for s in signalsPP:
counters[s] = Counter('Hpp3l')
counters[s].addProcess(s,sigMap[s],signal=True)
for s in signalsPPR:
counters[s] = Counter('Hpp3l')
counters[s].addProcess(s,sigMap[s],signal=True)
counters['data'] = Counter('Hpp3l')
counters['data'].addProcess('data',sigMap['data'])
for mode in modes:
for mass in masses:
logging.info('Producing datacard for {0} - {1} GeV'.format(mode,mass))
results = {}
limits = Limits()
limits.addEra('Era13TeV2016')
limits.addAnalysis('Hpp3l')
limits.addAnalysis('Hpp3lAP')
limits.addAnalysis('Hpp3lPP')
limits.addAnalysis('Hpp3lPPR')
recoChans = getRecoChans(mode)
for reco in recoChans:
limits.addChannel(reco)
limits.addChannel(reco+'_SB')
signalsAP = ['HppHm{0}GeV'.format(mass)]
signalsPP = ['HppHmm{0}GeV'.format(mass)]
signalsPPR = ['HppHmmR{0}GeV'.format(mass)]
for proc in samples:
hists += [histMap[proc]]
hist = sumHists('obs',*hists)
for b in range(hist.GetNbinsX()+1):
val = int(hist.GetBinContent(b))
if val<0: val = 0
err = val**0.5
hist.SetBinContent(b,val)
#hist.SetBinError(b,err)
histMap['data'] = hist
else:
hist = getBinned('data')
histMap['data'] = hist
# create limit object
limits = Limits(wsname)
limits.addEra('Run2016')
limits.addAnalysis('ThreePhoton')
limits.addChannel('ggg')
if doParametric:
limits.addMH(*binning[1:])
limits.addX(*binning[1:])
if doParametric:
limits.addProcess('sig',signal=True)
limits.addProcess('bg')
else:
for signal in signals:
limits.addProcess(signal,signal=True)
for s in signals:
counters[s] = Counter('Hpp4l')
counters[s].addProcess(s, sigMap[s], signal=True)
for s in signalsR:
counters[s] = Counter('Hpp4l')
counters[s].addProcess(s, sigMap[s], signal=True)
counters['data'] = Counter('Hpp4l')
counters['data'].addProcess('data', sigMap['data'])
for mode in modes:
for mass in masses:
logging.info('Producing datacard for {0} - {1} GeV'.format(mode, mass))
results = {}
limits = Limits()
limits.addEra('Era13TeV2016')
limits.addAnalysis('Hpp4l')
recoChans = getRecoChans(mode)
for reco in recoChans:
limits.addChannel(reco)
limits.addChannel(reco + '_SB')
signals = ['HppHmm{0}GeV'.format(mass)]
signalsR = ['HppHmmR{0}GeV'.format(mass)]
for sig in signals + signalsR:
limits.addProcess(sig, signal=True)
for background in backgrounds:
for s in signalsPP:
counters[s] = Counter('Hpp3l')
counters[s].addProcess(s, sigMap[s], signal=True)
for s in signalsPPR:
counters[s] = Counter('Hpp3l')
counters[s].addProcess(s, sigMap[s], signal=True)
counters['data'] = Counter('Hpp3l')
counters['data'].addProcess('data', sigMap['data'])
for mode in modes:
for mass in masses:
logging.info('Producing datacard for {0} - {1} GeV'.format(mode, mass))
results = {}
limits = Limits()
limits.addEra('13TeV80X')
limits.addAnalysis('Hpp3l')
limits.addAnalysis('Hpp3lAP')
limits.addAnalysis('Hpp3lPP')
limits.addAnalysis('Hpp3lPPR')
recoChans = getRecoChans(mode)
for reco in recoChans:
limits.addChannel(reco)
limits.addChannel(reco + '_SB')
signalsAP = ['HppHm{0}GeV'.format(mass)]
signalsPP = ['HppHmm{0}GeV'.format(mass)]
signalsPPR = ['HppHmmR{0}GeV'.format(mass)]
for proc in samples:
hists += [histMap[proc]]
hist = sumHists('obs', *hists)
for b in range(hist.GetNbinsX() + 1):
val = int(hist.GetBinContent(b))
if val < 0: val = 0
err = val**0.5
hist.SetBinContent(b, val)
#hist.SetBinError(b,err)
histMap['data'] = hist
