def loopOneFile(iSample, varName, hist, fs, varBins = 0):
file = r.TFile(iSample)
tree = file.Get('Ntuple')
weight = file.Get('eventCountWeighted').GetBinContent(1)
print weight
nEntries = tree.GetEntries()
hist.Sumw2()
for i in range(nEntries):
tree.GetEntry(i)
if passCut(tree, fs):
value = -1
if varName != 'nTruePU':
wegiht = weight*cutSampleTools.getPUWeight(tree.nTruePU)
if varName == 'm_eff':
met.SetCoordinates(tree.pfMetEt, 0.0, tree.pfMetPhi, 0)
l1.SetCoordinates(tree.pt_1, tree.eta_1, tree.phi_1, tree.m_1)
l2.SetCoordinates(tree.pt_2, tree.eta_2, tree.phi_2, tree.m_2)
value = (l1 + l2 + met).mass()
if value > varBins[len(varBins)-1]:
value = (varBins[len(varBins)-1]+varBins[len(varBins)-2]+0.0)/2.0
else:
value = getattr(tree, varName)
hist.Fill(value, 1.0/weight)
def loopOneFile(iSample, varName, hist, fs, varBins = 0):
file = r.TFile(iSample)
tree = file.Get('Ntuple')
sumWeights = file.Get('eventCountWeighted').GetBinContent(1)
print sumWeights
nEntries = tree.GetEntries()
hist.Sumw2()
counter = 0
for i in range(nEntries):
tree.GetEntry(i)
wegiht = 1.0
if passCut(tree, fs):
value = -1
weight = cutSampleTools.getPUWeight(tree.nTruePU)*Lumi*tree.xs*tree.genEventWeight*tree.trigweight_1*tree.trigweight_2/(sumWeights+0.0)
value = getattr(tree, varName)
if value > 1200:
counter += 1
if value < varBins[0]:
value = (varBins[0]+varBins[1])/2.0
elif value > varBins[len(varBins)-1]:
value = (varBins[len(varBins)-1]+varBins[len(varBins)-2]+0.0)/2.0
hist.Fill(value, weight)
print 'nevents with m_eff > 1200: ', counter
def loopOneFile(iSample, varName, hist, fs, varBins=0):
file = r.TFile(iSample)
tree = file.Get('Ntuple')
weight = file.Get('eventCountWeighted').GetBinContent(1)
print weight
nEntries = tree.GetEntries()
hist.Sumw2()
for i in range(nEntries):
tree.GetEntry(i)
if passCut(tree, fs):
value = -1
if varName != 'nTruePU':
wegiht = weight * cutSampleTools.getPUWeight(tree.nTruePU)
if varName == 'm_eff':
met.SetCoordinates(tree.pfMetEt, 0.0, tree.pfMetPhi, 0)
l1.SetCoordinates(tree.pt_1, tree.eta_1, tree.phi_1, tree.m_1)
l2.SetCoordinates(tree.pt_2, tree.eta_2, tree.phi_2, tree.m_2)
value = (l1 + l2 + met).mass()
if value > varBins[len(varBins) - 1]:
value = (varBins[len(varBins) - 1] +
varBins[len(varBins) - 2] + 0.0) / 2.0
else:
value = getattr(tree, varName)
hist.Fill(value, 1.0 / weight)
def loop_one_sample(iSample, iLocation, iCat, oTree, oTree_tmp,
floatVarsDict, intVarsDict, charVarsDict,
iFS, yieldDict, histDict):
print 'combininig sample [%s] for datacard' %(iSample)
if 'data' in iSample:
isData = True
else:
isData = False
if 'emb' in iSample:
isEmbedded = True
else:
isEmbedded = False
if ("Z'" in iSample):
isSignal = True
else:
isSignal = False
iFile = r.TFile(iLocation)
iTree = iFile.Get("Ntuple")
nEntries = iTree.GetEntries()
iTree.SetBranchStatus("*",1)
iTree.SetBranchStatus("sampleName",0)
eventCount = iFile.Get('eventCount')
eventCountWeighted = iFile.Get('eventCountWeighted')
sumPtWeights = -1.0
if "TTJets" in iSample:
sumPtWeights = iFile.Get('eventCountPtWeighted').GetBinContent(1)
yieldEstimator_OS = 0.0
yieldEstimator_SS = 0.0
fillcounter=0
met = lvClass()
for iEntry in range(nEntries):
iTree.GetEntry(iEntry)
tool.printProcessStatus(iEntry, nEntries, 'looping over file %s' %(iSample), iEntry-1)
uncWeight = 1.0
region = 'none'
met.SetCoordinates(iTree.met, 0.0, iTree.metphi, 0)
l1.SetCoordinates(iTree.pt_1, iTree.eta_1, iTree.phi_1, iTree.m_1)
l2.SetCoordinates(iTree.pt_2, iTree.eta_2, iTree.phi_2, iTree.m_2)
if not plots_dataDrivenQCDandWJ.passCut(iTree, iFS, isData, options.sys):
continue
if plots_dataDrivenQCDandWJ.regionSelection(iTree, iFS, "signal", plots_cfg.scanRange[0], plots_cfg.scanRange[1]):
if isData:
charVarsDict['sampleName'][:31] = 'data' + signalRegionName
else:
charVarsDict['sampleName'][:31] = iSample
if isSignal:
charVarsDict['sampleName'][:31] = "Zprime_%s" %iSample[iSample.find("(")+1: iSample.find(")")]
if iTree.q_1 != iTree.q_2:
region = 'A'
else:
if isSignal:
continue
region = 'B'
elif plots_dataDrivenQCDandWJ.regionSelection(iTree, iFS, "control_iso", plots_cfg.scanRange[0], plots_cfg.scanRange[1]):
if isSignal:
continue
if iTree.q_1 == -iTree.q_2: #C region
if iFS != "em":
if "WJets" in iSample:
continue
region = 'C'
else: #D region
charVarsDict['sampleName'][:31] = 'QCD_in_D'
region = 'D'
elif iFS != 'em' and plots_dataDrivenQCDandWJ.regionSelection(iTree, iFS, "control_anti_iso", plots_cfg.scanRange[0], plots_cfg.scanRange[1]):
if isSignal:
continue
if not isData:
continue
if iTree.q_1 == -iTree.q_2: #E region
charVarsDict['sampleName'][:31] = 'QCDLoose'
region = 'E'
else:
charVarsDict['sampleName'][:31] = 'QCD_in_F'
region = 'F'
else:
continue
floatVarsDict['pt_1'][0] = iTree.pt_1
floatVarsDict['pt_2'][0] = iTree.pt_2
floatVarsDict['eta_1'][0] = iTree.eta_1
floatVarsDict['eta_2'][0] = iTree.eta_2
floatVarsDict['mt_1'][0] = iTree.mt_1
floatVarsDict['genMass'][0] = 0.0
if hasattr(iTree, 'X_to_ll'):
floatVarsDict['genMass'][0] = iTree.X_to_ll
if hasattr(iTree, 'BDT_both'):
floatVarsDict['BDT'][0] = iTree.BDT_both
else:
floatVarsDict['BDT'][0] = -999
floatVarsDict['triggerEff'][0] = iTree.trigweight_1*iTree.trigweight_2
charVarsDict['Category'][:31] = iFS
floatVarsDict['xs'][0] = iTree.xs
if 'WJets' in iSample:
floatVarsDict['xs'][0] = floatVarsDict['xs'][0]*plots_cfg.WJetsScanRange[0]
if "Zprime" in iSample:
floatVarsDict['xs'][0] = plots.getZPrimeXS(iSample[7:])
if eventCount:
intVarsDict['initEvents'][0] = int(eventCount.GetBinContent(1))
else:
intVarsDict['initEvents'][0] = int(iTree.initEvents)
if eventCountWeighted:
intVarsDict['initSumWeights'][0] = int(eventCountWeighted.GetBinContent(1))
else:
intVarsDict['initSumWeights'][0] = int(iTree.initWeightedEvents)
# floatVarsDict['m_svfit'][0] = iTree.pfmet_svmc_mass
floatVarsDict['m_effective'][0] = (l1 + l2 + met).mass()
floatVarsDict['m_vis'][0] = (l1 + l2).mass()
# floatVarsDict['m_tt'][0] = iTree.m_tt
if (options.sys == 'up' or options.sys == 'down') and (not isData):
floatVarsDict['xs'][0] = floatVarsDict['xs'][0]*getPDFWeight(iFS, iSample, iCat, options.sys, floatVarsDict['m_effective'][0])
intVarsDict['nCSVL'][0] = int(iTree.nCSVL)
if iFS == 'et' or iFS == "mt":
intVarsDict['tauDecayMode'][0] = int(iTree.tDecayMode)
floatVarsDict['tauTightIso'][0] = iTree.tByTightCombinedIsolationDeltaBetaCorr3Hits
floatVarsDict['tauMediumIso'][0] = iTree.tByMediumCombinedIsolationDeltaBetaCorr3Hits
floatVarsDict['tauLooseIso'][0] = iTree.tByLooseCombinedIsolationDeltaBetaCorr3Hits
if options.sys == 'tauUncUp':
uncWeight += 0.05*iTree.pt_2/1000.
