def cleanFromLepton(objs, leptonCollection):
cleanStuffs = []
dicts = matchObjectCollectionMultiple(objs, leptonCollection, 0.4)
for key, value in dicts.iteritems():
#print "key : ", key , " ; value : ", value
if (value is not None and len(value) != 0) or (value is not None):
continue
cleanStuffs.append(key)
return cleanStuffs
def genRecoFinder(obj1s, obj2s):
dicts = matchObjectCollectionMultiple(obj1s, obj2s, 0.4,
lambda x, y: x.pdgId == y.pdgId)
genRecoflatten = []
for key, value in dicts.iteritems():
if value is None: continue # None means genparts list is empty.
if len(value) == 0:
continue # empty list mean unsuccessful deltaR matching from GEN to Reco
genRecoflatten.append([key, value[0]])
return genRecoflatten
def recoFinder(obj1s, obj2s):
dicts = matchObjectCollectionMultiple(obj1s, obj2s, 0.4,
lambda x, y: x.pdgId == y.pdgId)
#else:
# dicts=matchObjectCollectionMultiple(obj1s,obj2s,0.4,lambda x,y : x.partonFlavour==y.pdgId)
#print "dicts = ", dicts
recoflatten = []
for key, value in dicts.iteritems():
if value is None: continue # None means genparts list is empty.
if len(value) == 0:
continue # empty list mean unsuccessful deltaR matching from GEN to Reco
#print "key = ", key
#print "value[0].pdgId = ", value[0].pdgId
recoflatten.append(key)
return recoflatten
def matchLeptons(self, event):
def plausible(rec, gen):
if abs(rec.pdgId) == 11 and abs(gen.pdgId) != 11: return False
if abs(rec.pdgId) == 13 and abs(gen.pdgId) != 13: return False
dr = deltaR(rec.etc, rec.phi, gen.eta, gen.phi)
if dr < 0.3: return True
if rec.pt < 10 and abs(rec.pdgId) == 13 and gen.pdgId != rec.pdgId:
return False
if dr < 0.7: return True
if min(rec.pt, gen.pt) / max(rec.pt, gen.pt) < 0.3: return False
return True
leps = event.selectedLeptons
match = matchObjectCollectionMultiple(leps,
event.genleps + event.gentauleps,
dRmax=1.2,
presel=lambda plausible: True)
for lep in leps:
gen = match[lep]
lep.mcMatchId = (gen.sourceId if gen != None else 0)
lep.mcMatchTau = (gen in event.gentauleps if gen else -99)
lep.mcLep = gen
def analyze(self, event):
"""process event, return True (go to next module) or False (fail, go to next event)"""
jets = Collection(event, self.jetBranchName)
if not self.isData:
genJets = Collection(event, self.genJetBranchName)
if self.doGroomed:
subJets = Collection(event, self.subJetBranchName)
if not self.isData:
genSubJets = Collection(event, self.genSubJetBranchName)
genSubJetMatcher = matchObjectCollectionMultiple(genJets,
genSubJets,
dRmax=0.8)
if not self.isData:
self.jetSmearer.setSeed(event)
jets_pt_raw = []
jets_pt_nom = []
jets_mass_raw = []
jets_mass_nom = []
jets_corr_JEC = []
jets_corr_JER = []
jets_corr_JMS = []
jets_corr_JMR = []
jets_pt_jerUp = {}
jets_pt_jerDown = {}
jets_pt_jesUp = {}
jets_pt_jesDown = {}
jets_mass_jerUp = {}
jets_mass_jerDown = {}
jets_mass_jmrUp = []
jets_mass_jmrDown = []
jets_mass_jesUp = {}
jets_mass_jesDown = {}
jets_mass_jmsUp = []
jets_mass_jmsDown = []
for jerID in self.splitJERIDs:
jets_pt_jerUp[jerID] = []
jets_pt_jerDown[jerID] = []
jets_mass_jerUp[jerID] = []
jets_mass_jerDown[jerID] = []
for jesUncertainty in self.jesUncertainties:
jets_pt_jesUp[jesUncertainty] = []
jets_pt_jesDown[jesUncertainty] = []
jets_mass_jesUp[jesUncertainty] = []
jets_mass_jesDown[jesUncertainty] = []
if self.doGroomed:
jets_msdcorr_raw = []
jets_msdcorr_nom = []
jets_msdcorr_corr_JMR = []
jets_msdcorr_corr_JMS = []
jets_msdcorr_corr_PUPPI = []
jets_msdcorr_jerUp = {}
jets_msdcorr_jerDown = {}
jets_msdcorr_jmrUp = []
jets_msdcorr_jmrDown = []
jets_msdcorr_jesUp = {}
jets_msdcorr_jesDown = {}
jets_msdcorr_jmsUp = []
jets_msdcorr_jmsDown = []
jets_msdcorr_tau21DDT_nom = []
jets_msdcorr_tau21DDT_jerUp = {}
jets_msdcorr_tau21DDT_jerDown = {}
jets_msdcorr_tau21DDT_jmrUp = []
jets_msdcorr_tau21DDT_jmrDown = []
jets_msdcorr_tau21DDT_jmsUp = []
jets_msdcorr_tau21DDT_jmsDown = []
for jerID in self.splitJERIDs:
jets_msdcorr_jerUp[jerID] = []
jets_msdcorr_jerDown[jerID] = []
jets_msdcorr_tau21DDT_jerUp[jerID] = []
jets_msdcorr_tau21DDT_jerDown[jerID] = []
for jesUncertainty in self.jesUncertainties:
jets_msdcorr_jesUp[jesUncertainty] = []
jets_msdcorr_jesDown[jesUncertainty] = []
rho = getattr(event, self.rhoBranchName)
# match reconstructed jets to generator level ones
# (needed to evaluate JER scale factors and uncertainties)
if not self.isData:
pairs = matchObjectCollection(jets, genJets)
for jet in jets:
# jet pt and mass corrections
jet_pt = jet.pt
jet_mass = jet.mass
if hasattr(jet, "rawFactor"):
jet_rawpt = jet_pt * (1 - jet.rawFactor)
jet_rawmass = jet_mass * (1 - jet.rawFactor)
else:
jet_rawpt = -1.0 * jet_pt # If factor not present factor will be saved as -1
jet_rawmass = -1.0 * jet_mass # If factor not present factor will be saved as -1
(jet_pt, jet_mass) = self.jetReCalibrator.correct(jet, rho)
jet.pt = jet_pt
jet.mass = jet_mass
jets_pt_raw.append(jet_rawpt)
jets_mass_raw.append(jet_rawmass)
jets_corr_JEC.append(jet_pt / jet_rawpt)
if not self.isData:
genJet = pairs[jet]
# evaluate JER scale factors and uncertainties
# (cf. https://twiki.cern.ch/twiki/bin/view/CMS/JetResolution and https://twiki.cern.ch/twiki/bin/view/CMSPublic/WorkBookJetEnergyResolution )
if not self.isData:
(jet_pt_jerNomVal, jet_pt_jerUpVal,
jet_pt_jerDownVal) = self.jetSmearer.getSmearValsPt(
jet, genJet, rho)
else:
# set values to 1 for data so that jet_pt_nom is not smeared
(jet_pt_jerNomVal, jet_pt_jerUpVal, jet_pt_jerDownVal) = (1, 1,
1)
jets_corr_JER.append(jet_pt_jerNomVal)
jet_pt_nom = jet_pt_jerNomVal * jet_pt if self.applySmearing else jet_pt
if jet_pt_nom < 0.0:
jet_pt_nom *= -1.0
jets_pt_nom.append(jet_pt_nom)
# Evaluate JMS and JMR scale factors and uncertainties
jmsNomVal, jmsDownVal, jmsUpVal = self.jmsVals if not self.isData else (
1, 1, 1)
if not self.isData:
(jet_mass_jmrNomVal, jet_mass_jmrUpVal,
jet_mass_jmrDownVal) = self.jetSmearer.getSmearValsM(
jet, genJet)
else:
# set values to 1 for data so that jet_mass_nom is not smeared
(jet_mass_jmrNomVal, jet_mass_jmrUpVal,
jet_mass_jmrDownVal) = (1, 1, 1)
jets_corr_JMS.append(jmsNomVal)
jets_corr_JMR.append(jet_mass_jmrNomVal)
jet_mass_nom = jet_pt_jerNomVal * jet_mass_jmrNomVal * \
jmsNomVal * jet_mass if self.applySmearing else jet_mass
if jet_mass_nom < 0.0:
jet_mass_nom *= -1.0
jets_mass_nom.append(jet_mass_nom)
if not self.isData:
jet_pt_jerUp = {
jerID: jet_pt_nom
for jerID in self.splitJERIDs
}
jet_pt_jerDown = {
jerID: jet_pt_nom
for jerID in self.splitJERIDs
}
jet_mass_jerUp = {
jerID: jet_mass_nom
for jerID in self.splitJERIDs
}
jet_mass_jerDown = {
jerID: jet_mass_nom
