toUncorrelate = [
'ttcrosscheck', 'zllcrosscheck', 'btagcrosscheckmr',
'btagcrosscheckrsq', 'btaginvcrosscheck', 'vetolepetacrosscheck',
'vetotauetacrosscheck', 'vetolepptcrosscheck',
'vetotauptcrosscheck'
]
toUncorrelate += ['stat' + curBox + sample for sample in samples]
for sys in toUncorrelate:
if 'stat' in sys:
if args.noStat: continue
suppressLevel = 0.1
else:
if args.noSys: continue
suppressLevel = 0.0
uncorrelate(backgroundHists, sys, suppressLevel=suppressLevel)
#scale factor uncertainties are correlated according to the bin they are in
toUncorrelateSF = ['sfstatttjets', 'sfstatwjets', 'sfstatzinv']
cfg = Config.Config(config)
for sys in toUncorrelateSF:
uncorrelateSFs(backgroundHists,
sys,
sfHists,
cfg,
curBox,
unrollBins=unrollBins)
#call SMS template maker
dirToUse = SIGNAL_DIR
if args.noPathologies:
def makeSMSTemplates(box, inFile, uncertainties=[], debugLevel=0,
tag="Razor2016_MoriondRereco", opts=None, boostCuts=True,
doUncorrelate=True):
"""Returns a dictionary of histograms representing predicted
yields for the indicated signal sample.
'opts' should be an SMSOpts instance containing desired
values for configurable parameters."""
print '\nInput file is %s' % inFile
uncerts = copy.copy(uncertainties)
# process configuration options
if opts is None:
opts = SMSOpts()
xBR = opts.xBR
yBR = opts.yBR
doMCStat = True
if opts.doNPVExtrap:
uncerts.append('npvextrap')
doMCStat = False
if opts.doGenMetVsPFMet:
uncerts.append('genmetvspfmet')
# get mass point information
model, m1, m2 = getModelInfoFromFilename(inFile, xBR, yBR)
isGluinos = ("T1" in model or "T5" in model)
if isGluinos:
thyXsec, _ = getTheoryCrossSectionAndError(
mGluino=m1)
else:
thyXsec, _ = getTheoryCrossSectionAndError(
mStop=m1)
print "\n--- %s %s %s ---\n"%(model, m1, m2)
print "Theory cross section: %.3f pb"%thyXsec
minBtags = 0
maxBtags = getMaxBtags(box)
# special case: 1L control regions
if box == "TTJetsSingleLeptonForSignal":
minBtags = maxBtags = 1
elif box == "WJetsSingleLeptonForSignal":
minBtags = maxBtags = 0
hists = []
unrollBins = []
for nb in range(minBtags, maxBtags+1):
# get analysis info
nbMax = nb
if nb == maxBtags:
nbMax = -1
# special case: 1L control regions
if box == "WJetsSingleLeptonForSignal":
nbMax = 0
analysis = Analysis(box, tag, nbMin=nb, nbMax=nbMax,
boostCuts=boostCuts)
unrollBins.append(analysis.unrollBins)
# modify for signal sample
analysis.filenames = { 'Signal':inFile }
analysis.samples = ['Signal']
analysis.samplesReduced = analysis.samples
# scale according to cross section
f = rt.TFile.Open(inFile)
nEvents = f.Get('NEvents').Integral()
globalScaleFactor = thyXsec/nEvents
nisrFactor = getGlobalNISRScaleFactor(f)
globalScaleFactor *= nisrFactor
print "Number of events: %d"%nEvents
print "Integrated luminosity: %d /pb"%analysis.lumi
print "Overall NISR scale factor: {}".format(nisrFactor)
print "Overall scale factor: %.3f"%(
analysis.lumi * globalScaleFactor)
# fill the histograms
hists.append(makeControlSampleHistsForAnalysis(analysis,
treeName="RazorInclusive", shapeErrors=uncerts,
boxName=box, btags=nb, makePlots=False,
exportShapeErrs=True, propagateScaleFactorErrs=False,
lumiMC=1./globalScaleFactor, debugLevel=debugLevel))
makeRazor2DTable(pred=None,
boxName='{}-{}-{}-{}-'.format(box, model, m1, m2),
mcNames=['{} {} {}'.format(model, m1, m2)],
mcHists=[hists[-1]['Signal'][('MR','Rsq')]],
btags=nb, unrollBins=unrollBins[-1], listAllMC=True)
signalHists = unrollAndStitch(hists, box, samples=analysis.samples,
