def main(argv, dsetMgr, moduleInfoString):
COMBINEDHISTODIR = "ForQCDNormalization"
FAKEHISTODIR = "ForQCDNormalizationEWKFakeTaus"
GENUINEHISTODIR = "ForQCDNormalizationEWKGenuineTaus"
comparisonList = ["AfterStdSelections"]
dirs = []
dirs.append(sys.argv[1])
# Check multicrab consistency
consistencyCheck.checkConsistencyStandalone(dirs[0],
dsetMgr,
name="QCD inverted")
# As we use weighted counters for MC normalisation, we have to
# update the all event count to a separately defined value because
# the analysis job uses skimmed pattuple as an input
dsetMgr.updateNAllEventsToPUWeighted()
# Read integrated luminosities of data dsetMgr from lumi.json
dsetMgr.loadLuminosities()
# Include only 120 mass bin of HW and HH dsetMgr
dsetMgr.remove(
filter(lambda name: "TTToHplus" in name and not "M120" in name,
dsetMgr.getAllDatasetNames()))
dsetMgr.remove(
filter(lambda name: "HplusTB" in name, dsetMgr.getAllDatasetNames()))
# Default merging nad ordering of data and MC dsetMgr
# All data dsetMgr to "Data"
# All QCD dsetMgr to "QCD"
# All single top dsetMgr to "SingleTop"
# WW, WZ, ZZ to "Diboson"
plots.mergeRenameReorderForDataMC(dsetMgr)
# Set BR(t->H) to 0.05, keep BR(H->tau) in 1
xsect.setHplusCrossSectionsToBR(dsetMgr, br_tH=0.05, br_Htaunu=1)
# Merge WH and HH dsetMgr to one (for each mass bin)
plots.mergeWHandHH(dsetMgr)
# Merge MC EWK samples as one EWK sample
myMergeList = []
if "TT" in dsetMgr.getMCDatasetNames():
myMergeList.append("TT") # Powheg, no neg. weights -> large stats.
else:
myMergeList.append("TTJets") # Madgraph with negative weights
print "Warning: using TTJets as input, but this is suboptimal. Please switch to the TT sample (much more stats.)."
myMergeList.append("WJetsHT")
myMergeList.append("DYJetsToLLHT")
myMergeList.append("SingleTop")
if "Diboson" in dsetMgr.getMCDatasetNames():
myMergeList.append("Diboson")
print "Warning: ignoring diboson sample (since it does not exist) ..."
for item in myMergeList:
if not item in dsetMgr.getMCDatasetNames():
raise Exception(
"Error: tried to use dataset '%s' as part of the merged EWK dataset, but the dataset '%s' does not exist!"
% (item, item))
dsetMgr.merge("EWK", myMergeList)
# Apply TDR style
style = tdrstyle.TDRStyle()
style.setOptStat(True)
for HISTONAME in comparisonList:
BASELINETAUHISTONAME = "NormalizationMETBaselineTau" + HISTONAME + "/NormalizationMETBaselineTau" + HISTONAME
INVERTEDTAUHISTONAME = "NormalizationMETInvertedTau" + HISTONAME + "/NormalizationMETInvertedTau" + HISTONAME
FITMIN = None
FITMAX = None
#===== Infer binning information and labels
histonames = dsetMgr.getDataset("Data").getDirectoryContent(
COMBINEDHISTODIR + "/NormalizationMETBaselineTau" + HISTONAME)
bins = []
binLabels = []
if histonames == None:
# Assume that only inclusive bin exists
name = COMBINEDHISTODIR + "/NormalizationMETBaselineTau" + HISTONAME
if not dsetMgr.getDataset("Data").hasRootHisto(name):
raise Exception(
"Error: Cannot find histogram or directory of name '%s'!" %
name)
BASELINETAUHISTONAME = "NormalizationMETBaselineTau" + HISTONAME
INVERTEDTAUHISTONAME = "NormalizationMETInvertedTau" + HISTONAME
bins = [""]
binLabels = ["Inclusive"]
else:
for hname in histonames:
binIndex = hname.replace(
"NormalizationMETBaselineTau" + HISTONAME, "")
hDummy = dsetMgr.getDataset("Data").getDatasetRootHisto(
COMBINEDHISTODIR + "/" + BASELINETAUHISTONAME +
binIndex).getHistogram()
title = hDummy.GetTitle()
title = title.replace("METBaseline" + HISTONAME, "")
if hDummy.Integral() > 0.0:
bins.append(binIndex)
if binIndex == "Inclusive":
binLabels.append(binIndex)
else:
binLabels.append(
QCDNormalization.getModifiedBinLabelString(title))
if FITMIN == None:
FITMIN = hDummy.GetXaxis().GetXmin()
FITMAX = hDummy.GetXaxis().GetXmax()
hDummy.Delete()
else:
print "Skipping bin '%s' (%s) because it has no entries" % (
binIndex,
QCDNormalization.getModifiedBinLabelString(title))
print "\nHistogram bins available", bins
# Select bins by filter
if len(selectOnlyBins) > 0:
oldBinLabels = binLabels[:]
oldBins = bins[:]
binLabels = []
bins = []
for k in selectOnlyBins:
for i in range(len(oldBinLabels)):
if k == oldBinLabels[i] or k == oldBins[i]:
binLabels.append(oldBinLabels[i])
bins.append(oldBins[i])
print "Using bins ", bins
print "\nBin labels"
for i in range(len(binLabels)):
line = bins[i]
while len(line) < 10:
line += " "
line += ": " + binLabels[i]
print line
print
#===== Initialize normalization calculator
#manager = QCDNormalization.QCDNormalizationManagerExperimental1(binLabels)
manager = QCDNormalization.QCDNormalizationManagerDefault(
binLabels, dirs[0], moduleInfoString)
#===== Create templates (EWK fakes, EWK genuine, QCD; data template is created by manager)
template_EWKFakeTaus_Baseline = manager.createTemplate(
"EWKFakeTaus_Baseline")
template_EWKFakeTaus_Inverted = manager.createTemplate(
"EWKFakeTaus_Inverted")
template_EWKGenuineTaus_Baseline = manager.createTemplate(
"EWKGenuineTaus_Baseline")
template_EWKGenuineTaus_Inverted = manager.createTemplate(
"EWKGenuineTaus_Inverted")
template_EWKInclusive_Baseline = manager.createTemplate(
"EWKInclusive_Baseline")
template_EWKInclusive_Inverted = manager.createTemplate(
"EWKInclusive_Inverted")
template_QCD_Baseline = manager.createTemplate("QCD_Baseline")
template_QCD_Inverted = manager.createTemplate("QCD_Inverted")
#===== Define fit functions and fit parameters
# The available functions are defined in the FitFunction class in the QCDMeasurement/python/QCDNormalization.py file
# commented out fitter for EWK fake taus, since only the fit on inclusive EWK is used to obtain w_QCD
#boundary = 100
#template_EWKFakeTaus_Baseline.setFitter(QCDNormalization.FitFunction("EWKFunctionInv", boundary=boundary, norm=1, rejectPoints=1),
#FITMIN, FITMAX)
#template_EWKFakeTaus_Baseline.setDefaultFitParam(defaultInitialValue=[10.0, 100, 45, 0.02],
#defaultLowerLimit= [ 0.1, 70, 10, 0.001],
#defaultUpperLimit= [ 30, 300, 100, 0.1])
# commented out fitter for EWK genuine taus, since only the fit on inclusive EWK is used to obtain w_QCD
#boundary = 150
#template_EWKGenuineTaus_Baseline.setFitter(QCDNormalization.FitFunction("EWKFunction", boundary=boundary, norm=1, rejectPoints=1),
#FITMIN, FITMAX)
#template_EWKGenuineTaus_Baseline.setDefaultFitParam(defaultLowerLimit=[0.5, 90, 30, 0.0001],
#defaultUpperLimit=[ 20, 150, 50, 1.0])
# Inclusive EWK
boundary = 150
template_EWKInclusive_Baseline.setFitter(
QCDNormalization.FitFunction("EWKFunction",
boundary=boundary,
norm=1,
rejectPoints=1), FITMIN, FITMAX)
template_EWKInclusive_Baseline.setDefaultFitParam(
defaultLowerLimit=[0.5, 90, 30, 0.0001],
defaultUpperLimit=[20, 150, 50, 1.0])
# Note that the same function is used for QCD only and QCD+EWK fakes
template_QCD_Inverted.setFitter(
QCDNormalization.FitFunction("QCDFunction", norm=1), FITMIN,
FITMAX)
template_QCD_Inverted.setDefaultFitParam(
defaultLowerLimit=[0.0001, 0.001, 0.1, 0.0, 10, 0.0001, 0.001],
defaultUpperLimit=[200, 10, 10, 150, 100, 1, 0.05])
#===== Loop over tau pT bins
for i, binStr in enumerate(bins):
print "\n********************************"
print "*** Fitting bin %s" % binLabels[i]
print "********************************\n"
#===== Reset bin results
manager.resetBinResults()
#===== Obtain histograms for normalization
# Data
histoName = COMBINEDHISTODIR + "/" + BASELINETAUHISTONAME + binStr
hmetBase_data = plots.DataMCPlot(
dsetMgr, histoName).histoMgr.getHisto(
"Data").getRootHisto().Clone(histoName)
histoName = COMBINEDHISTODIR + "/" + INVERTEDTAUHISTONAME + binStr
hmetInverted_data = plots.DataMCPlot(
dsetMgr, histoName).histoMgr.getHisto(
"Data").getRootHisto().Clone(histoName)
# EWK genuine taus
histoName = GENUINEHISTODIR + "/" + BASELINETAUHISTONAME + binStr
hmetBase_EWK_GenuineTaus = plots.DataMCPlot(
dsetMgr, histoName).histoMgr.getHisto(
"EWK").getRootHisto().Clone(histoName)
histoName = GENUINEHISTODIR + "/" + INVERTEDTAUHISTONAME + binStr
hmetInverted_EWK_GenuineTaus = plots.DataMCPlot(
dsetMgr, histoName).histoMgr.getHisto(
"EWK").getRootHisto().Clone(histoName)
# EWK fake taus
histoName = FAKEHISTODIR + "/" + BASELINETAUHISTONAME + binStr
hmetBase_EWK_FakeTaus = plots.DataMCPlot(
dsetMgr, histoName).histoMgr.getHisto(
"EWK").getRootHisto().Clone(histoName)
