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 old_plots():
mtAllCuts = []
mtAllCutsBaseline = []
mtAfterMetCut = []
mtAfterMetCutBaseline = []
mtAfterMetPlusSoftBtagging = []
mtAfterMetPlusSoftBtaggingBaseline = []
mtAfterMetPlusBveto = []
mtAfterMetPlusBvetoBaseline = []
mtAfterCollinearCuts = []
mtAfterCollinearCutsBaseline = []
invertedNoScale = []
## histograms in bins, normalisation and substraction of EWK contribution
## mt with 2dim deltaPhi cut
for ptbin in ptbins:
## ------------- mt with all cuts -----------
mt_tmp = plots.PlotBase([datasets.getDataset("Data").getDatasetRootHisto("Inverted/MTInvertedTauIdAfterBackToBackCuts/MTInvertedTauIdAfterBackToBackCuts"+ptbin)])
mt_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(5))
mt = mt_tmp.histoMgr.getHisto("Data").getRootHisto().Clone()
mtEvents = mt_tmp.histoMgr.getHisto("Data").getRootHisto().Clone()
invertedNoScale.append(mtEvents)
mt.Scale(normData[ptbin])
mtEWK_tmp = plots.PlotBase([datasets.getDataset("EWK").getDatasetRootHisto("Inverted/MTInvertedTauIdAfterBackToBackCuts/MTInvertedTauIdAfterBackToBackCuts"+ptbin)])
mtEWK_tmp.histoMgr.normalizeMCToLuminosity(datasets.getDataset("Data").getLuminosity())
mtEWK_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(5))
mtEWK = mtEWK_tmp.histoMgr.getHisto("EWK").getRootHisto().Clone()
mtEWK.Scale(normEWK[ptbin])
if includeEWKscale:
mtEWK.Scale(EWKscale)
mt.Add(mtEWK, -1)
mtAllCuts.append(mt)
## baseline
mt_tmp = plots.PlotBase([datasets.getDataset("Data").getDatasetRootHisto("baseline/MTBaselineTauIdAfterBackToBackCuts/MTBaselineTauIdAfterBackToBackCuts"+ptbin)])
mt_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(5))
mtBaseline = mt_tmp.histoMgr.getHisto("Data").getRootHisto().Clone()
mtEWK_tmp = plots.PlotBase([datasets.getDataset("EWK").getDatasetRootHisto("baseline/MTBaselineTauIdAfterBackToBackCuts/MTBaselineTauIdAfterBackToBackCuts"+ptbin)])
mtEWK_tmp.histoMgr.normalizeMCToLuminosity(datasets.getDataset("Data").getLuminosity())
mtEWK_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(5))
mtEWKBaseline = mtEWK_tmp.histoMgr.getHisto("EWK").getRootHisto().Clone()
mtBaseline.Add(mtEWKBaseline, -1)
mtAllCutsBaseline.append(mtBaseline)
#################################################
### ---- mt after met cut
mtfac_tmp = plots.PlotBase([datasets.getDataset("Data").getDatasetRootHisto("Inverted/MTInvertedTauIdAfterMetPlusBackToBackCuts/MTInvertedTauIdAfterMetPlusBackToBackCuts"+ptbin)])
mtfac_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(5))
mtfac = mtfac_tmp.histoMgr.getHisto("Data").getRootHisto().Clone()
mtNoBtaggingTailKiller = mtfac_tmp.histoMgr.getHisto("Data").getRootHisto().Clone()
mtNoBtaggingTailKiller.Scale(normData[ptbin])
mtfacEWK_tmp = plots.PlotBase([datasets.getDataset("EWK").getDatasetRootHisto("Inverted/MTInvertedTauIdAfterMetPlusBackToBackCuts/MTInvertedTauIdAfterMetPlusBackToBackCuts"+ptbin)])
mtfacEWK_tmp.histoMgr.normalizeMCToLuminosity(datasets.getDataset("Data").getLuminosity())
mtfacEWK_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(5))
mtNoBtaggingTailKillerEWK = mtfacEWK_tmp.histoMgr.getHisto("EWK").getRootHisto().Clone()
mtNoBtaggingTailKillerEWK.Scale(normEWK[ptbin])
mtNoBtaggingTailKiller.Add(mtNoBtaggingTailKillerEWK, -1)
mtAfterMetCut.append(mtNoBtaggingTailKiller)
### ---- mt after met cut baseline
mtfac_tmp = plots.PlotBase([datasets.getDataset("Data").getDatasetRootHisto("baseline/MTBaselineTauIdAfterMetPlusBackToBackCuts/MTBaselineTauIdAfterMetPlusBackToBackCuts"+ptbin)])
mtfac_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(5))
mtfac = mtfac_tmp.histoMgr.getHisto("Data").getRootHisto().Clone()
mtNoBtaggingTailKillerBaseline = mtfac_tmp.histoMgr.getHisto("Data").getRootHisto().Clone()
mtfacEWK_tmp = plots.PlotBase([datasets.getDataset("EWK").getDatasetRootHisto("baseline/MTBaselineTauIdAfterMetPlusBackToBackCuts/MTBaselineTauIdAfterMetPlusBackToBackCuts"+ptbin)])
mtfacEWK_tmp.histoMgr.normalizeMCToLuminosity(datasets.getDataset("Data").getLuminosity())
mtfacEWK_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(5))
mtNoBtaggingTailKillerEWK = mtfacEWK_tmp.histoMgr.getHisto("EWK").getRootHisto().Clone()
mtNoBtaggingTailKillerBaseline.Add(mtNoBtaggingTailKillerEWK, -1)
mtAfterMetCutBaseline.append(mtNoBtaggingTailKillerBaseline)
########################################################
# ----------mt after SOFT b tagging ---------------
mtbSoft_tmp = plots.PlotBase([datasets.getDataset("Data").getDatasetRootHisto("Inverted/MTInvertedTauIdAfterMetPlusSoftBtaggingPlusBackToBackCuts/MTInvertedTauIdAfterMetPlusSoftBtaggingPlusBackToBackCuts"+ptbin)])
mtbSoft_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(5))
mtbSoft = mtbSoft_tmp.histoMgr.getHisto("Data").getRootHisto().Clone()
mtbSoft.Scale(normData[ptbin])
mtbSoftEWK_tmp = plots.PlotBase([datasets.getDataset("EWK").getDatasetRootHisto("Inverted/MTInvertedTauIdAfterMetPlusSoftBtaggingPlusBackToBackCuts/MTInvertedTauIdAfterMetPlusSoftBtaggingPlusBackToBackCuts"+ptbin)])
mtbSoftEWK_tmp.histoMgr.normalizeMCToLuminosity(datasets.getDataset("Data").getLuminosity())
mtbSoftEWK_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(5))
mtbSoftEWK = mtbSoftEWK_tmp.histoMgr.getHisto("EWK").getRootHisto().Clone()
mtbSoftEWK.Scale(normEWK[ptbin])
mtbSoft.Add(mtbSoftEWK, -1)
mtAfterMetPlusSoftBtagging.append(mtbSoft)
# ----------mt after SOFT b tagging baseline ---------------
mtbSoft_tmp = plots.PlotBase([datasets.getDataset("Data").getDatasetRootHisto("baseline/MTBaselineTauIdAfterMetPlusSoftBtaggingPlusBackToBackCuts/MTBaselineTauIdAfterMetPlusSoftBtaggingPlusBackToBackCuts"+ptbin)])
mtbSoft_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(5))
mtbSoftBaseline = mtbSoft_tmp.histoMgr.getHisto("Data").getRootHisto().Clone()
mtbSoftEWK_tmp = plots.PlotBase([datasets.getDataset("EWK").getDatasetRootHisto("baseline/MTBaselineTauIdAfterMetPlusSoftBtaggingPlusBackToBackCuts/MTBaselineTauIdAfterMetPlusSoftBtaggingPlusBackToBackCuts"+ptbin)])
mtbSoftEWK_tmp.histoMgr.normalizeMCToLuminosity(datasets.getDataset("Data").getLuminosity())
mtbSoftEWK_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(5))
mtbSoftEWKBaseline = mtbSoftEWK_tmp.histoMgr.getHisto("EWK").getRootHisto().Clone()
mtbSoftBaseline.Add(mtbSoftEWKBaseline, -1)
mtAfterMetPlusSoftBtaggingBaseline.append(mtbSoftBaseline)
########################################################
# mt after met cut and b-jet veto
mtPhiv_tmp = plots.PlotBase([datasets.getDataset("Data").getDatasetRootHisto("Inverted/MTInvertedTauIdAfterMetPlusBvetoPlusBackToBackCuts/MTInvertedTauIdAfterMetPlusBvetoPlusBackToBackCuts"+ptbin)])
mtPhiv_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(5))
mtPhiv = mtPhiv_tmp.histoMgr.getHisto("Data").getRootHisto().Clone()
mtPhiv.Scale(normData[ptbin])
mtPhiEWKv_tmp = plots.PlotBase([datasets.getDataset("EWK").getDatasetRootHisto("Inverted/MTInvertedTauIdAfterMetPlusBvetoPlusBackToBackCuts/MTInvertedTauIdAfterMetPlusBvetoPlusBackToBackCuts"+ptbin)])
mtPhiEWKv_tmp.histoMgr.normalizeMCToLuminosity(datasets.getDataset("Data").getLuminosity())
mtPhiEWKv_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(5))
mtPhiEWKv = mtPhiEWKv_tmp.histoMgr.getHisto("EWK").getRootHisto().Clone()
mtPhiEWKv.Scale(normEWK[ptbin])
mtPhiv.Add(mtPhiEWKv, -1)
mtAfterMetPlusBveto.append(mtPhiv)
mtPhiv_tmp = plots.PlotBase([datasets.getDataset("Data").getDatasetRootHisto("baseline/MTBaselineTauIdAfterMetPlusBvetoPlusBackToBackCuts/MTBaselineTauIdAfterMetPlusBvetoPlusBackToBackCuts"+ptbin)])
mtPhiv_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(5))
mtPhivBaseline = mtPhiv_tmp.histoMgr.getHisto("Data").getRootHisto().Clone()
mtPhiEWKv_tmp = plots.PlotBase([datasets.getDataset("EWK").getDatasetRootHisto("baseline/MTBaselineTauIdAfterMetPlusBvetoPlusBackToBackCuts/MTBaselineTauIdAfterMetPlusBvetoPlusBackToBackCuts"+ptbin)])
mtPhiEWKv_tmp.histoMgr.normalizeMCToLuminosity(datasets.getDataset("Data").getLuminosity())
mtPhiEWKv_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(5))
mtPhiEWKvBaseline = mtPhiEWKv_tmp.histoMgr.getHisto("EWK").getRootHisto().Clone()
mtPhivBaseline.Add(mtPhiEWKvBaseline, -1)
mtAfterMetPlusBvetoBaseline.append(mtPhivBaseline)
############################################################
# mt after jet selection and back-to-back cuts
mtj_tmp = plots.PlotBase([datasets.getDataset("Data").getDatasetRootHisto("Inverted/MTInvertedTauIdAfterCollinearCutsPlusBackToBackCuts/MTInvertedTauIdAfterCollinearCutsPlusBackToBackCuts"+ptbin)])
mtj_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(5))
mtj = mtj_tmp.histoMgr.getHisto("Data").getRootHisto().Clone()
mtj.Scale(normData[ptbin])
mtjEWK_tmp = plots.PlotBase([datasets.getDataset("EWK").getDatasetRootHisto("Inverted/MTInvertedTauIdAfterCollinearCutsPlusBackToBackCuts/MTInvertedTauIdAfterCollinearCutsPlusBackToBackCuts"+ptbin)])
mtjEWK_tmp.histoMgr.normalizeMCToLuminosity(datasets.getDataset("Data").getLuminosity())
mtjEWK_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(5))
mtjEWK = mtjEWK_tmp.histoMgr.getHisto("EWK").getRootHisto().Clone()
mtjEWK.Scale(normEWK[ptbin])
mtj.Add(mtjEWK, -1)
mtAfterCollinearCuts.append(mtj)
# mt after jet selection and back-to-back cuts baseline
mtj_tmp = plots.PlotBase([datasets.getDataset("Data").getDatasetRootHisto("baseline/MTBaselineTauIdAfterCollinearCutsPlusBackToBackCuts/MTBaselineTauIdAfterCollinearCutsPlusBackToBackCuts"+ptbin)])
mtj_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(5))
mtjBaseline = mtj_tmp.histoMgr.getHisto("Data").getRootHisto().Clone()
mtjEWK_tmp = plots.PlotBase([datasets.getDataset("EWK").getDatasetRootHisto("baseline/MTBaselineTauIdAfterCollinearCutsPlusBackToBackCuts/MTBaselineTauIdAfterCollinearCutsPlusBackToBackCuts"+ptbin)])
mtjEWK_tmp.histoMgr.normalizeMCToLuminosity(datasets.getDataset("Data").getLuminosity())
mtjEWK_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(5))
mtjEWKBaseline = mtjEWK_tmp.histoMgr.getHisto("EWK").getRootHisto().Clone()
mtjBaseline.Add(mtjEWKBaseline, -1)
mtAfterCollinearCutsBaseline.append(mtjBaseline)
############################################################
## end of loop
### sum and plot
invertedQCD = InvertedTauID()
invertedQCD.setLumi(datasets.getDataset("Data").getLuminosity())
events = invertedNoScale[0].Clone("inv")
events.SetName("inv")
events.SetTitle("Inverted tau ID")
events.Reset()
print "check events in inverted ",events.GetEntries()
for histo in invertedNoScale:
events.Add(histo)
print "All unormalised events - EWK = ",events.Integral()
## mt with all cuts
hAllCuts = mtAllCuts[0].Clone("mtSum")
hAllCuts.SetName("transverseMass")
hAllCuts.SetTitle("Inverted tau ID")
hAllCuts.Reset()
for histo in mtAllCuts:
hAllCuts.Add(histo)
print "All events with all cuts - EWK evets= ",hAllCuts.Integral()
hAllCutsBaseline = mtAllCutsBaseline[0].Clone("mtSumBaseline")
hAllCutsBaseline.SetName("transverseMass")
hAllCutsBaseline.SetTitle("Inverted tau ID")
hAllCutsBaseline.Reset()
for histo in mtAllCutsBaseline:
hAllCutsBaseline.Add(histo)
print "Baseline QCD events with all cuts = ",hAllCutsBaseline.Integral()
# mt plot with all cuts
allCuts_inverted = hAllCuts.Clone("AllCuts")
allCuts_baseline = hAllCutsBaseline.Clone("allcuts_QCD")
invertedQCD.setLabel("AllCuts")
invertedQCD.mtComparison(allCuts_inverted, allCuts_baseline,"AllCuts")
# mt plot inverted all cuts
invertedQCD.setLabel("MtWithAllCutsTailKiller")
invertedQCD.mtComparison(allCuts_inverted, allCuts_inverted,"MtWithAllCutsTailKiller")
########################################################
# mt with SOFT btagging
hmtAfterMetPlusSoftBtagging = mtAfterMetPlusSoftBtagging[0].Clone("mthmtbSoft")
hmtAfterMetPlusSoftBtagging.SetName("transverseMassBtag")
hmtAfterMetPlusSoftBtagging.SetTitle("Inverted tau ID")
hmtAfterMetPlusSoftBtagging.Reset()
# print "check hmtsum",hmtSum.GetEntries()
for histo in mtAfterMetPlusSoftBtagging:
hmtAfterMetPlusSoftBtagging.Add(histo)
print "Integral: after SOFT B tagging - EWK = ",hmtAfterMetPlusSoftBtagging.Integral()
# mt with SOFT btagging baseline
hmtAfterMetPlusSoftBtaggingBaseline = mtAfterMetPlusSoftBtaggingBaseline[0].Clone("mthmtbSoft")
hmtAfterMetPlusSoftBtaggingBaseline.SetName("transverseMassBtag")
hmtAfterMetPlusSoftBtaggingBaseline.SetTitle("Inverted tau ID")
hmtAfterMetPlusSoftBtaggingBaseline.Reset()
# print "check hmtsum",hmtSum.GetEntries()
for histo in mtAfterMetPlusSoftBtaggingBaseline:
hmtAfterMetPlusSoftBtaggingBaseline.Add(histo)
print "Integral Baseline: after SOFT B tagging - EWK = ",hmtAfterMetPlusSoftBtaggingBaseline.Integral()
# mt inverted-baseline comparison, SOFT b tagging, MET CUT
afterJets_inverted = hmtAfterMetPlusSoftBtagging.Clone("mtbSoft")
afterJets_baseline = hmtAfterMetPlusSoftBtaggingBaseline.Clone("mtbSoft_QCD")
invertedQCD.setLabel("AfterMetPlusSoftBtagging")
invertedQCD.mtComparison(afterJets_inverted,afterJets_baseline,"AfterMetPlusSoftBtagging")
################################
hmtAfterCollinearCuts = mtAfterCollinearCuts[0].Clone("AfterCollinearCuts")
hmtAfterCollinearCuts.SetName("transverseMassBtag")
hmtAfterCollinearCuts.SetTitle("Inverted tau ID")
hmtAfterCollinearCuts.Reset()
for histo in mtAfterCollinearCuts:
hmtAfterCollinearCuts.Add(histo)
print "Integral: after collinear cuts - EWK = ",hmtAfterCollinearCuts.Integral()
hmtAfterCollinearCutsBaseline = mtAfterCollinearCutsBaseline[0].Clone("AfterCollinearCutsBaseline")
hmtAfterCollinearCutsBaseline.SetName("transverseMassBtag")
hmtAfterCollinearCutsBaseline.SetTitle("Inverted tau ID")
hmtAfterCollinearCutsBaseline.Reset()
for histo in mtAfterCollinearCutsBaseline:
hmtAfterCollinearCutsBaseline.Add(histo)
print "Integral Baseline : after collinear cuts - EWK = ",hmtAfterCollinearCutsBaseline.Integral()
# mt inverted-baseline comparison at coll cuts
afterJets_inverted = hmtAfterCollinearCuts.Clone("AfterCollinearCuts")
afterJets_baseline = hmtAfterCollinearCutsBaseline.Clone("AfterCollinearCutsBaseline_QCD")
invertedQCD.setLabel("AfterCollinearCuts")
invertedQCD.mtComparison(afterJets_inverted,afterJets_baseline,"AfterCollinearCuts")
######################################################
## B veto for closure test With Met cut
hmtAfterMetPlusBveto = mtAfterMetPlusBveto[0].Clone("hClosureBveto")
hmtAfterMetPlusBveto.SetName("transverseMassBveto")
hmtAfterMetPlusBveto.SetTitle("Inverted tau ID")
hmtAfterMetPlusBveto.Reset()
for histo in mtAfterMetPlusBveto:
hmtAfterMetPlusBveto.Add(histo)
print "Integral: Bveto - EWK with met cut = ",hmtAfterMetPlusBveto.Integral()
hmtAfterMetPlusBvetoBaseline = mtAfterMetPlusBvetoBaseline[0].Clone("hClosureBvetoBaseline")
hmtAfterMetPlusBvetoBaseline.SetName("transverseMassBveto")
hmtAfterMetPlusBvetoBaseline.SetTitle("Inverted tau ID")
hmtAfterMetPlusBvetoBaseline.Reset()
for histo in mtAfterMetPlusBvetoBaseline:
hmtAfterMetPlusBvetoBaseline.Add(histo)
print "Integral baseline: Bveto - EWK with met cut = ",hmtAfterMetPlusBvetoBaseline.Integral()
# mt inverted-baseline comparison with bveto and MET CUT, closure
#bveto_inverted =hClosureBvetoTailKiller.Clone("hmtvSum")
bveto_inverted = hmtAfterMetPlusBveto.Clone("bveto")
bveto_baseline = hmtAfterMetPlusBvetoBaseline.Clone("bvetoBaseline_QCD")
invertedQCD.setLabel("AfterMetPlusBveto")
# invertedQCD.mtComparison(bveto_inverted, bveto_baseline,"BvetoTailKillerClosure",sysError=sysError)
invertedQCD.mtComparison(bveto_inverted, bveto_baseline,"AfterMetPlusBveto")
########################################################
## no btagging, with Met cut
hmtAfterMetCut = mtAfterMetCut[0].Clone("hClosureNoBtag")
hmtAfterMetCut.SetName("transverseMassNoBtagging")
hmtAfterMetCut.SetTitle("Inverted tau ID")
hmtAfterMetCut.Reset()
for histo in mtAfterMetCut:
hmtAfterMetCut.Add(histo)
