOption(statOption)
weight = 1
if dataset.isMC():
weight = dataset.getCrossSection()/d.GetEntries()
for i in range(1,d.GetNbinsX()+1):
print " bin",i,d.GetBinLowEdge(i),n.GetBinContent(i),d.GetBinContent(i)
eff.SetWeight(weight)
if first:
teff = eff
if dataset.isData():
tn = n
td = d
first = False
else:
teff.Add(eff)
if dataset.isData():
tn.Add(n)
td.Add(d)
if isData:
teff = ROOT.TEfficiency(tn, td)
teff.SetStatisticOption(self.statOption)
return teff
def GetCutEfficiencyHisto(dataset, histoName, statOpt, **kwargs):
'''
See https://root.cern.ch/doc/master/classTEfficiency.html
'''
HasKeys(["verbose", "normalizeTo", "cutDirection"], **kwargs)
verbose = kwargs.get("verbose")
normalizeTo = kwargs.get("normalizeTo")
cutDirection = kwargs.get("cutDirection")
Verbose("Calculating the cut-efficiency (%s) for histo with name %s" %
(cutDirection, histoName))
# Choose statistics options
statOpts = [
"kFCP", "kFNormal", "KFWilson", "kFAC", "kFFC", "kBJeffrey",
"kBUniform", "kBayesian"
]
if statOpt not in statOpts:
raise Exception(
"Invalid statistics option \"%s\". Please choose one from the following:\n\t%s"
% (statOpt, "\n\t".join(statOpts)))
if statOpt == "kFCP":
statOption = ROOT.TEfficiency.kFCP # Clopper-Pearson
elif statOpt == "kFNormal":
statOption = ROOT.TEfficiency.kFNormal # Normal Approximation
elif statOpt == "kFWilson":
statOption = ROOT.TEfficiency.kFWilson # Wilson
elif statOpt == "kFAC":
statOption = ROOT.TEfficiency.kFAC # Agresti-Coull
elif statOpt == "kFFC":
statOption = ROOT.TEfficiency.kFFC # Feldman-Cousins
elif statOpt == "kBJeffrey":
statOption = ROOT.TEfficiency.kBJeffrey # Jeffrey
elif statOpt == "kBUniform":
statOption = ROOT.TEfficiency.kBUniform # Uniform Prior
elif statOpt == "kBayesian":
statOption = ROOT.TEfficiency.kBayesian # Custom Prior
else:
raise Exception("This should never be reached")
# Declare variables & options
first = True
isData = False
teff = ROOT.TEfficiency()
# Get the ROOT histogram
rootHisto = dataset.getDatasetRootHisto(histoName)
# Normalise the histogram
NormalizeRootHisto(datasetsMgr, rootHisto, dataset.isMC(), normalizeTo)
#NormalizeRootHisto(datasetsMgr, rootHisto, d.isMC(), normalizeTo)
