if options.signal:
signalMassPoints.add((r.GenSusyMScan1, r.GenSusyMScan2))
genWeight = 1 if sample.isData else t.GetLeaf(
'genWeight').GetValue()
if allMC and not options.signal:
s.weight = lumiScaleFactor * genWeight
if allData:
s.weight = 1.
if options.signal:
s.weight = signalWeight[(r.GenSusyMScan1,
r.GenSusyMScan2)]['weight']
s.reweightXSecUp = signalWeight[(r.GenSusyMScan1,
r.GenSusyMScan2)]['xSecFacUp']
s.reweightXSecDown = signalWeight[(
r.GenSusyMScan1, r.GenSusyMScan2)]['xSecFacDown']
s.reweightTopPt = topPtReweightingFunc(getTopPtsForReweighting(
r)) / topScaleF if doTopPtReweighting else 1.
if not sample.isData:
s.weightPU = s.weight * puRW(r.nTrueInt)
s.weightPUDown = s.weight * puRWDown(r.nTrueInt)
s.weightPUUp = s.weight * puRWUp(r.nTrueInt)
else:
s.weightPU = 1
s.weightPUDown = 1
s.weightPUUp = 1
if sample.isData:
if not sample.lumiList.contains(r.run, r.lumi):
# print "Did not find run %i lumi %i in json file %s"%(r.run, r.lumi, sample.json)
s.weight = 0
s.weightPU = 0
s.weightPUUp = 0
s.weightPUDown = 0
if (i%10000 == 0) and i>0 :
print i,"/",nEvents , "name:" , chunk['name']
s.init()
r.init()
t.GetEntry(i)
if options.signal: signalMassPoints.add((r.GenSusyMScan1, r.GenSusyMScan2))
genWeight = 1 if sample.isData else t.GetLeaf('genWeight').GetValue()
if allMC and not options.signal:
s.weight = lumiScaleFactor*genWeight
if allData:
s.weight = 1.
if options.signal:
s.weight=signalWeight[(r.GenSusyMScan1, r.GenSusyMScan2)]['weight']
s.reweightXSecUp = signalWeight[(r.GenSusyMScan1, r.GenSusyMScan2)]['xSecFacUp']
s.reweightXSecDown = signalWeight[(r.GenSusyMScan1, r.GenSusyMScan2)]['xSecFacDown']
s.reweightTopPt = topPtReweightingFunc(getTopPtsForReweighting(r))/topScaleF if doTopPtReweighting else 1.
if not sample.isData:
s.weightPU = s.weight*puRW(r.nTrueInt)
s.weightPUDown = s.weight*puRWDown(r.nTrueInt)
s.weightPUUp = s.weight*puRWUp(r.nTrueInt)
else:
s.weightPU = 1
s.weightPUDown = 1
s.weightPUUp = 1
if sample.isData:
if not sample.lumiList.contains(r.run, r.lumi):
# print "Did not find run %i lumi %i in json file %s"%(r.run, r.lumi, sample.json)
s.weight=0
s.weightPU=0
s.weightPUUp=0
s.weightPUDown=0
def filler(s):
# shortcut
r = reader.data
if isMC: gPart = getGenPartsAll(r)
# weight
if options.T2tt:
s.weight=signalWeight[(r.GenSusyMScan1, r.GenSusyMScan2)]['weight']
s.mStop = r.GenSusyMScan1
s.mNeu = r.GenSusyMScan2
s.reweightXSecUp = signalWeight[(r.GenSusyMScan1, r.GenSusyMScan2)]['xSecFacUp']
s.reweightXSecDown = signalWeight[(r.GenSusyMScan1, r.GenSusyMScan2)]['xSecFacDown']
elif isMC:
s.weight = lumiScaleFactor*r.genWeight if lumiScaleFactor is not None else 1
elif isData:
s.weight = 1
else:
raise NotImplementedError( "isMC %r isData %r T2tt? %r TTDM?" % (isMC, isData, options.T2tt, options.TTDM) )
# lumi lists and vetos
if isData:
s.vetoPassed = vetoList.passesVeto(r.run, r.lumi, r.evt)
s.jsonPassed = lumiList.contains(r.run, r.lumi)
# store decision to use after filler has been executed
s.jsonPassed_ = s.jsonPassed
if isMC:
s.reweightPU = puRW(r.nTrueInt)
s.reweightPUDown = puRWDown(r.nTrueInt)
s.reweightPUUp = puRWUp(r.nTrueInt)
# top pt reweighting
if isMC: s.reweightTopPt = topPtReweightingFunc(getTopPtsForReweighting(r))/topScaleF if doTopPtReweighting else 1.