else:
hist = getBinned('data')
histMap['data'] = hist
# create limit object
limits = Limits(wsname)
limits.addEra('Run2016')
limits.addAnalysis('ThreePhoton')
limits.addChannel('ggg')
if doParametric:
limits.addMH(*binning[1:])
limits.addX(*binning[1:])
if doParametric:
limits.addProcess('sig', signal=True)
limits.addProcess('bg')
else:
for signal in signals:
limits.addProcess(signal, signal=True)
def create_datacard(args):
doMatrix = False
doParametric = args.parametric
doUnbinned = args.unbinned
do2D = len(args.fitVars) == 2
blind = not args.unblind
addSignal = args.addSignal
signalParams = {'h': args.higgs, 'a': args.pseudoscalar}
wsname = 'w'
var = args.fitVars
if do2D and doParametric:
logging.error('Parametric 2D fits are not yet supported')
raise
if doUnbinned and not doParametric:
logging.error('Unbinned only supported with parametric option')
raise
#############
### Setup ###
#############
sampleMap = getSampleMap()
backgrounds = ['datadriven']
data = ['data']
signals = [signame.format(h=h, a=a) for h in hmasses for a in amasses]
signalToAdd = signame.format(**signalParams)
signalSplines = [splinename.format(h=h) for h in hmasses]
wrappers = {}
for proc in backgrounds + signals + data:
if proc == 'datadriven': continue
for sample in sampleMap[proc]:
wrappers[sample] = NtupleWrapper('MuMuTauTau',
sample,
new=True,
version='80X')
for shift in shifts:
wrappers[sample + shift] = NtupleWrapper('MuMuTauTau',
sample,
new=True,
version='80X',
shift=shift)
##############################
### Create/read histograms ###
##############################
histMap = {}
# The definitons of which regions match to which arguments
# PP can take a fake rate datadriven estimate from PF, but PF can only take the observed values
regionArgs = {
'PP': {
'region': 'A',
'fakeRegion': 'B',
'source': 'B',
'sources': ['A', 'C'],
'fakeSources': ['B', 'D'],
},
'PF': {
'region': 'B',
'sources': ['B', 'D'],
},
}
for mode in ['PP', 'PF']:
histMap[mode] = {}
for shift in [''] + shifts:
histMap[mode][shift] = {}
for proc in backgrounds + signals:
logging.info('Getting {} {}'.format(proc, shift))
if proc == 'datadriven':
# TODO: unbinned, get the RooDataHist from flattenener first
if mode == 'PP':
if doMatrix:
histMap[mode][shift][
proc] = getMatrixDatadrivenHist(
var=var,
wrappers=wrappers,
shift=shift,
do2D=do2D,
**regionArgs[mode])
else:
histMap[mode][shift][proc] = getDatadrivenHist(
var=var,
wrappers=wrappers,
shift=shift,
do2D=do2D,
**regionArgs[mode])
else:
if doMatrix:
histMap[mode][shift][proc] = getMatrixHist(
'data',
var=var,
wrappers=wrappers,
shift=shift,
do2D=do2D,
**regionArgs[mode])
else:
histMap[mode][shift][proc] = getHist(
'data',
var=var,
wrappers=wrappers,
shift=shift,
do2D=do2D,
**regionArgs[mode])
else:
if doMatrix:
histMap[mode][shift][proc] = getMatrixHist(
proc,
var=var,
wrappers=wrappers,
shift=shift,
do2D=do2D,
**regionArgs[mode])
else:
histMap[mode][shift][proc] = getHist(
proc,
var=var,
wrappers=wrappers,
shift=shift,
do2D=do2D,
**regionArgs[mode])
if do2D:
pass # TODO, figure out how to rebin 2D
else:
histMap[mode][shift][proc].Rebin(rebinning[var[0]])
if shift: continue
logging.info('Getting observed')
if blind:
samples = backgrounds
if addSignal: samples = backgrounds + [signalToAdd]
hists = []
for proc in samples:
hists += [histMap[mode][shift][proc]]
hist = sumHists('obs', *hists)
#for b in range(hist.GetNbinsX()+1):
# val = int(hist.GetBinContent(b))
# if val<0: val = 0
# err = val**0.5
# hist.SetBinContent(b,val)
# #hist.SetBinError(b,err)
histMap[mode][shift]['data'] = hist
else:
hist = getHist('data',
var=var,
wrappers=wrappers,
do2D=do2D,
**regionArgs[mode])
histMap[mode][shift]['data'] = hist
if do2D:
pass
else:
histMap[mode][shift]['data'].Rebin(rebinning[var[0]])
#####################
### Create Limits ###
#####################
limits = Limits(wsname)
limits.addEra('Run2016')
limits.addAnalysis('HAA')
era = 'Run2016'
analysis = 'HAA'
reco = 'mmmt'
for mode in ['PP', 'PF']:
limits.addChannel(mode)
if doParametric:
binning = varBinning[var[0]]
limits.addMH(*binning[1:])
limits.addX(*binning[1:], unit='GeV', label='m_{#mu#mu}')
for h in hmasses:
limits.addProcess(splinename.format(h=h), signal=True)
for background in backgrounds:
limits.addProcess(background)
# add models
for h in hmasses:
model = getSpline(histMap[mode][''], h, tag=mode)
limits.setExpected(splinename.format(h=h), era, analysis, mode,
model)
if doUnbinned:
bg = buildModel(limits, tag=mode)
limits.setExpected('datadriven', era, analysis, mode, bg)
else:
# add histograms for background if not using an unbinned model
for bg in backgrounds:
limits.setExpected(bg, era, analysis, mode,
histMap[mode][''][bg])
# get roodatahist
limits.setObserved(era, analysis, mode, histMap[mode]['']['data'])
else:
for signal in signals:
limits.addProcess(signal, signal=True)
for background in backgrounds:
limits.addProcess(background)
for proc in backgrounds:
limits.setExpected(proc, era, analysis, mode,
histMap[mode][''][proc])
for proc in signals:
limits.setExpected(proc, era, analysis, mode,
histMap[mode][''][proc])
limits.setObserved(era, analysis, mode, histMap[mode]['']['data'])
#########################
### Add uncertainties ###
#########################
systproc = tuple(
[proc for proc in signals + backgrounds if 'datadriven' not in proc])
allproc = tuple([proc for proc in signals + backgrounds])
systsplineproc = tuple([
proc for proc in signalSplines + backgrounds
if 'datadriven' not in proc
])
allsplineproc = tuple([proc for proc in signalSplines + backgrounds])
bgproc = tuple([proc for proc in backgrounds])
sigsplineproc = tuple([proc for proc in signalSplines])
sigproc = tuple([proc for proc in signals])
############
### stat ###
############
def getStat(hist, direction):
newhist = hist.Clone('{0}{1}'.format(hist.GetName(), direction))
nb = hist.GetNbinsX() * hist.GetNbinsY()
for b in range(nb + 1):
val = hist.GetBinContent(b + 1)
err = hist.GetBinError(b + 1)
newval = val + err if direction == 'Up' else val - err
if newval < 0: newval = 0
newhist.SetBinContent(b + 1, newval)
newhist.SetBinError(b + 1, 0)
return newhist
logging.info('Adding stat systematic')
statMapUp = {}
statMapDown = {}
for proc in backgrounds + signals:
statMapUp[proc] = getStat(histMap[mode][''][proc], 'Up')
statMapDown[proc] = getStat(histMap[mode][''][proc], 'Down')
statsyst = {}
for mode in ['PP', 'PF']:
# background
if doUnbinned:
# TODO: add errors on params
pass
else:
for proc in bgproc:
statsyst[((proc, ), (era, ), (analysis, ),
(mode, ))] = (statMapUp[proc], statMapDown[proc])
# signal
if doParametric:
for h in hmasses:
statsyst[((splinename.format(h=h), ), (era, ), (analysis, ),
(mode, ))] = (getSpline(statMapUp,
h,
tag=mode + 'StatUp'),
getSpline(statMapDown,
h,
tag=mode + 'StatDown'))
else:
for proc in sigproc:
statsyst[((proc, ), (era, ), (analysis, ),
(mode, ))] = (statMapUp[proc], statMapDown[proc])
limits.addSystematic('stat_{process}_{channel}',
'shape',
systematics=statsyst)
##############
### shifts ###
##############
for shift in shiftTypes:
logging.info('Adding {} systematic'.format(shift))
shiftsyst = {}
for mode in ['PP', 'PF']:
# background
if doUnbinned:
# TODO rateParams on bg model
pass
else:
for proc in bgproc:
shiftsyst[((proc, ), (era, ), (analysis, ),
(mode, ))] = (histMap[mode][shift + 'Up'][proc],
histMap[mode][shift +
'Down'][proc])
# signal
if doParametric:
for h in hmasses:
shiftsyst[((splinename.format(h=h), ), (era, ),
(analysis, ),
(mode, ))] = (getSpline(
histMap[mode][shift + 'Up'],
h,
tag=mode + shift + 'Up'),
getSpline(
histMap[mode][shift + 'Down'],
h,
tag=mode + shift + 'Down'))
else:
for proc in sigproc:
shiftsyst[((proc, ), (era, ), (analysis, ),
(mode, ))] = (histMap[mode][shift + 'Up'][proc],
histMap[mode][shift +
'Down'][proc])
limits.addSystematic(shift, 'shape', systematics=shiftsyst)
############
### Lumi ###
############
# lumi 2.3% for 2015 and 2.5% for 2016
# https://twiki.cern.ch/twiki/bin/view/CMS/TWikiLUM#CurRec
logging.info('Adding lumi systematic')
lumiproc = systsplineproc if doParametric else systproc
lumisyst = {
(lumiproc, (era, ), ('all', ), ('all', )): 1.025,
}
limits.addSystematic('lumi', 'lnN', systematics=lumisyst)
############
### muon ###
############
# from z: 1 % + 0.5 % + 0.5 % per muon for id + iso + trig (pt>20)
logging.info('Adding mu id+iso systematic')
muproc = systsplineproc if doParametric else systproc
musyst = {
(muproc, (era, ), ('all', ), ('all', )):
1 + math.sqrt(sum([0.01**2, 0.005**2] * 2 +
[0.01**2])), # 2 lead have iso, tau_mu doesnt
}
limits.addSystematic('muid', 'lnN', systematics=musyst)
logging.info('Adding mu trig systematic')
musyst = {
(muproc, (era, ), ('all', ), ('all', )): 1.005, # 1 triggering muon
}
limits.addSystematic('mutrig', 'lnN', systematics=musyst)
###########
### tau ###
###########
# 5% on sf 0.99 (VL/L) or 0.97 (M)
logging.info('Adding mu id+iso systematic')
tauproc = systsplineproc if doParametric else systproc
tausyst = {
(tauproc, (era, ), ('all', ), ('all', )): 1.05,
}
limits.addSystematic('tauid', 'lnN', systematics=tausyst)
######################
### Print datacard ###
######################
directory = 'datacards_shape/{0}'.format('MuMuTauTau')
python_mkdir(directory)
datacard = '{0}/mmmt_{1}'.format(
directory, args.tag) if args.tag else '{}/mmmt'.format(directory)
processes = {}
if doParametric:
for h in hmasses:
processes[signame.format(
h=h, a='X')] = [splinename.format(h=h)] + backgrounds
else:
for signal in signals:
processes[signal] = [signal] + backgrounds
limits.printCard(datacard,
processes=processes,
blind=False,
saveWorkspace=doParametric)
def create_datacard(args):
doMatrix = False
doParametric = args.parametric
doUnbinned = args.unbinned
do2D = len(args.fitVars)==2
blind = not args.unblind
addSignal = args.addSignal
signalParams = {'h': args.higgs, 'a': args.pseudoscalar}
wsname = 'w'
var = args.fitVars
if do2D and doParametric:
logging.error('Parametric 2D fits are not yet supported')
raise
if doUnbinned and not doParametric:
logging.error('Unbinned only supported with parametric option')
raise
#############
### Setup ###
#############
sampleMap = getSampleMap()
backgrounds = ['datadriven']
data = ['data']
signals = [signame.format(h=h,a=a) for h in hmasses for a in amasses]
signalToAdd = signame.format(**signalParams)
signalSplines = [splinename.format(h=h) for h in hmasses]
wrappers = {}
for proc in backgrounds+signals+data:
if proc=='datadriven': continue
for sample in sampleMap[proc]:
wrappers[sample] = NtupleWrapper('MuMuTauTau',sample,new=True,version='80X')
for shift in shifts:
wrappers[sample+shift] = NtupleWrapper('MuMuTauTau',sample,new=True,version='80X',shift=shift)
##############################
### Create/read histograms ###
##############################
histMap = {}
# The definitons of which regions match to which arguments
# PP can take a fake rate datadriven estimate from PF, but PF can only take the observed values
regionArgs = {
'PP': {'region':'A','fakeRegion':'B','source':'B','sources':['A','C'],'fakeSources':['B','D'],},
'PF': {'region':'B','sources':['B','D'],},
}
for mode in ['PP','PF']:
histMap[mode] = {}
for shift in ['']+shifts:
histMap[mode][shift] = {}
for proc in backgrounds+signals:
logging.info('Getting {} {}'.format(proc,shift))
if proc=='datadriven':
# TODO: unbinned, get the RooDataHist from flattenener first
if mode=='PP':
if doMatrix:
histMap[mode][shift][proc] = getMatrixDatadrivenHist(var=var,wrappers=wrappers,shift=shift,do2D=do2D,**regionArgs[mode])
else:
histMap[mode][shift][proc] = getDatadrivenHist(var=var,wrappers=wrappers,shift=shift,do2D=do2D,**regionArgs[mode])
else:
if doMatrix:
histMap[mode][shift][proc] = getMatrixHist('data',var=var,wrappers=wrappers,shift=shift,do2D=do2D,**regionArgs[mode])
else:
histMap[mode][shift][proc] = getHist('data',var=var,wrappers=wrappers,shift=shift,do2D=do2D,**regionArgs[mode])
else:
if doMatrix:
histMap[mode][shift][proc] = getMatrixHist(proc,var=var,wrappers=wrappers,shift=shift,do2D=do2D,**regionArgs[mode])
else:
histMap[mode][shift][proc] = getHist(proc,var=var,wrappers=wrappers,shift=shift,do2D=do2D,**regionArgs[mode])
if do2D:
pass # TODO, figure out how to rebin 2D
else:
histMap[mode][shift][proc].Rebin(rebinning[var[0]])
if shift: continue
logging.info('Getting observed')
if blind:
samples = backgrounds
if addSignal: samples = backgrounds + [signalToAdd]
hists = []
for proc in samples:
hists += [histMap[mode][shift][proc]]
hist = sumHists('obs',*hists)
#for b in range(hist.GetNbinsX()+1):
# val = int(hist.GetBinContent(b))
# if val<0: val = 0
# err = val**0.5
# hist.SetBinContent(b,val)
# #hist.SetBinError(b,err)
histMap[mode][shift]['data'] = hist
else:
hist = getHist('data',var=var,wrappers=wrappers,do2D=do2D,**regionArgs[mode])
histMap[mode][shift]['data'] = hist
if do2D:
pass
else:
histMap[mode][shift]['data'].Rebin(rebinning[var[0]])
#####################
### Create Limits ###
#####################
limits = Limits(wsname)
limits.addEra('Run2016')
limits.addAnalysis('HAA')
era = 'Run2016'
analysis = 'HAA'
reco = 'mmmt'
for mode in ['PP','PF']:
limits.addChannel(mode)
if doParametric:
binning = varBinning[var[0]]
limits.addMH(*binning[1:])
limits.addX(*binning[1:],unit='GeV',label='m_{#mu#mu}')
