def refinedDataSets(dirs, dataEra, searchMode, analysis, optMode, removeTTJets):
datasets = dataset.getDatasetsFromMulticrabDirs(dirs,dataEra=dataEra, searchMode=searchMode, analysisName=analysis, optimizationMode=optMode)
datasets.updateNAllEventsToPUWeighted()
datasets.loadLuminosities()
datasets.remove(filter(lambda name: "TTToHplus" 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()))
if removeTTJets:
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()))
plots.mergeRenameReorderForDataMC(datasets)
datasets.merge("EWK", [
"TTJets",
"WJets",
"DYJetsToLL",
"SingleTop",
"Diboson"
])
return datasets
def main(argv):
interactive = True
interactive = False
# HISTONAME = "TauIdJets"
# HISTONAME = "TauIdBtag"
HISTONAME = "TauIdBveto"
dirs = []
if len(sys.argv) < 2:
usage()
dirs.append(sys.argv[1])
# Disable batch mode here to have the interactivity (see also the line with 'raw_input') below
if interactive:
ROOT.gROOT.SetBatch(False)
# Create all datasets from a multicrab task
# datasets = dataset.getDatasetsFromMulticrabCfg(counters=counters)
# datasets = dataset.getDatasetsFromMulticrabDirs(dirs,counters=counters, dataEra=dataEra)
datasets = dataset.getDatasetsFromMulticrabDirs(dirs,counters=counters, dataEra=dataEra, analysisbaseName = "signalAnalysisInvertedTau")
# 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()))
# 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 0.2, 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)
# Apply TDR style
style = tdrstyle.TDRStyle()
#dataMCExample(datasets)
distComparison(datasets)
# Script execution can be paused like this, it will continue after
# user has given some input (which must include enter)
if interactive:
raw_input("Hit enter to continue")
def main():
if len(sys.argv) < 2:
usage()
dirs = []
dirs.append(sys.argv[1])
datasets = dataset.getDatasetsFromMulticrabDirs(dirs,dataEra=dataEra, searchMode=searchMode, analysisName=analysis)
datasets.loadLuminosities()
datasets.updateNAllEventsToPUWeighted()
plots.mergeRenameReorderForDataMC(datasets)
datasets.merge("EWK", [
"TTJets",
"WJets",
"DYJetsToLL",
"SingleTop",
"Diboson"
])
style = tdrstyle.TDRStyle()
plot = plots.PlotBase()
legends = {}
name_re = re.compile("SelectedTau_pT_(?P<name>\S+)")
for i,histo in enumerate(HISTONAMES):
plot.histoMgr.appendHisto(purityGraph(i,datasets,histo))
name = histo
match = name_re.search(histo)
if match:
name = match.group("name")
legends["Purity%s"%i] = name
plot.createFrame("purity", opts={"xmin": 40, "ymin": 0., "ymax": 1.2})
plot.frame.GetXaxis().SetTitle("tau p_{T} (GeV/c)")
plot.frame.GetYaxis().SetTitle("Purity")
plot.setEnergy(datasets.getEnergies())
plot.setLuminosity(datasets.getDataset("Data").getLuminosity())
plot.histoMgr.setHistoLegendLabelMany(legends)
plot.setLegend(histograms.createLegend(0.6, 0.3, 0.8, 0.4))
plot.addStandardTexts()
plot.draw()
plot.save()
def getDatasets(multicrabPath, myDataEra):
'''
def getDatasets(multicrabPath):
'''
### Get the ROOT files for all datasets, merge datasets and reorder them
print "*** Obtaining datasets from: %s" % (multicrabPath)
datasets = dataset.getDatasetsFromMulticrabDirs(multicrabPath, dataEra=myDataEra)
### Print PSets used in ROOT-file generation
printPSet(bPrintPSet, folderName=myAnalysis+myDataEra)
### Take care of PU weighting, luminosity, signal merging etc... of the datatasets
manageDatasets(datasets)
return datasets
def getDatasets(multicrabPath, myDataEra):
'''
def getDatasets(multicrabPath):
'''
### Get the ROOT files for all datasets, merge datasets and reorder them
print "*** Obtaining datasets from: %s" % (multicrabPath)
datasets = dataset.getDatasetsFromMulticrabDirs(multicrabPath, dataEra=myDataEra)
### Print PSets used in ROOT-file generation
printPSet(bPrintPSet, folderName=myAnalysis+myDataEra)
### Take care of PU weighting, luminosity, signal merging etc... of the datatasets
manageDatasets(datasets)
return datasets
def main():
if len(sys.argv) < 2:
usage()
dirs = []
dirs.append(sys.argv[1])
datasets = dataset.getDatasetsFromMulticrabDirs(dirs,
dataEra=dataEra,
searchMode=searchMode,
analysisName=analysis)
datasets.loadLuminosities()
datasets.updateNAllEventsToPUWeighted()
plots.mergeRenameReorderForDataMC(datasets)
datasets.merge("EWK",
["TTJets", "WJets", "DYJetsToLL", "SingleTop", "Diboson"])
style = tdrstyle.TDRStyle()
plot = plots.PlotBase()
legends = {}
name_re = re.compile("SelectedTau_pT_(?P<name>\S+)")
for i, histo in enumerate(HISTONAMES):
plot.histoMgr.appendHisto(purityGraph(i, datasets, histo))
name = histo
match = name_re.search(histo)
if match:
name = match.group("name")
legends["Purity%s" % i] = name
plot.createFrame("purity", opts={"xmin": 40, "ymin": 0., "ymax": 1.2})
plot.frame.GetXaxis().SetTitle("tau p_{T} (GeV/c)")
plot.frame.GetYaxis().SetTitle("Purity")
plot.setEnergy(datasets.getEnergies())
plot.setLuminosity(datasets.getDataset("Data").getLuminosity())
plot.histoMgr.setHistoLegendLabelMany(legends)
plot.setLegend(histograms.createLegend(0.6, 0.3, 0.8, 0.4))
plot.addStandardTexts()
plot.draw()
plot.save()
def refinedDataSets(dirs, dataEra, searchMode, analysis, optMode,
removeTTJets):
datasets = dataset.getDatasetsFromMulticrabDirs(dirs,
dataEra=dataEra,
searchMode=searchMode,
analysisName=analysis,
optimizationMode=optMode)
datasets.updateNAllEventsToPUWeighted()
datasets.loadLuminosities()
datasets.remove(
filter(lambda name: "TTToHplus" 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()))
if removeTTJets:
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()))
plots.mergeRenameReorderForDataMC(datasets)
datasets.merge("EWK",
["TTJets", "WJets", "DYJetsToLL", "SingleTop", "Diboson"])
return datasets
def main(argv):
# HISTONAME = "TauIdJets"
# HISTONAME = "TauIdJetsCollinear"
# HISTONAME = "TauIdBtag"
# HISTONAME = "TauIdBvetoCollinear"
# HISTONAME = "TauIdBveto"
HISTONAME = "TauIdAfterCollinearCuts"
dirs = []
if len(sys.argv) < 2:
usage()
dirs.append(sys.argv[1])