# jet/met related quantities, also load the leptons already
allJets = getGoodJets(r, ptCut=0, jetVars = jetVarNames )
jets = filter(lambda j:jetId(j, ptCut=30, absEtaCut=2.4), allJets)
bJets = filter(lambda j:isBJet(j), jets)
nonBJets = filter(lambda j:not isBJet(j), jets)
if isVeryLoose:
# all leptons up to relIso 1
miniRelIso = 999.
ptCut = 20 if not isVeryLoosePt10 else 10
leptons_pt10 = getGoodAndOtherLeptons(r, ptCut=10, miniRelIso = miniRelIso , dz = 0.1, dxy = 1.)
leptons = filter(lambda l:l['pt']>ptCut, leptons_pt10)
elif isLoose:
# reliso 0.4
miniRelIso = 0.4
leptons_pt10 = getGoodLeptons(r, ptCut=10, miniRelIso = miniRelIso)
leptons = filter(lambda l:l['pt']>20, leptons_pt10)
else:
miniRelIso = 0.2
leptons_pt10 = getGoodLeptons(r, ptCut=10, miniRelIso = miniRelIso)
# relIso 0.2
leptons = filter(lambda l:l['pt']>20, leptons_pt10)
s.met_pt = r.met_pt
s.met_phi = r.met_phi
# Filling jets
s.nJetGood = len(jets)
for iJet, jet in enumerate(jets):
for b in jetVarNames:
getattr(s, "JetGood_"+b)[iJet] = jet[b]
if isSingleLep:
# Compute M3 and the three indiced of the jets entering m3
s.m3, s.m3_ind1, s.m3_ind2, s.m3_ind3 = m3( jets )
s.ht = sum([j['pt'] for j in jets])
s.metSig = s.met_pt/sqrt(s.ht) if s.ht>0 else float('nan')
s.nBTag = len(bJets)
jets_sys = {}
bjets_sys = {}
nonBjets_sys = {}
metVariants = [''] # default
# Keep photons and estimate met including (leading pt) photon
if options.keepPhotons:
photons = getGoodPhotons(r, ptCut=20, idLevel="loose", isData=isData)
s.nPhotonGood = len(photons)
if s.nPhotonGood > 0:
metVariants += ['_photonEstimated'] # do all met calculations also for the photonEstimated variant
s.photon_pt = photons[0]['pt']
s.photon_eta = photons[0]['eta']
s.photon_phi = photons[0]['phi']
s.photon_idCutBased = photons[0]['idCutBased']
if isMC:
genPhoton = getGenPhoton(gPart)
s.photon_genPt = genPhoton['pt'] if genPhoton is not None else float('nan')
s.photon_genEta = genPhoton['eta'] if genPhoton is not None else float('nan')
s.met_pt_photonEstimated, s.met_phi_photonEstimated = getMetPhotonEstimated(r.met_pt, r.met_phi, photons[0])
s.metSig_photonEstimated = s.met_pt_photonEstimated/sqrt(s.ht) if s.ht>0 else float('nan')
s.photonJetdR = min(deltaR(photons[0], j) for j in jets) if len(jets) > 0 else 999
s.photonLepdR = min(deltaR(photons[0], l) for l in leptons_pt10) if len(leptons_pt10) > 0 else 999
if options.checkTTGJetsOverlap and isMC:
s.TTGJetsEventType = getTTGJetsEventType(r)
if addSystematicVariations:
for j in allJets:
j['pt_JECUp'] =j['pt']/j['corr']*j['corr_JECUp']
j['pt_JECDown'] =j['pt']/j['corr']*j['corr_JECDown']
# JERUp, JERDown, JER
addJERScaling(j)
for var in ['JECUp', 'JECDown', 'JERUp', 'JERDown']:
jets_sys[var] = filter(lambda j:jetId(j, ptCut=30, absEtaCut=2.4, ptVar='pt_'+var), allJets)
bjets_sys[var] = filter(isBJet, jets_sys[var])
nonBjets_sys[var] = filter(lambda j: not isBJet(j), jets_sys[var])
setattr(s, "nJetGood_"+var, len(jets_sys[var]))
setattr(s, "ht_"+var, sum([j['pt_'+var] for j in jets_sys[var]]))
setattr(s, "nBTag_"+var, len(bjets_sys[var]))
for var in ['JECUp', 'JECDown', 'JERUp', 'JERDown', 'UnclusteredEnUp', 'UnclusteredEnDown']:
for i in metVariants:
