def wrapper(s):
logger.info("Now working on %s", s.name)
xSecScale = 1
c = cardFileWriter.cardFileWriter()
c.releaseLocation = combineReleaseLocation
for coup in nonZeroCouplings:
try:
modification_dict[coup] = getCouplingFromName(s.name, coup)
logger.info("The following coupling is set to non-zero value: %s: %s", coup, modification_dict[coup])
except ValueError:
logger.info("The following coupling is kept at zero: %s: %s", coup, modification_dict[coup])
continue
try:
p = Process(process = "ttZ_ll", nEvents = 5000, config = config, xsec_cache=xsecDB)
xsec = p.xsecDB.get(modification_dict)
except IndexError:
logger.info("Looking into backup DB for x-sec")
p = Process(process = "ttZ_ll", nEvents = 5000, config = config, xsec_cache=xsecDB_Backup)
xsec = p.xsecDB.get(modification_dict)
if not xsec:
try:
p = Process(process = "ttZ_ll", nEvents = 5000, config = config, xsec_cache=xsecDB_Backup)
xsec = p.xsecDB.get(modification_dict)
except IndexError:
logger.info("No x-sec found.")
logger.info("Found modified x-sec of %s", xsec)
cardFileName = os.path.join(limitDir, s.name+'.txt')
if not os.path.exists(cardFileName) or overWrite:
counter=0
c.reset()
c.setPrecision(3)
postfix = '_%s'%args.year
c.addUncertainty('PU', 'lnN') # correlated
c.addUncertainty('JEC'+postfix, 'lnN') # uncorrelated, for now!
c.addUncertainty('btag_heavy'+postfix, 'lnN') # uncorrelated, wait for offical recommendation
c.addUncertainty('btag_light'+postfix, 'lnN') # uncorrelated, wait for offical recommendation
c.addUncertainty('trigger'+postfix, 'lnN') # uncorrelated, statistics dominated
c.addUncertainty('leptonSF', 'lnN') # correlated
c.addUncertainty('scale', 'lnN') # correlated.
c.addUncertainty('scale_sig', 'lnN') # correlated.
c.addUncertainty('PDF', 'lnN') # correlated.
c.addUncertainty('PartonShower', 'lnN') # correlated.
c.addUncertainty('nonprompt', 'lnN') # correlated?!
c.addUncertainty('WZ_xsec', 'lnN') # correlated.
c.addUncertainty('WZ_bb', 'lnN') # correlated
c.addUncertainty('WZ_powheg', 'lnN') # correlated
c.addUncertainty('ZZ_xsec', 'lnN') # correlated.
c.addUncertainty('ZG_xsec', 'lnN') # correlated.
c.addUncertainty('rare', 'lnN') # correlated.
c.addUncertainty('ttX', 'lnN') # correlated.
c.addUncertainty('Lumi'+postfix, 'lnN')
uncList = ['PU', 'JEC', 'btag_heavy', 'btag_light', 'leptonSF', 'trigger']
for unc in uncList:
uncertainties[unc] = []
## use rate parameters??
#c.addRateParameter('WZ', 1, '[0,2]')
#c.addRateParameter('ZZ', 1, '[0,2]')
for setupPair in setups:
# extract the nominal and nonprompt setup from the pair
setup, setupNP = setupPair
signal = MCBasedEstimate(name="TTZ", sample=setup.samples["TTZ"], cacheDir=setup.defaultCacheDir())
#nonprompt = FakeEstimate(name="nonPromptDD", sample=setup.samples["Data"], setup=setupNP, cacheDir=setup.defaultCacheDir())
if args.unblind or (setup == setup3l_CR) or (setup == setup4l_CR):
observation = DataObservation(name="Data", sample=setup.samples["Data"], cacheDir=setup.defaultCacheDir())
logger.info("Using data!")
else:
observation = MCBasedEstimate(name="observation", sample=setup.samples["pseudoData"], cacheDir=setup.defaultCacheDir())
logger.info("Using pseudo-data!")