histoName = FAKEHISTODIR + "/" + INVERTEDTAUHISTONAME + binStr
hmetInverted_EWK_FakeTaus = plots.DataMCPlot(
dsetMgr, histoName).histoMgr.getHisto(
"EWK").getRootHisto().Clone(histoName)
# Finalize histograms by rebinning
for histogram in [
hmetBase_data, hmetInverted_data, hmetBase_EWK_GenuineTaus,
hmetInverted_EWK_GenuineTaus, hmetBase_EWK_FakeTaus,
hmetInverted_EWK_FakeTaus
]:
histogram.Rebin(_rebinFactor)
#===== Obtain inclusive EWK histograms
hmetBase_EWKinclusive = hmetBase_EWK_GenuineTaus.Clone(
"EWKinclusiveBase")
hmetBase_EWKinclusive.Add(hmetBase_EWK_FakeTaus, 1.0)
hmetInverted_EWKinclusive = hmetInverted_EWK_GenuineTaus.Clone(
"EWKinclusiveInv")
hmetInverted_EWKinclusive.Add(hmetInverted_EWK_FakeTaus, 1.0)
#===== Obtain histograms for QCD (subtract MC EWK events from data)
# QCD from baseline is usable only as a cross check
hmetBase_QCD = hmetBase_data.Clone("QCDbase")
hmetBase_QCD.Add(hmetBase_EWKinclusive, -1)
hmetInverted_QCD = hmetInverted_data.Clone("QCDinv")
hmetInverted_QCD.Add(hmetInverted_EWKinclusive, -1)
#===== Set histograms to the templates
template_EWKFakeTaus_Inverted.setHistogram(
hmetInverted_EWK_FakeTaus, binLabels[i])
template_EWKGenuineTaus_Inverted.setHistogram(
hmetInverted_EWK_GenuineTaus, binLabels[i])
template_EWKInclusive_Inverted.setHistogram(
hmetInverted_EWKinclusive, binLabels[i])
template_QCD_Inverted.setHistogram(hmetInverted_QCD, binLabels[i])
template_EWKFakeTaus_Baseline.setHistogram(hmetBase_EWK_FakeTaus,
binLabels[i])
template_EWKGenuineTaus_Baseline.setHistogram(
hmetBase_EWK_GenuineTaus, binLabels[i])
template_EWKInclusive_Baseline.setHistogram(
hmetBase_EWKinclusive, binLabels[i])
template_QCD_Baseline.setHistogram(hmetBase_QCD, binLabels[i])
#===== Make plots of templates
manager.plotTemplates()
#===== Fit individual templates to data
fitOptions = "R B" # RBLW
manager.calculateNormalizationCoefficients(hmetBase_data,
fitOptions, FITMIN,
FITMAX)
#===== Calculate combined normalisation coefficient (f_fakes = w*f_QCD + (1-w)*f_EWKfakes)
# Obtain histograms
histoName = "ForDataDrivenCtrlPlots/shapeTransverseMass/shapeTransverseMass" + binStr
dataMt = plots.DataMCPlot(dsetMgr, histoName).histoMgr.getHisto(
"Data").getRootHisto().Clone(histoName)
treatNegativeBins(dataMt, "Data_inverted mT")
histoName = "ForDataDrivenCtrlPlotsEWKFakeTaus/shapeTransverseMass/shapeTransverseMass" + binStr
ewkFakeTausMt = plots.DataMCPlot(
dsetMgr, histoName).histoMgr.getHisto(
"EWK").getRootHisto().Clone(histoName)
treatNegativeBins(ewkFakeTausMt, "ewkFakeTaus_inverted mT")
histoName = "ForDataDrivenCtrlPlotsEWKGenuineTaus/shapeTransverseMass/shapeTransverseMass" + binStr
ewkGenuineTausMt = plots.DataMCPlot(
dsetMgr, histoName).histoMgr.getHisto(
"EWK").getRootHisto().Clone(histoName)
treatNegativeBins(ewkGenuineTausMt, "ewkGenuineTaus_inverted mT")
qcdMt = dataMt.Clone("QCD")
qcdMt.Add(ewkFakeTausMt, -1)
qcdMt.Add(ewkGenuineTausMt, -1)
treatNegativeBins(qcdMt, "QCD_inverted mT")
# Do calculation
manager.calculateCombinedNormalizationCoefficient(
qcdMt, ewkFakeTausMt)
#===== Save normalization
outFileName = "QCDNormalizationFactors_%s_%s.py" % (HISTONAME,
moduleInfoString)
print argv[1], outFileName
outFileFullName = os.path.join(argv[1], outFileName)
manager.writeScaleFactorFile(outFileFullName, moduleInfoString)
def main(argv):
# HISTONAME = "TauIdJets"
# HISTONAME = "TauIdJetsCollinear"
# HISTONAME = "TauIdBtag"
# HISTONAME = "TauIdBvetoCollinear"
# HISTONAME = "TauIdBveto"
HISTONAME = "TauIdAfterCollinearCuts"
FAKEHISTO = "OnlyEWKFakeTaus"
GENUINEHISTO = "PlusFilteredEWKFakeTaus"
dirs = []
if len(sys.argv) < 2:
usage()
dirs.append(sys.argv[1])
# Create all datasets from a multicrab task
datasets = dataset.getDatasetsFromMulticrabDirs(dirs,dataEra=dataEra, searchMode=searchMode, analysisName="signalAnalysisInvertedTau",optimizationMode="") #no collinear
#datasets = dataset.getDatasetsFromMulticrabDirs(dirs,dataEra=dataEra, searchMode=searchMode, analysisName="signalAnalysisInvertedTau",optimizationMode="OptQCDTailKillerLoosePlus") #collinear
# Check multicrab consistency
consistencyCheck.checkConsistencyStandalone(dirs[0],datasets,name="QCD inverted")
# As we use weighted counters for MC normalisation, we have to
# update the all event count to a separately defined value because
# the analysis job uses skimmed pattuple as an input
datasets.updateNAllEventsToPUWeighted()
# Read integrated luminosities of data datasets from lumi.json
datasets.loadLuminosities()
# Include only 120 mass bin of HW and HH datasets
datasets.remove(filter(lambda name: "TTToHplus" in name and not "M120" in name, datasets.getAllDatasetNames()))
datasets.remove(filter(lambda name: "HplusTB" in name, datasets.getAllDatasetNames()))
datasets.remove(filter(lambda name: "Hplus_taunu_t-channel" in name, datasets.getAllDatasetNames()))
datasets.remove(filter(lambda name: "Hplus_taunu_tW-channel" in name, datasets.getAllDatasetNames()))
datasets.remove(filter(lambda name: "TTJets_SemiLept" in name, datasets.getAllDatasetNames()))
datasets.remove(filter(lambda name: "TTJets_FullLept" in name, datasets.getAllDatasetNames()))
datasets.remove(filter(lambda name: "TTJets_Hadronic" in name, datasets.getAllDatasetNames()))
# Default merging nad ordering of data and MC datasets
# All data datasets to "Data"
# All QCD datasets to "QCD"
# All single top datasets to "SingleTop"
# WW, WZ, ZZ to "Diboson"
plots.mergeRenameReorderForDataMC(datasets)
# Set BR(t->H) to 0.05, keep BR(H->tau) in 1
xsect.setHplusCrossSectionsToBR(datasets, br_tH=0.05, br_Htaunu=1)
# Merge WH and HH datasets to one (for each mass bin)
# TTToHplusBWB_MXXX and TTToHplusBHminusB_MXXX to "TTToHplus_MXXX"
plots.mergeWHandHH(datasets)
datasets.merge("EWK", [
"TTJets",
"WJets",
"DYJetsToLL",
"SingleTop",
"Diboson"
])
# Apply TDR style
style = tdrstyle.TDRStyle()
style.setOptStat(True)
invertedQCD = InvertedTauID(False)
invertedQCD.setLumi(datasets.getDataset("Data").getLuminosity())
invertedQCD.setInfo([dataEra,searchMode,HISTONAME])
invertedQCD_separatedFakes = InvertedTauID(True)
invertedQCD_separatedFakes.setLumi(datasets.getDataset("Data").getLuminosity())
invertedQCD_separatedFakes.setInfo([dataEra,searchMode,HISTONAME])
histonames = datasets.getDataset("Data").getDirectoryContent("baseline/METBaseline"+HISTONAME)
bins = []
binLabels = []
for histoname in histonames:
bins.append(histoname.replace("METBaseline"+HISTONAME,""))
title = datasets.getDataset("Data").getDatasetRootHisto("baseline/METBaseline"+HISTONAME+"/"+histoname).getHistogram().GetTitle()
title = title.replace("METBaseline"+HISTONAME,"")
title = title.replace("#tau p_{T}","taup_T")
title = title.replace("#tau eta","taueta")
title = title.replace("<","lt")
title = title.replace(">","gt")
title = title.replace("=","eq")
title = title.replace("..","to")
title = title.replace(".","p")
title = title.replace("/","_")
binLabels.append(title)
#binLabels = bins # for this data set
print
print "Histogram bins available",bins
# bins = ["Inclusive"]
# bins = ["taup_Tleq50","taup_Teq50to60"]
print "Using bins ",bins
print
print "Bin labels"
for i in range(len(binLabels)):
line = bins[i]
while len(line) < 10:
line += " "
line += ": "+binLabels[i]
print line
print
for i,bin in enumerate(bins):
invertedQCD.setLabel(binLabels[i])
invertedQCD_separatedFakes.setLabel(binLabels[i])
metBase = plots.DataMCPlot(datasets, "baseline/METBaseline"+HISTONAME+"/METBaseline"+HISTONAME+bin)
metInver = plots.DataMCPlot(datasets, "Inverted/METInverted"+HISTONAME+"/METInverted"+HISTONAME+bin)
#metBase_EWK = plots.DataMCPlot(datasets, "baseline/METBaseline"+HISTONAME+"/METBaseline"+HISTONAME+bin)
#metInver_EWK = plots.DataMCPlot(datasets, "Inverted/METInverted"+HISTONAME+"/METInverted"+HISTONAME+bin)
metBase_GenuineTaus = plots.DataMCPlot(datasets, "baseline/METBaseline"+HISTONAME+GENUINEHISTO+"/METBaseline"+HISTONAME+GENUINEHISTO+bin)
metInver_GenuineTaus = plots.DataMCPlot(datasets, "Inverted/METInverted"+HISTONAME+GENUINEHISTO+"/METInverted"+HISTONAME+GENUINEHISTO+bin)
metBase_FakeTaus = plots.DataMCPlot(datasets, "baseline/METBaseline"+HISTONAME+FAKEHISTO+"/METBaseline"+HISTONAME+FAKEHISTO+bin)
metInver_FakeTaus = plots.DataMCPlot(datasets, "Inverted/METInverted"+HISTONAME+FAKEHISTO+"/METInverted"+HISTONAME+FAKEHISTO+bin)