## Get a clone of the wrapped histogram normalized as requested.
h = rootHisto.getHistogram()
titleX = h.GetXaxis().GetTitle()
binWidth = h.GetXaxis().GetBinWidth(0)
titleY = "efficiency (%s) / %s" % (cutDirection,
GetBinwidthDecimals(binWidth) %
(binWidth))
# If empty return
if h.GetEntries() == 0:
return
# Create the numerator/denominator histograms
numerator = h.Clone("Numerator")
denominator = h.Clone("Denominator")
# Reset the numerator/denominator histograms
numerator.Reset()
denominator.Reset()
# Calculate the instances passing a given cut (all bins)
nBinsX = h.GetNbinsX() + 1
for iBin in range(1, nBinsX):
nTotal = h.Integral(0, nBinsX)
if cutDirection == ">":
nPass = h.Integral(iBin + 1, nBinsX)
elif cutDirection == "<":
nPass = nTotal - h.Integral(iBin + 1, nBinsX)
else:
raise Exception(
"Invalid cut direction \"%s\". Please choose either \">\" or \"<\""
% (cutDirection))
# Sanity check
if nPass < 0:
nPass = 0
# Fill the numerator/denominator histograms
# print "iBin = %s, nPass = %s, nTotal = %s" % (iBin, nPass, nTotal)
numerator.SetBinContent(iBin, nPass)
numerator.SetBinError(iBin, math.sqrt(nPass) / 10)
#
denominator.SetBinContent(iBin, nTotal)
denominator.SetBinError(iBin, math.sqrt(nTotal) / 10)
# Check for negative values
CheckNegatives(numerator, denominator)
# Create TEfficiency object using the two histos
eff = ROOT.TEfficiency(numerator, denominator)
eff.SetStatisticOption(statOption)
Verbose("The statistic option was set to %s" % (eff.GetStatisticOption()))
# Save info in a table (debugging)
table = []
hLine = "=" * 70
msgAlign = '{:<5} {:<20} {:<20} {:<20}'
title = msgAlign.format("Bin", "Efficiency", "Error-Low", "Error-Up")
table.append("\n" + hLine)
table.append(title)
table.append(hLine)
for iBin in range(1, nBinsX):
e = eff.GetEfficiency(iBin)
errLow = eff.GetEfficiencyErrorLow(iBin)
errUp = eff.GetEfficiencyErrorUp(iBin)
values = msgAlign.format(iBin, e, errLow, errUp)
table.append(values)
table.append(hLine)
# Verbose mode
if verbose:
for l in table:
print l
weight = 1
if dataset.isMC():
weight = dataset.getCrossSection()
eff.SetWeight(weight)
if first:
teff = eff
if dataset.isData():
tn = numerator
td = denominator
first = False
else:
teff.Add(eff)
if dataset.isData():
tn.Add(numerator)
td.Add(denominator)
if isData:
teff = ROOT.TEfficiency(tn, td)
teff.SetStatisticOption(self.statOption)
style = styleDict[dataset.getName()]
return Convert2TGraph(teff, dataset, style, titleX, titleY)
def GetCutEfficiencyHisto(dataset, histoName, statOpt, **kwargs):
'''
See https://root.cern.ch/doc/master/classTEfficiency.html
'''
HasKeys(["verbose", "normalizeTo", "cutDirection"], **kwargs)
verbose = kwargs.get("verbose")
normalizeTo = kwargs.get("normalizeTo")
cutDirection= kwargs.get("cutDirection")
Verbose("Calculating the cut-efficiency (%s) for histo with name %s" % (cutDirection, histoName) )
# Choose statistics options
statOpts = ["kFCP", "kFNormal", "KFWilson", "kFAC", "kFFC", "kBJeffrey", "kBUniform", "kBayesian"]
if statOpt not in statOpts:
raise Exception("Invalid statistics option \"%s\". Please choose one from the following:\n\t%s" % (statOpt, "\n\t".join(statOpts)))
if statOpt == "kFCP":
statOption = ROOT.TEfficiency.kFCP # Clopper-Pearson
elif statOpt == "kFNormal":
statOption = ROOT.TEfficiency.kFNormal # Normal Approximation
elif statOpt == "kFWilson":
statOption = ROOT.TEfficiency.kFWilson # Wilson
elif statOpt == "kFAC":
statOption = ROOT.TEfficiency.kFAC # Agresti-Coull
elif statOpt == "kFFC":
statOption = ROOT.TEfficiency.kFFC # Feldman-Cousins
elif statOpt == "kBJeffrey":
statOption = ROOT.TEfficiency.kBJeffrey # Jeffrey
elif statOpt == "kBUniform":
statOption = ROOT.TEfficiency.kBUniform # Uniform Prior
elif statOpt == "kBayesian":
statOption = ROOT.TEfficiency.kBayesian # Custom Prior
else:
raise Exception("This should never be reached")
# Declare variables & options
first = True
isData = False
teff = ROOT.TEfficiency()
# Get the ROOT histogram
rootHisto = dataset.getDatasetRootHisto(histoName)
# Normalise the histogram
NormalizeRootHisto(datasetsMgr, rootHisto, dataset.isMC(), normalizeTo)
#NormalizeRootHisto(datasetsMgr, rootHisto, d.isMC(), normalizeTo)
