def process(self, event):
run = event.input.eventAuxiliary().id().run()
lumi = event.input.eventAuxiliary().id().luminosityBlock()
eventId = event.input.eventAuxiliary().id().event()
self.readCollections( event.input )
# Will need who for jet calibration later
rho = self.handles["rho"].product()[0]
########
# AK8 Jets from MiniAOD + Subjet btags
########
setattr(event, "ak08", map(PhysicsObject, self.handles["ak08"].product()))
setattr(event, "ak0softdropsubjets", map(PhysicsObject, self.handles["ak08softdropsubjets"].product()))
# bb-tag Output
newtags = self.handles['ak08bbtag'].product()
# Loop over jets
for ij, jet in enumerate(getattr(event, "ak08")):
# Fill bb-tag
for i in xrange(len(newtags)) :
if jet.physObj == newtags.key(i).get():
jet.bbtag = newtags.value(i)
# bb-tag Inputs
muonTagInfos = self.handles['ak08muonTagInfos'].product()[ij]
elecTagInfos = self.handles['ak08elecTagInfos'].product()[ij]
ipTagInfo = self.handles['ak08ipTagInfos'].product()[ij]
svTagInfo = self.handles['ak08svTagInfos'].product()[ij]
calcBBTagVariables(jet,
muonTagInfos,
elecTagInfos,
ipTagInfo,
svTagInfo,
njettiness_08,
maxSVDeltaRToJet = 0.7,)
# end of loop over jets
########
# Ungroomed Fatjets + NSubjettiness + Hbb Tagging
########
for prefix in ["ca15"]:
if self.skip_ca15 and ("ca15" in prefix):
continue
# N-Subjettiness
tau1 = self.handles[prefix+'tau1'].product()
tau2 = self.handles[prefix+'tau2'].product()
tau3 = self.handles[prefix+'tau3'].product()
# bb-tag Output
newtags = self.handles[prefix+'bbtag'].product()
# Four Vector
setattr(event, prefix+"ungroomed", map(PhysicsObject, self.handles[prefix+'ungroomed'].product()))
# Loop over jets
for ij, jet in enumerate(getattr(event, prefix+"ungroomed")):
# Fill N-Subjettiness
jet.tau1 = tau1.get(ij)
jet.tau2 = tau2.get(ij)
jet.tau3 = tau3.get(ij)
# Fill bb-tag
for i in xrange(len(newtags)) :
if jet.physObj == newtags.key(i).get():
jet.bbtag = newtags.value(i)
# bb-tag Inputs
muonTagInfos = self.handles['ca15muonTagInfos'].product()[ij]
elecTagInfos = self.handles['ca15elecTagInfos'].product()[ij]
ipTagInfo = self.handles['ca15ipTagInfos'].product()[ij]
svTagInfo = self.handles['ca15svTagInfos'].product()[ij]
calcBBTagVariables(jet,
muonTagInfos,
elecTagInfos,
ipTagInfo,
svTagInfo,
njettiness_15,
maxSVDeltaRToJet = 1.3)
# end of loop over jets
########
# Softdrop Fatjets + NSubjettiness
########
for fj_name in ["ca15softdropz2b1"]:
if self.skip_ca15 and ("ca15" in fj_name):
continue
# Set the four-vector
setattr(event, fj_name, map(PhysicsObject, self.handles[fj_name].product()))
# N-Subjettiness
tau1 = self.handles[fj_name+'tau1'].product()
tau2 = self.handles[fj_name+'tau2'].product()
tau3 = self.handles[fj_name+'tau3'].product()
# Loop over jets
for ij, jet in enumerate(getattr(event, fj_name)):
# Fill N-Subjettiness
jet.tau1 = tau1.get(ij)
jet.tau2 = tau2.get(ij)
jet.tau3 = tau3.get(ij)
# end of loop over jets
########
# Groomed Uncalibrated Fatjets
########
for fj_name in ['ak08pruned', 'ca15trimmed', 'ca15softdrop', 'ca15pruned']:
if self.skip_ca15 and ("ca15" in fj_name):
continue
setattr(event, fj_name, map(PhysicsObject, self.handles[fj_name].product()))
