def getPlotter(blind=True,datadriven=False,control=False):
plotter = Plotter('Hpp4l')
plotter.setSelectionMap(sampleCuts)
if datadriven: plotter.addHistogramToStack('datadriven',datadrivenSamples)
mcSamples = samples if datadriven else allsamples
signalMap = sigMapDD if datadriven else sigMap
for s in mcSamples:
plotter.addHistogramToStack(s,signalMap[s])
if not control:
for signal in signals:
plotter.addHistogram(signal,signalMap[signal],signal=True)
if not blind: plotter.addHistogram('data',signalMap['data'])
return plotter
def getPlotter(blind=True, datadriven=False, control=False):
plotter = Plotter('Hpp4l')
plotter.setSelectionMap(sampleCuts)
if datadriven: plotter.addHistogramToStack('datadriven', datadrivenSamples)
mcSamples = samples if datadriven else allsamples
signalMap = sigMapDD if datadriven else sigMap
for s in mcSamples:
plotter.addHistogramToStack(s, signalMap[s])
if not control:
for signal in signals:
plotter.addHistogram(signal, signalMap[signal], signal=True)
if not blind: plotter.addHistogram('data', signalMap['data'])
return plotter
# z
'zMass' : {'xaxis': 'm^{#mu#mu} (GeV)', 'yaxis': 'Events / 0.5 GeV', 'numcol': 2, 'lumipos': 11, 'legendpos':34, 'rebin': map(lambda x: x*0.5, range(120,240,1)), 'logy': False, 'overflow': True},
'z1Pt' : {'xaxis': 'm^{#mu#mu} #mu_{1} p_{T} (GeV)', 'yaxis': 'Events / 5 GeV', 'numcol': 2, 'lumipos': 11, 'legendpos':34, 'rebin': range(0,150,5), 'logy': False, 'overflow': True},
'z2Pt' : {'xaxis': 'm^{#mu#mu} #mu_{2} p_{T} (GeV)', 'yaxis': 'Events / 5 GeV', 'numcol': 2, 'lumipos': 11, 'legendpos':34, 'rebin': range(0,150,5), 'logy': False, 'overflow': True},
# t
'mPt' : {'xaxis': '#mu p_{T} (GeV)', 'yaxis': 'Events / 5 GeV', 'numcol': 2, 'lumipos': 11, 'legendpos':34, 'rebin': range(0,150,5), 'logy': True, 'overflow': True},
# event
#'numVertices' : {'xaxis': 'Reconstructed Vertices', 'yaxis': 'Events'},
#'met' : {'xaxis': 'E_{T}^{miss} (GeV)', 'yaxis': 'Events / 20 GeV', 'rebin': range(0,320,20), 'numcol': 2, 'logy': False, 'overflow': True},
}
############################
### MC based BG estimate ###
############################
for s in samples:
plotter.addHistogramToStack(s,sigMap[s])
if not blind: plotter.addHistogram('data',sigMap['data'])
for plot in plots:
for sel in sels:
kwargs = deepcopy(plots[plot])
plotname = '{0}/{1}'.format(sel,plot)
savename = '{0}/mc/{1}'.format(sel,plot)
plotter.plot(plotname,savename,**kwargs)
# ratios of tight/loose as func of pt/eta
plotter.clearHistograms()
plotter.addHistogram('MC',sigMap['BG'])
if toPlot:
for p in plots.keys():
if p not in toPlot: plots.pop(p)
for s in special.keys():
for p in special[s].keys():
if p not in toPlot: special[s].pop(p)
############################
### MC based BG estimate ###
############################
if doMC:
for sel in sels:
plotter.clearHistograms()
for s in samples:
plotter.addHistogramToStack(s, sigMap[s])