## Get a clone of the wrapped histogram normalized as requested.
h = rootHisto.getHistogram()
titleX = h.GetXaxis().GetTitle()
binWidth = h.GetXaxis().GetBinWidth(0)
titleY = "efficiency (%s) / %s" % (cutDirection, GetBinwidthDecimals(binWidth) % (binWidth) )
# If empty return
if h.GetEntries() == 0:
return
# Create the numerator/denominator histograms
numerator = h.Clone("Numerator")
denominator = h.Clone("Denominator")
# Reset the numerator/denominator histograms
numerator.Reset()
denominator.Reset()
# Calculate the instances passing a given cut (all bins)
nBinsX = h.GetNbinsX()+1
for iBin in range(1, nBinsX):
nTotal = h.Integral(0, nBinsX)
if cutDirection == ">":
nPass = h.Integral(iBin+1, nBinsX)
elif cutDirection == "<":
nPass = nTotal - h.Integral(iBin+1, nBinsX)
else:
raise Exception("Invalid cut direction \"%s\". Please choose either \">\" or \"<\"" % (cutDirection))
# Sanity check
if nPass < 0:
nPass = 0
# Fill the numerator/denominator histograms
# print "iBin = %s, nPass = %s, nTotal = %s" % (iBin, nPass, nTotal)
numerator.SetBinContent(iBin, nPass)
numerator.SetBinError(iBin, math.sqrt(nPass)/10)
#
denominator.SetBinContent(iBin, nTotal)
denominator.SetBinError(iBin, math.sqrt(nTotal)/10)
# Check for negative values
CheckNegatives(numerator, denominator)
# Create TEfficiency object using the two histos
eff = ROOT.TEfficiency(numerator, denominator)
eff.SetStatisticOption(statOption)
Verbose("The statistic option was set to %s" % (eff.GetStatisticOption()) )
# Save info in a table (debugging)
table = []
hLine = "="*70
msgAlign = '{:<5} {:<20} {:<20} {:<20}'
title = msgAlign.format("Bin", "Efficiency", "Error-Low", "Error-Up")
table.append("\n" + hLine)
table.append(title)
table.append(hLine)
for iBin in range(1, nBinsX):
e = eff.GetEfficiency(iBin)
errLow = eff.GetEfficiencyErrorLow(iBin)
errUp = eff.GetEfficiencyErrorUp(iBin)
values = msgAlign.format(iBin, e, errLow, errUp)
table.append(values)
table.append(hLine)
# Verbose mode
if verbose:
for l in table:
print l
weight = 1
if dataset.isMC():
weight = dataset.getCrossSection()
eff.SetWeight(weight)
if first:
teff = eff
if dataset.isData():
tn = numerator
td = denominator
first = False
else:
teff.Add(eff)
if dataset.isData():
tn.Add(numerator)
td.Add(denominator)
if isData:
teff = ROOT.TEfficiency(tn, td)
teff.SetStatisticOption(self.statOption)
style = styleDict[dataset.getName()]
return Convert2TGraph(teff, dataset, style, titleX, titleY)
def GetEfficiency(datasetsMgr, datasets, numerator="Numerator",denominator="Denominator", **kwargs):
'''
TEfficiency method:
See https://root.cern.ch/doc/master/classTEfficiency.html
'''
lumi = GetLumi(datasetsMgr)
# Select Statistic Options
statOption = ROOT.TEfficiency.kFCP