if options.sys == 'tauUncDown':
uncWeight -= 0.35*iTree.pt_2/1000.
floatVarsDict['cosDPhi'][0] = math.cos(iTree.phi_1 - iTree.phi_2)
floatVarsDict['pZetaCut'][0] = getattr(iTree, "%s_%s_PZeta" %(iFS[0], iFS[1])) - 3.1*getattr(iTree, "%s_%s_PZetaVis" %(iFS[0], iFS[1]))
floatVarsDict['pfMEt'][0] = iTree.met
# floatVarsDict['eleRelIso'][0] = iTree.eRelIso
if options.PUWeight and not isData:
floatVarsDict['PUWeight'][0] = cutSampleTools.getPUWeight(iTree.nTruePU)
floatVarsDict['npv'][0] = iTree.nTruePU
else:
floatVarsDict['PUWeight'][0] = 1.0
floatVarsDict['npv'][0] = 0
if isData:
floatVarsDict['genEventWeight'][0] = 1.0
intVarsDict['initSumWeights'][0] = 1
floatVarsDict['xs'][0] = 1.0/plots.lumi
floatVarsDict['triggerEff'][0] = 1.0
else:
floatVarsDict['genEventWeight'][0] = uncWeight*iTree.genEventWeight
if options.topPt and sumPtWeights != -1.0:
intVarsDict['initSumWeights'][0] = int(sumPtWeights)
floatVarsDict['genEventWeight'][0] = iTree.topPtWeight*floatVarsDict['genEventWeight'][0]
if (iFS == 'et' or iFS == 'mt') and not isData:
floatVarsDict['genEventWeight'][0] = floatVarsDict['genEventWeight'][0]*0.95 #tauID
if options.againstLeptonSF:
if iFS == 'et':
if (iTree.tIsPromptElectron):
floatVarsDict['genEventWeight'][0] = floatVarsDict['genEventWeight'][0]*plots_dataDrivenQCDandWJ.getAgainstLeptonSF('electron', 'Tight', abs(iTree.eta_2))
if (iTree.tIsPromptMuon):
floatVarsDict['genEventWeight'][0] = floatVarsDict['genEventWeight'][0]*plots_dataDrivenQCDandWJ.getAgainstLeptonSF('muon', 'Loose', abs(iTree.eta_2))
if iFS == 'mt':
if (iTree.tIsPromptElectron):
floatVarsDict['genEventWeight'][0] = floatVarsDict['genEventWeight'][0]*plots_dataDrivenQCDandWJ.getAgainstLeptonSF('electron', 'VLoose', abs(iTree.eta_2))
if (iTree.tIsPromptMuon):
floatVarsDict['genEventWeight'][0] = floatVarsDict['genEventWeight'][0]*plots_dataDrivenQCDandWJ.getAgainstLeptonSF('muon', 'Tight', abs(iTree.eta_2))
if region == 'none':
print "ERROR!!!!!"
if region == "A":
if charVarsDict['sampleName'][:31] in yieldDict.keys():
yieldDict[str(charVarsDict['sampleName'][:31])] += floatVarsDict['triggerEff'][0]*floatVarsDict['PUWeight'][0]*floatVarsDict['genEventWeight'][0]*floatVarsDict['xs'][0]*plots.lumi/(intVarsDict['initSumWeights'][0]+ 0.0)
else:
yieldDict[str(charVarsDict['sampleName'][:31])] = floatVarsDict['triggerEff'][0]*floatVarsDict['PUWeight'][0]*floatVarsDict['genEventWeight'][0]*floatVarsDict['xs'][0]*plots.lumi/(intVarsDict['initSumWeights'][0]+ 0.0)
if "WJets" in iSample:
histDict["WJetsOSTight_MC"].Fill(1, floatVarsDict['triggerEff'][0]*floatVarsDict['PUWeight'][0]*floatVarsDict['genEventWeight'][0]*floatVarsDict['xs'][0]*plots.lumi/(intVarsDict['initSumWeights'][0]+ 0.0))
yieldDict["WJetsOSTight_MC"] += floatVarsDict['triggerEff'][0]*floatVarsDict['PUWeight'][0]*floatVarsDict['genEventWeight'][0]*floatVarsDict['xs'][0]*plots.lumi/(intVarsDict['initSumWeights'][0]+ 0.0)
oTree.Fill()
elif region == "B":
weight = 1
if not isData:
weight = -floatVarsDict['triggerEff'][0]*floatVarsDict['PUWeight'][0]*floatVarsDict['genEventWeight'][0]*floatVarsDict['xs'][0]*plots.lumi/(intVarsDict['initSumWeights'][0]+ 0.0)
yieldDict["QCD_in_B"] += weight
histDict["QCD_in_B"].Fill(1, weight)
elif region == "C":
if iFS == "em":
charVarsDict['sampleName'][:31] = 'QCDLoose'
if isData:
yieldDict["QCDLoose"] += 1
else:
yieldDict["QCDLoose"] -= floatVarsDict['triggerEff'][0]*floatVarsDict['PUWeight'][0]*floatVarsDict['genEventWeight'][0]*floatVarsDict['xs'][0]*plots.lumi/(intVarsDict['initSumWeights'][0]+ 0.0)
floatVarsDict['genEventWeight'][0] = -1.0*floatVarsDict['genEventWeight'][0]
oTree.Fill()
if "WJets" in iSample:
floatVarsDict['genEventWeight'][0] = -1.0*floatVarsDict['genEventWeight'][0]
yieldDict['WJets' + controlRegionName] += floatVarsDict['triggerEff'][0]*floatVarsDict['PUWeight'][0]*floatVarsDict['genEventWeight'][0]*floatVarsDict['xs'][0]*plots.lumi/(intVarsDict['initSumWeights'][0]+ 0.0)
histDict["WJetsOSLoose_MC"].Fill(1, floatVarsDict['triggerEff'][0]*floatVarsDict['PUWeight'][0]*floatVarsDict['genEventWeight'][0]*floatVarsDict['xs'][0]*plots.lumi/(intVarsDict['initSumWeights'][0]+ 0.0))
charVarsDict['sampleName'][:31] = 'WJets' + controlRegionName
oTree.Fill()
else:
charVarsDict['sampleName'][:31] = 'WJetsLoose'
if isData:
yieldDict['WJetsLoose'] += 1
else:
floatVarsDict['genEventWeight'][0] = -1.0*floatVarsDict['genEventWeight'][0]
yieldDict['WJetsLoose'] += floatVarsDict['triggerEff'][0]*floatVarsDict['PUWeight'][0]*floatVarsDict['genEventWeight'][0]*floatVarsDict['xs'][0]*plots.lumi/(intVarsDict['initSumWeights'][0]+ 0.0)
oTree.Fill()
elif region == "D":
weight = 1
if not isData:
weight = -floatVarsDict['triggerEff'][0]*floatVarsDict['PUWeight'][0]*floatVarsDict['genEventWeight'][0]*floatVarsDict['xs'][0]*plots.lumi/(intVarsDict['initSumWeights'][0]+ 0.0)
yieldDict["QCD_in_D"] += weight
histDict["QCD_in_D"].Fill(1, weight)
elif region == "E" and iFS != 'em' and isData:
yieldDict["QCDLoose"] += 1
oTree_tmp.Fill()
oTree.Fill()
elif region == "F" and iFS != 'em' and isData:
yieldDict["QCD_in_F"] += 1
histDict["QCD_in_F"].Fill(1, 1)
return yieldDict, histDict
def loop_one_sample(iSample, iCategory, histDict, varName, varBins, FS, scanPoints):
if options.antiIso:
iSample += "%s_antiIso.root" %FS
elif options.antiEIso:
iSample += "%s_antiEIso.root" %FS
elif options.antiMIso:
iSample += "%s_antiMIso.root" %FS
elif options.noIso:
iSample += "%s_noIso.root" %FS
elif options.BSM3G:
iSample += "%s_noIso.root" %FS
else:
iSample += "%s_inclusive.root" %FS
file = r.TFile(iSample)
tree = file.Get('Ntuple')
eventCount = file.Get('eventCount')
eventCountWeighted = file.Get('eventCountWeighted')
sumPtWeights = -1
if "TT" in iSample and options.sys == 'topPt':
sumPtWeights = file.Get('eventCountPtWeighted').GetBinContent(1)
nEntries = tree.GetEntries()
# if nEntries > 100:
# nEntries = 100
tmpHist = r.TH1F("tmp_%s_%s" %(iCategory, varName), '', len(varBins)-1, varBins)
tmpHist.Sumw2()
tmpHist_forROOTFile = r.TH1F("%s" %(varName), '', len(varBins)-1, varBins)
tmpHist_forROOTFile.Sumw2()
isData = False
isSignal = False
if eventCount:
initEvents = eventCount.GetBinContent(1)
else:
initEvents = tree.initEvents
if eventCountWeighted:
sumWeights = eventCountWeighted.GetBinContent(1)
else:
sumWeights = tree.initWeightedEvents