print "Integral: no btagging - EWK = ",hmtAfterMetCut.Integral()
hmtAfterMetCutBaseline = mtAfterMetCutBaseline[0].Clone("hClosureNoBtag")
hmtAfterMetCutBaseline.SetName("transverseMassNoBtagging")
hmtAfterMetCutBaseline.SetTitle("Inverted tau ID")
hmtAfterMetCutBaseline.Reset()
for histo in mtAfterMetCutBaseline:
hmtAfterMetCutBaseline.Add(histo)
print "Integral baseline: no btagging - EWK = ",hmtAfterMetCutBaseline.Integral()
# mt inverted-baseline comparison, no b tagging, with met cut, closure
afterMet_inverted = hmtAfterMetCut.Clone("afterMet")
afterMet_baseline = hmtAfterMetCutBaseline.Clone("aftermet_QCD")
invertedQCD.setLabel("AfterMetCut")
invertedQCD.mtComparison(afterMet_inverted,afterMet_baseline,"AfterMetCut")
def controlPlots(datasets):
normData, normEWK, normFactorisedData, normFactorisedEWK = normalisation()
norm_inc, normEWK_inc = normalisationInclusive()
hmet = []
hmetb = []
effArray = []
effErrArray = []
hmetbveto = []
hmtBtag = []
hmtBveto = []
hmtNoMetBtag = []
hmtNoMetBveto = []
effBvetoArray = []
effErrBvetoArray = []
effArrayMt = []
effErrArrayMt = []
effArrayMtNoMet = []
effErrArrayMtNoMet = []
## histograms in bins, normalisation and substraction of EWK contribution
## mt with 2dim deltaPhi cut
for ptbin in ptbins:
### MET
mmt_tmp = plots.PlotBase([
datasets.getDataset("Data").getDatasetRootHisto(
"Inverted/MET_InvertedTauIdJets" + ptbin)
])
mmt_tmp._setLegendStyles()
mmt_tmp._setLegendLabels()
mmt_tmp.histoMgr.setHistoDrawStyleAll("P")
mmt_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(20))
mmt = mmt_tmp.histoMgr.getHisto("Data").getRootHisto().Clone()
mmt.Scale(normData[ptbin])
# hmt.append(mt)
mmtEWK_tmp = plots.PlotBase([
datasets.getDataset("EWK").getDatasetRootHisto(
"Inverted/MET_InvertedTauIdJets" + ptbin)
])
mmtEWK_tmp.histoMgr.normalizeMCToLuminosity(
datasets.getDataset("Data").getLuminosity())
mmtEWK_tmp._setLegendStyles()
mmtEWK_tmp._setLegendLabels()
mmtEWK_tmp.histoMgr.setHistoDrawStyleAll("P")
mmtEWK_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(20))
mmtEWK = mmtEWK_tmp.histoMgr.getHisto("EWK").getRootHisto().Clone()
mmtEWK.Scale(normEWK[ptbin])
mmt.Add(mmtEWK, -1)
hmet.append(mmt)
### MET with btagging
mmtb_tmp = plots.PlotBase([
datasets.getDataset("Data").getDatasetRootHisto(
"Inverted/MET_InvertedTauIdBtag" + ptbin)
])
mmtb_tmp._setLegendStyles()
mmtb_tmp._setLegendLabels()
mmtb_tmp.histoMgr.setHistoDrawStyleAll("P")
mmtb_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(20))
mmtb = mmtb_tmp.histoMgr.getHisto("Data").getRootHisto().Clone()
mmtb.Scale(normData[ptbin])
# hmt.append(mt)
mmtbEWK_tmp = plots.PlotBase([
datasets.getDataset("EWK").getDatasetRootHisto(
"Inverted/MET_InvertedTauIdBtag" + ptbin)
])
mmtbEWK_tmp.histoMgr.normalizeMCToLuminosity(
datasets.getDataset("Data").getLuminosity())
mmtbEWK_tmp._setLegendStyles()
mmtbEWK_tmp._setLegendLabels()
mmtbEWK_tmp.histoMgr.setHistoDrawStyleAll("P")
mmtbEWK_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(20))
mmtbEWK = mmtbEWK_tmp.histoMgr.getHisto("EWK").getRootHisto().Clone()
mmtbEWK.Scale(normEWK[ptbin])
mmtb.Add(mmtbEWK, -1)
hmetb.append(mmtb)
eff = mmtb.Integral() / mmt.Integral()
ereff = sqrt(eff * (1 - eff) / mmt.Integral())
print " pt bin ", ptbin, " btag efficiency from MET = ", eff, " error ", ereff
effArray.append(eff)
effErrArray.append(ereff)
### MET with bveto
mmtbveto_tmp = plots.PlotBase([
datasets.getDataset("Data").getDatasetRootHisto(
"Inverted/MET_InvertedTauIdBveto" + ptbin)
])
mmtbveto_tmp._setLegendStyles()
mmtbveto_tmp._setLegendLabels()
mmtbveto_tmp.histoMgr.setHistoDrawStyleAll("P")
mmtbveto_tmp.histoMgr.forEachHisto(
lambda h: h.getRootHisto().Rebin(20))
mmtbveto = mmtbveto_tmp.histoMgr.getHisto(
"Data").getRootHisto().Clone()
mmtbveto.Scale(normData[ptbin])
# hmt.append(mt)
mmtbvetoEWK_tmp = plots.PlotBase([
datasets.getDataset("EWK").getDatasetRootHisto(
"Inverted/MET_InvertedTauIdBveto" + ptbin)
])
mmtbvetoEWK_tmp.histoMgr.normalizeMCToLuminosity(
datasets.getDataset("Data").getLuminosity())
mmtbvetoEWK_tmp._setLegendStyles()
mmtbvetoEWK_tmp._setLegendLabels()
mmtbvetoEWK_tmp.histoMgr.setHistoDrawStyleAll("P")
mmtbvetoEWK_tmp.histoMgr.forEachHisto(
lambda h: h.getRootHisto().Rebin(20))
mmtbvetoEWK = mmtbvetoEWK_tmp.histoMgr.getHisto(
"EWK").getRootHisto().Clone()
mmtbvetoEWK.Scale(normEWK[ptbin])
mmtbveto.Add(mmtbvetoEWK, -1)
hmetbveto.append(mmtbveto)
## normalization mT(btag/bveto)
eff = mmtb.Integral() / mmtbveto.Integral()
ereff = sqrt(eff * (1 - eff) / mmtbveto.Integral())
print " pt bin ", ptbin, " btag/bveto efficiency from MET = ", eff, " error ", ereff
effBvetoArray.append(eff)
effErrBvetoArray.append(ereff)
## with MT distribution
if False:
### no MET cut
mmtb_tmp = plots.PlotBase([
datasets.getDataset("Data").getDatasetRootHisto(
"Inverted/MTInvertedTauIdBtagNoMetCut" + ptbin)
])
mmtb_tmp._setLegendStyles()
mmtb_tmp._setLegendLabels()
mmtb_tmp.histoMgr.setHistoDrawStyleAll("P")
mmtb_tmp.histoMgr.forEachHisto(
lambda h: h.getRootHisto().Rebin(20))
mmtb = mmtb_tmp.histoMgr.getHisto("Data").getRootHisto().Clone()
mmtb.Scale(normData[ptbin])
# hmt.append(mt)
mmtbEWK_tmp = plots.PlotBase([
datasets.getDataset("EWK").getDatasetRootHisto(
"Inverted/MTInvertedTauIdBtagNoMetCut" + ptbin)
])
mmtbEWK_tmp.histoMgr.normalizeMCToLuminosity(
datasets.getDataset("Data").getLuminosity())
mmtbEWK_tmp._setLegendStyles()
mmtbEWK_tmp._setLegendLabels()
mmtbEWK_tmp.histoMgr.setHistoDrawStyleAll("P")
mmtbEWK_tmp.histoMgr.forEachHisto(
lambda h: h.getRootHisto().Rebin(20))
mmtbEWK = mmtbEWK_tmp.histoMgr.getHisto(
"EWK").getRootHisto().Clone()
mmtbEWK.Scale(normEWK[ptbin])
mmtb.Add(mmtbEWK, -1)
hmtNoMetBtag.append(mmtb)
### MET with bvet
mmtbveto_tmp = plots.PlotBase([
datasets.getDataset("Data").getDatasetRootHisto(
"Inverted/MTInvertedTauIdBvetoNoMetCut" + ptbin)
])
mmtbveto_tmp._setLegendStyles()
mmtbveto_tmp._setLegendLabels()
mmtbveto_tmp.histoMgr.setHistoDrawStyleAll("P")
mmtbveto_tmp.histoMgr.forEachHisto(
lambda h: h.getRootHisto().Rebin(20))
mmtbveto = mmtbveto_tmp.histoMgr.getHisto(
"Data").getRootHisto().Clone()
mmtbveto.Scale(normData[ptbin])
# hmt.append(mt)
mmtbvetoEWK_tmp = plots.PlotBase([
datasets.getDataset("EWK").getDatasetRootHisto(
"Inverted/MTInvertedTauIdBvetoNoMetCut" + ptbin)
])
mmtbvetoEWK_tmp.histoMgr.normalizeMCToLuminosity(
datasets.getDataset("Data").getLuminosity())
mmtbvetoEWK_tmp._setLegendStyles()
mmtbvetoEWK_tmp._setLegendLabels()
mmtbvetoEWK_tmp.histoMgr.setHistoDrawStyleAll("P")
mmtbvetoEWK_tmp.histoMgr.forEachHisto(
lambda h: h.getRootHisto().Rebin(20))
mmtbvetoEWK = mmtbvetoEWK_tmp.histoMgr.getHisto(
"EWK").getRootHisto().Clone()
mmtbvetoEWK.Scale(normEWK[ptbin])
mmtbveto.Add(mmtbvetoEWK, -1)
hmtNoMetBveto.append(mmtbveto)
## normalization mT(btag/bveto)
eff = mmtb.Integral() / mmtbveto.Integral()
ereff = sqrt(eff * (1 - eff) / mmtbveto.Integral())
print " pt bin ", ptbin, " btag/bveto efficiency from mt, no met cut = ", eff, " error ", ereff
effArrayMtNoMet.append(eff)
effErrArrayMtNoMet.append(ereff)
#############################################
### with MET cut
mmtb_tmp = plots.PlotBase([
datasets.getDataset("Data").getDatasetRootHisto(
"Inverted/MTInvertedTauIdBtag" + ptbin)
])
mmtb_tmp._setLegendStyles()
mmtb_tmp._setLegendLabels()
mmtb_tmp.histoMgr.setHistoDrawStyleAll("P")
mmtb_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(20))
mmtb = mmtb_tmp.histoMgr.getHisto("Data").getRootHisto().Clone()
mmtb.Scale(normData[ptbin])
# hmt.append(mt)
mmtbEWK_tmp = plots.PlotBase([
datasets.getDataset("EWK").getDatasetRootHisto(
"Inverted/MTInvertedTauIdBtag" + ptbin)
])
mmtbEWK_tmp.histoMgr.normalizeMCToLuminosity(
datasets.getDataset("Data").getLuminosity())
mmtbEWK_tmp._setLegendStyles()
mmtbEWK_tmp._setLegendLabels()
mmtbEWK_tmp.histoMgr.setHistoDrawStyleAll("P")
mmtbEWK_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(20))
mmtbEWK = mmtbEWK_tmp.histoMgr.getHisto("EWK").getRootHisto().Clone()
mmtbEWK.Scale(normEWK[ptbin])
mmtb.Add(mmtbEWK, -1)
hmtBtag.append(mmtb)
mmtbveto_tmp = plots.PlotBase([
datasets.getDataset("Data").getDatasetRootHisto(
"Inverted/MTInvertedTauIdBveto" + ptbin)
])
mmtbveto_tmp._setLegendStyles()
mmtbveto_tmp._setLegendLabels()
mmtbveto_tmp.histoMgr.setHistoDrawStyleAll("P")
mmtbveto_tmp.histoMgr.forEachHisto(
lambda h: h.getRootHisto().Rebin(20))
mmtbveto = mmtbveto_tmp.histoMgr.getHisto(
"Data").getRootHisto().Clone()
mmtbveto.Scale(normData[ptbin])
# hmt.append(mt)
mmtbvetoEWK_tmp = plots.PlotBase([
datasets.getDataset("EWK").getDatasetRootHisto(
"Inverted/MTInvertedTauIdBveto" + ptbin)
])
mmtbvetoEWK_tmp.histoMgr.normalizeMCToLuminosity(
datasets.getDataset("Data").getLuminosity())
mmtbvetoEWK_tmp._setLegendStyles()
mmtbvetoEWK_tmp._setLegendLabels()
mmtbvetoEWK_tmp.histoMgr.setHistoDrawStyleAll("P")
mmtbvetoEWK_tmp.histoMgr.forEachHisto(
lambda h: h.getRootHisto().Rebin(20))
mmtbvetoEWK = mmtbvetoEWK_tmp.histoMgr.getHisto(
"EWK").getRootHisto().Clone()
mmtbvetoEWK.Scale(normEWK[ptbin])
mmtbveto.Add(mmtbvetoEWK, -1)
hmtBveto.append(mmtbveto)
## normalization mT(btag/bveto)
eff = mmtb.Integral() / mmtbveto.Integral()
ereff = sqrt(eff * (1 - eff) / mmtbveto.Integral())
print " pt bin ", ptbin, " btag/bveto efficiency from mt = ", eff, " error ", ereff
effArrayMt.append(eff)
effErrArrayMt.append(ereff)
## sum histo bins
met = hmet[0].Clone("met")
met.SetName("MET")
met.SetTitle("Inverted tau Met")
met.Reset()
print "check met", met.GetEntries()
for histo in hmet:
met.Add(histo)
metb = hmetb[0].Clone("met")
metb.SetName("MET")
metb.SetTitle("Inverted tau Met")
metb.Reset()
print "check met btagging", metb.GetEntries()
for histo in hmetb:
metb.Add(histo)
metbveto = hmetbveto[0].Clone("met")
metbveto.SetName("METbveto")
metbveto.SetTitle("Inverted tau Met")
metbveto.Reset()
print "check met bveto", metbveto.GetEntries()
for histo in hmetbveto:
metbveto.Add(histo)
## with MT
if False:
mtNoMetBtag = hmtNoMetBtag[0].Clone("mt")
mtNoMetBtag.SetName("MET")
mtNoMetBtag.SetTitle("Inverted tau Met")
mtNoMetBtag.Reset()
print "check MT btagging", mtNoMetBtag.GetEntries()
for histo in hmtNoMetBtag:
mtNoMetBtag.Add(histo)
mtNoMetBveto = hmtNoMetBveto[0].Clone("mt")
mtNoMetBveto.SetName("MET")
mtNoMetBveto.SetTitle("Inverted tau Met")
mtNoMetBveto.Reset()
print "check MT bveto", mtNoMetBveto.GetEntries()
for histo in hmtNoMetBveto:
mtNoMetBveto.Add(histo)
mtBtag = hmtBtag[0].Clone("mt")
mtBtag.SetName("MET")
mtBtag.SetTitle("Inverted tau Met")
mtBtag.Reset()
print "check MT btagging", mtBtag.GetEntries()
for histo in hmtBtag:
mtBtag.Add(histo)
mtBveto = hmtBveto[0].Clone("mt")
mtBveto.SetName("MET")
mtBveto.SetTitle("Inverted tau Met")
mtBveto.Reset()
print "check MT bveto", mtBveto.GetEntries()
for histo in hmtBveto:
mtBveto.Add(histo)
##########################################
## plotting
invertedQCD = InvertedTauID()
invertedQCD.setLumi(datasets.getDataset("Data").getLuminosity())
### effisiency as a function of MET
metWithBtagging = metb.Clone("MET")
metWithBtagging.Divide(metbveto)
BtaggingEffVsMet = metWithBtagging.Clone("Eff")
invertedQCD.setLabel("BtagToBvetoEffVsMet")
invertedQCD.mtComparison(BtaggingEffVsMet, BtaggingEffVsMet,
"BtagToBvetoEffVsMet")
if False:
### effisiency as a function of MT
mtWithBtagging = mtNoMetBtag.Clone("MT")
mtWithBtagging.Divide(mtNoMetBveto)
BtaggingEffNoMetVsMt = mtWithBtagging.Clone("Eff")
invertedQCD.setLabel("BtagToBvetoEffNoMetVsMt")
invertedQCD.mtComparison(BtaggingEffNoMetVsMt, BtaggingEffNoMetVsMt,
"BtagToBvetoEffNoMetVsMt")
### effisiency as a function of MT
mtWithBtagging = mtBtag.Clone("MT")
mtWithBtagging.Divide(mtBveto)
BtaggingEffVsMt = mtWithBtagging.Clone("Eff")
invertedQCD.setLabel("BtagToBvetoEffVsMt")
invertedQCD.mtComparison(BtaggingEffVsMt, BtaggingEffVsMt,
"BtagToBvetoEffVsMt")
# efficiency metb/met
metbtag = metb.Clone("metb")
metnobtag = met.Clone("met")
metbtag.Divide(metnobtag)
invertedQCD.setLabel("BtagEffVsMet")
invertedQCD.mtComparison(metbtag, metbtag, "BtagEffVsMet")
# efficiency metb/metbveto
metbtag = metb.Clone("metb")
metbjetveto = metbveto.Clone("met")
invertedQCD.setLabel("BtagToBvetoEfficiency")
invertedQCD.mtComparison(metbtag, metbjetveto, "BtagToBvetoEfficiency")
### Create and customise TGraph
cEff = TCanvas("Efficiency", "Efficiency", 1)
cEff.cd()
ptbin_error = array.array("d", [5, 5, 5, 5, 10, 10, 30])
ptbin = array.array("d", [45, 55, 65, 75, 90, 110, 150])
if False:
## no MET cut
cEff = TCanvas("btaggingEffNoMet", "btaggingEffNoMet", 1)
cEff.cd()
graph = TGraphErrors(7, ptbin, array.array("d", effArrayMtNoMet),
ptbin_error, array.array("d", effErrArrayMtNoMet))
graph.SetMaximum(0.25)
graph.SetMinimum(0.0)
graph.SetMarkerStyle(kFullCircle)
graph.SetMarkerColor(kBlue)
graph.SetMarkerSize(1)
graph.GetYaxis().SetTitle("N_{b tagged}/N_{b veto}")
graph.GetXaxis().SetTitle("p_{T}^{#tau jet} [GeV]")
### Re-draw graph and update canvas and gPad
graph.Draw("AP")
tex4 = ROOT.TLatex(
0.2, 0.955,
"8 TeV 19.6 fb^{-1} CMS preliminary")
tex4.SetNDC()
tex4.SetTextSize(20)
tex4.Draw()
tex1 = ROOT.TLatex(0.2, 0.88, "All selection cuts")
tex1.SetNDC()
tex1.SetTextSize(22)
# tex1.Draw()
tex2 = ROOT.TLatex(0.5, 0.8, "No MET cut")
tex2.SetNDC()
tex2.SetTextSize(24)
tex2.Draw()
cEff.Update()
cEff.SaveAs("btagToBvetoEffNoMetVsPtTau_mt.png")
## with MET cut
cEff = TCanvas("btaggingEff", "btaggingEff", 1)
cEff.cd()
graph = TGraphErrors(7, ptbin, array.array("d", effArrayMt), ptbin_error,
array.array("d", effErrArrayMt))
graph.SetMaximum(0.25)
graph.SetMinimum(0.0)
graph.SetMarkerStyle(kFullCircle)
graph.SetMarkerColor(kBlue)
graph.SetMarkerSize(1)
graph.GetYaxis().SetTitle("N_{b tagged}/N_{b veto}")
graph.GetXaxis().SetTitle("p_{T}^{#tau jet} [GeV]")
### Re-draw graph and update canvas and gPad
graph.Draw("AP")
tex4 = ROOT.TLatex(
0.2, 0.955,
"8 TeV 19.6 fb^{-1} CMS preliminary")
tex4.SetNDC()
tex4.SetTextSize(20)
tex4.Draw()
tex1 = ROOT.TLatex(0.2, 0.88, "All selection cuts")
tex1.SetNDC()
tex1.SetTextSize(22)
# tex1.Draw()
tex2 = ROOT.TLatex(0.5, 0.8, "After MET cut")
tex2.SetNDC()
tex2.SetTextSize(24)
tex2.Draw()
cEff.Update()
cEff.SaveAs("btagToBvetoEffVsPtTau_mt.png")
## no MET cut
cEff = TCanvas("btaggingEffNoMet", "btaggingEffNoMet", 1)
cEff.cd()
graph = TGraphErrors(7, ptbin, array.array("d", effBvetoArray),
ptbin_error, array.array("d", effErrBvetoArray))
graph.SetMaximum(0.25)
graph.SetMinimum(0.0)
graph.SetMarkerStyle(kFullCircle)
graph.SetMarkerColor(kBlue)
graph.SetMarkerSize(1)
graph.GetYaxis().SetTitle("N_{b tagged}/N_{b veto}")
graph.GetXaxis().SetTitle("p_{T}^{#tau jet} [GeV]")
### Re-draw graph and update canvas and gPad
graph.Draw("AP")
tex4 = ROOT.TLatex(
0.2, 0.955,
"8 TeV 19.6 fb^{-1} CMS preliminary")
tex4.SetNDC()
tex4.SetTextSize(20)
tex4.Draw()
tex1 = ROOT.TLatex(0.2, 0.88, "All selection cuts")
tex1.SetNDC()
tex1.SetTextSize(22)
# tex1.Draw()
tex2 = ROOT.TLatex(0.5, 0.8, "No MET cut")
tex2.SetNDC()
tex2.SetTextSize(24)
tex2.Draw()
cEff.Update()
cEff.SaveAs("btagToBvetoEffNoMetVsPtTau.png")
## with MET cut
cEff = TCanvas("btaggingEfficiency", "btaggingEfficiency", 1)
cEff.cd()
graph = TGraphErrors(7, ptbin, array.array("d", effArray), ptbin_error,
array.array("d", effErrArray))
graph.SetMaximum(0.25)
graph.SetMinimum(0.0)
graph.SetMarkerStyle(kFullCircle)
graph.SetMarkerColor(kBlue)
graph.SetMarkerSize(1)
graph.GetYaxis().SetTitle("b-tagging efficiency")
graph.GetXaxis().SetTitle("p_{T}^{#tau jet} [GeV]")
### Re-draw graph and update canvas and gPad
graph.Draw("AP")
tex4 = ROOT.TLatex(
0.2, 0.955,
"8 TeV 19.6 fb^{-1} CMS preliminary")
tex4.SetNDC()
tex4.SetTextSize(20)
tex4.Draw()
tex1 = ROOT.TLatex(0.4, 0.85, "Inverted #tau identification")
tex1.SetNDC()
tex1.SetTextSize(22)
tex1.Draw()
tex2 = ROOT.TLatex(0.4, 0.78, "At least 3 jets")
tex2.SetNDC()
tex2.SetTextSize(24)