'''
statOption = ROOT.TEfficiency.kFCP # Clopper-Pearson
statOption = ROOT.TEfficiency.kFNormal # Normal Approximation
statOption = ROOT.TEfficiency.kFWilson # Wilson
statOption = ROOT.TEfficiency.kFAC # Agresti-Coull
statOption = ROOT.TEfficiency.kFFC # Feldman-Cousins
statOption = ROOT.TEfficiency.kBBJeffrey # Jeffrey
statOption = ROOT.TEfficiency.kBBUniform # Uniform Prior
statOption = ROOT.TEfficiency.kBBayesian # Custom Prior
'''
first = True
teff = ROOT.TEfficiency()
# teff.SetStatisticOption(statOption)
# For-loop: All datasets
for dataset in datasets:
num = dataset.getDatasetRootHisto(numerator)
den = dataset.getDatasetRootHisto(denominator)
#
if dataset.isMC():
num.normalizeToLuminosity(lumi)
den.normalizeToLuminosity(lumi)
# Get Numerator and Denominator
n = num.getHistogram()
d = den.getHistogram()
if d.GetEntries() == 0 or n.GetEntries() == 0:
msg = "Denominator Or Numerator has no entries"
Print(ErrorStyle() + msg + NormalStyle(), True)
continue
# Check Negatives
CheckNegatives(n, d, True)
# Remove Negatives
RemoveNegatives(n)
#RemoveNegatives(d)
NumeratorBins = n.GetNbinsX()
DenominatorBins = d.GetNbinsX()
# Sanity Check
if (NumeratorBins != DenominatorBins) :
raise Exception("Numerator and Denominator Bins are NOT equal!")
nBins = d.GetNbinsX()
xMin = d.GetXaxis().GetXmin()
xMax = d.GetXaxis().GetXmax()
# ----------------------------------------------------------------------------------------- #
# Ugly hack to ignore EMPTY (in the wanted range) histograms with overflows/underflows
# ----------------------------------------------------------------------------------------- #
if 0:
print "\n"
print "=========== getEfficiency:"
print "Dataset = ", dataset.getName()
print "Numerator :", n.GetName(), " entries=", n.GetEntries(), " Bins=", n.GetNbinsX(), " Low edge=", n.GetBinLowEdge(1)
print "Denominator:", d.GetName(), " entries=", d.GetEntries(), " Bins=", d.GetNbinsX(), " Low edge=", d.GetBinLowEdge(1)
print "\n"
print ">>>>>> Sanity Check: <<<<<<"
print "Numerator Mean = ", n.GetMean()
print "Numerator RMS = ", n.GetRMS()
print "Numerator Integral = ", n.Integral(1, nBins)
print "Denominator Mean = ", d.GetMean()
print "Denominator RMS = ", d.GetRMS()
print "Denominator Integral = ", d.Integral(1, nBins)
if (n.GetMean() == 0 or d.GetMean() == 0): continue
if (n.GetRMS() == 0 or d.GetRMS() == 0): continue
if (n.Integral(1,nBins) == 0 or d.Integral(1,nBins) == 0): continue
Verbose("Passed the sanity check", True)
eff = ROOT.TEfficiency(n, d)
eff.SetStatisticOption(statOption)
# For-loop: All bins
if 0:
for iBin in range(1, nBins+1):
print iBin, "x=", n.GetBinLowEdge(iBin), " Num=", n.GetBinContent(iBin), " Den=", d.GetBinContent(iBin)," Eff=", eff.GetEfficiency(iBin)