for e in setup.estimators: e.initCache(setup.defaultCacheDir())
for r in setup.regions:
totalBackground = u_float(0)
for channel in setup.channels:
niceName = ' '.join([channel.name, r.__str__()])
binname = 'Bin'+str(counter)
logger.info("Working on %s", binname)
counter += 1
c.addBin(binname, [e.name.split('-')[0] for e in setup.estimators]+["nonPromptDD"], niceName)
#c.addBin(binname, 'nonPromptDD', niceName)
for e in setup.estimators:
name = e.name.split('-')[0]
if name.count('WZ'):
logger.info("Using reweighting to powheg for WZ sample")
wzReweighting = WZReweighting( cacheDir = reweightCacheWZ )
f = wzReweighting.cachedReweightingFunc( setup.WZselection )
powhegExpected = e.reweight1D(r, channel, setup, f)
expected = e.cachedEstimate(r, channel, setup)
print expected
WZ_powheg_unc = (powhegExpected-expected)/expected
else:
expected = e.cachedEstimate(r, channel, setup)
logger.info("Adding expectation %s for process %s", expected.val, name)
c.specifyExpectation(binname, name, expected.val if expected.val > 0.01 else 0.01)
totalBackground += expected
if not args.statOnly:
# uncertainties
pu = 1 + e.PUSystematic( r, channel, setup).val if expected.val>0.01 else 1.1
jec = 1 + e.JECSystematic( r, channel, setup).val if expected.val>0.01 else 1.1
btag_heavy = 1 + e.btaggingSFbSystematic(r, channel, setup).val if expected.val>0.01 else 1.1
btag_light = 1 + e.btaggingSFlSystematic(r, channel, setup).val if expected.val>0.01 else 1.1
trigger = 1 + e.triggerSystematic(r, channel, setup).val if expected.val>0.01 else 1.1
leptonSF = 1 + e.leptonSFSystematic(r, channel, setup).val if expected.val>0.01 else 1.1
if name.count('WZ'):
WZ_powheg = 1 + WZ_powheg_unc.val if expected.val>0.01 else 1.1
c.specifyUncertainty('PU', binname, name, 1 + e.PUSystematic( r, channel, setup).val)
if not name.count('nonprompt'):
c.specifyUncertainty('JEC'+postfix, binname, name, jec)
c.specifyUncertainty('btag_heavy'+postfix, binname, name, btag_heavy)
c.specifyUncertainty('btag_light'+postfix, binname, name, btag_light)
c.specifyUncertainty('trigger'+postfix, binname, name, trigger)
c.specifyUncertainty('leptonSF', binname, name, leptonSF)
c.specifyUncertainty('scale', binname, name, 1.01)
c.specifyUncertainty('PDF', binname, name, 1.01)
c.specifyUncertainty('Lumi'+postfix, binname, name, 1.025 )
if name.count('ZZ'): c.specifyUncertainty('ZZ_xsec', binname, name, 1.10)
if name.count('ZG'): c.specifyUncertainty('ZG_xsec', binname, name, 1.20)
if name.count('WZ'):
c.specifyUncertainty('WZ_xsec', binname, name, 1.10)
if setup == setup3l:
c.specifyUncertainty('WZ_bb', binname, name, 1.08)
c.specifyUncertainty('WZ_powheg', binname, name, WZ_powheg)
if name.count('nonprompt'): c.specifyUncertainty('nonprompt', binname, name, 1.30)
if name.count('rare'): c.specifyUncertainty('rare', binname, name, 1.50)
if name.count('TTX'): c.specifyUncertainty('ttX', binname, name, 1.11)
#MC bkg stat (some condition to neglect the smaller ones?)
uname = 'Stat_'+binname+'_'+name+postfix
c.addUncertainty(uname, 'lnN')
if expected.val > 0:
c.specifyUncertainty(uname, binname, name, 1 + expected.sigma/expected.val )
else:
c.specifyUncertainty(uname, binname, name, 1.01 )
uname = 'Stat_'+binname+'_nonprompt'+postfix
c.addUncertainty(uname, 'lnN')
if setup.nLeptons == 3 and setupNP:
nonprompt = FakeEstimate(name="nonPromptDD", sample=setup.samples["Data"], setup=setupNP, cacheDir=setup.defaultCacheDir())
np = nonprompt.cachedEstimate(r, channel, setupNP)
if np.val < 0.01:
np = u_float(0.01,0.)