########
# Groomed Fatjets to calibrate
########
pruned_cal_jets = []
for groomed_fj in self.handles['ak08pruned'].product():
# 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, "ak08")):
closest_ung_fj_and_dr = sorted(
[(ung_fj, deltaR2(ung_fj, groomed_fj)) for ung_fj in getattr(event, "ak08")],
key=lambda x:x[1])[0]
else:
print "WARNING: No ungroomed fatjets found in event with groomed fatjet. Skipping"
continue
# Use the jet cone size for matching
minimal_dr_groomed_ungroomed = 0.8
if closest_ung_fj_and_dr[1] > minimal_dr_groomed_ungroomed:
print "WARNING: No ungroomed fatjet found close to groomed fatjet. Skipping"
continue
ungroomed_jet = Jet(closest_ung_fj_and_dr[0])
c = self.jetReCalibrator.getCorrection(ungroomed_jet, rho)
# Need to do a deep-copy. Otherwise the original jet will be modified
cal_groomed_fj = PhysicsObject(groomed_fj).__copy__()
cal_groomed_fj.scaleEnergy(c)
pruned_cal_jets.append(cal_groomed_fj)
setattr(event, 'ak08prunedcal', pruned_cal_jets)
########
# AK08 Regression
#######
self.regressionsAK08[0].evaluateRegressionAK08(event)
########
# Subjets
########
for fj_name in ['ak08pruned','ca15pruned']:
if self.skip_ca15 and ("ca15" in fj_name):
continue
setattr(event, fj_name + "subjets", map(PhysicsObject, self.handles[fj_name+"subjets"].product()))
newtags = self.handles[fj_name+'subjetbtag'].product()
for i in xrange(0,len(newtags)) :
for j in getattr(event, fj_name+"subjets"):
if j.physObj == newtags.key(i).get():
j.btag = newtags.value(i)
########
# HEPTopTagger
########
if not self.skip_ca15:
candJets = self.handles['httCandJets'].product()
candInfos = self.handles['httCandInfos'].product()
event.httCandidates = map(PhysicsObject, candJets)
sjbtags = self.handles['httSubjetBtags'].product()
for i in xrange(0, len(candJets)):
event.httCandidates[i].fRec = candInfos[i].properties().fRec
event.httCandidates[i].Ropt = candInfos[i].properties().Ropt
event.httCandidates[i].RoptCalc = candInfos[i].properties().RoptCalc
event.httCandidates[i].ptForRoptCalc = candInfos[i].properties().ptForRoptCalc
# HTT return the subjet-pair closest to the W-mass as W-subjets
# Could be improved by b-tagging if we run into a problem
[sj_w1, sj_w2, sj_nonw] = [con.__deref__() for con in candJets[i].getJetConstituents() if not con.isNull()]
event.httCandidates[i].sjW1pt = sj_w1.pt()
event.httCandidates[i].sjW1eta = sj_w1.eta()
event.httCandidates[i].sjW1phi = sj_w1.phi()
event.httCandidates[i].sjW1mass = sj_w1.mass()
# Get the correct b-tag
for ib in xrange(0, len(sjbtags)) :
if sj_w1 == sjbtags.key(ib).get():
event.httCandidates[i].sjW1btag = sjbtags.value(ib)
event.httCandidates[i].sjW2pt = sj_w2.pt()
event.httCandidates[i].sjW2eta = sj_w2.eta()
event.httCandidates[i].sjW2phi = sj_w2.phi()
event.httCandidates[i].sjW2mass = sj_w2.mass()
# Get the correct b-tag
for ib in xrange(0, len(sjbtags)) :
if sj_w2 == sjbtags.key(ib).get():
event.httCandidates[i].sjW2btag = sjbtags.value(ib)
event.httCandidates[i].sjNonWpt = sj_nonw.pt()
event.httCandidates[i].sjNonWeta = sj_nonw.eta()
event.httCandidates[i].sjNonWphi = sj_nonw.phi()
