def process(self, event):
self.readCollections(event.input)
miniaodjets = self.handles['jets'].product()
allJets = []
event.jets = []
event.bJets = []
event.cleanJets = []
event.cleanBJets = []
leptons = []
if hasattr(event, 'selectedLeptons'):
leptons = event.selectedLeptons
genJets = None
if self.cfg_comp.isMC:
genJets = map(GenJet, self.mchandles['genJets'].product())
for maodjet in miniaodjets:
jet = Jet(maodjet)
allJets.append(jet)
if self.cfg_comp.isMC and hasattr(self.cfg_comp, 'jetScale'):
scale = random.gauss(self.cfg_comp.jetScale,
self.cfg_comp.jetSmear)
jet.scaleEnergy(scale)
if genJets:
# Use DeltaR = 0.25 matching like JetMET
pairs = matchObjectCollection([jet], genJets, 0.25 * 0.25)
if pairs[jet] is None:
pass
else:
jet.matchedGenJet = pairs[jet]
#Add JER correction for MC jets. Requires gen-jet matching.
if self.cfg_comp.isMC and hasattr(
self.cfg_ana, 'jerCorr') and self.cfg_ana.jerCorr:
self.jerCorrection(jet)
#Add JES correction for MC jets.
if self.cfg_comp.isMC and hasattr(self.cfg_ana, 'jesCorr'):
self.jesCorrection(jet, self.cfg_ana.jesCorr)
if self.testJet(jet):
event.jets.append(jet)
if self.testBJet(jet):
event.bJets.append(jet)
self.counters.counter('jets').inc('all events')
event.cleanJets, dummy = cleanObjectCollection(event.jets,
masks=leptons,
deltaRMin=0.5)
event.cleanBJets, dummy = cleanObjectCollection(event.bJets,
masks=leptons,
deltaRMin=0.5)
pairs = matchObjectCollection(leptons, allJets, 0.5 * 0.5)
# associating a jet to each lepton
for lepton in leptons:
jet = pairs[lepton]
if jet is None:
lepton.jet = lepton
else:
lepton.jet = jet
# associating a leg to each clean jet
invpairs = matchObjectCollection(event.cleanJets, leptons, 99999.)
for jet in event.cleanJets:
leg = invpairs[jet]
jet.leg = leg
for jet in event.cleanJets:
jet.matchGenParton = 999.0
if self.cfg_comp.isMC and "BB" in self.cfg_comp.name:
genParticles = self.mchandles['genParticles'].product()
event.genParticles = map(GenParticle, genParticles)
for gen in genParticles:
if abs(gen.pdgId()) == 5 and gen.mother() and abs(
gen.mother().pdgId()) == 21:
for jet in event.cleanJets:
dR = deltaR2(jet.eta(), jet.phi(), gen.eta(),
gen.phi())
if dR < jet.matchGenParton:
jet.matchGenParton = dR
event.jets30 = [jet for jet in event.jets if jet.pt() > 30]
event.cleanJets30 = [jet for jet in event.cleanJets if jet.pt() > 30]
if len(event.jets30) >= 2:
self.counters.counter('jets').inc('at least 2 good jets')
if len(event.cleanJets30) >= 2:
self.counters.counter('jets').inc('at least 2 clean jets')
if len(event.cleanBJets) > 0:
self.counters.counter('jets').inc('at least 1 b jet')
if len(event.cleanBJets) > 1:
self.counters.counter('jets').inc('at least 2 b jets')
return True
def evaluateRegressionAK08(self, event):
#self.readCollections( event.input )
reg_fj = []
for ung_fj in getattr(event, "ak08"):
# We need the closest ungroomed fatjet to get the JEC: # - Make a list of pairs: deltaR(ungroomed fj, groomed fj) for all ungroomed fatjets # - Sort by deltaR # - And take the minimum
if len(getattr(event, "ak08pruned")):
closest_pr_fj_and_dr = sorted( [(pr_fj, deltaR2(ung_fj, pr_fj)) for pr_fj in getattr(event, "ak08pruned")], key=lambda x:x[1])[0]
else:
print "WARNING: No pruned fatjets found in event with ungroomed fatjet. Skipping"
continue
# Use the jet cone size for matching
minimal_dr_groomed_ungroomed = 0.8
if closest_pr_fj_and_dr[1] > minimal_dr_groomed_ungroomed:
print "WARNING: No pruned fatjet found close to ungroomed fatjet. Skipping"
continue
pr_jet = Jet(closest_pr_fj_and_dr[0])
if len(getattr(event, "ak08prunedcal")):
