cardDir += "_shape"
cardDir += "_lowUnc"
if args.WZtoPowheg:
cardDir += "_WZreweight"
if args.expected:
cardDir += "_expected"
## 2l setup ##
# not yet part of the game
## 3l setup ##
setup3l = Setup(year, nLeptons=3)
estimators3l = estimatorList(setup3l)
setup3l.estimators = estimators3l.constructEstimatorList(["WZ", "TTX", "TTW", "ZG", "rare", "ZZ"])# if not args.WZamc else estimators3l.constructEstimatorList(["WZ_amc", "TTX", "TTW", "ZG", "rare", "ZZ"])
setup3l.reweightRegions = regionsReweight
setup3l.channels = [channel(-1,-1)] # == 'all'
setup3l.regions = regionsE if not args.merged else noRegions
setup3l.year = year
## 3l NP setup ##
setup3l_NP = Setup(year, nLeptons=3, nonprompt=True)
setup3l_NP.channels = [channel(-1,-1)] # == 'all'
setup3l_NP.regions = regionsE if not args.merged else noRegions
## 4l setup ##
setup4l = Setup(year=year, nLeptons=4)
setup4l.parameters.update({'nJets':(1,-1), 'nBTags':(1,-1), 'zMassRange':20, 'zWindow2':"offZ"})
estimators4l = estimatorList(setup4l)
setup4l.estimators = estimators4l.constructEstimatorList(["ZZ", "rare","TTX"])
setup4l.reweightRegions = regionsReweight4l
setup4l.channels = [channel(-1,-1)] # == 'all'
jobs = []
# remove all so to avoid unnecessary concurrency. All will be calculated as sum of the individual channels later
seperateChannels3l = allTrilepChannels
allTrilepChannelNames = [c.name for c in allTrilepChannels]
seperateChannels3l.pop(allTrilepChannelNames.index('all'))
seperateChannels4l = allQuadlepChannels
allQuadlepChannelNames4l = [c.name for c in allQuadlepChannels]
seperateChannels4l.pop(allQuadlepChannelNames4l.index('all'))
if not options.skipCentral:
# First run over seperate channels
jobs.append((noRegions[0], channel(-1, -1), setupIncl3l))
jobs.append((noRegions[0], channel(-1, -1), setupIncl4l))
for var in variations:
for c in seperateChannels3l:
jobs.append((noRegions[0], c,
setupIncl3l.systematicClone(sys={'reweight': [var]})))
for c in seperateChannels4l:
jobs.append((noRegions[0], c,
setupIncl4l.systematicClone(sys={'reweight': [var]})))
## then one can sum up over all (currently done in the combine step)
#for var in variations:
# jobs.append((noRegions[0], "all", setupIncl.systematicClone(sys={'reweight':[var]})))
if not options.combine:
for region in regions:
def __init__(self, year=2017, nLeptons=3, nonprompt=False):
self.name = "defaultSetup"
self.channels = [channel(-1, -1)]
self.resultsFile = 'calculatedLimits_%s.db' % self.name
self.year = year
self.nLeptons = nLeptons
self.short = False
if nLeptons == 1:
self.tight_ID = "tight_3l" # for parton shower study
self.FO_ID = "FO_3l"
elif nLeptons == 2:
self.tight_ID = "tight_SS"
self.FO_ID = "FO_SS"
elif nLeptons == 3:
self.tight_ID = "tight_3l"
self.FO_ID = "FO_3l"
elif nLeptons == 4:
self.tight_ID = "tight_4l"