c.specifyExpectation(binname, 'nonPromptDD', np.val )
c.specifyUncertainty(uname, binname, "nonPromptDD", 1 + np.sigma/np.val )
c.specifyUncertainty('nonprompt', binname, "nonPromptDD", 1.30)
else:
np = u_float(0)
c.specifyExpectation(binname, 'nonPromptDD', np.val)
if args.expected:
sig = signal.cachedEstimate(r, channel, setup)
obs = totalBackground + sig + np
elif args.unblind or (setup == setup3l_CR) or (setup == setup4l_CR):
obs = observation.cachedObservation(r, channel, setup)
else:
obs = observation.cachedEstimate(r, channel, setup)
c.specifyObservation(binname, int(round(obs.val,0)) )
if args.useShape:
logger.info("Using 2D reweighting method for shapes")
if args.model == "dim6top_LO":
source_gen = dim6top_central
elif args.model == "ewkDM":
source_gen = ewkDM_central
signalReweighting = SignalReweighting( source_sample = source_gen, target_sample = s, cacheDir = reweightCache)
f = signalReweighting.cachedReweightingFunc( setup.genSelection )
sig = signal.reweight2D(r, channel, setup, f)
else:
sig = signal.cachedEstimate(r, channel, setup)
xSecMod = 1
if args.useXSec:
xSecMod = xsec.val/xsec_central.val
logger.info("x-sec is multiplied by %s",xSecMod)
c.specifyExpectation(binname, 'signal', sig.val * xSecScale * xSecMod )
logger.info('Adding signal %s'%(sig.val * xSecScale * xSecMod))
if sig.val>0:
c.specifyUncertainty('Lumi'+postfix, binname, 'signal', 1.025 )
if not args.statOnly:
# uncertainties
pu = 1 + e.PUSystematic( r, channel, setup).val
jec = 1 + e.JECSystematic( r, channel, setup).val
btag_heavy = 1 + e.btaggingSFbSystematic(r, channel, setup).val
btag_light = 1 + e.btaggingSFlSystematic(r, channel, setup).val
trigger = 1 + e.triggerSystematic(r, channel, setup).val
leptonSF = 1 + e.leptonSFSystematic(r, channel, setup).val
if sig.sigma/sig.val < 0.05:
uncertainties['PU'] += [pu]
uncertainties['JEC'] += [jec]
uncertainties['btag_heavy'] += [btag_heavy]
uncertainties['btag_light'] += [btag_light]
uncertainties['trigger'] += [trigger]
uncertainties['leptonSF'] += [leptonSF]
c.specifyUncertainty('PU', binname, "signal", pu)
c.specifyUncertainty('JEC'+postfix, binname, "signal", jec)
c.specifyUncertainty('btag_heavy'+postfix, binname, "signal", btag_heavy)
c.specifyUncertainty('btag_light'+postfix, binname, "signal", btag_light)
c.specifyUncertainty('trigger'+postfix, binname, "signal", trigger)
c.specifyUncertainty('leptonSF', binname, "signal", leptonSF)
# This doesn't get the right uncertainty in CRs. However, signal doesn't matter there anyway.
if setup in [setup3l, setup4l]:
c.specifyUncertainty('scale_sig', binname, "signal", 1 + scale_cache.get({"region":r, "channel":channel.name, "PDFset":"scale"}).val)
c.specifyUncertainty('PDF', binname, "signal", 1 + PDF_cache.get({"region":r, "channel":channel.name, "PDFset":PDFset}).val)
c.specifyUncertainty('PartonShower',binname, "signal", PS_cache.get({"region":r, "channel":channel.name, "PDFset":"PSscale"}).val) #something wrong here?