weight = 1
if dataset.isMC():
weight = dataset.getCrossSection()
eff.SetWeight(weight)
if first:
teff = eff
first = False
if dataset.isData():
tn = n
td = d
else:
teff.Add(eff)
if dataset.isData():
tn.Add(n)
td.Add(d)
if dataset.isData():
teff = ROOT.TEfficiency(tn, td)
teff.SetStatisticOption(statOption)
Verbose("Final tEff", True)
if 0:
for iBin in range(1,nBins+1):
print iBin, "x=", n.GetBinLowEdge(iBin)," Efficiency=", teff.GetEfficiency(iBin), " Weight = ", teff.GetWeight()
return convert2TGraph(teff)
def getEfficiency2D(datasetsMgr,
datasets,
numerator="Numerator",
denominator="Denominator",
**kwargs):
'''
TEfficiency method:
See https://root.cern.ch/doc/master/classTEfficiency.html
'''
HasKeys(["verbose"], **kwargs)
verbose = True #kwargs.get("verbose")
lumi = GetLumi(datasetsMgr)
# Select Statistic Options
statOption = ROOT.TEfficiency.kFCP
'''
statOption = ROOT.TEfficiency.kFCP # Clopper-Pearson
statOption = ROOT.TEfficiency.kFNormal # Normal Approximation
statOption = ROOT.TEfficiency.kFWilson # Wilson
statOption = ROOT.TEfficiency.kFAC # Agresti-Coull
statOption = ROOT.TEfficiency.kFFC # Feldman-Cousins
statOption = ROOT.TEfficiency.kBBJeffrey # Jeffrey
statOption = ROOT.TEfficiency.kBBUniform # Uniform Prior
statOption = ROOT.TEfficiency.kBBayesian # Custom Prior
'''
print "getEfficiency function"
first = True
teff = ROOT.TEfficiency()
# teff.SetStatisticOption(statOption)
print "Loop over Datasets"
for dataset in datasets:
print "Datasets"
#datasets.normalizeMCByLuminosity()
for dataset in datasets:
num = dataset.getDatasetRootHisto(numerator)
den = dataset.getDatasetRootHisto(denominator)
if dataset.isMC():
num.normalizeToLuminosity(lumi)
den.normalizeToLuminosity(lumi)
#num.normalizeMCByLuminosity()
#den.normalizeMCByLuminosity()
# Get Numerator and Denominator
n = num.getHistogram()
d = den.getHistogram()
#tn = None
#td = None
#n.normalizeMCByLuminosity()
#d.normalizeMCByLuminosity()
#n = dataset.getDatasetRootHisto(numerator).getHistogram()
#d = dataset.getDatasetRootHisto(denominator).getHistogram()
if d.GetEntries() == 0 or n.GetEntries() == 0:
print "Denominator Or Numerator has no entries"
continue
# Check Negatives
CheckNegatives(n, d, True)
# Remove Negatives
RemoveNegatives(n)
#RemoveNegatives(d)
NumeratorBins = n.GetNbinsX()
DenominatorBins = d.GetNbinsX()
# Sanity Check
if (NumeratorBins != DenominatorBins):
raise Exception("Numerator and Denominator Bins are NOT equal!")
nBinsX = d.GetNbinsX()
xMin = d.GetXaxis().GetXmin()
xMax = d.GetXaxis().GetXmax()
nBinsY = d.GetNbinsY()
#yMin = d.GetYaxis().GetYmin()
#yMax = d.GetYaxis().GetYmax()
print("NoProblem till here asdasd...")
