all_sb.texName += "high #sigma_{i#etai#eta}"
all_sb.color = ROOT.kRed + 2
all_fit = copy.deepcopy(all)
all_fit.name = "fit"
all_fit.texName = "MC "
if "sieie" in args.variable:
all_fit.texName += "low chg Iso"
elif "chg" in args.variable:
all_fit.texName += "low #sigma_{i#etai#eta}"
all_fit.color = ROOT.kCyan + 2
mc = [all_fit, all_sb]
stackSamples = [[s] for s in mc]
NanoVars = NanoVariables(args.year)
photonVariables = NanoVars.getVariables("Photon",
postprocessed=True,
data=False,
plot=True)
read_variables = [
"weight/F",
"isTTGamma/I",
"isZWGamma/I",
"isTGamma/I",
"overlapRemoval/I",
"reweightPU/F",
"reweightPUDown/F",
"reweightPUUp/F",
"reweightPUVDown/F",
logger.info("Re-using pickled histogram!")
else:
# change to EOS
# from TTGammaEFT.Tools.user import eos_directory
# data_directory = os.path.join( eos_directory, "nanoTuples" )
# fromEOS = "True"
# os.environ["gammaSkim"]="True"
from TTGammaEFT.Samples.nanoTuples_Summer16_private_semilep_postProcessed import *
sample = TTGSemiLep_16
#sample.files = sample.files[:1]
# add needed variables
NanoVars = NanoVariables(args.year)
genVarString = NanoVars.getVariableString("Gen",
postprocessed=True,
data=False,
plot=True)
photonVarString = NanoVars.getVariableString("Photon",
postprocessed=True,
data=True,
plot=True)
photonVariables = NanoVars.getVariables("Photon",
postprocessed=True,
data=True,
plot=True)
variables = [
TreeVariable.fromString("event/l"),
TreeVariable.fromString("weight/F"),
"nJetGood/I",
"nBTagGood/I",
"nLeptonTight/I",
"nElectronTight/I",
"nMuonTight/I",
"nPhotonGood/I",
"Photon[pt/F,eta/F,phi/F]",
"MET_pt/F",
"MET_phi/F",
"ht/F",
"mLtight0Gamma/F",
"m3/F",
"mT/F",
]
NanoVars = NanoVariables(2016)
readGenVarString = NanoVars.getVariableString("Gen",
postprocessed=True,
data=False)
readGenVarString = readGenVarString.replace("index/I,", "")
variables = ["pt/F", "eta/F", "phi/F", "pdgId/I", "genPartIdxMother/I"]
# add here variables that should be read only for MC samples
read_variables_MC = [
"overlapRemoval/I",
'nGenElectronCMSUnfold/I',
'nGenMuonCMSUnfold/I',
'nGenLeptonCMSUnfold/I',
'nGenPhotonCMSUnfold/I',
'nGenBJetCMSUnfold/I',
'nGenJetsCMSUnfold/I',
copyIndexPHP=True,
)
def getYieldPlot(index):
return Plot(
name='yield',
texX='yield',
texY='Number of Events',
attribute=lambda event, sample: 0.5 + index,
binning=[3, 0, 3],
)
# get nano variable lists
NanoVars = NanoVariables(2017)
jetVarString = NanoVars.getVariableString("Jet",
postprocessed=True,
data=True,
plot=True)
jetVariableNames = NanoVars.getVariableNameList("Jet",
postprocessed=True,
data=True,
plot=True)
bJetVariables = NanoVars.getVariables("BJet",
postprocessed=True,
data=True,
plot=True)
leptonVariables = NanoVars.getVariables("Lepton",
postprocessed=True,
data=True,
ttg181l = Sample.fromDirectory("ttg1l_18", directory = [os.path.join(data18_dir, name) for name in ["TTGSingleLep_LO","TTGSingleLep_ptG100To200_LO","TTGSingleLep_ptG200_LO"]])
ttg182l = Sample.fromDirectory("ttg2l_18", directory = [os.path.join(data18_dir, name) for name in ["TTGLep_LO","TTGLep_ptG100To200_LO","TTGLep_ptG200_LO"]])
#sample = Sample.combine( "ttg", [ttg160l, ttg161l, ttg162l, ttg170l, ttg171l, ttg172l, ttg180l, ttg181l, ttg182l] )
#sample = Sample.combine( "ttg", [ttg160l, ttg161l, ttg162l] )
sample = Sample.combine( "ttg", [ttg161l] )
def convertUnits( coll ):
for p in coll:
if abs(p['pdgId'])==11 and isinstance( p['lostHits'], basestring ): p['lostHits'] = ord( p['lostHits'] )
if abs(p['pdgId'])==13 and isinstance( p['pfIsoId'], basestring ): p['pfIsoId'] = ord( p['pfIsoId'] )
