########################
### normalized plots ###
########################
if plotNormalization:
hpp3lPlotter.clearHistograms()
hpp3lPlotter.addHistogram('BG',allSamplesDict['BG'])
for signal in signals:
hpp3lPlotter.addHistogram(signal,sigMap[signal],signal=True)
for plot in plots:
plotname = 'default/{0}'.format(plot)
savename = 'normalized/{0}'.format(plot)
kwargs = deepcopy(plots[plot])
if plot in norm_cust: kwargs.update(norm_cust[plot])
hpp3lPlotter.plotNormalized(plotname,savename,**kwargs)
for cat in cats:
plotnames = []
for subcat in subCatChannels[cat]:
plotnames += ['default/{0}/{1}'.format(chan,plot) for chan in subCatChannels[cat][subcat]]
savename = 'normalized/{0}/{1}'.format(cat,plot)
if doCat: hpp3lPlotter.plotNormalized(plotnames,savename,**kwargs)
####################################
### Signal over background plots ###
####################################
if plotSOverB:
hpp3lPlotter.clearHistograms()
hpp3lPlotter.addHistogram('BG',allSamplesDict['BG'])
sigOrder = []
for varname in ['wzLoose','wzMedium','wzTight','heepV60','mvaTrigWP80','mvaTrigWP90','mvaNonTrigWP80','mvaNonTrigWP90']:
plotname = 'electron_{0}'.format(varname)
num = 'h_{0}_numerator'.format(plotname)
numFake = 'h_{0}_fake_numerator'.format(plotname)
numJet = 'h_{0}_jet_numerator'.format(plotname)
denom = 'h_{0}_denominator'.format(plotname)
denomJet = 'h_{0}_jet_denominator'.format(plotname)
denomFake = 'h_{0}_fake_denominator'.format(plotname)
numMap = {'Z':num, 'TT':numJet, 'QCD': numFake, 'HppHmm500GeV':num}
denomMap = {'Z':denom, 'TT':denomJet, 'QCD': denomFake, 'HppHmm500GeV':denom}
effPlotter.plotRatio(numMap,denomMap,plotname,numcol=2,legendpos=34,ymax=1.3,ymin=0.,yaxis='Efficiency',xaxis='p^{e}_{T} (GeV)',rebin=5)
sigPlotter.plotRatio(num,denom,plotname,numcol=2,legendpos=34,ymax=1.3,ymin=0.,yaxis='Efficiency',xaxis='p^{e}_{T} (GeV)',rebin=5)
for region in ['barrel','endcap']:
for var in ['pt','sigmaIEtaIEta','absDEtaIn','absDPhiIn','hOverE','relIsoEA','ooEmooP','absDxy','absDz','conversionVeto']:
varname = 'h_electron_{0}_{1}'.format(var,region)
effPlotter.plotNormalized(varname,varname,legendps=34,yaxis='Unit Normalized',xaxis=xaxisMap[var],customOrder=['Z','HppHmm500GeV'],logy=0)
sigPlotter.plotNormalized(varname,varname,legendps=34,yaxis='Unit Normalized',xaxis=xaxisMap[var],logy=0)
for var in ['eta']:
varname = 'h_electron_{0}'.format(var)
effPlotter.plotNormalized(varname,varname,legendps=34,yaxis='Unit Normalized',xaxis=xaxisMap[var],customOrder=['Z','HppHmm500GeV'],logy=0)
sigPlotter.plotNormalized(varname,varname,legendps=34,yaxis='Unit Normalized',xaxis=xaxisMap[var],logy=0)
# muons
for varname in ['wzLoose','wzMedium','highPt_tightiso','tight_tightiso']:
plotname = 'muon_{0}'.format(varname)
num = 'h_{0}_numerator'.format(plotname)
numFake = 'h_{0}_fake_numerator'.format(plotname)
numJet = 'h_{0}_jet_numerator'.format(plotname)
denom = 'h_{0}_denominator'.format(plotname)
denomFake = 'h_{0}_fake_denominator'.format(plotname)
########################
### normalized plots ###
########################
if plotNormalization:
hpp4lPlotter.clearHistograms()
hpp4lPlotter.addHistogram('BG',allSamplesDict['BG'])