for jerID in self.splitJERIDs
}
thisJERID = self.getJERsplitID(jet_pt_nom, jet.eta)
jet_pt_jerUp[thisJERID] = jet_pt_jerUpVal * jet_pt
jet_pt_jerDown[thisJERID] = jet_pt_jerDownVal * jet_pt
jet_mass_jerUp[thisJERID] = jet_pt_jerUpVal * \
jet_mass_jmrNomVal * jmsNomVal * jet_mass
jet_mass_jerDown[thisJERID] = jet_pt_jerDownVal * \
jet_mass_jmrNomVal * jmsNomVal * jet_mass
for jerID in self.splitJERIDs:
jets_pt_jerUp[jerID].append(jet_pt_jerUp[jerID])
jets_pt_jerDown[jerID].append(jet_pt_jerDown[jerID])
jets_mass_jerUp[jerID].append(jet_mass_jerUp[jerID])
jets_mass_jerDown[jerID].append(jet_mass_jerDown[jerID])
jets_mass_jmrUp.append(jet_pt_jerNomVal * jet_mass_jmrUpVal *
jmsNomVal * jet_mass)
jets_mass_jmrDown.append(jet_pt_jerNomVal *
jet_mass_jmrDownVal * jmsNomVal *
jet_mass)
jets_mass_jmsUp.append(jet_pt_jerNomVal * jet_mass_jmrNomVal *
jmsUpVal * jet_mass)
jets_mass_jmsDown.append(jet_pt_jerNomVal *
jet_mass_jmrNomVal * jmsDownVal *
jet_mass)
if self.doGroomed:
if not self.isData:
genGroomedSubJets = genSubJetMatcher[
genJet] if genJet is not None else None
genGroomedJet = genGroomedSubJets[0].p4(
) + genGroomedSubJets[1].p4(
) if genGroomedSubJets is not None and len(
genGroomedSubJets) >= 2 else None
else:
genGroomedSubJets = None
genGroomedJet = None
if jet.subJetIdx1 >= 0 and jet.subJetIdx2 >= 0:
groomedP4 = subJets[jet.subJetIdx1].p4() + subJets[
jet.subJetIdx2].p4() # check subjet jecs
else:
groomedP4 = None
jet_msdcorr_raw = groomedP4.M(
) if groomedP4 is not None else 0.0
# raw value always stored withoud mass correction
jets_msdcorr_raw.append(jet_msdcorr_raw)
# LC: Apply PUPPI SD mass correction https://github.com/cms-jet/PuppiSoftdropMassCorr/
puppisd_genCorr = self.puppisd_corrGEN.Eval(jet.pt)
if abs(jet.eta) <= 1.3:
puppisd_recoCorr = self.puppisd_corrRECO_cen.Eval(jet.pt)
else:
puppisd_recoCorr = self.puppisd_corrRECO_for.Eval(jet.pt)
puppisd_total = puppisd_genCorr * puppisd_recoCorr
jets_msdcorr_corr_PUPPI.append(puppisd_total)
if groomedP4 is not None:
groomedP4.SetPtEtaPhiM(groomedP4.Perp(), groomedP4.Eta(),
groomedP4.Phi(),
groomedP4.M() * puppisd_total)
# now apply the mass correction to the raw value
jet_msdcorr_raw = groomedP4.M(
) if groomedP4 is not None else 0.0
if jet_msdcorr_raw < 0.0:
jet_msdcorr_raw *= -1.0
# Evaluate JMS and JMR scale factors and uncertainties
if not self.isData:
(jet_msdcorr_jmrNomVal, jet_msdcorr_jmrUpVal,
jet_msdcorr_jmrDownVal) = \
(self.jetSmearer.getSmearValsM(groomedP4,
genGroomedJet) if groomedP4 is not None
and genGroomedJet is not None else (0., 0., 0.))
else:
(jet_msdcorr_jmrNomVal, jet_msdcorr_jmrUpVal,
jet_msdcorr_jmrDownVal) = (1, 1, 1)
jets_msdcorr_corr_JMS.append(jmsNomVal)
jets_msdcorr_corr_JMR.append(jet_msdcorr_jmrNomVal)
jet_msdcorr_nom = jet_pt_jerNomVal * \
jet_msdcorr_jmrNomVal * jmsNomVal * jet_msdcorr_raw
# store the nominal mass value
jets_msdcorr_nom.append(jet_msdcorr_nom)
if not self.isData:
jet_msdcorr_jerUp = {
jerID: jet_msdcorr_nom
for jerID in self.splitJERIDs
}
jet_msdcorr_jerDown = {
jerID: jet_msdcorr_nom
for jerID in self.splitJERIDs
}
thisJERID = self.getJERsplitID(jet_pt_nom, jet.eta)
jet_msdcorr_jerUp[thisJERID] = jet_pt_jerUpVal * \
jet_msdcorr_jmrNomVal * jmsNomVal * jet_msdcorr_raw
jet_msdcorr_jerDown[thisJERID] = jet_pt_jerDownVal * \
jet_msdcorr_jmrNomVal * jmsNomVal * jet_msdcorr_raw
for jerID in self.splitJERIDs:
jets_msdcorr_jerUp[jerID].append(
jet_msdcorr_jerUp[jerID])
jets_msdcorr_jerDown[jerID].append(
jet_msdcorr_jerDown[jerID])
jets_msdcorr_jmrUp.append(
jet_pt_jerNomVal * jet_msdcorr_jmrUpVal * jmsNomVal *
jet_msdcorr_raw)
jets_msdcorr_jmrDown.append(
jet_pt_jerNomVal * jet_msdcorr_jmrDownVal * jmsNomVal *
jet_msdcorr_raw)
jets_msdcorr_jmsUp.append(
jet_pt_jerNomVal * jet_msdcorr_jmrNomVal * jmsUpVal *
jet_msdcorr_raw)
jets_msdcorr_jmsDown.append(
jet_pt_jerNomVal * jet_msdcorr_jmrNomVal * jmsDownVal *
jet_msdcorr_raw)
# Also evaluated JMS&JMR SD corr in tau21DDT region: https://twiki.cern.ch/twiki/bin/viewauth/CMS/JetWtagging#tau21DDT_0_43
if self.era in ["2016"]:
jmstau21DDTNomVal = 1.014
jmstau21DDTDownVal = 1.007
jmstau21DDTUpVal = 1.021
self.jetSmearer.jmr_vals = [1.086, 1.176, 0.996]
elif self.era in ["2017"]:
jmstau21DDTNomVal = 0.983
jmstau21DDTDownVal = 0.976
jmstau21DDTUpVal = 0.99
self.jetSmearer.jmr_vals = [1.080, 1.161, 0.999]
elif self.era in ["2018"]:
jmstau21DDTNomVal = 1.000 # tau21DDT < 0.43 WP
jmstau21DDTDownVal = 0.990
jmstau21DDTUpVal = 1.010
self.jetSmearer.jmr_vals = [1.124, 1.208, 1.040]
(
jet_msdcorr_tau21DDT_jmrNomVal,
jet_msdcorr_tau21DDT_jmrUpVal,
jet_msdcorr_tau21DDT_jmrDownVal
) = self.jetSmearer.getSmearValsM(
groomedP4, genGroomedJet
) if groomedP4 is not None and genGroomedJet is not None else (
0., 0., 0.)
jet_msdcorr_tau21DDT_nom = jet_pt_jerNomVal * \
jet_msdcorr_tau21DDT_jmrNomVal * jmstau21DDTNomVal * jet_msdcorr_raw
jets_msdcorr_tau21DDT_nom.append(jet_msdcorr_tau21DDT_nom)
jet_msdcorr_tau21DDT_jerUp = {
jerID: jet_msdcorr_tau21DDT_nom
for jerID in self.splitJERIDs
}
jet_msdcorr_tau21DDT_jerDown = {
jerID: jet_msdcorr_tau21DDT_nom
for jerID in self.splitJERIDs
}
jet_msdcorr_tau21DDT_jerUp[thisJERID] = jet_pt_jerUpVal * \
jet_msdcorr_tau21DDT_jmrNomVal * jmstau21DDTNomVal * jet_msdcorr_raw
jet_msdcorr_tau21DDT_jerDown[thisJERID] = jet_pt_jerDownVal * \
jet_msdcorr_tau21DDT_jmrNomVal * jmstau21DDTNomVal * jet_msdcorr_raw
for jerID in self.splitJERIDs:
jets_msdcorr_tau21DDT_jerUp[jerID].append(
jet_msdcorr_tau21DDT_jerUp[jerID])
jets_msdcorr_tau21DDT_jerDown[jerID].append(
jet_msdcorr_tau21DDT_jerDown[jerID])
jets_msdcorr_tau21DDT_jmrUp.append(
jet_pt_jerNomVal * jet_msdcorr_tau21DDT_jmrUpVal *
jmstau21DDTNomVal * jet_msdcorr_raw)
jets_msdcorr_tau21DDT_jmrDown.append(
jet_pt_jerNomVal * jet_msdcorr_tau21DDT_jmrDownVal *
jmstau21DDTNomVal * jet_msdcorr_raw)
jets_msdcorr_tau21DDT_jmsUp.append(
jet_pt_jerNomVal * jet_msdcorr_tau21DDT_jmrNomVal *
jmstau21DDTUpVal * jet_msdcorr_raw)
jets_msdcorr_tau21DDT_jmsDown.append(
jet_pt_jerNomVal * jet_msdcorr_tau21DDT_jmrNomVal *
jmstau21DDTDownVal * jet_msdcorr_raw)
# Restore original jmr_vals in jetSmearer
self.jetSmearer.jmr_vals = self.jmrVals
if not self.isData:
# evaluate JES uncertainties
jet_pt_jesUp = {}
jet_pt_jesDown = {}
jet_mass_jesUp = {}
jet_mass_jesDown = {}
jet_msdcorr_jesUp = {}
jet_msdcorr_jesDown = {}
for jesUncertainty in self.jesUncertainties:
# (cf. https://twiki.cern.ch/twiki/bin/view/CMSPublic/WorkBookJetEnergyCorrections#JetCorUncertainties)
# cf. https://hypernews.cern.ch/HyperNews/CMS/get/JetMET/2000.html
if jesUncertainty == "HEMIssue":
delta = 1.