# Rebin before subtracting
metBase.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(10)) #5
metInver.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(10)) #5
#metBase_EWK.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(10)) #5
#metInver_EWK.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(10)) #5
metBase_GenuineTaus.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(10)) #5
metInver_GenuineTaus.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(10)) #5
metBase_FakeTaus.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(10)) #5
metInver_FakeTaus.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(10)) #5
metInverted_data = metInver.histoMgr.getHisto("Data").getRootHisto().Clone("Inverted/METInverted"+HISTONAME+"/METInverted"+HISTONAME+bin)
metBase_data = metBase.histoMgr.getHisto("Data").getRootHisto().Clone("baseline/METBaseLine"+HISTONAME+"/METBaseline"+HISTONAME+bin)
metInverted_EWK = metInver.histoMgr.getHisto("EWK").getRootHisto().Clone("Inverted/METInverted"+HISTONAME+"/METInverted"+HISTONAME+bin)
metBase_EWK = metBase.histoMgr.getHisto("EWK").getRootHisto().Clone("baseline/METBaseLine"+HISTONAME+"/METBaseline"+HISTONAME+bin)
metInverted_EWK_GenuineTaus = metInver_GenuineTaus.histoMgr.getHisto("EWK").getRootHisto().Clone("Inverted/METInverted"+HISTONAME+GENUINEHISTO+"/METInverted"+HISTONAME+GENUINEHISTO+bin)
metBase_EWK_GenuineTaus = metBase_GenuineTaus.histoMgr.getHisto("EWK").getRootHisto().Clone("baseline/METBaseLine"+HISTONAME+GENUINEHISTO+"/METBaseline"+HISTONAME+GENUINEHISTO+bin)
metInverted_EWK_FakeTaus = metInver_FakeTaus.histoMgr.getHisto("EWK").getRootHisto().Clone("Inverted/METInverted"+HISTONAME+FAKEHISTO+"/METInverted"+HISTONAME+FAKEHISTO+bin)
metBase_EWK_FakeTaus = metBase_FakeTaus.histoMgr.getHisto("EWK").getRootHisto().Clone("baseline/METBaseLine"+HISTONAME+FAKEHISTO+"/METBaseline"+HISTONAME+FAKEHISTO+bin)
metBase_QCD = metBase_data.Clone("QCD")
metBase_QCD.Add(metBase_EWK,-1)
metInverted_QCD = metInverted_data.Clone("QCD")
metInverted_QCD.Add(metInverted_EWK,-1)
metBase_QCD_separatedFakes = metBase_data.Clone("QCD")
metBase_QCD_separatedFakes.Add(metBase_EWK_GenuineTaus,-1)
metBase_QCD_separatedFakes.Add(metBase_EWK_FakeTaus,-1)
metInverted_QCD_separatedFakes = metInverted_data.Clone("QCD")
metInverted_QCD_separatedFakes.Add(metInverted_EWK_GenuineTaus,-1)
metInverted_QCD_separatedFakes.Add(metInverted_EWK_FakeTaus,-1)
metInverted_data = addlabels(metInverted_data)
metInverted_EWK = addlabels(metInverted_EWK)
metInverted_EWK_GenuineTaus = addlabels(metInverted_EWK_GenuineTaus)
metInverted_EWK_FakeTaus = addlabels(metInverted_EWK_FakeTaus)
metBase_data = addlabels(metBase_data)
metBase_EWK = addlabels(metBase_EWK)
metBase_EWK_GenuineTaus = addlabels(metBase_EWK_GenuineTaus)
metBase_EWK_FakeTaus = addlabels(metBase_EWK_FakeTaus)
metInverted_QCD = addlabels(metInverted_QCD)
metInverted_QCD_separatedFakes = addlabels(metInverted_QCD_separatedFakes)
#non-separated
invertedQCD.plotHisto(metInverted_data,"inverted")
invertedQCD.plotHisto(metInverted_EWK,"invertedEWK")
invertedQCD.plotHisto(metBase_data,"baseline")
invertedQCD.plotHisto(metBase_EWK,"baselineEWK")
fitOptions = "LRB"
invertedQCD.fitEWK(metInverted_EWK,fitOptions)
invertedQCD.fitEWK(metBase_EWK,fitOptions)
invertedQCD.fitQCD(metInverted_QCD,fitOptions)
invertedQCD.fitData(metBase_data)
invertedQCD.getNormalization()
#separated
invertedQCD_separatedFakes.plotHisto(metInverted_data,"inverted")
invertedQCD_separatedFakes.plotHisto(metInverted_EWK_GenuineTaus,"invertedEWKGenuineTaus")
invertedQCD_separatedFakes.plotHisto(metInverted_EWK_FakeTaus,"invertedEWKFakeTaus")
invertedQCD_separatedFakes.plotHisto(metBase_data,"baseline")
invertedQCD_separatedFakes.plotHisto(metBase_EWK_GenuineTaus,"baselineEWKGenuineTaus")
invertedQCD_separatedFakes.plotHisto(metBase_EWK_FakeTaus,"baselineEWKFakeTaus")
invertedQCD_separatedFakes.fitEWK_GenuineTaus(metInverted_EWK_GenuineTaus,fitOptions)
invertedQCD_separatedFakes.fitEWK_GenuineTaus(metBase_EWK_GenuineTaus,fitOptions)
invertedQCD_separatedFakes.fitEWK_FakeTaus(metInverted_EWK_FakeTaus,fitOptions)
invertedQCD_separatedFakes.fitEWK_FakeTaus(metBase_EWK_FakeTaus,fitOptions)
invertedQCD_separatedFakes.fitQCD(metInverted_QCD_separatedFakes,fitOptions)
invertedQCD_separatedFakes.fitData(metBase_data)
invertedQCD_separatedFakes.getNormalization()
invertedQCD.Summary()
invertedQCD.WriteNormalizationToFile("QCDInvertedNormalizationFactors.py")
invertedQCD.WriteLatexOutput("fits.tex")
invertedQCD_separatedFakes.Summary()
invertedQCD_separatedFakes.WriteNormalizationToFile("QCDInvertedNormalizationFactorsSeparatedFakeTaus.py")
invertedQCD_separatedFakes.WriteLatexOutput("fits_separatedfaketaus.tex")
mergeNormalizations("QCDInvertedNormalizationFactors.py","QCDInvertedNormalizationFactorsSeparatedFakeTaus.py")
def main(argv):
COMBINEDHISTODIR = "ForQCDNormalization"
FAKEHISTODIR = "ForQCDNormalizationEWKFakeTaus"
GENUINEHISTODIR = "ForQCDNormalizationEWKGenuineTaus"
comparisonList = ["AfterStdSelections"]
dirs = []
if len(sys.argv) < 2:
usage()
dirs.append(sys.argv[1])
# Create all dsetMgr from a multicrab task
dsetMgr = dataset.getDatasetsFromMulticrabDirs(dirs,
dataEra=dataEra,
searchMode=searchMode,
analysisName=analysis)
#print dsetMgr
# Check multicrab consistency
consistencyCheck.checkConsistencyStandalone(dirs[0],
dsetMgr,
name="QCD inverted")
# As we use weighted counters for MC normalisation, we have to
# update the all event count to a separately defined value because
# the analysis job uses skimmed pattuple as an input
dsetMgr.updateNAllEventsToPUWeighted()
# Read integrated luminosities of data dsetMgr from lumi.json
dsetMgr.loadLuminosities()
# Include only 120 mass bin of HW and HH dsetMgr
dsetMgr.remove(
filter(lambda name: "TTToHplus" in name and not "M120" in name,
dsetMgr.getAllDatasetNames()))
dsetMgr.remove(
filter(lambda name: "HplusTB" in name, dsetMgr.getAllDatasetNames()))
# Default merging nad ordering of data and MC dsetMgr
# All data dsetMgr to "Data"
# All QCD dsetMgr to "QCD"
# All single top dsetMgr to "SingleTop"
# WW, WZ, ZZ to "Diboson"
plots.mergeRenameReorderForDataMC(dsetMgr)
# Set BR(t->H) to 0.05, keep BR(H->tau) in 1
xsect.setHplusCrossSectionsToBR(dsetMgr, br_tH=0.05, br_Htaunu=1)
# Merge WH and HH dsetMgr to one (for each mass bin)
plots.mergeWHandHH(dsetMgr)
dsetMgr.merge(
"EWK",
[
"TTJets",
"WJetsHT",
"DYJetsToLL",
"SingleTop",
#"Diboson"
])
# Apply TDR style
style = tdrstyle.TDRStyle()
style.setOptStat(True)
for HISTONAME in comparisonList:
BASELINETAUHISTONAME = "NormalizationMETBaselineTau" + HISTONAME + "/NormalizationMETBaselineTau" + HISTONAME
INVERTEDTAUHISTONAME = "NormalizationMETInvertedTau" + HISTONAME + "/NormalizationMETInvertedTau" + HISTONAME
#===== Infer binning information and labels
histonames = dsetMgr.getDataset("Data").getDirectoryContent(
COMBINEDHISTODIR + "/NormalizationMETBaselineTau" + HISTONAME)
bins = []
binLabels = []
if histonames == None:
# Assume that only inclusive bin exists
name = COMBINEDHISTODIR + "/NormalizationMETBaselineTau" + HISTONAME
if not dsetMgr.getDataset("Data").hasRootHisto(name):
raise Exception(
"Error: Cannot find histogram or directory of name '%s'!" %
name)
BASELINETAUHISTONAME = "NormalizationMETBaselineTau" + HISTONAME
INVERTEDTAUHISTONAME = "NormalizationMETInvertedTau" + HISTONAME
bins = [""]
binLabels = ["Inclusive"]
else:
for hname in histonames:
bins.append(
hname.replace("NormalizationMETBaselineTau" + HISTONAME,
""))
title = dsetMgr.getDataset("Data").getDatasetRootHisto(
COMBINEDHISTODIR + "/" + BASELINETAUHISTONAME + "/" +
hname).getHistogram().GetTitle()
title = title.replace("METBaseline" + HISTONAME, "")
binLabels.append(formatHistoTitle(title))
print "\nHistogram bins available", bins
print "Using bins ", bins
print "\nBin labels"
for i in range(len(binLabels)):
line = bins[i]
while len(line) < 10:
line += " "
line += ": " + binLabels[i]
print line
print
#===== Initialize normalization calculator
invertedQCD = InvertedTauID()
invertedQCD.setLumi(dsetMgr.getDataset("Data").getLuminosity())
invertedQCD.setInfo([dataEra, searchMode, HISTONAME])
#===== Loop over tau pT bins
for i, binStr in enumerate(bins):