#c.specifyUncertainty('scale_sig', binname, "signal", 1.05) #1.30
#c.specifyUncertainty('PDF', binname, "signal", 1.04) #1.15
uname = 'Stat_'+binname+'_signal'+postfix
c.addUncertainty(uname, 'lnN')
c.specifyUncertainty(uname, binname, 'signal', 1 + sig.sigma/sig.val )
else:
uname = 'Stat_'+binname+'_signal'+postfix
c.addUncertainty(uname, 'lnN')
c.specifyUncertainty(uname, binname, 'signal', 1 )
#c.addUncertainty('Lumi'+postfix, 'lnN')
#c.specifyFlatUncertainty('Lumi'+postfix, 1.026)
cardFileName = c.writeToFile(cardFileName)
else:
logger.info("File %s found. Reusing.",cardFileName)
res = {}
if not os.path.isdir(limitDir):
os.makedirs(limitDir)
resDB = resultsDB(limitDir+'/results.sq', "results", setup.resultsColumns)
res = {"signal":s.name}
if not overWrite and res.DB.contains(key):
res = resDB.getDicts(key)[0]
logger.info("Found result for %s, reusing", s.name)
else:
# We don't calculate limits here, but just in case we find a way how to do it, put placeholders here
res.update({"exp":0, "obs":0, "exp1up":0, "exp2up":0, "exp1down":0, "exp2down":0})
# Don't extract all the nuisances by default
signalRegions = range(15,30) ## shouldn't be hardcoded
masks = ['mask_ch1_Bin'+str(i)+'=1' for i in signalRegions]
masks = ','.join(masks)
if args.calcNuisances:
c.calcNuisances(cardFileName, masks=masks)
# extract the NLL
#nll = c.calcNLL(cardFileName, options="")
nll = c.physicsModel(cardFileName, options="", normList=["WZ_norm","ZZ_norm"], masks=masks) # fastScan turns of profiling
if nll["nll0"] > 0:
res.update({"dNLL_postfit_r1":nll["nll"], "dNLL_bestfit":nll["bestfit"], "NLL_prefit":nll["nll0"]})
else:
res.update({"dNLL_postfit_r1":-999, "dNLL_bestfit":-999, "NLL_prefit":-999})
logger.info("Fits failed, adding values -999 as results")
logger.info("Adding results to database")
resDB.add(res, nll['nll_abs'], overwrite=True)
print
print "NLL results:"
print "{:>15}{:>15}{:>15}".format("Pre-fit", "Post-fit r=1", "Best fit")
print "{:15.2f}{:15.2f}{:15.2f}".format(float(res["NLL_prefit"]), float(res["NLL_prefit"])+float(res["dNLL_postfit_r1"]), float(res["NLL_prefit"])+float(res["dNLL_bestfit"]))
print 'PU', min(uncertainties['PU']), max(uncertainties['PU'])
print 'JEC', min(uncertainties['JEC']), max(uncertainties['JEC'])
print 'btag_heavy', min(uncertainties['btag_heavy']), max(uncertainties['btag_heavy'])
print 'btag_light', min(uncertainties['btag_light']), max(uncertainties['btag_light'])
print 'trigger', min(uncertainties['trigger']), max(uncertainties['trigger'])
print 'leptonSF', min(uncertainties['leptonSF']), max(uncertainties['leptonSF'])
def wrapper(s):
logger.info("Now working on %s", s.name)
xSecScale = 1
c = cardFileWriter.cardFileWriter()
c.releaseLocation = combineReleaseLocation
for coup in nonZeroCouplings:
modification_dict[coup] = getCouplingFromName(s.name, coup)
logger.info("The following coupling is set to non-zero value: %s: %s", coup, modification_dict[coup])
xsec = p.xsecDB.get(modification_dict)
logger.info("Found modified x-sec of %s", xsec)
cardFileName = os.path.join(limitDir, s.name+'.txt')
if not os.path.exists(cardFileName) or overWrite:
counter=0
c.reset()
c.addUncertainty('PU', 'lnN')
c.addUncertainty('JEC', 'lnN')
c.addUncertainty('btag', 'lnN')
c.addUncertainty('trigger', 'lnN')
c.addUncertainty('leptonSF', 'lnN')
c.addUncertainty('scale', 'lnN')
c.addUncertainty('scale_sig', 'lnN')
c.addUncertainty('PDF', 'lnN')
