def process_shift(self, events, shift_name):
dataset = events.metadata['dataset']
isRealData = not hasattr(events, "genWeight")
selection = PackedSelection()
weights = Weights(len(events), storeIndividual=True)
output = self.make_output()
if shift_name is None and not isRealData:
output['sumw'] = ak.sum(events.genWeight)
if isRealData or self._newTrigger:
trigger = np.zeros(len(events), dtype='bool')
for t in self._triggers[self._year]:
if t in events.HLT.fields:
trigger = trigger | events.HLT[t]
selection.add('trigger', trigger)
del trigger
else:
selection.add('trigger', np.ones(len(events), dtype='bool'))
if isRealData:
selection.add(
'lumimask', lumiMasks[self._year](events.run,
events.luminosityBlock))
else:
selection.add('lumimask', np.ones(len(events), dtype='bool'))
if isRealData and self._skipRunB and self._year == '2017':
selection.add('dropB', events.run > 299329)
else:
selection.add('dropB', np.ones(len(events), dtype='bool'))
if isRealData:
trigger = np.zeros(len(events), dtype='bool')
for t in self._muontriggers[self._year]:
if t in events.HLT.fields:
trigger |= np.array(events.HLT[t])
selection.add('muontrigger', trigger)
del trigger
else:
selection.add('muontrigger', np.ones(len(events), dtype='bool'))
metfilter = np.ones(len(events), dtype='bool')
for flag in self._met_filters[
self._year]['data' if isRealData else 'mc']:
metfilter &= np.array(events.Flag[flag])
selection.add('metfilter', metfilter)
del metfilter
fatjets = events.FatJet
fatjets['msdcorr'] = corrected_msoftdrop(fatjets)
fatjets['qcdrho'] = 2 * np.log(fatjets.msdcorr / fatjets.pt)
fatjets['n2ddt'] = fatjets.n2b1 - n2ddt_shift(fatjets, year=self._year)
fatjets['msdcorr_full'] = fatjets['msdcorr'] * self._msdSF[self._year]
candidatejet = fatjets[
# https://github.com/DAZSLE/BaconAnalyzer/blob/master/Analyzer/src/VJetLoader.cc#L269
(fatjets.pt > 200)
& (abs(fatjets.eta) < 2.5)
& fatjets.isTight # this is loose in sampleContainer
]
candidatejet = candidatejet[:, :
2] # Only consider first two to match generators
if self._jet_arbitration == 'pt':
candidatejet = ak.firsts(candidatejet)
elif self._jet_arbitration == 'mass':
candidatejet = ak.firsts(candidatejet[ak.argmax(
candidatejet.msdcorr, axis=1, keepdims=True)])
elif self._jet_arbitration == 'n2':
candidatejet = ak.firsts(candidatejet[ak.argmin(candidatejet.n2ddt,
axis=1,
keepdims=True)])
elif self._jet_arbitration == 'ddb':
candidatejet = ak.firsts(candidatejet[ak.argmax(
candidatejet.btagDDBvLV2, axis=1, keepdims=True)])
elif self._jet_arbitration == 'ddc':
candidatejet = ak.firsts(candidatejet[ak.argmax(
candidatejet.btagDDCvLV2, axis=1, keepdims=True)])
else:
raise RuntimeError("Unknown candidate jet arbitration")
if self._tagger == 'v1':
bvl = candidatejet.btagDDBvL
cvl = candidatejet.btagDDCvL
cvb = candidatejet.btagDDCvB
elif self._tagger == 'v2':
bvl = candidatejet.btagDDBvLV2
cvl = candidatejet.btagDDCvLV2
cvb = candidatejet.btagDDCvBV2
elif self._tagger == 'v3':
bvl = candidatejet.particleNetMD_Xbb
cvl = candidatejet.particleNetMD_Xcc / (
1 - candidatejet.particleNetMD_Xbb)
cvb = candidatejet.particleNetMD_Xcc / (
candidatejet.particleNetMD_Xcc +
candidatejet.particleNetMD_Xbb)
elif self._tagger == 'v4':
bvl = candidatejet.particleNetMD_Xbb
cvl = candidatejet.btagDDCvLV2
cvb = candidatejet.particleNetMD_Xcc / (
candidatejet.particleNetMD_Xcc +
candidatejet.particleNetMD_Xbb)
else:
raise ValueError("Not an option")
selection.add('minjetkin', (candidatejet.pt >= 450)
& (candidatejet.pt < 1200)
& (candidatejet.msdcorr >= 40.)
& (candidatejet.msdcorr < 201.)
& (abs(candidatejet.eta) < 2.5))
selection.add('_strict_mass', (candidatejet.msdcorr > 85) &
(candidatejet.msdcorr < 130))
selection.add('_high_score', cvl > 0.8)
selection.add('minjetkinmu', (candidatejet.pt >= 400)
& (candidatejet.pt < 1200)
& (candidatejet.msdcorr >= 40.)
& (candidatejet.msdcorr < 201.)
& (abs(candidatejet.eta) < 2.5))
selection.add('minjetkinw', (candidatejet.pt >= 200)
& (candidatejet.pt < 1200)
& (candidatejet.msdcorr >= 40.)
& (candidatejet.msdcorr < 201.)
& (abs(candidatejet.eta) < 2.5))
selection.add('jetid', candidatejet.isTight)
selection.add('n2ddt', (candidatejet.n2ddt < 0.))
if not self._tagger == 'v2':
selection.add('ddbpass', (bvl >= 0.89))
selection.add('ddcpass', (cvl >= 0.83))
selection.add('ddcvbpass', (cvb >= 0.2))
else:
selection.add('ddbpass', (bvl >= 0.7))
selection.add('ddcpass', (cvl >= 0.45))
selection.add('ddcvbpass', (cvb >= 0.03))
jets = events.Jet
jets = jets[(jets.pt > 30.) & (abs(jets.eta) < 2.5) & jets.isTight]
# only consider first 4 jets to be consistent with old framework
jets = jets[:, :4]
dphi = abs(jets.delta_phi(candidatejet))
selection.add(
'antiak4btagMediumOppHem',
ak.max(jets[dphi > np.pi / 2][self._ak4tagBranch],
axis=1,
mask_identity=False) <
BTagEfficiency.btagWPs[self._ak4tagger][self._year]['medium'])
ak4_away = jets[dphi > 0.8]
selection.add(
'ak4btagMedium08',
ak.max(ak4_away[self._ak4tagBranch], axis=1, mask_identity=False) >
BTagEfficiency.btagWPs[self._ak4tagger][self._year]['medium'])
met = events.MET
selection.add('met', met.pt < 140.)