print "\n********************************"
print "*** Fitting bin %s" % binLabels[i]
print "********************************\n"
invertedQCD.resetBinResults()
invertedQCD.setLabel(binLabels[i])
#===== Obtain histograms for normalization
metBase = plots.DataMCPlot(
dsetMgr,
COMBINEDHISTODIR + "/" + BASELINETAUHISTONAME + binStr)
metInver = plots.DataMCPlot(
dsetMgr,
COMBINEDHISTODIR + "/" + INVERTEDTAUHISTONAME + binStr)
metBase_GenuineTaus = plots.DataMCPlot(
dsetMgr, GENUINEHISTODIR + "/" + BASELINETAUHISTONAME + binStr)
metInver_GenuineTaus = plots.DataMCPlot(
dsetMgr, GENUINEHISTODIR + "/" + INVERTEDTAUHISTONAME + binStr)
metBase_FakeTaus = plots.DataMCPlot(
dsetMgr, FAKEHISTODIR + "/" + BASELINETAUHISTONAME + binStr)
metInver_FakeTaus = plots.DataMCPlot(
dsetMgr, FAKEHISTODIR + "/" + INVERTEDTAUHISTONAME + binStr)
#===== Rebin histograms before subtracting
RebinFactor = 2 # Aim for 10 GeV binning
metBase.histoMgr.forEachHisto(
lambda h: h.getRootHisto().Rebin(RebinFactor))
metInver.histoMgr.forEachHisto(
lambda h: h.getRootHisto().Rebin(RebinFactor))
metBase_GenuineTaus.histoMgr.forEachHisto(
lambda h: h.getRootHisto().Rebin(RebinFactor))
metInver_GenuineTaus.histoMgr.forEachHisto(
lambda h: h.getRootHisto().Rebin(RebinFactor))
metBase_FakeTaus.histoMgr.forEachHisto(
lambda h: h.getRootHisto().Rebin(RebinFactor))
metInver_FakeTaus.histoMgr.forEachHisto(
lambda h: h.getRootHisto().Rebin(RebinFactor))
#===== Obtain templates for data and EWK
metInverted_data = metInver.histoMgr.getHisto(
"Data").getRootHisto().Clone(COMBINEDHISTODIR + "/" +
INVERTEDTAUHISTONAME + binStr)
treatHistogram(metInverted_data, "Data, inverted")
metInverted_EWK_GenuineTaus = metInver_GenuineTaus.histoMgr.getHisto(
"EWK").getRootHisto().Clone(GENUINEHISTODIR + "/" +
INVERTEDTAUHISTONAME + binStr)
treatHistogram(metInverted_EWK_GenuineTaus,
"EWK genuine taus, inverted")
metInverted_EWK_FakeTaus = metInver_FakeTaus.histoMgr.getHisto(
"EWK").getRootHisto().Clone(FAKEHISTODIR + "/" +
INVERTEDTAUHISTONAME + binStr)
treatHistogram(metInverted_EWK_FakeTaus, "EWK fake taus, inverted")
metBase_data = metBase.histoMgr.getHisto(
"Data").getRootHisto().Clone(COMBINEDHISTODIR + "/" +
BASELINETAUHISTONAME + binStr)
treatHistogram(metBase_data, "Data, baseline")
metBase_EWK_GenuineTaus = metBase_GenuineTaus.histoMgr.getHisto(
"EWK").getRootHisto().Clone(GENUINEHISTODIR + "/" +
BASELINETAUHISTONAME + binStr)
treatHistogram(metBase_EWK_GenuineTaus,
"EWK genuine taus, baseline")
metBase_EWK_FakeTaus = metBase_FakeTaus.histoMgr.getHisto(
"EWK").getRootHisto().Clone(FAKEHISTODIR + "/" +
BASELINETAUHISTONAME + binStr)
treatHistogram(metBase_EWK_FakeTaus, "EWK fake taus, baseline")
#===== Obtain templates for QCD (subtract MC EWK events from data)
# QCD from baseline is usable only as a cross check
#metBase_QCD = metBase_data.Clone("QCD")
#metBase_QCD.Add(metBase_EWK_GenuineTaus,-1)
#metBase_QCD.Add(metBase_EWK_FakeTaus,-1)
#addLabels(metBase_QCD, "QCD, baseline")
metInverted_QCD = metInverted_data.Clone("QCD")
metInverted_QCD.Add(metInverted_EWK_GenuineTaus, -1)
metInverted_QCD.Add(metInverted_EWK_FakeTaus, -1)
treatHistogram(metInverted_QCD, "QCD, inverted")
#===== Make plots of templates
print "\n*** Integrals of plotted templates"
#invertedQCD.plotHisto(metInverted_data,"template_Data_Inverted")
#invertedQCD.plotHisto(metInverted_EWK_GenuineTaus,"template_EWKGenuineTaus_Inverted")
#invertedQCD.plotHisto(metInverted_EWK_FakeTaus,"template_EWKFakeTaus_Inverted")
invertedQCD.plotHisto(metInverted_QCD, "template_QCD_Inverted")
invertedQCD.plotHisto(metBase_data, "template_Data_Baseline")
invertedQCD.plotHisto(metBase_EWK_GenuineTaus,
"template_EWKGenuineTaus_Baseline")
invertedQCD.plotHisto(metBase_EWK_FakeTaus,
"template_EWKFakeTaus_Baseline")
#invertedQCD.plotHisto(metBase_QCD,"template_QCD_Baseline")
#===== Fit individual templates and
# Fit first templates for QCD, EWK_genuine_taus, and EWK_fake_taus
# Then fit the shape of those parametrizations to baseline data to obtain normalization coefficients
fitOptions = "RB"
# Strategy: take EWK templates from baseline and QCD template from inverted; then fit to baseline data
invertedQCD.fitEWK_GenuineTaus(metInverted_EWK_GenuineTaus,
fitOptions)
invertedQCD.fitEWK_GenuineTaus(metBase_EWK_GenuineTaus, fitOptions)
invertedQCD.fitEWK_FakeTaus(metInverted_EWK_FakeTaus, fitOptions)
invertedQCD.fitEWK_FakeTaus(metBase_EWK_FakeTaus, fitOptions)
invertedQCD.fitQCD(metInverted_QCD, fitOptions)
invertedQCD.fitData(metBase_data)
#===== Calculate normalization
invertedQCD.getNormalization()
invertedQCD.Summary()
invertedQCD.WriteNormalizationToFile(
"QCDInvertedNormalizationFactorsFilteredEWKFakeTaus.py")
invertedQCD.WriteLatexOutput("fits.tex")
def main(opts):
Verbose("main function")
comparisonList = ["AfterStdSelections"]
# Setup & configure the dataset manager
datasetsMgr = GetDatasetsFromDir(opts)
datasetsMgr.updateNAllEventsToPUWeighted()
datasetsMgr.loadLuminosities() # from lumi.json
# Set/Overwrite cross-sections
for d in datasetsMgr.getAllDatasets():
if "ChargedHiggs" in d.getName():
print d.getName()
datasetsMgr.getDataset(d.getName()).setCrossSection(10.0)
if opts.verbose:
datasetsMgr.PrintCrossSections()
datasetsMgr.PrintLuminosities()
# Check multicrab consistency
if 0:
consistencyCheck.checkConsistencyStandalone(dirs[0],
datasets,
name="CorrelationAnalysis")
# Custom Filtering of datasets
if 0:
datasetsMgr.remove(
filter(lambda name: "ST" in name,
datasetsMgr.getAllDatasetNames()))
# Merge histograms (see NtupleAnalysis/python/tools/plots.py)
plots.mergeRenameReorderForDataMC(datasetsMgr)
# Get Integrated Luminosity
if opts.mcOnly:
# Determine integrated lumi
if opts.intLumi < 0.0:
opts.intLumi = GetLumi(datasetsMgr)
else:
pass
# Remove data datasets
datasetsMgr.remove(
filter(lambda name: "Data" in name,
datasetsMgr.getAllDatasetNames()))
# Re-order datasets (different for inverted than default=baseline)
newOrder = ["Data"]
newOrder.extend(["ChargedHiggs_HplusTB_HplusToTB_M_500"])
newOrder.extend(GetListOfEwkDatasets())
datasetsMgr.selectAndReorder(newOrder)
if opts.mcOnly:
newOrder.remove("Data")
# Merge EWK samples
if opts.mergeEWK:
datasetsMgr.merge("EWK", GetListOfEwkDatasets())
plots._plotStyles["EWK"] = styles.getAltEWKStyle()
# Print dataset information
datasetsMgr.PrintInfo()
# Apply TDR style
style = tdrstyle.TDRStyle()
style.setOptStat(True)
# Do the standard top-selections
analysisTypes = ["Baseline", "Inverted"]
for analysis in analysisTypes:
Print("Plotting Top Selection Histograms (%s)" % (analysis), True)
TopSelectionHistograms(opts, datasetsMgr, analysis)
# Do Data-MC histograms
#for analysis in analysisTypes:
# Print("Plotting Other Histograms (%s)" % (analysis), True)
# DataMCHistograms(datasetsMgr, analysis)
return
def main(opts):
#optModes = ["", "OptChiSqrCutValue50p0", "OptChiSqrCutValue100p0", "OptChiSqrCutValue150p0", "OptChiSqrCutValue200p0"]
optModes = ["OptChiSqrCutValue100"]
if opts.optMode != None:
optModes = [opts.optMode]
# For-loop: All opt Mode
for opt in optModes:
opts.optMode = opt
# Setup & configure the dataset manager
datasetsMgr = GetDatasetsFromDir(opts)
datasetsMgr.updateNAllEventsToPUWeighted()
datasetsMgr.loadLuminosities() # from lumi.json
# Set/Overwrite cross-sections
for d in datasetsMgr.getAllDatasets():
if "ChargedHiggs" in d.getName():
print d.getName()
datasetsMgr.getDataset(d.getName()).setCrossSection(
1.0) # ATLAS 13 TeV H->tb exclusion limits
if opts.verbose:
datasetsMgr.PrintCrossSections()
datasetsMgr.PrintLuminosities()
# Check multicrab consistency
if 0:
consistencyCheck.checkConsistencyStandalone(
dirs[0], datasets, name="CorrelationAnalysis")
# Custom Filtering of datasets
if 0:
datasetsMgr.remove(
filter(lambda name: "ST" in name,
datasetsMgr.getAllDatasetNames()))
# Merge histograms (see NtupleAnalysis/python/tools/plots.py)
plots.mergeRenameReorderForDataMC(datasetsMgr)
# Re-order datasets (different for inverted than default=baseline)
if 1:
newOrder = ["Data"]
newOrder.extend(["ChargedHiggs_HplusTB_HplusToTB_M_500"])
newOrder.extend(["QCD"])
newOrder.extend(GetListOfEwkDatasets())
datasetsMgr.selectAndReorder(newOrder)
# Merge EWK samples
if opts.mergeEWK:
datasetsMgr.merge("EWK", GetListOfEwkDatasets())