for signal in signals:
hpp4lPlotter.addHistogram(signal,sigMap[signal],signal=True)
for plot in plots:
plotname = 'default/{0}'.format(plot)
savename = 'normalized/{0}'.format(plot)
kwargs = deepcopy(plots[plot])
if plot in norm_cust: kwargs.update(norm_cust[plot])
hpp4lPlotter.plotNormalized(plotname,savename,**kwargs)
for cat in cats:
plotnames = []
for subcat in subCatChannels[cat]:
plotnames += ['default/{0}/{1}'.format(chan,plot) for chan in subCatChannels[cat][subcat]]
savename = 'normalized/{0}/{1}'.format(cat,plot)
if doCat: hpp4lPlotter.plotNormalized(plotnames,savename,**kwargs)
if plotSOverB:
hpp4lPlotter.clearHistograms()
hpp4lPlotter.addHistogram('BG',allSamplesDict['BG'])
sigOrder = []
bgOrder = []
for mass in masses:
name = 'HppHmm{0}GeV'.format(mass)
'name': 'Region {}'.format(region),
'linecolor': colors[region],
'fillcolor': colors[region]
})
for plot in plots:
kwargs = deepcopy(plots[plot])
plotname = {
'region{}'.format(region):
'lowmass/region{}/{}'.format(region, plot)
for region in regions
}
savename = 'lowmass/regions/{}'.format(plot)
plotter.plot(plotname, savename, **kwargs)
savename = 'lowmass/regions_normalized/{}'.format(plot)
plotter.plotNormalized(plotname, savename, **kwargs)
for s in special:
for plot in special[s]:
kwargs = deepcopy(special[s][plot])
plotname = {
'region{}'.format(region):
'lowmass/region{}/{}'.format(region, plot)
for region in regions
}
savename = 'lowmass/regions/{}_{}'.format(plot, s)
plotter.plot(plotname, savename, **kwargs)
savename = 'lowmass/regions_normalized/{}_{}'.format(plot, s)
plotter.plotNormalized(plotname, savename, **kwargs)
for plot in plots:
########################
### normalized plots ###
########################
if plotNormalization:
hpp4lPlotter.clearHistograms()
hpp4lPlotter.addHistogram('BG', allSamplesDict['BG'])
for signal in signals:
hpp4lPlotter.addHistogram(signal, sigMap[signal], signal=True)
for plot in plots:
plotname = 'default/{0}'.format(plot)
savename = 'normalized/{0}'.format(plot)
kwargs = deepcopy(plots[plot])
if plot in norm_cust: kwargs.update(norm_cust[plot])
hpp4lPlotter.plotNormalized(plotname, savename, **kwargs)
for cat in cats:
plotnames = []
for subcat in subCatChannels[cat]:
plotnames += [
'default/{0}/{1}'.format(chan, plot)
for chan in subCatChannels[cat][subcat]
]
savename = 'normalized/{0}/{1}'.format(cat, plot)
if doCat:
hpp4lPlotter.plotNormalized(plotnames, savename, **kwargs)
if plotSOverB:
hpp4lPlotter.clearHistograms()
hpp4lPlotter.addHistogram('BG', allSamplesDict['BG'])
"sigmaIEtaIEta",
"absDEtaIn",
"absDPhiIn",
"hOverE",
"relIsoEA",
"ooEmooP",
"absDxy",
"absDz",
"conversionVeto",
]:
varname = "h_electron_{0}_{1}".format(var, region)
effPlotter.plotNormalized(
varname,
varname,
legendps=34,
yaxis="Unit Normalized",
xaxis=xaxisMap[var],
customOrder=["Z", "HppHmm500GeV"],
logy=0,
)
sigPlotter.plotNormalized(varname, varname, legendps=34, yaxis="Unit Normalized", xaxis=xaxisMap[var], logy=0)
for var in ["eta"]:
varname = "h_electron_{0}".format(var)
effPlotter.plotNormalized(
varname,
varname,
legendps=34,
yaxis="Unit Normalized",
xaxis=xaxisMap[var],