tex2.Draw()
cEff.Update()
cEff.SaveAs("btaggingEffVsPtTau.png")
##################################3
fOUT = open("btaggingFactors", "w")
# now = datetime.datetime.now()
# fOUT.write("# Generated on %s\n"%now.ctime())
fOUT.write("# by %s\n" % os.path.basename(sys.argv[0]))
fOUT.write("\n")
fOUT.write("btaggingFactors = {\n")
i = 0
while i < len(effArray):
line = " \"" + ptbins[i] + "\": " + str(effArray[i])
if i < len(effArray) - 1:
line += ","
line += "\n"
fOUT.write(line)
i = i + 1
fOUT.write("}\n")
fOUT.close()
print "B-tagging efficiensies written in file", "btaggingFactors"
fOUT = open("btaggingToBvetoFactors.py", "w")
# now = datetime.datetime.now()
# fOUT.write("# Generated on %s\n"%now.ctime())
fOUT.write("# by %s\n" % os.path.basename(sys.argv[0]))
fOUT.write("\n")
fOUT.write("btaggingToBvetoFactors = {\n")
i = 0
while i < len(effBvetoArray):
line = " \"" + ptbins[i] + "\": " + str(effBvetoArray[i])
if i < len(effBvetoArray) - 1:
line += ","
line += "\n"
fOUT.write(line)
i = i + 1
fOUT.write("}\n")
fOUT.close()
print "BtaggingToBveto efficiensies written in file", "btaggingToBvetoFactors"
fOUT = open("btaggingToBvetoAfterMetFactors.py", "w")
# now = datetime.datetime.now()
# fOUT.write("# Generated on %s\n"%now.ctime())
fOUT.write("# by %s\n" % os.path.basename(sys.argv[0]))
fOUT.write("\n")
fOUT.write("btaggingToBvetoAfterMetFactors = {\n")
i = 0
while i < len(effArrayMt):
line = " \"" + ptbins[i] + "\": " + str(effArrayMt[i])
if i < len(effArrayMt) - 1:
line += ","
line += "\n"
fOUT.write(line)
i = i + 1
fOUT.write("}\n")
fOUT.close()
print "BtaggingToBvetoAfterMet efficiensies written in file", "btaggingToBvetoFactors"
def main():
datasets = dataset.getDatasetsFromMulticrabCfg(counters=counters)
datasets.loadLuminosities()
plots.mergeRenameReorderForDataMC(datasets)
datasets.merge("EWK", [
"TTJets",
"WJets",
"DYJetsToLL",
"SingleTop",
"Diboson"
])
style = tdrstyle.TDRStyle()
metInver = plots.DataMCPlot(datasets, analysis+"/MET_InvertedTauIdJets")
metBase = plots.DataMCPlot(datasets, analysis+"/MET_BaseLineTauIdJets")
metBasePP = plots.DataMCPlot(datasets, analysisPlusPlus+"/MET_BaseLineTauIdJets")
metBaseMP = plots.DataMCPlot(datasets, analysisMinusPlus+"/MET_BaseLineTauIdJets")
metBasePM = plots.DataMCPlot(datasets, analysisPlusMinus+"/MET_BaseLineTauIdJets")
metBaseMM = plots.DataMCPlot(datasets, analysisMinusMinus+"/MET_BaseLineTauIdJets")
# Rebin before subtracting
rebin = 20
metInver.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(rebin))
metBase.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(rebin))
metBasePP.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(rebin))
metBaseMP.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(rebin))
metBasePM.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(rebin))
metBaseMM.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(rebin))
metInverted_data = metInver.histoMgr.getHisto("Data").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_EWKplusplus = metBasePP.histoMgr.getHisto("EWK").getRootHisto().Clone(analysisPlusPlus+"/MET_BaselineTauIdJets")
metBase_EWKminusplus = metBaseMP.histoMgr.getHisto("EWK").getRootHisto().Clone(analysisMinusPlus+"/MET_BaselineTauIdJets")
metBase_EWKplusminus = metBasePM.histoMgr.getHisto("EWK").getRootHisto().Clone(analysisPlusMinus+"/MET_BaselineTauIdJets")
metBase_EWKminusminus = metBaseMM.histoMgr.getHisto("EWK").getRootHisto().Clone(analysisMinusMinus+"/MET_BaselineTauIdJets")
invertedQCD = InvertedTauID()
invertedQCD.setLabel("noError")
invertedQCD.fitQCD(metInverted_data)
invertedQCD.fitEWK(metBase_EWK)
invertedQCD.fitData(metBase_data)
normalizationWithEWK = invertedQCD.getNormalization()
invertedQCD.setLabel("plusplusError")
invertedQCD.fitEWK(metBase_EWKplusplus)
invertedQCD.fitData(metBase_data)
normalizationWithEWKplusplus = invertedQCD.getNormalization()
invertedQCD.setLabel("minusplusError")
invertedQCD.fitEWK(metBase_EWKminusplus)
invertedQCD.fitData(metBase_data)
normalizationWithEWKminusplus = invertedQCD.getNormalization()
invertedQCD.setLabel("plusminusError")
invertedQCD.fitEWK(metBase_EWKplusminus)
invertedQCD.fitData(metBase_data)
normalizationWithEWKplusminus = invertedQCD.getNormalization()
invertedQCD.setLabel("minusminusError")
invertedQCD.fitEWK(metBase_EWKminusminus)
invertedQCD.fitData(metBase_data)
normalizationWithEWKminusminus = invertedQCD.getNormalization()
invertedQCD.Summary()
print "Difference in normalization due to JES ++ variation",(normalizationWithEWK - normalizationWithEWKplusplus)/normalizationWithEWK
print "Difference in normalization due to JES -+ variation",(normalizationWithEWK - normalizationWithEWKminusplus)/normalizationWithEWK
print "Difference in normalization due to JES +- variation",(normalizationWithEWK - normalizationWithEWKplusminus)/normalizationWithEWK
print "Difference in normalization due to JES -- variation",(normalizationWithEWK - normalizationWithEWKminusminus)/normalizationWithEWK
def main():
# Create all datasets from a multicrab task
datasets = dataset.getDatasetsFromMulticrabCfg(counters=counters)
# 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 0.05, 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"])
# 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_QCD = metBase_data.Clone("QCD")
metBase_QCD.Add(metBase_EWK, -1)
metBase_EWK.SetTitle("MC EWK")
invertedQCD.setLabel("InvData")
invertedQCD.fitQCD(metInverted_data)
invertedQCD.setLabel("ChiSq")
invertedQCD.fitEWK(metBase_EWK, "R")
invertedQCD.fitData(metBase_data)
normalizationWithChiSq = invertedQCD.getNormalization()
invertedQCD.setLabel("LogLikelihood")
invertedQCD.fitEWK(metBase_EWK, "LR")
invertedQCD.fitData(metBase_data)
normalizationWithLogLL = invertedQCD.getNormalization()
print "Difference Signal vs no signal in EWK fit", (
normalizationWithLogLL -
normalizationWithChiSq) / normalizationWithChiSq
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"
])
# 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_QCD = metBase_data.Clone("QCD")
metBase_QCD.Add(metBase_EWK,-1)
metBase_EWK.SetTitle("MC EWK")
invertedQCD.setLabel("InvData")
invertedQCD.fitQCD(metInverted_data)
invertedQCD.setLabel("ChiSq")
invertedQCD.fitEWK(metBase_EWK,"R")
invertedQCD.fitData(metBase_data)
normalizationWithChiSq = invertedQCD.getNormalization()
invertedQCD.setLabel("LogLikelihood")
invertedQCD.fitEWK(metBase_EWK,"LR")
invertedQCD.fitData(metBase_data)
normalizationWithLogLL = invertedQCD.getNormalization()
print "Difference Signal vs no signal in EWK fit",(normalizationWithLogLL - normalizationWithChiSq)/normalizationWithChiSq
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():
errorBars = False
# 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())
invertedQCD.useErrorBars(errorBars)
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")
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):
# 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():
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():
# HISTONAME = "TauIdJets"
HISTONAME = "TauIdBtag"
# 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())
bins = ["inclusive"]
# bins = ["4050","5060","6070","7080","80100","100120","120150","150"]
# bins = ["4050"]
for bin in bins:
invertedQCD.setLabel(bin)
if bin == "inclusive":
bin = ""
metBase = plots.DataMCPlot(datasets, analysis+"/MET_BaseLine"+HISTONAME+bin)
metInver = plots.DataMCPlot(datasets, analysis+"/MET_Inverted"+HISTONAME+bin)
# 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_Inverted"+HISTONAME+bin)
metInverted_EWK = metInver.histoMgr.getHisto("EWK").getRootHisto().Clone(analysis+"/MET_Inverted"+HISTONAME+bin)
metBase_data = metBase.histoMgr.getHisto("Data").getRootHisto().Clone(analysis+"/MET_Baseline"+HISTONAME+bin)
metBase_EWK = metBase.histoMgr.getHisto("EWK").getRootHisto().Clone(analysis+"/MET_Baseline"+HISTONAME+bin)
metBase_QCD = metBase_data.Clone("QCD")
metBase_QCD.Add(metBase_EWK,-1)
invertedQCD.plotHisto(metInverted_data,"inverted")
invertedQCD.plotHisto(metBase_data,"baseline")
invertedQCD.fitQCD(metInverted_data)
invertedQCD.fitEWK(metBase_EWK,"LR")
invertedQCD.fitData(metBase_data)
invertedQCD.getNormalization()
invertedQCD.Summary()
def invertedPurities(datasets):
normData,normEWK,normFactorisedData,normFactorisedEWK=normalisation()
norm_inc,normEWK_inc = normalisationInclusive()
noDphi = []
noDphiEWK = []
Dphi160 = []
DphiEWK160 = []
DphiAll = []
DphiEWKAll = []
DphiAllremovett = []
DphiEWKAllremovett = []
DphiJet1=[]
DphiEWKJet1=[]
DphiJet2=[]
DphiEWKJet2=[]
hmt = []
hmtb = []
hmtv = []
hmtPhiv = []
hmet = []
hmetQCD = []
hmetEWK = []
hjetmet = []
hjetmetphi = []
hMHTJet1phi = []
hmtph = []
hmtphj1= []
hmtphj2= []
hmtremovett = []
DphiEWKAllbveto= []
DphiAllbveto= []
purityMet= []
purityErrMet= []
purityMtRemovett = []
purityErrMtRemovett = []
purityMtFirstDeltaPhiCut = []
purityErrMtFirstDeltaPhiCut = []
purityMtThirdDeltaPhiCut = []
purityErrMtThirdDeltaPhiCut = []
purityMtSecondDeltaPhiCut = []
purityErrMtSecondDeltaPhiCut = []
purityMtDeltaPhiCut = []
purityErrMtDeltaPhiCut = []
purityMtAfterBtagging = []
purityErrMtAfterBtagging = []
purityMTInvertedTauIdBvetoDphi = []
purityErrMTInvertedTauIdBvetoDphi = []
## histograms in bins, normalisation and substraction of EWK contribution
## mt with 2dim deltaPhi cut
for ptbin in ptbins:
mt_tmp = plots.PlotBase([datasets.getDataset("Data").getDatasetRootHisto("Inverted/MTInvertedSecondDeltaPhiCut"+ptbin)])
mt_tmp._setLegendStyles()
mt_tmp._setLegendLabels()
mt_tmp.histoMgr.setHistoDrawStyleAll("P")
mt_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(20))
mt = mt_tmp.histoMgr.getHisto("Data").getRootHisto().Clone()
# mt.Scale(normData[ptbin])
DphiJet2.append(mt)
mtEWK_tmp = plots.PlotBase([datasets.getDataset("EWK").getDatasetRootHisto("Inverted/MTInvertedSecondDeltaPhiCut"+ptbin)])
mtEWK_tmp.histoMgr.normalizeMCToLuminosity(datasets.getDataset("Data").getLuminosity())
mtEWK_tmp._setLegendStyles()
mtEWK_tmp._setLegendLabels()
mtEWK_tmp.histoMgr.setHistoDrawStyleAll("P")
mtEWK_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(20))
mtEWK = mtEWK_tmp.histoMgr.getHisto("EWK").getRootHisto().Clone()
# mtEWK.Scale(normEWK[ptbin])
# mt.Add(mtEWK, -1)
# hmt.append(mt)
DphiEWKJet2.append(mtEWK)
purity = -999
error = -999
if mt.Integral() > 0:
purity = (mt.Integral() - mtEWK.Integral())/ mt.Integral()
error = sqrt(purity*(1-purity)/mt.Integral())
purityMtSecondDeltaPhiCut.append(purity)
purityErrMtSecondDeltaPhiCut.append(error)
# print " pt bin ", ptbin, " purity Mt Second Delta Phi Cut = ",purity, " error ",error
############################################
# mt after b tagging
mtb_tmp = plots.PlotBase([datasets.getDataset("Data").getDatasetRootHisto("Inverted/MTInvertedTauIdBtag"+ptbin)])
mtb_tmp._setLegendStyles()
mtb_tmp._setLegendLabels()
mtb_tmp.histoMgr.setHistoDrawStyleAll("P")
mtb_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(20))
mtb = mtb_tmp.histoMgr.getHisto("Data").getRootHisto().Clone()
# mtb.Scale(normData[ptbin])
hmt.append(mtb)
noDphi.append(mtb)
mtbEWK_tmp = plots.PlotBase([datasets.getDataset("EWK").getDatasetRootHisto("Inverted/MTInvertedTauIdBtag"+ptbin)])
mtbEWK_tmp.histoMgr.normalizeMCToLuminosity(datasets.getDataset("Data").getLuminosity())
mtbEWK_tmp._setLegendStyles()
mtbEWK_tmp._setLegendLabels()
mtbEWK_tmp.histoMgr.setHistoDrawStyleAll("P")
mtbEWK_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(20))
mtbEWK = mtbEWK_tmp.histoMgr.getHisto("EWK").getRootHisto().Clone()
# mtbEWK.Scale(normEWK[ptbin])
mtb.Add(mtbEWK, -1)
hmtb.append(mtb)
noDphiEWK.append(mtbEWK)
purity = -999
error = -999
if mtb.Integral() > 0:
purity = (mtb.Integral() - mtbEWK.Integral())/ mtb.Integral()
error = sqrt(purity*(1-purity)/mtb.Integral())
purityMtAfterBtagging.append(purity)
purityErrMtAfterBtagging.append(error)
# print " pt bin ", ptbin, " purity Mt After Btagging = ",purity, " error ",error
############################################
# mt after deltaPhi cut
mtph_tmp = plots.PlotBase([datasets.getDataset("Data").getDatasetRootHisto("Inverted/MTInvertedTauIdJetDphi"+ptbin)])
mtph_tmp._setLegendStyles()
mtph_tmp._setLegendLabels()
mtph_tmp.histoMgr.setHistoDrawStyleAll("P")
mtph_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(20))
mtph = mtph_tmp.histoMgr.getHisto("Data").getRootHisto().Clone()
# mtph.Scale(normData[ptbin])
# hmt.append(mt)
Dphi160.append(mtph)
mtphEWK_tmp = plots.PlotBase([datasets.getDataset("EWK").getDatasetRootHisto("Inverted/MTInvertedTauIdJetDphi"+ptbin)])
mtphEWK_tmp.histoMgr.normalizeMCToLuminosity(datasets.getDataset("Data").getLuminosity())
mtphEWK_tmp._setLegendStyles()
mtphEWK_tmp._setLegendLabels()
mtphEWK_tmp.histoMgr.setHistoDrawStyleAll("P")
mtphEWK_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(20))
mtphEWK = mtphEWK_tmp.histoMgr.getHisto("EWK").getRootHisto().Clone()
# mtphEWK.Scale(normEWK[ptbin])
# mtph.Add(mtphEWK, -1)
# hmtph.append(mtph)
DphiEWK160.append(mtphEWK)
purity = -999
error = -999
if mtph.Integral() > 0:
purity = (mtph.Integral() - mtphEWK.Integral())/ mtph.Integral()
error = sqrt(purity*(1-purity)/mtph.Integral())
purityMtDeltaPhiCut.append(purity)
purityErrMtDeltaPhiCut.append(error)
# print " pt bin ", ptbin, " purity Mt DeltaPhi Cut = ",purity, " error ",error
############################################
# mt after deltaphi vs MHTjet1 cut
mtphj1_tmp = plots.PlotBase([datasets.getDataset("Data").getDatasetRootHisto("Inverted/MTInvertedFirstDeltaPhiCut"+ptbin)])
#mtphj1_tmp = plots.PlotBase([datasets.getDataset("Data").getDatasetRootHisto("MTInvertedTauIdMet"+ptbin)])
mtphj1_tmp._setLegendStyles()
mtphj1_tmp._setLegendLabels()
mtphj1_tmp.histoMgr.setHistoDrawStyleAll("P")
mtphj1_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(20))
mtphj1 = mtphj1_tmp.histoMgr.getHisto("Data").getRootHisto().Clone()
# mtphj1.Scale(normData[ptbin])
DphiJet1.append(mtphj1)
mtphj1EWK_tmp = plots.PlotBase([datasets.getDataset("EWK").getDatasetRootHisto("Inverted/MTInvertedFirstDeltaPhiCut"+ptbin)])
#mtphj1EWK_tmp = plots.PlotBase([datasets.getDataset("EWK").getDatasetRootHisto("MTInvertedTauIdMet"+ptbin)])
mtphj1EWK_tmp.histoMgr.normalizeMCToLuminosity(datasets.getDataset("Data").getLuminosity())
mtphj1EWK_tmp._setLegendStyles()
mtphj1EWK_tmp._setLegendLabels()
mtphj1EWK_tmp.histoMgr.setHistoDrawStyleAll("P")
mtphj1EWK_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(20))
mtphj1EWK = mtphj1EWK_tmp.histoMgr.getHisto("EWK").getRootHisto().Clone()
# mtphj1EWK.Scale(normEWK[ptbin])
# mtphj1.Add(mtphj1EWK, -1)
# hmtphj1.append(mtphj1)
DphiEWKJet1.append(mtphj1EWK)
purity = -999
error = -999
if mtphj1.Integral() > 0:
purity = (mtphj1.Integral() - mtphj1EWK.Integral())/ mtphj1.Integral()
error = sqrt(purity*(1-purity)/mtphj1.Integral())
purityMtFirstDeltaPhiCut.append(purity)
purityErrMtFirstDeltaPhiCut.append(error)
# print " pt bin ", ptbin, " purity Mt First Delta Phi Cut = ",purity, " error ",error
############################################
# mt after all cuts
mtphj2_tmp = plots.PlotBase([datasets.getDataset("Data").getDatasetRootHisto("Inverted/MTInvertedThirdDeltaPhiCut"+ptbin)])
mtphj2_tmp._setLegendStyles()
mtphj2_tmp._setLegendLabels()
mtphj2_tmp.histoMgr.setHistoDrawStyleAll("P")
mtphj2_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(20))
mtphj2 = mtphj2_tmp.histoMgr.getHisto("Data").getRootHisto().Clone()
# mtphj2.Scale(normData[ptbin])
# hmt.append(mt)
DphiAll.append(mtphj2)
mtphj2EWK_tmp = plots.PlotBase([datasets.getDataset("EWK").getDatasetRootHisto("Inverted/MTInvertedThirdDeltaPhiCut"+ptbin)])
mtphj2EWK_tmp.histoMgr.normalizeMCToLuminosity(datasets.getDataset("Data").getLuminosity())
mtphj2EWK_tmp._setLegendStyles()
mtphj2EWK_tmp._setLegendLabels()
mtphj2EWK_tmp.histoMgr.setHistoDrawStyleAll("P")