goodmuon = ((events.Muon.pt > 10)
& (abs(events.Muon.eta) < 2.4)
& (events.Muon.pfRelIso04_all < 0.25)
& events.Muon.looseId)
nmuons = ak.sum(goodmuon, axis=1)
leadingmuon = ak.firsts(events.Muon[goodmuon])
if self._looseTau:
goodelectron = ((events.Electron.pt > 10)
& (abs(events.Electron.eta) < 2.5)
&
(events.Electron.cutBased >= events.Electron.VETO))
nelectrons = ak.sum(goodelectron, axis=1)
ntaus = ak.sum(
((events.Tau.pt > 20)
& (abs(events.Tau.eta) < 2.3)
& events.Tau.idDecayMode
& ((events.Tau.idMVAoldDM2017v2 & 2) != 0)
& ak.all(events.Tau.metric_table(events.Muon[goodmuon]) > 0.4,
axis=2)
& ak.all(events.Tau.metric_table(
events.Electron[goodelectron]) > 0.4,
axis=2)),
axis=1,
)
else:
goodelectron = (
(events.Electron.pt > 10)
& (abs(events.Electron.eta) < 2.5)
& (events.Electron.cutBased >= events.Electron.LOOSE))
nelectrons = ak.sum(goodelectron, axis=1)
ntaus = ak.sum(
(events.Tau.pt > 20)
&
events.Tau.idDecayMode # bacon iso looser than Nano selection
& ak.all(events.Tau.metric_table(events.Muon[goodmuon]) > 0.4,
axis=2)
& ak.all(events.Tau.metric_table(events.Electron[goodelectron])
> 0.4,
axis=2),
axis=1,
)
selection.add('noleptons',
(nmuons == 0) & (nelectrons == 0) & (ntaus == 0))
selection.add('onemuon',
(nmuons == 1) & (nelectrons == 0) & (ntaus == 0))
selection.add('muonkin',
(leadingmuon.pt > 55.) & (abs(leadingmuon.eta) < 2.1))
selection.add('muonDphiAK8',
abs(leadingmuon.delta_phi(candidatejet)) > 2 * np.pi / 3)
# W-Tag (Tag and Probe)
# tag side
selection.add(
'ak4btagMediumOppHem',
ak.max(jets[dphi > np.pi / 2][self._ak4tagBranch],
axis=1,
mask_identity=False) >
BTagEfficiency.btagWPs[self._ak4tagger][self._year]['medium'])
selection.add('met40p', met.pt > 40.)
selection.add('tightMuon',
(leadingmuon.tightId) & (leadingmuon.pt > 53.))
# selection.add('ptrecoW', (leadingmuon + met).pt > 250.)
selection.add('ptrecoW200', (leadingmuon + met).pt > 200.)
selection.add(
'ak4btagNearMu',
leadingmuon.delta_r(leadingmuon.nearest(ak4_away, axis=None)) <
2.0)
_bjets = jets[self._ak4tagBranch] > BTagEfficiency.btagWPs[
self._ak4tagger][self._year]['medium']
# _nearAK8 = jets.delta_r(candidatejet) < 0.8
# _nearMu = jets.delta_r(ak.firsts(events.Muon)) < 0.3
# selection.add('ak4btagOld', ak.sum(_bjets & ~_nearAK8 & ~_nearMu, axis=1) >= 1)
_nearAK8 = jets.delta_r(candidatejet) < 0.8
_nearMu = jets.delta_r(leadingmuon) < 0.3
selection.add('ak4btagOld',
ak.sum(_bjets & ~_nearAK8 & ~_nearMu, axis=1) >= 1)
# _nearAK8 = jets.delta_r(candidatejet) < 0.8
# _nearMu = jets.delta_r(candidatejet.nearest(events.Muon[goodmuon], axis=None)) < 0.3
# selection.add('ak4btagNew', ak.sum(_bjets & ~_nearAK8 & ~_nearMu, axis=1) >= 1)
# probe side
selection.add('minWjetpteta',
(candidatejet.pt >= 200) & (abs(candidatejet.eta) < 2.4))
# selection.add('noNearMuon', candidatejet.delta_r(candidatejet.nearest(events.Muon[goodmuon], axis=None)) > 1.0)
selection.add('noNearMuon', candidatejet.delta_r(leadingmuon) > 1.0)
#####
if isRealData:
genflavor = ak.zeros_like(candidatejet.pt)
else:
if 'HToCC' in dataset or 'HToBB' in dataset:
if self._ewkHcorr:
add_HiggsEW_kFactors(weights, events.GenPart, dataset)
weights.add('genweight', events.genWeight)
if "PSWeight" in events.fields:
add_ps_weight(weights, events.PSWeight)
else:
add_ps_weight(weights, None)
if "LHEPdfWeight" in events.fields:
add_pdf_weight(weights, events.LHEPdfWeight)
else:
add_pdf_weight(weights, None)
if "LHEScaleWeight" in events.fields:
add_scalevar_7pt(weights, events.LHEScaleWeight)
add_scalevar_3pt(weights, events.LHEScaleWeight)
else:
add_scalevar_7pt(weights, [])
add_scalevar_3pt(weights, [])
add_pileup_weight(weights, events.Pileup.nPU, self._year, dataset)
bosons = getBosons(events.GenPart)
matchedBoson = candidatejet.nearest(bosons,
axis=None,
threshold=0.8)
if self._tightMatch:
match_mask = (
(candidatejet.pt - matchedBoson.pt) / matchedBoson.pt <
0.5) & ((candidatejet.msdcorr - matchedBoson.mass) /
matchedBoson.mass < 0.3)
selmatchedBoson = ak.mask(matchedBoson, match_mask)
genflavor = bosonFlavor(selmatchedBoson)
else:
genflavor = bosonFlavor(matchedBoson)
genBosonPt = ak.fill_none(ak.firsts(bosons.pt), 0)
if self._newVjetsKfactor:
add_VJets_kFactors(weights, events.GenPart, dataset)
else:
add_VJets_NLOkFactor(weights, genBosonPt, self._year, dataset)
if shift_name is None:
output['btagWeight'].fill(val=self._btagSF.addBtagWeight(
weights, ak4_away, self._ak4tagBranch))
if self._nnlops_rew and dataset in [
'GluGluHToCC_M125_13TeV_powheg_pythia8'
]:
weights.add('minlo_rew',
powheg_to_nnlops(ak.to_numpy(genBosonPt)))
if self._newTrigger:
add_jetTriggerSF(
weights, ak.firsts(fatjets),
self._year if not self._skipRunB else f'{self._year}CDEF',
selection)
else:
add_jetTriggerWeight(weights, candidatejet.msdcorr,
candidatejet.pt, self._year)
