# 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
stack = Stack(mc)
else:
# Data Sample definition
# define the name on the plot
postFix = " (%s)" % args.mode.replace("mu", "#mu").replace("all", "e+#mu")
data_sample.texName = "data" + postFix
data_sample.name = "data"
# add here variables that should be read only for Data samples
data_sample.read_variables = read_variables_Data
# set additional cuts specific for data
data_sample.setSelectionString([filterCutData, "triggered==1"])
# change the style of the data sample (dots with error bars)
data_sample.style = styles.errorStyle(ROOT.kBlack)
# you can scale the histograms of each sample by defining sample.scale (don't scale data)
data_sample.scale = 1
# Scale the MC histograms by the luminosity taken by CMS in each year
lumi_scale = data_sample.lumi * 0.001
# add all samples to the Stack
stack = Stack(mc, data_sample)
# settings for MC Samples
for sample in mc:
# add here variables that should be read only for MC samples
sample.read_variables = read_variables_MC
# set additional cuts specific for MC
# overlapremoval for separating samples with and without a photon (e.g. ttbar from ttgamma)
# 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",
# add all samples to the Stack
stack = Stack(mc)
else:
# Data Sample definition
# mc samples are defined in TTGammaEFT/Samples/python/nanoTuples_Run201X_14Dec2018_semilep_postProcessed.py
if args.year == 2016: data_sample = Run2016
elif args.year == 2017: data_sample = Run2017
elif args.year == 2018: data_sample = Run2018
# define the name on the plot
postFix = " (%s)" % args.mode.replace("mu", "#mu").replace("all", "e+#mu")
data_sample.texName = "data" + postFix
data_sample.name = "data"
# add here variables that should be read only for Data samples
data_sample.read_variables = read_variables_Data
# set additional cuts specific for data
data_sample.setSelectionString([filterCutData])
# change the style of the data sample (dots with error bars)
data_sample.style = styles.errorStyle(ROOT.kBlack)
# you can scale the histograms of each sample by defining sample.scale (don't scale data)
data_sample.scale = 1
# Scale the MC histograms by the luminosity taken by CMS in each year
lumi_scale = data_sample.lumi * 0.001
# add all samples to the Stack
stack = Stack(mc, data_sample)
# settings for MC Samples
for sample in mc:
# add here variables that should be read only for MC samples
sample.read_variables = read_variables_MC
# set additional cuts specific for MC
# overlapremoval for separating samples with and without a photon (e.g. ttbar from ttgamma)
logger.info("Computing plots for mode %s", mode)
yields[mode] = {}
# always initialize with [], elso you get in trouble with pythons references!
plots = []
plots += addPlots
# Define 2l selections
leptonSelection = cutInterpreter.cutString(mode)
if args.sideband == "chgIso":
data_sample.texName = "data (%s) chg Iso fit region" % mode.replace(
"mu", "#mu").replace("all", "e+#mu")
data_sample.setSelectionString([
cutInterpreter.cutString("lowChgIsoNoSieie"), filterCutData,
leptonSelection, "triggered==1"
])
mc_fit.setSelectionString(
[cutInterpreter.cutString("lowChgIsoNoSieie"), leptonSelection] +
mcSelection)
mc_sb.setSelectionString(
[cutInterpreter.cutString("highChgIsoNoSieie"), leptonSelection] +
mcSelection)
elif args.sideband == "sieie":
data_sample.texName = "data (%s) #sigma_{i#eta i#eta} fit region" % mode.replace(
"mu", "#mu").replace("all", "e+#mu")
data_sample.setSelectionString([
cutInterpreter.cutString("lowSieieNoChgIso"), filterCutData,
leptonSelection, "triggered==1"
])
mc_fit.setSelectionString(
