s.weightPU = 0 s.weightPUUp = 0 s.weightPUDown = 0 nVetoEvents += 1 # print "Found %i:%i:%i in %s"%(r.run, r.lumi, r.evt, vetoList.filename) # else: print [r.run, r.lumi, r.evt], vetoList_.events[0] # print "Found run %i lumi %i in json file %s"%(r.run, r.lumi, sample.json) allJets = getGoodJets( r, ptCut=0, jetVars=jetVars if options.skipVariations else jetVars + [ 'mcPt', 'corr', 'corr_JECUp', 'corr_JECDown', 'hadronFlavour' ]) jets = filter(lambda j: jetId(j, ptCut=30, absEtaCut=2.4), allJets) s.nGoodJets = len(jets) s.ht = sum([j['pt'] for j in jets]) s.nBTags = len(filter(isBJet, jets)) if not options.skipVariations: for j in allJets: j['pt_JECUp'] = j['pt'] / j['corr'] * j['corr_JECUp'] j['pt_JECDown'] = j['pt'] / j['corr'] * j['corr_JECDown'] addJERScaling(j) jets_ = {} bJets_ = {} nonBJets_ = {} metShifts_ = {} for var in ['JECUp', 'JECDown', 'JER', 'JERUp', 'JERDown']: jets_[var] = filter(
if r.lumi not in outputLumiList[r.run]: outputLumiList[r.run].append(r.lumi) if vetoList_: if (r.run, r.lumi, r.evt) in vetoList_.events: # print "Veto %i:%i:%i "%(r.run, r.lumi, r.evt) s.weight=0 s.weightPU=0 s.weightPUUp=0 s.weightPUDown=0 nVetoEvents+=1 # print "Found %i:%i:%i in %s"%(r.run, r.lumi, r.evt, vetoList.filename) # else: print [r.run, r.lumi, r.evt], vetoList_.events[0] # print "Found run %i lumi %i in json file %s"%(r.run, r.lumi, sample.json) allJets = getGoodJets(r, ptCut=0, jetVars=jetVars if options.skipVariations else jetVars+['mcPt', 'corr','corr_JECUp','corr_JECDown','hadronFlavour']) jets = filter(lambda j:jetId(j, ptCut=30, absEtaCut=2.4), allJets) s.nGoodJets = len(jets) s.ht = sum([j['pt'] for j in jets]) s.nBTags = len(filter(isBJet, jets)) if not options.skipVariations: for j in allJets: j['pt_JECUp'] =j['pt']/j['corr']*j['corr_JECUp'] j['pt_JECDown'] =j['pt']/j['corr']*j['corr_JECDown'] addJERScaling(j) jets_ = {} bJets_ = {} nonBJets_ = {} metShifts_ = {} for var in ['JECUp', 'JECDown', 'JER', 'JERUp', 'JERDown']: jets_[var] = filter(lambda j:jetId(j, ptCut=30, absEtaCut=2.4, ptVar='pt_'+var), allJets)
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]