# Create all datasets from a multicrab task
# datasets = dataset.getDatasetsFromMulticrabCfg(counters=counters, dataEra=dataEra, analysisBaseName="signalAnalysisInvertedTau")
#datasets = dataset.getDatasetsFromMulticrabDirs(dirs,counters=counters, dataEra=dataEra, analysisBaseName="signalAnalysisInvertedTau")
datasets = dataset.getDatasetsFromMulticrabDirs(dirs,dataEra=dataEra, searchMode=searchMode, analysisName=analysis, optimizationMode=optimizationMode)
# datasets = dataset.getDatasetsFromMulticrabDirs(dirs,counters=counters, dataEra=dataEra)
# 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()))
# 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()
invertedQCD.setLumi(datasets.getDataset("Data").getLuminosity())
invertedQCD.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)
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, "baseline/METBaseline"+HISTONAME+"/METBaseline"+HISTONAME+bin)
metInver = plots.DataMCPlot(datasets, "Inverted/METInverted"+HISTONAME+"/METInverted"+HISTONAME+bin)
# Rebin before subtracting
metBase.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(5))
metInver.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(5))
metInverted_data = metInver.histoMgr.getHisto("Data").getRootHisto().Clone("Inverted/METInverted"+HISTONAME+"/METInverted"+HISTONAME+bin)
metInverted_EWK = metInver.histoMgr.getHisto("EWK").getRootHisto().Clone("Inverted/METInverted"+HISTONAME+"/METInverted"+HISTONAME+bin)
metBase_data = metBase.histoMgr.getHisto("Data").getRootHisto().Clone("Baseline/METBaseLine"+HISTONAME+"/METBaseline"+HISTONAME+bin)
metBase_EWK = metBase.histoMgr.getHisto("EWK").getRootHisto().Clone("Baseline/METBaseLine"+HISTONAME+"/METBaseline"+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 = "LRB"
# fitOptions = "RB"
# fitOptions = "IEB"
# ROOT.TVirtualFitter.SetDefaultFitter("Minuit")
# ROOT.TVirtualFitter.SetDefaultFitter("Minuit2")
# fitOptions = "Q" # Chi2 Fit
# fitOptions = "VEB" # Chi2 Fit with Minos Error
# fitOptions = "IEB" # Chi2 Fit with Integral and Minos
# fitOptions = "VLEB" # Likelihood Fit with Minos Error
# ROOT.TVirtualFitter.SetDefaultFitter("Fumili")
# ROOT.TVirtualFitter.SetDefaultFitter("Fumili2")
# fitOptions = "Q" # Chi2 Fit
# fitOptions = "VE" # Chi2 Fit with Minos Error
# fitOptions = "IE" # Chi2 Fit with Integral and Minos
# fitOptions = "VLE" # Likelihood Fit with Minos Error
invertedQCD.fitEWK(metInverted_EWK,fitOptions)
invertedQCD.fitEWK(metBase_EWK,fitOptions)
invertedQCD.fitQCD(metInverted_QCD,fitOptions)
invertedQCD.fitData(metBase_data)
invertedQCD.getNormalization()
invertedQCD.Summary()
invertedQCD.WriteNormalizationToFile("QCDInvertedNormalizationFactors.py")
invertedQCD.WriteLatexOutput("fits.tex")
def main(argv):
# HISTONAME = "TauIdJets"
# HISTONAME = "TauIdJetsCollinear"
# HISTONAME = "TauIdBtag"
# HISTONAME = "TauIdBvetoCollinear"
# HISTONAME = "TauIdBveto"
HISTONAME = "TauIdAfterCollinearCuts"
FAKEHISTO = "OnlyEWKFakeTaus"
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()
invertedQCD.setLumi(datasets.getDataset("Data").getLuminosity())
invertedQCD.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])
metBase = plots.DataMCPlot(datasets, "baseline/METBaseline"+HISTONAME+"/METBaseline"+HISTONAME+bin)
metInver = plots.DataMCPlot(datasets, "Inverted/METInverted"+HISTONAME+"/METInverted"+HISTONAME+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_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_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)
metInverted_data = addlabels(metInverted_data)
metInverted_EWK = addlabels(metInverted_EWK)
metInverted_EWK_FakeTaus = addlabels(metInverted_EWK_FakeTaus)
metBase_data = addlabels(metBase_data)
metBase_EWK = addlabels(metBase_EWK)
metBase_EWK_FakeTaus = addlabels(metBase_EWK_FakeTaus)
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")
invertedQCD.plotHisto(metInverted_EWK_FakeTaus,"invertedEWK_FakeTaus")
invertedQCD.plotHisto(metBase_EWK_FakeTaus,"baselineEWK_FakeTaus")
fitOptions = "LRB"
#fitOptions = "RB"
#fitOptions = "IEB"
invertedQCD.fitEWK(metInverted_EWK,fitOptions)
invertedQCD.fitEWK(metBase_EWK,fitOptions)
invertedQCD.fitQCD(metInverted_QCD,fitOptions)
invertedQCD.fitData(metBase_data)
invertedQCD.fitEWK_FakeTaus(metInverted_EWK_FakeTaus,fitOptions)
invertedQCD.fitEWK_FakeTaus(metBase_EWK_FakeTaus,fitOptions)
invertedQCD.getNormalization()
invertedQCD.Summary()
invertedQCD.WriteNormalizationToFile("QCDInvertedNormalizationFactors.py")
invertedQCD.WriteLatexOutput("fits.tex")
def main():
if len(sys.argv) < 2:
usage()
# HISTONAME = "METInvertedTauIdAfterJets"
HISTONAME = "METInvertedTauIdAfterCollinearCuts"
# HISTONAME = "MET_InvertedTauIdBveto"
# HISTONAME = "METInvertedTauIdAfterCollinearCutsPlusBtag"
# HISTONAME = "METInvertedTauIdAfterCollinearCutsPlusBtag"
# HISTONAME = "MTInvertedTauIdJet"
# HISTONAME = "MTInvertedTauIdPhi"
invertedhisto = HISTONAME
baselinehisto = HISTONAME.replace("Inverted", "BaseLine")
dirs = []
if len(sys.argv) < 2:
usage()
dirs.append(sys.argv[1])
# Create all datasets from a multicrab task
#datasets = dataset.getDatasetsFromMulticrabCfg(counters=counters)
datasets = dataset.getDatasetsFromMulticrabDirs(dirs,
dataEra=dataEra,
searchMode=searchMode,
analysisName=analysis)
# datasets = dataset.getDatasetsFromMulticrabDirs(dirs,counters=counters, dataEra=dataEra, analysisBaseName="signalAnalysisInvertedTau" )
# As we use weighted counters for MC normalisation, we have to
# 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()))
# 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()
invertedQCD = InvertedTauID()
invertedQCD.setLumi(datasets.getDataset("Data").getLuminosity())
if ReBinning:
rebinfactor = 1.3
histobins = []
histobins.append(0)
histobins.append(1)
i = 1
while histobins[len(histobins) - 1] < 400:
edge = histobins[i] + (histobins[i] -
histobins[i - 1]) * rebinfactor
histobins.append(edge)
i += 1
print histobins