mtphj2EWK_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(20))
mtphj2EWK = mtphj2EWK_tmp.histoMgr.getHisto("EWK").getRootHisto().Clone()
# mtphj2EWK.Scale(normEWK[ptbin])
# mtphj2.Add(mtphj2EWK, -1)
# hmtphj2.append(mtphj2)
DphiEWKAll.append(mtphj2EWK)
purity = -999
error = -999
if mtphj2.Integral() > 0:
purity = (mtphj2.Integral() - mtphj2EWK.Integral())/ mtphj2.Integral()
error = sqrt(purity*(1-purity)/mtphj2.Integral())
purityMtThirdDeltaPhiCut.append(purity)
purityErrMtThirdDeltaPhiCut.append(error)
# print " pt bin ", ptbin, " purity Mt Third Delta Phi Cut = ",purity, " error ",error
#######################
# mt with cut against tt
mtremovett_tmp = plots.PlotBase([datasets.getDataset("Data").getDatasetRootHisto("Inverted/MTInvertedAgainstTTCut"+ptbin)])
mtremovett_tmp._setLegendStyles()
mtremovett_tmp._setLegendLabels()
mtremovett_tmp.histoMgr.setHistoDrawStyleAll("P")
mtremovett_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(20))
mtremovett = mtremovett_tmp.histoMgr.getHisto("Data").getRootHisto().Clone()
# mtremovett.Scale(normData[ptbin])
DphiAllremovett.append(mtremovett)
mtremovettEWK_tmp = plots.PlotBase([datasets.getDataset("EWK").getDatasetRootHisto("Inverted/MTInvertedAgainstTTCut"+ptbin)])
mtremovettEWK_tmp.histoMgr.normalizeMCToLuminosity(datasets.getDataset("Data").getLuminosity())
mtremovettEWK_tmp._setLegendStyles()
mtremovettEWK_tmp._setLegendLabels()
mtremovettEWK_tmp.histoMgr.setHistoDrawStyleAll("P")
mtremovettEWK_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(20))
mtremovettEWK = mtremovettEWK_tmp.histoMgr.getHisto("EWK").getRootHisto().Clone()
# mtremovettEWK.Scale(normEWK[ptbin])
# mtremovett.Add(mtremovettEWK, -1)
# hmtremovett.append(mtremovett)
DphiEWKAllremovett.append(mtremovettEWK)
purity = -999
error = -999
if mtremovett.Integral() > 0:
purity = (mtremovett.Integral() - mtremovettEWK.Integral())/ mtremovett.Integral()
error = sqrt(purity*(1-purity)/mtremovett.Integral())
purityMtRemovett.append(purity)
purityErrMtRemovett.append(error)
print "mtremovett.Integral() ",mtremovett.Integral(), " mmtEWK.Integral() ", mtremovettEWK.Integral()
#######################
### MET
mmt_tmp = plots.PlotBase([datasets.getDataset("Data").getDatasetRootHisto("Inverted/MET_InvertedTauIdJets"+ptbin)])
mmt_tmp._setLegendStyles()
mmt_tmp._setLegendLabels()
mmt_tmp.histoMgr.setHistoDrawStyleAll("P")
mmt_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(20))
mmt = mmt_tmp.histoMgr.getHisto("Data").getRootHisto().Clone()
## mmt.Scale(normData[ptbin])
hmet.append(mmt)
mmtEWK_tmp = plots.PlotBase([datasets.getDataset("EWK").getDatasetRootHisto("Inverted/MET_InvertedTauIdJets"+ptbin)])
mmtEWK_tmp.histoMgr.normalizeMCToLuminosity(datasets.getDataset("Data").getLuminosity())
mmtEWK_tmp._setLegendStyles()
mmtEWK_tmp._setLegendLabels()
mmtEWK_tmp.histoMgr.setHistoDrawStyleAll("P")
mmtEWK_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(20))
mmtEWK = mmtEWK_tmp.histoMgr.getHisto("EWK").getRootHisto().Clone()
## mmtEWK.Scale(normEWK[ptbin])
mmt.Add(mmtEWK, -1)
hmetQCD.append(mmt)
hmetEWK.append(mmtEWK)
purity = -999
error = -999
if mmt.Integral() > 0:
purity = (mmt.Integral() - mmtEWK.Integral())/ mmt.Integral()
error = sqrt(purity*(1-purity)/mmt.Integral())
purityMet.append(purity)
purityErrMet.append(error)
## print "mmt.Integral() ",mmt.Integral(), " mmtEWK.Integral() ", mmtEWK.Integral()
############################################
# mt after all cuts
mtphj2_tmp = plots.PlotBase([datasets.getDataset("Data").getDatasetRootHisto("Inverted/MTInvertedTauIdBvetoDphi"+ptbin)])
mtphj2_tmp._setLegendStyles()
mtphj2_tmp._setLegendLabels()
mtphj2_tmp.histoMgr.setHistoDrawStyleAll("P")
mtphj2_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(20))
mtphj2 = mtphj2_tmp.histoMgr.getHisto("Data").getRootHisto().Clone()
# mtphj2.Scale(normData[ptbin])
# hmt.append(mt)
DphiAllbveto.append(mtphj2)
mtphj2EWK_tmp = plots.PlotBase([datasets.getDataset("EWK").getDatasetRootHisto("Inverted/MTInvertedTauIdBvetoDphi"+ptbin)])
mtphj2EWK_tmp.histoMgr.normalizeMCToLuminosity(datasets.getDataset("Data").getLuminosity())
mtphj2EWK_tmp._setLegendStyles()
mtphj2EWK_tmp._setLegendLabels()
mtphj2EWK_tmp.histoMgr.setHistoDrawStyleAll("P")
mtphj2EWK_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(20))
mtphj2EWK = mtphj2EWK_tmp.histoMgr.getHisto("EWK").getRootHisto().Clone()
# mtphj2EWK.Scale(normEWK[ptbin])
# mtphj2.Add(mtphj2EWK, -1)
# hmtphj2.append(mtphj2)
DphiEWKAllbveto.append(mtphj2EWK)
purity = -999
error = -999
if mtphj2.Integral() > 0:
purity = (mtphj2.Integral() - mtphj2EWK.Integral())/ mtphj2.Integral()
error = sqrt(purity*(1-purity)/mtphj2.Integral())
purityMTInvertedTauIdBvetoDphi.append(purity)
purityErrMTInvertedTauIdBvetoDphi.append(error)
# print " pt bin ", ptbin, " purity Mt Third Delta Phi Cut = ",purity, " error ",error
print " "
print " purity met = ",purityMet, " error ",purityErrMet
print " purity Mt no DeltaPhi Cuts = ",purityMtAfterBtagging, " error ",purityErrMtAfterBtagging
print " purity Mt DeltaPhi160 Cut = ",purityMtDeltaPhiCut, " error ",purityErrMtDeltaPhiCut
print " purity Mt First DeltaPhi Cut = ",purityMtFirstDeltaPhiCut, " error ",purityErrMtFirstDeltaPhiCut
print " purity Mt Second DeltaPhi Cut = ",purityMtSecondDeltaPhiCut, " error ",purityErrMtSecondDeltaPhiCut
print " purity Mt Third Delta Phi Cut = ",purityMtThirdDeltaPhiCut, " error ",purityErrMtThirdDeltaPhiCut
print " purity Mt b veto deltaPhi Cuts = ",purityMTInvertedTauIdBvetoDphi, " error ",purityErrMTInvertedTauIdBvetoDphi
print " purity Mt Remove tt = ",purityMtRemovett, " error ",purityErrMtRemovett
invertedQCD = InvertedTauID()
invertedQCD.setLumi(datasets.getDataset("Data").getLuminosity())
### Met
met = hmet[0].Clone("met")
met.SetName("MET")
met.SetTitle("Inverted tau Met")
met.Reset()
for histo in hmet:
met.Add(histo)
metQCD = hmetQCD[0].Clone("met")
metQCD.SetName("MET")
metQCD.SetTitle("Inverted tau Met")
metQCD.Reset()
for histo in hmetQCD:
metQCD.Add(histo)
metEWK = hmetEWK[0].Clone("metewk")
metEWK.SetName("METewk")
metEWK.SetTitle("Inverted tau Met")
metEWK.Reset()
for histo in hmetEWK:
metEWK.Add(histo)
### Mt no DeltaPhi Cuts
mtNoDphi = noDphi[0].Clone("mt")
mtNoDphi.SetName("mt")
mtNoDphi.SetTitle("Inverted tau Mt")
mtNoDphi.Reset()
for histo in noDphi:
mtNoDphi.Add(histo)
mtNoDphiEWK = noDphiEWK[0].Clone("mtewk")
mtNoDphiEWK.SetName("MTewk")
mtNoDphiEWK.SetTitle("Inverted tau Met")
mtNoDphiEWK.Reset()
for histo in noDphiEWK:
mtNoDphiEWK.Add(histo)
### Mt DeltaPhi < 160 Cut
mtDphi160 = Dphi160[0].Clone("mt")
mtDphi160.SetName("mt")
mtDphi160.SetTitle("Inverted tau Mt")
mtDphi160.Reset()
for histo in Dphi160:
mtDphi160.Add(histo)
mtDphi160EWK = DphiEWK160[0].Clone("mtewk")
mtDphi160EWK.SetName("MTewk")
mtDphi160EWK.SetTitle("Inverted tau Met")
mtDphi160EWK.Reset()
for histo in DphiEWK160:
mtDphi160EWK.Add(histo)
### Mt Mt all dphi cuts
mtDphiAll = DphiAll[0].Clone("mt")
mtDphiAll.SetName("mt")
mtDphiAll.SetTitle("Inverted tau Mt")
mtDphiAll.Reset()
for histo in DphiAll:
mtDphiAll.Add(histo)
mtDphiAllEWK = DphiEWKAll[0].Clone("mtewk")
mtDphiAllEWK.SetName("MTewk")
mtDphiAllEWK.SetTitle("Inverted tau Met")
mtDphiAllEWK.Reset()
for histo in DphiEWKAll:
mtDphiAllEWK.Add(histo)
### Mt bveto all dphi cuts
mtDphiAllbveto = DphiAllbveto[0].Clone("mt")
mtDphiAllbveto.SetName("mt")
mtDphiAllbveto.SetTitle("Inverted tau Mt")
mtDphiAllbveto.Reset()
for histo in DphiAllbveto:
mtDphiAllbveto.Add(histo)
mtDphiAllEWKbveto = DphiEWKAllbveto[0].Clone("mtewk")
mtDphiAllEWKbveto.SetName("MTewk")
mtDphiAllEWKbveto.SetTitle("Inverted tau Met")
mtDphiAllEWKbveto.Reset()
for histo in DphiEWKAllbveto:
mtDphiAllEWKbveto.Add(histo)
### Mt Mt dphi jet1
mtDphiJet1 = DphiJet1[0].Clone("mt")
mtDphiJet1.SetName("mt")
mtDphiJet1.SetTitle("Inverted tau Mt")
mtDphiJet1.Reset()
for histo in DphiJet1:
mtDphiJet1.Add(histo)
mtDphiEWKJet1 = DphiEWKJet1[0].Clone("mtewk")
mtDphiEWKJet1.SetName("MTewk")
mtDphiEWKJet1.SetTitle("Inverted tau Met")
mtDphiEWKJet1.Reset()
for histo in DphiEWKJet1:
mtDphiEWKJet1.Add(histo)
### Mt Mt dphi jet2
mtDphiJet2 = DphiJet2[0].Clone("mt")
mtDphiJet2.SetName("mt")
mtDphiJet2.SetTitle("Inverted tau Mt")
mtDphiJet2.Reset()
for histo in DphiJet2:
mtDphiJet2.Add(histo)
mtDphiEWKJet2 = DphiEWKJet2[0].Clone("mtewk")
mtDphiEWKJet2.SetName("MTewk")
mtDphiEWKJet2.SetTitle("Inverted tau Met")
mtDphiEWKJet2.Reset()
for histo in DphiEWKJet2:
mtDphiEWKJet2.Add(histo)
### Mt all dphi + tt cuts
mtDphiAllremovett = DphiAllremovett[0].Clone("mt")
mtDphiAllremovett.SetName("mt")
mtDphiAllremovett.SetTitle("Inverted tau Mt")
mtDphiAllremovett.Reset()
for histo in DphiAllremovett:
mtDphiAllremovett.Add(histo)
mtDphiAllremovettEWK = DphiEWKAllremovett[0].Clone("mtewk")
mtDphiAllremovettEWK.SetName("MTewk")
mtDphiAllremovettEWK.SetTitle("Inverted tau Met")
mtDphiAllremovettEWK.Reset()
for histo in DphiEWKAllremovett:
mtDphiAllremovettEWK.Add(histo)
##########################################
# met purity
metqcd = metQCD.Clone("metqcd")
metinv = met.Clone("met")
invertedQCD.setLabel("MetPurity")
# invertedQCD.mtComparison(metqcd, metqcd,"MetPurity")
##########################################
# mt purity no deltaPhi
mtQCD = mtNoDphi.Clone("QCD")
mtQCD.Add(mtNoDphiEWK,-1)
mtQCD.Divide(mtNoDphi)
mtqcd = mtQCD.Clone("mtqcd")
invertedQCD.setLabel("MtNoDeltaPhiCuts")
invertedQCD.mtComparison(mtQCD, mtQCD,"MtNoDeltaPhiCuts")
##########################################
# mt purity deltaPhi 160
mtQCD = mtDphi160.Clone("QCD")
mtQCD.Add(mtDphi160EWK,-1)
mtQCD.Divide(mtDphi160)
mtqcd = mtQCD.Clone("mtqcd")
invertedQCD.setLabel("MtDeltaPhi160")
invertedQCD.mtComparison(mtQCD, mtQCD,"MtDeltaPhi160")
##########################################
# mt purity all deltaPhi cuts
mtQCD = mtDphiAll.Clone("QCD")
mtQCD.Add(mtDphiAllEWK,-1)
mtQCD.Divide(mtDphiAll)
mtqcd = mtQCD.Clone("mtqcd")
invertedQCD.setLabel("MtAllDeltaPhiCuts")
invertedQCD.mtComparison(mtQCD, mtQCD,"MtAllDeltaPhiCuts")
##########################################
# mt bveto purity all deltaPhi cuts
## test
invertedQCD.setLabel("testMtbveto")
# invertedQCD.mtComparison(mtDphiAllbveto, mtDphiAllbveto,"testMtbveto")
invertedQCD.setLabel("testEWKMtbveto")
# invertedQCD.mtComparison(mtDphiAllEWKbveto, mtDphiAllEWKbveto,"testEWKMtbveto")
mtQCD = mtDphiAllbveto.Clone("QCD")
mtQCD.Add(mtDphiAllEWKbveto,-1)
mtQCD.Divide(mtDphiAllbveto)
mtqcd = mtQCD.Clone("mtqcd")
invertedQCD.setLabel("MtbvetoAllDeltaPhiCuts")
invertedQCD.mtComparison(mtQCD, mtQCD,"MtbvetoAllDeltaPhiCuts")
##########################################
# mt purity jet1 deltaPhi cuts
mtQCD = mtDphiJet1.Clone("QCD")
mtQCD.Add(mtDphiEWKJet1,-1)
mtQCD.Divide(mtDphiJet1)
mtqcd = mtQCD.Clone("mtqcd")
invertedQCD.setLabel("MtDeltaPhiJet1Cuts")
invertedQCD.mtComparison(mtQCD, mtQCD,"MtDeltaPhiJet1Cuts")
##########################################
# mt purity jet2 deltaPhi cuts
mtQCD = mtDphiJet2.Clone("QCD")
mtQCD.Add(mtDphiEWKJet2,-1)
mtQCD.Divide(mtDphiJet2)
mtqcd = mtQCD.Clone("mtqcd")
invertedQCD.setLabel("MtDeltaPhiJet2Cuts")
invertedQCD.mtComparison(mtQCD, mtQCD,"MtDeltaPhiJet2Cuts")
##########################################
# mt purity all deltaPhi cuts and against tt cut
mtQCD = mtDphiAllremovett.Clone("QCD")
mtQCD.Add(mtDphiAllremovettEWK,-1)
mtQCD.Divide(mtDphiAllremovett)
mtqcd = mtQCD.Clone("mtqcd")
invertedQCD.setLabel("MtDeltaPhiAndAgainsttt")
invertedQCD.mtComparison(mtQCD, mtQCD,"MtDeltaPhiAndAgainsttt")
#################################################
## purities as a function of pt tau jet
### Create and customise TGraph
cEff = TCanvas ("MetPurity", "MetPurity", 1)
cEff.cd()
ptbin_error = array.array("d",[5, 5, 5, 5, 10, 10 ,30])
ptbin = array.array("d",[45, 55, 65, 75, 90, 110 ,150])
graph = TGraphErrors(7, ptbin, array.array("d",purityMTInvertedTauIdBvetoDphi),ptbin_error,array.array("d",purityErrMTInvertedTauIdBvetoDphi))
graph.SetMaximum(1.0)
graph.SetMinimum(0.6)
graph.SetMarkerStyle(kFullCircle)
graph.SetMarkerColor(kBlue)
graph.SetMarkerSize(1)
graph.GetYaxis().SetTitle("QCD purity")
graph.GetXaxis().SetTitle("p_{T}^{#tau jet} [GeV/c]")
### Re-draw graph and update canvas and gPad
graph.Draw("AP")
tex4 = ROOT.TLatex(0.2,0.955,"8 TeV 12.2 fb^{-1} CMS preliminary")
tex4.SetNDC()
tex4.SetTextSize(20)
tex4.Draw()
tex1 = ROOT.TLatex(0.35,0.35,"B-tagging factorisation")
tex1.SetNDC()
tex1.SetTextSize(25)
tex1.Draw()
tex2 = ROOT.TLatex(0.35,0.27,"#Delta#phi cuts" )
tex2.SetNDC()
tex2.SetTextSize(25)
tex2.Draw()
cEff.Update()
cEff.SaveAs("purityMTInvertedTauIdBvetoDphiBins.png")
graph = TGraphErrors(7, ptbin, array.array("d",purityMet),ptbin_error,array.array("d",purityErrMet))
graph.SetMaximum(1.1)
graph.SetMinimum(0.8)
graph.SetMarkerStyle(kFullCircle)
graph.SetMarkerColor(kBlue)
graph.SetMarkerSize(1)
graph.GetYaxis().SetTitle("QCD purity")
graph.GetXaxis().SetTitle("p_{T}^{#tau jet} [GeV/c]")
### Re-draw graph and update canvas and gPad
graph.Draw("AP")
tex4 = ROOT.TLatex(0.2,0.955,"8 TeV 12.2 fb^{-1} CMS preliminary")
tex4.SetNDC()
tex4.SetTextSize(20)
tex4.Draw()
tex1 = ROOT.TLatex(0.35,0.35,"Inverted #tau jet isolation")
tex1.SetNDC()
tex1.SetTextSize(20)
tex1.Draw()
tex2 = ROOT.TLatex(0.35,0.3,"at least 3 jets" )
tex2.SetNDC()
tex2.SetTextSize(25)
tex2.Draw()
cEff.Update()
cEff.SaveAs("purityMetPtBins.png")
## Mt without deltaPhi cuts
cEff = TCanvas ("MtNoDeltaPhiCutsPurity", "MtNoDeltaPhiCutsPurity", 1)
cEff.cd()
graph = TGraphErrors(7, ptbin, array.array("d",purityMtAfterBtagging),ptbin_error,array.array("d",purityErrMtAfterBtagging))
graph.SetMaximum(1.0)
graph.SetMinimum(0.4)
graph.SetMarkerStyle(kFullCircle)
graph.SetMarkerColor(kBlue)
graph.SetMarkerSize(1)
graph.GetYaxis().SetTitle("QCD purity")
graph.GetXaxis().SetTitle("p_{T}^{#tau jet} [GeV]")
### Re-draw graph and update canvas and gPad
graph.Draw("AP")
tex4 = ROOT.TLatex(0.2,0.955,"8 TeV 12.2 fb^{-1} CMS preliminary")
tex4.SetNDC()
tex4.SetTextSize(20)
tex4.Draw()
tex1 = ROOT.TLatex(0.5,0.38,"All selection cuts")
tex1.SetNDC()
tex1.SetTextSize(22)
tex1.Draw()
tex2 = ROOT.TLatex(0.5,0.3,"no #Delta#phi cuts" )
tex2.SetNDC()
tex2.SetTextSize(25)
tex2.Draw()
cEff.Update()
cEff.SaveAs("purityMtNoDeltaPhiCutsBins.png")
## Mt without deltaPhi cuts
cEff = TCanvas ("MtDeltaPhi160Purity", "MtDeltaPhi160Purity", 1)
cEff.cd()
graph = TGraphErrors(7, ptbin, array.array("d",purityMtDeltaPhiCut),ptbin_error,array.array("d",purityErrMtDeltaPhiCut))
graph.SetMaximum(1.0)
graph.SetMinimum(0.4)
graph.SetMarkerStyle(kFullCircle)
graph.SetMarkerColor(kBlue)
graph.SetMarkerSize(1)
graph.GetYaxis().SetTitle("QCD purity")
graph.GetXaxis().SetTitle("p_{T}^{#tau jet} [GeV]")
### Re-draw graph and update canvas and gPad
graph.Draw("AP")
tex4 = ROOT.TLatex(0.2,0.955,"8 TeV 12.2 fb^{-1} CMS preliminary")
tex4.SetNDC()
tex4.SetTextSize(20)
tex4.Draw()
tex1 = ROOT.TLatex(0.5,0.35,"All selection cuts")
tex1.SetNDC()
tex1.SetTextSize(24)
tex1.Draw()
tex2 = ROOT.TLatex(0.5,0.25,"#Delta#phi(#tau jet,MET) < 160^{o}" )
tex2.SetNDC()
tex2.SetTextSize(24)
tex2.Draw()
cEff.Update()
cEff.SaveAs("purityMtDeltaPhi160Bins.png")
## Mt 1st deltaPhi cut
cEff = TCanvas ("MtFirstDeltaCutPurity", "MtFirstDeltaPhiCutPurity", 1)
cEff.cd()
graph = TGraphErrors(7, ptbin, array.array("d",purityMtFirstDeltaPhiCut),ptbin_error,array.array("d",purityErrMtFirstDeltaPhiCut))
graph.SetMaximum(1.0)
graph.SetMinimum(0.4)
graph.SetMarkerStyle(kFullCircle)
graph.SetMarkerColor(kBlue)
graph.SetMarkerSize(1)
graph.GetYaxis().SetTitle("QCD purity")
graph.GetXaxis().SetTitle("p_{T}^{#tau jet} [GeV]")
### Re-draw graph and update canvas and gPad
graph.Draw("AP")
tex4 = ROOT.TLatex(0.2,0.955,"8 TeV 12.2 fb^{-1} CMS preliminary")
tex4.SetNDC()
tex4.SetTextSize(20)
tex4.Draw()
tex1 = ROOT.TLatex(0.4,0.4,"All selection cuts")
tex1.SetNDC()