add_mutriggerSF(weights, leadingmuon, self._year, selection)
add_mucorrectionsSF(weights, leadingmuon, self._year, selection)
if self._year in ("2016", "2017"):
weights.add("L1Prefiring", events.L1PreFiringWeight.Nom,
events.L1PreFiringWeight.Up,
events.L1PreFiringWeight.Dn)
logger.debug("Weight statistics: %r" % weights.weightStatistics)
msd_matched = candidatejet.msdcorr * self._msdSF[self._year] * (
genflavor > 0) + candidatejet.msdcorr * (genflavor == 0)
regions = {
'signal': [
'noleptons', 'minjetkin', 'met', 'metfilter', 'jetid',
'antiak4btagMediumOppHem', 'n2ddt', 'trigger', 'lumimask'
],
'signal_noddt': [
'noleptons', 'minjetkin', 'met', 'jetid',
'antiak4btagMediumOppHem', 'trigger', 'lumimask', 'metfilter'
],
# 'muoncontrol': ['minjetkinmu', 'jetid', 'n2ddt', 'ak4btagMedium08', 'onemuon', 'muonkin', 'muonDphiAK8', 'muontrigger', 'lumimask', 'metfilter'],
'muoncontrol': [
'onemuon', 'muonkin', 'muonDphiAK8', 'metfilter',
'minjetkinmu', 'jetid', 'ak4btagMedium08', 'n2ddt',
'muontrigger', 'lumimask'
],
'muoncontrol_noddt': [
'onemuon', 'muonkin', 'muonDphiAK8', 'jetid', 'metfilter',
'minjetkinmu', 'jetid', 'ak4btagMedium08', 'muontrigger',
'lumimask'
],
'wtag': [
'onemuon', 'tightMuon', 'minjetkinw', 'jetid', 'met40p',
'metfilter', 'ptrecoW200', 'ak4btagOld', 'muontrigger',
'lumimask'
],
'wtag0': [
'onemuon', 'tightMuon', 'met40p', 'metfilter', 'ptrecoW200',
'ak4btagOld', 'muontrigger', 'lumimask'
],
'wtag2': [
'onemuon', 'tightMuon', 'minjetkinw', 'jetid',
'ak4btagMediumOppHem', 'met40p', 'metfilter', 'ptrecoW200',
'ak4btagOld', 'muontrigger', 'lumimask'
],
'noselection': [],
}
def normalize(val, cut):
if cut is None:
ar = ak.to_numpy(ak.fill_none(val, np.nan))
return ar
else:
ar = ak.to_numpy(ak.fill_none(val[cut], np.nan))
return ar
import time
tic = time.time()
if shift_name is None:
for region, cuts in regions.items():
allcuts = set([])
cut = selection.all(*allcuts)
output['cutflow_msd'].fill(region=region,
genflavor=normalize(
genflavor, None),
cut=0,
weight=weights.weight(),
msd=normalize(msd_matched, None))
output['cutflow_eta'].fill(region=region,
genflavor=normalize(genflavor, cut),
cut=0,
weight=weights.weight()[cut],
eta=normalize(
candidatejet.eta, cut))
output['cutflow_pt'].fill(region=region,
genflavor=normalize(genflavor, cut),
cut=0,
weight=weights.weight()[cut],
pt=normalize(candidatejet.pt, cut))
for i, cut in enumerate(cuts + ['ddcvbpass', 'ddcpass']):
allcuts.add(cut)
cut = selection.all(*allcuts)
output['cutflow_msd'].fill(region=region,
genflavor=normalize(
genflavor, cut),
cut=i + 1,
weight=weights.weight()[cut],
msd=normalize(msd_matched, cut))
output['cutflow_eta'].fill(
region=region,
genflavor=normalize(genflavor, cut),
cut=i + 1,
weight=weights.weight()[cut],
eta=normalize(candidatejet.eta, cut))
output['cutflow_pt'].fill(
region=region,
genflavor=normalize(genflavor, cut),
cut=i + 1,
weight=weights.weight()[cut],
pt=normalize(candidatejet.pt, cut))
if self._evtVizInfo and 'ddcpass' in allcuts and isRealData and region == 'signal':
if 'event' not in events.fields:
continue
_cut = selection.all(*allcuts, '_strict_mass',
'_high_score')
# _cut = selection.all('_strict_mass'')
output['to_check'][
'mass'] += processor.column_accumulator(
normalize(msd_matched, _cut))
nfatjet = ak.sum(
((fatjets.pt > 200) &
(abs(fatjets.eta) < 2.5) & fatjets.isTight),
axis=1)
output['to_check'][
'njet'] += processor.column_accumulator(
normalize(nfatjet, _cut))
output['to_check'][
'fname'] += processor.column_accumulator(
np.array([events.metadata['filename']] *
len(normalize(msd_matched, _cut))))
output['to_check'][
'event'] += processor.column_accumulator(
normalize(events.event, _cut))
output['to_check'][
'luminosityBlock'] += processor.column_accumulator(
normalize(events.luminosityBlock, _cut))
output['to_check'][
'run'] += processor.column_accumulator(
normalize(events.run, _cut))
if shift_name is None:
systematics = [None] + list(weights.variations)
else:
systematics = [shift_name]
def fill(region, systematic, wmod=None):
selections = regions[region]
cut = selection.all(*selections)
sname = 'nominal' if systematic is None else systematic
if wmod is None:
if systematic in weights.variations:
weight = weights.weight(modifier=systematic)[cut]
else:
weight = weights.weight()[cut]
else:
weight = weights.weight()[cut] * wmod[cut]
output['templates'].fill(
region=region,
systematic=sname,
runid=runmap(events.run)[cut],
genflavor=normalize(genflavor, cut),
pt=normalize(candidatejet.pt, cut),
msd=normalize(msd_matched, cut),
ddb=normalize(bvl, cut),
ddc=normalize(cvl, cut),
ddcvb=normalize(cvb, cut),
weight=weight,
)
if region in [
'wtag', 'wtag0', 'wtag2', 'wtag3', 'wtag4', 'wtag5',
'wtag6', 'wtag7', 'noselection'
]: # and sname in ['nominal', 'pileup_weightDown', 'pileup_weightUp', 'jet_triggerDown', 'jet_triggerUp']:
output['wtag'].fill(
region=region,
systematic=sname,
genflavor=normalize(genflavor, cut),
pt=normalize(candidatejet.pt, cut),
msd=normalize(msd_matched, cut),
n2ddt=normalize(candidatejet.n2ddt, cut),
ddc=normalize(cvl, cut),
ddcvb=normalize(cvb, cut),
weight=weight,
)
# if region in ['signal', 'noselection']:
# output['etaphi'].fill(
# region=region,
# systematic=sname,
# runid=runmap(events.run)[cut],
# genflavor=normalize(genflavor, cut),
# pt=normalize(candidatejet.pt, cut),
# eta=normalize(candidatejet.eta, cut),
# phi=normalize(candidatejet.phi, cut),
# ddc=normalize(cvl, cut),
# ddcvb=normalize(cvb, cut),
# ),
if not isRealData:
if wmod is not None:
_custom_weight = events.genWeight[cut] * wmod[cut]
else:
_custom_weight = np.ones_like(weight)
output['genresponse_noweight'].fill(
region=region,
systematic=sname,
pt=normalize(candidatejet.pt, cut),
genpt=normalize(genBosonPt, cut),
weight=_custom_weight,
)
output['genresponse'].fill(
region=region,
systematic=sname,
pt=normalize(candidatejet.pt, cut),
genpt=normalize(genBosonPt, cut),
weight=weight,
)
if systematic is None:
output['signal_opt'].fill(
region=region,
genflavor=normalize(genflavor, cut),
ddc=normalize(cvl, cut),
ddcvb=normalize(cvb, cut),
msd=normalize(msd_matched, cut),
weight=weight,
)
output['signal_optb'].fill(
region=region,
genflavor=normalize(genflavor, cut),
ddb=normalize(bvl, cut),
msd=normalize(msd_matched, cut),
weight=weight,
)
for region in regions:
cut = selection.all(*(set(regions[region]) - {'n2ddt'}))
if shift_name is None:
output['nminus1_n2ddt'].fill(
region=region,
n2ddt=normalize(candidatejet.n2ddt, cut),
weight=weights.weight()[cut],
)
for systematic in systematics:
if isRealData and systematic is not None:
continue
fill(region, systematic)
if shift_name is None and 'GluGluH' in dataset and 'LHEWeight' in events.fields:
for i in range(9):
fill(region, 'LHEScale_%d' % i, events.LHEScaleWeight[:,
i])
for c in events.LHEWeight.fields[1:]:
fill(region, 'LHEWeight_%s' % c, events.LHEWeight[c])
toc = time.time()
output["filltime"] = toc - tic
if shift_name is None:
output["weightStats"] = weights.weightStatistics
return {dataset: output}
def process(self, events):
dataset = events.metadata['dataset']
isRealData = not hasattr(events, "genWeight")
selection = PackedSelection()
weights = Weights(len(events))
output = self.accumulator.identity()
if not isRealData:
output['sumw'][dataset] += ak.sum(events.genWeight)
if isRealData:
trigger = np.zeros(len(events), dtype='bool')
for t in self._triggers[self._year]:
trigger = trigger | events.HLT[t]
else:
trigger = np.ones(len(events), dtype='bool')
selection.add('trigger', trigger)
if isRealData:
trigger = np.zeros(len(events), dtype='bool')
for t in self._muontriggers[self._year]:
trigger = trigger | events.HLT[t]
else:
trigger = np.ones(len(events), dtype='bool')
selection.add('muontrigger', trigger)
fatjets = events.FatJet
fatjets['msdcorr'] = corrected_msoftdrop(fatjets)
fatjets['qcdrho'] = 2 * np.log(fatjets.msdcorr / fatjets.pt)
fatjets['n2ddt'] = fatjets.n2b1 - n2ddt_shift(fatjets, year=self._year)
fatjets['msdcorr_full'] = fatjets['msdcorr'] * self._msdSF[self._year]
candidatejet = fatjets[
# https://github.com/DAZSLE/BaconAnalyzer/blob/master/Analyzer/src/VJetLoader.cc#L269
(fatjets.pt > 200)
& (abs(fatjets.eta) < 2.5)
& fatjets.isTight # this is loose in sampleContainer
]
if self._jet_arbitration == 'pt':
candidatejet = ak.firsts(candidatejet)
elif self._jet_arbitration == 'mass':
candidatejet = candidatejet[ak.argmax(candidatejet.msdcorr)]
elif self._jet_arbitration == 'n2':
candidatejet = candidatejet[ak.argmin(candidatejet.n2ddt)]
elif self._jet_arbitration == 'ddb':
candidatejet = candidatejet[ak.argmax(candidatejet.btagDDBvL)]
else:
raise RuntimeError("Unknown candidate jet arbitration")
selection.add('minjetkin', (candidatejet.pt >= 450)
& (candidatejet.msdcorr >= 40.)
& (abs(candidatejet.eta) < 2.5))
selection.add('jetacceptance', (candidatejet.msdcorr >= 47.)
& (candidatejet.pt < 1200)
& (candidatejet.msdcorr < 201.))
selection.add('jetid', candidatejet.isTight)
selection.add('n2ddt', (candidatejet.n2ddt < 0.))
selection.add('ddbpass', (candidatejet.btagDDBvL >= 0.89))
jets = events.Jet[(events.Jet.pt > 30.)
& (abs(events.Jet.eta) < 2.5)
& events.Jet.isTight]
# only consider first 4 jets to be consistent with old framework
jets = jets[:, :4]
dphi = abs(jets.delta_phi(candidatejet))
selection.add(
'antiak4btagMediumOppHem',
ak.max(
jets[dphi > np.pi / 2].btagDeepB, axis=1, mask_identity=False)
< BTagEfficiency.btagWPs[self._year]['medium'])
ak4_away = jets[dphi > 0.8]
selection.add(
'ak4btagMedium08',
ak.max(ak4_away.btagDeepB, axis=1, mask_identity=False) >
BTagEfficiency.btagWPs[self._year]['medium'])
selection.add('met', events.MET.pt < 140.)