closest_cal_fj_and_dr = sorted( [(cal_fj, deltaR2(ung_fj, cal_fj)) for cal_fj in getattr(event, "ak08prunedcal")], key=lambda x:x[1])[0]
else:
print "WARNING: No calib groomed fatjets found in event with ungroomed fatjet. Skipping"
continue
if closest_cal_fj_and_dr[1] > minimal_dr_groomed_ungroomed:
print "WARNING: No calib fatjet found close to ungroomed fatjet. Skipping"
continue
cal_jet = Jet(closest_cal_fj_and_dr[0])
# now check the AK08 jet is not a lepton
# if len(getattr(event, "selectedLeptons")):
# closest_pr_lep_and_dr = sorted( [(lep, deltaR2(cal_jet,lep)) for lep in getattr(event, "vLeptons")], key=lambda x:x[1])[0]
# if closest_pr_lep_and_dr[1] < (0.4 * 0.4):
# print "WARNING: No groomed fatjet is overlapping with a vLepton. Skipping"
# continue
# Need to do a deep-copy. Otherwise the original jet will be modified
reg_groomed_fj = PhysicsObject(closest_cal_fj_and_dr[0]).__copy__()
# for j in event.FatjetAK08pruned:
self.FatjetAK08ungroomed_pt[0] = ung_fj.pt()
# print 'ung_fj.pt() ', ung_fj.pt()
self.FatjetAK08pruned_pt[0] = pr_jet.pt()
# print 'pr_jet.pt() ', pr_jet.pt()
self.FatjetAK08prunedCal_pt[0] = cal_jet.pt()
# print 'cal_jet.pt() ', cal_jet.pt()
self.FatjetAK08prunedCal_eta[0] = cal_jet.eta()
self.FatjetAK08ungroomed_vertexNTracks[0] = ung_fj.vertexNTracks
self.FatjetAK08ungroomed_SV_mass_0[0] = ung_fj.SV_mass_0
self.FatjetAK08ungroomed_SV_EnergyRatio_0[0] = ung_fj.SV_EnergyRatio_0
self.FatjetAK08ungroomed_SV_EnergyRatio_1[0] = ung_fj.SV_EnergyRatio_1
self.FatjetAK08ungroomed_PFLepton_ptrel[0] = ung_fj.PFLepton_ptrel
self.FatjetAK08ungroomed_nSL[0] = ung_fj.nSL
# print 'ung_fj.nSL ', ung_fj.nSL
reg_groomed_fj.scaleEnergy(self.reader.EvaluateRegression(self.name)[0])
reg_fj.append(reg_groomed_fj)
# print 'reg_groomed_fj.pt() ', reg_groomed_fj.pt()
setattr(event, 'ak08prunedreg', reg_fj )
def process(self, event):
self.readCollections(event.input)
miniaodjets = self.handles['jets'].product()
allJets = []
event.jets = []
event.bJets = []
event.cleanJets = []
event.cleanBJets = []
leptons = []
if hasattr(event, 'selectedLeptons'):
leptons = event.selectedLeptons
genJets = None
if self.cfg_comp.isMC:
genJets = map(GenJet, self.mchandles['genJets'].product())
for maodjet in miniaodjets:
jet = Jet(maodjet)
allJets.append(jet)
if self.cfg_comp.isMC and hasattr(self.cfg_comp, 'jetScale'):
scale = random.gauss(self.cfg_comp.jetScale,
self.cfg_comp.jetSmear)
jet.scaleEnergy(scale)
if genJets:
# Use DeltaR = 0.25 matching like JetMET
pairs = matchObjectCollection([jet], genJets, 0.25 * 0.25)
if pairs[jet] is None:
pass
else:
jet.matchedGenJet = pairs[jet]
# Add JER correction for MC jets. Requires gen-jet matching.
if self.cfg_comp.isMC and hasattr(self.cfg_ana, 'jerCorr') and self.cfg_ana.jerCorr:
self.jerCorrection(jet)
# Add JES correction for MC jets.
if self.cfg_comp.isMC and hasattr(self.cfg_ana, 'jesCorr'):
self.jesCorrection(jet, self.cfg_ana.jesCorr)
if self.testJet(jet):
event.jets.append(jet)
if self.testBJet(jet):
event.bJets.append(jet)
self.counters.counter('jets').inc('all events')
event.cleanJets, dummy = cleanObjectCollection(event.jets,
masks=leptons,
deltaRMin=0.5)
event.cleanBJets, dummy = cleanObjectCollection(event.bJets,
masks=leptons,
deltaRMin=0.5)
pairs = matchObjectCollection(leptons, allJets, 0.5 * 0.5)
# associating a jet to each lepton
for lepton in leptons:
jet = pairs[lepton]
if jet is None:
lepton.jet = lepton
else:
lepton.jet = jet
# associating a leg to each clean jet
invpairs = matchObjectCollection(event.cleanJets, leptons, 99999.)