# metBase_data = metBase_data.Rebin(len(histobins)-1, metInverted_EWK = metInver.histoMgr.getHisto("EWK").getRootHisto().Clone(analysis+"/"+invertedhisto) "",array.array("d", histobins))
metInverted_data = metInverted_data.Rebin(
len(histobins) - 1, "", array.array("d", histobins))
metBase_EWK = metBase_EWK.Rebin(
len(histobins) - 1, "", array.array("d", histobins))
metInverted_EWK = metInverted_EWK.Rebin(
len(histobins) - 1, "", array.array("d", histobins))
normData, normEWK = normalisation()
metInverted = []
metBaseline = []
metBaselineQCD = []
for ptbin in ptbins:
## inverted
met_tmp = plots.PlotBase([
datasets.getDataset("Data").getDatasetRootHisto(
"Inverted/METInvertedTauIdAfterCollinearCuts/METInvertedTauIdAfterCollinearCuts"
+ ptbin)
])
#met_tmp = plots.PlotBase([datasets.getDataset("Data").getDatasetRootHisto("Inverted/"+invertedhisto+ptbin)])
met_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(5))
met = met_tmp.histoMgr.getHisto("Data").getRootHisto().Clone()
met.Scale(normData[ptbin])
metEWK_tmp = plots.PlotBase([
datasets.getDataset("EWK").getDatasetRootHisto(
"Inverted/METInvertedTauIdAfterCollinearCuts/METInvertedTauIdAfterCollinearCuts"
+ ptbin)
])
#metEWK_tmp = plots.PlotBase([datasets.getDataset("EWK").getDatasetRootHisto("Inverted/"+invertedhisto+ptbin)])
metEWK_tmp.histoMgr.normalizeMCToLuminosity(
datasets.getDataset("Data").getLuminosity())
metEWK_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(5))
metEWK = metEWK_tmp.histoMgr.getHisto("EWK").getRootHisto().Clone()
metEWK.Scale(normEWK[ptbin])
met.Add(metEWK, -1)
metInverted.append(met)
## baseline
met_tmp = plots.PlotBase([
datasets.getDataset("Data").getDatasetRootHisto(
"baseline/METBaselineTauIdAfterCollinearCuts/METBaselineTauIdAfterCollinearCuts"
+ ptbin)
])
#met_tmp = plots.PlotBase([datasets.getDataset("Data").getDatasetRootHisto("Inverted/"+invertedhisto+ptbin)])
met_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(5))
metbaseline = met_tmp.histoMgr.getHisto("Data").getRootHisto().Clone()
metEWK_tmp = plots.PlotBase([
datasets.getDataset("EWK").getDatasetRootHisto(
"baseline/METBaselineTauIdAfterCollinearCuts/METBaselineTauIdAfterCollinearCuts"
+ ptbin)
])
#metEWK_tmp = plots.PlotBase([datasets.getDataset("EWK").getDatasetRootHisto("Inverted/"+invertedhisto+ptbin)])
metEWK_tmp.histoMgr.normalizeMCToLuminosity(
datasets.getDataset("Data").getLuminosity())
metEWK_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(5))
metEWKbaseline = metEWK_tmp.histoMgr.getHisto(
"EWK").getRootHisto().Clone()
metBaseline.append(metbaseline)
metbaseline.Add(metEWKbaseline, -1)
metBaselineQCD.append(metbaseline)
metInverted_data = metInverted[0].Clone("met")
metInverted_data.SetName("met")
metInverted_data.SetTitle("Inverted tau ID")
metInverted_data.Reset()
for histo in metInverted:
metInverted_data.Add(histo)
metBaseline_data = metBaseline[0].Clone("met")
metBaseline_data.SetName("met")
metBaseline_data.SetTitle("baseline tau ID")
metBaseline_data.Reset()
for histo in metBaseline:
metBaseline_data.Add(histo)
metBaseline_QCD = metBaselineQCD[0].Clone("met")
metBaseline_QCD.SetName("met")
metBaseline_QCD.SetTitle("baseline tau ID")
metBaseline_QCD.Reset()
for histo in metBaselineQCD:
metBaseline_QCD.Add(histo)
#metBase_data.SetTitle("Data: BaseLine TauID")
#metInverted_data.SetTitle("Data: Inverted TauID")
#metBase_QCD = metBase_data.Clone("QCD")
#metBase_QCD.Add(metBase_EWK,-1)
#metBase_QCD.SetTitle("Data - EWK MC: BaseLine TauID")
invertedQCD.setLabel("BaseVsInverted")
invertedQCD.comparison(metInverted_data, metBaseline_data)
invertedQCD.setLabel("BaseMinusEWKVsInverted")
invertedQCD.comparison(metInverted_data, metBaseline_QCD)
invertedQCD.setLabel("McVsInverted")
# invertedQCD.comparison(metInverted_data,metInverted_MC)
invertedQCD.setLabel("EfficiencyBaseMinusEWKVsInverted")
invertedQCD.cutefficiency(metInverted_data, metBaseline_QCD)
def getDatasetNames(multiCrabDir):
datasets = dataset.getDatasetsFromMulticrabDirs([multiCrabDir],counters=counters)
return datasets.getAllDatasetNames()
def main():
if len(sys.argv) < 2:
usage()
dirs = []
dirs.append(sys.argv[1])
# Read the datasets
# datasets = dataset.getDatasetsFromMulticrabDirs(dirs,counters=counters, dataEra=dataEra, analysisBaseName="signalAnalysisInvertedTau")
datasets = dataset.getDatasetsFromMulticrabDirs(dirs,dataEra=dataEra, searchMode=searchMode, analysisName=analysis)
# datasets = dataset.getDatasetsFromMulticrabDirs(dirs,counters=counters)
# datasets = dataset.getDatasetsFromMulticrabCfg(counters=counters, dataEra=dataEra)
# datasets.updateNAllEventsToPUWeighted()
datasets.loadLuminosities()
datasets.updateNAllEventsToPUWeighted()
# Take QCD from data
datasetsQCD = None
if QCDfromData:
datasetsQCD = dataset.getDatasetsFromMulticrabCfg(cfgfile="/home/rkinnune/signalAnalysis/CMSSW_4_2_8_patch2/src/HiggsAnalysis/HeavyChHiggsToTauNu/test/multicrab_111123_132128/multicrab.cfg", counters=counters)
datasetsQCD.loadLuminosities()
print "QCDfromData", QCDfromData
datasetsQCD.mergeData()
datasetsQCD.remove(datasetsQCD.getMCDatasetNames())
datasetsQCD.rename("Data", "QCD")
plots.mergeRenameReorderForDataMC(datasets)
print "Int.Lumi",datasets.getDataset("Data").getLuminosity()
# Remove signals other than M120
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.merge("EWK", ["WJets", "DYJetsToLL", "SingleTop", "Diboson","TTJets"], keepSources=True)
datasets.remove(filter(lambda name: "W2Jets" in name, datasets.getAllDatasetNames()))
datasets.remove(filter(lambda name: "W3Jets" in name, datasets.getAllDatasetNames()))
datasets.remove(filter(lambda name: "W4Jets" in name, datasets.getAllDatasetNames()))
datasets.remove(filter(lambda name: "Hplus_taunu_s-channel" 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_lands = datasets.deepCopy()
# Set the signal cross sections to the ttbar for datasets for lands
# xsect.setHplusCrossSectionsToTop(datasets_lands)
# Set the signal cross sections to a given BR(t->H), BR(h->taunu)
xsect.setHplusCrossSectionsToBR(datasets, br_tH=0.01, br_Htaunu=1)
