def checkReferencePoint(sample):
''' check if sample is simulated with a reference point
'''
return pickle.load(file(sample.reweight_pkl))['ref_point'] != {}
# somehow this has to be done first, not in the next loop
if args.small:
for s in [ttXSample] + bg:
s.reduceFiles(to=10)
# configure samples
for s in [ttXSample] + bg:
# Scale the plots with number of events used (implemented in ref_lumiweight1fb)
s.event_factor = s.nEvents / float(s.chain.GetEntries())
s.setSelectionString(cutInterpreter.cutString(args.selection))
if checkReferencePoint(s):
s.read_variables = [
"ref_lumiweight1fb/F",
VectorTreeVariable.fromString('p[C/F]', nMax=2000)
]
catPhoton_variables = [
"signalPhoton/I", "isrPhoton/I", "lepPhoton/I", "nonIsoPhoton/I",
"fakePhoton/I"
]
catZ_variables = ["signalZ/I"]
if args.processFile.split('_')[0] == 'ttgamma':
# overlap removal + signal categorization for ttgamma
def checkReferencePoint( sample ):
''' check if sample is simulated with a reference point
'''
return pickle.load(file(sample.reweight_pkl))['ref_point'] != {}
# somehow this has to be done first, not in the next loop
if args.small:
for s in [ttXSample] + bg:
s.reduceFiles( to = 1 )
# configure samples
for s in [ttXSample] + bg:
# Scale the plots with number of events used (implemented in ref_lumiweight1fb)
s.event_factor = s.nEvents / float( s.chain.GetEntries() )
s.setSelectionString( cutInterpreter.cutString(args.selection) )
if checkReferencePoint( s ):
s.read_variables = ["ref_lumiweight1fb/F", VectorTreeVariable.fromString('p[C/F]', nMax=2000)]
catPhoton_variables = [ "signalPhoton/I", "isrPhoton/I", "lepPhoton/I", "nonIsoPhoton/I", "fakePhoton/I"]
catZ_variables = [ "signalZ/I" ]
if args.processFile.split('_')[0] == 'ttgamma':
# overlap removal + signal categorization for ttgamma
ttXSample.read_variables += catPhoton_variables
ttgammaIsrSample.read_variables += catPhoton_variables
ttgammaLepSample.read_variables = catPhoton_variables
ttSample.read_variables = catPhoton_variables
ttgammabSample.read_variables = catPhoton_variables
ttgammaFakeSample.read_variables = catPhoton_variables
if args.nonInfoSignal:
ttZISRSample = copy.deepcopy(
ttXSample
) #select ttgamma events with isolated gamma from ISR (cat a2)
ttZISRSample.name = 'fwlite_nonInfo_LO_order2_15weights_ref'
nonInfo.append(ttZISRSample)
def checkReferencePoint(sample):
''' check if sample is simulated with a reference point
'''
return pickle.load(file(sample.reweight_pkl))['ref_point'] != {}
# set selection string
selectionString = cutInterpreter.cutString(args.selection)
selectionString_up = selectionString.replace('nBTag', 'nBTag_JEC_up').replace(
'nrecoJet', 'nrecoJets_JEC_up')
selectionString_down = selectionString.replace(
'nBTag', 'nBTag_JEC_down').replace('nrecoJet', 'nrecoJets_JEC_down')
# somehow has to be separate from the next loop
if args.small:
for s in [ttXSample] + bg + nonInfo:
s.reduceFiles(to=5)
# configure samples
for i, s in enumerate([ttXSample] + bg + nonInfo):
s.shortname = s.name.split('_')[1]
s.event_factor = s.nEvents / float(s.chain.GetEntries())