for jet in event.cleanJets:
leg = invpairs[jet]
jet.leg = leg
for jet in event.cleanJets:
jet.matchGenParton = 999.0
if self.cfg_comp.isMC and "BB" in self.cfg_comp.name:
genParticles = self.mchandles['genParticles'].product()
event.genParticles = map(GenParticle, genParticles)
for gen in genParticles:
if abs(gen.pdgId()) == 5 and gen.mother() and abs(gen.mother().pdgId()) == 21:
for jet in event.cleanJets:
dR = deltaR2(jet.eta(), jet.phi(), gen.eta(), gen.phi())
if dR < jet.matchGenParton:
jet.matchGenParton = dR
event.jets30 = [jet for jet in event.jets if jet.pt() > 30]
event.cleanJets30 = [jet for jet in event.cleanJets if jet.pt() > 30]
if len(event.jets30) >= 2:
self.counters.counter('jets').inc('at least 2 good jets')
if len(event.cleanJets30) >= 2:
self.counters.counter('jets').inc('at least 2 clean jets')
if len(event.cleanBJets) > 0:
self.counters.counter('jets').inc('at least 1 b jet')
if len(event.cleanBJets) > 1:
self.counters.counter('jets').inc('at least 2 b jets')
return True
def evaluateRegressionAK08(self, event):
#self.readCollections( event.input )
reg_fj = []
for ung_fj in getattr(event, "ak08"):
# We need the closest ungroomed fatjet to get the JEC: # - Make a list of pairs: deltaR(ungroomed fj, groomed fj) for all ungroomed fatjets # - Sort by deltaR # - And take the minimum
if len(getattr(event, "ak08pruned")):
closest_pr_fj_and_dr = sorted(
[(pr_fj, deltaR2(ung_fj, pr_fj))
for pr_fj in getattr(event, "ak08pruned")],
key=lambda x: x[1])[0]
else:
print "WARNING: No pruned fatjets found in event with ungroomed fatjet. Skipping"
continue
# Use the jet cone size for matching
minimal_dr_groomed_ungroomed = 0.8
if closest_pr_fj_and_dr[1] > minimal_dr_groomed_ungroomed:
print "WARNING: No pruned fatjet found close to ungroomed fatjet. Skipping"
continue
pr_jet = Jet(closest_pr_fj_and_dr[0])
if len(getattr(event, "ak08prunedcal")):
closest_cal_fj_and_dr = sorted(
[(cal_fj, deltaR2(ung_fj, cal_fj))
for cal_fj in getattr(event, "ak08prunedcal")],
key=lambda x: x[1])[0]
else:
print "WARNING: No calib groomed fatjets found in event with ungroomed fatjet. Skipping"
continue
if closest_cal_fj_and_dr[1] > minimal_dr_groomed_ungroomed:
print "WARNING: No calib fatjet found close to ungroomed fatjet. Skipping"
continue
cal_jet = Jet(closest_cal_fj_and_dr[0])
# now check the AK08 jet is not a lepton
# if len(getattr(event, "selectedLeptons")):
# closest_pr_lep_and_dr = sorted( [(lep, deltaR2(cal_jet,lep)) for lep in getattr(event, "vLeptons")], key=lambda x:x[1])[0]
# if closest_pr_lep_and_dr[1] < (0.4 * 0.4):
# print "WARNING: No groomed fatjet is overlapping with a vLepton. Skipping"
# continue
# Need to do a deep-copy. Otherwise the original jet will be modified
reg_groomed_fj = PhysicsObject(closest_cal_fj_and_dr[0]).__copy__()
# for j in event.FatjetAK08pruned:
self.FatjetAK08ungroomed_pt[0] = ung_fj.pt()
# print 'ung_fj.pt() ', ung_fj.pt()
self.FatjetAK08pruned_pt[0] = pr_jet.pt()
# print 'pr_jet.pt() ', pr_jet.pt()
self.FatjetAK08prunedCal_pt[0] = cal_jet.pt()
# print 'cal_jet.pt() ', cal_jet.pt()
self.FatjetAK08prunedCal_eta[0] = cal_jet.eta()