if jet_pt_nom > 15 and jet.jetId & 2 and jet.phi > -1.57 and jet.phi < -0.87:
if jet.eta > -2.5 and jet.eta < -1.3:
delta = 0.8
elif jet.eta <= -2.5 and jet.eta > -3:
delta = 0.65
jet_pt_jesUp[jesUncertainty] = jet_pt_nom
jet_pt_jesDown[jesUncertainty] = delta * jet_pt_nom
jet_mass_jesUp[jesUncertainty] = jet_mass_nom
jet_mass_jesDown[jesUncertainty] = delta * jet_mass_nom
if self.doGroomed:
jet_msdcorr_jesUp[jesUncertainty] = jet_msdcorr_nom
jet_msdcorr_jesDown[jesUncertainty] = delta * \
jet_msdcorr_nom
else:
self.jesUncertainty[jesUncertainty].setJetPt(
jet_pt_nom)
self.jesUncertainty[jesUncertainty].setJetEta(jet.eta)
delta = self.jesUncertainty[
jesUncertainty].getUncertainty(True)
jet_pt_jesUp[jesUncertainty] = jet_pt_nom * \
(1. + delta)
jet_pt_jesDown[jesUncertainty] = jet_pt_nom * \
(1. - delta)
jet_mass_jesUp[jesUncertainty] = jet_mass_nom * \
(1. + delta)
jet_mass_jesDown[jesUncertainty] = jet_mass_nom * \
(1. - delta)
if self.doGroomed:
jet_msdcorr_jesUp[jesUncertainty] = jet_msdcorr_nom * \
(1. + delta)
jet_msdcorr_jesDown[
jesUncertainty] = jet_msdcorr_nom * (1. -
delta)
jets_pt_jesUp[jesUncertainty].append(
jet_pt_jesUp[jesUncertainty])
jets_pt_jesDown[jesUncertainty].append(
jet_pt_jesDown[jesUncertainty])
jets_mass_jesUp[jesUncertainty].append(
jet_mass_jesUp[jesUncertainty])
jets_mass_jesDown[jesUncertainty].append(
jet_mass_jesDown[jesUncertainty])
if self.doGroomed:
jets_msdcorr_jesUp[jesUncertainty].append(
jet_msdcorr_jesUp[jesUncertainty])
jets_msdcorr_jesDown[jesUncertainty].append(
jet_msdcorr_jesDown[jesUncertainty])
self.out.fillBranch("%s_pt_raw" % self.jetBranchName, jets_pt_raw)
self.out.fillBranch("%s_pt_nom" % self.jetBranchName, jets_pt_nom)
self.out.fillBranch("%s_corr_JEC" % self.jetBranchName, jets_corr_JEC)
self.out.fillBranch("%s_mass_raw" % self.jetBranchName, jets_mass_raw)
self.out.fillBranch("%s_mass_nom" % self.jetBranchName, jets_mass_nom)
if not self.isData:
self.out.fillBranch("%s_corr_JER" % self.jetBranchName,
jets_corr_JER)
self.out.fillBranch("%s_corr_JMS" % self.jetBranchName,
jets_corr_JMS)
self.out.fillBranch("%s_corr_JMR" % self.jetBranchName,
jets_corr_JMR)
for jerID in self.splitJERIDs:
self.out.fillBranch(
"%s_pt_jer%sUp" % (self.jetBranchName, jerID),
jets_pt_jerUp[jerID])
self.out.fillBranch(
"%s_pt_jer%sDown" % (self.jetBranchName, jerID),
jets_pt_jerDown[jerID])
self.out.fillBranch(
"%s_mass_jer%sUp" % (self.jetBranchName, jerID),
jets_mass_jerUp[jerID])
self.out.fillBranch(
"%s_mass_jer%sDown" % (self.jetBranchName, jerID),
jets_mass_jerDown[jerID])
self.out.fillBranch("%s_mass_jmrUp" % self.jetBranchName,
jets_mass_jmrUp)
self.out.fillBranch("%s_mass_jmrDown" % self.jetBranchName,
jets_mass_jmrDown)
self.out.fillBranch("%s_mass_jmsUp" % self.jetBranchName,
jets_mass_jmsUp)
self.out.fillBranch("%s_mass_jmsDown" % self.jetBranchName,
jets_mass_jmsDown)
if self.doGroomed:
self.out.fillBranch("%s_msoftdrop_raw" % self.jetBranchName,
jets_msdcorr_raw)
self.out.fillBranch("%s_msoftdrop_nom" % self.jetBranchName,
jets_msdcorr_nom)
self.out.fillBranch("%s_msoftdrop_corr_JMS" % self.jetBranchName,
jets_msdcorr_corr_JMS)
self.out.fillBranch("%s_msoftdrop_corr_JMR" % self.jetBranchName,
jets_msdcorr_corr_JMR)
self.out.fillBranch("%s_msoftdrop_corr_PUPPI" % self.jetBranchName,
jets_msdcorr_corr_PUPPI)
if not self.isData:
self.out.fillBranch(
"%s_msoftdrop_tau21DDT_nom" % self.jetBranchName,
jets_msdcorr_tau21DDT_nom)
for jerID in self.splitJERIDs:
self.out.fillBranch(
"%s_msoftdrop_jer%sUp" % (self.jetBranchName, jerID),
jets_msdcorr_jerUp[jerID])
self.out.fillBranch(
"%s_msoftdrop_jer%sDown" % (self.jetBranchName, jerID),
jets_msdcorr_jerDown[jerID])
self.out.fillBranch(
"%s_msoftdrop_tau21DDT_jer%sUp" %
(self.jetBranchName, jerID),
jets_msdcorr_tau21DDT_jerUp[jerID])
self.out.fillBranch(
"%s_msoftdrop_tau21DDT_jer%sDown" %
(self.jetBranchName, jerID),
jets_msdcorr_tau21DDT_jerDown[jerID])
self.out.fillBranch("%s_msoftdrop_jmrUp" % self.jetBranchName,
jets_msdcorr_jmrUp)
self.out.fillBranch(
"%s_msoftdrop_jmrDown" % self.jetBranchName,
jets_msdcorr_jmrDown)
self.out.fillBranch("%s_msoftdrop_jmsUp" % self.jetBranchName,
jets_msdcorr_jmsUp)
self.out.fillBranch(
"%s_msoftdrop_jmsDown" % self.jetBranchName,
jets_msdcorr_jmsDown)
self.out.fillBranch(
"%s_msoftdrop_tau21DDT_jmrUp" % self.jetBranchName,
jets_msdcorr_tau21DDT_jmrUp)
self.out.fillBranch(
"%s_msoftdrop_tau21DDT_jmrDown" % self.jetBranchName,
jets_msdcorr_tau21DDT_jmrDown)
self.out.fillBranch(
"%s_msoftdrop_tau21DDT_jmsUp" % self.jetBranchName,
jets_msdcorr_tau21DDT_jmsUp)
self.out.fillBranch(
"%s_msoftdrop_tau21DDT_jmsDown" % self.jetBranchName,
jets_msdcorr_tau21DDT_jmsDown)
if not self.isData:
for jesUncertainty in self.jesUncertainties:
self.out.fillBranch(
"%s_pt_jes%sUp" % (self.jetBranchName, jesUncertainty),
jets_pt_jesUp[jesUncertainty])
self.out.fillBranch(
"%s_pt_jes%sDown" % (self.jetBranchName, jesUncertainty),
jets_pt_jesDown[jesUncertainty])
self.out.fillBranch(
"%s_mass_jes%sUp" % (self.jetBranchName, jesUncertainty),
jets_mass_jesUp[jesUncertainty])
self.out.fillBranch(
"%s_mass_jes%sDown" % (self.jetBranchName, jesUncertainty),
jets_mass_jesDown[jesUncertainty])
if self.doGroomed:
self.out.fillBranch(
"%s_msoftdrop_jes%sUp" %
(self.jetBranchName, jesUncertainty),
jets_msdcorr_jesUp[jesUncertainty])
self.out.fillBranch(
"%s_msoftdrop_jes%sDown" %
(self.jetBranchName, jesUncertainty),
jets_msdcorr_jesDown[jesUncertainty])
return True
def analyze(self, event):
"""process event, return True (go to next module) or False (fail, go to next event)"""
jets = Collection(event, self.jetBranchName)
genJets = Collection(event, self.genJetBranchName)
if self.doGroomed:
subJets = Collection(event, self.subJetBranchName)
genSubJets = Collection(event, self.genSubJetBranchName)
genSubJetMatcher = matchObjectCollectionMultiple(genJets,
genSubJets,
dRmax=0.8)
jets_pt_nom = []
jets_pt_jerUp = []
jets_pt_jerDown = []
jets_pt_jesUp = {}
jets_pt_jesDown = {}
jets_mass_nom = []
jets_mass_jerUp = []
jets_mass_jerDown = []
jets_mass_jmrUp = []
jets_mass_jmrDown = []
jets_mass_jesUp = {}
jets_mass_jesDown = {}
jets_mass_jmsUp = []
jets_mass_jmsDown = []
for jesUncertainty in self.jesUncertainties:
jets_pt_jesUp[jesUncertainty] = []