self.FO_ID = "FO_4l"
else:
raise NotImplementedError("Can't handle 0,1,5,.. lepton cases")
self.nonprompt = nonprompt
self.leptonId = self.FO_ID if self.nonprompt else self.tight_ID
self.default_sys = {
'weight': 'weight',
'reweight': ['reweightPU36fb', 'reweightBTagDeepCSV_SF'],
'selectionModifier': None
} # 'reweightTrigger_%s'%self.leptonId, 'reweightLeptonTrackingSF_%s'%self.leptonId
if nLeptons == 1:
# no trigger/lepton reweighting
pass
#elif nLeptons == 3:
# self.default_sys['reweight'] += ['reweightTrigger_tight_3l', 'reweightLeptonSF_tight_3l']
# if self.year == 2017: #in 2016 already included in leptonSF
# self.default_sys['reweight'] += ['reweightLeptonTrackingSF_tight_3l']
#elif nLeptons == 4:
# self.default_sys['reweight'] += ['reweightTrigger_tight_4l', 'reweightLeptonSF_tight_4l']
# if self.year == 2017: #in 2016 already included in leptonSF
# self.default_sys['reweight'] += ['reweightLeptonTrackingSF_tight_4l']
elif nLeptons == 3:
self.default_sys['reweight'] += [
'reweightTrigger_tight_3l', 'reweightLeptonSFSyst_tight_3l',
'reweightEleSFStat_tight_3l', 'reweightMuSFStat_tight_3l',
'reweightLeptonTrackingSF_tight_3l'
]
elif nLeptons == 4:
self.default_sys['reweight'] += [
'reweightTrigger_tight_4l', 'reweightLeptonSFSyst_tight_4l',
'reweightEleSFStat_tight_4l', 'reweightMuSFStat_tight_4l',
'reweightLeptonTrackingSF_tight_4l'
]
self.resultsColumns = [
'signal', 'exp', 'obs', 'exp1up', 'exp1down', 'exp2up', 'exp2down',
'NLL_prefit', 'dNLL_postfit_r1', 'dNLL_bestfit'
]
self.uncertaintyColumns = ["region", "channel", "PDFset"]
self.analysis_results = analysis_results
self.prefixes = []
self.externalCuts = []
#Default cuts and requirements. Those three things below are used to determine the key in the cache!
self.parameters = copy.deepcopy(default_parameters)
self.sys = self.default_sys
if year == 2017:
self.lumi = dataLumi2017
self.dataLumi = dataLumi2017
elif year == 2016:
self.lumi = dataLumi2016
self.dataLumi = dataLumi2016
elif year == 20167:
self.lumi = dataLumi201617
self.dataLumi = dataLumi201617
elif year == "run2":
self.lumi = dataLumi20161718
self.dataLumi = dataLumi20161718
elif year == "HLLHC":
self.lumi = dataHighLumi
self.dataLumi = dataHighLumi
self.genSelection = "Sum$(GenJet_pt>30)>=3&& abs(Z_mass-91.2)<10&&(abs(Z_daughterPdg)==11 || abs(Z_daughterPdg)==13 || abs(Z_daughterPdg)==15 )"
self.WZselection = cutInterpreter.cutString(
'trilep-Zcand-onZ-lepSelTTZ-njet1p')
# Data
if year == 2017:
data = Run2017
else:
data = Run2016
# MC
if year == 2017:
TTZSample = TTZtoLLNuNu_17
WZSample = WZ_amcatnlo_17
TTXSample = TTX_17
TTWSample = TTW_17
TZQSample = TZQ_17
ZGSample = ZGTo2LG
ZZSample = ZZ_17
rareSample = rare_17
nonpromptSample = nonpromptMC_17
pseudoDataSample = pseudoData_17
ttbarSample = TTLep_pow_17
else:
