from TTGammaEFT.Tools.user import plot_directory
tt0l = Sample.fromDirectory("tt0l", directory = ["/scratch/robert.schoefbeck/TTGammaEFT/nanoTuples/postprocessed/TTGammaEFT_PP_2016_TTG_private_v45/inclusive/TTHad_pow_CP5/"])
tt1l = Sample.fromDirectory("tt1l", directory = ["/scratch/robert.schoefbeck/TTGammaEFT/nanoTuples/postprocessed/TTGammaEFT_PP_2016_TTG_private_v45/inclusive/TTSingleLep_pow_CP5/"])
tt2l = Sample.fromDirectory("tt2l", directory = ["/scratch/robert.schoefbeck/TTGammaEFT/nanoTuples/postprocessed/TTGammaEFT_PP_2016_TTG_private_v45/inclusive/TTLep_pow_CP5/"])
ttg0l = Sample.fromDirectory("ttg0l", directory = ["/scratch/robert.schoefbeck/TTGammaEFT/nanoTuples/postprocessed/TTGammaEFT_PP_2016_TTG_private_v45/inclusive/TTGHad_LO/"])
ttg1l = Sample.fromDirectory("ttg1l", directory = ["/scratch/robert.schoefbeck/TTGammaEFT/nanoTuples/postprocessed/TTGammaEFT_PP_2016_TTG_private_v45/inclusive/TTGSingleLep_LO/"])
ttg2l = Sample.fromDirectory("ttg2l", directory = ["/scratch/robert.schoefbeck/TTGammaEFT/nanoTuples/postprocessed/TTGammaEFT_PP_2016_TTG_private_v45/inclusive/TTGLep_LO/"])
ttg = Sample.combine( "ttg", [ttg0l, ttg1l, ttg2l] )
## Missing: Add TT contribution + 'overlapremoval'
## Missing: Add years
triggerSelector = TriggerSelector(2016)
lumi_weight_str = "(%f*(year==2016)+%f*(year==2017)+%f*(year==2018))"%(Run2016.lumi/1000,Run2017.lumi/1000,Run2018.lumi/1000)
weight = "%s*weight*reweightHEM*reweightTrigger*reweightL1Prefire*reweightPU*reweightLeptonTightSF*reweightLeptonTrackingTightSF*reweightPhotonSF*reweightPhotonElectronVetoSF*reweightBTag_SF" % lumi_weight_str
reco_cut = "nLeptonTight==1&&nLeptonVetoIsoCorr==1&&nJetGood>=3&&nBTagGood>=1&&nPhotonGood==1&&nPhotonNoChgIsoNoSieie==1&&triggered==1&&reweightHEM>0&&overlapRemoval==1&&(PhotonGood0_photonCatMagic==0||PhotonGood0_photonCatMagic==2)&&((year==2016&&Flag_goodVertices&&Flag_globalSuperTightHalo2016Filter&&Flag_HBHENoiseFilter&&Flag_HBHENoiseIsoFilter&&Flag_EcalDeadCellTriggerPrimitiveFilter&&Flag_BadPFMuonFilter&&PV_ndof>4&&sqrt(PV_x*PV_x+PV_y*PV_y)<=2&&abs(PV_z)<=24)||(year==2017&&Flag_goodVertices&&Flag_globalSuperTightHalo2016Filter&&Flag_HBHENoiseFilter&&Flag_HBHENoiseIsoFilter&&Flag_EcalDeadCellTriggerPrimitiveFilter&&Flag_BadPFMuonFilter&&PV_ndof>4&&sqrt(PV_x*PV_x+PV_y*PV_y)<=2&&abs(PV_z)<=24)||(year==2018&&Flag_goodVertices&&Flag_globalSuperTightHalo2016Filter&&Flag_HBHENoiseFilter&&Flag_HBHENoiseIsoFilter&&Flag_EcalDeadCellTriggerPrimitiveFilter&&Flag_BadPFMuonFilter&&PV_ndof>4&&sqrt(PV_x*PV_x+PV_y*PV_y)<=2&&abs(PV_z)<=24))"
fiducial_cut = "overlapRemoval==1&&nGenLeptonCMSUnfold==1&&nGenJetsCMSUnfold>=3&&nGenBJetCMSUnfold>=1&&nGenPhotonCMSUnfold==1"
MET_filter_cut = "(year==2016&&Flag_goodVertices&&Flag_globalSuperTightHalo2016Filter&&Flag_HBHENoiseFilter&&Flag_HBHENoiseIsoFilter&&Flag_EcalDeadCellTriggerPrimitiveFilter&&Flag_BadPFMuonFilter&&PV_ndof>4&&sqrt(PV_x*PV_x+PV_y*PV_y)<=2&&abs(PV_z)<=24)"
gen_match_photon_cut = "sqrt(acos(cos(GenPhotonCMSUnfold0_phi-PhotonGood0_phi))**2+(GenPhotonCMSUnfold0_eta-PhotonGood0_eta)**2)<0.1"
weightString = "weight*reweightPhotonSF*reweightPhotonElectronVetoSF*reweightLeptonTightSF*reweightLeptonTrackingTightSF*reweightPU*reweightTrigger*reweightHEM*reweightL1Prefire*reweightBTag_SF"
pt_thresholds = [20, 35, 50, 65, 80, 120, 160, 200, 260, 320, 520]
ROOT.TH1.SetDefaultSumw2(0)
f_out_ref = ttg.get1DHistoFromDraw("PhotonGood0_pt", selectionString = reco_cut, weightString = weightString, binning = pt_thresholds, binningIsExplicit = True)
stack.extend([[s] for s in signals])
for sample in mc + signals:
sample.read_variables = read_variables_MC
sample.scale = lumi_scale
sample.style = styles.fillStyle(sample.color)
sample.weight = lambda event, sample: event.reweightDilepTriggerBackup * event.reweightPU * event.reweightLeptonSF * event.reweightLeptonTrackingSF * event.reweightPhotonSF * event.reweightPhotonElectronVetoSF * event.reweightBTag_SF
if args.small:
for sample in stack.samples:
sample.normalization = 1.