plots._plotStyles["EWK"] = styles.getAltEWKStyle()
# Print dataset information
datasetsMgr.PrintInfo()
# Apply TDR style
style = tdrstyle.TDRStyle()
style.setOptStat(True)
# Do the standard top-selections
analysisTypes = ["Baseline", "Inverted"]
for analysis in analysisTypes:
Print("Plotting Top Selection Histograms (%s)" % (analysis), True)
PlotSignal(datasetsMgr, analysis, opts)
# Do Data-MC histograms
# for analysis in analysisTypes:
# Print("Plotting Other Histograms (%s)" % (analysis), True)
# DataMCHistograms(datasetsMgr, analysis)
return
def main(argv):
dirs = []
if len(sys.argv) < 2:
usage()
dirs.append(sys.argv[1])
comparisonList = ["AfterStdSelections"]
# Create all datasets from a multicrab task
datasets = dataset.getDatasetsFromMulticrabDirs(dirs,
dataEra=dataEra,
searchMode=searchMode,
analysisName=analysis)
#print datasets.getDatasetNames()
#print datasets
# Check multicrab consistency
consistencyCheck.checkConsistencyStandalone(dirs[0],
datasets,
name="CorrelationAnalysis")
# As we use weighted counters for MC normalisation, we have to
# update the all event count to a separately defined value because
# the analysis job uses skimmed pattuple as an input
datasets.updateNAllEventsToPUWeighted()
# Read integrated luminosities of data datasets from lumi.json
datasets.loadLuminosities()
# Include only 120 mass bin of HW and HH datasets
#datasets.remove(filter(lambda name: "TTToHplus" in name and not "M120" in name, datasets.getAllDatasetNames()))
datasets.remove(
filter(lambda name: "TTToHplusBWB" in name,
datasets.getAllDatasetNames()))
datasets.remove(
filter(lambda name: "HplusTB" in name and not "M_500" in name,
datasets.getAllDatasetNames()))
# datasets.remove(filter(lambda name: "HplusTB" in name, datasets.getAllDatasetNames()))
datasets.remove(
filter(lambda name: "Hplus_taunu_t-channel" in name,
datasets.getAllDatasetNames()))
datasets.remove(
filter(lambda name: "Hplus_taunu_tW-channel" in name,
datasets.getAllDatasetNames()))
datasets.remove(
filter(lambda name: "TTJets_SemiLept" in name,
datasets.getAllDatasetNames()))
datasets.remove(
filter(lambda name: "TTJets_FullLept" in name,
datasets.getAllDatasetNames()))
datasets.remove(
filter(lambda name: "TTJets_Hadronic" in name,
datasets.getAllDatasetNames()))
# datasets.remove(filter(lambda name: "QCD" in name, datasets.getAllDatasetNames()))
# datasets.remove(filter(lambda name: "WJetsToLNu" in name, datasets.getAllDatasetNames()))
# datasets.remove(filter(lambda name: "DYJetsToLL" in name, datasets.getAllDatasetNames()))
datasets.remove(
filter(
lambda name: ("DYJetsToLL_M_10to50" in name or "DYJetsToLL_M_50" in
name) and not "DYJetsToLL_M_50_HT" in name,
datasets.getAllDatasetNames()))
datasets.remove(
filter(
lambda name: "WJetsToLNu" in name and not "WJetsToLNu_HT" in name,
datasets.getAllDatasetNames()))
# Default merging nad ordering of data and MC datasets
# All data datasets to "Data"
# All QCD datasets to "QCD"
# All single top datasets to "SingleTop"
# WW, WZ, ZZ to "Diboson"
plots.mergeRenameReorderForDataMC(datasets)
# Set BR(t->H) to 0.05, keep BR(H->tau) in 1
xsect.setHplusCrossSectionsToBR(datasets, br_tH=0.05, br_Htaunu=1)
# Merge WH and HH datasets to one (for each mass bin)
# TTToHplusBWB_MXXX and TTToHplusBHminusB_MXXX to "TTToHplus_MXXX"
plots.mergeWHandHH(datasets)
# datasets.getDataset("TTbar_HBWB_HToTauNu_M_160_13TeV_pythia6").setCrossSection(0.336902*2*0.955592) # pb
# At the moment the collision energy must be set by hand
# for dset in datasets.getMCDatasets():
# dset.setEnergy("13")
# At the moment the cross sections must be set by hand
#xsect.setBackgroundCrossSections(datasets)
if False:
datasets.merge(
"EWK", ["TT", "WJetsHT", "DYJetsToLLHT", "SingleTop", "Diboson"])
# Apply TDR style
style = tdrstyle.TDRStyle()
style.setOptStat(True)
dataMCExample(datasets)
MtComparison(datasets)
# MetComparison(datasets)
# TauPtComparison(datasets)
# Print counters
doCounters(datasets)
# Script execution can be paused like this, it will continue after
# user has given some input (which must include enter)
if drawToScreen:
raw_input("Hit enter to continue")
def main(argv):
dirs = []
if len(sys.argv) < 2:
usage()
dirs.append(sys.argv[1])
comparisonList = ["AfterStdSelections"]
# Create all datasets from a multicrab task
datasets = dataset.getDatasetsFromMulticrabDirs(dirs,
dataEra=dataEra,
searchMode=searchMode,
analysisName=analysis)
#print datasets.getDatasetNames()
#print datasets
# Check multicrab consistency
consistencyCheck.checkConsistencyStandalone(dirs[0],
datasets,
name="QCD inverted")
# As we use weighted counters for MC normalisation, we have to
# update the all event count to a separately defined value because
# the analysis job uses skimmed pattuple as an input
datasets.updateNAllEventsToPUWeighted()
# Read integrated luminosities of data datasets from lumi.json
datasets.loadLuminosities()
# Include only 120 mass bin of HW and HH datasets
datasets.remove(
filter(lambda name: "TTToHplus" in name and not "M120" in name,
datasets.getAllDatasetNames()))
datasets.remove(
filter(lambda name: "HplusTB" in name, datasets.getAllDatasetNames()))
datasets.remove(
filter(lambda name: "Hplus_taunu_t-channel" in name,
datasets.getAllDatasetNames()))
datasets.remove(
filter(lambda name: "Hplus_taunu_tW-channel" in name,
datasets.getAllDatasetNames()))
#datasets.remove(filter(lambda name: "TTJets_SemiLept" in name, datasets.getAllDatasetNames()))
#datasets.remove(filter(lambda name: "TTJets_FullLept" in name, datasets.getAllDatasetNames()))
#datasets.remove(filter(lambda name: "TTJets_Hadronic" in name, datasets.getAllDatasetNames()))
# Default merging nad ordering of data and MC datasets
# All data datasets to "Data"
# All QCD datasets to "QCD"
# All single top datasets to "SingleTop"
# WW, WZ, ZZ to "Diboson"
plots.mergeRenameReorderForDataMC(datasets)
# Set BR(t->H) to 0.05, keep BR(H->tau) in 1
xsect.setHplusCrossSectionsToBR(datasets, br_tH=0.05, br_Htaunu=1)
# Merge WH and HH datasets to one (for each mass bin)
# TTToHplusBWB_MXXX and TTToHplusBHminusB_MXXX to "TTToHplus_MXXX"
plots.mergeWHandHH(datasets)
datasets.merge(
"EWK",
[
"TTJets",
"WJets",
"DYJetsToLL",
"SingleTop",
# "Diboson"
])
# Apply TDR style
style = tdrstyle.TDRStyle()
style.setOptStat(True)
for HISTONAME in comparisonList:
invertedQCD = InvertedTauID()
invertedQCD.setLumi(datasets.getDataset("Data").getLuminosity())
invertedQCD.setInfo([dataEra, searchMode, HISTONAME])
histonames = datasets.getDataset("Data").getDirectoryContent(
"ForQCDNormalization/NormalizationMETBaselineTau" + HISTONAME)
bins = []
binLabels = []
for histoname in histonames:
bins.append(
histoname.replace("NormalizationMETBaselineTau" + HISTONAME,
""))
title = datasets.getDataset("Data").getDatasetRootHisto(
"ForQCDNormalization/NormalizationMETBaselineTau" + HISTONAME +
"/" + histoname).getHistogram().GetTitle()
title = title.replace("METBaseline" + HISTONAME, "")
title = title.replace("#tau p_{T}", "taup_T")
title = title.replace("#tau eta", "taueta")
title = title.replace("<", "lt")
title = title.replace(">", "gt")
title = title.replace("=", "eq")
title = title.replace("..", "to")
title = title.replace(".", "p")
title = title.replace("/", "_")
binLabels.append(title)
binLabels = bins # for this data set
print
print "Histogram bins available", bins
# bins = ["Inclusive"]
# bins = ["taup_Tleq50","taup_Teq50to60"]
print "Using bins ", bins
print
print "Bin labels"
for i in range(len(binLabels)):
line = bins[i]
while len(line) < 10:
line += " "
line += ": " + binLabels[i]
print line
print
for i, bin in enumerate(bins):
invertedQCD.setLabel(binLabels[i])
metBase = plots.DataMCPlot(
datasets, "ForQCDNormalization/NormalizationMETBaselineTau" +
HISTONAME + "/NormalizationMETBaselineTau" + HISTONAME + bin)
metInver = plots.DataMCPlot(
datasets, "ForQCDNormalization/NormalizationMETInvertedTau" +
HISTONAME + "/NormalizationMETInvertedTau" + HISTONAME + bin)
# Rebin before subtracting
RebinFactor = 10
metBase.histoMgr.forEachHisto(
lambda h: h.getRootHisto().Rebin(RebinFactor))
metInver.histoMgr.forEachHisto(
lambda h: h.getRootHisto().Rebin(RebinFactor))
metInverted_data = metInver.histoMgr.getHisto("Data").getRootHisto(
).Clone("ForQCDNormalization/NormalizationMETInvertedTau" +