debugLevel=debugLevel, unrollBins=unrollBins,
addStatUnc=doMCStat)
# apply pileup weight extrapolation procedure
if 'Signal_npvextrapUp' in signalHists:
npvLowHighHist = getNPVLowHighHist(analysis)
npvHist = getNPVHist(tag)
doNPVExtrapolation(signalHists, npvHist, npvLowHighHist,
scale=analysis.lumi*globalScaleFactor)
# need this extrapolation to be uncorrelated across bins & boxes
for sysName in ['Signal_npvextrapUp', 'Signal_npvextrapDown']:
signalHists[sysName.replace(
'npvextrap','npvextrap'+box)] = signalHists[sysName]
del signalHists[sysName]
if doUncorrelate:
uncorrelate(signalHists, 'npvextrap'+box, suppressLevel=0.1)
# apply gen-MET vs PF MET systematic
if 'Signal_genmetvspfmetUp' in signalHists:
doGenMetVsPFMetSystematic(signalHists)
return signalHists
backgroundHists = unrollAndStitch(curBox, samples=samples, inDir=BACKGROUND_DIR, outDir=outDir, unrollBins=unrollBins, noSys=args.noSys, addStatUnc=(not args.noStat), addMCVsFit=args.addMCVsFit, debugLevel=debugLevel, signalContaminationHists=contamHistsToUse, sfHistsForSignalContamination=sfHists)
if args.noCombine: continue #if not setting a limit, we are done
#treat appropriate uncertainties as uncorrelated bin to bin
toUncorrelate = ['ttcrosscheck','zllcrosscheck','btagcrosscheckmr','btagcrosscheckrsq','btaginvcrosscheck','vetolepetacrosscheck','vetotauetacrosscheck','vetolepptcrosscheck','vetotauptcrosscheck']
toUncorrelate += ['stat'+curBox+sample for sample in samples]
for sys in toUncorrelate:
if 'stat' in sys:
if args.noStat: continue
suppressLevel = 0.1
else:
if args.noSys: continue
suppressLevel = 0.0
uncorrelate(backgroundHists, sys, suppressLevel=suppressLevel)
#scale factor uncertainties are correlated according to the bin they are in
toUncorrelateSF = ['sfstatttjets','sfstatwjets','sfstatzinv']
cfg = Config.Config(config)
for sys in toUncorrelateSF:
uncorrelateSFs(backgroundHists, sys, sfHists, cfg, curBox, unrollBins=unrollBins)
#call SMS template maker
dirToUse = SIGNAL_DIR
if args.noPathologies:
dirToUse = NOPATHOLOGIES_SIGNAL_DIR
elif args.noPileupWeights:
dirToUse = NOPILEUPWEIGHTS_SIGNAL_DIR
if 'T1' in args.model or 'T5' in args.model:
modelName = 'SMS-'+args.model+'_'+str(args.mGluino)+'_'+str(args.mLSP)
# for bx in range(1,ds[0].GetNbinsX()+1):
# if pdfEnvelopeDown.GetBinContent(bx) < 0:
# pdfEnvelopeDown.SetBinContent(bx,0)
# #normalize
# pdfEnvelopeUp.Scale(ds[0].Integral()*1.0/pdfEnvelopeUp.Integral())
# pdfEnvelopeDown.Scale(ds[0].Integral()*1.0/pdfEnvelopeDown.Integral())
# #append
# ds.append(pdfEnvelopeUp)
# ds.append(pdfEnvelopeDown)
#convert dataset list to dict
for d in ds: dsDict[d.GetName()] = d
#perform uncorrelation procedure (for MC stat and pdf uncertainty)
if 'pdf%s'%box.lower() in shapes:
uncorrelate(dsDict, 'pdf%s'%box.lower())
# remove empty bins
for bx in range(1,ds[0].GetNbinsX()+1):
if ds[0].GetBinContent(bx) == 0 or ds[0].GetBinError(bx) == 0:
print 'Removing empty PDF uncertainty bin %i'%(bx)
del dsDict['%s_pdf%s%iUp'%(ds[0].GetName(),box.lower(),bx)]
del dsDict['%s_pdf%s%iDown'%(ds[0].GetName(),box.lower(),bx)]
if 'mcstat%s'%box.lower() in shapes:
uncorrelate(dsDict, 'mcstat%s'%box.lower())
# for now: don't remove unncessary MC stat bins
# remove unnecessary MC stat bins (relative uncertainty < 10%) see htt recommendation
# https://indico.cern.ch/event/373752/session/6/contribution/14/attachments/744534/1021298/bbb-HCG.pdf
#for bx in range(1,ds[0].GetNbinsX()+1):