jets_pt_jesDown[jesUncertainty] = []
jets_mass_jesUp[jesUncertainty] = []
jets_mass_jesDown[jesUncertainty] = []
if self.corrMET:
met = Object(event, self.metBranchName)
(met_px, met_py) = (met.pt * math.cos(met.phi),
met.pt * math.sin(met.phi))
(met_px_nom, met_py_nom) = (met_px, met_py)
(met_px_jerUp, met_py_jerUp) = (met_px, met_py)
(met_px_jerDown, met_py_jerDown) = (met_px, met_py)
(met_px_jesUp, met_py_jesUp) = ({}, {})
(met_px_jesDown, met_py_jesDown) = ({}, {})
for jesUncertainty in self.jesUncertainties:
met_px_jesUp[jesUncertainty] = met_px
met_py_jesUp[jesUncertainty] = met_py
met_px_jesDown[jesUncertainty] = met_px
met_py_jesDown[jesUncertainty] = met_py
if self.doGroomed:
jets_msdcorr_nom = []
jets_msdcorr_jerUp = []
jets_msdcorr_jerDown = []
jets_msdcorr_jmrUp = []
jets_msdcorr_jmrDown = []
jets_msdcorr_jesUp = {}
jets_msdcorr_jesDown = {}
jets_msdcorr_jmsUp = []
jets_msdcorr_jmsDown = []
for jesUncertainty in self.jesUncertainties:
jets_msdcorr_jesUp[jesUncertainty] = []
jets_msdcorr_jesDown[jesUncertainty] = []
rho = getattr(event, self.rhoBranchName)
# match reconstructed jets to generator level ones
# (needed to evaluate JER scale factors and uncertainties)
pairs = matchObjectCollection(jets, genJets)
for jet in jets:
genJet = pairs[jet]
if self.doGroomed:
genGroomedSubJets = genSubJetMatcher[
genJet] if genJet != None else None
genGroomedJet = genGroomedSubJets[0].p4() + genGroomedSubJets[
1].p4() if genGroomedSubJets != None and len(
genGroomedSubJets) >= 2 else None
if jet.subJetIdx1 >= 0 and jet.subJetIdx2 >= 0:
groomedP4 = subJets[jet.subJetIdx1].p4() + subJets[
jet.subJetIdx2].p4()
else:
groomedP4 = None
# evaluate JER scale factors and uncertainties
# (cf. https://twiki.cern.ch/twiki/bin/view/CMS/JetResolution and https://twiki.cern.ch/twiki/bin/view/CMSPublic/WorkBookJetEnergyResolution )
(jet_pt_jerNomVal, jet_pt_jerUpVal,
jet_pt_jerDownVal) = self.jetSmearer.getSmearValsPt(
jet, genJet, rho)
jet_pt = jet.pt
if self.redoJEC:
jet_pt = self.jetReCalibrator.correct(jet, rho)
jet_pt_nom = jet_pt_jerNomVal * jet_pt
if jet_pt_nom < 0.0:
jet_pt_nom *= -1.0
jet_pt_jerUp = jet_pt_jerUpVal * jet_pt
jet_pt_jerDown = jet_pt_jerDownVal * jet_pt
jets_pt_nom.append(jet_pt_nom)
jets_pt_jerUp.append(jet_pt_jerUpVal * jet_pt)
jets_pt_jerDown.append(jet_pt_jerDownVal * jet_pt)
# evaluate JES uncertainties
jet_pt_jesUp = {}
jet_pt_jesDown = {}
jet_mass_jesUp = {}
jet_mass_jesDown = {}
jet_mass_jmsUp = []
jet_mass_jmsDown = []
# Evaluate JMS and JMR scale factors and uncertainties
jmsNomVal = self.jmsVals[0]
jmsDownVal = self.jmsVals[1]
jmsUpVal = self.jmsVals[2]
(jet_mass_jmrNomVal, jet_mass_jmrUpVal,
jet_mass_jmrDownVal) = self.jetSmearer.getSmearValsM(jet, genJet)
jet_mass_nom = jet_pt_jerNomVal * jet_mass_jmrNomVal * jmsNomVal * jet.mass
if jet_mass_nom < 0.0:
jet_mass_nom *= -1.0
jets_mass_nom.append(jet_mass_nom)
jets_mass_jerUp.append(jet_pt_jerUpVal * jet_mass_jmrNomVal *
jmsNomVal * jet.mass)
jets_mass_jerDown.append(jet_pt_jerDownVal * jet_mass_jmrNomVal *
jmsNomVal * jet.mass)
jets_mass_jmrUp.append(jet_pt_jerNomVal * jet_mass_jmrUpVal *
jmsNomVal * jet.mass)
jets_mass_jmrDown.append(jet_pt_jerNomVal * jet_mass_jmrDownVal *
jmsNomVal * jet.mass)
jets_mass_jmsUp.append(jet_pt_jerNomVal * jet_mass_jmrNomVal *
jmsUpVal * jet.mass)
jets_mass_jmsDown.append(jet_pt_jerNomVal * jet_mass_jmrNomVal *
jmsDownVal * jet.mass)
if self.doGroomed:
# to evaluate JES uncertainties
jet_msdcorr_jmsUp = []
jet_msdcorr_jmsDown = []
jet_msdcorr_jesUp = {}
jet_msdcorr_jesDown = {}
(jet_msdcorr_jmrNomVal, jet_msdcorr_jmrUpVal,
jet_msdcorr_jmrDownVal) = self.jetSmearer.getSmearValsM(
groomedP4, genGroomedJet
) if groomedP4 != None and genGroomedJet != None else (0., 0.,
0.)
jet_msdcorr_raw = groomedP4.M() if groomedP4 != None else 0.0
if jet_msdcorr_raw < 0.0:
jet_msdcorr_raw *= -1.0
jet_msdcorr_nom = jet_pt_jerNomVal * jet_msdcorr_jmrNomVal * jet_msdcorr_raw
jets_msdcorr_nom.append(jet_msdcorr_nom)
jets_msdcorr_jerUp.append(jet_pt_jerUpVal *
jet_msdcorr_jmrNomVal * jmsNomVal *
jet_msdcorr_raw)
jets_msdcorr_jerDown.append(jet_pt_jerDownVal *
jet_msdcorr_jmrNomVal * jmsNomVal *
jet_msdcorr_raw)
jets_msdcorr_jmrUp.append(jet_pt_jerNomVal *
jet_msdcorr_jmrUpVal * jmsNomVal *
jet_msdcorr_raw)
jets_msdcorr_jmrDown.append(
jet_pt_jerNomVal * jet_msdcorr_jmrDownVal * jmsNomVal *
jet_msdcorr_raw)
jets_msdcorr_jmsUp.append(jet_pt_jerNomVal *
jet_msdcorr_jmrNomVal * jmsUpVal *
jet_msdcorr_raw)
jets_msdcorr_jmsDown.append(
jet_pt_jerNomVal * jet_msdcorr_jmrNomVal * jmsDownVal *
jet_msdcorr_raw)
for jesUncertainty in self.jesUncertainties:
# (cf. https://twiki.cern.ch/twiki/bin/view/CMSPublic/WorkBookJetEnergyCorrections#JetCorUncertainties )
self.jesUncertainty[jesUncertainty].setJetPt(jet_pt_nom)
self.jesUncertainty[jesUncertainty].setJetEta(jet.eta)
delta = self.jesUncertainty[jesUncertainty].getUncertainty(
True)
jet_pt_jesUp[jesUncertainty] = jet_pt_nom * (1. + delta)
jet_pt_jesDown[jesUncertainty] = jet_pt_nom * (1. - delta)
jets_pt_jesUp[jesUncertainty].append(
jet_pt_jesUp[jesUncertainty])
jets_pt_jesDown[jesUncertainty].append(
jet_pt_jesDown[jesUncertainty])
jet_mass_jesUp[jesUncertainty] = jet_mass_nom * (1. + delta)
jet_mass_jesDown[jesUncertainty] = jet_mass_nom * (1. - delta)
jets_mass_jesUp[jesUncertainty].append(
jet_mass_jesUp[jesUncertainty])
jets_mass_jesDown[jesUncertainty].append(
jet_mass_jesDown[jesUncertainty])
if self.doGroomed:
jet_msdcorr_jesUp[jesUncertainty] = jet_msdcorr_nom * (
1. + delta)
jet_msdcorr_jesDown[jesUncertainty] = jet_msdcorr_nom * (
1. - delta)
jets_msdcorr_jesUp[jesUncertainty].append(
jet_msdcorr_jesUp[jesUncertainty])
jets_msdcorr_jesDown[jesUncertainty].append(
jet_msdcorr_jesDown[jesUncertainty])
# progate JER and JES corrections and uncertainties to MET
if self.corrMET and jet_pt_nom > self.unclEnThreshold:
jet_cosPhi = math.cos(jet.phi)
jet_sinPhi = math.sin(jet.phi)
met_px_nom = met_px_nom - (jet_pt_nom - jet_pt) * jet_cosPhi
met_py_nom = met_py_nom - (jet_pt_nom - jet_pt) * jet_sinPhi
met_px_jerUp = met_px_jerUp - (jet_pt_jerUp -
jet_pt_nom) * jet_cosPhi
met_py_jerUp = met_py_jerUp - (jet_pt_jerUp -
jet_pt_nom) * jet_sinPhi
met_px_jerDown = met_px_jerDown - (jet_pt_jerDown -
jet_pt_nom) * jet_cosPhi