c.addUncertainty('nonprompt', 'lnN')
c.addUncertainty('WZ_xsec', 'lnN')
c.addUncertainty('ZZ_xsec', 'lnN')
c.addUncertainty('rare', 'lnN')
c.addUncertainty('ttX', 'lnN')
c.addUncertainty('tZq', 'lnN')
for setup in setups:
signal = MCBasedEstimate(name="TTZ", sample=setup.samples["TTZ"], cacheDir=setup.defaultCacheDir())
observation = MCBasedEstimate(name="observation", sample=setup.samples["pseudoData"], cacheDir=setup.defaultCacheDir())
#observation = DataObservation(name='Data', sample=setup.sample['pseudoData'], cacheDir=setup.defaultCacheDir())
for e in setup.estimators: e.initCache(setup.defaultCacheDir())
for r in setup.regions:
for channel in setup.channels:
niceName = ' '.join([channel, r.__str__()])
binname = 'Bin'+str(counter)
counter += 1
c.addBin(binname, [e.name.split('-')[0] for e in setup.estimators], niceName)
for e in setup.estimators:
name = e.name.split('-')[0]
expected = e.cachedEstimate(r, channel, setup)
c.specifyExpectation(binname, name, round(expected.val,3) if expected.val > 0 else 0.01)
if expected.val>0:
if not args.statOnly:
c.specifyUncertainty('PU', binname, name, 1.01)
c.specifyUncertainty('JEC', binname, name, 1.03) #1.05
c.specifyUncertainty('btag', binname, name, 1.03) #1.05
c.specifyUncertainty('trigger', binname, name, 1.03) #1.04
c.specifyUncertainty('leptonSF', binname, name, 1.05) #1.07
c.specifyUncertainty('scale', binname, name, 1.01)
c.specifyUncertainty('PDF', binname, name, 1.01)
if name.count('ZZ'): c.specifyUncertainty('ZZ_xsec', binname, name, 1.20) #1.20
if name.count('WZ'): c.specifyUncertainty('WZ_xsec', binname, name, 1.10) #1.20
if name.count('nonprompt'): c.specifyUncertainty('nonprompt', binname, name, 1.30)
if name.count('rare'): c.specifyUncertainty('rare', binname, name, 1.50)
if name.count('TTX'): c.specifyUncertainty('ttX', binname, name, 1.10) #1.15
if name.count('TZQ'): c.specifyUncertainty('tZq', binname, name, 1.10) #1.15
#MC bkg stat (some condition to neglect the smaller ones?)
uname = 'Stat_'+binname+'_'+name
c.addUncertainty(uname, 'lnN')
c.specifyUncertainty(uname, binname, name, round(1+expected.sigma/expected.val,3) )
obs = observation.cachedEstimate(r, channel, setup)
c.specifyObservation(binname, int(round(obs.val,0)))
if args.useShape:
logger.info("Using 2D reweighting method for shapes")
if args.model == "dim6top_LO":
source_gen = dim6top_LO_ttZ_ll_ctZ_0p00_ctZI_0p00
elif args.model == "ewkDM":
source_gen = ewkDM_central
target_gen = s
signalReweighting = SignalReweighting( source_sample = source_gen, target_sample = s, cacheDir = reweightCache)
f = signalReweighting.cachedReweightingFunc( setup.genSelection )
sig = signal.reweight2D(r, channel, setup, f)
else:
sig = signal.cachedEstimate(r, channel, setup)
xSecMod = 1
if args.useXSec:
xSecMod = xsec.val/xsec_central.val
logger.info("x-sec is multiplied by %s",xSecMod)
c.specifyExpectation(binname, 'signal', round(sig.val*xSecScale * xSecMod, 3) )
if sig.val>0:
if not args.statOnly:
c.specifyUncertainty('PU', binname, "signal", 1.01)
c.specifyUncertainty('JEC', binname, "signal", 1.03) #1.05
c.specifyUncertainty('btag', binname, "signal", 1.03) #1.05
c.specifyUncertainty('trigger', binname, "signal", 1.03) #1.04
c.specifyUncertainty('leptonSF', binname, "signal", 1.05) #1.07
c.specifyUncertainty('scale_sig', binname, "signal", 1.10) #1.30
c.specifyUncertainty('PDF', binname, "signal", 1.05) #1.15