# Set the signal cross sections to a value from MSSM
# xsect.setHplusCrossSectionsToMSSM(datasets, tanbeta=20, mu=200)
plots.mergeWHandHH(datasets) # merging of WH and HH signals must be done after setting the cross section
# Apply TDR style
style = tdrstyle.TDRStyle()
# Create plots
doPlots(datasets)
# Write mt histograms to ROOT file
# writeTransverseMass(datasets_lands)
# Print counters
doCounters(datasets)
def main():
errorBars = False
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=analysis)
# datasets = dataset.getDatasetsFromMulticrabDirs(dirs, counters=counters, dataEra=dataEra, analysisBaseName="signalAnalysisInvertedTau")
## datasets = dataset.getDatasetsFromMulticrabCfg(counters=counters, dataEra=dataEra)
# 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()))
# 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()
invertedQCD = InvertedTauID()
invertedQCD.setLumi(datasets.getDataset("Data").getLuminosity())
invertedQCD.useErrorBars(errorBars)
metBase = plots.DataMCPlot(datasets, "BaseLine/MET_BaseLineTauIdBvetoCollinear")
metInver = plots.DataMCPlot(datasets, "Inverted/MET_InvertedTauIdBvetoCollinear")
# metBase = plots.DataMCPlot(datasets, "BaseLine/MET_BaseLineTauIdJetsCollinear")
# metInver = plots.DataMCPlot(datasets, "Inverted/MET_InvertedTauIdJetsCollinear")
# metBase = plots.DataMCPlot(datasets, "BaseLine/MET_BaseLineTauIdBveto")
# metInver = plots.DataMCPlot(datasets, "Inverted/MET_InvertedTauIdBveto")
# Rebin before subtracting
metBase.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(1))
metInver.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(1))
# metInverted_data = metInver.histoMgr.getHisto("Data").getRootHisto().Clone("Inverted/MET_InvertedTauIdBveto")
# metInverted_EWK = metInver.histoMgr.getHisto("EWK").getRootHisto().Clone("Inverted/MET_InvertedTauIdBveto")
# metBase_data = metBase.histoMgr.getHisto("Data").getRootHisto().Clone("BaseLine/MET_BaselineTauIdBveto")
# metBase_EWK = metBase.histoMgr.getHisto("EWK").getRootHisto().Clone("BaseLine/MET_BaselineTauIdBveto")
metInverted_data = metInver.histoMgr.getHisto("Data").getRootHisto().Clone("Inverted/MET_InvertedTauIdBvetoCollinear")
metInverted_EWK = metInver.histoMgr.getHisto("EWK").getRootHisto().Clone("Inverted/MET_InvertedTauIdBvetoCollinear")
metBase_data = metBase.histoMgr.getHisto("Data").getRootHisto().Clone("BaseLine/MET_BaselineTauIdBvetoCollinear")
metBase_EWK = metBase.histoMgr.getHisto("EWK").getRootHisto().Clone("BaseLine/MET_BaselineTauIdBvetoCollinear")
# metInverted_data = metInver.histoMgr.getHisto("Data").getRootHisto().Clone("Inverted/MET_InvertedTauIdJetsCollinear")
# metInverted_EWK = metInver.histoMgr.getHisto("EWK").getRootHisto().Clone("Inverted/MET_InvertedTauIdJetsCollinear")
# metBase_data = metBase.histoMgr.getHisto("Data").getRootHisto().Clone("BaseLine/MET_BaselineTauIdJetsCollinear")
# metBase_EWK = metBase.histoMgr.getHisto("EWK").getRootHisto().Clone("BaseLine/MET_BaselineTauIdJetsCollinear")
metBase_data.SetTitle("Data: BaseLine TauID")
metInverted_data.SetTitle("Data: Inverted TauID")
metBase_QCD = metBase_data.Clone("QCD")
metBase_QCD.Add(metBase_EWK,-1)
metBase_QCD.SetTitle("Data - EWK MC: BaseLine TauID")
metInverted_data.GetXaxis().SetTitle("PFMET (GeV)")
invertedQCD.setLabel("BaseVsInverted")
# invertedQCD.comparison(metInverted_data,metBase_data)
invertedQCD.setLabel("BaseMinusEWKVsInverted")
# invertedQCD.comparison(metInverted_data,metBase_QCD)
invertedQCD.cutefficiency(metInverted_data,metBase_QCD)
def main(argv):
dirs = []
if len(sys.argv) < 2:
usage()
dirs.append(sys.argv[1])
QCDInvertedNormalization = sort(QCDInvertedNormalizationFactors.QCDInvertedNormalization)
analysis = "signalAnalysisInvertedTau"
optModes = []
optModes.append("OptQCDTailKillerZeroPlus")
optModes.append("OptQCDTailKillerLoosePlus")
optModes.append("OptQCDTailKillerMediumPlus")
optModes.append("OptQCDTailKillerTightPlus")
optModes.append("OptQCDTailKillerVeryTightPlus")
plot = plots.PlotBase()
color = 1
for optMode in optModes:
datasets = dataset.getDatasetsFromMulticrabDirs(dirs,dataEra=dataEra, searchMode=searchMode, analysisName=analysis, optimizationMode=optMode)
datasets.updateNAllEventsToPUWeighted()
datasets.loadLuminosities()
plots.mergeRenameReorderForDataMC(datasets)
histonames = datasets.getDataset("Data").getDirectoryContent(HISTONAME)
bins = []
for histoname in histonames:
binname = histoname.replace(HISTONAME,"")
if not binname == "Inclusive":
bins.append(binname)
for i,bin in enumerate(bins):
mtplot = plots.DataMCPlot(datasets, HISTONAME+"/"+HISTONAME+bin)
if i == 0:
mt = mtplot.histoMgr.getHisto("Data").getRootHisto().Clone(HISTONAME+"/"+HISTONAME+bin)
legendName = optMode.replace("OptQCDTailKiller","R_{BB} ")
legendName = legendName.replace("Plus","")
mt.SetName(legendName)
mt.SetLineColor(color)
mt.Scale(QCDInvertedNormalization[i])
color += 1
if color == 5:
color += 1
else:
h = mtplot.histoMgr.getHisto("Data").getRootHisto().Clone(HISTONAME+"/"+HISTONAME+bin)
h.Scale(QCDInvertedNormalization[i])
mt.Add(h)
plot.histoMgr.appendHisto(histograms.Histo(mt,mt.GetName()))
style = tdrstyle.TDRStyle()
plot.createFrame("mt")
moveLegend={"dx": -0.15,"dy": 0.}
plot.setLegend(histograms.moveLegend(histograms.createLegend(), **moveLegend))
plot.setLuminosity(datasets.getDataset("Data").getLuminosity())
plot.addStandardTexts()
plot.draw()
plot.save()
#dataEra = "Run2011A"
#dataEra = "Run2011B"
dataEra = "Run2011AB"
# Go to batch mode, comment if interactive mode is wanted
ROOT.gROOT.SetBatch(True)
#-------------------------------------------------------------------------#
# Apply TDR style
style = tdrstyle.TDRStyle()
# Construct the datasets
datasets = dataset.getDatasetsFromMulticrabDirs(
dirs,
counters=counters,
dataEra=dataEra,
analysisBaseName="signalAnalysis")
if not mcOnly:
datasets.loadLuminosities()
datasets.updateNAllEventsToPUWeighted()