# use cmg MET correction values ecept for JER where it is zero. There, propagate jet variations.
if 'JER' in var:
(met_corr_pt, met_corr_phi) = getMetJetCorrected(getattr(s, "met_pt" + i), getattr(s,"met_phi" + i), jets_sys[var], var)
else:
(met_corr_pt, met_corr_phi) = getMetCorrected(r, var, photons[0] if i.count("photonEstimated") else None)
setattr(s, "met_pt" +i+"_"+var, met_corr_pt)
setattr(s, "met_phi"+i+"_"+var, met_corr_phi)
ht = getattr(s, "ht_"+var) if 'Unclustered' not in var else s.ht
setattr(s, "metSig" +i+"_"+var, getattr(s, "met_pt"+i+"_"+var)/sqrt( ht ) if ht>0 else float('nan') )
if isSingleLep or isDiLep:
s.nGoodMuons = len(filter( lambda l:abs(l['pdgId'])==13, leptons))
s.nGoodElectrons = len(filter( lambda l:abs(l['pdgId'])==11, leptons))
if len(leptons)>=1:
s.l1_pt = leptons[0]['pt']
s.l1_eta = leptons[0]['eta']
s.l1_phi = leptons[0]['phi']
s.l1_pdgId = leptons[0]['pdgId']
s.l1_index = leptons[0]['index']
s.l1_jetPtRelv2 = leptons[0]['jetPtRelv2']
s.l1_jetPtRatiov2 = leptons[0]['jetPtRatiov2']
s.l1_jetPtRelv2 = leptons[0]['jetPtRelv2']
s.l1_miniRelIso = leptons[0]['miniRelIso']
s.l1_dxy = leptons[0]['dxy']
s.l1_dz = leptons[0]['dz']
# For TTZ studies: find Z boson candidate, and use third lepton to calculate mt
(s.mlmZ_mass, zl1, zl2) = closestOSDLMassToMZ(leptons_pt10)
# if len(leptons_pt10) >= 3:
# thirdLepton = leptons_pt10[[x for x in range(len(leptons_pt10)) if x != zl1 and x != zl2][0]]
# for i in metVariants:
# setattr(s, "mt"+i, sqrt(2*thirdLepton['pt']*getattr(s, "met_pt"+i)*(1-cos(thirdLepton['phi']-getattr(s, "met_phi"+i)))))
if options.fastSim:
s.reweightLeptonFastSimSF = reduce(mul, [leptonFastSimSF.get3DSF(pdgId=l['pdgId'], pt=l['pt'], eta=l['eta'] , nvtx = r.nVert) for l in leptons], 1)
s.reweightLeptonFastSimSFUp = reduce(mul, [leptonFastSimSF.get3DSF(pdgId=l['pdgId'], pt=l['pt'], eta=l['eta'] , nvtx = r.nVert, sigma = +1) for l in leptons], 1)
s.reweightLeptonFastSimSFDown = reduce(mul, [leptonFastSimSF.get3DSF(pdgId=l['pdgId'], pt=l['pt'], eta=l['eta'] , nvtx = r.nVert, sigma = -1) for l in leptons], 1)
if isDiLep:
if len(leptons)>=2:# and leptons[0]['pdgId']*leptons[1]['pdgId']<0 and abs(leptons[0]['pdgId'])==abs(leptons[1]['pdgId']): #OSSF choice
mt2Calc.reset()
s.l2_pt = leptons[1]['pt']
s.l2_eta = leptons[1]['eta']
s.l2_phi = leptons[1]['phi']
s.l2_pdgId = leptons[1]['pdgId']
s.l2_index = leptons[1]['index']
s.l2_jetPtRatiov2 = leptons[1]['jetPtRatiov2']
s.l2_jetPtRelv2 = leptons[1]['jetPtRelv2']
s.l2_miniRelIso = leptons[1]['miniRelIso']
s.l2_dxy = leptons[1]['dxy']
s.l2_dz = leptons[1]['dz']
l_pdgs = [abs(leptons[0]['pdgId']), abs(leptons[1]['pdgId'])]
l_pdgs.sort()
s.isMuMu = l_pdgs==[13,13]
s.isEE = l_pdgs==[11,11]
s.isEMu = l_pdgs==[11,13]
s.isOS = s.l1_pdgId*s.l2_pdgId<0
l1 = ROOT.TLorentzVector()