## use 2016 samples as default (we do combine on card file level)
TTZSample = TTZtoLLNuNu
WZSample = WZ_amcatnlo
TTXSample = TTX
TTWSample = TTW
TZQSample = TZQ
ZGSample = ZGTo2LG
ZZSample = ZZ
rareSample = rare
nonpromptSample = nonpromptMC
pseudoDataSample = pseudoData
ttbarSample = TTLep_pow
# removed the channel dependence.
self.samples = {
'TTZ': TTZSample,
'WZ': WZSample,
'TTX': TTXSample,
'TTW': TTWSample,
'TZQ': TZQSample,
'ZG': ZGSample,
'rare': rareSample,
'ZZ': ZZSample,
'nonprompt': nonpromptSample,
'ttbar': ttbarSample,
'pseudoData': pseudoDataSample,
'Data': data,
}
for s in samples17:
estimates17.append(MCBasedEstimate(name=s.name, sample=s ))
estimates17[-1].initCache(cacheDir)
jobs = []
def wrapper(args):
e, r, c, setup = args
res = e.cachedEstimate(r, c, setup, save=True, overwrite=options.overwrite)
return (e.uniqueKey(r, c, setup), res )
print regions
for r in regions:
for c in [channel(1,0), channel(0,1)]:
logger.info("Working on 2016 results")
for e in estimates16:
jobs.append((e, r, c, setup16))
#res = e.cachedEstimate(r, channel(-1,-1), setup16, save=True)
#logger.info("Result: %s", res.val)
logger.info("Working on 2017 results")
for e in estimates17:
jobs.append((e,r,c, setup17.systematicClone(sys={'reweight':['PSweight_central']})))
#res = e.cachedEstimate(r, channel(-1,-1), setup17, save=True)
#logger.info("Result: %s", res.val)
for weight in PS_indices:
jobs.append((e,r,c, setup17.systematicClone(sys={'reweight':['(%s)'%weight]})))
#res = e.cachedEstimate(r, channel(-1,-1), setup17.systematicClone(sys={'reweight':['LHEweight_wgt[%s]/LHEweight_wgt[1080]'%weight]}), save=True)
#logger.info("Result: %s", res.val)
estimates17.append(MCBasedEstimate(name=s.name, sample=s))
estimates17[-1].initCache(cacheDir)
jobs = []
def wrapper(args):
e, r, c, setup = args
res = e.cachedEstimate(r, c, setup, save=True, overwrite=options.overwrite)
return (e.uniqueKey(r, c, setup), res)
print regions
for r in regions:
for c in [channel(1, 0), channel(0, 1)]:
logger.info("Working on 2016 results")
for e in estimates16:
jobs.append((e, r, c, setup16))
#res = e.cachedEstimate(r, channel(-1,-1), setup16, save=True)
#logger.info("Result: %s", res.val)
logger.info("Working on 2017 results")
for e in estimates17:
jobs.append((e, r, c,
setup17.systematicClone(
sys={'reweight': ['PSweight_central']})))
#res = e.cachedEstimate(r, channel(-1,-1), setup17, save=True)
#logger.info("Result: %s", res.val)
for weight in PS_indices:
jobs.append((e, r, c,
setup17.systematicClone(
estimates16.append(MCBasedEstimate(name=s.name, sample=s))
estimates16[-1].initCache(cacheDir)
jobs = []
def wrapper(args):
e, r, c, setup = args
res = e.cachedEstimate(r, c, setup, save=True, overwrite=options.overwrite)
return (e.uniqueKey(r, c, setup), res)
print regions
for r in noRegionsB:
for c in [channel(1, 0), channel(0, 1)]:
for e in estimates16:
jobs.append((e, r, c, setup16))
for r in regions:
for c in [channel(1, 0), channel(0, 1)]:
logger.info("Working on 2016 results")
for e in estimates16:
jobs.append((e, r, c, setup16))
#res = e.cachedEstimate(r, channel(-1,-1), setup16, save=True)
#logger.info("Result: %s", res.val)
if options.noMultiThreading:
results = map(wrapper, jobs)
else:
from multiprocessing import Pool
estimate = MCBasedEstimate(name='TTZ', sample=TTZ_CR)
estimate.initCache(cacheDir)
jobs = []
def wrapper(args):
e, r, c, setup = args
res = e.cachedEstimate(r, c, setup, save=True, overwrite=options.overwrite)
return (e.uniqueKey(r, c, setup), res)
print regions
for r in regions:
for c in [channel(3, 0), channel(2, 1), channel(1, 2), channel(0, 3)]:
logger.info("Working on 2016 results")
jobs.append((estimate, r, c, setup16))
jobs.append(
(estimate, r, c,
setup16.systematicClone(sys={'reweight': ['reweightCR']})))
if options.noMultiThreading:
results = map(wrapper, jobs)
else:
from multiprocessing import Pool
pool = Pool(processes=8)
results = pool.map(wrapper, jobs)
pool.close()
pool.join()