tex1.SetTextSize(24)
tex1.Draw()
tex2 = ROOT.TLatex(0.3,0.3,"#Delta#phi(#tau jet,MET) vs #Delta#phi(jet1,MET) cut" )
tex2.SetNDC()
tex2.SetTextSize(24)
tex2.Draw()
cEff.Update()
cEff.SaveAs("purityMtFirstDeltaPhiCutBins.png")
## Mt 2nd deltaPhi cut
cEff = TCanvas ("MtSecondDeltaCutPurity", "MtFirstSecondPhiCutPurity", 1)
cEff.cd()
graph = TGraphErrors(7, ptbin, array.array("d",purityMtSecondDeltaPhiCut),ptbin_error,array.array("d",purityErrMtSecondDeltaPhiCut))
graph.SetMaximum(1.0)
graph.SetMinimum(0.4)
graph.SetMarkerStyle(kFullCircle)
graph.SetMarkerColor(kBlue)
graph.SetMarkerSize(1)
graph.GetYaxis().SetTitle("QCD purity")
graph.GetXaxis().SetTitle("p_{T}^{#tau jet} [GeV]")
### Re-draw graph and update canvas and gPad
graph.Draw("AP")
tex4 = ROOT.TLatex(0.2,0.955,"8 TeV 12.2 fb^{-1} CMS preliminary")
tex4.SetNDC()
tex4.SetTextSize(20)
tex4.Draw()
tex1 = ROOT.TLatex(0.2,0.88,"All selection cuts")
tex1.SetNDC()
tex1.SetTextSize(22)
tex1.Draw()
tex2 = ROOT.TLatex(0.2,0.8,"#Delta#phi(#tau jet,MET) vs #Delta#phi(jet1/2,MET) cuts" )
tex2.SetNDC()
tex2.SetTextSize(24)
tex2.Draw()
cEff.Update()
cEff.SaveAs("purityMtSecondDeltaPhiCutBins.png")
## Mt 2nd deltaPhi cut
cEff = TCanvas ("MtThirdDeltaCutPurity", "MtThirdDeltaPhiCutPurity", 1)
cEff.cd()
graph = TGraphErrors(7, ptbin, array.array("d",purityMtThirdDeltaPhiCut),ptbin_error,array.array("d",purityErrMtThirdDeltaPhiCut ))
graph.SetMaximum(1.0)
graph.SetMinimum(0.4)
graph.SetMarkerStyle(kFullCircle)
graph.SetMarkerColor(kBlue)
graph.SetMarkerSize(1)
graph.GetYaxis().SetTitle("QCD purity")
graph.GetXaxis().SetTitle("p_{T}^{#tau jet} [GeV]")
### Re-draw graph and update canvas and gPad
graph.Draw("AP")
tex4 = ROOT.TLatex(0.2,0.955,"8 TeV 12.2 fb^{-1} CMS preliminary")
tex4.SetNDC()
tex4.SetTextSize(20)
tex4.Draw()
tex1 = ROOT.TLatex(0.2,0.88, "All selection cuts")
tex1.SetNDC()
tex1.SetTextSize(22)
tex1.Draw()
tex2 = ROOT.TLatex(0.2,0.8,"#Delta#phi(#tau jet,MET) vs #Delta#phi(jet1/2/3,MET) cuts" )
tex2.SetNDC()
tex2.SetTextSize(24)
tex2.Draw()
cEff.Update()
cEff.SaveAs("purityMtThirdDeltaPhiCutBins.png")
## Mt deltaPhi cuts and against tt
cEff = TCanvas ("MtMtRemovettPurity", "MtMtRemovettPurity", 1)
cEff.cd()
graph = TGraphErrors(7, ptbin, array.array("d",purityMtRemovett),ptbin_error,array.array("d",purityErrMtRemovett))
graph.SetMaximum(1.0)
graph.SetMinimum(0.4)
graph.SetMarkerStyle(kFullCircle)
graph.SetMarkerColor(kBlue)
graph.SetMarkerSize(1)
graph.GetYaxis().SetTitle("QCD purity")
graph.GetXaxis().SetTitle("p_{T}^{#tau jet} [GeV]")
### Re-draw graph and update canvas and gPad
graph.Draw("AP")
tex4 = ROOT.TLatex(0.2,0.955,"8 TeV 12.2 fb^{-1} CMS preliminary")
tex4.SetNDC()
tex4.SetTextSize(20)
tex4.Draw()
tex1 = ROOT.TLatex(0.2,0.88, "All selection cuts ")
tex1.SetNDC()
tex1.SetTextSize(22)
tex1.Draw()
tex2 = ROOT.TLatex(0.2,0.8,"#Delta#phi(#tau jet,MET) vs #Delta#phi(jet1/2/3,MET) cuts" )
tex2.SetNDC()
tex2.SetTextSize(22)
tex2.Draw()
tex3 = ROOT.TLatex(0.2,0.72,"#Delta#phi cut against tt+jets" )
tex3.SetNDC()
tex3.SetTextSize(22)
tex3.Draw()
cEff.Update()
cEff.SaveAs("purityMtAgainstttCutBins.png")
def old_plots():
mtAllCuts = []
mtAllCutsBaseline = []
mtAfterMetCut = []
mtAfterMetCutBaseline = []
mtAfterMetPlusSoftBtagging = []
mtAfterMetPlusSoftBtaggingBaseline = []
mtAfterMetPlusBveto = []
mtAfterMetPlusBvetoBaseline = []
mtAfterCollinearCuts = []
mtAfterCollinearCutsBaseline = []
invertedNoScale = []
## histograms in bins, normalisation and substraction of EWK contribution
## mt with 2dim deltaPhi cut
for ptbin in ptbins:
## ------------- mt with all cuts -----------
mt_tmp = plots.PlotBase([
datasets.getDataset("Data").getDatasetRootHisto(
"Inverted/MTInvertedTauIdAfterBackToBackCuts/MTInvertedTauIdAfterBackToBackCuts"
+ ptbin)
])
mt_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(5))
mt = mt_tmp.histoMgr.getHisto("Data").getRootHisto().Clone()
mtEvents = mt_tmp.histoMgr.getHisto("Data").getRootHisto().Clone()
invertedNoScale.append(mtEvents)
mt.Scale(normData[ptbin])
mtEWK_tmp = plots.PlotBase([
datasets.getDataset("EWK").getDatasetRootHisto(
"Inverted/MTInvertedTauIdAfterBackToBackCuts/MTInvertedTauIdAfterBackToBackCuts"
+ ptbin)
])
mtEWK_tmp.histoMgr.normalizeMCToLuminosity(
datasets.getDataset("Data").getLuminosity())
mtEWK_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(5))
mtEWK = mtEWK_tmp.histoMgr.getHisto("EWK").getRootHisto().Clone()
mtEWK.Scale(normEWK[ptbin])
if includeEWKscale:
mtEWK.Scale(EWKscale)
mt.Add(mtEWK, -1)
mtAllCuts.append(mt)
## baseline
mt_tmp = plots.PlotBase([
datasets.getDataset("Data").getDatasetRootHisto(
"baseline/MTBaselineTauIdAfterBackToBackCuts/MTBaselineTauIdAfterBackToBackCuts"
+ ptbin)
])
mt_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(5))
mtBaseline = mt_tmp.histoMgr.getHisto("Data").getRootHisto().Clone()
mtEWK_tmp = plots.PlotBase([
datasets.getDataset("EWK").getDatasetRootHisto(
"baseline/MTBaselineTauIdAfterBackToBackCuts/MTBaselineTauIdAfterBackToBackCuts"
+ ptbin)
])
mtEWK_tmp.histoMgr.normalizeMCToLuminosity(
datasets.getDataset("Data").getLuminosity())
mtEWK_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(5))
mtEWKBaseline = mtEWK_tmp.histoMgr.getHisto(
"EWK").getRootHisto().Clone()
mtBaseline.Add(mtEWKBaseline, -1)
mtAllCutsBaseline.append(mtBaseline)
#################################################
### ---- mt after met cut
mtfac_tmp = plots.PlotBase([
datasets.getDataset("Data").getDatasetRootHisto(
"Inverted/MTInvertedTauIdAfterMetPlusBackToBackCuts/MTInvertedTauIdAfterMetPlusBackToBackCuts"
+ ptbin)
])
mtfac_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(5))
mtfac = mtfac_tmp.histoMgr.getHisto("Data").getRootHisto().Clone()
mtNoBtaggingTailKiller = mtfac_tmp.histoMgr.getHisto(
"Data").getRootHisto().Clone()
mtNoBtaggingTailKiller.Scale(normData[ptbin])
mtfacEWK_tmp = plots.PlotBase([
datasets.getDataset("EWK").getDatasetRootHisto(
"Inverted/MTInvertedTauIdAfterMetPlusBackToBackCuts/MTInvertedTauIdAfterMetPlusBackToBackCuts"
+ ptbin)
])
mtfacEWK_tmp.histoMgr.normalizeMCToLuminosity(
datasets.getDataset("Data").getLuminosity())
mtfacEWK_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(5))
mtNoBtaggingTailKillerEWK = mtfacEWK_tmp.histoMgr.getHisto(
"EWK").getRootHisto().Clone()
mtNoBtaggingTailKillerEWK.Scale(normEWK[ptbin])
mtNoBtaggingTailKiller.Add(mtNoBtaggingTailKillerEWK, -1)
mtAfterMetCut.append(mtNoBtaggingTailKiller)
### ---- mt after met cut baseline
mtfac_tmp = plots.PlotBase([
datasets.getDataset("Data").getDatasetRootHisto(
"baseline/MTBaselineTauIdAfterMetPlusBackToBackCuts/MTBaselineTauIdAfterMetPlusBackToBackCuts"
+ ptbin)
])
mtfac_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(5))
mtfac = mtfac_tmp.histoMgr.getHisto("Data").getRootHisto().Clone()
mtNoBtaggingTailKillerBaseline = mtfac_tmp.histoMgr.getHisto(
"Data").getRootHisto().Clone()
mtfacEWK_tmp = plots.PlotBase([
datasets.getDataset("EWK").getDatasetRootHisto(
"baseline/MTBaselineTauIdAfterMetPlusBackToBackCuts/MTBaselineTauIdAfterMetPlusBackToBackCuts"
+ ptbin)
])
mtfacEWK_tmp.histoMgr.normalizeMCToLuminosity(
datasets.getDataset("Data").getLuminosity())
mtfacEWK_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(5))
mtNoBtaggingTailKillerEWK = mtfacEWK_tmp.histoMgr.getHisto(
"EWK").getRootHisto().Clone()
mtNoBtaggingTailKillerBaseline.Add(mtNoBtaggingTailKillerEWK, -1)
mtAfterMetCutBaseline.append(mtNoBtaggingTailKillerBaseline)
########################################################
# ----------mt after SOFT b tagging ---------------
mtbSoft_tmp = plots.PlotBase([
datasets.getDataset("Data").getDatasetRootHisto(
"Inverted/MTInvertedTauIdAfterMetPlusSoftBtaggingPlusBackToBackCuts/MTInvertedTauIdAfterMetPlusSoftBtaggingPlusBackToBackCuts"
+ ptbin)
])
mtbSoft_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(5))
mtbSoft = mtbSoft_tmp.histoMgr.getHisto("Data").getRootHisto().Clone()
mtbSoft.Scale(normData[ptbin])
mtbSoftEWK_tmp = plots.PlotBase([
datasets.getDataset("EWK").getDatasetRootHisto(
"Inverted/MTInvertedTauIdAfterMetPlusSoftBtaggingPlusBackToBackCuts/MTInvertedTauIdAfterMetPlusSoftBtaggingPlusBackToBackCuts"
+ ptbin)
])
mtbSoftEWK_tmp.histoMgr.normalizeMCToLuminosity(
datasets.getDataset("Data").getLuminosity())
mtbSoftEWK_tmp.histoMgr.forEachHisto(
lambda h: h.getRootHisto().Rebin(5))
mtbSoftEWK = mtbSoftEWK_tmp.histoMgr.getHisto(
"EWK").getRootHisto().Clone()
mtbSoftEWK.Scale(normEWK[ptbin])
mtbSoft.Add(mtbSoftEWK, -1)
mtAfterMetPlusSoftBtagging.append(mtbSoft)
# ----------mt after SOFT b tagging baseline ---------------
mtbSoft_tmp = plots.PlotBase([
datasets.getDataset("Data").getDatasetRootHisto(
"baseline/MTBaselineTauIdAfterMetPlusSoftBtaggingPlusBackToBackCuts/MTBaselineTauIdAfterMetPlusSoftBtaggingPlusBackToBackCuts"
+ ptbin)
])
mtbSoft_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(5))
mtbSoftBaseline = mtbSoft_tmp.histoMgr.getHisto(
"Data").getRootHisto().Clone()
mtbSoftEWK_tmp = plots.PlotBase([
datasets.getDataset("EWK").getDatasetRootHisto(
"baseline/MTBaselineTauIdAfterMetPlusSoftBtaggingPlusBackToBackCuts/MTBaselineTauIdAfterMetPlusSoftBtaggingPlusBackToBackCuts"
+ ptbin)
])
mtbSoftEWK_tmp.histoMgr.normalizeMCToLuminosity(
datasets.getDataset("Data").getLuminosity())
mtbSoftEWK_tmp.histoMgr.forEachHisto(
lambda h: h.getRootHisto().Rebin(5))
mtbSoftEWKBaseline = mtbSoftEWK_tmp.histoMgr.getHisto(
"EWK").getRootHisto().Clone()
mtbSoftBaseline.Add(mtbSoftEWKBaseline, -1)
mtAfterMetPlusSoftBtaggingBaseline.append(mtbSoftBaseline)
########################################################
# mt after met cut and b-jet veto
mtPhiv_tmp = plots.PlotBase([
datasets.getDataset("Data").getDatasetRootHisto(
"Inverted/MTInvertedTauIdAfterMetPlusBvetoPlusBackToBackCuts/MTInvertedTauIdAfterMetPlusBvetoPlusBackToBackCuts"
+ ptbin)
])
mtPhiv_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(5))
mtPhiv = mtPhiv_tmp.histoMgr.getHisto("Data").getRootHisto().Clone()
mtPhiv.Scale(normData[ptbin])
mtPhiEWKv_tmp = plots.PlotBase([
datasets.getDataset("EWK").getDatasetRootHisto(
"Inverted/MTInvertedTauIdAfterMetPlusBvetoPlusBackToBackCuts/MTInvertedTauIdAfterMetPlusBvetoPlusBackToBackCuts"
+ ptbin)
])
mtPhiEWKv_tmp.histoMgr.normalizeMCToLuminosity(
datasets.getDataset("Data").getLuminosity())
mtPhiEWKv_tmp.histoMgr.forEachHisto(
lambda h: h.getRootHisto().Rebin(5))
mtPhiEWKv = mtPhiEWKv_tmp.histoMgr.getHisto(
"EWK").getRootHisto().Clone()
mtPhiEWKv.Scale(normEWK[ptbin])
mtPhiv.Add(mtPhiEWKv, -1)
mtAfterMetPlusBveto.append(mtPhiv)
mtPhiv_tmp = plots.PlotBase([
datasets.getDataset("Data").getDatasetRootHisto(
"baseline/MTBaselineTauIdAfterMetPlusBvetoPlusBackToBackCuts/MTBaselineTauIdAfterMetPlusBvetoPlusBackToBackCuts"
+ ptbin)
])
mtPhiv_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(5))
mtPhivBaseline = mtPhiv_tmp.histoMgr.getHisto(
"Data").getRootHisto().Clone()
mtPhiEWKv_tmp = plots.PlotBase([
datasets.getDataset("EWK").getDatasetRootHisto(
"baseline/MTBaselineTauIdAfterMetPlusBvetoPlusBackToBackCuts/MTBaselineTauIdAfterMetPlusBvetoPlusBackToBackCuts"
+ ptbin)
])
mtPhiEWKv_tmp.histoMgr.normalizeMCToLuminosity(
datasets.getDataset("Data").getLuminosity())
mtPhiEWKv_tmp.histoMgr.forEachHisto(
lambda h: h.getRootHisto().Rebin(5))
mtPhiEWKvBaseline = mtPhiEWKv_tmp.histoMgr.getHisto(
"EWK").getRootHisto().Clone()
mtPhivBaseline.Add(mtPhiEWKvBaseline, -1)
mtAfterMetPlusBvetoBaseline.append(mtPhivBaseline)
############################################################
# mt after jet selection and back-to-back cuts
mtj_tmp = plots.PlotBase([
datasets.getDataset("Data").getDatasetRootHisto(
"Inverted/MTInvertedTauIdAfterCollinearCutsPlusBackToBackCuts/MTInvertedTauIdAfterCollinearCutsPlusBackToBackCuts"
+ ptbin)
])
mtj_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(5))
mtj = mtj_tmp.histoMgr.getHisto("Data").getRootHisto().Clone()
mtj.Scale(normData[ptbin])
mtjEWK_tmp = plots.PlotBase([
datasets.getDataset("EWK").getDatasetRootHisto(
"Inverted/MTInvertedTauIdAfterCollinearCutsPlusBackToBackCuts/MTInvertedTauIdAfterCollinearCutsPlusBackToBackCuts"
+ ptbin)
])
mtjEWK_tmp.histoMgr.normalizeMCToLuminosity(
datasets.getDataset("Data").getLuminosity())
mtjEWK_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(5))
mtjEWK = mtjEWK_tmp.histoMgr.getHisto("EWK").getRootHisto().Clone()
mtjEWK.Scale(normEWK[ptbin])
mtj.Add(mtjEWK, -1)
mtAfterCollinearCuts.append(mtj)
# mt after jet selection and back-to-back cuts baseline
mtj_tmp = plots.PlotBase([
datasets.getDataset("Data").getDatasetRootHisto(
"baseline/MTBaselineTauIdAfterCollinearCutsPlusBackToBackCuts/MTBaselineTauIdAfterCollinearCutsPlusBackToBackCuts"
+ ptbin)
])
mtj_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(5))
mtjBaseline = mtj_tmp.histoMgr.getHisto("Data").getRootHisto().Clone()
mtjEWK_tmp = plots.PlotBase([
datasets.getDataset("EWK").getDatasetRootHisto(
"baseline/MTBaselineTauIdAfterCollinearCutsPlusBackToBackCuts/MTBaselineTauIdAfterCollinearCutsPlusBackToBackCuts"
+ ptbin)
])
mtjEWK_tmp.histoMgr.normalizeMCToLuminosity(
datasets.getDataset("Data").getLuminosity())
mtjEWK_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(5))
mtjEWKBaseline = mtjEWK_tmp.histoMgr.getHisto(
"EWK").getRootHisto().Clone()
mtjBaseline.Add(mtjEWKBaseline, -1)
mtAfterCollinearCutsBaseline.append(mtjBaseline)
############################################################
## end of loop
### sum and plot
invertedQCD = InvertedTauID()
invertedQCD.setLumi(datasets.getDataset("Data").getLuminosity())
events = invertedNoScale[0].Clone("inv")
events.SetName("inv")
events.SetTitle("Inverted tau ID")
events.Reset()
print "check events in inverted ", events.GetEntries()
for histo in invertedNoScale:
events.Add(histo)
print "All unormalised events - EWK = ", events.Integral()
## mt with all cuts
hAllCuts = mtAllCuts[0].Clone("mtSum")
hAllCuts.SetName("transverseMass")
hAllCuts.SetTitle("Inverted tau ID")
hAllCuts.Reset()
for histo in mtAllCuts:
hAllCuts.Add(histo)
print "All events with all cuts - EWK evets= ", hAllCuts.Integral()
hAllCutsBaseline = mtAllCutsBaseline[0].Clone("mtSumBaseline")
hAllCutsBaseline.SetName("transverseMass")
hAllCutsBaseline.SetTitle("Inverted tau ID")
hAllCutsBaseline.Reset()
for histo in mtAllCutsBaseline:
hAllCutsBaseline.Add(histo)
print "Baseline QCD events with all cuts = ", hAllCutsBaseline.Integral()
# mt plot with all cuts
allCuts_inverted = hAllCuts.Clone("AllCuts")
allCuts_baseline = hAllCutsBaseline.Clone("allcuts_QCD")
invertedQCD.setLabel("AllCuts")
invertedQCD.mtComparison(allCuts_inverted, allCuts_baseline, "AllCuts")
# mt plot inverted all cuts
invertedQCD.setLabel("MtWithAllCutsTailKiller")
invertedQCD.mtComparison(allCuts_inverted, allCuts_inverted,
"MtWithAllCutsTailKiller")
########################################################
# mt with SOFT btagging
hmtAfterMetPlusSoftBtagging = mtAfterMetPlusSoftBtagging[0].Clone(
"mthmtbSoft")
hmtAfterMetPlusSoftBtagging.SetName("transverseMassBtag")
hmtAfterMetPlusSoftBtagging.SetTitle("Inverted tau ID")
hmtAfterMetPlusSoftBtagging.Reset()
# print "check hmtsum",hmtSum.GetEntries()
for histo in mtAfterMetPlusSoftBtagging:
hmtAfterMetPlusSoftBtagging.Add(histo)
print "Integral: after SOFT B tagging - EWK = ", hmtAfterMetPlusSoftBtagging.Integral(
)
# mt with SOFT btagging baseline
hmtAfterMetPlusSoftBtaggingBaseline = mtAfterMetPlusSoftBtaggingBaseline[
0].Clone("mthmtbSoft")
hmtAfterMetPlusSoftBtaggingBaseline.SetName("transverseMassBtag")
hmtAfterMetPlusSoftBtaggingBaseline.SetTitle("Inverted tau ID")
hmtAfterMetPlusSoftBtaggingBaseline.Reset()
# print "check hmtsum",hmtSum.GetEntries()
for histo in mtAfterMetPlusSoftBtaggingBaseline:
hmtAfterMetPlusSoftBtaggingBaseline.Add(histo)
print "Integral Baseline: after SOFT B tagging - EWK = ", hmtAfterMetPlusSoftBtaggingBaseline.Integral(