# ----------------------------------------------------------------------------------------- #
# Ugly hack to ignore EMPTY (in the wanted range) histograms with overflows/underflows
# ----------------------------------------------------------------------------------------- #
print "\n"
print "=========== getEfficiency:"
print "Dataset = ", dataset.getName()
#print "Numerator :", n.GetName(), " entries=", n.GetEntries(), " Bins=", n.GetNbinsX(), " Low edge=", n.GetBinLowEdge(1)
#print "Denominator:", d.GetName(), " entries=", d.GetEntries(), " Bins=", d.GetNbinsX(), " Low edge=", d.GetBinLowEdge(1)
print "\n"
print ">>>>>> Sanity Check: <<<<<<"
print "Numerator Mean = ", n.GetMean()
print "Numerator RMS = ", n.GetRMS()
print "Numerator Integral = ", n.Integral()
print "Denominator Mean = ", d.GetMean()
print "Denominator RMS = ", d.GetRMS()
print "Denominator Integral = ", d.Integral()
if (n.GetMean() == 0 or d.GetMean() == 0): continue
if (n.GetRMS() == 0 or d.GetRMS() == 0): continue
if (n.Integral() == 0 or d.Integral() == 0): continue
print "Passed the sanity check"
eff = ROOT.TEfficiency(n, d)
eff.SetStatisticOption(statOption)
# if "TT" in dataset.getName():
# print " "
# print " TT sample"
#for iBin in range(1, nBins+1):
# print iBin, "x=", n.GetBinLowEdge(iBin), " Num=", n.GetBinContent(iBin), " Den=", d.GetBinContent(iBin)," Eff=", eff.GetEfficiency(iBin)
# "Contrib. =", d.GetBinContent(iBin)/d.Integral(1, nBins)*100.0, "Contrib. = ", n.GetBinContent(iBin)/n.Integral(1, nBins)*100.0,
'''
#if (verbose):
print "\n"
for iBin in range(1,nBins+1):
#print iBin, "x=", n.GetBinLowEdge(iBin), " Numerator=", n.GetBinContent(iBin), " Denominator=", d.GetBinContent(iBin), " Efficiency=", eff.GetEfficiency(iBin\
), " Weight=", eff.GetWeight()
print "\n"
'''
weight = 1
if dataset.isMC():
weight = dataset.getCrossSection()
eff.SetWeight(weight)
#print "dataset=", dataset.getName(), "has weight=", weight
#print " Efficiency plot has weight=", eff.GetWeight()
if first:
teff = eff
first = False
if dataset.isData():
tn = n
td = d
else:
teff.Add(eff)
#print " "
#print "Adding eff to TEfficiency="
#for iBin in range(1, nBins+1):
# print iBin, "x=", n.GetBinLowEdge(iBin), " Numerator=", n.GetBinContent(iBin), "Contrib. = ", n.GetBinContent(iBin)/n.Integral(1, nBins)*100.0, " Denominator=", d.GetBinContent(iBin), "Contrib. =", d.GetBinContent(iBin)/d.Integral(1, nBins)*100.0, " Efficiency=", teff.GetEfficiency(iBin), " Weight=", teff.GetWeight()
if dataset.isData():
tn.Add(n)
td.Add(d)
if dataset.isData():
teff = ROOT.TEfficiency(tn, td)
teff.SetStatisticOption(statOption)
'''
print " ------------------------- Final Data Plot ------------------------- "
print "Integral = ", tn.Integral(1, nBins)
print "Numerator:"
for iBin in range(1, nBins+1):
print iBin, "x=", tn.GetBinLowEdge(iBin), " Bin Content = ", tn.GetBinContent(iBin), " Percentage=", tn.GetBinContent(iBin)/tn.Integral(1, nBins)*100.0
print "Denominator: "
print "Integral = ", td.Integral(1,nBins)
for iBin in range(1, nBins+1):
print iBin, "x=", td.GetBinLowEdge(iBin), " Bin Content = ", td.GetBinContent(iBin), " Percentage=", td.GetBinContent(iBin)/td.Integral(1, nBins)*100
print "-------------------------------------------------------------------- "
'''
print " -----------------> Final tEff"
#for iBin in range(1,nBins+1):
# print iBin, "x=", n.GetBinLowEdge(iBin)," Efficiency=", teff.GetEfficiency(iBin), " Weight = ", teff.GetWeight()
return teff