if 'lowmass' not in sel and 'highmass' not in sel:
for signal in signals:
plotter.addHistogram(signal, sigMap[signal], signal=True)
if not blind or 'regionD' in sel or 'lowmass' in sel or 'highmass' in sel:
plotter.addHistogram('data', sigMap['data'])
for plot in plots:
kwargs = deepcopy(plots[plot])
plotname = '{0}/{1}'.format(sel, plot)
savename = '{0}/mc/{1}'.format(sel, plot)
plotter.plot(plotname, savename, **kwargs)
if blind and 'regionD' not in sel and 'lowmass' not in sel and 'highmass' not in sel:
'numVertices' : {'xaxis': 'Reconstructed Vertices', 'yaxis': 'Events'},
'met' : {'xaxis': 'E_{T}^{miss} (GeV)', 'yaxis': 'Events / 20 GeV', 'rebin': 2},
'metPhi' : {'xaxis': '#phi(E_{T}^{miss})', 'yaxis': 'Events', 'rebin': 5},
'mass' : {'xaxis': 'm_{4l} (GeV)', 'yaxis': 'Events / 20 GeV', 'rebin': 2, 'rangex': [0,1000]},
'st' : {'xaxis': '#Sigma p_{T}^{l} (GeV)', 'yaxis': 'Events / 20 GeV', 'rebin': 2, 'rangex': [0,1000]},
'nJets' : {'xaxis': 'Number of jets (p_{T} > 30 GeV)', 'yaxis': 'Events'},
}
############################
### MC based BG estimate ###
############################
if plotMC:
for s in allsamples:
zzPlotter.addHistogramToStack(s,sigMap[s])
zzPlotter.addHistogram('data',sigMap['data'])
if plotCount: plotCounts(zzPlotter,baseDir='default',saveDir='mc')
if plotCount: plotCounts(zzPlotter,baseDir='zWindow',saveDir='mc-zWindow')
for plot in plots:
kwargs = deepcopy(plots[plot])
plotWithChannels(zzPlotter,plot,baseDir='default',saveDir='mc',**kwargs)
plotWithChannels(zzPlotter,plot,baseDir='zWindow',saveDir='mc-zWindow',**kwargs)
##############################
### datadriven backgrounds ###
##############################
if plotDatadriven:
'QCD_Pt_1800to2400_TuneCUETP8M1_13TeV_pythia8',
'QCD_Pt_2400to3200_TuneCUETP8M1_13TeV_pythia8',
'QCD_Pt_3200toInf_TuneCUETP8M1_13TeV_pythia8',
],
'data': [
'SingleMuon',
'SingleElectron',
],
}
chans = ['me','em','mm','ee']
samples = ['W','T','TT','Z','WW']
for s in samples:
fakeratePlotter.addHistogramToStack(s,sigMap[s])
fakeratePlotter.addHistogram('data',sigMap['data'])
plots = {
# z cand
'zMass' : {'xaxis': 'm_{l^{+}l^{-}} (GeV)', 'yaxis': 'Events / 10 GeV', 'rebin': 10},
# m cand
'wtMt' : {'xaxis': 'm_{T}^{tag} (GeV)', 'yaxis': 'Events / 10 GeV', 'rebin': 10},
'tPt' : {'xaxis': 'p_{T}^{tag} (GeV)', 'yaxis': 'Events / 10 GeV', 'rebin': 10},
'tEta' : {'xaxis': '#eta^{tag}', 'yaxis': 'Events', 'rebin': 10},
# t cand
'wlMt' : {'xaxis': 'm_{T}^{l} (GeV)', 'yaxis': 'Events / 10 GeV', 'rebin': 10},
'lPt' : {'xaxis': 'p_{T}^{l} (GeV)', 'yaxis': 'Events / 10 GeV', 'rebin': 10},
'lEta' : {'xaxis': '#eta^{l}', 'yaxis': 'Events', 'rebin': 10},
# event
for s in samples3P1F + ['data']: histMap[s] += ['/'.join(['3P1F']+plotdirs)]
histMap['ZX'] += ['/'.join(['for3P1F','2P2F']+plotdirs)]
return histMap
############################
### MC based BG estimate ###
############################
if doMC:
for sel in selections:
for region in regions:
plotter.clearHistograms()
thisblind = blind and sel in ['4P0F'] and region in ['','hzz4l']
for s in ['ZX']+samples:
plotter.addHistogramToStack(s,sigMap[s])
if not thisblind: plotter.addHistogram('data',sigMap['data'])
for plot in plots:
kwargs = deepcopy(plots[plot])
plotname = '{}/{}/{}'.format(sel,region,plot) if region else '{}/{}'.format(sel,plot)
savename = '{}/{}/mc/{}'.format(sel,region,plot) if region else '{}/mc/{}'.format(sel,plot)
plotChannels(plotter,plotname,savename,**kwargs)
for alt in alt_plots.get(plot,[]):
kwargs = deepcopy(plots[plot])
kwargs.update(alt_plots[plot][alt])
savename = '{}/{}/mc/{}'.format(sel,region,alt) if region else '{}/mc/{}'.format(sel,alt)
plotChannels(plotter,plotname,savename,**kwargs)
'ZZTo2L2Nu_13TeV_powheg_pythia8',
'ZZTo2L2Q_13TeV_amcatnloFXFX_madspin_pythia8'
],
'VVV': ['WZZ_TuneCUETP8M1_13TeV-amcatnlo-pythia8'],
'TTV': ['TTWJetsToLNu_TuneCUETP8M1_13TeV-amcatnloFXFX-madspin-pythia8'],
'ZG': ['ZGTo2LG_TuneCUETP8M1_13TeV-amcatnloFXFX-pythia8'],
'WW': ['WWTo2L2Nu_13TeV-powheg'],
'Z': [
'DYJetsToLL_M-50_TuneCUETP8M1_13TeV-amcatnloFXFX-pythia8',
'DYJetsToLL_M-10to50_TuneCUETP8M1_13TeV-amcatnloFXFX-pythia8'
],
'TT': ['TTJets_TuneCUETP8M1_13TeV-amcatnloFXFX-pythia8'],
'data':
['DoubleMuon', 'DoubleEG', 'MuonEG', 'SingleMuon', 'SingleElectron'],
}
for s in ['TT', 'TTV', 'Z', 'ZG', 'WW', 'VVV', 'ZZ', 'WZ']:
wzPlotter.addHistogramToStack(s, sigMap[s])
wzPlotter.addHistogram('data', sigMap['data'])
plotStyles = {
'zMass': {
'xaxis': 'm_{l^{+}l^{#font[122]{\55}}}',
'yaxis': 'Events/1 GeV'
},
}
for plot in ['zMass']:
wzPlotter.plot(plot, plot, **plotStyles[plot])
'TTJets_SingleLeptFromT_TuneCUETP8M1_13TeV-madgraphMLM-pythia8',
'TTJets_SingleLeptFromTbar_TuneCUETP8M1_13TeV-madgraphMLM-pythia8',
],
'data': [
'DoubleMuon',
'DoubleEG',
'SingleMuon',
'SingleElectron',
],
}
#samples = ['WW','TT','Z']
samples = ['TT','Z']
for s in samples:
chargePlotter.addHistogramToStack(s,sigMap[s])
chargePlotter.addHistogram('data',sigMap['data'])
# plot definitions
plots = {
# z cand
'zMass' : {'xaxis': 'm_{l^{+}l^{-}} (GeV)', 'yaxis': 'Events / 1 GeV', 'rebin': range(81,101,1)},
'zLeadingLeptonPt' : {'xaxis': 'p_{T}^{Z_{lead}} (GeV)', 'yaxis': 'Events / 1 GeV', 'rebin': range(0,150,1)},
'zLeadingLeptonEta' : {'xaxis': '|#eta^{Z_{lead}}|', 'yaxis': 'Events',},
'zSubLeadingLeptonPt' : {'xaxis': 'p_{T}^{Z_{sublead}} (GeV)', 'yaxis': 'Events / 1 GeV', 'rebin': range(0,150,1)},
'zSubLeadingLeptonEta' : {'xaxis': '|#eta^{Z_{sublead}}|', 'yaxis': 'Events',},
}
# signal region
for plot in plots:
'numcol': 3,
'lumipos': 11,
'legendpos': 34,
'logy': False,
'yscale': 1.5,
'binlabels': ['']
},
#'numVertices' : {'xaxis': 'Reconstructed Vertices', 'yaxis': 'Events'},
#'met' : {'xaxis': 'E_{T}^{miss} (GeV)', 'yaxis': 'Events / 20 GeV', 'rebin': range(0,320,20), 'numcol': 2, 'logy': False, 'overflow': True},