# # Construct datasets as stated in the multicrab.cfg of the execution
# # directory. The returned object is of type DatasetManager.
# #datasets = dataset.getDatasetsFromMulticrabCfg(counters = "qcdMeasurementMethod2Part1Counters")
# #datasets.updateNAllEventsToPUWeighted()
# # Construct datasets from the given list of CRAB task directories
# datasets = dataset.getDatasetsFromCrabDirs(["TTToHplusBWB_M120_Fall11"])
# #datasets = dataset.getDatasetsFromCrabDirs(["TTbar_Htaunu_M80"])
mcOnly = False
mcOnlyLumi = 5000 # pb
#dataEra = "Run2011A"
#dataEra = "Run2011B"
dataEra = "Run2011AB"
# Go to batch mode, comment if interactive mode is wanted
ROOT.gROOT.SetBatch(True)
#-------------------------------------------------------------------------#
# Apply TDR style
style = tdrstyle.TDRStyle()
# Construct the datasets
datasets = dataset.getDatasetsFromMulticrabDirs(dirs, counters=counters, dataEra=dataEra, analysisBaseName="signalAnalysis")
if not mcOnly:
datasets.loadLuminosities()
datasets.updateNAllEventsToPUWeighted()
plots.mergeRenameReorderForDataMC(datasets)
# This can be used to merge datasets:
#datasets.merge("TTJetsPlusWJets", ["TTJets", "WJets"], keepSources=True)
# Override the data luminosity (should not be used except for testing)
#datasets.getDataset("Data").setLuminosity(35)
# Create the plot, the latter argument is the path to the histogram in the ROOT files
def main():
if len(sys.argv) < 2:
usage()
dirs = []
dirs.append(sys.argv[1])
datasets = dataset.getDatasetsFromMulticrabDirs(dirs,dataEra=dataEra, searchMode=searchMode, analysisName=analysis, optimizationMode=optMode)
datasets.loadLuminosities()
datasets.updateNAllEventsToPUWeighted()
plots.mergeRenameReorderForDataMC(datasets)
datasets.merge("EWK", [
"TTJets",
"WJets",
"DYJetsToLL",
"SingleTop",
"Diboson"
])
style = tdrstyle.TDRStyle()
plot = plots.PlotBase()
legends = {}
name_re = re.compile("SelectedTau_pT_(?P<name>\S+)")
for i,histo in enumerate(HISTONAMES):
plot.histoMgr.appendHisto(purityGraph(i,datasets,histo))
name = histo
match = name_re.search(histo)
if match:
name = match.group("name")
legends["Purity%s"%i] = name
# if "AfterMetCut" in name:
# legends["Purity%s"%i] = "MET > 60 GeV"
if "SelectedTau_pT_CollinearCuts" in name:
legends["Purity%s"%i] = "Collinear cuts"
if "AfterBtagging" in name:
legends["Purity%s"%i] = "B tagging"
if "AfterBveto" in name:
legends["Purity%s"%i] = "B-jet veto"
if "AfterBvetoPhiCuts" in name:
legends["Purity%s"%i] = "B-jet veto, TailKiller"
if "SelectedTau_pT_BackToBackCuts" in name:
legends["Purity%s"%i] = "BackToBack cuts"
plot.createFrame("purityLoose", opts={"xmin": 40, "xmax": 160, "ymin": 0., "ymax": 1.05})
plot.frame.GetXaxis().SetTitle("p_{T}^{#tau jet} (GeV/c)")
plot.frame.GetYaxis().SetTitle("QCD purity")
# plot.setEnergy(datasets.getEnergies())
plot.histoMgr.setHistoLegendLabelMany(legends)
plot.setLegend(histograms.createLegend(0.3, 0.35, 0.6, 0.5))
# histograms.addText(0.2, 0.3, "TailKiller: MediumPlus", 18)
histograms.addText(0.35, 0.28, "BackToBack cuts: TightPlus", 20)
histograms.addText(0.35, 0.22, "2011B", 20)
histograms.addText(0.2, 0.3, "TailKiller: MediumPlus", 18)
# histograms.addText(0.2, 0.3, "TailKiller: TightPlus", 18)
plot.setEnergy(datasets.getEnergies())
plot.setLuminosity(datasets.getDataset("Data").getLuminosity())
plot.addStandardTexts()
plot.draw()
plot.save()
def main():
### Get the ROOT files for all datasets, merge datasets and reorder them
print "*** Obtaining ROOT files from:\n %s" % (multicrabPath)
datasets = dataset.getDatasetsFromMulticrabDirs(multicrabPath, dataEra="Run2011A")
printPSet(bPrintPSet, folderName="signalAnalysisRun2011A")
print "*** Calling datasets.updateNAllEventsToPUWeighted():"
datasets.updateNAllEventsToPUWeighted()
print "*** Loading luminosities"
datasets.loadLuminosities()
print "*** Calling plots.mergeRenameReorderForDataMC(datasets):"
plots.mergeRenameReorderForDataMC(datasets)
if mcOnly:
datasets.remove(datasets.getDataDatasetNames())
histograms.cmsTextMode = histograms.CMSMode.SIMULATION
### Merge desirable datasets
if bMergeEwk:
print "*** Merging EWK MC"
datasets.merge("EWK MC", ["WJets", "TTJets", "DYJetsToLL", "SingleTop", "Diboson"])
plots._plotStyles["EWK MC"] = styles.ttStyle #plots._plotStyles["EWK"] = styles.getEWKStyle()
### Remove signals other than M120
print "*** Removing all signal except M120"
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: "HplusTB" in name, datasets.getAllDatasetNames()))
### Setup style
print "*** Setting up style"
styleGenerator = styles.generator(fill=True)
style = tdrstyle.TDRStyle()
histograms.createLegend.moveDefaults(dx=-0.05, dy=+0.0)
# Set the signal cross sections to a given BR(t->H), BR(h->taunu)
print "*** Setting the signal cross sections to a given BR(t->bH+) and BR(H+ -> tau+ nu)"
xsect.setHplusCrossSectionsToBR(datasets, br_tH=0.01, br_Htaunu=1)
### Merge signals into one histo
print "*** Merging WH and HH signals into one histogram"
plots.mergeWHandHH(datasets) # merging of WH and HH signals must be done after setting the cross section
### Print desirable information here
print "*** Available datasets:\n %s" % (datasets.getAllDatasetNames())
if mcOnly:
print "*** Integrated Luminosity:\n %s (pb)" % (mcOnlyLumi)
else:
print "*** Integrated Luminosity:\n %s (pb)" % (datasets.getDataset("Data").getLuminosity())
### Do all plots defined in histoDict function
doPlots(datasets, histoDict, JetSelectionCuts, SaveExtension = "JetSelection")
#doPlots(datasets, histoDict, MtCut, SaveExtension = "Mt_"+MtCutValue.replace(" >= ","GE").replace(" <= ","LE"))
#doPlots(datasets, histoDict, MetBtagDeltaPhiCuts, SaveExtension = "MetBtagDeltaPhi")
#doPlots(datasets, histoDict, MetBtagDeltaPhiMtCuts, SaveExtension = "MetBtagDeltaPhiMt_"+MtCutValue.replace(" >= ","GE").replace(" <= ","LE".replace(".", ""))
#doPlots(datasets, histoDict, MetBtagDeltaPhiPlanCuts, SaveExtension = "MetBtagDeltaPhi_"+PlanarityCut.replace(" >= ","GE").replace(" <= ","LE".replace(".", ""))
#doPlots(datasets, histoDict, MetBtagDeltaPhiCircCuts, SaveExtension = "MetBtagDeltaPhi_"+CircularityCut.replace(" >= ","GE").replace(" <= ","LE".replace(".", ""))
#doPlots(datasets, histoDict, MetBtagDeltaPhiSpherCuts, SaveExtension = "MetBtagDeltaPhi_"+SphericityCut.replace(" >= ","GE").replace(" <= ","LE".replace(".", ""))
#doPlots(datasets, histoDict, MetBtagDeltaPhiAplanCuts, SaveExtension = "MetBtagDeltaPhi_"+AplanarityCut.replace(" >= ","_GE").replace(" <= ","_LE").replace(".", ""))
#doPlots(datasets, histoDict, MetBtagDeltaPhiAlphaTCuts, SaveExtension = "MetBtagDeltaPhi_"+AlphaTCut.replace(" >= ","_GE").replace(" <= ","_LE").replace(".", ""))
### Keep session alive (otherwise canvases close automatically)
if bBatchMode == False:
raw_input("*** Press \"any\" key to exit pyROOT: ")
def main():
### Get the ROOT files for all datasets, merge datasets and reorder them
print "*** Obtaining ROOT files from:\n %s" % (multicrabPath)
datasets = dataset.getDatasetsFromMulticrabDirs(multicrabPath,
dataEra="Run2011A")
printPSet(bPrintPSet, folderName="signalAnalysisRun2011A")
print "*** Calling datasets.updateNAllEventsToPUWeighted():"
datasets.updateNAllEventsToPUWeighted()
print "*** Loading luminosities"
datasets.loadLuminosities()
print "*** Calling plots.mergeRenameReorderForDataMC(datasets):"
plots.mergeRenameReorderForDataMC(datasets)
if mcOnly:
datasets.remove(datasets.getDataDatasetNames())
histograms.cmsTextMode = histograms.CMSMode.SIMULATION
### Merge desirable datasets
if bMergeEwk:
print "*** Merging EWK MC"
datasets.merge(
"EWK MC",
["WJets", "TTJets", "DYJetsToLL", "SingleTop", "Diboson"])
plots._plotStyles[
"EWK MC"] = styles.ttStyle #plots._plotStyles["EWK"] = styles.getEWKStyle()
### Remove signals other than M120
print "*** Removing all signal except M120"
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: "HplusTB" in name, datasets.getAllDatasetNames()))
### Setup style
print "*** Setting up style"
styleGenerator = styles.generator(fill=True)
style = tdrstyle.TDRStyle()
histograms.createLegend.moveDefaults(dx=-0.05, dy=+0.0)
# Set the signal cross sections to a given BR(t->H), BR(h->taunu)
print "*** Setting the signal cross sections to a given BR(t->bH+) and BR(H+ -> tau+ nu)"
xsect.setHplusCrossSectionsToBR(datasets, br_tH=0.01, br_Htaunu=1)
### Merge signals into one histo
print "*** Merging WH and HH signals into one histogram"
plots.mergeWHandHH(
datasets
) # merging of WH and HH signals must be done after setting the cross section
### Print desirable information here
print "*** Available datasets:\n %s" % (datasets.getAllDatasetNames())
if mcOnly:
print "*** Integrated Luminosity:\n %s (pb)" % (mcOnlyLumi)
else:
print "*** Integrated Luminosity:\n %s (pb)" % (
datasets.getDataset("Data").getLuminosity())
### Do all plots defined in histoDict function
doPlots(datasets,
histoDict,
JetSelectionCuts,
SaveExtension="JetSelection_Validation")
#doPlots(datasets, histoDict, MetBtagDeltaPhiCuts, SaveExtension = "MetBtagDeltaPhi")
#doPlots(datasets, histoDict, MetBtagDeltaPhiMtCuts, SaveExtension = "MetBtagDeltaPhiMt")
#doPlots(datasets, histoDict, MtCut, SaveExtension = "Mt")
#doPlots(datasets, histoDict, MetBtagDeltaPhiCircCuts, SaveExtension = "MetBtagDeltaPhiCirc")
#doPlots(datasets, histoDict, MetBtagDeltaPhiSpherCuts, SaveExtension = "MetBtagDeltaPhiSpher")
#doPlots(datasets, histoDict, MetBtagDeltaPhiAplanCuts, SaveExtension = "MetBtagDeltaPhiAplan")
#doPlots(datasets, histoDict, MetBtagDeltaPhiPlanCuts, SaveExtension = "MetBtagDeltaPhiPlan")
#doPlots(datasets, histoDict, MetBtagDeltaPhiAlphaTCuts, SaveExtension = "MetBtagDeltaPhiAlphaT")
### Keep session alive (otherwise canvases close automatically)
if bBatchMode == False:
raw_input("*** Press \"any\" key to exit pyROOT: ")
def getDatasetNames(multiCrabDir):
datasets = dataset.getDatasetsFromMulticrabDirs([multiCrabDir],
counters=counters)
return datasets.getAllDatasetNames()
def main():
if len(sys.argv) < 2:
usage()
dirs = []
dirs.append(sys.argv[1])
# Read the datasets
# datasets = dataset.getDatasetsFromMulticrabDirs(dirs,counters=counters, dataEra=dataEra, analysisBaseName="signalAnalysisInvertedTau")
datasets = dataset.getDatasetsFromMulticrabDirs(dirs,
dataEra=dataEra,
searchMode=searchMode,
analysisName=analysis,
optimizationMode=optMode)