HISTONAME + "/NormalizationMETInvertedTau" + HISTONAME +
bin)
metInverted_EWK = metInver.histoMgr.getHisto("EWK").getRootHisto(
).Clone("ForQCDNormalization/NormalizationMETInvertedTau" +
HISTONAME + "/NormalizationMETInvertedTau" + HISTONAME +
bin)
metBase_data = metBase.histoMgr.getHisto("Data").getRootHisto(
).Clone("ForQCDNormalization/NormalizationMETBaselineTau" +
HISTONAME + "/NormalizationMETBaselineTau" + HISTONAME +
bin)
metBase_EWK = metBase.histoMgr.getHisto("EWK").getRootHisto(
).Clone("ForQCDNormalization/NormalizationMETBaselineTau" +
HISTONAME + "/NormalizationMETBaselineTau" + HISTONAME +
bin)
metBase_QCD = metBase_data.Clone("QCD")
metBase_QCD.Add(metBase_EWK, -1)
metInverted_QCD = metInverted_data.Clone("QCD")
metInverted_QCD.Add(metInverted_EWK, -1)
metInverted_data = addlabels(metInverted_data)
metInverted_EWK = addlabels(metInverted_EWK)
metBase_data = addlabels(metBase_data)
metBase_EWK = addlabels(metBase_EWK)
metInverted_QCD = addlabels(metInverted_QCD)
invertedQCD.plotHisto(metInverted_data, "inverted")
invertedQCD.plotHisto(metInverted_EWK, "invertedEWK")
invertedQCD.plotHisto(metBase_data, "baseline")
invertedQCD.plotHisto(metBase_EWK, "baselineEWK")
fitOptions = "RB"
invertedQCD.fitEWK(metInverted_EWK, fitOptions)
invertedQCD.fitEWK(metBase_EWK, fitOptions)
invertedQCD.fitQCD(metInverted_QCD, fitOptions)
invertedQCD.fitData(metBase_data)
invertedQCD.getNormalization()
invertedQCD.Summary()
normalizationFileName = HISTONAME #.replace("TauIdAfterCollinearCuts","")
if HISTONAME == "TauIdAfterCollinearCutsPlusFilteredEWKFakeTaus":
normalizationFileName = normalizationFileName.replace("Plus", "")
invertedQCD.WriteNormalizationToFile(
"QCDInvertedNormalizationFactors" + normalizationFileName + ".py")
invertedQCD.WriteLatexOutput("fits" + normalizationFileName + ".tex")
def main(argv, dsetMgr, moduleInfoString):
COMBINEDHISTODIR = "ForQCDNormalization"
FAKEHISTODIR = "ForQCDNormalizationEWKFakeTaus"
GENUINEHISTODIR = "ForQCDNormalizationEWKGenuineTaus"
comparisonList = ["AfterStdSelections"]
dirs = []
dirs.append(sys.argv[1])
# Check multicrab consistency
consistencyCheck.checkConsistencyStandalone(dirs[0],dsetMgr,name="QCD inverted")
# As we use weighted counters for MC normalisation, we have to
# update the all event count to a separately defined value because
# the analysis job uses skimmed pattuple as an input
dsetMgr.updateNAllEventsToPUWeighted()
# Read integrated luminosities of data dsetMgr from lumi.json
dsetMgr.loadLuminosities()
# Include only 120 mass bin of HW and HH dsetMgr
dsetMgr.remove(filter(lambda name: "TTToHplus" in name and not "M120" in name, dsetMgr.getAllDatasetNames()))
dsetMgr.remove(filter(lambda name: "HplusTB" in name, dsetMgr.getAllDatasetNames()))
# Default merging nad ordering of data and MC dsetMgr
# All data dsetMgr to "Data"
# All QCD dsetMgr to "QCD"
# All single top dsetMgr to "SingleTop"
# WW, WZ, ZZ to "Diboson"
plots.mergeRenameReorderForDataMC(dsetMgr)
# Set BR(t->H) to 0.05, keep BR(H->tau) in 1
xsect.setHplusCrossSectionsToBR(dsetMgr, br_tH=0.05, br_Htaunu=1)
# Merge WH and HH dsetMgr to one (for each mass bin)
plots.mergeWHandHH(dsetMgr)
# Merge MC EWK samples as one EWK sample
myMergeList = []
if "TT" in dsetMgr.getMCDatasetNames():
myMergeList.append("TT") # Powheg, no neg. weights -> large stats.
else:
myMergeList.append("TTJets") # Madgraph with negative weights
print "Warning: using TTJets as input, but this is suboptimal. Please switch to the TT sample (much more stats.)."
myMergeList.append("WJetsHT")
myMergeList.append("DYJetsToLLHT")
myMergeList.append("SingleTop")
if "Diboson" in dsetMgr.getMCDatasetNames():
myMergeList.append("Diboson")
print "Warning: ignoring diboson sample (since it does not exist) ..."
for item in myMergeList:
if not item in dsetMgr.getMCDatasetNames():
raise Exception("Error: tried to use dataset '%s' as part of the merged EWK dataset, but the dataset '%s' does not exist!"%(item,item))
dsetMgr.merge("EWK", myMergeList)
# Apply TDR style
style = tdrstyle.TDRStyle()
style.setOptStat(True)
for HISTONAME in comparisonList:
BASELINETAUHISTONAME = "NormalizationMETBaselineTau"+HISTONAME+"/NormalizationMETBaselineTau"+HISTONAME
INVERTEDTAUHISTONAME = "NormalizationMETInvertedTau"+HISTONAME+"/NormalizationMETInvertedTau"+HISTONAME
FITMIN = None
FITMAX = None
#===== Infer binning information and labels
histonames = dsetMgr.getDataset("Data").getDirectoryContent(COMBINEDHISTODIR+"/NormalizationMETBaselineTau"+HISTONAME)
bins = []
binLabels = []
if histonames == None:
# Assume that only inclusive bin exists
name = COMBINEDHISTODIR+"/NormalizationMETBaselineTau"+HISTONAME
if not dsetMgr.getDataset("Data").hasRootHisto(name):
raise Exception("Error: Cannot find histogram or directory of name '%s'!"%name)
BASELINETAUHISTONAME = "NormalizationMETBaselineTau"+HISTONAME
INVERTEDTAUHISTONAME = "NormalizationMETInvertedTau"+HISTONAME
bins = [""]
binLabels = ["Inclusive"]
else:
for hname in histonames:
binIndex = hname.replace("NormalizationMETBaselineTau"+HISTONAME,"")
hDummy = dsetMgr.getDataset("Data").getDatasetRootHisto(COMBINEDHISTODIR+"/"+BASELINETAUHISTONAME+binIndex).getHistogram()
title = hDummy.GetTitle()
title = title.replace("METBaseline"+HISTONAME,"")
if hDummy.Integral() > 0.0:
bins.append(binIndex)
if binIndex == "Inclusive":
binLabels.append(binIndex)
else:
binLabels.append(QCDNormalization.getModifiedBinLabelString(title))
if FITMIN == None:
FITMIN = hDummy.GetXaxis().GetXmin()
FITMAX = hDummy.GetXaxis().GetXmax()
hDummy.Delete()
else:
print "Skipping bin '%s' (%s) because it has no entries"%(binIndex, QCDNormalization.getModifiedBinLabelString(title))
print "\nHistogram bins available",bins
# Select bins by filter
if len(selectOnlyBins) > 0:
oldBinLabels = binLabels[:]
oldBins = bins[:]
binLabels = []
bins = []
for k in selectOnlyBins:
for i in range(len(oldBinLabels)):
if k == oldBinLabels[i] or k == oldBins[i]:
binLabels.append(oldBinLabels[i])
bins.append(oldBins[i])
print "Using bins ",bins
print "\nBin labels"
for i in range(len(binLabels)):
line = bins[i]
while len(line) < 10:
line += " "
line += ": "+binLabels[i]
print line
print
#===== Initialize normalization calculator
#manager = QCDNormalization.QCDNormalizationManagerExperimental1(binLabels)
manager = QCDNormalization.QCDNormalizationManagerDefault(binLabels, dirs[0], moduleInfoString)
#===== Create templates (EWK fakes, EWK genuine, QCD; data template is created by manager)
template_EWKFakeTaus_Baseline = manager.createTemplate("EWKFakeTaus_Baseline")
template_EWKFakeTaus_Inverted = manager.createTemplate("EWKFakeTaus_Inverted")
template_EWKGenuineTaus_Baseline = manager.createTemplate("EWKGenuineTaus_Baseline")
template_EWKGenuineTaus_Inverted = manager.createTemplate("EWKGenuineTaus_Inverted")
template_EWKInclusive_Baseline = manager.createTemplate("EWKInclusive_Baseline")
template_EWKInclusive_Inverted = manager.createTemplate("EWKInclusive_Inverted")
template_QCD_Baseline = manager.createTemplate("QCD_Baseline")
template_QCD_Inverted = manager.createTemplate("QCD_Inverted")
#===== Define fit functions and fit parameters
# The available functions are defined in the FitFunction class in the QCDMeasurement/python/QCDNormalization.py file
# commented out fitter for EWK fake taus, since only the fit on inclusive EWK is used to obtain w_QCD
#boundary = 100
#template_EWKFakeTaus_Baseline.setFitter(QCDNormalization.FitFunction("EWKFunctionInv", boundary=boundary, norm=1, rejectPoints=1),
#FITMIN, FITMAX)
#template_EWKFakeTaus_Baseline.setDefaultFitParam(defaultInitialValue=[10.0, 100, 45, 0.02],
#defaultLowerLimit= [ 0.1, 70, 10, 0.001],
#defaultUpperLimit= [ 30, 300, 100, 0.1])
# commented out fitter for EWK genuine taus, since only the fit on inclusive EWK is used to obtain w_QCD
#boundary = 150
#template_EWKGenuineTaus_Baseline.setFitter(QCDNormalization.FitFunction("EWKFunction", boundary=boundary, norm=1, rejectPoints=1),
#FITMIN, FITMAX)
#template_EWKGenuineTaus_Baseline.setDefaultFitParam(defaultLowerLimit=[0.5, 90, 30, 0.0001],
#defaultUpperLimit=[ 20, 150, 50, 1.0])
# Inclusive EWK
boundary = 150
template_EWKInclusive_Baseline.setFitter(QCDNormalization.FitFunction("EWKFunction", boundary=boundary, norm=1, rejectPoints=1),