sample.reduceFiles(factor=15)
sample.scale /= sample.normalization
weight_ = lambda event, sample: event.weight
tr = TriggerSelector(args.year, None)
# Use some defaults (set defaults before you create/import list of Plots!!)
Plot.setDefaults(stack=stack,
weight=staticmethod(weight_),
selectionString=cutInterpreter.cutString(args.selection),
addOverFlowBin='upper')
# Import plots list (AFTER setDefaults!!)
plotListFile = os.path.join(os.path.dirname(os.path.realpath(__file__)),
'plotLists', args.plotFile + '.py')
if not os.path.isfile(plotListFile):
logger.info("Plot file not found: %s", plotListFile)
sys.exit(1)
plotModule = imp.load_source("plotLists", os.path.expandvars(plotListFile))
# MC samples need to be corrected by certain scale factors to correct e.g. detector effects. Define the "weight" here:
mcWeight = lambda event, sample: event.reweightL1Prefire * event.reweightPU * event.reweightLeptonTightSF * event.reweightLeptonTrackingTightSF * event.reweightPhotonSF * event.reweightPhotonElectronVetoSF * event.reweightBTag_SF
# a weight for all samples incluuding data is defined here
weight = lambda event, sample: event.weight
# get some additional cuts specific for MC and/or data
# MET Filter cut for data and MC
filterCutData = getFilterCut(args.year,
isData=True,
skipBadChargedCandidate=True)
filterCutMc = getFilterCut(args.year,
isData=False,
skipBadChargedCandidate=True)
# Trigger cuts for MC (already applied for Data)
tr = TriggerSelector(args.year,
singleLepton=True) #single lepton trigger also for DY CR
triggerCutMc = tr.getSelection("MC")
# Sample definition
mc = [
mc_samples.TTG, mc_samples.TT_pow, mc_samples.DY_LO, mc_samples.WJets,
mc_samples.WG, mc_samples.ZG, mc_samples.rest
]
ttg = mc_samples.TTG
if args.noData:
# Scale the histograms by the luminosity taken by CMS in each year
if args.year == 2016: lumi_scale = 35.92
elif args.year == 2017: lumi_scale = 41.53
elif args.year == 2018: lumi_scale = 59.74
# add all samples to the Stack
# sample.weight = lambda event, sample: event.reweightDilepTriggerBackup
# sample.weight = lambda event, sample: event.reweightPU36fb
# sample.weight = lambda event, sample: event.reweightLeptonSF
# sample.weight = lambda event, sample: event.reweightLeptonTrackingSF
# sample.weight = lambda event, sample: event.reweightPhotonSF
# sample.weight = lambda event, sample: event.reweightPhotonElectronVetoSF
# sample.weight = lambda event, sample: event.reweightBTag_SF
if args.small:
for sample in stack.samples:
sample.normalization = 1.