l1.SetPtEtaPhiM(leptons[0]['pt'], leptons[0]['eta'], leptons[0]['phi'], 0 )
l2 = ROOT.TLorentzVector()
l2.SetPtEtaPhiM(leptons[1]['pt'], leptons[1]['eta'], leptons[1]['phi'], 0 )
dl = l1+l2
s.dl_pt = dl.Pt()
s.dl_eta = dl.Eta()
s.dl_phi = dl.Phi()
s.dl_mass = dl.M()
mt2Calc.setLeptons(s.l1_pt, s.l1_eta, s.l1_phi, s.l2_pt, s.l2_eta, s.l2_phi)
# To check MC truth when looking at the TTZToLLNuNu sample
if isMC:
zBoson = getGenZ(gPart)
s.zBoson_genPt = zBoson['pt'] if zBoson is not None else float('nan')
s.zBoson_genEta = zBoson['eta'] if zBoson is not None else float('nan')
if options.keepPhotons and s.nPhotonGood > 0:
gamma = ROOT.TLorentzVector()
gamma.SetPtEtaPhiM(photons[0]['pt'], photons[0]['eta'], photons[0]['phi'], photons[0]['mass'] )
dlg = dl + gamma
s.dlg_mass = dlg.M()
for i in metVariants:
mt2Calc.setMet(getattr(s, 'met_pt'+i), getattr(s, 'met_phi', i))
setattr(s, "dl_mt2ll"+i, mt2Calc.mt2ll())
if len(jets)>=2:
bj0, bj1 = (bJets+nonBJets)[:2]
mt2Calc.setBJets(bj0['pt'], bj0['eta'], bj0['phi'], bj1['pt'], bj1['eta'], bj1['phi'])
setattr(s, "dl_mt2bb"+i, mt2Calc.mt2bb())
setattr(s, "dl_mt2blbl"+i, mt2Calc.mt2blbl())
if addSystematicVariations:
for var in ['JECUp', 'JECDown', 'JERUp', 'JERDown', 'UnclusteredEnUp', 'UnclusteredEnDown']:
mt2Calc.setMet( getattr(s, "met_pt"+i+"_"+var), getattr(s, "met_phi"+i+"_"+var) )
setattr(s, "dl_mt2ll"+i+"_"+var, mt2Calc.mt2ll())
bj0_, bj1_ = bj0, bj1
if not 'Unclustered' in var:
if len(jets_sys[var])>=2:
bj0_, bj1_ = (bjets_sys[var]+nonBjets_sys[var])[:2]
else:
bj0_, bj1_ = None, None
if bj0_ and bj1_:
mt2Calc.setBJets(bj0_['pt'], bj0_['eta'], bj0_['phi'], bj1_['pt'], bj1_['eta'], bj1_['phi'])
setattr(s, 'dl_mt2bb' +i+'_'+var, mt2Calc.mt2bb())
setattr(s, 'dl_mt2blbl'+i+'_'+var, mt2Calc.mt2blbl())
if addSystematicVariations:
# B tagging weights method 1a
for j in jets:
btagEff.addBTagEffToJet(j)
for var in btagEff.btagWeightNames:
if var!='MC':
setattr(s, 'reweightBTag_'+var, btagEff.getBTagSF_1a( var, bJets, nonBJets ) )
# gen information on extra leptons
if isMC and not options.skipGenLepMatching:
genSearch.init( gPart )
# Start with status 1 gen leptons in acceptance
gLep = filter( lambda p:abs(p['pdgId']) in [11, 13] and p['status']==1 and p['pt']>20 and abs(p['eta'])<2.5, gPart )
for l in gLep:
ancestry = [ gPart[x]['pdgId'] for x in genSearch.ancestry( l ) ]
l["n_D"] = sum([ancestry.count(p) for p in D_mesons])
l["n_B"] = sum([ancestry.count(p) for p in B_mesons])
l["n_W"] = sum([ancestry.count(p) for p in [24, -24]])
l["n_t"] = sum([ancestry.count(p) for p in [6, -6]])
l["n_tau"] = sum([ancestry.count(p) for p in [15, -15]])
matched_lep = bestDRMatchInCollection(l, leptons_pt10)
if matched_lep:
l["lepGoodMatchIndex"] = matched_lep['index']
if isSingleLep:
l["matchesPromptGoodLepton"] = l["lepGoodMatchIndex"] in [s.l1_index]