}
############################
### MC based BG estimate ###
############################
for s in samples:
plotter.addHistogramToStack(s, sigMap[s])
if not blind: plotter.addHistogram('data', sigMap['data'])
for plot in plots:
for sel in sels:
kwargs = deepcopy(plots[plot])
plotname = '{0}/{1}'.format(sel, plot)
savename = '{0}/mc/{1}'.format(sel, plot)
plotter.plot(plotname, savename, **kwargs)
plotter.clearHistograms()
plotter.addHistogramToStack('W', sigMap['W'])
plotter.addHistogramToStack('TT', sigMap['TT'])
plotter.addHistogramToStack('VV', sigMap['WW'] + sigMap['WZ'] + sigMap['ZZ'])
plotter.addHistogramToStack('DYB', sigMap['Z'])
'numcol': 1
},
'mllMinusZ': {
'yaxis': '|m_{l^{+}l^{-}}-m_{Z}| (GeV)',
'xaxis': 'm_{#Phi^{#pm#pm}} (GeV)',
'legendpos': 13,
'numcol': 1
},
}
############################
### MC based BG estimate ###
############################
if plotMC:
for s in allsamples:
hpp4lPlotter.addHistogramToStack(s, sigMap[s])
for signal in signals:
hpp4lPlotter.addHistogram(signal, sigMap[signal], signal=True)
if not blind: hpp4lPlotter.addHistogram('data', sigMap['data'])
if plotCount: plotCounts(hpp4lPlotter, baseDir='default', saveDir='mc')
for plot in plots:
kwargs = deepcopy(plots[plot])
plotWithCategories(hpp4lPlotter,
plot,
baseDir='default',
saveDir='mc',
perCatBins=True,
samples = ['TTV', 'ZG', 'VVV', 'ZZ', 'WZ']
allsamples = ['W', 'TT', 'Z', 'WW', 'TTV', 'VVV', 'ZZall', 'WZall']
def addUncertainties(plotter, datadriven=False):
if datadriven:
plotter.addUncertainty('datadriven', nonprompt=0.3)
#################
### MC driven ###
#################
for s in allsamples:
name = s.replace('all', '')
wzPlotter.addHistogramToStack(name, sigMap[s])
wzPlotter.addHistogram('data', sigMap['data'])
addUncertainties(wzPlotter)
if doCounts and doMC:
plotCounts(wzPlotter, saveDir='mc', baseDir='default')
if doVBS: plotCounts(wzPlotter, saveDir='vbs', baseDir='default-vbs')
for cut in nMinusOneCuts:
if doNMinusOne:
plotCounts(wzPlotter,
baseDir='default/{0}'.format(cut),
saveDir='nMinusOne/{0}'.format(cut))
for cut in vbsNMinusOneCuts:
if doNMinusOne and doVBS:
for p in plots.keys():
if p not in toPlot: plots.pop(p)
for s in special.keys():
for p in special[s].keys():
if p not in toPlot: special[s].pop(p)
############################
### MC based BG estimate ###
############################
if doMC:
for sel in sels:
plotter.clearHistograms()
for s in samples:
plotter.addHistogramToStack(s,sigMap[s])
if 'lowmass' not in sel and 'highmass' not in sel:
for signal in signals:
plotter.addHistogram(signal,sigMap[signal],signal=True)
if not blind or 'regionA' not in sel or 'lowmass' in sel or 'highmass' in sel: plotter.addHistogram('data',sigMap['data'])
for plot in plots:
kwargs = deepcopy(plots[plot])
plotname = '{0}/{1}'.format(sel,plot)
savename = '{0}/mc/{1}'.format(sel,plot)
plotter.plot(plotname,savename,**kwargs)