self.FatjetAK08ungroomed_vertexNTracks[0] = ung_fj.vertexNTracks
self.FatjetAK08ungroomed_SV_mass_0[0] = ung_fj.SV_mass_0
self.FatjetAK08ungroomed_SV_EnergyRatio_0[
0] = ung_fj.SV_EnergyRatio_0
self.FatjetAK08ungroomed_SV_EnergyRatio_1[
0] = ung_fj.SV_EnergyRatio_1
self.FatjetAK08ungroomed_PFLepton_ptrel[0] = ung_fj.PFLepton_ptrel
self.FatjetAK08ungroomed_nSL[0] = ung_fj.nSL
# print 'ung_fj.nSL ', ung_fj.nSL
reg_groomed_fj.scaleEnergy(
self.reader.EvaluateRegression(self.name)[0])
reg_fj.append(reg_groomed_fj)
# print 'reg_groomed_fj.pt() ', reg_groomed_fj.pt()
setattr(event, 'ak08prunedreg', reg_fj)
def process(self, event):
# needed when doing handle.product(), goes back to
# PhysicsTools.Heppy.analyzers.core.Analyzer
self.readCollections(event.input)
if not eval(self.skimFunction):
return False
ptSelGentauleps = []
ptSelGenleps = []
ptSelGenSummary = []
if self.cfg_comp.isMC:
event.genJets = self.mchandles['genJets'].product()
ptcut = 8.
ptSelGentauleps = [
lep for lep in event.gentauleps if lep.pt() > ptcut
]
ptSelGenleps = [lep for lep in event.genleps if lep.pt() > ptcut]
ptSelGenSummary = [
p for p in event.generatorSummary if p.pt() > ptcut
and abs(p.pdgId()) not in [6, 23, 24, 25, 35, 36, 37]
]
for i_dil, dil in enumerate(event.selDiLeptons):
muon = dil.leg1()
jet = dil.leg2()
found = False
for corr_jet in event.jets:
if deltaR2(jet.eta(), jet.phi(), corr_jet.eta(),
corr_jet.phi()) < 0.01:
pt = max(
corr_jet.pt(),
corr_jet.pt() * corr_jet.corrJECUp / corr_jet.corr,
corr_jet.pt() * corr_jet.corrJECDown / corr_jet.corr)
if pt < 20.:
continue
found = True
if not found:
continue
tau = jet.tau if hasattr(jet, 'tau') else None
if self.cfg_comp.isMC:
if tau:
DYJetsFakeAnalyzer.genMatch(event, tau, ptSelGentauleps,
ptSelGenleps, ptSelGenSummary)
DYJetsFakeAnalyzer.attachGenStatusFlag(tau)
DYJetsFakeAnalyzer.genMatch(event, muon, ptSelGentauleps,
ptSelGenleps, ptSelGenSummary)
DYJetsFakeAnalyzer.attachGenStatusFlag(muon)
self.tree.reset()
self.fillEvent(self.tree, event)
self.fillDiLepton(self.tree, event.diLepton, fill_svfit=False)
self.fillExtraMetInfo(self.tree, event)
self.fillGenInfo(self.tree, event)
self.fillJetMETVars(event)
self.fillMuon(self.tree, 'muon', muon)
jet = Jet(jet)
jet.btagMVA = jet.btag(
'pfCombinedInclusiveSecondaryVertexV2BJetTags')
jet.btagFlag = jet.btagMVA > 0.8
self.fillJet(self.tree, 'oriJet', jet)
self.fill(self.tree, 'jet_nth', i_dil)
for corr_jet in event.jets:
if deltaR2(jet.eta(), jet.phi(), corr_jet.eta(),
corr_jet.phi()) < 0.01:
self.fillJet(self.tree, 'jet', corr_jet)
self.fill(self.tree, 'jet_nooverlap',
True if corr_jet in event.cleanJets else False)
self.fill(self.tree, 'jet_corrJECUp',
corr_jet.corrJECUp / corr_jet.corr)
self.fill(self.tree, 'jet_corrJECDown',
corr_jet.corrJECDown / corr_jet.corr)
self.fill(self.tree, 'jet_corr', corr_jet.corr)
if tau:
self.fillTau(self.tree, 'tau', tau)
if hasattr(tau, 'genp') and tau.genp:
self.fillGenParticle(self.tree, 'tau_gen', tau.genp)
if tau.genJet():
self.fillGenParticle(self.tree, 'tau_gen_vis',
tau.genJet())