)
# mt inverted-baseline comparison, SOFT b tagging, MET CUT
afterJets_inverted = hmtAfterMetPlusSoftBtagging.Clone("mtbSoft")
afterJets_baseline = hmtAfterMetPlusSoftBtaggingBaseline.Clone(
"mtbSoft_QCD")
invertedQCD.setLabel("AfterMetPlusSoftBtagging")
invertedQCD.mtComparison(afterJets_inverted, afterJets_baseline,
"AfterMetPlusSoftBtagging")
################################
hmtAfterCollinearCuts = mtAfterCollinearCuts[0].Clone("AfterCollinearCuts")
hmtAfterCollinearCuts.SetName("transverseMassBtag")
hmtAfterCollinearCuts.SetTitle("Inverted tau ID")
hmtAfterCollinearCuts.Reset()
for histo in mtAfterCollinearCuts:
hmtAfterCollinearCuts.Add(histo)
print "Integral: after collinear cuts - EWK = ", hmtAfterCollinearCuts.Integral(
)
hmtAfterCollinearCutsBaseline = mtAfterCollinearCutsBaseline[0].Clone(
"AfterCollinearCutsBaseline")
hmtAfterCollinearCutsBaseline.SetName("transverseMassBtag")
hmtAfterCollinearCutsBaseline.SetTitle("Inverted tau ID")
hmtAfterCollinearCutsBaseline.Reset()
for histo in mtAfterCollinearCutsBaseline:
hmtAfterCollinearCutsBaseline.Add(histo)
print "Integral Baseline : after collinear cuts - EWK = ", hmtAfterCollinearCutsBaseline.Integral(
)
# mt inverted-baseline comparison at coll cuts
afterJets_inverted = hmtAfterCollinearCuts.Clone("AfterCollinearCuts")
afterJets_baseline = hmtAfterCollinearCutsBaseline.Clone(
"AfterCollinearCutsBaseline_QCD")
invertedQCD.setLabel("AfterCollinearCuts")
invertedQCD.mtComparison(afterJets_inverted, afterJets_baseline,
"AfterCollinearCuts")
######################################################
## B veto for closure test With Met cut
hmtAfterMetPlusBveto = mtAfterMetPlusBveto[0].Clone("hClosureBveto")
hmtAfterMetPlusBveto.SetName("transverseMassBveto")
hmtAfterMetPlusBveto.SetTitle("Inverted tau ID")
hmtAfterMetPlusBveto.Reset()
for histo in mtAfterMetPlusBveto:
hmtAfterMetPlusBveto.Add(histo)
print "Integral: Bveto - EWK with met cut = ", hmtAfterMetPlusBveto.Integral(
)
hmtAfterMetPlusBvetoBaseline = mtAfterMetPlusBvetoBaseline[0].Clone(
"hClosureBvetoBaseline")
hmtAfterMetPlusBvetoBaseline.SetName("transverseMassBveto")
hmtAfterMetPlusBvetoBaseline.SetTitle("Inverted tau ID")
hmtAfterMetPlusBvetoBaseline.Reset()
for histo in mtAfterMetPlusBvetoBaseline:
hmtAfterMetPlusBvetoBaseline.Add(histo)
print "Integral baseline: Bveto - EWK with met cut = ", hmtAfterMetPlusBvetoBaseline.Integral(
)
# mt inverted-baseline comparison with bveto and MET CUT, closure
#bveto_inverted =hClosureBvetoTailKiller.Clone("hmtvSum")
bveto_inverted = hmtAfterMetPlusBveto.Clone("bveto")
bveto_baseline = hmtAfterMetPlusBvetoBaseline.Clone("bvetoBaseline_QCD")
invertedQCD.setLabel("AfterMetPlusBveto")
# invertedQCD.mtComparison(bveto_inverted, bveto_baseline,"BvetoTailKillerClosure",sysError=sysError)
invertedQCD.mtComparison(bveto_inverted, bveto_baseline,
"AfterMetPlusBveto")
########################################################
## no btagging, with Met cut
hmtAfterMetCut = mtAfterMetCut[0].Clone("hClosureNoBtag")
hmtAfterMetCut.SetName("transverseMassNoBtagging")
hmtAfterMetCut.SetTitle("Inverted tau ID")
hmtAfterMetCut.Reset()
for histo in mtAfterMetCut:
hmtAfterMetCut.Add(histo)
print "Integral: no btagging - EWK = ", hmtAfterMetCut.Integral()
hmtAfterMetCutBaseline = mtAfterMetCutBaseline[0].Clone("hClosureNoBtag")
hmtAfterMetCutBaseline.SetName("transverseMassNoBtagging")
hmtAfterMetCutBaseline.SetTitle("Inverted tau ID")
hmtAfterMetCutBaseline.Reset()
for histo in mtAfterMetCutBaseline:
hmtAfterMetCutBaseline.Add(histo)
print "Integral baseline: no btagging - EWK = ", hmtAfterMetCutBaseline.Integral(
)
# mt inverted-baseline comparison, no b tagging, with met cut, closure
afterMet_inverted = hmtAfterMetCut.Clone("afterMet")
afterMet_baseline = hmtAfterMetCutBaseline.Clone("aftermet_QCD")
invertedQCD.setLabel("AfterMetCut")
invertedQCD.mtComparison(afterMet_inverted, afterMet_baseline,
"AfterMetCut")
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 controlPlots(datasets):
normData,normEWK,normFactorisedData,normFactorisedEWK=normalisation()
norm_inc,normEWK_inc = normalisationInclusive()
hmet = []
hmetb = []
effArray = []
effErrArray = []
hmetbveto = []
hmtBtag = []
hmtBveto = []
hmtNoMetBtag = []
hmtNoMetBveto = []
effBvetoArray = []
effErrBvetoArray = []
effArrayMt= []
effErrArrayMt= []
effArrayMtNoMet= []
effErrArrayMtNoMet= []
## histograms in bins, normalisation and substraction of EWK contribution
## mt with 2dim deltaPhi cut
for ptbin in ptbins:
### MET
mmt_tmp = plots.PlotBase([datasets.getDataset("Data").getDatasetRootHisto("Inverted/MET_InvertedTauIdJets"+ptbin)])
mmt_tmp._setLegendStyles()
mmt_tmp._setLegendLabels()
mmt_tmp.histoMgr.setHistoDrawStyleAll("P")
mmt_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(20))
mmt = mmt_tmp.histoMgr.getHisto("Data").getRootHisto().Clone()
mmt.Scale(normData[ptbin])
# hmt.append(mt)
mmtEWK_tmp = plots.PlotBase([datasets.getDataset("EWK").getDatasetRootHisto("Inverted/MET_InvertedTauIdJets"+ptbin)])
mmtEWK_tmp.histoMgr.normalizeMCToLuminosity(datasets.getDataset("Data").getLuminosity())
mmtEWK_tmp._setLegendStyles()
mmtEWK_tmp._setLegendLabels()
mmtEWK_tmp.histoMgr.setHistoDrawStyleAll("P")
mmtEWK_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(20))
mmtEWK = mmtEWK_tmp.histoMgr.getHisto("EWK").getRootHisto().Clone()
mmtEWK.Scale(normEWK[ptbin])
mmt.Add(mmtEWK, -1)
hmet.append(mmt)
### MET with btagging
mmtb_tmp = plots.PlotBase([datasets.getDataset("Data").getDatasetRootHisto("Inverted/MET_InvertedTauIdBtag"+ptbin)])
mmtb_tmp._setLegendStyles()
mmtb_tmp._setLegendLabels()
mmtb_tmp.histoMgr.setHistoDrawStyleAll("P")
mmtb_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(20))
mmtb = mmtb_tmp.histoMgr.getHisto("Data").getRootHisto().Clone()
mmtb.Scale(normData[ptbin])
# hmt.append(mt)
mmtbEWK_tmp = plots.PlotBase([datasets.getDataset("EWK").getDatasetRootHisto("Inverted/MET_InvertedTauIdBtag"+ptbin)])
mmtbEWK_tmp.histoMgr.normalizeMCToLuminosity(datasets.getDataset("Data").getLuminosity())
mmtbEWK_tmp._setLegendStyles()
mmtbEWK_tmp._setLegendLabels()
mmtbEWK_tmp.histoMgr.setHistoDrawStyleAll("P")
mmtbEWK_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(20))
mmtbEWK = mmtbEWK_tmp.histoMgr.getHisto("EWK").getRootHisto().Clone()
mmtbEWK.Scale(normEWK[ptbin])
mmtb.Add(mmtbEWK, -1)
hmetb.append(mmtb)
eff = mmtb.Integral()/mmt.Integral()
ereff = sqrt(eff*(1-eff)/mmt.Integral())
print " pt bin ", ptbin, " btag efficiency from MET = ",eff, " error ",ereff
effArray.append(eff)
effErrArray.append(ereff)
### MET with bveto
mmtbveto_tmp = plots.PlotBase([datasets.getDataset("Data").getDatasetRootHisto("Inverted/MET_InvertedTauIdBveto"+ptbin)])
mmtbveto_tmp._setLegendStyles()
mmtbveto_tmp._setLegendLabels()
mmtbveto_tmp.histoMgr.setHistoDrawStyleAll("P")
mmtbveto_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(20))
mmtbveto = mmtbveto_tmp.histoMgr.getHisto("Data").getRootHisto().Clone()
mmtbveto.Scale(normData[ptbin])
# hmt.append(mt)
mmtbvetoEWK_tmp = plots.PlotBase([datasets.getDataset("EWK").getDatasetRootHisto("Inverted/MET_InvertedTauIdBveto"+ptbin)])
mmtbvetoEWK_tmp.histoMgr.normalizeMCToLuminosity(datasets.getDataset("Data").getLuminosity())
mmtbvetoEWK_tmp._setLegendStyles()
mmtbvetoEWK_tmp._setLegendLabels()
mmtbvetoEWK_tmp.histoMgr.setHistoDrawStyleAll("P")
mmtbvetoEWK_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(20))
mmtbvetoEWK = mmtbvetoEWK_tmp.histoMgr.getHisto("EWK").getRootHisto().Clone()
mmtbvetoEWK.Scale(normEWK[ptbin])
mmtbveto.Add(mmtbvetoEWK, -1)
hmetbveto.append(mmtbveto)
## normalization mT(btag/bveto)
eff = mmtb.Integral()/mmtbveto.Integral()
ereff = sqrt(eff*(1-eff)/mmtbveto.Integral())
print " pt bin ", ptbin, " btag/bveto efficiency from MET = ",eff, " error ",ereff
effBvetoArray.append(eff)
effErrBvetoArray.append(ereff)
## with MT distribution
if False:
### no MET cut
mmtb_tmp = plots.PlotBase([datasets.getDataset("Data").getDatasetRootHisto("Inverted/MTInvertedTauIdBtagNoMetCut"+ptbin)])
mmtb_tmp._setLegendStyles()
mmtb_tmp._setLegendLabels()
mmtb_tmp.histoMgr.setHistoDrawStyleAll("P")
mmtb_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(20))
mmtb = mmtb_tmp.histoMgr.getHisto("Data").getRootHisto().Clone()
mmtb.Scale(normData[ptbin])
# hmt.append(mt)
mmtbEWK_tmp = plots.PlotBase([datasets.getDataset("EWK").getDatasetRootHisto("Inverted/MTInvertedTauIdBtagNoMetCut"+ptbin)])
mmtbEWK_tmp.histoMgr.normalizeMCToLuminosity(datasets.getDataset("Data").getLuminosity())
mmtbEWK_tmp._setLegendStyles()
mmtbEWK_tmp._setLegendLabels()
mmtbEWK_tmp.histoMgr.setHistoDrawStyleAll("P")
mmtbEWK_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(20))
mmtbEWK = mmtbEWK_tmp.histoMgr.getHisto("EWK").getRootHisto().Clone()
mmtbEWK.Scale(normEWK[ptbin])
mmtb.Add(mmtbEWK, -1)
hmtNoMetBtag.append(mmtb)
### MET with bvet
mmtbveto_tmp = plots.PlotBase([datasets.getDataset("Data").getDatasetRootHisto("Inverted/MTInvertedTauIdBvetoNoMetCut"+ptbin)])
mmtbveto_tmp._setLegendStyles()
mmtbveto_tmp._setLegendLabels()
mmtbveto_tmp.histoMgr.setHistoDrawStyleAll("P")
mmtbveto_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(20))
mmtbveto = mmtbveto_tmp.histoMgr.getHisto("Data").getRootHisto().Clone()
mmtbveto.Scale(normData[ptbin])
# hmt.append(mt)
mmtbvetoEWK_tmp = plots.PlotBase([datasets.getDataset("EWK").getDatasetRootHisto("Inverted/MTInvertedTauIdBvetoNoMetCut"+ptbin)])
mmtbvetoEWK_tmp.histoMgr.normalizeMCToLuminosity(datasets.getDataset("Data").getLuminosity())
mmtbvetoEWK_tmp._setLegendStyles()
mmtbvetoEWK_tmp._setLegendLabels()
mmtbvetoEWK_tmp.histoMgr.setHistoDrawStyleAll("P")
mmtbvetoEWK_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(20))
mmtbvetoEWK = mmtbvetoEWK_tmp.histoMgr.getHisto("EWK").getRootHisto().Clone()
mmtbvetoEWK.Scale(normEWK[ptbin])
mmtbveto.Add(mmtbvetoEWK, -1)
hmtNoMetBveto.append(mmtbveto)
## normalization mT(btag/bveto)
eff = mmtb.Integral()/mmtbveto.Integral()
ereff = sqrt(eff*(1-eff)/mmtbveto.Integral())
print " pt bin ", ptbin, " btag/bveto efficiency from mt, no met cut = ",eff, " error ",ereff
effArrayMtNoMet.append(eff)
effErrArrayMtNoMet.append(ereff)
#############################################
### with MET cut
mmtb_tmp = plots.PlotBase([datasets.getDataset("Data").getDatasetRootHisto("Inverted/MTInvertedTauIdBtag"+ptbin)])
mmtb_tmp._setLegendStyles()
mmtb_tmp._setLegendLabels()
mmtb_tmp.histoMgr.setHistoDrawStyleAll("P")
mmtb_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(20))
mmtb = mmtb_tmp.histoMgr.getHisto("Data").getRootHisto().Clone()
mmtb.Scale(normData[ptbin])
# hmt.append(mt)
mmtbEWK_tmp = plots.PlotBase([datasets.getDataset("EWK").getDatasetRootHisto("Inverted/MTInvertedTauIdBtag"+ptbin)])
mmtbEWK_tmp.histoMgr.normalizeMCToLuminosity(datasets.getDataset("Data").getLuminosity())
mmtbEWK_tmp._setLegendStyles()
mmtbEWK_tmp._setLegendLabels()
mmtbEWK_tmp.histoMgr.setHistoDrawStyleAll("P")
mmtbEWK_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(20))
mmtbEWK = mmtbEWK_tmp.histoMgr.getHisto("EWK").getRootHisto().Clone()
mmtbEWK.Scale(normEWK[ptbin])
mmtb.Add(mmtbEWK, -1)
hmtBtag.append(mmtb)
mmtbveto_tmp = plots.PlotBase([datasets.getDataset("Data").getDatasetRootHisto("Inverted/MTInvertedTauIdBveto"+ptbin)])
mmtbveto_tmp._setLegendStyles()
mmtbveto_tmp._setLegendLabels()
mmtbveto_tmp.histoMgr.setHistoDrawStyleAll("P")
mmtbveto_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(20))
mmtbveto = mmtbveto_tmp.histoMgr.getHisto("Data").getRootHisto().Clone()
mmtbveto.Scale(normData[ptbin])
# hmt.append(mt)
mmtbvetoEWK_tmp = plots.PlotBase([datasets.getDataset("EWK").getDatasetRootHisto("Inverted/MTInvertedTauIdBveto"+ptbin)])
mmtbvetoEWK_tmp.histoMgr.normalizeMCToLuminosity(datasets.getDataset("Data").getLuminosity())
mmtbvetoEWK_tmp._setLegendStyles()
mmtbvetoEWK_tmp._setLegendLabels()
mmtbvetoEWK_tmp.histoMgr.setHistoDrawStyleAll("P")
mmtbvetoEWK_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(20))
mmtbvetoEWK = mmtbvetoEWK_tmp.histoMgr.getHisto("EWK").getRootHisto().Clone()
mmtbvetoEWK.Scale(normEWK[ptbin])
mmtbveto.Add(mmtbvetoEWK, -1)
hmtBveto.append(mmtbveto)
## normalization mT(btag/bveto)
eff = mmtb.Integral()/mmtbveto.Integral()
ereff = sqrt(eff*(1-eff)/mmtbveto.Integral())
print " pt bin ", ptbin, " btag/bveto efficiency from mt = ",eff, " error ",ereff
effArrayMt.append(eff)
effErrArrayMt.append(ereff)
## sum histo bins
met = hmet[0].Clone("met")
met.SetName("MET")
met.SetTitle("Inverted tau Met")
met.Reset()
print "check met",met.GetEntries()
for histo in hmet:
met.Add(histo)
metb = hmetb[0].Clone("met")
metb.SetName("MET")
metb.SetTitle("Inverted tau Met")
metb.Reset()
print "check met btagging",metb.GetEntries()
for histo in hmetb:
metb.Add(histo)
metbveto = hmetbveto[0].Clone("met")
metbveto.SetName("METbveto")
metbveto.SetTitle("Inverted tau Met")
metbveto.Reset()
print "check met bveto",metbveto.GetEntries()
for histo in hmetbveto:
metbveto.Add(histo)
## with MT
if False:
mtNoMetBtag = hmtNoMetBtag[0].Clone("mt")
mtNoMetBtag.SetName("MET")
mtNoMetBtag.SetTitle("Inverted tau Met")
mtNoMetBtag.Reset()
print "check MT btagging",mtNoMetBtag.GetEntries()
for histo in hmtNoMetBtag:
mtNoMetBtag.Add(histo)
mtNoMetBveto = hmtNoMetBveto[0].Clone("mt")
mtNoMetBveto.SetName("MET")
mtNoMetBveto.SetTitle("Inverted tau Met")
mtNoMetBveto.Reset()
print "check MT bveto",mtNoMetBveto.GetEntries()
for histo in hmtNoMetBveto:
mtNoMetBveto.Add(histo)
mtBtag = hmtBtag[0].Clone("mt")
mtBtag.SetName("MET")
mtBtag.SetTitle("Inverted tau Met")
mtBtag.Reset()
print "check MT btagging",mtBtag.GetEntries()
for histo in hmtBtag:
mtBtag.Add(histo)
mtBveto = hmtBveto[0].Clone("mt")
mtBveto.SetName("MET")
mtBveto.SetTitle("Inverted tau Met")
mtBveto.Reset()
print "check MT bveto",mtBveto.GetEntries()
for histo in hmtBveto:
mtBveto.Add(histo)
##########################################
## plotting
invertedQCD = InvertedTauID()
invertedQCD.setLumi(datasets.getDataset("Data").getLuminosity())
### effisiency as a function of MET
metWithBtagging = metb.Clone("MET")
metWithBtagging.Divide(metbveto)
BtaggingEffVsMet = metWithBtagging.Clone("Eff")
invertedQCD.setLabel("BtagToBvetoEffVsMet")
invertedQCD.mtComparison(BtaggingEffVsMet, BtaggingEffVsMet,"BtagToBvetoEffVsMet")
if False:
### effisiency as a function of MT
mtWithBtagging = mtNoMetBtag.Clone("MT")
mtWithBtagging.Divide(mtNoMetBveto)
BtaggingEffNoMetVsMt = mtWithBtagging.Clone("Eff")
invertedQCD.setLabel("BtagToBvetoEffNoMetVsMt")
invertedQCD.mtComparison(BtaggingEffNoMetVsMt, BtaggingEffNoMetVsMt,"BtagToBvetoEffNoMetVsMt")
### effisiency as a function of MT
mtWithBtagging = mtBtag.Clone("MT")
mtWithBtagging.Divide(mtBveto)
BtaggingEffVsMt = mtWithBtagging.Clone("Eff")
invertedQCD.setLabel("BtagToBvetoEffVsMt")
invertedQCD.mtComparison(BtaggingEffVsMt, BtaggingEffVsMt,"BtagToBvetoEffVsMt")
# efficiency metb/met
metbtag = metb.Clone("metb")
metnobtag = met.Clone("met")
metbtag.Divide(metnobtag)
invertedQCD.setLabel("BtagEffVsMet")
invertedQCD.mtComparison(metbtag, metbtag,"BtagEffVsMet")
# efficiency metb/metbveto
metbtag = metb.Clone("metb")
metbjetveto = metbveto.Clone("met")
invertedQCD.setLabel("BtagToBvetoEfficiency")
invertedQCD.mtComparison(metbtag, metbjetveto,"BtagToBvetoEfficiency")
### Create and customise TGraph
cEff = TCanvas ("Efficiency", "Efficiency", 1)
cEff.cd()
ptbin_error = array.array("d",[5, 5, 5, 5, 10, 10 ,30])
ptbin = array.array("d",[45, 55, 65, 75, 90, 110 ,150])
if False:
## no MET cut
cEff = TCanvas ("btaggingEffNoMet", "btaggingEffNoMet", 1)
cEff.cd()
graph = TGraphErrors(7, ptbin, array.array("d",effArrayMtNoMet),ptbin_error,array.array("d",effErrArrayMtNoMet))
graph.SetMaximum(0.25)
graph.SetMinimum(0.0)
graph.SetMarkerStyle(kFullCircle)