elif isDiLep:
l["matchesPromptGoodLepton"] = l["lepGoodMatchIndex"] in [s.l1_index, s.l2_index]
else:
l["matchesPromptGoodLepton"] = 0
else:
l["lepGoodMatchIndex"] = -1
l["matchesPromptGoodLepton"] = 0
# if l["n_t"]>0 and l["n_W"]>0 and l["n_B"]==0 and l["n_D"]==0 and l["n_tau"]==0:
# print "t->W->l"
# elif l["n_t"]>0 and l["n_W"]==0 and l["n_B"]>0 and l["n_D"]==0 and l["n_tau"]==0:
# print "t->b->B->l"
# elif l["n_t"]>0 and l["n_W"]==0 and l["n_B"]>0 and l["n_D"]>0 and l["n_tau"]==0:
# print "t->b->B->D->l"
# elif l["n_t"]>0 and l["n_W"]>0 and l["n_B"]==0 and l["n_D"]==0 and l["n_tau"]>0 :
# print "t->W->tau->l"
# elif l["n_t"]>0 and l["n_W"]>0 and l["n_B"]==0 and l["n_D"]>0 and l["n_tau"]==0:
# print "t->W->c->D->l"
# elif l["n_t"]==0 and l["n_W"]==0 and l["n_B"]>0 and l["n_D"]>=0 and l["n_tau"]==0:
# print l['pdgId'], l['pt'], l['phi'], l['eta'], ",".join(pdgToName( gPart[x]['pdgId']) for x in genSearch.ancestry(l) )
# for p in genSearch.ancestry(l):
# print p, gPart[p]
# else:
# pass
# print l['pdgId'], l['pt'], l['phi'], l['eta'], ",".join(pdgToName(gPart[x]['pdgId']) for x in genSearch.ancestry(l))
s.nGenLep = len(gLep)
for iLep, lep in enumerate(gLep):
for b in genLepVarNames:
getattr(s, "GenLep_"+b)[iLep] = lep[b]
def filler(s):
# shortcut
r = reader.data
if isMC: gPart = getGenPartsAll(r)
# weight
if options.T2tt:
s.weight = signalWeight[(r.GenSusyMScan1, r.GenSusyMScan2)]['weight']
s.mStop = r.GenSusyMScan1
s.mNeu = r.GenSusyMScan2
s.reweightXSecUp = signalWeight[(r.GenSusyMScan1,
r.GenSusyMScan2)]['xSecFacUp']
s.reweightXSecDown = signalWeight[(r.GenSusyMScan1,
r.GenSusyMScan2)]['xSecFacDown']
elif isMC:
s.weight = lumiScaleFactor * r.genWeight if lumiScaleFactor is not None else 1
elif isData:
s.weight = 1
else:
raise NotImplementedError("isMC %r isData %r T2tt? %r TTDM?" %
(isMC, isData, options.T2tt, options.TTDM))
# lumi lists and vetos
if isData:
s.vetoPassed = vetoList.passesVeto(r.run, r.lumi, r.evt)
s.jsonPassed = lumiList.contains(r.run, r.lumi)
# store decision to use after filler has been executed
s.jsonPassed_ = s.jsonPassed
if isMC:
s.reweightPU = puRW(r.nTrueInt)
s.reweightPUDown = puRWDown(r.nTrueInt)
s.reweightPUUp = puRWUp(r.nTrueInt)
# top pt reweighting
if isMC:
s.reweightTopPt = topPtReweightingFunc(getTopPtsForReweighting(
r)) / topScaleF if doTopPtReweighting else 1.
# jet/met related quantities, also load the leptons already
allJets = getGoodJets(r, ptCut=0, jetVars=jetVarNames)
jets = filter(lambda j: jetId(j, ptCut=30, absEtaCut=2.4), allJets)
bJets = filter(lambda j: isBJet(j), jets)
nonBJets = filter(lambda j: not isBJet(j), jets)
if isVeryLoose:
# all leptons up to relIso 1
miniRelIso = 999.
ptCut = 20 if not isVeryLoosePt10 else 10
leptons_pt10 = getGoodAndOtherLeptons(r,
ptCut=10,
miniRelIso=miniRelIso,
dz=0.1,
dxy=1.)