sample.reduceFiles(factor=5)
sample.scale /= sample.normalization
weight_ = lambda event, sample: event.weight
tr = TriggerSelector(2017)
# Use some defaults (set defaults before you create/import list of Plots!!)
Plot.setDefaults(stack=stack,
weight=staticmethod(weight_),
selectionString=cutInterpreter.cutString(args.selection),
addOverFlowBin='upper')
# Import plots list (AFTER setDefaults!!)
plotListFile = os.path.join(os.path.dirname(os.path.realpath(__file__)),
'plotLists', args.plotFile + '.py')
if not os.path.isfile(plotListFile):
logger.info("Plot file not found: %s", plotListFile)
sys.exit(1)
plotModule = imp.load_source("plotLists", os.path.expandvars(plotListFile))
# sample.weight = lambda event, sample: event.reweightDilepTriggerBackup
# sample.weight = lambda event, sample: event.reweightPU36fb
# sample.weight = lambda event, sample: event.reweightLeptonSF
# sample.weight = lambda event, sample: event.reweightLeptonTrackingSF
# sample.weight = lambda event, sample: event.reweightPhotonSF
# sample.weight = lambda event, sample: event.reweightPhotonElectronVetoSF
# sample.weight = lambda event, sample: event.reweightBTag_SF
if args.small:
for sample in stack.samples:
sample.normalization = 1.
sample.reduceFiles(factor=15)
sample.scale /= sample.normalization
weight_ = lambda event, sample: event.weight
tr = TriggerSelector(2016)
# Use some defaults (set defaults before you create/import list of Plots!!)
Plot.setDefaults(stack=stack,
weight=staticmethod(weight_),
selectionString=cutInterpreter.cutString(args.selection),
addOverFlowBin='upper')
# Import plots list (AFTER setDefaults!!)
plotListFile = os.path.join(os.path.dirname(os.path.realpath(__file__)),
'plotLists', args.plotFile + '.py')
if not os.path.isfile(plotListFile):
logger.info("Plot file not found: %s", plotListFile)
sys.exit(1)
plotModule = imp.load_source("plotLists", os.path.expandvars(plotListFile))
stack = Stack([Run2017], Run2017_noPreFiring)
Run2017.texName = "data"
Run2017.name = "data"
Run2017.style = styles.errorStyle(
ROOT.kBlue) #styles.fillStyle( ROOT.kOrange-3 )
Run2017.scale = 1
if args.small:
for sample in stack.samples:
sample.normalization = 1.
sample.reduceFiles(factor=50)
sample.scale /= sample.normalization
weight_ = lambda event, sample: event.weight
tr = TriggerSelector(2017, singleLepton=True)
triggerCond = tr.getSelection("SingleMuon")
# Use some defaults (set defaults before you create/import list of Plots!!)
Plot.setDefaults(stack=stack,
weight=staticmethod(weight_),
selectionString=cutInterpreter.cutString(args.selection),
addOverFlowBin='upper')
# Import plots list (AFTER setDefaults!!)
plotListFile = os.path.join(os.path.dirname(os.path.realpath(__file__)),
'plotLists', args.plotFile + '.py')
if not os.path.isfile(plotListFile):
logger.info("Plot file not found: %s", plotListFile)
sys.exit(1)
# Loop over channels
if args.mode != "None":
allModes = [args.mode]
elif args.nJobs != 1:
allModes = ['mumu', 'mue', 'ee', 'SF', 'all'
] if "dilep" in args.selection else ["mu", "e", "all"]
allModes = splitList(allModes, args.nJobs)[args.job]
else:
allModes = ['mumu', 'mue', 'ee'
] if "dilep" in args.selection else ["mu", "e"]
filterCutMc = getFilterCut(args.year,
isData=False,
skipBadChargedCandidate=True)
tr = TriggerSelector(args.year, singleLepton=True)
triggerCutMc = tr.getSelection("MC")
for index, mode in enumerate(allModes):
logger.info("Computing plots for mode %s", mode)
# always initialize with [], elso you get in trouble with pythons references!
plots = []
# plots += plotList
# plots += [ getYieldPlot( index ) ]
plots += addPlots
# Define lepton selections
leptonSelection = cutInterpreter.cutString(mode)
mcSelection = [
filterCutMc, leptonSelection, triggerCutMc, "pTStitching==1",
allPlots = {}
if args.mode != "None":
allModes = [args.mode]
elif args.nJobs != 1:
allModes = ['mumu', 'mue', 'ee', 'SF', 'all']
allModes = splitList(allModes, args.nJobs)[args.job]
else:
allModes = ['mumu', 'mue', 'ee']
filterCutData = getFilterCut(args.year,
isData=True,
skipBadChargedCandidate=True)
filterCutMc = getFilterCut(args.year,
isData=False,
skipBadChargedCandidate=True)
tr = TriggerSelector(args.year, singleLepton="nLepTight1" in args.selection)
triggerCutMc = tr.getSelection("MC")
cat_sel0 = ["%s_photonCat==0" % args.categoryPhoton]
cat_sel1 = ["%s_photonCat==1" % args.categoryPhoton]
cat_sel2 = ["%s_photonCat==2" % args.categoryPhoton]
cat_sel3 = ["%s_photonCat==3" % args.categoryPhoton]
for index, mode in enumerate(allModes):
logger.info("Computing plots for mode %s", mode)
yields[mode] = {}
# always initialize with [], elso you get in trouble with pythons references!
plots = []
plots += plotList