if iCategory == 'Observed':
isData = True
for iEntry in range(nEntries):
tree.GetEntry(iEntry)
tool.printProcessStatus(iEntry, nEntries, 'Looping sample %s' %(iSample), iEntry-1)
weight = 1.0
QCD_weight = 1.0
if not passCut(tree, FS, isData, options.sys):
continue
if not isData:
xs = tree.xs
if (80.94 < xs < 80.96) or (136.01 < xs < 136.03):
xs = xs*0.108*3
if options.sys == 'topPt' and sumPtWeights != -1.0:
sumWeights = sumPtWeights
weight = Lumi*xs*tree.genEventWeight*tree.trigweight_1*tree.trigweight_2*tree.topPtWeight/(sumWeights+0.0)
else:
weight = Lumi*xs*tree.genEventWeight*tree.trigweight_1*tree.trigweight_2/(sumWeights+0.0)
if options.PUWeight:
weight = weight*cutSampleTools.getPUWeight(tree.nTruePU)
if 'WJets' in iSample:
weight = 1.0*weight
if 'ZPrime' in iSample:
weight = getZPrimeXS(iSample[iSample.rfind("/")+8:iSample.rfind("_all")])*weight
isSignal = True
if varName == 'm_withMET':
value = tree.m_eff
elif varName == 'mVis':
value = tree.m_vis
elif varName == 'pZeta - 3.1pZetaVis':
value = tree.ZetaCut
elif varName == "nCSVL":
value = tree.nCSVL
elif varName == "cos_phi_tau1_tau2":
value = math.cos(tree.phi_1 - tree.phi_2)
elif varName == "j1Pt":
jets = getJets(tree)
if len(jets)>0:
value = jets[0].pt()
else:
value = -1
elif varName == 'm_gen':
if tree.eGenTauMass < 0 or tree.tGenMass < 0:
continue
l1.SetCoordinates(tree.eGenTauPt, tree.eGenTauEta, tree.eGenTauPhi, tree.eGenTauMass)
l2.SetCoordinates(tree.tGenPt, tree.tGenEta, tree.tGenPhi, tree.tGenMass)
value = (l1 + l2).mass()
else:
if hasattr(tree, varName):
value = getattr(tree, varName)
else:
value = -1
if options.overFlow:
if value > varBins[len(varBins)-1]:
value = (varBins[len(varBins)-1]+varBins[len(varBins)-2]+0.0)/2.0
if regionSelection(tree, FS, "OSsignal", plots_cfg.scanRange[0], plots_cfg.scanRange[1]):
fill = True
if isData:
fill = False
if options.unblind or (options.unblindPartial and passUnblindPartial(varName, value)):
fill = True
histDict['WJets_OSsignal'].Fill(value, weight)
if fill:
tmpHist.Fill(value, weight)
tmpHist_forROOTFile.Fill(value, weight)
if iCategory != 'WJets' and (not isData) and (not isSignal):
histDict['WJets_OSsignal'].Fill(value, -weight)
else:
if isSignal:
continue
#region B
if regionSelection(tree, FS, "SSsignal", plots_cfg.scanRange[0], plots_cfg.scanRange[1]):
if not isData:
histDict['QCD_B'].Fill(value, -weight*QCD_weight)
else:
histDict['QCD_B'].Fill(value, weight*QCD_weight)
#region C
if regionSelection(tree, FS, "OScontrol", plots_cfg.scanRange[0], plots_cfg.scanRange[1]):
if not isData:
histDict['QCD_C'].Fill(value, -weight*QCD_weight)
if iCategory != 'WJets':
histDict['WJets_OScontrol'].Fill(value, -weight)
else:
histDict['QCD_C'].Fill(value, weight*QCD_weight)
histDict['WJets_OScontrol'].Fill(value, weight)
#region D
if regionSelection(tree, FS, "SScontrol", plots_cfg.scanRange[0], plots_cfg.scanRange[1]):
if not isData:
histDict['QCD_D'].Fill(value, -weight*QCD_weight)
histDict['QCD_D_for_C'].Fill(value, -weight*QCD_weight)
histDict['QCD_D_for_A'].Fill(value, -weight*QCD_weight)
else:
histDict['QCD_D'].Fill(value, weight*QCD_weight)
histDict['QCD_D_for_C'].Fill(value, weight*QCD_weight)
histDict['QCD_D_for_A'].Fill(value, weight*QCD_weight)
print iCategory, tmpHist.Integral(0, len(varBins)+1)
histDict[iCategory].Add(tmpHist)
del tmpHist
print ''
def runOneFile(iFS, inputFile, signSelect, isoSelect, output_dir, split):
inputFile = inputFile + "%s_noIso.root" %iFS
ifile = r.TFile(inputFile)
iTree = ifile.Get("Ntuple")
eventCountWeighted = ifile.Get('eventCountWeighted')
oString = signSelect + isoSelect
outputFile = "%s_%s.root" %(output_dir+inputFile[inputFile.rfind("/"):inputFile.find(".root")], oString)
oFile = r.TFile(outputFile,"recreate")
iTree.SetBranchStatus("*",1)
oTree = r.TTree('eventTree','')
oTree_train = r.TTree('eventTree_train','')
oTree_test = r.TTree('eventTree_test','')
total = iTree.GetEntries()
Lumi = plots.lumi
isData = False
isSignal = False
floatVarsDict = setUpFloatVarsDict()
for iVar in floatVarsDict.keys():
oTree.Branch("%s" %iVar, floatVarsDict[iVar], "%s/f" %iVar)
oTree_train.Branch("%s" %iVar, floatVarsDict[iVar], "%s/f" %iVar)
oTree_test.Branch("%s" %iVar, floatVarsDict[iVar], "%s/f" %iVar)
isoSelection = 'control'
if isoSelect == 'Tight':
isoSelection = 'signal'
for i in range(total):
r.gStyle.SetOptStat(0)
tool.printProcessStatus(iCurrent=i, total=total, processName = 'Looping sample %s' %inputFile)
iTree.GetEntry(i)
if 'data' in iTree.sampleName:
isData = True
if 'ZPrime' in iTree.sampleName:
isSignal = True
if signSelect == 'OS' and iTree.q_1 == iTree.q_2:
continue
elif signSelect == 'SS' and iTree.q_1 != iTree.q_2:
continue
if plots.regionSelection(iTree, iFS, isoSelection, 'Loose', plots_cfg.scanRange[0], plots_cfg.scanRange[1]):
if isData:
floatVarsDict['weightWithPU'][0] = 1.0
else:
floatVarsDict['weightWithPU'][0] = cutSampleTools.getPUWeight(iTree.nTruePU)*iTree.xs*Lumi*iTree.genEventWeight/(int(eventCountWeighted.GetBinContent(1)))
if split:
floatVarsDict['weightWithPU'][0] = 2*floatVarsDict['weightWithPU'][0]
met.SetCoordinates(iTree.pfMetEt, 0.0, iTree.pfMetPhi, 0)
l1.SetCoordinates(iTree.pt_1, iTree.eta_1, iTree.phi_1, iTree.m_1)
l2.SetCoordinates(iTree.pt_2, iTree.eta_2, iTree.phi_2, iTree.m_2)
floatVarsDict['pt_1'][0] = iTree.pt_1
floatVarsDict['eta_1'][0] = iTree.eta_1
floatVarsDict['phi_1'][0] = iTree.phi_1
floatVarsDict['m_1'][0] = iTree.m_1
floatVarsDict['pt_2'][0] = iTree.pt_2
floatVarsDict['eta_2'][0] = iTree.eta_2
floatVarsDict['phi_2'][0] = iTree.phi_2
floatVarsDict['m_2'][0] = iTree.m_2
floatVarsDict['met'][0] = iTree.pfMetEt
floatVarsDict['met_phi'][0] = iTree.pfMetPhi
floatVarsDict['jpt_1'][0] = iTree.jpt_1
if iTree.jpt_1 > 0:
floatVarsDict['jeta_1'][0] = iTree.jeta_1
floatVarsDict['jphi_1'][0] = iTree.jphi_1
else:
floatVarsDict['jeta_1'][0] = -999.0
floatVarsDict['jphi_1'][0] = -999.0
floatVarsDict['jpt_2'][0] = iTree.jpt_2
if iTree.jpt_2 > 0:
floatVarsDict['jeta_2'][0] = iTree.jeta_2
floatVarsDict['jphi_2'][0] = iTree.jphi_2
else:
floatVarsDict['jeta_2'][0] = -999.0
floatVarsDict['jphi_2'][0] = -999.0
floatVarsDict['pZetaCut'][0] = iTree.pZetaCut
floatVarsDict['m_eff'][0] = iTree.m_eff
oTree.Fill()
if random.random() < 0.5:
oTree_train.Fill()
else:
oTree_test.Fill()
print ''
oFile.cd()
if not split:
oTree.Write()