leptons = filter(lambda l: l['pt'] > ptCut, leptons_pt10)
elif isLoose:
# reliso 0.4
miniRelIso = 0.4
leptons_pt10 = getGoodLeptons(r, ptCut=10, miniRelIso=miniRelIso)
leptons = filter(lambda l: l['pt'] > 20, leptons_pt10)
else:
miniRelIso = 0.2
leptons_pt10 = getGoodLeptons(r, ptCut=10, miniRelIso=miniRelIso)
# relIso 0.2
leptons = filter(lambda l: l['pt'] > 20, leptons_pt10)
s.met_pt = r.met_pt
s.met_phi = r.met_phi
# Filling jets
s.nJetGood = len(jets)
for iJet, jet in enumerate(jets):
for b in jetVarNames:
getattr(s, "JetGood_" + b)[iJet] = jet[b]
if isSingleLep:
# Compute M3 and the three indiced of the jets entering m3
s.m3, s.m3_ind1, s.m3_ind2, s.m3_ind3 = m3(jets)
s.ht = sum([j['pt'] for j in jets])
s.metSig = s.met_pt / sqrt(s.ht) if s.ht > 0 else float('nan')
s.nBTag = len(bJets)
jets_sys = {}
bjets_sys = {}
nonBjets_sys = {}
metVariants = [''] # default
# Keep photons and estimate met including (leading pt) photon
if options.keepPhotons:
photons = getGoodPhotons(r, ptCut=20, idLevel="loose", isData=isData)
s.nPhotonGood = len(photons)
if s.nPhotonGood > 0:
metVariants += [
'_photonEstimated'
] # do all met calculations also for the photonEstimated variant
s.photon_pt = photons[0]['pt']
s.photon_eta = photons[0]['eta']
s.photon_phi = photons[0]['phi']
s.photon_idCutBased = photons[0]['idCutBased']
if isMC:
genPhoton = getGenPhoton(gPart)
s.photon_genPt = genPhoton[
'pt'] if genPhoton is not None else float('nan')
s.photon_genEta = genPhoton[
'eta'] if genPhoton is not None else float('nan')
s.met_pt_photonEstimated, s.met_phi_photonEstimated = getMetPhotonEstimated(
r.met_pt, r.met_phi, photons[0])
s.metSig_photonEstimated = s.met_pt_photonEstimated / sqrt(
s.ht) if s.ht > 0 else float('nan')
s.photonJetdR = min(deltaR(photons[0], j)
for j in jets) if len(jets) > 0 else 999
s.photonLepdR = min(
deltaR(photons[0], l)
for l in leptons_pt10) if len(leptons_pt10) > 0 else 999
if options.checkTTGJetsOverlap and isMC:
s.TTGJetsEventType = getTTGJetsEventType(r)
if addSystematicVariations:
for j in allJets:
j['pt_JECUp'] = j['pt'] / j['corr'] * j['corr_JECUp']
j['pt_JECDown'] = j['pt'] / j['corr'] * j['corr_JECDown']
# JERUp, JERDown, JER
addJERScaling(j)
for var in ['JECUp', 'JECDown', 'JERUp', 'JERDown']:
jets_sys[var] = filter(
lambda j: jetId(j, ptCut=30, absEtaCut=2.4, ptVar='pt_' + var),
allJets)
bjets_sys[var] = filter(isBJet, jets_sys[var])
nonBjets_sys[var] = filter(lambda j: not isBJet(j), jets_sys[var])
setattr(s, "nJetGood_" + var, len(jets_sys[var]))
setattr(s, "ht_" + var,
sum([j['pt_' + var] for j in jets_sys[var]]))
setattr(s, "nBTag_" + var, len(bjets_sys[var]))
for var in [
'JECUp', 'JECDown', 'JERUp', 'JERDown', 'UnclusteredEnUp',
'UnclusteredEnDown'
]:
for i in metVariants:
# use cmg MET correction values ecept for JER where it is zero. There, propagate jet variations.
if 'JER' in var:
(met_corr_pt, met_corr_phi) = getMetJetCorrected(
getattr(s, "met_pt" + i), getattr(s, "met_phi" + i),
jets_sys[var], var)
else:
(met_corr_pt, met_corr_phi) = getMetCorrected(
r, var,
photons[0] if i.count("photonEstimated") else None)
setattr(s, "met_pt" + i + "_" + var, met_corr_pt)
setattr(s, "met_phi" + i + "_" + var, met_corr_phi)
ht = getattr(s, "ht_" +