met_py_jerDown = met_py_jerDown - (jet_pt_jerDown -
jet_pt_nom) * jet_sinPhi
for jesUncertainty in self.jesUncertainties:
met_px_jesUp[jesUncertainty] = met_px_jesUp[
jesUncertainty] - (jet_pt_jesUp[jesUncertainty] -
jet_pt_nom) * jet_cosPhi
met_py_jesUp[jesUncertainty] = met_py_jesUp[
jesUncertainty] - (jet_pt_jesUp[jesUncertainty] -
jet_pt_nom) * jet_sinPhi
met_px_jesDown[jesUncertainty] = met_px_jesDown[
jesUncertainty] - (jet_pt_jesDown[jesUncertainty] -
jet_pt_nom) * jet_cosPhi
met_py_jesDown[jesUncertainty] = met_py_jesDown[
jesUncertainty] - (jet_pt_jesDown[jesUncertainty] -
jet_pt_nom) * jet_sinPhi
# propagate "unclustered energy" uncertainty to MET
if self.corrMET:
(met_px_unclEnUp, met_py_unclEnUp) = (met_px, met_py)
(met_px_unclEnDown, met_py_unclEnDown) = (met_px, met_py)
met_deltaPx_unclEn = getattr(
event, self.metBranchName + "_MetUnclustEnUpDeltaX")
met_deltaPy_unclEn = getattr(
event, self.metBranchName + "_MetUnclustEnUpDeltaY")
met_px_unclEnUp = met_px_unclEnUp + met_deltaPx_unclEn
met_py_unclEnUp = met_py_unclEnUp + met_deltaPy_unclEn
met_px_unclEnDown = met_px_unclEnDown - met_deltaPx_unclEn
met_py_unclEnDown = met_py_unclEnDown - met_deltaPy_unclEn
# propagate effect of jet energy smearing to MET
met_px_jerUp = met_px_jerUp + (met_px_nom - met_px)
met_py_jerUp = met_py_jerUp + (met_py_nom - met_py)
met_px_jerDown = met_px_jerDown + (met_px_nom - met_px)
met_py_jerDown = met_py_jerDown + (met_py_nom - met_py)
for jesUncertainty in self.jesUncertainties:
met_px_jesUp[jesUncertainty] = met_px_jesUp[jesUncertainty] + (
met_px_nom - met_px)
met_py_jesUp[jesUncertainty] = met_py_jesUp[jesUncertainty] + (
met_py_nom - met_py)
met_px_jesDown[jesUncertainty] = met_px_jesDown[
jesUncertainty] + (met_px_nom - met_px)
met_py_jesDown[jesUncertainty] = met_py_jesDown[
jesUncertainty] + (met_py_nom - met_py)
met_px_unclEnUp = met_px_unclEnUp + (met_px_nom - met_px)
met_py_unclEnUp = met_py_unclEnUp + (met_py_nom - met_py)
met_px_unclEnDown = met_px_unclEnDown + (met_px_nom - met_px)
met_py_unclEnDown = met_py_unclEnDown + (met_py_nom - met_py)
self.out.fillBranch("%s_pt_nom" % self.jetBranchName, jets_pt_nom)
self.out.fillBranch("%s_pt_jerUp" % self.jetBranchName, jets_pt_jerUp)
self.out.fillBranch("%s_pt_jerDown" % self.jetBranchName,
jets_pt_jerDown)
self.out.fillBranch("%s_mass_nom" % self.jetBranchName, jets_mass_nom)
self.out.fillBranch("%s_mass_jerUp" % self.jetBranchName,
jets_mass_jerUp)
self.out.fillBranch("%s_mass_jerDown" % self.jetBranchName,
jets_mass_jerDown)
self.out.fillBranch("%s_mass_jmrUp" % self.jetBranchName,
jets_mass_jmrUp)
self.out.fillBranch("%s_mass_jmrDown" % self.jetBranchName,
jets_mass_jmrDown)
self.out.fillBranch("%s_mass_jmsUp" % self.jetBranchName,
jets_mass_jmsUp)
self.out.fillBranch("%s_mass_jmsDown" % self.jetBranchName,
jets_mass_jmsDown)
if self.doGroomed:
self.out.fillBranch("%s_msoftdrop_nom" % self.jetBranchName,
jets_msdcorr_nom)
self.out.fillBranch("%s_msoftdrop_jerUp" % self.jetBranchName,
jets_msdcorr_jerUp)
self.out.fillBranch("%s_msoftdrop_jerDown" % self.jetBranchName,
jets_msdcorr_jerDown)
self.out.fillBranch("%s_msoftdrop_jmrUp" % self.jetBranchName,
jets_msdcorr_jmrUp)
self.out.fillBranch("%s_msoftdrop_jmrDown" % self.jetBranchName,
jets_msdcorr_jmrDown)
self.out.fillBranch("%s_msoftdrop_jmsUp" % self.jetBranchName,
jets_msdcorr_jmsUp)
self.out.fillBranch("%s_msoftdrop_jmsDown" % self.jetBranchName,
jets_msdcorr_jmsDown)
if self.corrMET:
self.out.fillBranch("%s_pt_nom" % self.metBranchName,
math.sqrt(met_px_nom**2 + met_py_nom**2))
self.out.fillBranch("%s_phi_nom" % self.metBranchName,
math.atan2(met_py_nom, met_px_nom))
self.out.fillBranch("%s_pt_jerUp" % self.metBranchName,
math.sqrt(met_px_jerUp**2 + met_py_jerUp**2))
self.out.fillBranch("%s_phi_jerUp" % self.metBranchName,
math.atan2(met_py_jerUp, met_px_jerUp))
self.out.fillBranch(
"%s_pt_jerDown" % self.metBranchName,
math.sqrt(met_px_jerDown**2 + met_py_jerDown**2))
self.out.fillBranch("%s_phi_jerDown" % self.metBranchName,
math.atan2(met_py_jerDown, met_px_jerDown))
for jesUncertainty in self.jesUncertainties:
self.out.fillBranch(
"%s_pt_jes%sUp" % (self.jetBranchName, jesUncertainty),
jets_pt_jesUp[jesUncertainty])
self.out.fillBranch(
"%s_pt_jes%sDown" % (self.jetBranchName, jesUncertainty),
jets_pt_jesDown[jesUncertainty])
self.out.fillBranch(
"%s_mass_jes%sUp" % (self.jetBranchName, jesUncertainty),
jets_mass_jesUp[jesUncertainty])
self.out.fillBranch(
"%s_mass_jes%sDown" % (self.jetBranchName, jesUncertainty),
jets_mass_jesDown[jesUncertainty])
if self.doGroomed:
self.out.fillBranch(
"%s_msoftdrop_jes%sUp" %
(self.jetBranchName, jesUncertainty),
jets_msdcorr_jesUp[jesUncertainty])
self.out.fillBranch(
"%s_msoftdrop_jes%sDown" %
(self.jetBranchName, jesUncertainty),
jets_msdcorr_jesDown[jesUncertainty])
if self.corrMET:
self.out.fillBranch(
"%s_pt_jes%sUp" % (self.metBranchName, jesUncertainty),
math.sqrt(met_px_jesUp[jesUncertainty]**2 +
met_py_jesUp[jesUncertainty]**2))
self.out.fillBranch(
"%s_phi_jes%sUp" % (self.metBranchName, jesUncertainty),
math.atan2(met_py_jesUp[jesUncertainty],
met_px_jesUp[jesUncertainty]))
self.out.fillBranch(
"%s_pt_jes%sDown" % (self.metBranchName, jesUncertainty),
math.sqrt(met_px_jesDown[jesUncertainty]**2 +
met_py_jesDown[jesUncertainty]**2))
self.out.fillBranch(
"%s_phi_jes%sDown" % (self.metBranchName, jesUncertainty),
math.atan2(met_py_jesDown[jesUncertainty],
met_px_jesDown[jesUncertainty]))
if self.corrMET:
self.out.fillBranch(
"%s_pt_unclustEnUp" % self.metBranchName,
math.sqrt(met_px_unclEnUp**2 + met_py_unclEnUp**2))
self.out.fillBranch("%s_phi_unclustEnUp" % self.metBranchName,
math.atan2(met_py_unclEnUp, met_px_unclEnUp))
self.out.fillBranch(
"%s_pt_unclustEnDown" % self.metBranchName,
math.sqrt(met_px_unclEnDown**2 + met_py_unclEnDown**2))
self.out.fillBranch(
"%s_phi_unclustEnDown" % self.metBranchName,
math.atan2(met_py_unclEnDown, met_px_unclEnDown))
return True
def analyze(self, event):
"""process event, return True (go to next module) or False (fail, go to next event)"""
jets = Collection(event, self.jetBranchName )
genJets = Collection(event, self.genJetBranchName )
if self.doGroomed :
subJets = Collection(event, self.subJetBranchName )