event.httCandidates[i].sjNonWmass = sj_nonw.mass()
# Get the correct b-tag
for ib in xrange(0, len(sjbtags)) :
if sj_nonw == sjbtags.key(ib).get():
event.httCandidates[i].sjNonWbtag = sjbtags.value(ib)
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 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):
run = event.input.eventAuxiliary().id().run()
lumi = event.input.eventAuxiliary().id().luminosityBlock()
eventId = event.input.eventAuxiliary().id().event()
self.readCollections( event.input )
# Will need who for jet calibration later
rho = self.handles["rho"].product()[0]
########
# Softdrop Fatjets + NSubjettiness
########
for fj_name in ["ca15softdropz2b1"]:
if self.skip_ca15 and ("ca15" in fj_name):
continue
# Set the four-vector
setattr(event, fj_name, map(PhysicsObject, self.handles[fj_name].product()))
# N-Subjettiness
tau1 = self.handles[fj_name+'tau1'].product()
tau2 = self.handles[fj_name+'tau2'].product()
tau3 = self.handles[fj_name+'tau3'].product()
# Loop over jets
for ij, jet in enumerate(getattr(event, fj_name)):
# Fill N-Subjettiness
jet.tau1 = tau1.get(ij)
jet.tau2 = tau2.get(ij)
jet.tau3 = tau3.get(ij)
# end of loop over jets
########
# Groomed Uncalibrated Fatjets
########
for fj_name in ['ca15trimmed', 'ca15softdrop', 'ca15pruned']:
setattr(event, fj_name, map(PhysicsObject, self.handles[fj_name].product()))
#
# ########
# # Groomed Fatjets to calibrate
# ########
#
# pruned_cal_jets = []
#
# for groomed_fj in self.handles['ak08pruned'].product():
#
# # 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, "ak08")):
# closest_ung_fj_and_dr = sorted(
# [(ung_fj, deltaR2(ung_fj, groomed_fj)) for ung_fj in getattr(event, "ak08")],
# key=lambda x:x[1])[0]
# else:
# print "WARNING: No ungroomed fatjets found in event with groomed fatjet. Skipping"
# continue
#
# # Use the jet cone size for matching
# minimal_dr_groomed_ungroomed = 0.8
# if closest_ung_fj_and_dr[1] > minimal_dr_groomed_ungroomed:
# print "WARNING: No ungroomed fatjet found close to groomed fatjet. Skipping"
# continue
#
# ungroomed_jet = Jet(closest_ung_fj_and_dr[0])
#
# c = self.jetReCalibratorAK8L2L3.getCorrection(ungroomed_jet, rho)
#
#
# # Need to do a deep-copy. Otherwise the original jet will be modified
# cal_groomed_fj = PhysicsObject(groomed_fj).__copy__()
# cal_groomed_fj.scaleEnergy(c)
#
# pruned_cal_jets.append(cal_groomed_fj)
#
# setattr(event, 'ak08prunedcal', pruned_cal_jets)
#
########
# Subjets
########
for fj_name in ['ca15pruned']:
if self.skip_ca15 and ("ca15" in fj_name):
continue
setattr(event, fj_name + "subjets", map(PhysicsObject, self.handles[fj_name+"subjets"].product()))
newtags = self.handles[fj_name+'subjetbtag'].product()
for i in xrange(0,len(newtags)) :
for j in getattr(event, fj_name+"subjets"):
if j.physObj == newtags.key(i).get():
j.btag = newtags.value(i)
########
# HEPTopTagger
########
if not self.skip_ca15:
candJets = self.handles['httCandJets'].product()
candInfos = self.handles['httCandInfos'].product()
event.httCandidates = map(PhysicsObject, candJets)
sjbtags = self.handles['httSubjetBtags'].product()
for i in xrange(0, len(candJets)):