self.fill(self.tree, 'tau_gen_decayMode',
tauDecayModes.genDecayModeInt(tau.genJet()))
self.fillTree(event)
def process(self, event):
self.readCollections(event.input)
cmgJets = self.handles['jets'].product()
allJets = []
event.jets = []
event.bJets = []
event.cleanJets = []
event.cleanBJets = []
leg1 = event.diLepton.leg1()
leg2 = event.diLepton.leg2()
genJets = None
if self.cfg_comp.isMC:
genJets = map(GenJet, self.mchandles['genJets'].product())
for cmgJet in cmgJets:
jet = Jet(cmgJet)
allJets.append(jet)
if self.cfg_comp.isMC and hasattr(self.cfg_comp, 'jetScale'):
scale = random.gauss(self.cfg_comp.jetScale,
self.cfg_comp.jetSmear)
jet.scaleEnergy(scale)
if self.testJet(jet):
event.jets.append(jet)
if self.testBJet(jet):
event.bJets.append(jet)
if genJets:
# Use DeltaR = 0.25 matching like JetMET
pairs = matchObjectCollection([jet], genJets, 0.25 * 0.25)
if pairs[jet] is None:
pass
else:
jet.matchedGenJet = pairs[jet]
#Add JER correction for MC jets. Requires gen-jet matching
if self.cfg_comp.isMC and hasattr(
self.cfg_ana, 'jerCorr') and self.cfg_ana.jerCorr:
self.jerCorrection(jet)
self.counters.counter('VBF').inc('all events')
event.cleanJets, dummy = cleanObjectCollection(event.jets,
masks=[leg1, leg2],
deltaRMin=0.5)
event.cleanBJets, dummy = cleanObjectCollection(event.bJets,
masks=[leg1, leg2],
deltaRMin=0.5)
pairs = matchObjectCollection([leg1, leg2], allJets, 0.5 * 0.5)
# associating a jet to each leg
leg1.jet = pairs[leg1]
leg2.jet = pairs[leg2]
if leg1.jet is None: #COLIN: I don't understand the code below...
leg1.jet = leg1
if leg2.jet is None:
leg2.jet = leg2
# associating a leg to each clean jet
invpairs = matchObjectCollection(event.cleanJets, [leg1, leg2], 99999.)
for jet in event.cleanJets:
leg = invpairs[jet]
jet.leg = leg
for jet in event.cleanJets:
jet.matchGenParton = 999.0
if self.cfg_comp.isMC and "BB" in self.cfg_comp.name:
genParticles = self.mchandles['genParticles'].product()
event.genParticles = map(GenParticle, genParticles)
for gen in genParticles:
if abs(gen.pdgId()) == 5 and gen.mother() and abs(
gen.mother().pdgId()) == 21:
for jet in event.cleanJets:
dR = deltaR2(jet.eta(), jet.phi(), gen.eta(),
gen.phi())
if dR < jet.matchGenParton:
jet.matchGenParton = dR
event.jets30 = [jet for jet in event.jets if jet.pt() > 30]
event.cleanJets30 = [jet for jet in event.cleanJets if jet.pt() > 30]
if len(event.jets30) >= 2:
self.counters.counter('VBF').inc('at least 2 good jets')
if len(event.cleanJets30) >= 2:
self.counters.counter('VBF').inc('at least 2 clean jets')
if len(event.cleanJets) < 2:
return True
event.vbf = VBF(event.cleanJets, event.diLepton, None,
self.cfg_ana.cjvPtCut)
if event.vbf.mjj > self.cfg_ana.Mjj:
self.counters.counter('VBF').inc(
'M_jj > {cut:3.1f}'.format(cut=self.cfg_ana.Mjj))
else:
return True
if abs(event.vbf.deta) > self.cfg_ana.deltaEta:
self.counters.counter('VBF').inc(
'delta Eta > {cut:3.1f}'.format(cut=self.cfg_ana.deltaEta))
else:
return True
if len(event.vbf.centralJets) == 0:
self.counters.counter('VBF').inc('no central jets')
else:
return True
return True