# ATTENTION IF U USE MORE THAN ONE SIGNAL SAMPLE!!!
w = WeightInfo(ttXSample.reweight_pkl)
w.set_order(int(args.order))
if len(args.variables) == 0: args.variables = w.variables
def checkReferencePoint(sample):
''' check if sample is simulated with a reference point
'''
print pickle.load(file(sample.reweight_pkl))['ref_point']
return pickle.load(file(sample.reweight_pkl))['ref_point'] != {}
ttXSample.event_factor = ttXSample.nEvents / float(
ttXSample.chain.GetEntries())
ttXSample.setSelectionString(cutInterpreter.cutString(args.selection))
ttXSample.read_variables = ["ref_lumiweight1fb/F"]
ttXSample.read_variables.append(
VectorTreeVariable.fromString('p[C/F]', nMax=2000))
ttXSampleNoRef.event_factor = ttXSampleNoRef.nEvents / float(
ttXSampleNoRef.chain.GetEntries())
ttXSampleNoRef.setSelectionString(cutInterpreter.cutString(args.selection))
print ttXSample.event_factor
print ttXSampleNoRef.event_factor
signal = ttXSample
#stackList = [ [signal], [ttXSampleNoRef] ]
stackList = [[signal], [ttXSampleNoRef]]
stack = Stack(*stackList)
WC = {coeff: vals[i] for i, coeff in enumerate(coeffs)}
WC_string = '_'.join(args.parameters)
def get_reweight_function():
''' return a weight function
'''
def reweight(event, sample):
return event.ref_lumiweight1fb * float(args.luminosity) * event_factor
# return sample.xsec * 1000 / sample.nEvents / event.p_C[0] * float(args.luminosity) * event_factor
return reweight
selection_string = cutInterpreter.cutString(args.selection)
selection_addon = "(1)"
# overlap removal + signal categorization
if args.process == 'ttgamma':
selection_addon = "(signalPhoton==1)"
selection_string += '&&' + selection_addon
#elif args.process == 'ttZ':
# selection_addon = "(signalZ==1)"
# selection_string += '&&' + selection_addon
# Make sure that weightString contains the same as weightFunction!!!
weightString = 'ref_lumiweight1fb*%s*%s' % (str(
args.luminosity), str(event_factor))
#weightString = '%s*1000/%s*%s*%s/p_C[0]' %( str(sample.xsec), str(sample.nEvents), str(args.luminosity), str(event_factor) )
weightFunction = get_reweight_function()
w.set_order(int(args.order))
def checkReferencePoint( sample ):
''' check if sample is simulated with a reference point
'''
return pickle.load(file(sample.reweight_pkl))['ref_point'] != {}
if args.small:
for s in [sample] + bg:
s.reduceFiles( to = 10 )
# configure samples
for s in [sample] + bg:
# Scale the plots with number of events used (implemented in ref_lumiweight1fb)
s.event_factor = s.nEvents / float( s.chain.GetEntries() )
print cutInterpreter.cutString(args.selection)
s.setSelectionString( cutInterpreter.cutString(args.selection) )
if checkReferencePoint( s ):
s.read_variables = ["ref_lumiweight1fb/F", VectorTreeVariable.fromString('p[C/F]', nMax=2000)]
# draw all WC + SM as line
stackList = [ [sample] for param in params ]
# draw all bg filled
bgParams = []
stackList += [ bg ]
bgParams = [[ {'legendText':s.name.split('_')[1], 'WC':{}, 'color':colorsBg[i]} for i, s in enumerate(bg) ]]
stack = Stack( *stackList )
allParams = params + bgParams
WC = {coeff: vals[i] for i, coeff in enumerate(coeffs)}
WC_string = '_'.join(args.parameters)
def get_reweight_function():
''' return a weight function
'''
def reweight(event, sample):
return event.ref_lumiweight1fb * args.luminosity * event_factor
# return sample.xsec * 1000 / sample.nEvents / event.p_C[0] * args.luminosity * event_factor
return reweight
selection_string = cutInterpreter.cutString(args.selection)
selection_addon = "(1)"
# overlap removal + signal categorization
if args.process == 'ttgamma':
selection_addon = "(signalPhoton==1)"
selection_string += '&&' + selection_addon
#elif args.process == 'ttZ':
# selection_addon = "(signalZ==1)"
# selection_string += '&&'+selection_addon
# Make sure that weightString contains the same as weightFunction!!!
weightString = 'ref_lumiweight1fb*%s*%s' % (str(
args.luminosity), str(event_factor))
#weightString = '%s*1000/%s*%s*%s/p_C[0]' %( str(sample.xsec), str(sample.nEvents), str(args.luminosity), str(event_factor) )
weightFunction = get_reweight_function()