var) if 'Unclustered' not in var else s.ht
setattr(
s, "metSig" + i + "_" + var,
getattr(s, "met_pt" + i + "_" + var) /
sqrt(ht) if ht > 0 else float('nan'))
if isSingleLep or isDiLep:
s.nGoodMuons = len(filter(lambda l: abs(l['pdgId']) == 13, leptons))
s.nGoodElectrons = len(filter(lambda l: abs(l['pdgId']) == 11,
leptons))
if len(leptons) >= 1:
s.l1_pt = leptons[0]['pt']
s.l1_eta = leptons[0]['eta']
s.l1_phi = leptons[0]['phi']
s.l1_pdgId = leptons[0]['pdgId']
s.l1_index = leptons[0]['index']
s.l1_jetPtRelv2 = leptons[0]['jetPtRelv2']
s.l1_jetPtRatiov2 = leptons[0]['jetPtRatiov2']
s.l1_jetPtRelv2 = leptons[0]['jetPtRelv2']
s.l1_miniRelIso = leptons[0]['miniRelIso']
s.l1_dxy = leptons[0]['dxy']
s.l1_dz = leptons[0]['dz']
# For TTZ studies: find Z boson candidate, and use third lepton to calculate mt
(s.mlmZ_mass, zl1, zl2) = closestOSDLMassToMZ(leptons_pt10)
# if len(leptons_pt10) >= 3:
# thirdLepton = leptons_pt10[[x for x in range(len(leptons_pt10)) if x != zl1 and x != zl2][0]]
# for i in metVariants:
# setattr(s, "mt"+i, sqrt(2*thirdLepton['pt']*getattr(s, "met_pt"+i)*(1-cos(thirdLepton['phi']-getattr(s, "met_phi"+i)))))
if options.fastSim:
s.reweightLeptonFastSimSF = reduce(mul, [
leptonFastSimSF.get3DSF(
pdgId=l['pdgId'], pt=l['pt'], eta=l['eta'], nvtx=r.nVert)
for l in leptons
], 1)
s.reweightLeptonFastSimSFUp = reduce(mul, [
leptonFastSimSF.get3DSF(pdgId=l['pdgId'],
pt=l['pt'],
eta=l['eta'],
nvtx=r.nVert,
sigma=+1) for l in leptons
], 1)
s.reweightLeptonFastSimSFDown = reduce(mul, [
leptonFastSimSF.get3DSF(pdgId=l['pdgId'],
pt=l['pt'],
eta=l['eta'],
nvtx=r.nVert,
sigma=-1) for l in leptons
], 1)
if isDiLep:
if len(
leptons
) >= 2: # and leptons[0]['pdgId']*leptons[1]['pdgId']<0 and abs(leptons[0]['pdgId'])==abs(leptons[1]['pdgId']): #OSSF choice
mt2Calc.reset()
s.l2_pt = leptons[1]['pt']
s.l2_eta = leptons[1]['eta']
s.l2_phi = leptons[1]['phi']
s.l2_pdgId = leptons[1]['pdgId']
s.l2_index = leptons[1]['index']
s.l2_jetPtRatiov2 = leptons[1]['jetPtRatiov2']
s.l2_jetPtRelv2 = leptons[1]['jetPtRelv2']
s.l2_miniRelIso = leptons[1]['miniRelIso']
s.l2_dxy = leptons[1]['dxy']
s.l2_dz = leptons[1]['dz']
l_pdgs = [abs(leptons[0]['pdgId']), abs(leptons[1]['pdgId'])]
l_pdgs.sort()
s.isMuMu = l_pdgs == [13, 13]
s.isEE = l_pdgs == [11, 11]
s.isEMu = l_pdgs == [11, 13]
s.isOS = s.l1_pdgId * s.l2_pdgId < 0
l1 = ROOT.TLorentzVector()
l1.SetPtEtaPhiM(leptons[0]['pt'], leptons[0]['eta'],
leptons[0]['phi'], 0)
l2 = ROOT.TLorentzVector()
l2.SetPtEtaPhiM(leptons[1]['pt'], leptons[1]['eta'],
leptons[1]['phi'], 0)
dl = l1 + l2
s.dl_pt = dl.Pt()
s.dl_eta = dl.Eta()
s.dl_phi = dl.Phi()
s.dl_mass = dl.M()
mt2Calc.setLeptons(s.l1_pt, s.l1_eta, s.l1_phi, s.l2_pt, s.l2_eta,
s.l2_phi)
# To check MC truth when looking at the TTZToLLNuNu sample
if isMC:
zBoson = getGenZ(gPart)
s.zBoson_genPt = zBoson['pt'] if zBoson is not None else float(
'nan')
s.zBoson_genEta = zBoson[
'eta'] if zBoson is not None else float('nan')
if options.keepPhotons and s.nPhotonGood > 0:
gamma = ROOT.TLorentzVector()
gamma.SetPtEtaPhiM(photons[0]['pt'], photons[0]['eta'],
photons[0]['phi'], photons[0]['mass'])
dlg = dl + gamma
s.dlg_mass = dlg.M()
for i in metVariants:
mt2Calc.setMet(getattr(s, 'met_pt' + i),
getattr(s, 'met_phi', i))
