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 main():
# Create all datasets from a multicrab task
datasets = dataset.getDatasetsFromMulticrabCfg(counters=counters)
# 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 0.05, 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)
ttjets2 = datasets.getDataset("TTJets").deepCopy()
ttjets2.setCrossSection(ttjets2.getCrossSection()-datasets.getDataset("TTToHplus_M120").getCrossSection())
print "Set TTJets2 cross section to %f" % ttjets2.getCrossSection()
ttjets2.setName("TTJets2")
datasets.append(ttjets2)
datasets.merge("EWKnott", [
"WJets",
"DYJetsToLL",
"SingleTop",
"Diboson"
])
tmp = datasets.getDataset("EWKnott").deepCopy()
tmp.setName("EWKnott2")
datasets.append(tmp)
datasets.merge("EWK", [
"EWKnott",
"TTJets"
])
datasets.merge("EWKS", [
"EWKnott2",
"TTJets2",
"TTToHplus_M120",
])
# Apply TDR style
style = tdrstyle.TDRStyle()
invertedQCD = InvertedTauID()
metBase = plots.DataMCPlot(datasets, analysis+"/MET_BaseLineTauIdJets")
metInver = plots.DataMCPlot(datasets, analysis+"/MET_InvertedTauIdJets")
# Rebin before subtracting
metBase.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(20))
metInver.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(20))
metInverted_data = metInver.histoMgr.getHisto("Data").getRootHisto().Clone(analysis+"/MET_InvertedTauIdJets")
metInverted_EWK = metInver.histoMgr.getHisto("EWK").getRootHisto().Clone(analysis+"/MET_InvertedTauIdJets")
metBase_data = metBase.histoMgr.getHisto("Data").getRootHisto().Clone(analysis+"/MET_BaselineTauIdJets")
metBase_EWK = metBase.histoMgr.getHisto("EWK").getRootHisto().Clone(analysis+"/MET_BaselineTauIdJets")
metBase_EWKS = metBase.histoMgr.getHisto("EWKS").getRootHisto().Clone(analysis+"/MET_BaselineTauIdJets")
metBase_QCD = metBase_data.Clone("QCD")
metBase_QCD.Add(metBase_EWK,-1)
metBase_EWK.SetTitle("MC EWK")
metBase_EWKS.SetTitle("MC EWK + Signal")
invertedQCD.setLabel("EWK_vs_SEWK")
invertedQCD.comparison(metBase_EWK,metBase_EWKS,0)
invertedQCD.setLabel("EWK_vs_SEWK_Normalized")
invertedQCD.comparison(metBase_EWK,metBase_EWKS,1)
invertedQCD.setLabel("InvData")
invertedQCD.fitQCD(metInverted_data)
invertedQCD.setLabel("EWK")
invertedQCD.fitEWK(metBase_EWK)
invertedQCD.fitData(metBase_data)
normalizationWithEWK = invertedQCD.getNormalization()
invertedQCD.setLabel("EWKS")
invertedQCD.fitEWK(metBase_EWKS)
invertedQCD.fitData(metBase_data)
normalizationWithEWKS = invertedQCD.getNormalization()
print "Difference Signal vs no signal in EWK fit",(normalizationWithEWKS - normalizationWithEWK)/normalizationWithEWK
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 main():
# Create all datasets from a multicrab task
datasets = dataset.getDatasetsFromMulticrabCfg(counters=counters)
# 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 0.05, 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())
metBase = plots.DataMCPlot(datasets, analysis+"/MET_BaseLineTauIdJets")
metInver = plots.DataMCPlot(datasets, analysis+"/MET_InvertedTauIdJets")
# Rebin before subtracting
metBase.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(10))
metInver.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(10))
metInverted_data = metInver.histoMgr.getHisto("Data").getRootHisto().Clone(analysis+"/MET_InvertedTauIdJets")
metInverted_EWK = metInver.histoMgr.getHisto("EWK").getRootHisto().Clone(analysis+"/MET_InvertedTauIdJets")
metBase_data = metBase.histoMgr.getHisto("Data").getRootHisto().Clone(analysis+"/MET_BaselineTauIdJets")
metBase_EWK = metBase.histoMgr.getHisto("EWK").getRootHisto().Clone(analysis+"/MET_BaselineTauIdJets")
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,metBase_data)
invertedQCD.setLabel("BaseMinusEWKVsInverted")
invertedQCD.comparison(metInverted_data,metBase_QCD)
invertedQCD.cutefficiency(metInverted_data,metBase_QCD)