genSubJets = Collection(event, self.genSubJetBranchName )
genSubJetMatcher = matchObjectCollectionMultiple( genJets, genSubJets, dRmax=0.8 )
self.jetSmearer.setSeed(event)
jet_L1 = []
jet_L2L3 = []
jet_LRes = []
jets_pt_raw = []
jets_pt_nom = []
jets_mass_raw = []
jets_mass_nom = []
jets_corr_JEC = []
jets_corr_JER = []
jets_corr_JMS = []
jets_corr_JMR = []
jets_pt_jerUp = []
jets_pt_jerDown = []
jets_pt_jesUp = {}
jets_pt_jesDown = {}
jets_mass_jerUp = []
jets_mass_jerDown = []
jets_mass_jmrUp = []
jets_mass_jmrDown = []
jets_mass_jesUp = {}
jets_mass_jesDown = {}
jets_mass_jmsUp = []
jets_mass_jmsDown = []
for jesUncertainty in self.jesUncertainties:
jets_pt_jesUp[jesUncertainty] = []
jets_pt_jesDown[jesUncertainty] = []
jets_mass_jesUp[jesUncertainty] = []
jets_mass_jesDown[jesUncertainty] = []
if self.corrMET :
met = Object(event, self.metBranchName)
( met_px, met_py ) = ( met.pt*math.cos(met.phi), met.pt*math.sin(met.phi) )
( met_px_nom, met_py_nom ) = ( met_px, met_py )
( met_px_jerUp, met_py_jerUp ) = ( met_px, met_py )
( met_px_jerDown, met_py_jerDown ) = ( met_px, met_py )
( met_px_jesUp, met_py_jesUp ) = ( {}, {} )
( met_px_jesDown, met_py_jesDown ) = ( {}, {} )
for jesUncertainty in self.jesUncertainties:
met_px_jesUp[jesUncertainty] = met_px
met_py_jesUp[jesUncertainty] = met_py
met_px_jesDown[jesUncertainty] = met_px
met_py_jesDown[jesUncertainty] = met_py
if self.doGroomed:
jets_msdcorr_raw = []
jets_msdcorr_nom = []
jets_msdcorr_corr_JMR = []
jets_msdcorr_corr_JMS = []
jets_msdcorr_corr_PUPPI = []
jets_msdcorr_jerUp = []
jets_msdcorr_jerDown = []
jets_msdcorr_jmrUp = []
jets_msdcorr_jmrDown = []
jets_msdcorr_jesUp = {}
jets_msdcorr_jesDown = {}
jets_msdcorr_jmsUp = []
jets_msdcorr_jmsDown = []
jets_msdcorr_tau21DDT_nom = []
jets_msdcorr_tau21DDT_jerUp = []
jets_msdcorr_tau21DDT_jerDown = []
jets_msdcorr_tau21DDT_jmrUp = []
jets_msdcorr_tau21DDT_jmrDown = []
jets_msdcorr_tau21DDT_jmsUp = []
jets_msdcorr_tau21DDT_jmsDown = []
for jesUncertainty in self.jesUncertainties:
jets_msdcorr_jesUp[jesUncertainty] = []
jets_msdcorr_jesDown[jesUncertainty] = []
rho = getattr(event, self.rhoBranchName)
# match reconstructed jets to generator level ones
# (needed to evaluate JER scale factors and uncertainties)
pairs = matchObjectCollection(jets, genJets)
for jet in jets:
#jet pt and mass corrections
jet_pt=jet.pt
jet_mass=jet.mass
jet_L1.append(self.jetReCalibrator.getCorrection(jet,rho, corrector=self.jetReCalibrator.separateJetCorrectors["myL1"]))
jet_L2L3.append(self.jetReCalibrator.getCorrection(jet,rho, corrector=self.jetReCalibrator.separateJetCorrectors["myL2L3"]))
jet_LRes.append(self.jetReCalibrator.getCorrection(jet,rho, corrector=self.jetReCalibrator.separateJetCorrectors["myRes"]))
#redo JECs if desired
if hasattr(jet, "rawFactor"):
jet_rawpt = jet_pt * (1 - jet.rawFactor)
jet_rawmass = jet_mass * (1 - jet.rawFactor)
else:
jet_rawpt = -1.0 * jet_pt #If factor not present factor will be saved as -1
jet_rawmass = -1.0 * jet_mass #If factor not present factor will be saved as -1
if self.redoJEC :
(jet_pt, jet_mass) = self.jetReCalibrator.correct(jet,rho)
jet.pt = jet_pt
jet.mass = jet_mass
jets_pt_raw.append(jet_rawpt)
jets_mass_raw.append(jet_rawmass)
jets_corr_JEC.append(jet_pt/jet_rawpt)
genJet = pairs[jet]
if self.doGroomed:
genGroomedSubJets = genSubJetMatcher[genJet] if genJet != None else None
genGroomedJet = genGroomedSubJets[0].p4() + genGroomedSubJets[1].p4() if genGroomedSubJets != None and len(genGroomedSubJets) >= 2 else None
if jet.subJetIdx1 >= 0 and jet.subJetIdx2 >= 0 :
groomedP4 = subJets[ jet.subJetIdx1 ].p4() + subJets[ jet.subJetIdx2].p4() #check subjet jecs
else :
groomedP4 = None
# LC: Apply PUPPI SD mass correction https://github.com/cms-jet/PuppiSoftdropMassCorr/
puppisd_genCorr = self.puppisd_corrGEN.Eval(jet.pt)
if abs(jet.eta) <= 1.3:
puppisd_recoCorr = self.puppisd_corrRECO_cen.Eval(jet.pt)
else:
puppisd_recoCorr = self.puppisd_corrRECO_for.Eval(jet.pt)
puppisd_total = puppisd_genCorr * puppisd_recoCorr
jets_msdcorr_corr_PUPPI.append(puppisd_total)
if groomedP4 != None:
groomedP4.SetPtEtaPhiM(groomedP4.Perp(), groomedP4.Eta(), groomedP4.Phi(), groomedP4.M()*puppisd_total)
# evaluate JER scale factors and uncertainties
# (cf. https://twiki.cern.ch/twiki/bin/view/CMS/JetResolution and https://twiki.cern.ch/twiki/bin/view/CMSPublic/WorkBookJetEnergyResolution )
( jet_pt_jerNomVal, jet_pt_jerUpVal, jet_pt_jerDownVal ) = self.jetSmearer.getSmearValsPt(jet, genJet, rho)
jets_corr_JER.append(jet_pt_jerNomVal)
jet_pt_nom = jet_pt_jerNomVal *jet_pt
if jet_pt_nom < 0.0:
jet_pt_nom *= -1.0
jet_pt_jerUp = jet_pt_jerUpVal *jet_pt
jet_pt_jerDown = jet_pt_jerDownVal*jet_pt
jets_pt_nom .append(jet_pt_nom)
jets_pt_jerUp .append(jet_pt_jerUpVal*jet_pt)
jets_pt_jerDown.append(jet_pt_jerDownVal*jet_pt)
# evaluate JES uncertainties
jet_pt_jesUp = {}
jet_pt_jesDown = {}
jet_mass_jesUp = {}
jet_mass_jesDown = {}
jet_mass_jmsUp = []
jet_mass_jmsDown = []
# Evaluate JMS and JMR scale factors and uncertainties
jmsNomVal = self.jmsVals[0]
jmsDownVal = self.jmsVals[1]
jmsUpVal = self.jmsVals[2]
( jet_mass_jmrNomVal, jet_mass_jmrUpVal, jet_mass_jmrDownVal ) = self.jetSmearer.getSmearValsM(jet, genJet)
jets_corr_JMS .append(jmsNomVal)
jets_corr_JMR .append(jet_mass_jmrNomVal)
jet_mass_nom = jet_pt_jerNomVal*jet_mass_jmrNomVal*jmsNomVal*jet_mass
if jet_mass_nom < 0.0:
jet_mass_nom *= -1.0
jets_mass_nom .append(jet_mass_nom)
jets_mass_jerUp .append(jet_pt_jerUpVal *jet_mass_jmrNomVal *jmsNomVal *jet_mass)
jets_mass_jerDown.append(jet_pt_jerDownVal*jet_mass_jmrNomVal *jmsNomVal *jet_mass)
jets_mass_jmrUp .append(jet_pt_jerNomVal *jet_mass_jmrUpVal *jmsNomVal *jet_mass)
jets_mass_jmrDown.append(jet_pt_jerNomVal *jet_mass_jmrDownVal*jmsNomVal *jet_mass)
jets_mass_jmsUp .append(jet_pt_jerNomVal *jet_mass_jmrNomVal *jmsUpVal *jet_mass)
jets_mass_jmsDown.append(jet_pt_jerNomVal *jet_mass_jmrNomVal *jmsDownVal *jet_mass)
if self.doGroomed :
# evaluate JES uncertainties
jet_msdcorr_jesUp = {}
jet_msdcorr_jesDown = {}
# Evaluate JMS and JMR scale factors and uncertainties
( jet_msdcorr_jmrNomVal, jet_msdcorr_jmrUpVal, jet_msdcorr_jmrDownVal ) = self.jetSmearer.getSmearValsM(groomedP4, genGroomedJet) if groomedP4 != None and genGroomedJet != None else (0.,0.,0.)