goodmuon = ((events.Muon.pt > 10)
& (abs(events.Muon.eta) < 2.4)
& (events.Muon.pfRelIso04_all < 0.25)
& events.Muon.looseId)
nmuons = ak.sum(goodmuon, axis=1)
leadingmuon = ak.firsts(events.Muon[goodmuon])
nelectrons = ak.sum(
(events.Electron.pt > 10)
& (abs(events.Electron.eta) < 2.5)
& (events.Electron.cutBased >= events.Electron.LOOSE),
axis=1,
)
ntaus = ak.sum(
(events.Tau.pt > 20)
& events.Tau.idDecayMode, # bacon iso looser than Nano selection
axis=1,
)
selection.add('noleptons',
(nmuons == 0) & (nelectrons == 0) & (ntaus == 0))
selection.add('onemuon',
(nmuons == 1) & (nelectrons == 0) & (ntaus == 0))
selection.add('muonkin',
(leadingmuon.pt > 55.) & (abs(leadingmuon.eta) < 2.1))
selection.add('muonDphiAK8',
abs(leadingmuon.delta_phi(candidatejet)) > 2 * np.pi / 3)
if isRealData:
genflavor = 0
else:
weights.add('genweight', events.genWeight)
add_pileup_weight(weights, events.Pileup.nPU, self._year, dataset)
bosons = getBosons(events.GenPart)
matchedBoson = candidatejet.nearest(bosons,
axis=None,
threshold=0.8)
genflavor = bosonFlavor(matchedBoson)
genBosonPt = ak.fill_none(ak.firsts(bosons.pt), 0)
add_VJets_NLOkFactor(weights, genBosonPt, self._year, dataset)
add_jetTriggerWeight(weights, candidatejet.msdcorr,
candidatejet.pt, self._year)
output['btagWeight'].fill(dataset=dataset,
val=self._btagSF.addBtagWeight(
weights, ak4_away))
logger.debug("Weight statistics: %r" % weights.weightStatistics)
msd_matched = candidatejet.msdcorr * self._msdSF[self._year] * (
genflavor > 0) + candidatejet.msdcorr * (genflavor == 0)
regions = {
'signal': [
'trigger', 'minjetkin', 'jetacceptance', 'jetid', 'n2ddt',
'antiak4btagMediumOppHem', 'met', 'noleptons'
],
'muoncontrol': [
'muontrigger', 'minjetkin', 'jetacceptance', 'jetid', 'n2ddt',
'ak4btagMedium08', 'onemuon', 'muonkin', 'muonDphiAK8'
],
'noselection': [],
}
for region, cuts in regions.items():
allcuts = set()
output['cutflow'].fill(dataset=dataset,
region=region,
genflavor=genflavor,
cut=0,
weight=weights.weight())
for i, cut in enumerate(cuts + ['ddbpass']):
allcuts.add(cut)
cut = selection.all(*allcuts)
output['cutflow'].fill(dataset=dataset,
region=region,
genflavor=genflavor[cut],
cut=i + 1,
weight=weights.weight()[cut])
systematics = [
None,
'jet_triggerUp',
'jet_triggerDown',
'btagWeightUp',
'btagWeightDown',
'btagEffStatUp',
'btagEffStatDown',
]
def normalize(val, cut):
return ak.to_numpy(ak.fill_none(val[cut], np.nan))
def fill(region, systematic, wmod=None):
selections = regions[region]
cut = selection.all(*selections)
sname = 'nominal' if systematic is None else systematic
if wmod is None:
weight = weights.weight(modifier=systematic)[cut]
else:
weight = weights.weight()[cut] * wmod[cut]
output['templates'].fill(
dataset=dataset,
region=region,
systematic=sname,
genflavor=genflavor[cut],
pt=normalize(candidatejet.pt, cut),
msd=normalize(msd_matched, cut),
ddb=normalize(candidatejet.btagDDBvL, cut),
weight=weight,
)
if wmod is not None:
output['genresponse_noweight'].fill(
dataset=dataset,
region=region,
systematic=sname,
pt=normalize(candidatejet.pt, cut),
genpt=normalize(genBosonPt, cut),
weight=events.genWeight[cut] * wmod[cut],
)
output['genresponse'].fill(
dataset=dataset,
region=region,
systematic=sname,
pt=normalize(candidatejet.pt, cut),
genpt=normalize(genBosonPt, cut),
weight=weight,
)
for region in regions:
cut = selection.all(*(set(regions[region]) - {'n2ddt'}))
output['nminus1_n2ddt'].fill(
dataset=dataset,
region=region,
n2ddt=normalize(candidatejet.n2ddt, cut),
weight=weights.weight()[cut],
)
for systematic in systematics:
fill(region, systematic)
if 'GluGluHToBB' in dataset:
for i in range(9):
fill(region, 'LHEScale_%d' % i, events.LHEScaleWeight[:,
i])
for c in events.LHEWeight.columns[1:]:
fill(region, 'LHEWeight_%s' % c, events.LHEWeight[c])
output["weightStats"] = weights.weightStatistics
return output
def process(self, events):
dataset = events.metadata['dataset']
isRealData = 'genWeight' not in events.columns
selection = processor.PackedSelection()
weights = processor.Weights(len(events))
output = self.accumulator.identity()
if not isRealData:
output['sumw'][dataset] += events.genWeight.sum()
if isRealData:
trigger = np.zeros(events.size, dtype='bool')
for t in self._triggers[self._year]:
trigger = trigger | events.HLT[t]
else:
trigger = np.ones(events.size, dtype='bool')
selection.add('trigger', trigger)
if isRealData:
trigger = np.zeros(events.size, dtype='bool')
for t in self._muontriggers[self._year]:
trigger = trigger | events.HLT[t]
else:
trigger = np.ones(events.size, dtype='bool')
selection.add('muontrigger', trigger)
try:
fatjets = events.FatJet
except AttributeError:
# early pancakes
fatjets = events.CustomAK8Puppi
fatjets['msdcorr'] = corrected_msoftdrop(fatjets)
fatjets['rho'] = 2 * np.log(fatjets.msdcorr / fatjets.pt)
fatjets['n2ddt'] = fatjets.n2b1 - n2ddt_shift(fatjets, year=self._year)
fatjets['msdcorr_full'] = fatjets['msdcorr'] * self._msdSF[self._year]
candidatejet = fatjets[
# https://github.com/DAZSLE/BaconAnalyzer/blob/master/Analyzer/src/VJetLoader.cc#L269
(fatjets.pt > 200)
& (abs(fatjets.eta) < 2.5)
# & fatjets.isLoose # not always available
][:, 0:1]
selection.add('minjetkin', (
(candidatejet.pt >= 450)
& (candidatejet.msdcorr >= 47.)
& (abs(candidatejet.eta) < 2.5)
).any())
selection.add('jetacceptance', (
(candidatejet.msdcorr >= 47.)
& (candidatejet.pt < 1200)
& (candidatejet.msdcorr < 201.)
).any())
selection.add('jetid', candidatejet.isTight.any())
selection.add('n2ddt', (candidatejet.n2ddt < 0.).any())
selection.add('ddbpass', (candidatejet.btagDDBvL >= 0.89).any())
jets = events.Jet[
(events.Jet.pt > 30.)
& (abs(events.Jet.eta) < 2.5)
& events.Jet.isTight
]
# only consider first 4 jets to be consistent with old framework
jets = jets[:, :4]
ak4_ak8_pair = jets.cross(candidatejet, nested=True)
dphi = abs(ak4_ak8_pair.i0.delta_phi(ak4_ak8_pair.i1))
ak4_opposite = jets[(dphi > np.pi / 2).all()]
selection.add('antiak4btagMediumOppHem', ak4_opposite.btagDeepB.max() < BTagEfficiency.btagWPs[self._year]['medium'])
ak4_away = jets[(dphi > 0.8).all()]
selection.add('ak4btagMedium08', ak4_away.btagDeepB.max() > BTagEfficiency.btagWPs[self._year]['medium'])
selection.add('met', events.MET.pt < 140.)
goodmuon = (
(events.Muon.pt > 10)
& (abs(events.Muon.eta) < 2.4)
& (events.Muon.pfRelIso04_all < 0.25)
& (events.Muon.looseId).astype(bool)
)
nmuons = goodmuon.sum()
leadingmuon = events.Muon[goodmuon][:, 0:1]
muon_ak8_pair = leadingmuon.cross(candidatejet, nested=True)
nelectrons = (
(events.Electron.pt > 10)
& (abs(events.Electron.eta) < 2.5)
& (events.Electron.cutBased >= events.Electron.LOOSE)
).sum()
ntaus = (
(events.Tau.pt > 20)
& (events.Tau.idDecayMode).astype(bool)
# bacon iso looser than Nano selection
).sum()
selection.add('noleptons', (nmuons == 0) & (nelectrons == 0) & (ntaus == 0))
selection.add('onemuon', (nmuons == 1) & (nelectrons == 0) & (ntaus == 0))
selection.add('muonkin', (
(leadingmuon.pt > 55.)