for h in hmasses:
limits.addProcess(splinename.format(h=h),signal=True)
for background in backgrounds:
limits.addProcess(background)
# add models
for h in hmasses:
model = getSpline(histMap[mode][''],h,tag=mode)
limits.setExpected(splinename.format(h=h),era,analysis,mode,model)
if doUnbinned:
bg = buildModel(limits,tag=mode)
limits.setExpected('datadriven', era, analysis, mode, bg)
else:
# add histograms for background if not using an unbinned model
for bg in backgrounds:
limits.setExpected(bg,era,analysis,mode,histMap[mode][''][bg])
# get roodatahist
limits.setObserved(era,analysis,mode,histMap[mode]['']['data'])
else:
for signal in signals:
limits.addProcess(signal,signal=True)
for background in backgrounds:
limits.addProcess(background)
for proc in backgrounds:
limits.setExpected(proc,era,analysis,mode,histMap[mode][''][proc])
for proc in signals:
limits.setExpected(proc,era,analysis,mode,histMap[mode][''][proc])
limits.setObserved(era,analysis,mode,histMap[mode]['']['data'])
#########################
### Add uncertainties ###
#########################
systproc = tuple([proc for proc in signals + backgrounds if 'datadriven' not in proc])
allproc = tuple([proc for proc in signals + backgrounds])
systsplineproc = tuple([proc for proc in signalSplines + backgrounds if 'datadriven' not in proc])
allsplineproc = tuple([proc for proc in signalSplines + backgrounds])
bgproc = tuple([proc for proc in backgrounds])
sigsplineproc = tuple([proc for proc in signalSplines])
sigproc = tuple([proc for proc in signals])
############
### stat ###
############
def getStat(hist,direction):
newhist = hist.Clone('{0}{1}'.format(hist.GetName(),direction))
nb = hist.GetNbinsX()*hist.GetNbinsY()
for b in range(nb+1):
val = hist.GetBinContent(b+1)
err = hist.GetBinError(b+1)
newval = val+err if direction=='Up' else val-err
if newval<0: newval = 0
newhist.SetBinContent(b+1,newval)
newhist.SetBinError(b+1,0)
return newhist
logging.info('Adding stat systematic')
statMapUp = {}
statMapDown = {}
for proc in backgrounds+signals:
statMapUp[proc] = getStat(histMap[mode][''][proc],'Up')
statMapDown[proc] = getStat(histMap[mode][''][proc],'Down')
statsyst = {}
for mode in ['PP','PF']:
# background
if doUnbinned:
# TODO: add errors on params
pass
else:
for proc in bgproc:
statsyst[((proc,),(era,),(analysis,),(mode,))] = (statMapUp[proc],statMapDown[proc])
# signal
if doParametric:
for h in hmasses:
statsyst[((splinename.format(h=h),),(era,),(analysis,),(mode,))] = (getSpline(statMapUp,h,tag=mode+'StatUp'),getSpline(statMapDown,h,tag=mode+'StatDown'))
else:
for proc in sigproc:
statsyst[((proc,),(era,),(analysis,),(mode,))] = (statMapUp[proc],statMapDown[proc])
limits.addSystematic('stat_{process}_{channel}','shape',systematics=statsyst)
##############
### shifts ###
##############
for shift in shiftTypes:
logging.info('Adding {} systematic'.format(shift))
shiftsyst = {}
for mode in ['PP','PF']:
# background
if doUnbinned:
# TODO rateParams on bg model
pass
else:
for proc in bgproc:
shiftsyst[((proc,),(era,),(analysis,),(mode,))] = (histMap[mode][shift+'Up'][proc], histMap[mode][shift+'Down'][proc])
# signal
if doParametric:
for h in hmasses:
shiftsyst[((splinename.format(h=h),),(era,),(analysis,),(mode,))] = (getSpline(histMap[mode][shift+'Up'],h,tag=mode+shift+'Up'),getSpline(histMap[mode][shift+'Down'],h,tag=mode+shift+'Down'))
