"LeptonTightNoSieie0": "LeptonTightInvIsoNoSieie0",
}
filterCutData = getFilterCut(args.year,
isData=True,
skipBadChargedCandidate=True)
filterCutMc = getFilterCut(args.year,
isData=False,
skipBadChargedCandidate=True)
blinding = []
if args.year != 2016 and args.blind:
blinding += [cutInterpreter.cutString("highSieieLep")]
data_sample.setSelectionString([filterCutData, "reweightHEM>0"] + blinding)
data_sample.setWeightString("weight")
if args.small:
data_sample.normalization = 1.
data_sample.reduceFiles(factor=5)
data_sample.setWeightString("weight*%f" % (1. / data_sample.normalization))
for s in mc:
s.setSelectionString([filterCutMc, "pTStitching==1", "overlapRemoval==1"])
s.read_variables = read_variables_MC
sampleWeight = "1"
if args.small:
s.normalization = 1.
s.reduceFiles(factor=100)
sampleWeight = "%f" % (1. / s.normalization)
selection = cutInterpreter.cutString(args.selection)
# Sample definition
os.environ["gammaSkim"] = "False" #always false for QCD estimate
if args.year == 2016:
from TTGammaEFT.Samples.nanoTuples_Run2016_14Dec2018_semilep_postProcessed import Run2016 as data_sample
elif args.year == 2017:
from TTGammaEFT.Samples.nanoTuples_Run2017_14Dec2018_semilep_postProcessed import Run2017 as data_sample
elif args.year == 2018:
from TTGammaEFT.Samples.nanoTuples_Run2018_14Dec2018_semilep_postProcessed import Run2018 as data_sample
elif args.year == "RunII":
from TTGammaEFT.Samples.nanoTuples_RunII_postProcessed import RunII as data_sample
filterCutData = getFilterCut(args.year,
isData=True,
skipBadChargedCandidate=True)
data_sample.setSelectionString([filterCutData, "reweightHEM>0"])
data_sample.setWeightString("weight")
lumi_scale = data_sample.lumi * 0.001
replaceSelection = {
# "nLeptonVetoIsoCorr": "nLeptonVetoNoIso",
"nLeptonTight": "nLeptonTightInvIso",
"nMuonTight": "nMuonTightInvIso",
"nElectronTight": "nElectronTightInvIso",
"mLtight0Gamma": "mLinvtight0Gamma",
"nPhotonGood": "nPhotonGoodInvLepIso",
"nJetGood": "nJetGoodInvLepIso",
"nBTagGood": "nBTagGoodInvLepIso",
"mT": "mTinv",
"m3": "m3inv",
"LeptonTight0": "LeptonTightInvIso0",
"JetGood0": "JetGoodInvLepIso0",
weightStringAR = ws + ws16 + ws17 + ws18
wsInv = "(%s*weight*
reweightHEM*reweightInvIsoTrigger*reweightL1Prefire*reweightPU*reweightLeptonTightSFInvIso*reweightLeptonTrackingTightSFInvIso*reweightPhotonSF*reweightPhotonElectronVetoSF*reweightBTag_SF)"%lumiString
wsInv16 = "+(%s*(PhotonNoChgIsoNoSieieInvLepIso0_photonCatMagic==2)*(%f-1)*(year==2016))" %(wsInv, misIDSF_val[2016].val)
wsInv17 = "+(%s*(PhotonNoChgIsoNoSieieInvLepIso0_photonCatMagic==2)*(%f-1)*(year==2017))" %(wsInv, misIDSF_val[2017].val)
wsInv18 = "+(%s*(PhotonNoChgIsoNoSieieInvLepIso0_photonCatMagic==2)*(%f-1)*(year==2018))" %(wsInv, misIDSF_val[2018].val)
weightStringInv = wsInv + wsInv16 + wsInv17 + wsInv18
filterCutData = getFilterCut( args.year, isData=True, skipBadChargedCandidate=True )
filterCutMc = getFilterCut( args.year, isData=False, skipBadChargedCandidate=True )
#tr = TriggerSelector( args.year, singleLepton=True )
#triggerCutMc = tr.getSelection( "MC" )
data_sample.setSelectionString( filterCutData )
data_sample.setWeightString( "weight*reweightHEM" )
for s in mc:
s.setSelectionString( [ filterCutMc, "pTStitching==1", "overlapRemoval==1" ] )
sampleWeight = "1"
replaceSelection = {
# "nLeptonVetoIsoCorr": "nLeptonVetoNoIso",
"nLeptonTight": "nLeptonTightInvIso",
"nMuonTight": "nMuonTightInvIso",
"nElectronTight": "nElectronTightInvIso",
"mLtight0Gamma": "mLinvtight0Gamma",
"nPhotonGood": "nPhotonGoodInvLepIso",
"nJetGood": "nJetGoodInvLepIso",
"nBTagGood": "nBTagGoodInvLepIso",
"mT": "mTinv",