nSaved = oTree.GetEntries()
print 'saved %i events out of %i at: %s' %(nSaved,total,outputFile)
else:
oTree_train.Write()
nSaved = oTree_train.GetEntries()
print 'for training: saved %i events out of %i at: %s' %(nSaved,total,outputFile)
oTree_test.Write()
nSaved = oTree_test.GetEntries()
print 'for testing: saved %i events out of %i at: %s' %(nSaved,total,outputFile)
oFile.Close()
def loop_one_sample(iSample, iCategory, histDict, varName, varBins, FS, scanPoints):
if options.antiIso:
iSample += "%s_antiIso.root" %FS
elif options.antiEIso:
iSample += "%s_antiEIso.root" %FS
elif options.antiMIso:
iSample += "%s_antiMIso.root" %FS
elif options.noIso:
iSample += "%s_noIso.root" %FS
elif options.BSM3G:
iSample += "%s_noIso.root" %FS
else:
iSample += "%s_inclusive.root" %FS
file = r.TFile(iSample)
tree = file.Get('Ntuple')
eventCount = file.Get('eventCount')
eventCountWeighted = file.Get('eventCountWeighted')
nEntries = tree.GetEntries()
# if nEntries > 100:
# nEntries = 100
tmpHist = r.TH1F("tmp_%s_%s" %(iCategory, varName), '', len(varBins)-1, varBins)
tmpHist.Sumw2()
tmpHist_forROOTFile = r.TH1F("%s" %(varName), '', len(varBins)-1, varBins)
tmpHist_forROOTFile.Sumw2()
isData = False
isSignal = False
if iCategory == 'Observed':
isData = True
for iEntry in range(nEntries):
tree.GetEntry(iEntry)
tool.printProcessStatus(iEntry, nEntries, 'Looping sample %s' %(iSample), iEntry-1)
weight = 1.0
QCD_weight = 1.0
if options.sys == 'jetECUp' and not isData:
met.SetCoordinates(tree.pfMet_jesUp_Et, 0.0, tree.pfMet_jesUp_Phi, 0)
elif options.sys == 'jetECDown' and not isData:
met.SetCoordinates(tree.pfMet_jesDown_Et, 0.0, tree.pfMet_jesDown_Phi, 0)
elif options.sys == 'tauECUp' and not isData:
met.SetCoordinates(tree.pfMet_tesUp_Et, 0.0, tree.pfMet_tesUp_Phi, 0)
elif options.sys == 'tauECDown' and not isData:
met.SetCoordinates(tree.pfMet_tesDown_Et, 0.0, tree.pfMet_tesDown_Phi, 0)
else:
met.SetCoordinates(tree.pfMetEt, 0.0, tree.pfMetPhi, 0)
l1.SetCoordinates(tree.pt_1, tree.eta_1, tree.phi_1, tree.m_1)
l2.SetCoordinates(tree.pt_2, tree.eta_2, tree.phi_2, tree.m_2)
if eventCount:
initEvents = eventCount.GetBinContent(1)
else:
initEvents = tree.initEvents
if eventCountWeighted:
sumWeights = eventCountWeighted.GetBinContent(1)
else:
sumWeights = tree.initWeightedEvents
if not passCut(tree, FS, isData, l1, l2, met, options.sys):
continue
if not isData:
xs = tree.xs
if (80.94 < xs < 80.96) or (136.01 < xs < 136.03):
xs = xs*0.108*3
weight = Lumi*xs*tree.genEventWeight*tree.trigweight_1*tree.trigweight_2/(sumWeights+0.0)
if options.PUWeight:
weight = weight*cutSampleTools.getPUWeight(tree.nTruePU)
if options.diffQCD:
if tree.tDecayMode < 4:
QCD_weight = plots_cfg.SF_prong1[0]
elif tree.tDecayMode > 8:
QCD_weight = plots_cfg.SF_prong3[0]
if 'WJets' in iSample:
weight = 1.0*weight
if 'ZPrime' in iSample:
weight = getZPrimeXS(iCategory[7:])*weight
isSignal = True
if varName == 'm_withMET':
value = (l1 + l2 + met).mass()
elif varName == 'mVis':
value = (l1 + l2).mass()
elif varName == 'pZeta - 3.1pZetaVis':
value = pZetaCut(l1, l2, met)
elif varName == "nCSVL":
value = getNCSVLJets(tree, options.sys, isData, True)
elif varName == "cos_phi_tau1_tau2":
value = math.cos(tree.phi_1 - tree.phi_2)
elif varName == "j1Pt":
jets = getJets(tree)
if len(jets)>0:
value = jets[0].pt()
else:
value = -1
elif varName == 'm_gen':
if tree.eGenTauMass < 0 or tree.tGenMass < 0:
continue
l1.SetCoordinates(tree.eGenTauPt, tree.eGenTauEta, tree.eGenTauPhi, tree.eGenTauMass)
l2.SetCoordinates(tree.tGenPt, tree.tGenEta, tree.tGenPhi, tree.tGenMass)
value = (l1 + l2).mass()
else:
if hasattr(tree, varName):
value = getattr(tree, varName)
else:
value = -1
if options.overFlow:
if value > varBins[len(varBins)-1]:
value = (varBins[len(varBins)-1]+varBins[len(varBins)-2]+0.0)/2.0
if regionSelection(tree, FS, "OSsignal", plots_cfg.scanRange[0], plots_cfg.scanRange[1]):
fill = True
if isData:
fill = False
if options.unblind or (options.unblindPartial and passUnblindPartial(varName, value)):
fill = True
histDict['WJets_OSsignal'].Fill(value, weight)
if fill:
tmpHist.Fill(value, weight)
tmpHist_forROOTFile.Fill(value, weight)
if iCategory != 'WJets' and (not isData) and (not isSignal):
histDict['WJets_OSsignal'].Fill(value, -weight)
else:
if isSignal:
continue
#region B
if regionSelection(tree, FS, "SSsignal", plots_cfg.scanRange[0], plots_cfg.scanRange[1]):
if not isData:
histDict['QCD_B'].Fill(value, -weight*QCD_weight)
else:
histDict['QCD_B'].Fill(value, weight*QCD_weight)
#region C
if regionSelection(tree, FS, "OScontrol", plots_cfg.scanRange[0], plots_cfg.scanRange[1]):
if not isData:
histDict['QCD_C'].Fill(value, -weight*QCD_weight)
histDict['QCD_C_for_A'].Fill(value, -weight*QCD_weight)
if iCategory != 'WJets':
histDict['WJets_OScontrol'].Fill(value, -weight)
else:
histDict['QCD_C'].Fill(value, weight*QCD_weight)
histDict['QCD_C_for_A'].Fill(value, weight*QCD_weight)
histDict['WJets_OScontrol'].Fill(value, weight)
#region D
if regionSelection(tree, FS, "SScontrol", plots_cfg.scanRange[0], plots_cfg.scanRange[1]):
if not isData:
histDict['QCD_D'].Fill(value, -weight*QCD_weight)
histDict['QCD_D_for_C'].Fill(value, -weight*QCD_weight)
histDict['QCD_D_for_A'].Fill(value, -weight*QCD_weight)
else:
histDict['QCD_D'].Fill(value, weight*QCD_weight)
histDict['QCD_D_for_C'].Fill(value, weight*QCD_weight)
histDict['QCD_D_for_A'].Fill(value, weight*QCD_weight)
#highMT signal region
if regionSelection(tree, FS, "OSsignal", plots_cfg.scanRange[0], plots_cfg.scanRange[1], True):
if not isData:
if iCategory != 'WJets':
histDict['WJets_A_'].Fill(value, -weight)
else:
histDict['WJets_MC_A_'].Fill(value, weight)
else:
histDict['WJets_A_'].Fill(value, weight)
#highMT C region
if regionSelection(tree, FS, "OScontrol", plots_cfg.scanRange[0], plots_cfg.scanRange[1], True):
if not isData:
histDict['QCD_C_for_A_'].Fill(value, -weight*QCD_weight)
else:
histDict['QCD_C_for_A_'].Fill(value, weight*QCD_weight)
print iCategory, tmpHist.Integral(0, len(varBins)+1)
histDict[iCategory].Add(tmpHist)
del tmpHist
if options.saveHisto:
oFile = r.TFile('/user_data/zmao/ZPrimeHistos/%s/%s_%s.root' %(FS, iSample[iSample.rfind('/'): iSample.rfind('_SYNC')], varName),"recreate")
oFile.cd()
tmpHist_forROOTFile = fixNegativBins(tmpHist_forROOTFile)
tmpHist_forROOTFile.Write()