# datasets = dataset.getDatasetsFromMulticrabDirs(dirs,counters=counters)
# datasets = dataset.getDatasetsFromMulticrabCfg(counters=counters, dataEra=dataEra)
# datasets.updateNAllEventsToPUWeighted()
datasets.loadLuminosities()
datasets.updateNAllEventsToPUWeighted()
# Take QCD from data
datasetsQCD = None
if QCDfromData:
datasetsQCD = dataset.getDatasetsFromMulticrabCfg(
cfgfile=
"/home/rkinnune/signalAnalysis/CMSSW_4_2_8_patch2/src/HiggsAnalysis/HeavyChHiggsToTauNu/test/multicrab_111123_132128/multicrab.cfg",
counters=counters)
datasetsQCD.loadLuminosities()
print "QCDfromData", QCDfromData
datasetsQCD.mergeData()
datasetsQCD.remove(datasetsQCD.getMCDatasetNames())
datasetsQCD.rename("Data", "QCD")
plots.mergeRenameReorderForDataMC(datasets)
print "Int.Lumi", datasets.getDataset("Data").getLuminosity()
# Remove signals other than M120
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.merge("EWK",
["WJets", "DYJetsToLL", "SingleTop", "Diboson", "TTJets"],
keepSources=True)
datasets.remove(
filter(lambda name: "W2Jets" in name, datasets.getAllDatasetNames()))
datasets.remove(
filter(lambda name: "W3Jets" in name, datasets.getAllDatasetNames()))
datasets.remove(
filter(lambda name: "W4Jets" in name, datasets.getAllDatasetNames()))
datasets.remove(
filter(lambda name: "Hplus_taunu_s-channel" in name,
datasets.getAllDatasetNames()))
datasets_lands = datasets.deepCopy()
# Set the signal cross sections to the ttbar for datasets for lands
# xsect.setHplusCrossSectionsToTop(datasets_lands)
# Set the signal cross sections to a given BR(t->H), BR(h->taunu)
xsect.setHplusCrossSectionsToBR(datasets, br_tH=0.01, br_Htaunu=1)
# Set the signal cross sections to a value from MSSM
# xsect.setHplusCrossSectionsToMSSM(datasets, tanbeta=20, mu=200)
plots.mergeWHandHH(
datasets
) # merging of WH and HH signals must be done after setting the cross section
# Apply TDR style
style = tdrstyle.TDRStyle()
# Create plots
doPlots(datasets)
# Write mt histograms to ROOT file
# writeTransverseMass(datasets_lands)
# Print counters
doCounters(datasets)
def main():
if len(sys.argv) < 2:
usage()
dirs = []
dirs.append(sys.argv[1])
datasets = dataset.getDatasetsFromMulticrabDirs(dirs,
dataEra=dataEra,
searchMode=searchMode,
analysisName=analysis,
optimizationMode=optMode)
datasets.loadLuminosities()
datasets.updateNAllEventsToPUWeighted()
plots.mergeRenameReorderForDataMC(datasets)
datasets.merge("EWK",
["TTJets", "WJets", "DYJetsToLL", "SingleTop", "Diboson"])
style = tdrstyle.TDRStyle()
plot = plots.PlotBase()
legends = {}
name_re = re.compile("SelectedTau_pT_(?P<name>\S+)")
for i, histo in enumerate(HISTONAMES):
plot.histoMgr.appendHisto(purityGraph(i, datasets, histo))
name = histo
match = name_re.search(histo)
if match:
name = match.group("name")
legends["Purity%s" % i] = name
# if "AfterMetCut" in name:
# legends["Purity%s"%i] = "MET > 60 GeV"
if "SelectedTau_pT_CollinearCuts" in name:
legends["Purity%s" % i] = "Collinear cuts"
if "AfterBtagging" in name:
legends["Purity%s" % i] = "B tagging"
if "AfterBveto" in name:
legends["Purity%s" % i] = "B-jet veto"
if "AfterBvetoPhiCuts" in name:
legends["Purity%s" % i] = "B-jet veto, TailKiller"
if "SelectedTau_pT_BackToBackCuts" in name:
legends["Purity%s" % i] = "BackToBack cuts"
plot.createFrame("purityLoose",
opts={
"xmin": 40,
"xmax": 160,
"ymin": 0.,
"ymax": 1.05
})
plot.frame.GetXaxis().SetTitle("p_{T}^{#tau jet} (GeV/c)")
plot.frame.GetYaxis().SetTitle("QCD purity")
# plot.setEnergy(datasets.getEnergies())
plot.histoMgr.setHistoLegendLabelMany(legends)
plot.setLegend(histograms.createLegend(0.3, 0.35, 0.6, 0.5))
# histograms.addText(0.2, 0.3, "TailKiller: MediumPlus", 18)
histograms.addText(0.35, 0.28, "BackToBack cuts: TightPlus", 20)
histograms.addText(0.35, 0.22, "2011B", 20)
histograms.addText(0.2, 0.3, "TailKiller: MediumPlus", 18)
# histograms.addText(0.2, 0.3, "TailKiller: TightPlus", 18)
plot.setEnergy(datasets.getEnergies())
plot.setLuminosity(datasets.getDataset("Data").getLuminosity())
plot.addStandardTexts()
plot.draw()
plot.save()
def main(argv):
interactive = True
interactive = False
# HISTONAME = "TauIdJets"
# HISTONAME = "TauIdBtag"
HISTONAME = "TauIdBveto"
dirs = []
if len(sys.argv) < 2:
usage()
dirs.append(sys.argv[1])
# Disable batch mode here to have the interactivity (see also the line with 'raw_input') below
if interactive:
ROOT.gROOT.SetBatch(False)
# Create all datasets from a multicrab task
# datasets = dataset.getDatasetsFromMulticrabCfg(counters=counters)
# datasets = dataset.getDatasetsFromMulticrabDirs(dirs,counters=counters, dataEra=dataEra)
datasets = dataset.getDatasetsFromMulticrabDirs(
dirs,
counters=counters,
dataEra=dataEra,
analysisbaseName="signalAnalysisInvertedTau")
# 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()))
# 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 0.2, 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)
# Apply TDR style
style = tdrstyle.TDRStyle()
#dataMCExample(datasets)
distComparison(datasets)
# Script execution can be paused like this, it will continue after
# user has given some input (which must include enter)
if interactive:
raw_input("Hit enter to continue")
def main():
if len(sys.argv) < 2:
usage()