& (abs(leadingmuon.eta) < 2.1)
).all())
selection.add('muonDphiAK8', (
abs(muon_ak8_pair.i0.delta_phi(muon_ak8_pair.i1)) > 2*np.pi/3
).all().all())
if isRealData:
genflavor = candidatejet.pt.zeros_like()
else:
weights.add('genweight', events.genWeight)
add_pileup_weight(weights, events.Pileup.nPU, self._year, dataset)
bosons = getBosons(events)
genBosonPt = bosons.pt.pad(1, clip=True).fillna(0)
add_VJets_NLOkFactor(weights, genBosonPt, self._year, dataset)
genflavor = matchedBosonFlavor(candidatejet, bosons).pad(1, clip=True).fillna(-1).flatten()
add_jetTriggerWeight(weights, candidatejet.msdcorr, candidatejet.pt, self._year)
output['btagWeight'].fill(dataset=dataset, val=self._btagSF.addBtagWeight(weights, ak4_away))
logger.debug("Weight statistics: %r" % weights._weightStats)
msd_matched = candidatejet.msdcorr * self._msdSF[self._year] * (genflavor > 0) + candidatejet.msdcorr * (genflavor == 0)
regions = {
'signal': ['trigger','minjetkin',],#'noleptons','jetacceptance', 'noleptons','jetid',],#'jetid', 'noleptons',],# 'n2ddt','antiak4btagMediumOppHem'],#, 'met',],
'muoncontrol': ['muontrigger','minjetkin', 'jetid','muonDphiAK8','muonkin', 'ak4btagMedium08', 'onemuon',],# 'muonkin', 'muonDphiAK8'],
'noselection': [],
}
for region, cuts in regions.items():
allcuts = set()
logger.debug(f"Filling cutflow with: {dataset}, {region}, {genflavor}, {weights.weight()}")
#output['cutflow'].fill(dataset=dataset, region=region, genflavor=genflavor, cut=0, weight=weights.weight())
#for i, cut in enumerate(cuts + ['ddbpass']):
# allcuts.add(cut)
# cut = selection.all(*allcuts)
# output['cutflow'].fill(dataset=dataset, region=region, genflavor=genflavor[cut], cut=i + 1, weight=weights.weight()[cut])
systematics = [
None,
'jet_triggerUp',
'jet_triggerDown',
'btagWeightUp',
'btagWeightDown',
'btagEffStatUp',
'btagEffStatDown',
]
def normalize(val, cut):
return val[cut].pad(1, clip=True).fillna(0).flatten()
def fill(region, systematic=None, wmod=None):
selections = regions[region]
cut = selection.all(*selections)
sname = 'nominal' if systematic is None else systematic
if wmod is None:
weight = weights.weight(modifier=systematic)[cut]
else:
weight = weights.weight()[cut] * wmod[cut]
output['templates'].fill(
dataset=dataset,
region=region,
#systematic=sname,
#genflavor=genflavor[cut],
pt=normalize(candidatejet.pt, cut),
msd=normalize(msd_matched, cut),
#ddb=normalize(candidatejet.btagDDBvL, cut),
weight=weight,
)
if wmod is not None:
output['genresponse_noweight'].fill(
dataset=dataset,
region=region,
systematic=sname,
pt=normalize(candidatejet.pt, cut),
genpt=normalize(genBosonPt, cut),
weight=events.genWeight[cut] * wmod[cut],
)
output['genresponse'].fill(
dataset=dataset,
region=region,
systematic=sname,
pt=normalize(candidatejet.pt, cut),
genpt=normalize(genBosonPt, cut),
weight=weight,
)
for region in regions:
cut = selection.all(*(set(regions[region]) - {'n2ddt'}))
output['nminus1_n2ddt'].fill(
dataset=dataset,
region=region,
n2ddt=normalize(candidatejet.n2ddt, cut),
weight=weights.weight()[cut],
)
#for systematic in systematics:
fill(region)#, systematic)
if 'GluGluHToBB' in dataset:
for i in range(9):
fill(region, 'LHEScale_%d' % i, events.LHEScaleWeight[:, i])
for c in events.LHEWeight.columns[1:]:
fill(region, 'LHEWeight_%s' % c, events.LHEWeight[c])
return output
def process(self, events):
def normalize(val, cut):
return ak.to_numpy(ak.fill_none(
val[cut],
np.nan)) #val[cut].pad(1, clip=True).fillna(0).flatten()
def fill(region, cuts, systematic=None, wmod=None):
print('filling %s' % region)
selections = cuts
cut = selection.all(*selections)
if 'signal' in region: weight = weights_signal.weight()[cut]
elif 'muonCR' in region: weight = weights_muonCR.weight()[cut]
elif 'VtaggingCR' in region:
weight = weights_VtaggingCR.weight()[cut]
output['templates'].fill(
dataset=dataset,
region=region,
pt=normalize(candidatejet.pt, cut),
msd=normalize(candidatejet.msdcorr, cut),
n2ddt=normalize(candidatejet.n2ddt, cut),
#gruddt=normalize(candidatejet.gruddt, cut),
in_v3_ddt=normalize(candidatejet.in_v3_ddt, cut),
hadW=normalize(candidatejet.nmatcheddau, cut),
weight=weight,
),
output['event'].fill(
dataset=dataset,
region=region,
MET=events.MET.pt[cut],
#nJet=fatjets.counts[cut],
nPFConstituents=normalize(candidatejet.nPFConstituents, cut),
weight=weight,
),
output['deepAK8'].fill(
dataset=dataset,
region=region,
deepTagMDWqq=normalize(candidatejet.deepTagMDWqq, cut),
deepTagMDZqq=normalize(candidatejet.deepTagMDZqq, cut),
msd=normalize(candidatejet.msdcorr, cut),
#genflavor=genflavor[cut],