if blind and 'regionA' in sel and 'lowmass' not in sel and 'highmass' not in sel: plotter.addHistogram('data',sigMap['data'])
'TTJets_SingleLeptFromT_TuneCUETP8M1_13TeV-madgraphMLM-pythia8',
'TTJets_SingleLeptFromTbar_TuneCUETP8M1_13TeV-madgraphMLM-pythia8',
],
'data': [
'DoubleMuon',
'DoubleEG',
'SingleMuon',
'SingleElectron',
],
}
#samples = ['WW','TT','Z']
samples = ['TT', 'Z']
for s in samples:
chargePlotter.addHistogramToStack(s, sigMap[s])
chargePlotter.addHistogram('data', sigMap['data'])
# plot definitions
plots = {
# z cand
'zMass': {
'xaxis': 'm_{l^{+}l^{-}} (GeV)',
'yaxis': 'Events / 1 GeV',
'rebin': range(81, 101, 1)
},
'zLeadingLeptonPt': {
'xaxis': 'p_{T}^{Z_{lead}} (GeV)',
'yaxis': 'Events / 1 GeV',
'rebin': range(0, 150, 1)
histMap['ZX'] += ['/'.join(['for3P1F', '2P2F'] + plotdirs)]
return histMap
############################
### MC based BG estimate ###
############################
if doMC:
for sel in selections:
for region in regions:
plotter.clearHistograms()
thisblind = blind and sel in ['4P0F'] and region in ['', 'hzz4l']
for s in ['ZX'] + samples:
plotter.addHistogramToStack(s, sigMap[s])
if not thisblind: plotter.addHistogram('data', sigMap['data'])
for plot in plots:
kwargs = deepcopy(plots[plot])
plotname = '{}/{}/{}'.format(
sel, region, plot) if region else '{}/{}'.format(
sel, plot)
savename = '{}/{}/mc/{}'.format(
sel, region, plot) if region else '{}/mc/{}'.format(
sel, plot)
plotChannels(plotter, plotname, savename, **kwargs)
for alt in alt_plots.get(plot, []):
kwargs = deepcopy(plots[plot])
# hpp
#'hppMass' : {'yaxis': 'm_{l^{#pm}l^{#pm}} (GeV)', 'xaxis': 'm_{#Phi^{#pm#pm}} (GeV)', 'legendpos':13, 'numcol': 1},
#'st' : {'yaxis': '#Sigma p_{T}^{l} (GeV)', 'xaxis': 'm_{#Phi^{#pm#pm}} (GeV)', 'legendpos':13, 'numcol': 1},
#'hppDeltaR' : {'yaxis': '#DeltaR(l^{+}l^{+})', 'xaxis': 'm_{#Phi^{#pm#pm}} (GeV)', 'legendpos':13, 'numcol': 1},
#'met' : {'yaxis': 'E_{T}^{miss} (GeV)', 'xaxis': 'm_{#Phi^{#pm#pm}} (GeV)', 'legendpos':13, 'numcol': 1},
#'mllMinusZ' : {'yaxis': '|m_{l^{+}l^{-}}-m_{Z}| (GeV)', 'xaxis': 'm_{#Phi^{#pm#pm}} (GeV)', 'legendpos':13, 'numcol': 1},
}
############################
### MC based BG estimate ###
############################
if plotMC:
for s in allsamples:
hpp4lPlotter.addHistogramToStack(s,sigMap[s])
for signal in signals:
hpp4lPlotter.addHistogram(signal,sigMap[signal],signal=True)
if not blind: hpp4lPlotter.addHistogram('data',sigMap['data'])
if plotCount: plotCounts(hpp4lPlotter,baseDir='default',saveDir='mc')
for plot in plots:
kwargs = deepcopy(plots[plot])
plotWithCategories(hpp4lPlotter,plot,baseDir='default',saveDir='mc',perCatBins=True,**kwargs)
if plot=='hppMass' or plotAllChannels: plotChannels(hpp4lPlotter,plot,baseDir='default',saveDir='mc',**kwargs)
# selection assuming 500
if plotSig500:
'z1Pt' : {'xaxis': 'm^{#mu#mu} #mu_{1} p_{T} (GeV)', 'yaxis': 'Events / 5 GeV', 'numcol': 2, 'lumipos': 11, 'legendpos':34, 'rebin': range(25,150,5), 'logy': False, 'overflow': True},