jet_msdcorr_raw = groomedP4.M() if groomedP4 != None else 0.0
jets_msdcorr_corr_JMS.append(jmsNomVal)
jets_msdcorr_corr_JMR.append(jet_msdcorr_jmrNomVal)
if jet_msdcorr_raw < 0.0:
jet_msdcorr_raw *= -1.0
jet_msdcorr_nom = jet_pt_jerNomVal*jet_msdcorr_jmrNomVal*jmsNomVal*jet_msdcorr_raw
jets_msdcorr_raw .append(jet_msdcorr_raw) #fix later so jec's not applied - LC: Current PUPPI SD mass correction implementation needs the JECs applied before looking up the correction so make sure that's accounted for if this is changed.
jets_msdcorr_nom .append(jet_msdcorr_nom)
jets_msdcorr_jerUp .append(jet_pt_jerUpVal *jet_msdcorr_jmrNomVal *jmsNomVal *jet_msdcorr_raw)
jets_msdcorr_jerDown.append(jet_pt_jerDownVal*jet_msdcorr_jmrNomVal *jmsNomVal *jet_msdcorr_raw)
jets_msdcorr_jmrUp .append(jet_pt_jerNomVal *jet_msdcorr_jmrUpVal *jmsNomVal *jet_msdcorr_raw)
jets_msdcorr_jmrDown.append(jet_pt_jerNomVal *jet_msdcorr_jmrDownVal*jmsNomVal *jet_msdcorr_raw)
jets_msdcorr_jmsUp .append(jet_pt_jerNomVal *jet_msdcorr_jmrNomVal *jmsUpVal *jet_msdcorr_raw)
jets_msdcorr_jmsDown.append(jet_pt_jerNomVal *jet_msdcorr_jmrNomVal *jmsDownVal *jet_msdcorr_raw)
#Also evaluated JMS&JMR SD corr in tau21DDT region: https://twiki.cern.ch/twiki/bin/viewauth/CMS/JetWtagging#tau21DDT_0_43
if self.era in ["2016"]:
jmstau21DDTNomVal = 1.014
jmstau21DDTDownVal = 1.007
jmstau21DDTUpVal = 1.021
self.jetSmearer.jmr_vals = [1.086,1.176,0.996]
elif self.era in ["2017","2018"]:
jmstau21DDTNomVal = 0.983
jmstau21DDTDownVal = 0.976
jmstau21DDTUpVal = 0.99
self.jetSmearer.jmr_vals = [1.080,1.161,0.999]
( jet_msdcorr_tau21DDT_jmrNomVal, jet_msdcorr_tau21DDT_jmrUpVal, jet_msdcorr_tau21DDT_jmrDownVal ) = self.jetSmearer.getSmearValsM(groomedP4, genGroomedJet) if groomedP4 != None and genGroomedJet != None else (0.,0.,0.)
jet_msdcorr_tau21DDT_nom = jet_pt_jerNomVal*jet_msdcorr_tau21DDT_jmrNomVal*jmstau21DDTNomVal*jet_msdcorr_raw
jets_msdcorr_tau21DDT_nom .append(jet_msdcorr_tau21DDT_nom)
jets_msdcorr_tau21DDT_jerUp .append(jet_pt_jerUpVal *jet_msdcorr_tau21DDT_jmrNomVal *jmstau21DDTNomVal *jet_msdcorr_raw)
jets_msdcorr_tau21DDT_jerDown.append(jet_pt_jerDownVal*jet_msdcorr_tau21DDT_jmrNomVal *jmstau21DDTNomVal *jet_msdcorr_raw)
jets_msdcorr_tau21DDT_jmrUp .append(jet_pt_jerNomVal *jet_msdcorr_tau21DDT_jmrUpVal *jmstau21DDTNomVal *jet_msdcorr_raw)
jets_msdcorr_tau21DDT_jmrDown.append(jet_pt_jerNomVal *jet_msdcorr_tau21DDT_jmrDownVal*jmstau21DDTNomVal *jet_msdcorr_raw)
jets_msdcorr_tau21DDT_jmsUp .append(jet_pt_jerNomVal *jet_msdcorr_tau21DDT_jmrNomVal *jmstau21DDTUpVal *jet_msdcorr_raw)
jets_msdcorr_tau21DDT_jmsDown.append(jet_pt_jerNomVal *jet_msdcorr_tau21DDT_jmrNomVal *jmstau21DDTDownVal *jet_msdcorr_raw)
#Restore original jmr_vals in jetSmearer
self.jetSmearer.jmr_vals = self.jmrVals
for jesUncertainty in self.jesUncertainties:
# (cf. https://twiki.cern.ch/twiki/bin/view/CMSPublic/WorkBookJetEnergyCorrections#JetCorUncertainties )
self.jesUncertainty[jesUncertainty].setJetPt(jet_pt_nom)
self.jesUncertainty[jesUncertainty].setJetEta(jet.eta)
delta = self.jesUncertainty[jesUncertainty].getUncertainty(True)
jet_pt_jesUp[jesUncertainty] = jet_pt_nom*(1. + delta)
jet_pt_jesDown[jesUncertainty] = jet_pt_nom*(1. - delta)
jets_pt_jesUp[jesUncertainty].append(jet_pt_jesUp[jesUncertainty])
jets_pt_jesDown[jesUncertainty].append(jet_pt_jesDown[jesUncertainty])
jet_mass_jesUp [jesUncertainty] = jet_mass_nom*(1. + delta)
jet_mass_jesDown [jesUncertainty] = jet_mass_nom*(1. - delta)
jets_mass_jesUp [jesUncertainty].append(jet_mass_jesUp[jesUncertainty])
jets_mass_jesDown[jesUncertainty].append(jet_mass_jesDown[jesUncertainty])
if self.doGroomed :
jet_msdcorr_jesUp [jesUncertainty] = jet_msdcorr_nom*(1. + delta)
jet_msdcorr_jesDown [jesUncertainty] = jet_msdcorr_nom*(1. - delta)
jets_msdcorr_jesUp [jesUncertainty].append(jet_msdcorr_jesUp[jesUncertainty])
jets_msdcorr_jesDown[jesUncertainty].append(jet_msdcorr_jesDown[jesUncertainty])
# propagate JER and JES corrections and uncertainties to MET
if self.corrMET and jet_pt_nom > self.unclEnThreshold:
jet_cosPhi = math.cos(jet.phi)
jet_sinPhi = math.sin(jet.phi)
met_px_nom = met_px_nom - (jet_pt_nom - jet_pt)*jet_cosPhi
met_py_nom = met_py_nom - (jet_pt_nom - jet_pt)*jet_sinPhi
met_px_jerUp = met_px_jerUp - (jet_pt_jerUp - jet_pt_nom)*jet_cosPhi
met_py_jerUp = met_py_jerUp - (jet_pt_jerUp - jet_pt_nom)*jet_sinPhi
met_px_jerDown = met_px_jerDown - (jet_pt_jerDown - jet_pt_nom)*jet_cosPhi
met_py_jerDown = met_py_jerDown - (jet_pt_jerDown - jet_pt_nom)*jet_sinPhi
for jesUncertainty in self.jesUncertainties:
met_px_jesUp[jesUncertainty] = met_px_jesUp[jesUncertainty] - (jet_pt_jesUp[jesUncertainty] - jet_pt_nom)*jet_cosPhi
met_py_jesUp[jesUncertainty] = met_py_jesUp[jesUncertainty] - (jet_pt_jesUp[jesUncertainty] - jet_pt_nom)*jet_sinPhi
met_px_jesDown[jesUncertainty] = met_px_jesDown[jesUncertainty] - (jet_pt_jesDown[jesUncertainty] - jet_pt_nom)*jet_cosPhi
met_py_jesDown[jesUncertainty] = met_py_jesDown[jesUncertainty] - (jet_pt_jesDown[jesUncertainty] - jet_pt_nom)*jet_sinPhi
# propagate "unclustered energy" uncertainty to MET
if self.corrMET :
( met_px_unclEnUp, met_py_unclEnUp ) = ( met_px, met_py )
( met_px_unclEnDown, met_py_unclEnDown ) = ( met_px, met_py )
met_deltaPx_unclEn = getattr(event, self.metBranchName + "_MetUnclustEnUpDeltaX")
met_deltaPy_unclEn = getattr(event, self.metBranchName + "_MetUnclustEnUpDeltaY")
met_px_unclEnUp = met_px_unclEnUp + met_deltaPx_unclEn
met_py_unclEnUp = met_py_unclEnUp + met_deltaPy_unclEn
met_px_unclEnDown = met_px_unclEnDown - met_deltaPx_unclEn
met_py_unclEnDown = met_py_unclEnDown - met_deltaPy_unclEn
# propagate effect of jet energy smearing to MET
met_px_jerUp = met_px_jerUp + (met_px_nom - met_px)
met_py_jerUp = met_py_jerUp + (met_py_nom - met_py)
met_px_jerDown = met_px_jerDown + (met_px_nom - met_px)
met_py_jerDown = met_py_jerDown + (met_py_nom - met_py)
for jesUncertainty in self.jesUncertainties:
met_px_jesUp[jesUncertainty] = met_px_jesUp[jesUncertainty] + (met_px_nom - met_px)