uname = 'Stat_'+binname+'_signal'
c.addUncertainty(uname, 'lnN')
c.specifyUncertainty(uname, binname, 'signal', round(1 + sig.sigma/sig.val,3) )
else:
uname = 'Stat_'+binname+'_signal'
c.addUncertainty(uname, 'lnN')
c.specifyUncertainty(uname, binname, 'signal', 1 )
c.addUncertainty('Lumi', 'lnN')
c.specifyFlatUncertainty('Lumi', 1.026)
cardFileName = c.writeToFile(cardFileName)
else:
logger.info("File %s found. Reusing.",cardFileName)
res = {}
if getResult(s) and not overWrite:
res = getResult(s)
logger.info("Found result for %s, reusing", s.name)
else:
# calculate the limit
#limit = c.calcLimit(cardFileName)#, options="--run blind")
#res.update({"exp":limit['0.500'], "obs":limit['-1.000'], "exp1up":limit['0.840'], "exp2up":limit['0.975'], "exp1down":limit['0.160'], "exp2down":limit['0.025']})
res.update({"exp":0, "obs":0, "exp1up":0, "exp2up":0, "exp1down":0, "exp2down":0})
# run the checks
c.calcNuisances(cardFileName)
# extract the NLL
nll = c.calcNLL(cardFileName, options="--fastScan")
if nll["nll0"] > 0:
res.update({"dNLL_postfit_r1":nll["nll"], "dNLL_bestfit":nll["bestfit"], "NLL_prefit":nll["nll0"]})
else:
res.update({"dNLL_postfit_r1":-999, "dNLL_bestfit":-999, "NLL_prefit":-999})
logger.info("Fits failed, adding values -999 as results")
logger.info("Adding results to database")
addResult(s, res, nll['nll_abs'], overwrite=True)
print
print "NLL results:"
print "{:>15}{:>15}{:>15}".format("Pre-fit", "Post-fit r=1", "Best fit")
print "{:15.2f}{:15.2f}{:15.2f}".format(float(res["NLL_prefit"]), float(res["NLL_prefit"])+float(res["dNLL_postfit_r1"]), float(res["NLL_prefit"])+float(res["dNLL_bestfit"]))
if xSecScale != 1:
for k in res:
res[k] *= xSecScale
def wrapper(s):
logger.info("Now working on %s", s.name)
c = cardFileWriter.cardFileWriter()
c.releaseLocation = combineReleaseLocation
cards = {}
# get the seperated cards
for year in [2016,2017]:
subDir = ''
cardDir = "regionsE_%s"%(year)
if args.useXSec: cardDir += "_xsec"
if args.useShape: cardDir += "_shape"
exp = "_expected" if args.expected else ''
cardDir += "_lowUnc%s"%exp
if args.includeCR: cardDir += "_SRandCR"
baseDir = os.path.join(analysis_results)
limitDir = os.path.join(baseDir, 'cardFiles', cardDir, subDir, '_'.join([args.model, args.signal]))
#resDB = resultsDB(limitDir+'/results.sq', "results", setup.resultsColumns)
cardFileName = os.path.join(limitDir, s.name+'.txt')
if not os.path.isfile(cardFileName):
raise IOError("File %s doesn't exist!"%cardFileName)
cards[year] = cardFileName
limitDir = limitDir.replace('2017','COMBINED')
# run combine and store results in sqlite database
if not os.path.isdir(limitDir):
os.makedirs(limitDir)
resDB = resultsDB(limitDir+'/results.sq', "results", setup.resultsColumns)
res = {"signal":s.name}
overWrite = True
if not overWrite and res.DB.contains(key):
res = resDB.getDicts(key)[0]
logger.info("Found result for %s, reusing", s.name)
else:
signalRegions = range(15,30)
masks_2016 = ['mask_ch1_dc_2016_Bin'+str(i)+'=1' for i in signalRegions]
masks_2017 = ['mask_ch1_dc_2017_Bin'+str(i)+'=1' for i in signalRegions]
masks = ','.join(masks_2016+masks_2017)
combinedCard = c.combineCards( cards )
# We don't calculate limits here, but just in case we find a way how to do it, put placeholders here
res.update({"exp":0, "obs":0, "exp1up":0, "exp2up":0, "exp1down":0, "exp2down":0})
# Don't extract all the nuisances by default
if args.calcNuisances:
c.calcNuisances(combinedCard, masks=masks)
#nll = c.physicsModel(combinedCard, options="", normList=["WZ_norm","ZZ_norm"], masks=masks) # fastScan turns of profiling
nll = c.calcNLL(combinedCard)
if nll["nll0"] > 0:
res.update({"dNLL_postfit_r1":nll["nll"], "dNLL_bestfit":nll["bestfit"], "NLL_prefit":nll["nll0"]})
else:
res.update({"dNLL_postfit_r1":-999, "dNLL_bestfit":-999, "NLL_prefit":-999})
logger.info("Fits failed, adding values -999 as results")
logger.info("Adding results to database")
resDB.add(res, nll['nll_abs'], overwrite=True)
logger.info("Results stored in %s", limitDir)
def wrapper(s):
xSecScale = 1
c = cardFileWriter.cardFileWriter()
c.releaseLocation = combineReleaseLocation
cardFileName = os.path.join(limitDir, s.name + '.txt')
if not os.path.exists(cardFileName) or overWrite:
counter = 0
c.reset()
c.addUncertainty('PU', 'lnN')
c.addUncertainty('JEC', 'lnN')
c.addUncertainty('btag', 'lnN')
c.addUncertainty('trigger', 'lnN')
c.addUncertainty('leptonSF', 'lnN')
c.addUncertainty('scale', 'lnN')
c.addUncertainty('scale_sig', 'lnN')
c.addUncertainty('PDF', 'lnN')
c.addUncertainty('nonprompt', 'lnN')
c.addUncertainty('WZ_xsec', 'lnN')
c.addUncertainty('ZZ_xsec', 'lnN')
c.addUncertainty('rare', 'lnN')
c.addUncertainty('ttX', 'lnN')
c.addUncertainty('tZq', 'lnN')
for setup in setups:
for r in setup.regions:
for channel in setup.channels:
niceName = ' '.join([channel, r.__str__()])
binname = 'Bin' + str(counter)
counter += 1
c.addBin(binname, [p.name for p in processes], niceName)
for p in processes:
name = p.name
expected = getEstimate(
p, r, channel
) #{"process":nam, "region":r, "lumi":35.9, "presel":"nLep==3&&isTTZ&&nJetGodd>2&&nBTag>0", "weightString":"weight", "channel":channel})
c.specifyExpectation(binname, name, expected.val)
if expected.val > 0:
c.specifyUncertainty('PU', binname, name, 1.01)
c.specifyUncertainty('JEC', binname, name, 1.05)
c.specifyUncertainty('btag', binname, name, 1.05)
c.specifyUncertainty('trigger', binname, name,
1.04)
c.specifyUncertainty('leptonSF', binname, name,
1.07)
c.specifyUncertainty('scale', binname, name, 1.01)
c.specifyUncertainty('PDF', binname, name, 1.01)
if name.count('ZZ'):
c.specifyUncertainty('ZZ_xsec', binname, name,
1.20)
if name.count('WZ'):
c.specifyUncertainty('WZ_xsec', binname, name,
1.20)
if name.count('nonprompt'):
c.specifyUncertainty('nonprompt', binname,
name, 1.30)
if name.count('rare'):
c.specifyUncertainty('rare', binname, name,
1.50)
if name.count('TTX'):
c.specifyUncertainty('ttX', binname, name,
1.15)
if name.count('TZQ'):
c.specifyUncertainty('tZq', binname, name,
1.15)
#MC bkg stat (some condition to neglect the smaller ones?)
uname = 'Stat_' + binname + '_' + name
c.addUncertainty(uname, 'lnN')
c.specifyUncertainty(
uname, binname, name,
1 + expected.sigma / expected.val)
c.specifyObservation(
binname,
int(round((getEstimate(
pseudoData, r,
channel).val)))) #can also be pseudoDataPriv
#signalSetup = setup.systematicClone()
signal = getEstimate(
s, r, channel
) #resultsCache.get({"process":s.name, "region":r, "lumi":35.9, "presel":"nLep==3&&isTTZ&&nJetGodd>2&&nBTag>0", "weightString":"weight", "channel":channel})
c.specifyExpectation(binname, 'signal',
signal.val * xSecScale)
if signal.val > 0:
c.specifyUncertainty('PU', binname, "signal", 1.01)
c.specifyUncertainty('JEC', binname, "signal", 1.05)
c.specifyUncertainty('btag', binname, "signal", 1.05)