'z2Pt' : {'xaxis': 'm^{#mu#mu} #mu_{2} p_{T} (GeV)', 'yaxis': 'Events / 5 GeV', 'numcol': 2, 'lumipos': 11, 'legendpos':34, 'rebin': range(10,150,5), 'logy': False, 'overflow': True},
# t
'tPt' : {'xaxis': '#tau p_{T} (GeV)', 'yaxis': 'Events / 5 GeV', 'numcol': 2, 'lumipos': 11, 'legendpos':34, 'rebin': range(10,150,5), 'logy': False, 'overflow': True},
'tDM' : {'xaxis': '#tau Decay Mode', 'yaxis': 'Events', 'numcol': 3, 'lumipos': 11, 'legendpos':34, 'logy': False, 'yscale': 1.5,},
# event
'count' : {'xaxis': '', 'yaxis': 'Events', 'numcol': 3, 'lumipos': 11, 'legendpos':34, 'logy': False, 'yscale': 1.5,'binlabels': ['']},
#'numVertices' : {'xaxis': 'Reconstructed Vertices', 'yaxis': 'Events'},
#'met' : {'xaxis': 'E_{T}^{miss} (GeV)', 'yaxis': 'Events / 20 GeV', 'rebin': range(0,320,20), 'numcol': 2, 'logy': False, 'overflow': True},
}
############################
### MC based BG estimate ###
############################
for s in samples:
plotter.addHistogramToStack(s,sigMap[s])
if not blind: plotter.addHistogram('data',sigMap['data'])
for plot in plots:
for sel in sels:
kwargs = deepcopy(plots[plot])
plotname = '{0}/{1}'.format(sel,plot)
savename = '{0}/mc/{1}'.format(sel,plot)
plotter.plot(plotname,savename,**kwargs)
plotter.clearHistograms()
plotter.addHistogramToStack('W',sigMap['W'])
plotter.addHistogramToStack('TT',sigMap['TT'])
plotter.addHistogramToStack('VV',sigMap['WW']+sigMap['WZ']+sigMap['ZZ'])
envelope_cust = {
# hpp
'hppMass' : {'yaxis': 'm_{l^{#pm}l^{#pm}} (GeV)', 'xaxis': 'm_{#Phi^{#pm#pm}} (GeV)', 'legendpos':13, 'numcol': 1},
'st' : {'yaxis': '#Sigma p_{T}^{l} (GeV)', 'xaxis': 'm_{#Phi^{#pm#pm}} (GeV)', 'legendpos':13, 'numcol': 1},
'hppDeltaR' : {'yaxis': '#DeltaR(l^{+}l^{+})', 'xaxis': 'm_{#Phi^{#pm#pm}} (GeV)', 'legendpos':13, 'numcol': 1},
'met' : {'yaxis': 'E_{T}^{miss} (GeV)', 'xaxis': 'm_{#Phi^{#pm#pm}} (GeV)', 'legendpos':13, 'numcol': 1},
#'mllMinusZ' : {'yaxis': '|m_{l^{+}l^{-}}-m_{Z}| (GeV)', 'xaxis': 'm_{#Phi^{#pm#pm}} (GeV)', 'legendpos':13, 'numcol': 1},
}
############################
### MC based BG estimate ###
############################
if plotMC:
for s in allsamples:
hpp3lPlotter.addHistogramToStack(s,sigMap[s])
for signal in signals:
hpp3lPlotter.addHistogram(signal,sigMap[signal],signal=True)
if not blind: hpp3lPlotter.addHistogram('data',sigMap['data'])
if plotCount: plotCounts(hpp3lPlotter,saveDir='mc',baseDir='default')
for plot in plots:
kwargs = deepcopy(plots[plot])
plotWithCategories(hpp3lPlotter,plot,saveDir='mc',baseDir='default',perCatBins=True,**kwargs)
if plot=='hppMass': plotChannels(hpp3lPlotter,plot,saveDir='mc',baseDir='default',**kwargs)
# selection assuming mass 500
if plotSig500:
'GluGluToContinToZZTo4e_13TeV_MCFM701_pythia8',
'GluGluToContinToZZTo4mu_13TeV_MCFM701_pythia8',
'GluGluToContinToZZTo4tau_13TeV_MCFM701_pythia8',