oFile.Close()
del tmpHist_forROOTFile
print ''
def loop_one_sample(iSample, iLocation, iCat, oTree, oTree_tmp, floatVarsDict,
intVarsDict, charVarsDict, iFS, yieldDict, histDict):
print 'combininig sample [%s] for datacard' % (iSample)
if 'data' in iSample:
isData = True
else:
isData = False
if 'emb' in iSample:
isEmbedded = True
else:
isEmbedded = False
if ("Z'" in iSample):
isSignal = True
else:
isSignal = False
iFile = r.TFile(iLocation)
iTree = iFile.Get("Ntuple")
nEntries = iTree.GetEntries()
iTree.SetBranchStatus("*", 1)
iTree.SetBranchStatus("sampleName", 0)
eventCount = iFile.Get('eventCount')
eventCountWeighted = iFile.Get('eventCountWeighted')
sumPtWeights = -1.0
if "TTJets" in iSample:
sumPtWeights = iFile.Get('eventCountPtWeighted').GetBinContent(1)
yieldEstimator_OS = 0.0
yieldEstimator_SS = 0.0
fillcounter = 0
met = lvClass()
for iEntry in range(nEntries):
iTree.GetEntry(iEntry)
tool.printProcessStatus(iEntry, nEntries,
'looping over file %s' % (iSample), iEntry - 1)
uncWeight = 1.0
region = 'none'
met.SetCoordinates(iTree.met, 0.0, iTree.metphi, 0)
l1.SetCoordinates(iTree.pt_1, iTree.eta_1, iTree.phi_1, iTree.m_1)
l2.SetCoordinates(iTree.pt_2, iTree.eta_2, iTree.phi_2, iTree.m_2)
if not plots_dataDrivenQCDandWJ.passCut(iTree, iFS, isData,
options.sys):
continue
if plots_dataDrivenQCDandWJ.regionSelection(iTree, iFS, "signal",
plots_cfg.scanRange[0],
plots_cfg.scanRange[1]):
if isData:
charVarsDict['sampleName'][:31] = 'data' + signalRegionName
else:
charVarsDict['sampleName'][:31] = iSample
if isSignal:
charVarsDict['sampleName'][:31] = "Zprime_%s" % iSample[
iSample.find("(") + 1:iSample.find(")")]
if iTree.q_1 != iTree.q_2:
region = 'A'
else:
if isSignal:
continue
region = 'B'
elif plots_dataDrivenQCDandWJ.regionSelection(iTree, iFS,
"control_iso",
plots_cfg.scanRange[0],
plots_cfg.scanRange[1]):
if isSignal:
continue
if iTree.q_1 == -iTree.q_2: #C region
if iFS != "em":
if "WJets" in iSample:
continue
region = 'C'
else: #D region
charVarsDict['sampleName'][:31] = 'QCD_in_D'
region = 'D'
elif iFS != 'em' and plots_dataDrivenQCDandWJ.regionSelection(
iTree, iFS, "control_anti_iso", plots_cfg.scanRange[0],
plots_cfg.scanRange[1]):
if isSignal:
continue
if not isData:
continue
if iTree.q_1 == -iTree.q_2: #E region
charVarsDict['sampleName'][:31] = 'QCDLoose'
region = 'E'
else:
charVarsDict['sampleName'][:31] = 'QCD_in_F'
region = 'F'
else:
continue
floatVarsDict['pt_1'][0] = iTree.pt_1
floatVarsDict['pt_2'][0] = iTree.pt_2
floatVarsDict['eta_1'][0] = iTree.eta_1
floatVarsDict['eta_2'][0] = iTree.eta_2
floatVarsDict['mt_1'][0] = iTree.mt_1
floatVarsDict['genMass'][0] = 0.0
if hasattr(iTree, 'X_to_ll'):
floatVarsDict['genMass'][0] = iTree.X_to_ll
if hasattr(iTree, 'BDT_both'):
floatVarsDict['BDT'][0] = iTree.BDT_both
else:
floatVarsDict['BDT'][0] = -999
floatVarsDict['triggerEff'][
0] = iTree.trigweight_1 * iTree.trigweight_2
charVarsDict['Category'][:31] = iFS
floatVarsDict['xs'][0] = iTree.xs
if 'WJets' in iSample:
floatVarsDict['xs'][
0] = floatVarsDict['xs'][0] * plots_cfg.WJetsScanRange[0]
if "Zprime" in iSample:
floatVarsDict['xs'][0] = plots.getZPrimeXS(iSample[7:])
if eventCount:
intVarsDict['initEvents'][0] = int(eventCount.GetBinContent(1))
else:
intVarsDict['initEvents'][0] = int(iTree.initEvents)
if eventCountWeighted:
intVarsDict['initSumWeights'][0] = int(
eventCountWeighted.GetBinContent(1))
else:
intVarsDict['initSumWeights'][0] = int(iTree.initWeightedEvents)
# floatVarsDict['m_svfit'][0] = iTree.pfmet_svmc_mass
floatVarsDict['m_effective'][0] = (l1 + l2 + met).mass()
floatVarsDict['m_vis'][0] = (l1 + l2).mass()
# floatVarsDict['m_tt'][0] = iTree.m_tt
if (options.sys == 'up' or options.sys == 'down') and (not isData):
floatVarsDict['xs'][0] = floatVarsDict['xs'][0] * getPDFWeight(
iFS, iSample, iCat, options.sys,
floatVarsDict['m_effective'][0])
intVarsDict['nCSVL'][0] = int(iTree.nCSVL)
if iFS == 'et' or iFS == "mt":
intVarsDict['tauDecayMode'][0] = int(iTree.tDecayMode)
floatVarsDict['tauTightIso'][
0] = iTree.tByTightCombinedIsolationDeltaBetaCorr3Hits
floatVarsDict['tauMediumIso'][
0] = iTree.tByMediumCombinedIsolationDeltaBetaCorr3Hits
floatVarsDict['tauLooseIso'][
0] = iTree.tByLooseCombinedIsolationDeltaBetaCorr3Hits
if options.sys == 'tauUncUp':
uncWeight += 0.05 * iTree.pt_2 / 1000.
if options.sys == 'tauUncDown':
uncWeight -= 0.35 * iTree.pt_2 / 1000.
floatVarsDict['cosDPhi'][0] = math.cos(iTree.phi_1 - iTree.phi_2)
floatVarsDict['pZetaCut'][0] = getattr(
iTree, "%s_%s_PZeta" % (iFS[0], iFS[1])) - 3.1 * getattr(
iTree, "%s_%s_PZetaVis" % (iFS[0], iFS[1]))
floatVarsDict['pfMEt'][0] = iTree.met
# floatVarsDict['eleRelIso'][0] = iTree.eRelIso
if options.PUWeight and not isData:
floatVarsDict['PUWeight'][0] = cutSampleTools.getPUWeight(
iTree.nTruePU)
floatVarsDict['npv'][0] = iTree.nTruePU
else:
floatVarsDict['PUWeight'][0] = 1.0
floatVarsDict['npv'][0] = 0
if isData:
floatVarsDict['genEventWeight'][0] = 1.0
intVarsDict['initSumWeights'][0] = 1
floatVarsDict['xs'][0] = 1.0 / plots.lumi
floatVarsDict['triggerEff'][0] = 1.0
else:
floatVarsDict['genEventWeight'][
0] = uncWeight * iTree.genEventWeight
if options.topPt and sumPtWeights != -1.0:
intVarsDict['initSumWeights'][0] = int(sumPtWeights)
floatVarsDict['genEventWeight'][
0] = iTree.topPtWeight * floatVarsDict['genEventWeight'][0]
if (iFS == 'et' or iFS == 'mt') and not isData:
floatVarsDict['genEventWeight'][
0] = floatVarsDict['genEventWeight'][0] * 0.95 #tauID
if options.againstLeptonSF:
if iFS == 'et':
if (iTree.tIsPromptElectron):
floatVarsDict['genEventWeight'][
0] = floatVarsDict['genEventWeight'][
0] * plots_dataDrivenQCDandWJ.getAgainstLeptonSF(
'electron', 'Tight', abs(iTree.eta_2))
if (iTree.tIsPromptMuon):
floatVarsDict['genEventWeight'][
0] = floatVarsDict['genEventWeight'][
0] * plots_dataDrivenQCDandWJ.getAgainstLeptonSF(
'muon', 'Loose', abs(iTree.eta_2))
if iFS == 'mt':
if (iTree.tIsPromptElectron):
floatVarsDict['genEventWeight'][
0] = floatVarsDict['genEventWeight'][
0] * plots_dataDrivenQCDandWJ.getAgainstLeptonSF(
'electron', 'VLoose', abs(iTree.eta_2))
if (iTree.tIsPromptMuon):
floatVarsDict['genEventWeight'][
0] = floatVarsDict['genEventWeight'][
0] * plots_dataDrivenQCDandWJ.getAgainstLeptonSF(
'muon', 'Tight', abs(iTree.eta_2))
if region == 'none':
print "ERROR!!!!!"