met_py_jesUp[jesUncertainty] = met_py_jesUp[jesUncertainty] + (met_py_nom - met_py)
met_px_jesDown[jesUncertainty] = met_px_jesDown[jesUncertainty] + (met_px_nom - met_px)
met_py_jesDown[jesUncertainty] = met_py_jesDown[jesUncertainty] + (met_py_nom - met_py)
met_px_unclEnUp = met_px_unclEnUp + (met_px_nom - met_px)
met_py_unclEnUp = met_py_unclEnUp + (met_py_nom - met_py)
met_px_unclEnDown = met_px_unclEnDown + (met_px_nom - met_px)
met_py_unclEnDown = met_py_unclEnDown + (met_py_nom - met_py)
self.out.fillBranch("%s_pt_raw" % self.jetBranchName, jets_pt_raw)
self.out.fillBranch("%s_pt_nom" % self.jetBranchName, jets_pt_nom)
self.out.fillBranch("%s_corr_JEC" % self.jetBranchName, jets_corr_JEC)
self.out.fillBranch("%s_corr_JER" % self.jetBranchName, jets_corr_JER)
self.out.fillBranch("%s_pt_jerUp" % self.jetBranchName, jets_pt_jerUp)
self.out.fillBranch("%s_pt_jerDown" % self.jetBranchName, jets_pt_jerDown)
self.out.fillBranch("%s_mass_raw" % self.jetBranchName, jets_mass_raw)
self.out.fillBranch("%s_mass_nom" % self.jetBranchName, jets_mass_nom)
self.out.fillBranch("%s_corr_JMS" % self.jetBranchName, jets_corr_JMS)
self.out.fillBranch("%s_corr_JMR" % self.jetBranchName, jets_corr_JMR)
self.out.fillBranch("%s_mass_jerUp" % self.jetBranchName, jets_mass_jerUp)
self.out.fillBranch("%s_mass_jerDown" % self.jetBranchName, jets_mass_jerDown)
self.out.fillBranch("%s_mass_jmrUp" % self.jetBranchName, jets_mass_jmrUp)
self.out.fillBranch("%s_mass_jmrDown" % self.jetBranchName, jets_mass_jmrDown)
self.out.fillBranch("%s_mass_jmsUp" % self.jetBranchName, jets_mass_jmsUp)
self.out.fillBranch("%s_mass_jmsDown" % self.jetBranchName, jets_mass_jmsDown)
self.out.fillBranch("%s_L1" % self.jetBranchName, jet_L1)
self.out.fillBranch("%s_L2L3" % self.jetBranchName, jet_L2L3)
self.out.fillBranch("%s_LRes" % self.jetBranchName, jet_LRes)
if self.doGroomed :
self.out.fillBranch("%s_msoftdrop_raw" % self.jetBranchName, jets_msdcorr_raw)
self.out.fillBranch("%s_msoftdrop_nom" % self.jetBranchName, jets_msdcorr_nom)
self.out.fillBranch("%s_msoftdrop_corr_JMS" % self.jetBranchName, jets_msdcorr_corr_JMS)
self.out.fillBranch("%s_msoftdrop_corr_JMR" % self.jetBranchName, jets_msdcorr_corr_JMR)
self.out.fillBranch("%s_msoftdrop_jerUp" % self.jetBranchName, jets_msdcorr_jerUp)
self.out.fillBranch("%s_msoftdrop_jerDown" % self.jetBranchName, jets_msdcorr_jerDown)
self.out.fillBranch("%s_msoftdrop_corr_PUPPI" % self.jetBranchName, jets_msdcorr_corr_PUPPI)
self.out.fillBranch("%s_msoftdrop_jmrUp" % self.jetBranchName, jets_msdcorr_jmrUp)
self.out.fillBranch("%s_msoftdrop_jmrDown" % self.jetBranchName, jets_msdcorr_jmrDown)
self.out.fillBranch("%s_msoftdrop_jmsUp" % self.jetBranchName, jets_msdcorr_jmsUp)
self.out.fillBranch("%s_msoftdrop_jmsDown" % self.jetBranchName, jets_msdcorr_jmsDown)
self.out.fillBranch("%s_msoftdrop_tau21DDT_nom" % self.jetBranchName, jets_msdcorr_tau21DDT_nom)
self.out.fillBranch("%s_msoftdrop_tau21DDT_jerUp" % self.jetBranchName, jets_msdcorr_tau21DDT_jerUp)
self.out.fillBranch("%s_msoftdrop_tau21DDT_jerDown" % self.jetBranchName, jets_msdcorr_tau21DDT_jerDown)
self.out.fillBranch("%s_msoftdrop_tau21DDT_jmrUp" % self.jetBranchName, jets_msdcorr_tau21DDT_jmrUp)
self.out.fillBranch("%s_msoftdrop_tau21DDT_jmrDown" % self.jetBranchName, jets_msdcorr_tau21DDT_jmrDown)
self.out.fillBranch("%s_msoftdrop_tau21DDT_jmsUp" % self.jetBranchName, jets_msdcorr_tau21DDT_jmsUp)
self.out.fillBranch("%s_msoftdrop_tau21DDT_jmsDown" % self.jetBranchName, jets_msdcorr_tau21DDT_jmsDown)
if self.corrMET :
self.out.fillBranch("%s_pt_nom" % self.metBranchName, math.sqrt(met_px_nom**2 + met_py_nom**2))
self.out.fillBranch("%s_phi_nom" % self.metBranchName, math.atan2(met_py_nom, met_px_nom))
self.out.fillBranch("%s_pt_jerUp" % self.metBranchName, math.sqrt(met_px_jerUp**2 + met_py_jerUp**2))
self.out.fillBranch("%s_phi_jerUp" % self.metBranchName, math.atan2(met_py_jerUp, met_px_jerUp))
self.out.fillBranch("%s_pt_jerDown" % self.metBranchName, math.sqrt(met_px_jerDown**2 + met_py_jerDown**2))
self.out.fillBranch("%s_phi_jerDown" % self.metBranchName, math.atan2(met_py_jerDown, met_px_jerDown))
for jesUncertainty in self.jesUncertainties:
self.out.fillBranch("%s_pt_jes%sUp" % (self.jetBranchName, jesUncertainty), jets_pt_jesUp[jesUncertainty])
self.out.fillBranch("%s_pt_jes%sDown" % (self.jetBranchName, jesUncertainty), jets_pt_jesDown[jesUncertainty])
self.out.fillBranch("%s_mass_jes%sUp" % (self.jetBranchName, jesUncertainty), jets_mass_jesUp[jesUncertainty])
self.out.fillBranch("%s_mass_jes%sDown" % (self.jetBranchName, jesUncertainty), jets_mass_jesDown[jesUncertainty])
if self.doGroomed :
self.out.fillBranch("%s_msoftdrop_jes%sUp" % (self.jetBranchName, jesUncertainty), jets_msdcorr_jesUp[jesUncertainty])
self.out.fillBranch("%s_msoftdrop_jes%sDown" % (self.jetBranchName, jesUncertainty), jets_msdcorr_jesDown[jesUncertainty])
if self.corrMET:
self.out.fillBranch("%s_pt_jes%sUp" % (self.metBranchName, jesUncertainty), math.sqrt(met_px_jesUp[jesUncertainty]**2 + met_py_jesUp[jesUncertainty]**2))
self.out.fillBranch("%s_phi_jes%sUp" % (self.metBranchName, jesUncertainty), math.atan2(met_py_jesUp[jesUncertainty], met_px_jesUp[jesUncertainty]))
self.out.fillBranch("%s_pt_jes%sDown" % (self.metBranchName, jesUncertainty), math.sqrt(met_px_jesDown[jesUncertainty]**2 + met_py_jesDown[jesUncertainty]**2))
self.out.fillBranch("%s_phi_jes%sDown" % (self.metBranchName, jesUncertainty), math.atan2(met_py_jesDown[jesUncertainty], met_px_jesDown[jesUncertainty]))
if self.corrMET:
self.out.fillBranch("%s_pt_unclustEnUp" % self.metBranchName, math.sqrt(met_px_unclEnUp**2 + met_py_unclEnUp**2))
self.out.fillBranch("%s_phi_unclustEnUp" % self.metBranchName, math.atan2(met_py_unclEnUp, met_px_unclEnUp))
self.out.fillBranch("%s_pt_unclustEnDown" % self.metBranchName, math.sqrt(met_px_unclEnDown**2 + met_py_unclEnDown**2))
self.out.fillBranch("%s_phi_unclustEnDown" % self.metBranchName, math.atan2(met_py_unclEnDown, met_px_unclEnDown))
return True