else:
for proc in sigproc:
shiftsyst[((proc,),(era,),(analysis,),(mode,))] = (histMap[mode][shift+'Up'][proc], histMap[mode][shift+'Down'][proc])
limits.addSystematic(shift,'shape',systematics=shiftsyst)
############
### Lumi ###
############
# lumi 2.3% for 2015 and 2.5% for 2016
# https://twiki.cern.ch/twiki/bin/view/CMS/TWikiLUM#CurRec
logging.info('Adding lumi systematic')
lumiproc = systsplineproc if doParametric else systproc
lumisyst = {
(lumiproc,(era,),('all',),('all',)): 1.025,
}
limits.addSystematic('lumi','lnN',systematics=lumisyst)
############
### muon ###
############
# from z: 1 % + 0.5 % + 0.5 % per muon for id + iso + trig (pt>20)
logging.info('Adding mu id+iso systematic')
muproc = systsplineproc if doParametric else systproc
musyst = {
(muproc,(era,),('all',),('all',)): 1+math.sqrt(sum([0.01**2,0.005**2]*2+[0.01**2])), # 2 lead have iso, tau_mu doesnt
}
limits.addSystematic('muid','lnN',systematics=musyst)
logging.info('Adding mu trig systematic')
musyst = {
(muproc,(era,),('all',),('all',)): 1.005, # 1 triggering muon
}
limits.addSystematic('mutrig','lnN',systematics=musyst)
###########
### tau ###
###########
# 5% on sf 0.99 (VL/L) or 0.97 (M)
logging.info('Adding mu id+iso systematic')
tauproc = systsplineproc if doParametric else systproc
tausyst = {
(tauproc,(era,),('all',),('all',)): 1.05,
}
limits.addSystematic('tauid','lnN',systematics=tausyst)
######################
### Print datacard ###
######################
directory = 'datacards_shape/{0}'.format('MuMuTauTau')
python_mkdir(directory)
datacard = '{0}/mmmt_{1}'.format(directory, args.tag) if args.tag else '{}/mmmt'.format(directory)
processes = {}
if doParametric:
for h in hmasses:
processes[signame.format(h=h,a='X')] = [splinename.format(h=h)] + backgrounds
else:
for signal in signals:
processes[signal] = [signal]+backgrounds
limits.printCard(datacard,processes=processes,blind=False,saveWorkspace=doParametric)
for proc in samples:
hists += [histMap[proc]]
hist = sumHists('obs',*hists)
for b in range(hist.GetNbinsX()+1):
val = int(hist.GetBinContent(b))
if val<0: val = 0
err = val**0.5
hist.SetBinContent(b,val)
#hist.SetBinError(b,err)
histMap['data'] = hist
else:
hist = getBinned('data')
histMap['data'] = hist
# create limit object
limits = Limits(wsname)
limits.addEra('Run2016')
limits.addAnalysis('ThreePhoton')
limits.addChannel('ggg')
if doParametric:
limits.addMH(*binning[1:])
limits.addX(*binning[1:])
if doParametric:
limits.addProcess('sig',signal=True)
limits.addProcess('bg')
else:
for signal in signals:
limits.addProcess(signal,signal=True)
for proc in samples:
hists += [histMap[proc]]
hist = sumHists('obs', *hists)
for b in range(hist.GetNbinsX() + 1):
val = int(hist.GetBinContent(b))
if val < 0: val = 0
err = val**0.5
hist.SetBinContent(b, val)
#hist.SetBinError(b,err)
histMap['data'] = hist
else:
hist = getBinned('data')
histMap['data'] = hist
# create limit object
limits = Limits(wsname)
limits.addEra('Run2016')
limits.addAnalysis('ThreePhoton')
limits.addChannel('ggg')
if doParametric:
limits.addMH(*binning[1:])
limits.addX(*binning[1:])
if doParametric:
limits.addProcess('sig', signal=True)
limits.addProcess('bg')
else:
for signal in signals:
limits.addProcess(signal, signal=True)