setattr(s, "dl_mt2ll" + i, mt2Calc.mt2ll())
if len(jets) >= 2:
bj0, bj1 = (bJets + nonBJets)[:2]
mt2Calc.setBJets(bj0['pt'], bj0['eta'], bj0['phi'],
bj1['pt'], bj1['eta'], bj1['phi'])
setattr(s, "dl_mt2bb" + i, mt2Calc.mt2bb())
setattr(s, "dl_mt2blbl" + i, mt2Calc.mt2blbl())
if addSystematicVariations:
for var in [
'JECUp', 'JECDown', 'JERUp', 'JERDown',
'UnclusteredEnUp', 'UnclusteredEnDown'
]:
mt2Calc.setMet(getattr(s, "met_pt" + i + "_" + var),
getattr(s, "met_phi" + i + "_" + var))
setattr(s, "dl_mt2ll" + i + "_" + var, mt2Calc.mt2ll())
bj0_, bj1_ = bj0, bj1
if not 'Unclustered' in var:
if len(jets_sys[var]) >= 2:
bj0_, bj1_ = (bjets_sys[var] +
nonBjets_sys[var])[:2]
else:
bj0_, bj1_ = None, None
if bj0_ and bj1_:
mt2Calc.setBJets(bj0_['pt'], bj0_['eta'],
bj0_['phi'], bj1_['pt'],
bj1_['eta'], bj1_['phi'])
setattr(s, 'dl_mt2bb' + i + '_' + var,
mt2Calc.mt2bb())
setattr(s, 'dl_mt2blbl' + i + '_' + var,
mt2Calc.mt2blbl())
if addSystematicVariations:
# B tagging weights method 1a
for j in jets:
btagEff.addBTagEffToJet(j)
for var in btagEff.btagWeightNames:
if var != 'MC':
setattr(s, 'reweightBTag_' + var,
btagEff.getBTagSF_1a(var, bJets, nonBJets))
# gen information on extra leptons
if isMC and not options.skipGenLepMatching:
genSearch.init(gPart)
# Start with status 1 gen leptons in acceptance
gLep = filter(
lambda p: abs(p['pdgId']) in [11, 13] and p['status'] == 1 and p[
'pt'] > 20 and abs(p['eta']) < 2.5, gPart)
for l in gLep:
ancestry = [gPart[x]['pdgId'] for x in genSearch.ancestry(l)]
l["n_D"] = sum([ancestry.count(p) for p in D_mesons])
l["n_B"] = sum([ancestry.count(p) for p in B_mesons])
l["n_W"] = sum([ancestry.count(p) for p in [24, -24]])
l["n_t"] = sum([ancestry.count(p) for p in [6, -6]])
l["n_tau"] = sum([ancestry.count(p) for p in [15, -15]])
matched_lep = bestDRMatchInCollection(l, leptons_pt10)
if matched_lep:
l["lepGoodMatchIndex"] = matched_lep['index']
if isSingleLep:
l["matchesPromptGoodLepton"] = l["lepGoodMatchIndex"] in [
s.l1_index
]
elif isDiLep:
l["matchesPromptGoodLepton"] = l["lepGoodMatchIndex"] in [
s.l1_index, s.l2_index
]
else:
l["matchesPromptGoodLepton"] = 0
else:
l["lepGoodMatchIndex"] = -1
l["matchesPromptGoodLepton"] = 0
# if l["n_t"]>0 and l["n_W"]>0 and l["n_B"]==0 and l["n_D"]==0 and l["n_tau"]==0:
# print "t->W->l"
# elif l["n_t"]>0 and l["n_W"]==0 and l["n_B"]>0 and l["n_D"]==0 and l["n_tau"]==0:
# print "t->b->B->l"
# elif l["n_t"]>0 and l["n_W"]==0 and l["n_B"]>0 and l["n_D"]>0 and l["n_tau"]==0:
# print "t->b->B->D->l"
# elif l["n_t"]>0 and l["n_W"]>0 and l["n_B"]==0 and l["n_D"]==0 and l["n_tau"]>0 :
# print "t->W->tau->l"
# elif l["n_t"]>0 and l["n_W"]>0 and l["n_B"]==0 and l["n_D"]>0 and l["n_tau"]==0:
# print "t->W->c->D->l"
# elif l["n_t"]==0 and l["n_W"]==0 and l["n_B"]>0 and l["n_D"]>=0 and l["n_tau"]==0:
# print l['pdgId'], l['pt'], l['phi'], l['eta'], ",".join(pdgToName( gPart[x]['pdgId']) for x in genSearch.ancestry(l) )
# for p in genSearch.ancestry(l):
# print p, gPart[p]
# else:
# pass
# print l['pdgId'], l['pt'], l['phi'], l['eta'], ",".join(pdgToName(gPart[x]['pdgId']) for x in genSearch.ancestry(l))
s.nGenLep = len(gLep)
for iLep, lep in enumerate(gLep):
for b in genLepVarNames:
getattr(s, "GenLep_" + b)[iLep] = lep[b]