'ZZTo2L2Nu_13TeV_powheg_pythia8',
'ZZTo2L2Q_13TeV_amcatnloFXFX_madspin_pythia8'],
'VVV' : ['WZZ_TuneCUETP8M1_13TeV-amcatnlo-pythia8'],
'TTV' : ['TTWJetsToLNu_TuneCUETP8M1_13TeV-amcatnloFXFX-madspin-pythia8'],
'ZG' : ['ZGTo2LG_TuneCUETP8M1_13TeV-amcatnloFXFX-pythia8'],
'WW' : ['WWTo2L2Nu_13TeV-powheg'],
'Z' : ['DYJetsToLL_M-50_TuneCUETP8M1_13TeV-amcatnloFXFX-pythia8',
'DYJetsToLL_M-10to50_TuneCUETP8M1_13TeV-amcatnloFXFX-pythia8'],
'TT' : ['TTJets_TuneCUETP8M1_13TeV-amcatnloFXFX-pythia8'],
'data': ['DoubleMuon',
'DoubleEG',
'MuonEG',
'SingleMuon',
'SingleElectron'],
'HppHmm500GeV' : ['HPlusPlusHMinusMinusHTo4L_M-500_13TeV-pythia8'],
}
for s in ['TT','TTV','Z','ZG','WW','VVV','ZZ','WZ']:
hpp4lPlotter.addHistogramToStack(s,sigMap[s])
hpp4lPlotter.addHistogram('HppHmm500GeV',sigMap['HppHmm500GeV'])
if not blind:
hpp4lPlotter.addHistogram('data',sigMap['data'])
for plot in ['hppMass']:
hpp4lPlotter.plot(plot,plot,logy=True,numcol=2,legendpos=34)
'DoubleMuon',
'DoubleEG',
],
}
#samples = ['TT','Z','W','T','WW','QCD']
samples = ['TT','Z','W','T','WW']
allSamplesDict = {'MC':[]}
for s in samples:
if s=='QCD': continue
allSamplesDict['MC'] += sigMap[s]
for s in samples:
dijetFakeRatePlotter.addHistogramToStack(s,sigMap[s])
dijetFakeRatePlotter.addHistogram('data',sigMap['data'])
# plot definitions
plots = {
'pt' : {'xaxis': 'p_{T} (GeV)', 'yaxis': 'Events/0.5 GeV', 'rebin': 5, 'rangex': [0,150]},
'eta' : {'xaxis': '#eta', 'yaxis': 'Events', 'rebin': 5, 'rangex': [-2.5,2.5]},
#'met' : {'xaxis': 'E_{T}^{miss} (GeV)', 'yaxis': 'Events/0.2 GeV', 'rebin': 2, 'rangex': [0,200]},
'wMass' : {'xaxis': 'm_{T}^{l^{+},MET} (GeV)', 'yaxis': 'Events/0.5 GeV', 'rebin': 5, 'rangex': [0,200]},
}
# signal region
for plot in plots:
for lepton in ['loose','medium','tight']:
for chan in chans:
controls = ['dy','tt']
samples = ['TTV','ZG','VVV','ZZ','WZ']
allsamples = ['W','TT','Z','WW','TTV','VVV','ZZall','WZall']
def addUncertainties(plotter,datadriven=False):
if datadriven:
plotter.addUncertainty('datadriven',nonprompt=0.3)
#################
### MC driven ###
#################
for s in allsamples:
name = s.replace('all','')
wzPlotter.addHistogramToStack(name,sigMap[s])
wzPlotter.addHistogram('data',sigMap['data'])
addUncertainties(wzPlotter)
if doCounts and doMC:
plotCounts(wzPlotter,saveDir='mc',baseDir='default')
if doVBS: plotCounts(wzPlotter,saveDir='vbs',baseDir='default-vbs')
for cut in nMinusOneCuts:
if doNMinusOne: plotCounts(wzPlotter,baseDir='default/{0}'.format(cut),saveDir='nMinusOne/{0}'.format(cut))
for cut in vbsNMinusOneCuts:
if doNMinusOne and doVBS: plotCounts(wzPlotter,baseDir='default-vbs/{0}'.format(cut),saveDir='vbsNMinusOne/{0}'.format(cut))
for control in controls:
if doControls: plotCounts(wzPlotter,saveDir=control,baseDir=control)
if doVBS and doControls: plotCounts(wzPlotter,saveDir=control+'-vbs',baseDir=control+'-vbs')