weight=weight,
),
output['in_v3'].fill(
dataset=dataset,
region=region,
#genflavor=genflavor[cut],
in_v3=normalize(candidatejet.in_v3, cut),
n2=normalize(candidatejet.n2b1, cut),
gru=normalize(candidatejet.gru, cut),
weight=weight,
),
if 'muonCR' in dataset or 'VtaggingCR' in dataset:
output['muon'].fill(
dataset=dataset,
region=region,
mu_pt=normalize(candidatemuon.pt, cut),
mu_eta=normalize(candidatemuon.eta, cut),
mu_pfRelIso04_all=normalize(candidatemuon.pfRelIso04_all,
cut),
weight=weight,
),
#common jet kinematics
gru = events.GRU
IN = events.IN
fatjets = events.FatJet
fatjets['msdcorr'] = corrected_msoftdrop(fatjets)
fatjets['qcdrho'] = 2 * np.log(fatjets.msdcorr / fatjets.pt)
fatjets['gruddt'] = gru.v25 - shift(
fatjets, algo='gruddt', year='2017')
fatjets['gru'] = gru.v25
fatjets['in_v3'] = IN.v3
fatjets['in_v3_ddt'] = IN.v3 - shift(
fatjets, algo='inddt', year='2017')
fatjets['in_v3_ddt_90pctl'] = IN.v3 - shift(
fatjets, algo='inddt90pctl', year='2017')
fatjets['n2ddt'] = fatjets.n2b1 - n2ddt_shift(fatjets, year='2017')
fatjets['nmatcheddau'] = TTsemileptonicmatch(events)
dataset = events.metadata['dataset']
print('process dataset', dataset)
isRealData = not hasattr(events, 'genWeight')
output = self.accumulator.identity()
if (len(events) == 0): return output
selection = PackedSelection('uint64')
weights_signal = Weights(len(events))
weights_muonCR = Weights(len(events))
weights_VtaggingCR = Weights(len(events))
if not isRealData:
output['sumw'][dataset] += ak.sum(events.genWeight)
#######################
if 'signal' in self._region:
if isRealData:
trigger_fatjet = np.zeros(len(events), dtype='bool')
for t in self._triggers[self._year]:
try:
trigger_fatjet = trigger_fatjet | events.HLT[t]
except:
print('trigger %s not available' % t)
continue
else:
trigger_fatjet = np.ones(len(events), dtype='bool')
fatjets["genMatchFull"] = VQQgenmatch(events)
candidatejet = ak.firsts(fatjets)
candidatejet["genMatchFull"] = VQQgenmatch(events)
nelectrons = ak.sum(
(events.Electron.pt > 10.)
& (abs(events.Electron.eta) < 2.5)
& (events.Electron.cutBased >= events.Electron.VETO),
axis=1,
)
nmuons = ak.sum(
(events.Muon.pt > 10)
& (abs(events.Muon.eta) < 2.1)
& (events.Muon.pfRelIso04_all < 0.4)
& (events.Muon.looseId),
axis=1,
)
ntaus = ak.sum(
(events.Tau.pt > 20.)
& (events.Tau.idDecayMode)
& (events.Tau.rawIso < 5)
& (abs(events.Tau.eta) < 2.3),
axis=1,
)
cuts = {
"S_fatjet_trigger":
trigger_fatjet,
"S_pt":
candidatejet.pt > 525,
"S_eta": (abs(candidatejet.eta) < 2.5),
"S_msdcorr": (candidatejet.msdcorr > 40),
"S_rho":
((candidatejet.qcdrho > -5.5) & (candidatejet.qcdrho < -2.)),
"S_jetid": (candidatejet.isTight),
"S_VQQgenmatch": (candidatejet.genMatchFull),
"S_noelectron": (nelectrons == 0),
"S_nomuon": (nmuons == 0),
"S_notau": (ntaus == 0),
}
for name, cut in cuts.items():
print(name, cut)
selection.add(name, cut)
if isRealData:
genflavor = 0 #candidatejet.pt.zeros_like().pad(1, clip=True).fillna(-1).flatten()
if not isRealData:
weights_signal.add('genweight', events.genWeight)
#add_pileup_weight(weights_signal, events.Pileup.nPU, self._year, dataset)
add_jetTriggerWeight(weights_signal, candidatejet.msdcorr,
candidatejet.pt, self._year)
bosons = getBosons(events.GenPart)
genBosonPt = ak.fill_none(ak.firsts(bosons.pt), 0)
add_VJets_NLOkFactor(weights_signal, genBosonPt, self._year,
dataset)
#genflavor = matchedBosonFlavor(candidatejet, bosons).pad(1, clip=True).fillna(-1).flatten()
allcuts_signal = set()
output['cutflow_signal'][dataset]['none'] += float(
weights_signal.weight().sum())
for cut in cuts:
allcuts_signal.add(cut)
output['cutflow_signal'][dataset][cut] += float(
weights_signal.weight()[selection.all(
*allcuts_signal)].sum())
fill('signal', cuts.keys())
#######################
if 'muonCR' in self._region:
if isRealData:
trigger_muon = np.zeros(len(events), dtype='bool')
for t in self._muontriggers[self._year]:
trigger_muon = trigger_muon | events.HLT[t]
else:
trigger_muon = np.ones(len(events), dtype='bool')
candidatejet = ak.firsts(fatjets)
candidatemuon = events.Muon[:, :5]
jets = events.Jet[((events.Jet.pt > 50.)
& (abs(events.Jet.eta) < 2.5)
& (events.Jet.isTight))][:, :4]
dphi = abs(jets.delta_phi(candidatejet))
ak4_away = jets[(dphi > 0.8)]
nelectrons = ak.sum(
(events.Electron.pt > 10.)
& (abs(events.Electron.eta) < 2.5)
& (events.Electron.cutBased >= events.Electron.VETO),
axis=1,
)
nmuons = ak.sum(
(events.Muon.pt > 10)
& (abs(events.Muon.eta) < 2.4)
& (events.Muon.pfRelIso04_all < 0.25)
& (events.Muon.looseId),
axis=1,
)
ntaus = ak.sum(
(events.Tau.pt > 20.)