c.specifyUncertainty('trigger', binname, "signal",
1.04)
c.specifyUncertainty('leptonSF', binname, "signal",
1.07)
c.specifyUncertainty('scale_sig', binname, "signal",
1.30)
c.specifyUncertainty('PDF', binname, "signal", 1.15)
uname = 'Stat_' + binname + '_signal'
c.addUncertainty(uname, 'lnN')
c.specifyUncertainty(uname, binname, 'signal',
1 + signal.sigma / signal.val)
else:
uname = 'Stat_' + binname + '_signal'
c.addUncertainty(uname, 'lnN')
c.specifyUncertainty(uname, binname, 'signal', 1)
## alternative signal model
#signal_ALT = getEstimate(s, r, channel)
#c.specifyExpectation(binname, 'signal_ALT', signal_ALT.val*xSecScale )
#
#if signal_ALT.val>0:
# c.specifyUncertainty('PU', binname, "signal_ALT", 1.01)
# c.specifyUncertainty('JEC', binname, "signal_ALT", 1.05)
# c.specifyUncertainty('btag', binname, "signal_ALT", 1.05)
# c.specifyUncertainty('trigger', binname, "signal_ALT", 1.04)
# c.specifyUncertainty('leptonSF', binname, "signal_ALT", 1.07)
# c.specifyUncertainty('scale_sig', binname, "signal_ALT", 1.30)
# c.specifyUncertainty('PDF', binname, "signal_ALT", 1.15)
# uname = 'Stat_'+binname+'_signal_ALT'
# c.addUncertainty(uname, 'lnN')
# c.specifyUncertainty(uname, binname, 'signal_ALT', 1 + signal_ALT.sigma/signal_ALT.val )
#else:
# uname = 'Stat_'+binname+'_signal_ALT'
# c.addUncertainty(uname, 'lnN')
# c.specifyUncertainty(uname, binname, 'signal_ALT', 1 )
c.addUncertainty('Lumi', 'lnN')
c.specifyFlatUncertainty('Lumi', 1.026)
cardFileName = c.writeToFile(cardFileName)
else:
print "File %s found. Reusing." % cardFileName
res = {}
if getResult(s) and not overWrite:
res = getResult(s)
print "Found result for %s, reusing" % s.name
else:
# calculate the limit
limit = c.calcLimit(cardFileName) #, options="--run blind")
res.update({
"exp": limit['0.500'],
"obs": limit['-1.000'],
"exp1up": limit['0.840'],
"exp2up": limit['0.975'],
"exp1down": limit['0.160'],
"exp2down": limit['0.025']
})
# run the checks
c.calcNuisances(cardFileName)
# extract the NLL
nll = c.calcNLL(cardFileName)
res.update({
"dNLL_postfit_r1": nll["nll"],
"dNLL_bestfit": nll["bestfit"],
"NLL_prefit": nll["nll0"]
})
addResult(s, res, nll['nll_abs'], overwrite=True)
print "NLL results:"
print "{:>15}{:>15}{:>15}".format("Pre-fit", "Post-fit r=1", "Best fit")
print "{:15.2f}{:15.2f}{:15.2f}".format(
float(res["NLL_prefit"]),
float(res["NLL_prefit"]) + float(res["dNLL_postfit_r1"]),
float(res["NLL_prefit"]) + float(res["dNLL_bestfit"]))
if xSecScale != 1:
for k in res:
res[k] *= xSecScale
histo.Add(histos[i_histo + 1])
histos[i_histo + 1].Scale(-1)
# compute differences
h_signal, h_backgrounds = histos[0], histos[1:]
logger.info("Total signal %s %f", h_signal.GetName(), h_signal.Integral())
for i_h, h in enumerate(h_backgrounds):
logger.info("Total bkg %i %s: %f", i_h, h.GetName(), h.Integral())
result = {}
lumi_factor = 136. / 300.
signal_strengths = [0, 0.25, 0.5, 0.75, 1., 1.5, 2, 2.2]
for signal_strength in signal_strengths:
c = cardFileWriter.cardFileWriter()
bkg_sys = 1.1
bkg_shape_sys = 1.1
for i in range(len(h_backgrounds)):
c.addUncertainty('bkg_sys_%i' % i, 'lnN')
c.addUncertainty('bkg_shape_sys_%i' % i, 'lnN')
c.addUncertainty('sig_sys', 'lnN')
sig_sys = 1.25
for i_bin in range(1, 1 + h_signal.GetNbinsX()):
c.addBin('Bin' + str(i_bin), [h.GetName() for h in h_backgrounds],
'Bin' + str(i_bin))
y_signal = h_signal.GetBinContent(i_bin)