graph.SetMarkerColor(kBlue)
graph.SetMarkerSize(1)
graph.GetYaxis().SetTitle("N_{b tagged}/N_{b veto}")
graph.GetXaxis().SetTitle("p_{T}^{#tau jet} [GeV]")
### Re-draw graph and update canvas and gPad
graph.Draw("AP")
tex4 = ROOT.TLatex(0.2,0.955,"8 TeV 19.6 fb^{-1} CMS preliminary")
tex4.SetNDC()
tex4.SetTextSize(20)
tex4.Draw()
tex1 = ROOT.TLatex(0.2,0.88,"All selection cuts")
tex1.SetNDC()
tex1.SetTextSize(22)
# tex1.Draw()
tex2 = ROOT.TLatex(0.5,0.8,"No MET cut" )
tex2.SetNDC()
tex2.SetTextSize(24)
tex2.Draw()
cEff.Update()
cEff.SaveAs("btagToBvetoEffNoMetVsPtTau_mt.png")
## with MET cut
cEff = TCanvas ("btaggingEff", "btaggingEff", 1)
cEff.cd()
graph = TGraphErrors(7, ptbin, array.array("d",effArrayMt),ptbin_error,array.array("d",effErrArrayMt))
graph.SetMaximum(0.25)
graph.SetMinimum(0.0)
graph.SetMarkerStyle(kFullCircle)
graph.SetMarkerColor(kBlue)
graph.SetMarkerSize(1)
graph.GetYaxis().SetTitle("N_{b tagged}/N_{b veto}")
graph.GetXaxis().SetTitle("p_{T}^{#tau jet} [GeV]")
### Re-draw graph and update canvas and gPad
graph.Draw("AP")
tex4 = ROOT.TLatex(0.2,0.955,"8 TeV 19.6 fb^{-1} CMS preliminary")
tex4.SetNDC()
tex4.SetTextSize(20)
tex4.Draw()
tex1 = ROOT.TLatex(0.2,0.88,"All selection cuts")
tex1.SetNDC()
tex1.SetTextSize(22)
# tex1.Draw()
tex2 = ROOT.TLatex(0.5,0.8,"After MET cut" )
tex2.SetNDC()
tex2.SetTextSize(24)
tex2.Draw()
cEff.Update()
cEff.SaveAs("btagToBvetoEffVsPtTau_mt.png")
## no MET cut
cEff = TCanvas ("btaggingEffNoMet", "btaggingEffNoMet", 1)
cEff.cd()
graph = TGraphErrors(7, ptbin, array.array("d",effBvetoArray),ptbin_error,array.array("d",effErrBvetoArray))
graph.SetMaximum(0.25)
graph.SetMinimum(0.0)
graph.SetMarkerStyle(kFullCircle)
graph.SetMarkerColor(kBlue)
graph.SetMarkerSize(1)
graph.GetYaxis().SetTitle("N_{b tagged}/N_{b veto}")
graph.GetXaxis().SetTitle("p_{T}^{#tau jet} [GeV]")
### Re-draw graph and update canvas and gPad
graph.Draw("AP")
tex4 = ROOT.TLatex(0.2,0.955,"8 TeV 19.6 fb^{-1} CMS preliminary")
tex4.SetNDC()
tex4.SetTextSize(20)
tex4.Draw()
tex1 = ROOT.TLatex(0.2,0.88,"All selection cuts")
tex1.SetNDC()
tex1.SetTextSize(22)
# tex1.Draw()
tex2 = ROOT.TLatex(0.5,0.8,"No MET cut" )
tex2.SetNDC()
tex2.SetTextSize(24)
tex2.Draw()
cEff.Update()
cEff.SaveAs("btagToBvetoEffNoMetVsPtTau.png")
## with MET cut
cEff = TCanvas ("btaggingEfficiency", "btaggingEfficiency", 1)
cEff.cd()
graph = TGraphErrors(7, ptbin, array.array("d",effArray),ptbin_error,array.array("d",effErrArray))
graph.SetMaximum(0.25)
graph.SetMinimum(0.0)
graph.SetMarkerStyle(kFullCircle)
graph.SetMarkerColor(kBlue)
graph.SetMarkerSize(1)
graph.GetYaxis().SetTitle("b-tagging efficiency")
graph.GetXaxis().SetTitle("p_{T}^{#tau jet} [GeV]")
### Re-draw graph and update canvas and gPad
graph.Draw("AP")
tex4 = ROOT.TLatex(0.2,0.955,"8 TeV 19.6 fb^{-1} CMS preliminary")
tex4.SetNDC()
tex4.SetTextSize(20)
tex4.Draw()
tex1 = ROOT.TLatex(0.4,0.85,"Inverted #tau identification")
tex1.SetNDC()
tex1.SetTextSize(22)
tex1.Draw()
tex2 = ROOT.TLatex(0.4,0.78,"At least 3 jets" )
tex2.SetNDC()
tex2.SetTextSize(24)
tex2.Draw()
cEff.Update()
cEff.SaveAs("btaggingEffVsPtTau.png")
##################################3
fOUT = open("btaggingFactors","w")
# now = datetime.datetime.now()
# fOUT.write("# Generated on %s\n"%now.ctime())
fOUT.write("# by %s\n"%os.path.basename(sys.argv[0]))
fOUT.write("\n")
fOUT.write("btaggingFactors = {\n")
i = 0
while i < len(effArray):
line = " \"" + ptbins[i] + "\": " + str(effArray[i])
if i < len(effArray) - 1:
line += ","
line += "\n"
fOUT.write(line)
i = i + 1
fOUT.write("}\n")
fOUT.close()
print "B-tagging efficiensies written in file","btaggingFactors"
fOUT = open("btaggingToBvetoFactors.py","w")
# now = datetime.datetime.now()
# fOUT.write("# Generated on %s\n"%now.ctime())
fOUT.write("# by %s\n"%os.path.basename(sys.argv[0]))
fOUT.write("\n")
fOUT.write("btaggingToBvetoFactors = {\n")
i = 0
while i < len(effBvetoArray):
line = " \"" + ptbins[i] + "\": " + str(effBvetoArray[i])
if i < len(effBvetoArray) - 1:
line += ","
line += "\n"
fOUT.write(line)
i = i + 1
fOUT.write("}\n")
fOUT.close()
print "BtaggingToBveto efficiensies written in file","btaggingToBvetoFactors"
fOUT = open("btaggingToBvetoAfterMetFactors.py","w")
# now = datetime.datetime.now()
# fOUT.write("# Generated on %s\n"%now.ctime())
fOUT.write("# by %s\n"%os.path.basename(sys.argv[0]))
fOUT.write("\n")
fOUT.write("btaggingToBvetoAfterMetFactors = {\n")
i = 0
while i < len(effArrayMt):
line = " \"" + ptbins[i] + "\": " + str(effArrayMt[i])
if i < len(effArrayMt) - 1:
line += ","
line += "\n"
fOUT.write(line)
i = i + 1
fOUT.write("}\n")
fOUT.close()
print "BtaggingToBvetoAfterMet efficiensies written in file","btaggingToBvetoFactors"
def invertedPurities(datasets):
normData, normEWK, normFactorisedData, normFactorisedEWK = normalisation()
norm_inc, normEWK_inc = normalisationInclusive()
noDphi = []
noDphiEWK = []
Dphi160 = []
DphiEWK160 = []
DphiAll = []
DphiEWKAll = []
DphiAllremovett = []
DphiEWKAllremovett = []
DphiJet1 = []
DphiEWKJet1 = []
DphiJet2 = []
DphiEWKJet2 = []
hmt = []
hmtb = []
hmtv = []
hmtPhiv = []
hmet = []
hmetQCD = []
hmetEWK = []
hjetmet = []
hjetmetphi = []
hMHTJet1phi = []
hmtph = []
hmtphj1 = []
hmtphj2 = []
hmtremovett = []
DphiEWKAllbveto = []
DphiAllbveto = []
purityMet = []
purityErrMet = []
purityMtRemovett = []
purityErrMtRemovett = []
purityMtFirstDeltaPhiCut = []
purityErrMtFirstDeltaPhiCut = []
purityMtThirdDeltaPhiCut = []
purityErrMtThirdDeltaPhiCut = []
purityMtSecondDeltaPhiCut = []
purityErrMtSecondDeltaPhiCut = []
purityMtDeltaPhiCut = []
purityErrMtDeltaPhiCut = []
purityMtAfterBtagging = []
purityErrMtAfterBtagging = []
purityMTInvertedTauIdBvetoDphi = []
purityErrMTInvertedTauIdBvetoDphi = []
## histograms in bins, normalisation and substraction of EWK contribution
## mt with 2dim deltaPhi cut
for ptbin in ptbins:
mt_tmp = plots.PlotBase([
datasets.getDataset("Data").getDatasetRootHisto(
"Inverted/MTInvertedSecondDeltaPhiCut" + ptbin)
])
mt_tmp._setLegendStyles()
mt_tmp._setLegendLabels()
mt_tmp.histoMgr.setHistoDrawStyleAll("P")
mt_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(20))
mt = mt_tmp.histoMgr.getHisto("Data").getRootHisto().Clone()
# mt.Scale(normData[ptbin])
DphiJet2.append(mt)
mtEWK_tmp = plots.PlotBase([
datasets.getDataset("EWK").getDatasetRootHisto(
"Inverted/MTInvertedSecondDeltaPhiCut" + ptbin)
])
mtEWK_tmp.histoMgr.normalizeMCToLuminosity(
datasets.getDataset("Data").getLuminosity())
mtEWK_tmp._setLegendStyles()
mtEWK_tmp._setLegendLabels()
mtEWK_tmp.histoMgr.setHistoDrawStyleAll("P")
mtEWK_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(20))
mtEWK = mtEWK_tmp.histoMgr.getHisto("EWK").getRootHisto().Clone()
# mtEWK.Scale(normEWK[ptbin])
# mt.Add(mtEWK, -1)
# hmt.append(mt)
DphiEWKJet2.append(mtEWK)
purity = -999
error = -999
if mt.Integral() > 0:
purity = (mt.Integral() - mtEWK.Integral()) / mt.Integral()
error = sqrt(purity * (1 - purity) / mt.Integral())
purityMtSecondDeltaPhiCut.append(purity)
purityErrMtSecondDeltaPhiCut.append(error)
# print " pt bin ", ptbin, " purity Mt Second Delta Phi Cut = ",purity, " error ",error
############################################
# mt after b tagging
mtb_tmp = plots.PlotBase([
datasets.getDataset("Data").getDatasetRootHisto(
"Inverted/MTInvertedTauIdBtag" + ptbin)
])
mtb_tmp._setLegendStyles()
mtb_tmp._setLegendLabels()
mtb_tmp.histoMgr.setHistoDrawStyleAll("P")
mtb_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(20))
mtb = mtb_tmp.histoMgr.getHisto("Data").getRootHisto().Clone()
# mtb.Scale(normData[ptbin])
hmt.append(mtb)
noDphi.append(mtb)
mtbEWK_tmp = plots.PlotBase([
datasets.getDataset("EWK").getDatasetRootHisto(
"Inverted/MTInvertedTauIdBtag" + ptbin)
])
mtbEWK_tmp.histoMgr.normalizeMCToLuminosity(
datasets.getDataset("Data").getLuminosity())
mtbEWK_tmp._setLegendStyles()
mtbEWK_tmp._setLegendLabels()
mtbEWK_tmp.histoMgr.setHistoDrawStyleAll("P")
mtbEWK_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(20))
mtbEWK = mtbEWK_tmp.histoMgr.getHisto("EWK").getRootHisto().Clone()
# mtbEWK.Scale(normEWK[ptbin])
mtb.Add(mtbEWK, -1)
hmtb.append(mtb)
noDphiEWK.append(mtbEWK)
purity = -999
error = -999
if mtb.Integral() > 0:
purity = (mtb.Integral() - mtbEWK.Integral()) / mtb.Integral()
error = sqrt(purity * (1 - purity) / mtb.Integral())
purityMtAfterBtagging.append(purity)
purityErrMtAfterBtagging.append(error)
# print " pt bin ", ptbin, " purity Mt After Btagging = ",purity, " error ",error
############################################
# mt after deltaPhi cut
mtph_tmp = plots.PlotBase([
datasets.getDataset("Data").getDatasetRootHisto(
"Inverted/MTInvertedTauIdJetDphi" + ptbin)
])
mtph_tmp._setLegendStyles()
mtph_tmp._setLegendLabels()
mtph_tmp.histoMgr.setHistoDrawStyleAll("P")
mtph_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(20))
mtph = mtph_tmp.histoMgr.getHisto("Data").getRootHisto().Clone()
# mtph.Scale(normData[ptbin])
# hmt.append(mt)
Dphi160.append(mtph)
mtphEWK_tmp = plots.PlotBase([
datasets.getDataset("EWK").getDatasetRootHisto(
"Inverted/MTInvertedTauIdJetDphi" + ptbin)
])
mtphEWK_tmp.histoMgr.normalizeMCToLuminosity(
datasets.getDataset("Data").getLuminosity())
mtphEWK_tmp._setLegendStyles()
mtphEWK_tmp._setLegendLabels()
mtphEWK_tmp.histoMgr.setHistoDrawStyleAll("P")
mtphEWK_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(20))
mtphEWK = mtphEWK_tmp.histoMgr.getHisto("EWK").getRootHisto().Clone()
# mtphEWK.Scale(normEWK[ptbin])
# mtph.Add(mtphEWK, -1)
# hmtph.append(mtph)
DphiEWK160.append(mtphEWK)
purity = -999
error = -999
if mtph.Integral() > 0:
purity = (mtph.Integral() - mtphEWK.Integral()) / mtph.Integral()
error = sqrt(purity * (1 - purity) / mtph.Integral())
purityMtDeltaPhiCut.append(purity)
purityErrMtDeltaPhiCut.append(error)
# print " pt bin ", ptbin, " purity Mt DeltaPhi Cut = ",purity, " error ",error
############################################
# mt after deltaphi vs MHTjet1 cut
mtphj1_tmp = plots.PlotBase([
datasets.getDataset("Data").getDatasetRootHisto(
"Inverted/MTInvertedFirstDeltaPhiCut" + ptbin)
])
#mtphj1_tmp = plots.PlotBase([datasets.getDataset("Data").getDatasetRootHisto("MTInvertedTauIdMet"+ptbin)])
mtphj1_tmp._setLegendStyles()
mtphj1_tmp._setLegendLabels()
mtphj1_tmp.histoMgr.setHistoDrawStyleAll("P")
mtphj1_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(20))
mtphj1 = mtphj1_tmp.histoMgr.getHisto("Data").getRootHisto().Clone()
# mtphj1.Scale(normData[ptbin])
DphiJet1.append(mtphj1)
mtphj1EWK_tmp = plots.PlotBase([
datasets.getDataset("EWK").getDatasetRootHisto(
"Inverted/MTInvertedFirstDeltaPhiCut" + ptbin)
])
#mtphj1EWK_tmp = plots.PlotBase([datasets.getDataset("EWK").getDatasetRootHisto("MTInvertedTauIdMet"+ptbin)])
mtphj1EWK_tmp.histoMgr.normalizeMCToLuminosity(
datasets.getDataset("Data").getLuminosity())
mtphj1EWK_tmp._setLegendStyles()
mtphj1EWK_tmp._setLegendLabels()
mtphj1EWK_tmp.histoMgr.setHistoDrawStyleAll("P")
mtphj1EWK_tmp.histoMgr.forEachHisto(
lambda h: h.getRootHisto().Rebin(20))
mtphj1EWK = mtphj1EWK_tmp.histoMgr.getHisto(
"EWK").getRootHisto().Clone()
# mtphj1EWK.Scale(normEWK[ptbin])
# mtphj1.Add(mtphj1EWK, -1)
# hmtphj1.append(mtphj1)
DphiEWKJet1.append(mtphj1EWK)
purity = -999
error = -999
if mtphj1.Integral() > 0:
purity = (mtphj1.Integral() -
mtphj1EWK.Integral()) / mtphj1.Integral()
error = sqrt(purity * (1 - purity) / mtphj1.Integral())
purityMtFirstDeltaPhiCut.append(purity)
purityErrMtFirstDeltaPhiCut.append(error)
# print " pt bin ", ptbin, " purity Mt First Delta Phi Cut = ",purity, " error ",error
############################################
# mt after all cuts
mtphj2_tmp = plots.PlotBase([
datasets.getDataset("Data").getDatasetRootHisto(
"Inverted/MTInvertedThirdDeltaPhiCut" + ptbin)
])
mtphj2_tmp._setLegendStyles()
mtphj2_tmp._setLegendLabels()
mtphj2_tmp.histoMgr.setHistoDrawStyleAll("P")
mtphj2_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(20))
mtphj2 = mtphj2_tmp.histoMgr.getHisto("Data").getRootHisto().Clone()
# mtphj2.Scale(normData[ptbin])
# hmt.append(mt)
DphiAll.append(mtphj2)
mtphj2EWK_tmp = plots.PlotBase([
datasets.getDataset("EWK").getDatasetRootHisto(
"Inverted/MTInvertedThirdDeltaPhiCut" + ptbin)
])
mtphj2EWK_tmp.histoMgr.normalizeMCToLuminosity(
datasets.getDataset("Data").getLuminosity())
mtphj2EWK_tmp._setLegendStyles()
mtphj2EWK_tmp._setLegendLabels()
mtphj2EWK_tmp.histoMgr.setHistoDrawStyleAll("P")
mtphj2EWK_tmp.histoMgr.forEachHisto(
lambda h: h.getRootHisto().Rebin(20))
mtphj2EWK = mtphj2EWK_tmp.histoMgr.getHisto(
"EWK").getRootHisto().Clone()
# mtphj2EWK.Scale(normEWK[ptbin])
# mtphj2.Add(mtphj2EWK, -1)
# hmtphj2.append(mtphj2)
DphiEWKAll.append(mtphj2EWK)
purity = -999
error = -999
if mtphj2.Integral() > 0:
purity = (mtphj2.Integral() -
mtphj2EWK.Integral()) / mtphj2.Integral()
error = sqrt(purity * (1 - purity) / mtphj2.Integral())
purityMtThirdDeltaPhiCut.append(purity)
purityErrMtThirdDeltaPhiCut.append(error)
# print " pt bin ", ptbin, " purity Mt Third Delta Phi Cut = ",purity, " error ",error
#######################
# mt with cut against tt
mtremovett_tmp = plots.PlotBase([
datasets.getDataset("Data").getDatasetRootHisto(
"Inverted/MTInvertedAgainstTTCut" + ptbin)
])
mtremovett_tmp._setLegendStyles()
mtremovett_tmp._setLegendLabels()
mtremovett_tmp.histoMgr.setHistoDrawStyleAll("P")
mtremovett_tmp.histoMgr.forEachHisto(
lambda h: h.getRootHisto().Rebin(20))
mtremovett = mtremovett_tmp.histoMgr.getHisto(
"Data").getRootHisto().Clone()
# mtremovett.Scale(normData[ptbin])
DphiAllremovett.append(mtremovett)
mtremovettEWK_tmp = plots.PlotBase([
datasets.getDataset("EWK").getDatasetRootHisto(
"Inverted/MTInvertedAgainstTTCut" + ptbin)
])
mtremovettEWK_tmp.histoMgr.normalizeMCToLuminosity(
datasets.getDataset("Data").getLuminosity())
mtremovettEWK_tmp._setLegendStyles()
mtremovettEWK_tmp._setLegendLabels()
mtremovettEWK_tmp.histoMgr.setHistoDrawStyleAll("P")
mtremovettEWK_tmp.histoMgr.forEachHisto(
lambda h: h.getRootHisto().Rebin(20))
mtremovettEWK = mtremovettEWK_tmp.histoMgr.getHisto(
"EWK").getRootHisto().Clone()
# mtremovettEWK.Scale(normEWK[ptbin])
# mtremovett.Add(mtremovettEWK, -1)
# hmtremovett.append(mtremovett)
DphiEWKAllremovett.append(mtremovettEWK)
purity = -999
error = -999
if mtremovett.Integral() > 0:
purity = (mtremovett.Integral() -
mtremovettEWK.Integral()) / mtremovett.Integral()
error = sqrt(purity * (1 - purity) / mtremovett.Integral())
purityMtRemovett.append(purity)
purityErrMtRemovett.append(error)
print "mtremovett.Integral() ", mtremovett.Integral(
), " mmtEWK.Integral() ", mtremovettEWK.Integral()
#######################
### MET
mmt_tmp = plots.PlotBase([
datasets.getDataset("Data").getDatasetRootHisto(
"Inverted/MET_InvertedTauIdJets" + ptbin)
])
mmt_tmp._setLegendStyles()
mmt_tmp._setLegendLabels()
mmt_tmp.histoMgr.setHistoDrawStyleAll("P")
mmt_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(20))
mmt = mmt_tmp.histoMgr.getHisto("Data").getRootHisto().Clone()
## mmt.Scale(normData[ptbin])
hmet.append(mmt)
mmtEWK_tmp = plots.PlotBase([
datasets.getDataset("EWK").getDatasetRootHisto(
"Inverted/MET_InvertedTauIdJets" + ptbin)
])
mmtEWK_tmp.histoMgr.normalizeMCToLuminosity(
datasets.getDataset("Data").getLuminosity())
mmtEWK_tmp._setLegendStyles()
mmtEWK_tmp._setLegendLabels()
mmtEWK_tmp.histoMgr.setHistoDrawStyleAll("P")
mmtEWK_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(20))
mmtEWK = mmtEWK_tmp.histoMgr.getHisto("EWK").getRootHisto().Clone()
## mmtEWK.Scale(normEWK[ptbin])
mmt.Add(mmtEWK, -1)
hmetQCD.append(mmt)
hmetEWK.append(mmtEWK)
purity = -999
error = -999
if mmt.Integral() > 0:
purity = (mmt.Integral() - mmtEWK.Integral()) / mmt.Integral()
error = sqrt(purity * (1 - purity) / mmt.Integral())
purityMet.append(purity)
purityErrMet.append(error)