FITMIN, FITMAX)
template_EWKInclusive_Baseline.setDefaultFitParam(defaultLowerLimit=[0.5, 90, 30, 0.0001],
defaultUpperLimit=[ 20, 150, 50, 1.0])
# Note that the same function is used for QCD only and QCD+EWK fakes
template_QCD_Inverted.setFitter(QCDNormalization.FitFunction("QCDFunction", norm=1), FITMIN, FITMAX)
template_QCD_Inverted.setDefaultFitParam(defaultLowerLimit=[0.0001, 0.001, 0.1, 0.0, 10, 0.0001, 0.001],
defaultUpperLimit=[ 200, 10, 10, 150, 100, 1, 0.05])
#===== Loop over tau pT bins
for i,binStr in enumerate(bins):
print "\n********************************"
print "*** Fitting bin %s"%binLabels[i]
print "********************************\n"
#===== Reset bin results
manager.resetBinResults()
#===== Obtain histograms for normalization
# Data
histoName = COMBINEDHISTODIR+"/"+BASELINETAUHISTONAME+binStr
hmetBase_data = plots.DataMCPlot(dsetMgr, histoName).histoMgr.getHisto("Data").getRootHisto().Clone(histoName)
histoName = COMBINEDHISTODIR+"/"+INVERTEDTAUHISTONAME+binStr
hmetInverted_data = plots.DataMCPlot(dsetMgr, histoName).histoMgr.getHisto("Data").getRootHisto().Clone(histoName)
# EWK genuine taus
histoName = GENUINEHISTODIR+"/"+BASELINETAUHISTONAME+binStr
hmetBase_EWK_GenuineTaus = plots.DataMCPlot(dsetMgr, histoName).histoMgr.getHisto("EWK").getRootHisto().Clone(histoName)
histoName = GENUINEHISTODIR+"/"+INVERTEDTAUHISTONAME+binStr
hmetInverted_EWK_GenuineTaus = plots.DataMCPlot(dsetMgr, histoName).histoMgr.getHisto("EWK").getRootHisto().Clone(histoName)
# EWK fake taus
histoName = FAKEHISTODIR+"/"+BASELINETAUHISTONAME+binStr
hmetBase_EWK_FakeTaus = plots.DataMCPlot(dsetMgr, histoName).histoMgr.getHisto("EWK").getRootHisto().Clone(histoName)
histoName = FAKEHISTODIR+"/"+INVERTEDTAUHISTONAME+binStr
hmetInverted_EWK_FakeTaus = plots.DataMCPlot(dsetMgr, histoName).histoMgr.getHisto("EWK").getRootHisto().Clone(histoName)
# Finalize histograms by rebinning
for histogram in [hmetBase_data, hmetInverted_data, hmetBase_EWK_GenuineTaus, hmetInverted_EWK_GenuineTaus, hmetBase_EWK_FakeTaus, hmetInverted_EWK_FakeTaus]:
histogram.Rebin(_rebinFactor)
#===== Obtain inclusive EWK histograms
hmetBase_EWKinclusive = hmetBase_EWK_GenuineTaus.Clone("EWKinclusiveBase")
hmetBase_EWKinclusive.Add(hmetBase_EWK_FakeTaus, 1.0)
hmetInverted_EWKinclusive = hmetInverted_EWK_GenuineTaus.Clone("EWKinclusiveInv")
hmetInverted_EWKinclusive.Add(hmetInverted_EWK_FakeTaus, 1.0)
#===== Obtain histograms for QCD (subtract MC EWK events from data)
# QCD from baseline is usable only as a cross check
hmetBase_QCD = hmetBase_data.Clone("QCDbase")
hmetBase_QCD.Add(hmetBase_EWKinclusive,-1)
hmetInverted_QCD = hmetInverted_data.Clone("QCDinv")
hmetInverted_QCD.Add(hmetInverted_EWKinclusive,-1)
#===== Set histograms to the templates
template_EWKFakeTaus_Inverted.setHistogram(hmetInverted_EWK_FakeTaus, binLabels[i])
template_EWKGenuineTaus_Inverted.setHistogram(hmetInverted_EWK_GenuineTaus, binLabels[i])
template_EWKInclusive_Inverted.setHistogram(hmetInverted_EWKinclusive, binLabels[i])
template_QCD_Inverted.setHistogram(hmetInverted_QCD, binLabels[i])
template_EWKFakeTaus_Baseline.setHistogram(hmetBase_EWK_FakeTaus, binLabels[i])
template_EWKGenuineTaus_Baseline.setHistogram(hmetBase_EWK_GenuineTaus, binLabels[i])
template_EWKInclusive_Baseline.setHistogram(hmetBase_EWKinclusive, binLabels[i])
template_QCD_Baseline.setHistogram(hmetBase_QCD, binLabels[i])
#===== Make plots of templates
manager.plotTemplates()
#===== Fit individual templates to data
fitOptions = "R B" # RBLW
manager.calculateNormalizationCoefficients(hmetBase_data, fitOptions, FITMIN, FITMAX)
#===== Calculate combined normalisation coefficient (f_fakes = w*f_QCD + (1-w)*f_EWKfakes)
# Obtain histograms
histoName = "ForDataDrivenCtrlPlots/shapeTransverseMass/shapeTransverseMass"+binStr
dataMt = plots.DataMCPlot(dsetMgr, histoName).histoMgr.getHisto("Data").getRootHisto().Clone(histoName)
treatNegativeBins(dataMt, "Data_inverted mT")
histoName = "ForDataDrivenCtrlPlotsEWKFakeTaus/shapeTransverseMass/shapeTransverseMass"+binStr
ewkFakeTausMt = plots.DataMCPlot(dsetMgr, histoName).histoMgr.getHisto("EWK").getRootHisto().Clone(histoName)
treatNegativeBins(ewkFakeTausMt, "ewkFakeTaus_inverted mT")
histoName = "ForDataDrivenCtrlPlotsEWKGenuineTaus/shapeTransverseMass/shapeTransverseMass"+binStr
ewkGenuineTausMt = plots.DataMCPlot(dsetMgr, histoName).histoMgr.getHisto("EWK").getRootHisto().Clone(histoName)
treatNegativeBins(ewkGenuineTausMt, "ewkGenuineTaus_inverted mT")
qcdMt = dataMt.Clone("QCD")
qcdMt.Add(ewkFakeTausMt, -1)
qcdMt.Add(ewkGenuineTausMt, -1)
treatNegativeBins(qcdMt, "QCD_inverted mT")
# Do calculation
manager.calculateCombinedNormalizationCoefficient(qcdMt, ewkFakeTausMt)
#===== Save normalization
outFileName = "QCDNormalizationFactors_%s_%s.py"%(HISTONAME, moduleInfoString)
print argv[1],outFileName
outFileFullName = os.path.join(argv[1],outFileName)
manager.writeScaleFactorFile(outFileFullName, moduleInfoString)
def main(argv):
COMBINEDHISTODIR = "ForQCDNormalization"
FAKEHISTODIR = "ForQCDNormalizationEWKFakeTaus"
GENUINEHISTODIR = "ForQCDNormalizationEWKGenuineTaus"
comparisonList = ["AfterStdSelections"]
dirs = []
if len(sys.argv) < 2:
usage()
dirs.append(sys.argv[1])
# Create all dsetMgr from a multicrab task
dsetMgr = dataset.getDatasetsFromMulticrabDirs(dirs,dataEra=dataEra, searchMode=searchMode, analysisName=analysis)
#print dsetMgr
# Check multicrab consistency
consistencyCheck.checkConsistencyStandalone(dirs[0],dsetMgr,name="QCD inverted")
# As we use weighted counters for MC normalisation, we have to
# update the all event count to a separately defined value because
# the analysis job uses skimmed pattuple as an input
dsetMgr.updateNAllEventsToPUWeighted()
# Read integrated luminosities of data dsetMgr from lumi.json
dsetMgr.loadLuminosities()
# Include only 120 mass bin of HW and HH dsetMgr
dsetMgr.remove(filter(lambda name: "TTToHplus" in name and not "M120" in name, dsetMgr.getAllDatasetNames()))
dsetMgr.remove(filter(lambda name: "HplusTB" in name, dsetMgr.getAllDatasetNames()))
# Default merging nad ordering of data and MC dsetMgr
# All data dsetMgr to "Data"
# All QCD dsetMgr to "QCD"
# All single top dsetMgr to "SingleTop"
# WW, WZ, ZZ to "Diboson"
plots.mergeRenameReorderForDataMC(dsetMgr)
# Set BR(t->H) to 0.05, keep BR(H->tau) in 1
xsect.setHplusCrossSectionsToBR(dsetMgr, br_tH=0.05, br_Htaunu=1)
# Merge WH and HH dsetMgr to one (for each mass bin)
plots.mergeWHandHH(dsetMgr)
dsetMgr.merge("EWK", [
"TTJets",
"WJetsHT",
"DYJetsToLL",
"SingleTop",
#"Diboson"
])
# Apply TDR style
style = tdrstyle.TDRStyle()
style.setOptStat(True)
for HISTONAME in comparisonList:
BASELINETAUHISTONAME = "NormalizationMETBaselineTau"+HISTONAME+"/NormalizationMETBaselineTau"+HISTONAME
INVERTEDTAUHISTONAME = "NormalizationMETInvertedTau"+HISTONAME+"/NormalizationMETInvertedTau"+HISTONAME
#===== Infer binning information and labels
histonames = dsetMgr.getDataset("Data").getDirectoryContent(COMBINEDHISTODIR+"/NormalizationMETBaselineTau"+HISTONAME)
bins = []
binLabels = []
if histonames == None:
# Assume that only inclusive bin exists
name = COMBINEDHISTODIR+"/NormalizationMETBaselineTau"+HISTONAME
if not dsetMgr.getDataset("Data").hasRootHisto(name):
raise Exception("Error: Cannot find histogram or directory of name '%s'!"%name)
BASELINETAUHISTONAME = "NormalizationMETBaselineTau"+HISTONAME
INVERTEDTAUHISTONAME = "NormalizationMETInvertedTau"+HISTONAME
bins = [""]
binLabels = ["Inclusive"]
else:
for hname in histonames:
bins.append(hname.replace("NormalizationMETBaselineTau"+HISTONAME,""))
title = dsetMgr.getDataset("Data").getDatasetRootHisto(COMBINEDHISTODIR+"/"+BASELINETAUHISTONAME+"/"+hname).getHistogram().GetTitle()
title = title.replace("METBaseline"+HISTONAME,"")
binLabels.append(formatHistoTitle(title))
print "\nHistogram bins available",bins
print "Using bins ",bins
print "\nBin labels"
for i in range(len(binLabels)):
line = bins[i]
while len(line) < 10:
line += " "
line += ": "+binLabels[i]
print line
print
#===== Initialize normalization calculator
invertedQCD = InvertedTauID()
invertedQCD.setLumi(dsetMgr.getDataset("Data").getLuminosity())
invertedQCD.setInfo([dataEra,searchMode,HISTONAME])