event.httCandidates[i].fRec = candInfos[i].properties().fRec
event.httCandidates[i].Ropt = candInfos[i].properties().Ropt
event.httCandidates[i].RoptCalc = candInfos[i].properties().RoptCalc
event.httCandidates[i].ptForRoptCalc = candInfos[i].properties().ptForRoptCalc
# HTT return the subjet-pair closest to the W-mass as W-subjets
# Could be improved by b-tagging if we run into a problem
[sj_w1, sj_w2, sj_nonw] = [con.__deref__() for con in candJets[i].getJetConstituents() if not con.isNull()]
# Calibrate the subjets: W1
sj_w1_uncal = Jet(sj_w1)
c = self.jetReCalibratorAK4.getCorrection(sj_w1_uncal, rho, isHttSubjet=True)
sj_w1_cal = PhysicsObject(sj_w1).__copy__()
sj_w1_cal.scaleEnergy(c)
# Calibrate the subjets: W2
sj_w2_uncal = Jet(sj_w2)
c = self.jetReCalibratorAK4.getCorrection(sj_w2_uncal, rho, isHttSubjet=True)
sj_w2_cal = PhysicsObject(sj_w2).__copy__()
sj_w2_cal.scaleEnergy(c)
# Calibrate the subjets: NonW
sj_nonw_uncal = Jet(sj_nonw)
c = self.jetReCalibratorAK4.getCorrection(sj_nonw_uncal, rho, isHttSubjet=True)
sj_nonw_cal = PhysicsObject(sj_nonw).__copy__()
sj_nonw_cal.scaleEnergy(c)
# Make TLVs so we can add them and get the top quark
# candidate
tlv_sj_w1_cal = ROOT.TLorentzVector()
tlv_sj_w2_cal = ROOT.TLorentzVector()
tlv_sj_nonw_cal = ROOT.TLorentzVector()
tlv_sj_w1_cal.SetPtEtaPhiM(sj_w1_cal.pt(),
sj_w1_cal.eta(),
sj_w1_cal.phi(),
sj_w1_cal.mass())
tlv_sj_w2_cal.SetPtEtaPhiM(sj_w2_cal.pt(),
sj_w2_cal.eta(),
sj_w2_cal.phi(),
sj_w2_cal.mass())
tlv_sj_nonw_cal.SetPtEtaPhiM(sj_nonw_cal.pt(),
sj_nonw_cal.eta(),
sj_nonw_cal.phi(),
sj_nonw_cal.mass())
tlv_top_cal = tlv_sj_w1_cal + tlv_sj_w2_cal + tlv_sj_nonw_cal
# Store calibrated top candidate variables
event.httCandidates[i].ptcal = tlv_top_cal.Pt()
event.httCandidates[i].etacal = tlv_top_cal.Eta()
event.httCandidates[i].phical = tlv_top_cal.Phi()
event.httCandidates[i].masscal = tlv_top_cal.M()
# Store SJ W1 Variables
event.httCandidates[i].sjW1ptcal = sj_w1_cal.pt()
event.httCandidates[i].sjW1masscal = sj_w1_cal.mass()
event.httCandidates[i].sjW1pt = sj_w1.pt()
event.httCandidates[i].sjW1eta = sj_w1.eta()
event.httCandidates[i].sjW1phi = sj_w1.phi()
event.httCandidates[i].sjW1mass = sj_w1.mass()
# Get the correct b-tag
for ib in xrange(0, len(sjbtags)) :
if sj_w1 == sjbtags.key(ib).get():
event.httCandidates[i].sjW1btag = sjbtags.value(ib)
# Store SJ W2 Variables
event.httCandidates[i].sjW2ptcal = sj_w2_cal.pt()
event.httCandidates[i].sjW2masscal = sj_w2_cal.mass()
event.httCandidates[i].sjW2pt = sj_w2.pt()
event.httCandidates[i].sjW2eta = sj_w2.eta()
event.httCandidates[i].sjW2phi = sj_w2.phi()
event.httCandidates[i].sjW2mass = sj_w2.mass()
# Get the correct b-tag
for ib in xrange(0, len(sjbtags)) :
if sj_w2 == sjbtags.key(ib).get():
event.httCandidates[i].sjW2btag = sjbtags.value(ib)
# Store SJ Non W Variables
event.httCandidates[i].sjNonWptcal = sj_nonw_cal.pt()
event.httCandidates[i].sjNonWmasscal = sj_nonw_cal.mass()
event.httCandidates[i].sjNonWpt = sj_nonw.pt()
event.httCandidates[i].sjNonWeta = sj_nonw.eta()
event.httCandidates[i].sjNonWphi = sj_nonw.phi()
event.httCandidates[i].sjNonWmass = sj_nonw.mass()