## print "mmt.Integral() ",mmt.Integral(), " mmtEWK.Integral() ", mmtEWK.Integral()
############################################
# mt after all cuts
mtphj2_tmp = plots.PlotBase([
datasets.getDataset("Data").getDatasetRootHisto(
"Inverted/MTInvertedTauIdBvetoDphi" + ptbin)
])
mtphj2_tmp._setLegendStyles()
mtphj2_tmp._setLegendLabels()
mtphj2_tmp.histoMgr.setHistoDrawStyleAll("P")
mtphj2_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(20))
mtphj2 = mtphj2_tmp.histoMgr.getHisto("Data").getRootHisto().Clone()
# mtphj2.Scale(normData[ptbin])
# hmt.append(mt)
DphiAllbveto.append(mtphj2)
mtphj2EWK_tmp = plots.PlotBase([
datasets.getDataset("EWK").getDatasetRootHisto(
"Inverted/MTInvertedTauIdBvetoDphi" + ptbin)
])
mtphj2EWK_tmp.histoMgr.normalizeMCToLuminosity(
datasets.getDataset("Data").getLuminosity())
mtphj2EWK_tmp._setLegendStyles()
mtphj2EWK_tmp._setLegendLabels()
mtphj2EWK_tmp.histoMgr.setHistoDrawStyleAll("P")
mtphj2EWK_tmp.histoMgr.forEachHisto(
lambda h: h.getRootHisto().Rebin(20))
mtphj2EWK = mtphj2EWK_tmp.histoMgr.getHisto(
"EWK").getRootHisto().Clone()
# mtphj2EWK.Scale(normEWK[ptbin])
# mtphj2.Add(mtphj2EWK, -1)
# hmtphj2.append(mtphj2)
DphiEWKAllbveto.append(mtphj2EWK)
purity = -999
error = -999
if mtphj2.Integral() > 0:
purity = (mtphj2.Integral() -
mtphj2EWK.Integral()) / mtphj2.Integral()
error = sqrt(purity * (1 - purity) / mtphj2.Integral())
purityMTInvertedTauIdBvetoDphi.append(purity)
purityErrMTInvertedTauIdBvetoDphi.append(error)
# print " pt bin ", ptbin, " purity Mt Third Delta Phi Cut = ",purity, " error ",error
print " "
print " purity met = ", purityMet, " error ", purityErrMet
print " purity Mt no DeltaPhi Cuts = ", purityMtAfterBtagging, " error ", purityErrMtAfterBtagging
print " purity Mt DeltaPhi160 Cut = ", purityMtDeltaPhiCut, " error ", purityErrMtDeltaPhiCut
print " purity Mt First DeltaPhi Cut = ", purityMtFirstDeltaPhiCut, " error ", purityErrMtFirstDeltaPhiCut
print " purity Mt Second DeltaPhi Cut = ", purityMtSecondDeltaPhiCut, " error ", purityErrMtSecondDeltaPhiCut
print " purity Mt Third Delta Phi Cut = ", purityMtThirdDeltaPhiCut, " error ", purityErrMtThirdDeltaPhiCut
print " purity Mt b veto deltaPhi Cuts = ", purityMTInvertedTauIdBvetoDphi, " error ", purityErrMTInvertedTauIdBvetoDphi
print " purity Mt Remove tt = ", purityMtRemovett, " error ", purityErrMtRemovett
invertedQCD = InvertedTauID()
invertedQCD.setLumi(datasets.getDataset("Data").getLuminosity())
### Met
met = hmet[0].Clone("met")
met.SetName("MET")
met.SetTitle("Inverted tau Met")
met.Reset()
for histo in hmet:
met.Add(histo)
metQCD = hmetQCD[0].Clone("met")
metQCD.SetName("MET")
metQCD.SetTitle("Inverted tau Met")
metQCD.Reset()
for histo in hmetQCD:
metQCD.Add(histo)
metEWK = hmetEWK[0].Clone("metewk")
metEWK.SetName("METewk")
metEWK.SetTitle("Inverted tau Met")
metEWK.Reset()
for histo in hmetEWK:
metEWK.Add(histo)
### Mt no DeltaPhi Cuts
mtNoDphi = noDphi[0].Clone("mt")
mtNoDphi.SetName("mt")
mtNoDphi.SetTitle("Inverted tau Mt")
mtNoDphi.Reset()
for histo in noDphi:
mtNoDphi.Add(histo)
mtNoDphiEWK = noDphiEWK[0].Clone("mtewk")
mtNoDphiEWK.SetName("MTewk")
mtNoDphiEWK.SetTitle("Inverted tau Met")
mtNoDphiEWK.Reset()
for histo in noDphiEWK:
mtNoDphiEWK.Add(histo)
### Mt DeltaPhi < 160 Cut
mtDphi160 = Dphi160[0].Clone("mt")
mtDphi160.SetName("mt")
mtDphi160.SetTitle("Inverted tau Mt")
mtDphi160.Reset()
for histo in Dphi160:
mtDphi160.Add(histo)
mtDphi160EWK = DphiEWK160[0].Clone("mtewk")
mtDphi160EWK.SetName("MTewk")
mtDphi160EWK.SetTitle("Inverted tau Met")
mtDphi160EWK.Reset()
for histo in DphiEWK160:
mtDphi160EWK.Add(histo)
### Mt Mt all dphi cuts
mtDphiAll = DphiAll[0].Clone("mt")
mtDphiAll.SetName("mt")
mtDphiAll.SetTitle("Inverted tau Mt")
mtDphiAll.Reset()
for histo in DphiAll:
mtDphiAll.Add(histo)
mtDphiAllEWK = DphiEWKAll[0].Clone("mtewk")
mtDphiAllEWK.SetName("MTewk")
mtDphiAllEWK.SetTitle("Inverted tau Met")
mtDphiAllEWK.Reset()
for histo in DphiEWKAll:
mtDphiAllEWK.Add(histo)
### Mt bveto all dphi cuts
mtDphiAllbveto = DphiAllbveto[0].Clone("mt")
mtDphiAllbveto.SetName("mt")
mtDphiAllbveto.SetTitle("Inverted tau Mt")
mtDphiAllbveto.Reset()
for histo in DphiAllbveto:
mtDphiAllbveto.Add(histo)
mtDphiAllEWKbveto = DphiEWKAllbveto[0].Clone("mtewk")
mtDphiAllEWKbveto.SetName("MTewk")
mtDphiAllEWKbveto.SetTitle("Inverted tau Met")
mtDphiAllEWKbveto.Reset()
for histo in DphiEWKAllbveto:
mtDphiAllEWKbveto.Add(histo)
### Mt Mt dphi jet1
mtDphiJet1 = DphiJet1[0].Clone("mt")
mtDphiJet1.SetName("mt")
mtDphiJet1.SetTitle("Inverted tau Mt")
mtDphiJet1.Reset()
for histo in DphiJet1:
mtDphiJet1.Add(histo)
mtDphiEWKJet1 = DphiEWKJet1[0].Clone("mtewk")
mtDphiEWKJet1.SetName("MTewk")
mtDphiEWKJet1.SetTitle("Inverted tau Met")
mtDphiEWKJet1.Reset()
for histo in DphiEWKJet1:
mtDphiEWKJet1.Add(histo)
### Mt Mt dphi jet2
mtDphiJet2 = DphiJet2[0].Clone("mt")
mtDphiJet2.SetName("mt")
mtDphiJet2.SetTitle("Inverted tau Mt")
mtDphiJet2.Reset()
for histo in DphiJet2:
mtDphiJet2.Add(histo)
mtDphiEWKJet2 = DphiEWKJet2[0].Clone("mtewk")
mtDphiEWKJet2.SetName("MTewk")
mtDphiEWKJet2.SetTitle("Inverted tau Met")
mtDphiEWKJet2.Reset()
for histo in DphiEWKJet2:
mtDphiEWKJet2.Add(histo)
### Mt all dphi + tt cuts
mtDphiAllremovett = DphiAllremovett[0].Clone("mt")
mtDphiAllremovett.SetName("mt")
mtDphiAllremovett.SetTitle("Inverted tau Mt")
mtDphiAllremovett.Reset()
for histo in DphiAllremovett:
mtDphiAllremovett.Add(histo)
mtDphiAllremovettEWK = DphiEWKAllremovett[0].Clone("mtewk")
mtDphiAllremovettEWK.SetName("MTewk")
mtDphiAllremovettEWK.SetTitle("Inverted tau Met")
mtDphiAllremovettEWK.Reset()
for histo in DphiEWKAllremovett:
mtDphiAllremovettEWK.Add(histo)
##########################################
# met purity
metqcd = metQCD.Clone("metqcd")
metinv = met.Clone("met")
invertedQCD.setLabel("MetPurity")
# invertedQCD.mtComparison(metqcd, metqcd,"MetPurity")
##########################################
# mt purity no deltaPhi
mtQCD = mtNoDphi.Clone("QCD")
mtQCD.Add(mtNoDphiEWK, -1)
mtQCD.Divide(mtNoDphi)
mtqcd = mtQCD.Clone("mtqcd")
invertedQCD.setLabel("MtNoDeltaPhiCuts")
invertedQCD.mtComparison(mtQCD, mtQCD, "MtNoDeltaPhiCuts")
##########################################
# mt purity deltaPhi 160
mtQCD = mtDphi160.Clone("QCD")
mtQCD.Add(mtDphi160EWK, -1)
mtQCD.Divide(mtDphi160)
mtqcd = mtQCD.Clone("mtqcd")
invertedQCD.setLabel("MtDeltaPhi160")
invertedQCD.mtComparison(mtQCD, mtQCD, "MtDeltaPhi160")
##########################################
# mt purity all deltaPhi cuts
mtQCD = mtDphiAll.Clone("QCD")
mtQCD.Add(mtDphiAllEWK, -1)
mtQCD.Divide(mtDphiAll)
mtqcd = mtQCD.Clone("mtqcd")
invertedQCD.setLabel("MtAllDeltaPhiCuts")
invertedQCD.mtComparison(mtQCD, mtQCD, "MtAllDeltaPhiCuts")
##########################################
# mt bveto purity all deltaPhi cuts
## test
invertedQCD.setLabel("testMtbveto")
# invertedQCD.mtComparison(mtDphiAllbveto, mtDphiAllbveto,"testMtbveto")
invertedQCD.setLabel("testEWKMtbveto")
# invertedQCD.mtComparison(mtDphiAllEWKbveto, mtDphiAllEWKbveto,"testEWKMtbveto")
mtQCD = mtDphiAllbveto.Clone("QCD")
mtQCD.Add(mtDphiAllEWKbveto, -1)
mtQCD.Divide(mtDphiAllbveto)
mtqcd = mtQCD.Clone("mtqcd")
invertedQCD.setLabel("MtbvetoAllDeltaPhiCuts")
invertedQCD.mtComparison(mtQCD, mtQCD, "MtbvetoAllDeltaPhiCuts")
##########################################
# mt purity jet1 deltaPhi cuts
mtQCD = mtDphiJet1.Clone("QCD")
mtQCD.Add(mtDphiEWKJet1, -1)
mtQCD.Divide(mtDphiJet1)
mtqcd = mtQCD.Clone("mtqcd")
invertedQCD.setLabel("MtDeltaPhiJet1Cuts")
invertedQCD.mtComparison(mtQCD, mtQCD, "MtDeltaPhiJet1Cuts")
##########################################
# mt purity jet2 deltaPhi cuts
mtQCD = mtDphiJet2.Clone("QCD")
mtQCD.Add(mtDphiEWKJet2, -1)
mtQCD.Divide(mtDphiJet2)
mtqcd = mtQCD.Clone("mtqcd")
invertedQCD.setLabel("MtDeltaPhiJet2Cuts")
invertedQCD.mtComparison(mtQCD, mtQCD, "MtDeltaPhiJet2Cuts")
##########################################
# mt purity all deltaPhi cuts and against tt cut
mtQCD = mtDphiAllremovett.Clone("QCD")
mtQCD.Add(mtDphiAllremovettEWK, -1)
mtQCD.Divide(mtDphiAllremovett)
mtqcd = mtQCD.Clone("mtqcd")
invertedQCD.setLabel("MtDeltaPhiAndAgainsttt")
invertedQCD.mtComparison(mtQCD, mtQCD, "MtDeltaPhiAndAgainsttt")
#################################################
## purities as a function of pt tau jet
### Create and customise TGraph
cEff = TCanvas("MetPurity", "MetPurity", 1)
cEff.cd()
ptbin_error = array.array("d", [5, 5, 5, 5, 10, 10, 30])
ptbin = array.array("d", [45, 55, 65, 75, 90, 110, 150])
graph = TGraphErrors(7, ptbin,
array.array("d", purityMTInvertedTauIdBvetoDphi),
ptbin_error,
array.array("d", purityErrMTInvertedTauIdBvetoDphi))
graph.SetMaximum(1.0)
graph.SetMinimum(0.6)
graph.SetMarkerStyle(kFullCircle)
graph.SetMarkerColor(kBlue)
graph.SetMarkerSize(1)
graph.GetYaxis().SetTitle("QCD purity")
graph.GetXaxis().SetTitle("p_{T}^{#tau jet} [GeV/c]")
### Re-draw graph and update canvas and gPad
graph.Draw("AP")
tex4 = ROOT.TLatex(
0.2, 0.955,
"8 TeV 12.2 fb^{-1} CMS preliminary")
tex4.SetNDC()
tex4.SetTextSize(20)
tex4.Draw()
tex1 = ROOT.TLatex(0.35, 0.35, "B-tagging factorisation")
tex1.SetNDC()
tex1.SetTextSize(25)
tex1.Draw()
tex2 = ROOT.TLatex(0.35, 0.27, "#Delta#phi cuts")
tex2.SetNDC()
tex2.SetTextSize(25)
tex2.Draw()
cEff.Update()
cEff.SaveAs("purityMTInvertedTauIdBvetoDphiBins.png")
graph = TGraphErrors(7, ptbin, array.array("d", purityMet), ptbin_error,
array.array("d", purityErrMet))
graph.SetMaximum(1.1)
graph.SetMinimum(0.8)
graph.SetMarkerStyle(kFullCircle)
graph.SetMarkerColor(kBlue)
graph.SetMarkerSize(1)
graph.GetYaxis().SetTitle("QCD purity")
graph.GetXaxis().SetTitle("p_{T}^{#tau jet} [GeV/c]")
### Re-draw graph and update canvas and gPad
graph.Draw("AP")
tex4 = ROOT.TLatex(
0.2, 0.955,
"8 TeV 12.2 fb^{-1} CMS preliminary")
tex4.SetNDC()
tex4.SetTextSize(20)
tex4.Draw()
tex1 = ROOT.TLatex(0.35, 0.35, "Inverted #tau jet isolation")
tex1.SetNDC()
tex1.SetTextSize(20)
tex1.Draw()
tex2 = ROOT.TLatex(0.35, 0.3, "at least 3 jets")
tex2.SetNDC()
tex2.SetTextSize(25)
tex2.Draw()
cEff.Update()
cEff.SaveAs("purityMetPtBins.png")
## Mt without deltaPhi cuts
cEff = TCanvas("MtNoDeltaPhiCutsPurity", "MtNoDeltaPhiCutsPurity", 1)
cEff.cd()
graph = TGraphErrors(7, ptbin,
array.array("d", purityMtAfterBtagging), ptbin_error,
array.array("d", purityErrMtAfterBtagging))
graph.SetMaximum(1.0)
graph.SetMinimum(0.4)
graph.SetMarkerStyle(kFullCircle)
graph.SetMarkerColor(kBlue)
graph.SetMarkerSize(1)
graph.GetYaxis().SetTitle("QCD purity")
graph.GetXaxis().SetTitle("p_{T}^{#tau jet} [GeV]")
### Re-draw graph and update canvas and gPad
graph.Draw("AP")
tex4 = ROOT.TLatex(
0.2, 0.955,
"8 TeV 12.2 fb^{-1} CMS preliminary")
tex4.SetNDC()
tex4.SetTextSize(20)
tex4.Draw()
tex1 = ROOT.TLatex(0.5, 0.38, "All selection cuts")
tex1.SetNDC()
tex1.SetTextSize(22)
tex1.Draw()
tex2 = ROOT.TLatex(0.5, 0.3, "no #Delta#phi cuts")
tex2.SetNDC()
tex2.SetTextSize(25)
tex2.Draw()
cEff.Update()
cEff.SaveAs("purityMtNoDeltaPhiCutsBins.png")
## Mt without deltaPhi cuts
cEff = TCanvas("MtDeltaPhi160Purity", "MtDeltaPhi160Purity", 1)
cEff.cd()
graph = TGraphErrors(7, ptbin, array.array("d", purityMtDeltaPhiCut),
ptbin_error, array.array("d", purityErrMtDeltaPhiCut))
graph.SetMaximum(1.0)
graph.SetMinimum(0.4)
graph.SetMarkerStyle(kFullCircle)
graph.SetMarkerColor(kBlue)
graph.SetMarkerSize(1)
graph.GetYaxis().SetTitle("QCD purity")
graph.GetXaxis().SetTitle("p_{T}^{#tau jet} [GeV]")
### Re-draw graph and update canvas and gPad
graph.Draw("AP")
tex4 = ROOT.TLatex(
0.2, 0.955,
"8 TeV 12.2 fb^{-1} CMS preliminary")
tex4.SetNDC()
tex4.SetTextSize(20)
tex4.Draw()
tex1 = ROOT.TLatex(0.5, 0.35, "All selection cuts")
tex1.SetNDC()
tex1.SetTextSize(24)
tex1.Draw()
tex2 = ROOT.TLatex(0.5, 0.25, "#Delta#phi(#tau jet,MET) < 160^{o}")
tex2.SetNDC()
tex2.SetTextSize(24)
tex2.Draw()
cEff.Update()
cEff.SaveAs("purityMtDeltaPhi160Bins.png")
## Mt 1st deltaPhi cut
cEff = TCanvas("MtFirstDeltaCutPurity", "MtFirstDeltaPhiCutPurity", 1)
cEff.cd()
graph = TGraphErrors(7, ptbin, array.array("d", purityMtFirstDeltaPhiCut),
ptbin_error,
array.array("d", purityErrMtFirstDeltaPhiCut))
graph.SetMaximum(1.0)
graph.SetMinimum(0.4)
graph.SetMarkerStyle(kFullCircle)
graph.SetMarkerColor(kBlue)
graph.SetMarkerSize(1)
graph.GetYaxis().SetTitle("QCD purity")
graph.GetXaxis().SetTitle("p_{T}^{#tau jet} [GeV]")
### Re-draw graph and update canvas and gPad
graph.Draw("AP")
tex4 = ROOT.TLatex(
0.2, 0.955,
"8 TeV 12.2 fb^{-1} CMS preliminary")
tex4.SetNDC()
tex4.SetTextSize(20)
tex4.Draw()
tex1 = ROOT.TLatex(0.4, 0.4, "All selection cuts")
tex1.SetNDC()
tex1.SetTextSize(24)
tex1.Draw()
tex2 = ROOT.TLatex(0.3, 0.3,
"#Delta#phi(#tau jet,MET) vs #Delta#phi(jet1,MET) cut")
tex2.SetNDC()
tex2.SetTextSize(24)
tex2.Draw()
cEff.Update()
cEff.SaveAs("purityMtFirstDeltaPhiCutBins.png")
## Mt 2nd deltaPhi cut
cEff = TCanvas("MtSecondDeltaCutPurity", "MtFirstSecondPhiCutPurity", 1)
cEff.cd()
graph = TGraphErrors(7, ptbin, array.array("d", purityMtSecondDeltaPhiCut),
ptbin_error,
array.array("d", purityErrMtSecondDeltaPhiCut))
graph.SetMaximum(1.0)
graph.SetMinimum(0.4)
graph.SetMarkerStyle(kFullCircle)
graph.SetMarkerColor(kBlue)
graph.SetMarkerSize(1)
graph.GetYaxis().SetTitle("QCD purity")
graph.GetXaxis().SetTitle("p_{T}^{#tau jet} [GeV]")
### Re-draw graph and update canvas and gPad
graph.Draw("AP")
tex4 = ROOT.TLatex(
0.2, 0.955,
"8 TeV 12.2 fb^{-1} CMS preliminary")
tex4.SetNDC()
tex4.SetTextSize(20)
tex4.Draw()
tex1 = ROOT.TLatex(0.2, 0.88, "All selection cuts")
tex1.SetNDC()
tex1.SetTextSize(22)
tex1.Draw()
tex2 = ROOT.TLatex(
0.2, 0.8, "#Delta#phi(#tau jet,MET) vs #Delta#phi(jet1/2,MET) cuts")
tex2.SetNDC()
tex2.SetTextSize(24)
tex2.Draw()
cEff.Update()
cEff.SaveAs("purityMtSecondDeltaPhiCutBins.png")
## Mt 2nd deltaPhi cut
cEff = TCanvas("MtThirdDeltaCutPurity", "MtThirdDeltaPhiCutPurity", 1)
cEff.cd()
graph = TGraphErrors(7, ptbin, array.array("d", purityMtThirdDeltaPhiCut),
ptbin_error,
array.array("d", purityErrMtThirdDeltaPhiCut))
graph.SetMaximum(1.0)
graph.SetMinimum(0.4)
graph.SetMarkerStyle(kFullCircle)
graph.SetMarkerColor(kBlue)
graph.SetMarkerSize(1)
graph.GetYaxis().SetTitle("QCD purity")
graph.GetXaxis().SetTitle("p_{T}^{#tau jet} [GeV]")
### Re-draw graph and update canvas and gPad
graph.Draw("AP")
tex4 = ROOT.TLatex(
0.2, 0.955,
"8 TeV 12.2 fb^{-1} CMS preliminary")
tex4.SetNDC()
tex4.SetTextSize(20)
tex4.Draw()
tex1 = ROOT.TLatex(0.2, 0.88, "All selection cuts")
tex1.SetNDC()
tex1.SetTextSize(22)
tex1.Draw()
tex2 = ROOT.TLatex(
0.2, 0.8, "#Delta#phi(#tau jet,MET) vs #Delta#phi(jet1/2/3,MET) cuts")
tex2.SetNDC()
tex2.SetTextSize(24)
tex2.Draw()
cEff.Update()
cEff.SaveAs("purityMtThirdDeltaPhiCutBins.png")
## Mt deltaPhi cuts and against tt
cEff = TCanvas("MtMtRemovettPurity", "MtMtRemovettPurity", 1)
cEff.cd()
graph = TGraphErrors(7, ptbin, array.array("d", purityMtRemovett),
ptbin_error, array.array("d", purityErrMtRemovett))
graph.SetMaximum(1.0)
graph.SetMinimum(0.4)
graph.SetMarkerStyle(kFullCircle)
graph.SetMarkerColor(kBlue)
graph.SetMarkerSize(1)
graph.GetYaxis().SetTitle("QCD purity")
graph.GetXaxis().SetTitle("p_{T}^{#tau jet} [GeV]")
### Re-draw graph and update canvas and gPad
graph.Draw("AP")
tex4 = ROOT.TLatex(
0.2, 0.955,
"8 TeV 12.2 fb^{-1} CMS preliminary")
tex4.SetNDC()
tex4.SetTextSize(20)
tex4.Draw()
tex1 = ROOT.TLatex(0.2, 0.88, "All selection cuts ")
tex1.SetNDC()
tex1.SetTextSize(22)
tex1.Draw()
tex2 = ROOT.TLatex(
0.2, 0.8, "#Delta#phi(#tau jet,MET) vs #Delta#phi(jet1/2/3,MET) cuts")
tex2.SetNDC()
tex2.SetTextSize(22)
tex2.Draw()
tex3 = ROOT.TLatex(0.2, 0.72, "#Delta#phi cut against tt+jets")
tex3.SetNDC()
tex3.SetTextSize(22)
tex3.Draw()
cEff.Update()
cEff.SaveAs("purityMtAgainstttCutBins.png")
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