customOrder=["Z", "HppHmm500GeV"],
logy=0,
for varname in ['wzLoose','wzMedium','wzTight','heepV60','mvaTrigWP80','mvaTrigWP90','mvaNonTrigWP80','mvaNonTrigWP90']:
plotname = 'electron_{0}'.format(varname)
num = 'h_{0}_numerator'.format(plotname)
numFake = 'h_{0}_fake_numerator'.format(plotname)
numJet = 'h_{0}_jet_numerator'.format(plotname)
denom = 'h_{0}_denominator'.format(plotname)
denomJet = 'h_{0}_jet_denominator'.format(plotname)
denomFake = 'h_{0}_fake_denominator'.format(plotname)
numMap = {'Z':num, 'TT':numJet, 'QCD': numFake, 'HppHmm500GeV':num}
denomMap = {'Z':denom, 'TT':denomJet, 'QCD': denomFake, 'HppHmm500GeV':denom}
effPlotter.plotRatio(numMap,denomMap,plotname,numcol=2,legendpos=34,ymax=1.3,ymin=0.,yaxis='Efficiency',xaxis='p^{e}_{T} (GeV)',rebin=5)
sigPlotter.plotRatio(num,denom,plotname,numcol=2,legendpos=34,ymax=1.3,ymin=0.,yaxis='Efficiency',xaxis='p^{e}_{T} (GeV)',rebin=5)
for region in ['barrel','endcap']:
for var in ['pt','sigmaIEtaIEta','absDEtaIn','absDPhiIn','hOverE','relIsoEA','ooEmooP','absDxy','absDz','conversionVeto']:
varname = 'h_electron_{0}_{1}'.format(var,region)
effPlotter.plotNormalized(varname,varname,legendps=34,yaxis='Unit Normalized',xaxis=xaxisMap[var],customOrder=['Z','HppHmm500GeV'],logy=0)
sigPlotter.plotNormalized(varname,varname,legendps=34,yaxis='Unit Normalized',xaxis=xaxisMap[var],logy=0)
for var in ['eta']:
varname = 'h_electron_{0}'.format(var)
effPlotter.plotNormalized(varname,varname,legendps=34,yaxis='Unit Normalized',xaxis=xaxisMap[var],customOrder=['Z','HppHmm500GeV'],logy=0)
sigPlotter.plotNormalized(varname,varname,legendps=34,yaxis='Unit Normalized',xaxis=xaxisMap[var],logy=0)
# muons
for varname in ['wzLoose','wzMedium','highPt_tightiso','tight_tightiso']:
plotname = 'muon_{0}'.format(varname)
num = 'h_{0}_numerator'.format(plotname)
numFake = 'h_{0}_fake_numerator'.format(plotname)
numJet = 'h_{0}_jet_numerator'.format(plotname)
denom = 'h_{0}_denominator'.format(plotname)
denomFake = 'h_{0}_fake_denominator'.format(plotname)
plotter.clearHistograms()
name = signame.format(h=h,a=a)
ggname = ggsigname.format(h=h,a=a)
vbfname = vbfsigname.format(h=h,a=a)
plotter.addHistogram(ggname,sigMap[ggname],signal=True)
plotter.addHistogram(vbfname,sigMap[vbfname],signal=True)
for plot in plots:
for sel in signalsels:
if 'lowmass' in sel or 'highmass' in sel: continue
kwargs = deepcopy(plots[plot])
if plot in plotsSignal: kwargs.update(deepcopy(plotsSignal[plot]))
plotname = '{0}/{1}'.format(sel,plot)
savename = '{0}/h{h}a{a}/{1}'.format(sel,plot,a=a,h=h)
plotter.plotNormalized(plotname,savename,plotratio=False,**kwargs)
##################
### Datadriven ###
##################
# for now, only data (no mc subtraction)
def getDatadrivenPlot(*plots,**kwargs):
region = kwargs.pop('region','A')
source = kwargs.pop('source','D')
looseMVA = kwargs.pop('looseMVA',None)
tag = kwargs.pop('tag',None)
histMap = {}
for s in samples+signals+allsignals+['data','datadriven']: histMap[s] = []
for plot in plots:
aplot = '{}region{}/{}'.format(tag+'/' if tag else '',region,plot)
bplot = '{}region{}_fakeFor{}/{}'.format(tag+'/' if tag else '',source,region,plot)