# HISTONAME = "METInvertedTauIdAfterJets"
HISTONAME = "METInvertedTauIdAfterCollinearCuts"
# HISTONAME = "MET_InvertedTauIdBveto"
# HISTONAME = "METInvertedTauIdAfterCollinearCutsPlusBtag"
# HISTONAME = "METInvertedTauIdAfterCollinearCutsPlusBtag"
# HISTONAME = "MTInvertedTauIdJet"
# HISTONAME = "MTInvertedTauIdPhi"
invertedhisto = HISTONAME
baselinehisto = HISTONAME.replace("Inverted","BaseLine")
dirs = []
if len(sys.argv) < 2:
usage()
dirs.append(sys.argv[1])
# Create all datasets from a multicrab task
#datasets = dataset.getDatasetsFromMulticrabCfg(counters=counters)
datasets = dataset.getDatasetsFromMulticrabDirs(dirs,dataEra=dataEra, searchMode=searchMode, analysisName=analysis)
# datasets = dataset.getDatasetsFromMulticrabDirs(dirs,counters=counters, dataEra=dataEra, analysisBaseName="signalAnalysisInvertedTau" )
# As we use weighted counters for MC normalisation, we have to
# 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()))
# 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()
invertedQCD = InvertedTauID()
invertedQCD.setLumi(datasets.getDataset("Data").getLuminosity())
if ReBinning:
rebinfactor = 1.3
histobins = []
histobins.append(0)
histobins.append(1)
i = 1
while histobins[len(histobins)-1] < 400:
edge = histobins[i] + (histobins[i]-histobins[i-1])*rebinfactor
histobins.append(edge)
i += 1
print histobins
# metBase_data = metBase_data.Rebin(len(histobins)-1, metInverted_EWK = metInver.histoMgr.getHisto("EWK").getRootHisto().Clone(analysis+"/"+invertedhisto) "",array.array("d", histobins))
metInverted_data = metInverted_data.Rebin(len(histobins)-1,"",array.array("d", histobins))
metBase_EWK = metBase_EWK.Rebin(len(histobins)-1,"",array.array("d", histobins))
metInverted_EWK = metInverted_EWK.Rebin(len(histobins)-1,"",array.array("d", histobins))
normData,normEWK=normalisation()
metInverted = []
metBaseline = []
metBaselineQCD = []
for ptbin in ptbins:
## inverted
met_tmp = plots.PlotBase([datasets.getDataset("Data").getDatasetRootHisto("Inverted/METInvertedTauIdAfterCollinearCuts/METInvertedTauIdAfterCollinearCuts"+ptbin)])
#met_tmp = plots.PlotBase([datasets.getDataset("Data").getDatasetRootHisto("Inverted/"+invertedhisto+ptbin)])
met_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(5))
met = met_tmp.histoMgr.getHisto("Data").getRootHisto().Clone()
met.Scale(normData[ptbin])
metEWK_tmp = plots.PlotBase([datasets.getDataset("EWK").getDatasetRootHisto("Inverted/METInvertedTauIdAfterCollinearCuts/METInvertedTauIdAfterCollinearCuts"+ptbin)])
#metEWK_tmp = plots.PlotBase([datasets.getDataset("EWK").getDatasetRootHisto("Inverted/"+invertedhisto+ptbin)])
metEWK_tmp.histoMgr.normalizeMCToLuminosity(datasets.getDataset("Data").getLuminosity())
metEWK_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(5))
metEWK = metEWK_tmp.histoMgr.getHisto("EWK").getRootHisto().Clone()
metEWK.Scale(normEWK[ptbin])
met.Add(metEWK, -1)
metInverted.append(met)
## baseline
met_tmp = plots.PlotBase([datasets.getDataset("Data").getDatasetRootHisto("baseline/METBaselineTauIdAfterCollinearCuts/METBaselineTauIdAfterCollinearCuts"+ptbin)])
#met_tmp = plots.PlotBase([datasets.getDataset("Data").getDatasetRootHisto("Inverted/"+invertedhisto+ptbin)])
met_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(5))
metbaseline = met_tmp.histoMgr.getHisto("Data").getRootHisto().Clone()
metEWK_tmp = plots.PlotBase([datasets.getDataset("EWK").getDatasetRootHisto("baseline/METBaselineTauIdAfterCollinearCuts/METBaselineTauIdAfterCollinearCuts"+ptbin)])
#metEWK_tmp = plots.PlotBase([datasets.getDataset("EWK").getDatasetRootHisto("Inverted/"+invertedhisto+ptbin)])
metEWK_tmp.histoMgr.normalizeMCToLuminosity(datasets.getDataset("Data").getLuminosity())
metEWK_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(5))
metEWKbaseline = metEWK_tmp.histoMgr.getHisto("EWK").getRootHisto().Clone()
metBaseline.append(metbaseline)
metbaseline.Add(metEWKbaseline, -1)
metBaselineQCD.append(metbaseline)
metInverted_data = metInverted[0].Clone("met")
metInverted_data.SetName("met")
metInverted_data.SetTitle("Inverted tau ID")
metInverted_data.Reset()
for histo in metInverted:
metInverted_data.Add(histo)
metBaseline_data = metBaseline[0].Clone("met")
metBaseline_data.SetName("met")
metBaseline_data.SetTitle("baseline tau ID")
metBaseline_data.Reset()
for histo in metBaseline:
metBaseline_data.Add(histo)
metBaseline_QCD = metBaselineQCD[0].Clone("met")
metBaseline_QCD .SetName("met")
metBaseline_QCD.SetTitle("baseline tau ID")
metBaseline_QCD.Reset()
for histo in metBaselineQCD:
metBaseline_QCD.Add(histo)
#metBase_data.SetTitle("Data: BaseLine TauID")
#metInverted_data.SetTitle("Data: Inverted TauID")
#metBase_QCD = metBase_data.Clone("QCD")
#metBase_QCD.Add(metBase_EWK,-1)
#metBase_QCD.SetTitle("Data - EWK MC: BaseLine TauID")
invertedQCD.setLabel("BaseVsInverted")
invertedQCD.comparison(metInverted_data,metBaseline_data)
invertedQCD.setLabel("BaseMinusEWKVsInverted")
invertedQCD.comparison(metInverted_data,metBaseline_QCD)
invertedQCD.setLabel("McVsInverted")
# invertedQCD.comparison(metInverted_data,metInverted_MC)
invertedQCD.setLabel("EfficiencyBaseMinusEWKVsInverted")
invertedQCD.cutefficiency(metInverted_data,metBaseline_QCD )