& (events.Tau.idDecayMode)
& (events.Tau.rawIso < 5)
& (abs(events.Tau.eta) < 2.3)
& (events.Tau.idMVAoldDM2017v1 >= 16),
axis=1,
)
cuts = {
"CR1_muon_trigger":
trigger_muon,
"CR1_jet_pt": (candidatejet.pt > 525),
"CR1_jet_eta": (abs(candidatejet.eta) < 2.5),
"CR1_jet_msd": (candidatejet.msdcorr > 40),
"CR1_jet_rho":
((candidatejet.qcdrho > -5.5) & (candidatejet.qcdrho < -2.)),
"CR1_mu_pt":
ak.any(candidatemuon.pt > 55, axis=1),
"CR1_mu_eta":
ak.any(abs(candidatemuon.eta) < 2.1, axis=1),
"CR1_mu_IDLoose":
ak.any(candidatemuon.looseId, axis=1),
"CR1_mu_isolationTight":
ak.any(candidatemuon.pfRelIso04_all < 0.15, axis=1),
"CR1_muonDphiAK8":
ak.any(
abs(candidatemuon.delta_phi(candidatejet)) > 2 * np.pi / 3,
axis=1),
"CR1_ak4btagMedium08":
(ak.max(ak4_away.btagCSVV2, axis=1, mask_identity=False) >
BTagEfficiency.btagWPs[self._year]['medium']
), #(ak4_away.btagCSVV2.max() > 0.8838),
"CR1_noelectron": (nelectrons == 0),
"CR1_onemuon": (nmuons == 1),
"CR1_notau": (ntaus == 0),
}
for name, cut in cuts.items():
selection.add(name, cut)
if isRealData:
genflavor = 0 #candidatejet.pt.zeros_like().pad(1, clip=True).fillna(-1).flatten()
if not isRealData:
weights_muonCR.add('genweight', events.genWeight)
#add_pileup_weight(weights_muonCR, events.Pileup.nPU, self._year, dataset)
#add_singleMuTriggerWeight(weights, candidatejet.msdcorr, candidatejet.pt, self._year)
bosons = getBosons(events.GenPart)
genBosonPt = ak.fill_none(ak.firsts(bosons.pt), 0)
#add_VJets_NLOkFactor(weights, genBosonPt, self._year, dataset)
#genflavor = matchedBosonFlavor(candidatejet, bosons).pad(1, clip=True).fillna(-1).flatten()
allcuts_ttbar_muoncontrol = set()
output['cutflow_muonCR'][dataset]['none'] += float(
weights_muonCR.weight().sum())
for cut in cuts:
allcuts_ttbar_muoncontrol.add(cut)
output['cutflow_muonCR'][dataset][cut] += float(
weights_muonCR.weight()[selection.all(
*allcuts_ttbar_muoncontrol)].sum())
fill('muonCR', cuts.keys())
#######################
if 'VtaggingCR' in self._region:
if isRealData:
trigger_muon = np.zeros(len(events), dtype='bool')
for t in self._muontriggers[self._year]:
trigger_muon = trigger_muon | events.HLT[t]
else:
trigger_muon = np.ones(len(events), dtype='bool')
candidatejet = ak.firsts(fatjets)
candidatemuon = ak.firsts(events.Muon)
jets = events.Jet[((events.Jet.pt > 30.)
& (abs(events.Jet.eta) < 2.4))][:, :4]
dr_ak4_ak8 = jets.delta_r(candidatejet)
dr_ak4_muon = jets.delta_r(candidatemuon)
ak4_away = jets[(dr_ak4_ak8 > 0.8)] # & (dr_ak4_muon > 0.4)]
mu_p4 = ak.zip(
{
"pt": ak.fill_none(candidatemuon.pt, 0),
"eta": ak.fill_none(candidatemuon.eta, 0),
"phi": ak.fill_none(candidatemuon.phi, 0),
"mass": ak.fill_none(candidatemuon.mass, 0),
},
with_name="PtEtaPhiMLorentzVector")
met_p4 = ak.zip(
{
"pt": ak.from_iter([[v] for v in events.MET.pt]),
"eta": ak.from_iter([[v] for v in np.zeros(len(events))]),
"phi": ak.from_iter([[v] for v in events.MET.phi]),
"mass": ak.from_iter([[v] for v in np.zeros(len(events))]),
},
with_name="PtEtaPhiMLorentzVector")
Wleptoniccandidate = mu_p4 + met_p4
nelectrons = ak.sum(
((events.Electron.pt > 10.)
& (abs(events.Electron.eta) < 2.5)
& (events.Electron.cutBased >= events.Electron.VETO)),
axis=1,
)
n_tight_muon = ak.sum(
((events.Muon.pt > 53)
& (abs(events.Muon.eta) < 2.1)
& (events.Muon.tightId)),
axis=1,
)
n_loose_muon = ak.sum(
((events.Muon.pt > 20)
& (events.Muon.looseId)
& (abs(events.Muon.eta) < 2.4)),
axis=1,
)
ntaus = ak.sum(
((events.Tau.pt > 20.)
& (events.Tau.idDecayMode)
& (events.Tau.rawIso < 5)
& (abs(events.Tau.eta) < 2.3)
& (events.Tau.idMVAoldDM2017v1 >= 16)),
axis=1,
)
cuts = {
"CR2_muon_trigger":
trigger_muon,
"CR2_jet_pt": (candidatejet.pt > 200),
"CR2_jet_eta": (abs(candidatejet.eta) < 2.5),
"CR2_jet_msd": (candidatejet.msdcorr > 40),
"CR2_mu_pt":
candidatemuon.pt > 53,
"CR2_mu_eta": (abs(candidatemuon.eta) < 2.1),
"CR2_mu_IDTight":
candidatemuon.tightId,
"CR2_mu_isolationTight": (candidatemuon.pfRelIso04_all < 0.15),
"CR2_muonDphiAK8":
abs(candidatemuon.delta_phi(candidatejet)) > 2 * np.pi / 3,
"CR2_ak4btagMedium08":
(ak.max(ak4_away.btagCSVV2, axis=1, mask_identity=False) >
BTagEfficiency.btagWPs[self._year]['medium']),
"CR2_leptonicW":
ak.flatten(Wleptoniccandidate.pt > 200),
"CR2_MET": (events.MET.pt > 40.),
"CR2_noelectron": (nelectrons == 0),
"CR2_one_tightMuon": (n_tight_muon == 1),
"CR2_one_looseMuon": (n_loose_muon == 1),
#"CR2_notau" : (ntaus==0),
}
for name, cut in cuts.items():
print(name, cut)
selection.add(name, cut)
#weights.add('metfilter', events.Flag.METFilters)
if isRealData:
genflavor = 0 #candidatejet.pt.zeros_like().pad(1, clip=True).fillna(-1).flatten()
if not isRealData:
weights_VtaggingCR.add('genweight', events.genWeight)
#add_pileup_weight(weights_VtaggingCR, events.Pileup.nPU, self._year, dataset)
#add_singleMuTriggerWeight(weights, abs(candidatemuon.eta), candidatemuon.pt, self._year)
bosons = getBosons(events.GenPart)
genBosonPt = ak.fill_none(ak.firsts(bosons.pt), 0)
#add_VJets_NLOkFactor(weights, genBosonPt, self._year, dataset)
#genflavor = matchedBosonFlavor(candidatejet, bosons).pad(1, clip=True).fillna(-1).flatten()
#b-tag weights
allcuts_vselection = set()
output['cutflow_VtaggingCR'][dataset]['none'] += float(
weights_VtaggingCR.weight().sum())
for cut in cuts:
allcuts_vselection.add(cut)
output['cutflow_VtaggingCR'][dataset][cut] += float(
weights_VtaggingCR.weight()[selection.all(
*allcuts_vselection)].sum())
fill('VtaggingCR', cuts.keys())
return output