def GetEfficiency(datasetsMgr, datasets, numerator="Numerator",denominator="Denominator", **kwargs):
'''
TEfficiency method:
See https://root.cern.ch/doc/master/classTEfficiency.html
'''
lumi = GetLumi(datasetsMgr)
# Select Statistic Options
statOption = ROOT.TEfficiency.kFCP
'''
statOption = ROOT.TEfficiency.kFCP # Clopper-Pearson
statOption = ROOT.TEfficiency.kFNormal # Normal Approximation
statOption = ROOT.TEfficiency.kFWilson # Wilson
statOption = ROOT.TEfficiency.kFAC # Agresti-Coull
statOption = ROOT.TEfficiency.kFFC # Feldman-Cousins
statOption = ROOT.TEfficiency.kBBJeffrey # Jeffrey
statOption = ROOT.TEfficiency.kBBUniform # Uniform Prior
statOption = ROOT.TEfficiency.kBBayesian # Custom Prior
'''
first = True
teff = ROOT.TEfficiency()
# teff.SetStatisticOption(statOption)
# For-loop: All datasets
for dataset in datasets:
num = dataset.getDatasetRootHisto(numerator)
den = dataset.getDatasetRootHisto(denominator)
#
if dataset.isMC():
num.normalizeToLuminosity(lumi)
den.normalizeToLuminosity(lumi)
# Get Numerator and Denominator
n = num.getHistogram()
d = den.getHistogram()
if d.GetEntries() == 0 or n.GetEntries() == 0:
msg = "Denominator Or Numerator has no entries"
Print(ErrorStyle() + msg + NormalStyle(), True)
continue
# Check Negatives
CheckNegatives(n, d, True)
# Remove Negatives
RemoveNegatives(n)
#RemoveNegatives(d)
NumeratorBins = n.GetNbinsX()
DenominatorBins = d.GetNbinsX()
# Sanity Check
if (NumeratorBins != DenominatorBins) :
raise Exception("Numerator and Denominator Bins are NOT equal!")
nBins = d.GetNbinsX()
xMin = d.GetXaxis().GetXmin()
xMax = d.GetXaxis().GetXmax()
# ----------------------------------------------------------------------------------------- #
# Ugly hack to ignore EMPTY (in the wanted range) histograms with overflows/underflows
# ----------------------------------------------------------------------------------------- #
if 0:
print "\n"
print "=========== getEfficiency:"
print "Dataset = ", dataset.getName()
print "Numerator :", n.GetName(), " entries=", n.GetEntries(), " Bins=", n.GetNbinsX(), " Low edge=", n.GetBinLowEdge(1)
print "Denominator:", d.GetName(), " entries=", d.GetEntries(), " Bins=", d.GetNbinsX(), " Low edge=", d.GetBinLowEdge(1)
print "\n"
print ">>>>>> Sanity Check: <<<<<<"
print "Numerator Mean = ", n.GetMean()
print "Numerator RMS = ", n.GetRMS()
print "Numerator Integral = ", n.Integral(1, nBins)
print "Denominator Mean = ", d.GetMean()
print "Denominator RMS = ", d.GetRMS()
print "Denominator Integral = ", d.Integral(1, nBins)
if (n.GetMean() == 0 or d.GetMean() == 0): continue
if (n.GetRMS() == 0 or d.GetRMS() == 0): continue
if (n.Integral(1,nBins) == 0 or d.Integral(1,nBins) == 0): continue
Verbose("Passed the sanity check", True)
eff = ROOT.TEfficiency(n, d)
eff.SetStatisticOption(statOption)
# For-loop: All bins
if 0:
for iBin in range(1, nBins+1):
print iBin, "x=", n.GetBinLowEdge(iBin), " Num=", n.GetBinContent(iBin), " Den=", d.GetBinContent(iBin)," Eff=", eff.GetEfficiency(iBin)
weight = 1
if dataset.isMC():
weight = dataset.getCrossSection()
eff.SetWeight(weight)
if first:
teff = eff
first = False
if dataset.isData():
tn = n
td = d
else:
teff.Add(eff)
if dataset.isData():
tn.Add(n)
td.Add(d)
if dataset.isData():
teff = ROOT.TEfficiency(tn, td)
teff.SetStatisticOption(statOption)
Verbose("Final tEff", True)
if 0:
for iBin in range(1,nBins+1):
print iBin, "x=", n.GetBinLowEdge(iBin)," Efficiency=", teff.GetEfficiency(iBin), " Weight = ", teff.GetWeight()
return convert2TGraph(teff)