#===== Loop over tau pT bins
for i,binStr in enumerate(bins):
print "\n********************************"
print "*** Fitting bin %s"%binLabels[i]
print "********************************\n"
invertedQCD.resetBinResults()
invertedQCD.setLabel(binLabels[i])
#===== Obtain histograms for normalization
metBase = plots.DataMCPlot(dsetMgr, COMBINEDHISTODIR+"/"+BASELINETAUHISTONAME+binStr)
metInver = plots.DataMCPlot(dsetMgr, COMBINEDHISTODIR+"/"+INVERTEDTAUHISTONAME+binStr)
metBase_GenuineTaus = plots.DataMCPlot(dsetMgr, GENUINEHISTODIR+"/"+BASELINETAUHISTONAME+binStr)
metInver_GenuineTaus = plots.DataMCPlot(dsetMgr, GENUINEHISTODIR+"/"+INVERTEDTAUHISTONAME+binStr)
metBase_FakeTaus = plots.DataMCPlot(dsetMgr, FAKEHISTODIR+"/"+BASELINETAUHISTONAME+binStr)
metInver_FakeTaus = plots.DataMCPlot(dsetMgr, FAKEHISTODIR+"/"+INVERTEDTAUHISTONAME+binStr)
#===== Rebin histograms before subtracting
RebinFactor = 2 # Aim for 10 GeV binning
metBase.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(RebinFactor))
metInver.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(RebinFactor))
metBase_GenuineTaus.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(RebinFactor))
metInver_GenuineTaus.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(RebinFactor))
metBase_FakeTaus.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(RebinFactor))
metInver_FakeTaus.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(RebinFactor))
#===== Obtain templates for data and EWK
metInverted_data = metInver.histoMgr.getHisto("Data").getRootHisto().Clone(COMBINEDHISTODIR+"/"+INVERTEDTAUHISTONAME+binStr)
treatHistogram(metInverted_data, "Data, inverted")
metInverted_EWK_GenuineTaus = metInver_GenuineTaus.histoMgr.getHisto("EWK").getRootHisto().Clone(GENUINEHISTODIR+"/"+INVERTEDTAUHISTONAME+binStr)
treatHistogram(metInverted_EWK_GenuineTaus, "EWK genuine taus, inverted")
metInverted_EWK_FakeTaus = metInver_FakeTaus.histoMgr.getHisto("EWK").getRootHisto().Clone(FAKEHISTODIR+"/"+INVERTEDTAUHISTONAME+binStr)
treatHistogram(metInverted_EWK_FakeTaus, "EWK fake taus, inverted")
metBase_data = metBase.histoMgr.getHisto("Data").getRootHisto().Clone(COMBINEDHISTODIR+"/"+BASELINETAUHISTONAME+binStr)
treatHistogram(metBase_data, "Data, baseline")
metBase_EWK_GenuineTaus = metBase_GenuineTaus.histoMgr.getHisto("EWK").getRootHisto().Clone(GENUINEHISTODIR+"/"+BASELINETAUHISTONAME+binStr)
treatHistogram(metBase_EWK_GenuineTaus, "EWK genuine taus, baseline")
metBase_EWK_FakeTaus = metBase_FakeTaus.histoMgr.getHisto("EWK").getRootHisto().Clone(FAKEHISTODIR+"/"+BASELINETAUHISTONAME+binStr)
treatHistogram(metBase_EWK_FakeTaus, "EWK fake taus, baseline")
#===== Obtain templates for QCD (subtract MC EWK events from data)
# QCD from baseline is usable only as a cross check
#metBase_QCD = metBase_data.Clone("QCD")
#metBase_QCD.Add(metBase_EWK_GenuineTaus,-1)
#metBase_QCD.Add(metBase_EWK_FakeTaus,-1)
#addLabels(metBase_QCD, "QCD, baseline")
metInverted_QCD = metInverted_data.Clone("QCD")
metInverted_QCD.Add(metInverted_EWK_GenuineTaus,-1)
metInverted_QCD.Add(metInverted_EWK_FakeTaus,-1)
treatHistogram(metInverted_QCD, "QCD, inverted")
#===== Make plots of templates
print "\n*** Integrals of plotted templates"
#invertedQCD.plotHisto(metInverted_data,"template_Data_Inverted")
#invertedQCD.plotHisto(metInverted_EWK_GenuineTaus,"template_EWKGenuineTaus_Inverted")
#invertedQCD.plotHisto(metInverted_EWK_FakeTaus,"template_EWKFakeTaus_Inverted")
invertedQCD.plotHisto(metInverted_QCD,"template_QCD_Inverted")
invertedQCD.plotHisto(metBase_data,"template_Data_Baseline")
invertedQCD.plotHisto(metBase_EWK_GenuineTaus,"template_EWKGenuineTaus_Baseline")
invertedQCD.plotHisto(metBase_EWK_FakeTaus,"template_EWKFakeTaus_Baseline")
#invertedQCD.plotHisto(metBase_QCD,"template_QCD_Baseline")
#===== Fit individual templates and
# Fit first templates for QCD, EWK_genuine_taus, and EWK_fake_taus
# Then fit the shape of those parametrizations to baseline data to obtain normalization coefficients
fitOptions = "RB"
# Strategy: take EWK templates from baseline and QCD template from inverted; then fit to baseline data
invertedQCD.fitEWK_GenuineTaus(metInverted_EWK_GenuineTaus,fitOptions)
invertedQCD.fitEWK_GenuineTaus(metBase_EWK_GenuineTaus,fitOptions)
invertedQCD.fitEWK_FakeTaus(metInverted_EWK_FakeTaus,fitOptions)
invertedQCD.fitEWK_FakeTaus(metBase_EWK_FakeTaus,fitOptions)
invertedQCD.fitQCD(metInverted_QCD,fitOptions)
invertedQCD.fitData(metBase_data)
#===== Calculate normalization
invertedQCD.getNormalization()
invertedQCD.Summary()
invertedQCD.WriteNormalizationToFile("QCDInvertedNormalizationFactorsFilteredEWKFakeTaus.py")
invertedQCD.WriteLatexOutput("fits.tex")
def main(argv):
dirs = []
if len(sys.argv) < 2:
usage()
dirs.append(sys.argv[1])
comparisonList = ["AfterStdSelections"]
# Create all datasets from a multicrab task
datasets = dataset.getDatasetsFromMulticrabDirs(dirs,dataEra=dataEra, searchMode=searchMode, analysisName=analysis)
#print datasets.getDatasetNames()
#print datasets
# Check multicrab consistency
consistencyCheck.checkConsistencyStandalone(dirs[0],datasets,name="CorrelationAnalysis")
# As we use weighted counters for MC normalisation, we have to
# update the all event count to a separately defined value because
# the analysis job uses skimmed pattuple as an input
datasets.updateNAllEventsToPUWeighted()
# Read integrated luminosities of data datasets from lumi.json
datasets.loadLuminosities()
# Include only 120 mass bin of HW and HH datasets
#datasets.remove(filter(lambda name: "TTToHplus" in name and not "M120" in name, datasets.getAllDatasetNames()))
datasets.remove(filter(lambda name: "TTToHplusBWB" in name, datasets.getAllDatasetNames()))
datasets.remove(filter(lambda name: "HplusTB" in name and not "M_500" in name, datasets.getAllDatasetNames()))
# datasets.remove(filter(lambda name: "HplusTB" in name, datasets.getAllDatasetNames()))
datasets.remove(filter(lambda name: "Hplus_taunu_t-channel" in name, datasets.getAllDatasetNames()))
datasets.remove(filter(lambda name: "Hplus_taunu_tW-channel" in name, datasets.getAllDatasetNames()))
datasets.remove(filter(lambda name: "TTJets_SemiLept" in name, datasets.getAllDatasetNames()))
datasets.remove(filter(lambda name: "TTJets_FullLept" in name, datasets.getAllDatasetNames()))
datasets.remove(filter(lambda name: "TTJets_Hadronic" in name, datasets.getAllDatasetNames()))
# datasets.remove(filter(lambda name: "QCD" in name, datasets.getAllDatasetNames()))
# datasets.remove(filter(lambda name: "WJetsToLNu" in name, datasets.getAllDatasetNames()))
# datasets.remove(filter(lambda name: "DYJetsToLL" in name, datasets.getAllDatasetNames()))
datasets.remove(filter(lambda name: ("DYJetsToLL_M_10to50" in name or "DYJetsToLL_M_50" in name) and not "DYJetsToLL_M_50_HT" in name, datasets.getAllDatasetNames()))
datasets.remove(filter(lambda name: "WJetsToLNu" in name and not "WJetsToLNu_HT" in name, datasets.getAllDatasetNames()))
# Default merging nad ordering of data and MC datasets
# All data datasets to "Data"
# All QCD datasets to "QCD"
# All single top datasets to "SingleTop"
# WW, WZ, ZZ to "Diboson"
plots.mergeRenameReorderForDataMC(datasets)
# Set BR(t->H) to 0.05, keep BR(H->tau) in 1
xsect.setHplusCrossSectionsToBR(datasets, br_tH=0.05, br_Htaunu=1)
# Merge WH and HH datasets to one (for each mass bin)
# TTToHplusBWB_MXXX and TTToHplusBHminusB_MXXX to "TTToHplus_MXXX"
plots.mergeWHandHH(datasets)
# datasets.getDataset("TTbar_HBWB_HToTauNu_M_160_13TeV_pythia6").setCrossSection(0.336902*2*0.955592) # pb
# At the moment the collision energy must be set by hand
# for dset in datasets.getMCDatasets():
# dset.setEnergy("13")
# At the moment the cross sections must be set by hand
#xsect.setBackgroundCrossSections(datasets)
if False:
datasets.merge("EWK", [
"TT",
"WJetsHT",
"DYJetsToLLHT",
"SingleTop",
"Diboson"
])
# Apply TDR style
style = tdrstyle.TDRStyle()
style.setOptStat(True)
dataMCExample(datasets)
MtComparison(datasets)
# MetComparison(datasets)
# TauPtComparison(datasets)
# Print counters
doCounters(datasets)
# Script execution can be paused like this, it will continue after
# user has given some input (which must include enter)
if drawToScreen:
raw_input("Hit enter to continue")