if region == "A":
if charVarsDict['sampleName'][:31] in yieldDict.keys():
yieldDict[str(
charVarsDict['sampleName']
[:31])] += floatVarsDict['triggerEff'][0] * floatVarsDict[
'PUWeight'][0] * floatVarsDict['genEventWeight'][
0] * floatVarsDict['xs'][0] * plots.lumi / (
intVarsDict['initSumWeights'][0] + 0.0)
else:
yieldDict[str(
charVarsDict['sampleName']
[:31])] = floatVarsDict['triggerEff'][0] * floatVarsDict[
'PUWeight'][0] * floatVarsDict['genEventWeight'][
0] * floatVarsDict['xs'][0] * plots.lumi / (
intVarsDict['initSumWeights'][0] + 0.0)
if "WJets" in iSample:
histDict["WJetsOSTight_MC"].Fill(
1, floatVarsDict['triggerEff'][0] *
floatVarsDict['PUWeight'][0] *
floatVarsDict['genEventWeight'][0] *
floatVarsDict['xs'][0] * plots.lumi /
(intVarsDict['initSumWeights'][0] + 0.0))
yieldDict["WJetsOSTight_MC"] += floatVarsDict['triggerEff'][
0] * floatVarsDict['PUWeight'][0] * floatVarsDict[
'genEventWeight'][0] * floatVarsDict['xs'][
0] * plots.lumi / (
intVarsDict['initSumWeights'][0] + 0.0)
oTree.Fill()
elif region == "B":
weight = 1
if not isData:
weight = -floatVarsDict['triggerEff'][0] * floatVarsDict[
'PUWeight'][0] * floatVarsDict['genEventWeight'][
0] * floatVarsDict['xs'][0] * plots.lumi / (
intVarsDict['initSumWeights'][0] + 0.0)
yieldDict["QCD_in_B"] += weight
histDict["QCD_in_B"].Fill(1, weight)
elif region == "C":
if iFS == "em":
charVarsDict['sampleName'][:31] = 'QCDLoose'
if isData:
yieldDict["QCDLoose"] += 1
else:
yieldDict["QCDLoose"] -= floatVarsDict['triggerEff'][
0] * floatVarsDict['PUWeight'][0] * floatVarsDict[
'genEventWeight'][0] * floatVarsDict['xs'][
0] * plots.lumi / (
intVarsDict['initSumWeights'][0] + 0.0)
floatVarsDict['genEventWeight'][
0] = -1.0 * floatVarsDict['genEventWeight'][0]
oTree.Fill()
if "WJets" in iSample:
floatVarsDict['genEventWeight'][
0] = -1.0 * floatVarsDict['genEventWeight'][0]
yieldDict['WJets' + controlRegionName] += floatVarsDict[
'triggerEff'][0] * floatVarsDict['PUWeight'][
0] * floatVarsDict['genEventWeight'][
0] * floatVarsDict['xs'][0] * plots.lumi / (
intVarsDict['initSumWeights'][0] + 0.0)
histDict["WJetsOSLoose_MC"].Fill(
1, floatVarsDict['triggerEff'][0] *
floatVarsDict['PUWeight'][0] *
floatVarsDict['genEventWeight'][0] *
floatVarsDict['xs'][0] * plots.lumi /
(intVarsDict['initSumWeights'][0] + 0.0))
charVarsDict[
'sampleName'][:31] = 'WJets' + controlRegionName
oTree.Fill()
else:
charVarsDict['sampleName'][:31] = 'WJetsLoose'
if isData:
yieldDict['WJetsLoose'] += 1
else:
floatVarsDict['genEventWeight'][
0] = -1.0 * floatVarsDict['genEventWeight'][0]
yieldDict['WJetsLoose'] += floatVarsDict['triggerEff'][
0] * floatVarsDict['PUWeight'][0] * floatVarsDict[
'genEventWeight'][0] * floatVarsDict['xs'][
0] * plots.lumi / (
intVarsDict['initSumWeights'][0] + 0.0)
oTree.Fill()
elif region == "D":
weight = 1
if not isData:
weight = -floatVarsDict['triggerEff'][0] * floatVarsDict[
'PUWeight'][0] * floatVarsDict['genEventWeight'][
0] * floatVarsDict['xs'][0] * plots.lumi / (
intVarsDict['initSumWeights'][0] + 0.0)
yieldDict["QCD_in_D"] += weight
histDict["QCD_in_D"].Fill(1, weight)
elif region == "E" and iFS != 'em' and isData:
yieldDict["QCDLoose"] += 1
oTree_tmp.Fill()
oTree.Fill()
elif region == "F" and iFS != 'em' and isData:
yieldDict["QCD_in_F"] += 1
histDict["QCD_in_F"].Fill(1, 1)
return yieldDict, histDict
def loop_one_sample(iSample, varName, hist1, hist2, category = 'mt', isData = False, region = 'barrel', triggerLeg = 'e'):
file = r.TFile(iSample)
tree = file.Get('Ntuple')
nEntries = tree.GetEntries()
hist1.Sumw2()
hist2.Sumw2()
eventCountWeighted = file.Get('eventCountWeighted')
# if not isData and nEntries > 50000:
# nEntries = 50000
for iEntry in range(nEntries):
tree.GetEntry(iEntry)
tool.printProcessStatus(iEntry, nEntries, 'Looping sample %s' %(iSample), iEntry-1)
weight = 1.0
if not isData:
puweight = cutSampleTools.getPUWeight(tree.nTruePU)
sumWeights = eventCountWeighted.GetBinContent(1)
weight = 10000*tree.xs*tree.genEventWeight*puweight/(sumWeights+0.0)
if category == 'mt':
if tree.mPt <= 25:
continue
if not (tree.singleMu24Pass and tree.mIsoMu24):
continue
elif category == 'et':
if tree.ePt <= 33:
continue
if isData:
if not (tree.singleETightPass and tree.eSingleEleTight):
continue
else:
if not (tree.singleEPass and tree.eSingleEle):
continue
elif category == 'ee':
if isData:
if 'e2' in varName:
if tree.singleETightPass and tree.e1SingleEleTight:
if tree.e1Pt <= 34:
continue
else:
continue
if 'e1' in varName:
if tree.singleETightPass and tree.e2SingleEleTight:
if tree.e2Pt <= 34:
continue
else:
continue
else:
if 'e2' in varName:
if tree.singleEPass and tree.e1SingleEle:
if tree.e1Pt <= 34:
continue
else:
continue
if 'e1' in varName:
if tree.singleEPass and tree.e2SingleEle:
if tree.e2Pt <= 34:
continue
else:
continue
elif category == 'em':
if abs(tree.eEta) >= 2.1:
continue
if abs(tree.mEta) >= 2.1:
continue
if triggerLeg == 'e':
if tree.mPt <= 24:
continue
if region == 'barrel' and abs(tree.eEta) > 1.479:
continue
elif region == 'endcap' and abs(tree.eEta) <= 1.479:
continue
else:
if region == 'barrel' and abs(tree.mEta) > 0.8:
continue
elif region == 'middle' and not (0.8 < abs(tree.mEta) < 1.24):
continue
elif region == 'endcap' and not (1.24 < abs(tree.mEta) < 2.4):
continue
if tree.ePt <= 28:
continue
if tree.q_1 == tree.q_2:
continue
value = getattr(tree, varName)
hist1.Fill(value)
if category == 'mt':
if tree.muTauPass and tree.mMuTau and tree.mMuTauOverlap and tree.tTau20AgainstMuon and tree.tTauOverlapMu:
hist2.Fill(value)
elif category == 'et':
if isData:
if tree.eTau_WPLoosePass and tree.eEle22Loose and tree.eOverlapEle22Loose and tree.tTau20 and tree.tTauOverlapEleLoose:
hist2.Fill(value)
else:
if tree.eTauPass and tree.eEle22 and tree.eOverlapEle22 and tree.tTau20 and tree.tTauOverlapEle:
hist2.Fill(value)
elif category == 'ee':
if isData:
if tree.doubleE_WPLoosePass:
if (tree.e1DoubleE_WPLooseLeg1 and tree.e2DoubleE_WPLooseLeg2) or (tree.e1DoubleE_WPLooseLeg2 and tree.e2DoubleE_WPLooseLeg1):
if tree.e2Pt > 25 and tree.e1Pt > 25:
hist2.Fill(value)
else:
if tree.doubleE_WP75Pass:
if (tree.e1DoubleE_WP75Leg1 and tree.e2DoubleE_WP75Leg2) or (tree.e1DoubleE_WP75Leg2 and tree.e2DoubleE_WP75Leg1):
if tree.e2Pt > 25 and tree.e1Pt > 25:
hist2.Fill(value)
elif category == 'em':
if triggerLeg == 'e':
if tree.singleE27_2p1_WPTightPass and tree.eSingleEle27_2p1_WPTight:
hist2.Fill(value)
elif triggerLeg == 'mu':
if tree.singleMu24Pass and tree.mIsoMu24:
hist2.Fill(value)
print ''
def loop_one_sample(iSample, iCategory, histDict, varName, varBins, FS,
scanPoints):
if options.antiIso:
iSample += "%s_antiIso.root" % FS
elif options.antiEIso:
iSample += "%s_antiEIso.root" % FS
elif options.antiMIso:
iSample += "%s_antiMIso.root" % FS
elif options.noIso:
iSample += "%s_noIso.root" % FS
elif options.BSM3G:
iSample += "%s_noIso.root" % FS
else:
iSample += "%s_inclusive.root" % FS
file = r.TFile(iSample)
tree = file.Get('Ntuple')
eventCount = file.Get('eventCount')
eventCountWeighted = file.Get('eventCountWeighted')
sumPtWeights = -1
if "TT" in iSample and options.sys == 'topPt':
sumPtWeights = file.Get('eventCountPtWeighted').GetBinContent(1)
nEntries = tree.GetEntries()
# if nEntries > 100:
# nEntries = 100
tmpHist = r.TH1F("tmp_%s_%s" % (iCategory, varName), '',
len(varBins) - 1, varBins)
tmpHist.Sumw2()
tmpHist_forROOTFile = r.TH1F("%s" % (varName), '',
len(varBins) - 1, varBins)
tmpHist_forROOTFile.Sumw2()
isData = False
isSignal = False
if eventCount:
initEvents = eventCount.GetBinContent(1)
else:
initEvents = tree.initEvents
if eventCountWeighted:
sumWeights = eventCountWeighted.GetBinContent(1)
else:
sumWeights = tree.initWeightedEvents
if iCategory == 'Observed':
isData = True
for iEntry in range(nEntries):
tree.GetEntry(iEntry)
tool.printProcessStatus(iEntry, nEntries,
'Looping sample %s' % (iSample), iEntry - 1)
weight = 1.0
QCD_weight = 1.0
if not passCut(tree, FS, isData, options.sys):
continue
if not isData:
xs = tree.xs
if (80.94 < xs < 80.96) or (136.01 < xs < 136.03):
xs = xs * 0.108 * 3
if options.sys == 'topPt' and sumPtWeights != -1.0:
sumWeights = sumPtWeights
weight = Lumi * xs * tree.genEventWeight * tree.trigweight_1 * tree.trigweight_2 * tree.topPtWeight / (
sumWeights + 0.0)
else:
weight = Lumi * xs * tree.genEventWeight * tree.trigweight_1 * tree.trigweight_2 / (
sumWeights + 0.0)
if options.PUWeight:
weight = weight * cutSampleTools.getPUWeight(tree.nTruePU)
if 'WJets' in iSample:
weight = 1.0 * weight
if 'ZPrime' in iSample:
weight = getZPrimeXS(
iSample[iSample.rfind("/") + 8:iSample.rfind("_all")]) * weight
isSignal = True
if varName == 'm_withMET':
value = tree.m_eff
elif varName == 'mVis':
value = tree.m_vis
elif varName == 'pZeta - 3.1pZetaVis':
value = tree.ZetaCut
elif varName == "nCSVL":
value = tree.nCSVL
elif varName == "cos_phi_tau1_tau2":
value = math.cos(tree.phi_1 - tree.phi_2)
elif varName == "j1Pt":
jets = getJets(tree)
if len(jets) > 0:
value = jets[0].pt()
else:
value = -1
elif varName == 'm_gen':
if tree.eGenTauMass < 0 or tree.tGenMass < 0:
continue
l1.SetCoordinates(tree.eGenTauPt, tree.eGenTauEta, tree.eGenTauPhi,
tree.eGenTauMass)
l2.SetCoordinates(tree.tGenPt, tree.tGenEta, tree.tGenPhi,
tree.tGenMass)
value = (l1 + l2).mass()
else:
if hasattr(tree, varName):
value = getattr(tree, varName)
else:
value = -1
if options.overFlow:
if value > varBins[len(varBins) - 1]:
value = (varBins[len(varBins) - 1] +
varBins[len(varBins) - 2] + 0.0) / 2.0
if regionSelection(tree, FS, "OSsignal", plots_cfg.scanRange[0],
plots_cfg.scanRange[1]):
fill = True
if isData:
fill = False
if options.unblind or (options.unblindPartial
and passUnblindPartial(varName, value)):
fill = True
histDict['WJets_OSsignal'].Fill(value, weight)
if fill:
tmpHist.Fill(value, weight)
tmpHist_forROOTFile.Fill(value, weight)
if iCategory != 'WJets' and (not isData) and (not isSignal):
histDict['WJets_OSsignal'].Fill(value, -weight)
else:
if isSignal:
continue
#region B
if regionSelection(tree, FS, "SSsignal", plots_cfg.scanRange[0],
plots_cfg.scanRange[1]):
if not isData:
histDict['QCD_B'].Fill(value, -weight * QCD_weight)
else:
histDict['QCD_B'].Fill(value, weight * QCD_weight)
#region C
if regionSelection(tree, FS, "OScontrol", plots_cfg.scanRange[0],
plots_cfg.scanRange[1]):
if not isData:
histDict['QCD_C'].Fill(value, -weight * QCD_weight)
if iCategory != 'WJets':
histDict['WJets_OScontrol'].Fill(value, -weight)
else:
histDict['QCD_C'].Fill(value, weight * QCD_weight)
histDict['WJets_OScontrol'].Fill(value, weight)
#region D
if regionSelection(tree, FS, "SScontrol", plots_cfg.scanRange[0],
plots_cfg.scanRange[1]):
if not isData:
histDict['QCD_D'].Fill(value, -weight * QCD_weight)
histDict['QCD_D_for_C'].Fill(value, -weight * QCD_weight)
histDict['QCD_D_for_A'].Fill(value, -weight * QCD_weight)
else:
histDict['QCD_D'].Fill(value, weight * QCD_weight)
histDict['QCD_D_for_C'].Fill(value, weight * QCD_weight)
histDict['QCD_D_for_A'].Fill(value, weight * QCD_weight)
print iCategory, tmpHist.Integral(0, len(varBins) + 1)
histDict[iCategory].Add(tmpHist)
del tmpHist
print ''
def loop_one_sample(iSample,
varName,
hist1,
hist2,
category='mt',
isData=False,
region='barrel',
triggerLeg='e'):
file = r.TFile(iSample)
tree = file.Get('Ntuple')
nEntries = tree.GetEntries()
hist1.Sumw2()
hist2.Sumw2()
eventCountWeighted = file.Get('eventCountWeighted')
# if not isData and nEntries > 50000:
# nEntries = 50000
for iEntry in range(nEntries):
tree.GetEntry(iEntry)
tool.printProcessStatus(iEntry, nEntries,
'Looping sample %s' % (iSample), iEntry - 1)
weight = 1.0
if not isData:
puweight = cutSampleTools.getPUWeight(tree.nTruePU)
sumWeights = eventCountWeighted.GetBinContent(1)
weight = 10000 * tree.xs * tree.genEventWeight * puweight / (
sumWeights + 0.0)
if category == 'mt':
if tree.mPt <= 25:
continue
if not (tree.singleMu24Pass and tree.mIsoMu24):
continue
elif category == 'et':
if tree.ePt <= 33:
continue
if isData:
if not (tree.singleETightPass and tree.eSingleEleTight):
continue
else:
if not (tree.singleEPass and tree.eSingleEle):
continue
elif category == 'ee':
if isData:
if 'e2' in varName:
if tree.singleETightPass and tree.e1SingleEleTight:
if tree.e1Pt <= 34:
continue
else:
continue
if 'e1' in varName:
if tree.singleETightPass and tree.e2SingleEleTight:
if tree.e2Pt <= 34:
continue
else:
continue
else:
if 'e2' in varName:
if tree.singleEPass and tree.e1SingleEle:
if tree.e1Pt <= 34:
continue
else:
continue
if 'e1' in varName:
if tree.singleEPass and tree.e2SingleEle:
if tree.e2Pt <= 34:
continue
else:
continue
elif category == 'em':
if abs(tree.eEta) >= 2.1:
continue
if abs(tree.mEta) >= 2.1:
continue
if triggerLeg == 'e':
if tree.mPt <= 24:
continue
if region == 'barrel' and abs(tree.eEta) > 1.479:
continue
elif region == 'endcap' and abs(tree.eEta) <= 1.479:
continue
else:
if region == 'barrel' and abs(tree.mEta) > 0.8:
continue
elif region == 'middle' and not (0.8 < abs(tree.mEta) < 1.24):
continue
elif region == 'endcap' and not (1.24 < abs(tree.mEta) < 2.4):
continue
if tree.ePt <= 28:
continue
if tree.q_1 == tree.q_2:
continue
value = getattr(tree, varName)
hist1.Fill(value)
if category == 'mt':
if tree.muTauPass and tree.mMuTau and tree.mMuTauOverlap and tree.tTau20AgainstMuon and tree.tTauOverlapMu:
hist2.Fill(value)
elif category == 'et':
if isData:
if tree.eTau_WPLoosePass and tree.eEle22Loose and tree.eOverlapEle22Loose and tree.tTau20 and tree.tTauOverlapEleLoose:
hist2.Fill(value)
else:
if tree.eTauPass and tree.eEle22 and tree.eOverlapEle22 and tree.tTau20 and tree.tTauOverlapEle:
hist2.Fill(value)
elif category == 'ee':
if isData:
if tree.doubleE_WPLoosePass:
if (tree.e1DoubleE_WPLooseLeg1
and tree.e2DoubleE_WPLooseLeg2) or (
tree.e1DoubleE_WPLooseLeg2
and tree.e2DoubleE_WPLooseLeg1):
if tree.e2Pt > 25 and tree.e1Pt > 25:
hist2.Fill(value)
else:
if tree.doubleE_WP75Pass:
if (tree.e1DoubleE_WP75Leg1 and tree.e2DoubleE_WP75Leg2
) or (tree.e1DoubleE_WP75Leg2
and tree.e2DoubleE_WP75Leg1):
if tree.e2Pt > 25 and tree.e1Pt > 25:
hist2.Fill(value)
elif category == 'em':
if triggerLeg == 'e':
if tree.singleE27_2p1_WPTightPass and tree.eSingleEle27_2p1_WPTight:
hist2.Fill(value)
elif triggerLeg == 'mu':
if tree.singleMu24Pass and tree.mIsoMu24:
hist2.Fill(value)
print ''
def loop_one_sample(iSample, iLocation, iFS):
print 'combining sample [%s] for LUT' % (iSample)
if 'data' in iSample:
return 0
if ('H2hh' in iSample) or ('ggH' in iSample) or ('Zprime' in iSample):
return 0
#pdfWeights
pdfHists = []
nWeights = 101
initSumPDF = []
for i in range(nWeights):
pdfHists.append(r.TH1F("pdf_%i" % i, "", len(bins) - 1, bins))
pdfHists[i].Sumw2()
iFile = r.TFile(iLocation)
iTree = iFile.Get("Ntuple")
nEntries = iTree.GetEntries()
iTree.SetBranchStatus("*", 1)
iTree.SetBranchStatus("sampleName", 0)
eventCount = iFile.Get('eventCount')
eventCountWeighted = iFile.Get('eventCountWeighted')
if options.signal:
for i in range(100):
initSumPDF.append(
int((iFile.Get('eventCountWeightedPDF_%i' %
i)).GetBinContent(1)))
hist_highPUWeight = r.TH1F("hist_highPUWeight", "", 50, 0.5, 1.5)
hist_lowPUWeight = r.TH1F("hist_lowPUWeight", "", 50, 0.5, 1.5)
yieldEstimator_OS = 0.0
yieldEstimator_SS = 0.0
fillcounter = 0
met = lvClass()
nBins = len(plots_cfg.list)
# nEntries = int(nEntries/3.)
for iEntry in range(nEntries):
iTree.GetEntry(iEntry)
tool.printProcessStatus(iEntry, nEntries,
'looping over file %s' % (iSample), iEntry - 1)
met.SetCoordinates(iTree.pfMetEt, 0.0, iTree.pfMetPhi, 0)
l1.SetCoordinates(iTree.pt_1, iTree.eta_1, iTree.phi_1, iTree.m_1)
l2.SetCoordinates(iTree.pt_2, iTree.eta_2, iTree.phi_2, iTree.m_2)
if not plots.passCut(iTree, iFS, False, l1, l2, met, ''):
continue
if options.method != 'SS' and iTree.q_1 == iTree.q_2:
continue
if plots.regionSelection(iTree, iFS, "control", options.method,
plots_cfg.scanRange[0],
plots_cfg.scanRange[1]):
sampleName = 'MC' + controlRegionName
elif plots.regionSelection(iTree, iFS, "signal", options.method,
plots_cfg.scanRange[0],
plots_cfg.scanRange[1]):
sampleName = iSample
else:
continue
triggerEff = iTree.trigweight_1 * iTree.trigweight_2
xs = iTree.xs
if 'WJets' in iSample:
xs = xs * plots_cfg.WJetsScanRange[0]
if "Zprime" in iSample:
xs = plots.getZPrimeXS(iSample[7:])
if eventCount:
initEvents = int(eventCount.GetBinContent(1))
else:
initEvents = int(iTree.initEvents)
if eventCountWeighted:
initSumWeights = int(eventCountWeighted.GetBinContent(1))
else:
initSumWeights = int(iTree.initWeightedEvents)
m_effective = (l1 + l2 + met).mass()
#overflow and underflow
if m_effective < plots_cfg.list[0]:
m_effective = (plots_cfg.list[0] + plots_cfg.list[1]) / 2.0
if m_effective > plots_cfg.list[nBins - 1]:
m_effective = (plots_cfg.list[nBins - 1] +
plots_cfg.list[nBins - 2]) / 2.0
if options.PUWeight:
PUWeight = cutSampleTools.getPUWeight(iTree.nTruePU)
else:
PUWeight = 1.0
genEventWeight = iTree.genEventWeight
if options.noNegGenEvts and genEventWeight < 0:
continue
if options.noGenEvtWeight:
genEventWeight = 1.0
if (sampleName == 'MC' + controlRegionName) and ("WJets" in iSample):
totalNormalWeights = triggerEff * xs * (
plots.lumi) * genEventWeight * PUWeight
pdfHists[0].Fill(m_effective, totalNormalWeights)
for i in range(1, nWeights):
pdfID = getPDFWeightID(i, iSample, iTree.pdfID)
pdfHists[i].Fill(
m_effective,
totalNormalWeights * iTree.pdfWeight.at(pdfID))
elif (controlRegionName not in sampleName) and ("WJets"
not in iSample):
totalNormalWeights = triggerEff * xs * (
plots.lumi) * genEventWeight * PUWeight
absMax = 0
for i in range(1, nWeights):
pdfID = getPDFWeightID(i, iSample, iTree.pdfID)
if abs(iTree.pdfWeight.at(pdfID)) > absMax:
absMax = iTree.pdfWeight.at(pdfID)
if absMax > 2.5:
continue
pdfHists[0].Fill(m_effective, totalNormalWeights)
for i in range(1, nWeights):
pdfID = getPDFWeightID(i, iSample, iTree.pdfID)
pdfHists[i].Fill(
m_effective,
totalNormalWeights * iTree.pdfWeight.at(pdfID))
#hist_highPUWeight.Fill(PUWeight)
if PUWeight > 1.0:
hist_highPUWeight.Fill(iTree.pdfWeight.at(pdfID),
genEventWeight * PUWeight)
else:
hist_lowPUWeight.Fill(iTree.pdfWeight.at(pdfID),
genEventWeight * PUWeight)
c = r.TCanvas("c", "Test", 600, 600)
c.Range(0, 0, 1, 1)
c.SetFillColor(0)
# r.gPad.SetLogy()
hist_highPUWeight.SetLineColor(r.kBlue)
hist_lowPUWeight.SetLineColor(r.kRed)
# hist_lowPUWeight.Scale(1./hist_lowPUWeight.Integral(0, hist_lowPUWeight.GetNbinsX()+1))
hist_highPUWeight.Draw()
hist_lowPUWeight.Draw("same")
position = (0.15, 0.85 - 0.05 * 2, 0.47, 0.85)
histList = []
histList.append((hist_highPUWeight, "high PU-weight events", 'l'))
histList.append((hist_lowPUWeight, "low PU-weight events", 'l'))
legends = tool.setMyLegend(position, histList)
legends.Draw("same")
c.Print('pdfSys.pdf')
return getVariations(pdfHists, initSumPDF, initSumWeights)