# Get the correct b-tag
for ib in xrange(0, len(sjbtags)) :
if sj_nonw == sjbtags.key(ib).get():
event.httCandidates[i].sjNonWbtag = sjbtags.value(ib)
########
# AK8 Jets from MiniAOD + Subjet btags
########
setattr(event, "ak08", map(PhysicsObject, self.handles["ak08"].product()))
setattr(event, "ak08softdropsubjets", map(PhysicsObject, self.handles["ak08softdropsubjets"].product()))
do_calc_bb = False
# Calc BB tag
# -- if there is a HTT candidate and at least one lepton
if len(event.selectedLeptons):
for cand in event.httCandidates:
if cand.fRec < 0.2 and cand.mass() > 100 and cand.mass() < 200:
do_calc_bb = True
# -- or if there are at least two fatjets with pT > 250 GeV and the scalar sum of the jet pTs is > 600
fj_pt250 = [j for j in event.ak08 if j.pt() > 250]
if len(fj_pt250) >= 2 and sum([j.pt() for j in fj_pt250]) > 600:
do_calc_bb = True
# bb-tag Output
newtags = self.handles['ak08bbtag'].product()
# Loop over jets
for ij, jet in enumerate(getattr(event, "ak08")):
# Fill bb-tag
for i in xrange(len(newtags)) :
if jet.physObj == newtags.key(i).get():
jet.bbtag = newtags.value(i)
corr = self.jetReCalibratorAK8L2L3.getCorrection(Jet(jet),rho)
jet.mprunedcorr= jet.userFloat("ak8PFJetsCHSPrunedMass")*corr
jet.JEC_L2L3 = corr
jet.JEC_L1L2L3 = self.jetReCalibratorAK8L1L2L3.getCorrection(Jet(jet),rho)
# bb-tag Inputs
muonTagInfos = self.handles['ak08muonTagInfos'].product()[ij]
elecTagInfos = self.handles['ak08elecTagInfos'].product()[ij]
ipTagInfo = self.handles['ak08ipTagInfos'].product()[ij]
svTagInfo = self.handles['ak08svTagInfos'].product()[ij]
orig_jet = self.handles["ak08"].product()[ij]
if do_calc_bb:
calcBBTagVariables(jet,
orig_jet,
muonTagInfos,
elecTagInfos,
ipTagInfo,
svTagInfo,
njettiness_08,
maxSVDeltaRToJet = 0.7,)
else:
calcBBTagVariables_dummy(jet)
# end of loop over jets
########
# Ungroomed Fatjets + NSubjettiness + Hbb Tagging
########
for prefix in ["ca15"]:
if self.skip_ca15 and ("ca15" in prefix):
continue
# N-Subjettiness
tau1 = self.handles[prefix+'tau1'].product()
tau2 = self.handles[prefix+'tau2'].product()
tau3 = self.handles[prefix+'tau3'].product()
# bb-tag Output
newtags = self.handles[prefix+'bbtag'].product()
# Four Vector
setattr(event, prefix+"ungroomed", map(PhysicsObject, self.handles[prefix+'ungroomed'].product()))
# Loop over jets
for ij, jet in enumerate(getattr(event, prefix+"ungroomed")):
# Fill N-Subjettiness
jet.tau1 = tau1.get(ij)
jet.tau2 = tau2.get(ij)
jet.tau3 = tau3.get(ij)
# Fill bb-tag
for i in xrange(len(newtags)) :
if jet.physObj == newtags.key(i).get():
jet.bbtag = newtags.value(i)
# bb-tag Inputs
muonTagInfos = self.handles['ca15muonTagInfos'].product()[ij]
elecTagInfos = self.handles['ca15elecTagInfos'].product()[ij]
ipTagInfo = self.handles['ca15ipTagInfos'].product()[ij]
svTagInfo = self.handles['ca15svTagInfos'].product()[ij]
orig_jet = self.handles[prefix+'ungroomed'].product()[ij]
if do_calc_bb:
calcBBTagVariables(jet,
orig_jet,
muonTagInfos,
elecTagInfos,
ipTagInfo,
svTagInfo,
njettiness_15,
maxSVDeltaRToJet = 1.3)
else:
calcBBTagVariables_dummy(jet)
# end of loop over jets
return True