# if ds[0].GetBinContent(bx) == 0 or ds[0].GetBinError(bx) == 0:
# print 'Relative MC stat uncertainty bin %i = %.1f%% is less than 10%%'%(bx,0.)
# print 'Removing histogram: %s_mcstat%s%iUp'%(ds[0].GetName(),box.lower(),bx)
'sfstatttjetsNJetsTTJets', 'sfstatwjetsNJetsWJets',
'sfstatzinvNJetsInv']
for unc in jet_closure_uncs:
hists = addHistSuffix(hists, unc, jets)
# treat appropriate uncertainties as uncorrelated bin to bin
toUncorrelate = ['stat'+curBox+sample for sample in samples
if sample != 'QCD']
for sys in toUncorrelate:
if 'stat' in sys:
if args.noStat: continue
suppressLevel = 0.1
else:
if args.noSys: continue
suppressLevel = 0.0
uncorrelate(hists, sys,
suppressLevel=suppressLevel)
# scale factor uncertainties are correlated according to
# the bin they are in
toUncorrelateSF = ['sfstatttjets','sfstatwjets','sfstatzinv']
for sys in toUncorrelateSF:
uncorrelateSFs(hists, sys, sfHists, cfg,
curBox, unrollBins=unrollBins)
toUncorrelateSF1D = ['btaginvcrosscheck', 'btagcrosscheckrsq']
for sys in toUncorrelateSF1D:
uncorrelateSFs1D(hists, sys, sfHistsForUncorrSFs1D, unrollBins)
toUncorrelateSF1DMR = ['sfstatttjetsMR', 'sfstatwjetsMR',
'sfstatzinvMRInv']
for sys in toUncorrelateSF1DMR:
uncorrelateSFs1D(hists, sys, sfHistsForUncorrSFs1DMR, unrollBins,
useRsq=False)
# if pdfEnvelopeDown.GetBinContent(bx) < 0:
# pdfEnvelopeDown.SetBinContent(bx,0)
# #normalize
# pdfEnvelopeUp.Scale(ds[0].Integral()*1.0/pdfEnvelopeUp.Integral())
# pdfEnvelopeDown.Scale(ds[0].Integral()*1.0/pdfEnvelopeDown.Integral())
# #append
# ds.append(pdfEnvelopeUp)
# ds.append(pdfEnvelopeDown)
#convert dataset list to dict
for d in ds:
dsDict[d.GetName()] = d
#perform uncorrelation procedure (for MC stat and pdf uncertainty)
if 'pdf%s' % box.lower() in shapes:
uncorrelate(dsDict, 'pdf%s' % box.lower())
# remove empty bins
for bx in range(1, ds[0].GetNbinsX() + 1):
if ds[0].GetBinContent(bx) == 0 or ds[0].GetBinError(
bx) == 0:
print 'Removing empty PDF uncertainty bin %i' % (bx)
del dsDict['%s_pdf%s%iUp' %
(ds[0].GetName(), box.lower(), bx)]
del dsDict['%s_pdf%s%iDown' %
(ds[0].GetName(), box.lower(), bx)]
if 'mcstat%s' % box.lower() in shapes:
uncorrelate(dsDict, 'mcstat%s' % box.lower())
# for now: don't remove unncessary MC stat bins
# remove unnecessary MC stat bins (relative uncertainty < 10%) see htt recommendation
# https://indico.cern.ch/event/373752/session/6/contribution/14/attachments/744534/1021298/bbb-HCG.pdf
#for bx in range(1,ds[0].GetNbinsX()+1):