if isinstance( p['genPartFlav'], basestring ): p['genPartFlav'] = ord( p['genPartFlav'] )
selectionString = "overlapRemoval==1"
NanoVars = NanoVariables( 2016 )
readGenVarString = NanoVars.getVariableString( "Gen", postprocessed=True, data=False )
readGenVarList = NanoVars.getVariableNameList( "Gen", postprocessed=True, data=False )
readPhotonVarList = NanoVars.getVariableNameList( "Photon", postprocessed=True, data=False, skipSyst=True)
readPhotonVarString = NanoVars.getVariableString( "Photon", postprocessed=True, data=False, skipSyst=True)
genLeptonSel_CMSUnfold = genLeptonSelector( "CMSUnfolding" )
genPhotonSel_CMSUnfold = genPhotonSelector( "CMSUnfolding" )
recoPhotonSel_medium = photonSelector( 'medium', year=2016 )
read_variables = [ TreeVariable.fromString('weight/F'),TreeVariable.fromString('nGenPart/I'),
VectorTreeVariable.fromString('GenPart[%s]'%readGenVarString, nMax = 1000) ] # all needed for genMatching
read_variables += [ TreeVariable.fromString('nPhoton/I'),
VectorTreeVariable.fromString('Photon[%s]'%readPhotonVarString) ]
lumiScaleFactor = None
branchKeepStrings = branchKeepStrings_DATAMC + branchKeepStrings_DATA
json = allSamples[0].json
from FWCore.PythonUtilities.LumiList import LumiList
lumiList = LumiList( os.path.expandvars( json ) )
logger.info( "Loaded json %s", json )
lumi = 1
else:
lumiScaleFactor = xSection * targetLumi / float( sample.normalization ) if xSection is not None else None
branchKeepStrings = branchKeepStrings_DATAMC + branchKeepStrings_MC
if options.year == 2016: lumi = 35.92
elif options.year == 2017: lumi = 41.53
elif options.year == 2018: lumi = 59.74
# get nano variable lists
NanoVars = NanoVariables( options.year )
#VarString ... "var1/type,var2/type"
#Variables ... ["var1/type","var2/type"]
#VarList ... ["var1", "var2"]
readGenVarString = NanoVars.getVariableString( "Gen", postprocessed=False, data=sample.isData )
readGenJetVarString = NanoVars.getVariableString( "GenJet", postprocessed=False, data=sample.isData )
readJetVarString = NanoVars.getVariableString( "Jet", postprocessed=False, data=sample.isData, skipSyst=True )
readElectronVarString = NanoVars.getVariableString( "Electron", postprocessed=False, data=sample.isData )
readMuonVarString = NanoVars.getVariableString( "Muon", postprocessed=False, data=sample.isData )
readPhotonVarString = NanoVars.getVariableString( "Photon", postprocessed=False, data=sample.isData )
readGenVarList = NanoVars.getVariableNameList( "Gen", postprocessed=False, data=sample.isData )
readGenJetVarList = NanoVars.getVariableNameList( "GenJet", postprocessed=False, data=sample.isData )
readJetVarList = NanoVars.getVariableNameList( "Jet", postprocessed=False, data=sample.isData, skipSyst=True )
readElectronVarList = NanoVars.getVariableNameList( "Electron", postprocessed=False, data=sample.isData )
},
logX=False,
logY=log,
sorting=False,
yRange=(0.03, "auto") if log else (0.001, "auto"),
scaling=scaling,
legend=[
(0.2, 0.87 - 0.04 * sum(map(len, plot.histos)),
0.8, 0.87), 1
],
drawObjects=drawObjects(True, lumi_scale),
copyIndexPHP=True)
# get nano variable lists
NanoVars = NanoVariables(args.year)
jetVarString = NanoVars.getVariableString("Jet",
postprocessed=True,
data=False,
plot=True)
jetVariableNames = NanoVars.getVariableNameList("Jet",
postprocessed=True,
data=False,
plot=True)
jetVarList = NanoVars.getVariableNameList("Jet",
postprocessed=True,
data=False,
plot=True)
jetVariables = NanoVars.getVariables("Jet",
postprocessed=True,
data=False,