def getMT2ll( event ):
l1 = ROOT.TLorentzVector()
l2 = ROOT.TLorentzVector()
event.lep_pt[event.nonZ1_l1_index_4l]
l1.SetPtEtaPhiM(event.lep_pt[event.nonZ1_l1_index_4l], event.lep_eta[event.nonZ1_l1_index_4l], event.lep_phi[event.nonZ1_l1_index_4l], 0 )
l2.SetPtEtaPhiM(event.lep_pt[event.nonZ1_l2_index_4l], event.lep_eta[event.nonZ1_l2_index_4l], event.lep_phi[event.nonZ1_l2_index_4l], 0 )
mt2Calc.setLeptons(l1.Pt(), l1.Eta(), l1.Phi(), l2.Pt(), l2.Eta(), l2.Phi())
#print event.nonZ1_l1_index_4l, event.nonZ1_l2_index_4l, event.Z1_l1_index_4l, event.Z1_l2_index_4l
met = ROOT.TLorentzVector()
met.SetPtEtaPhiM( event.met_pt, 0, event.met_phi, 0)
genZ = ROOT.TLorentzVector()
genZ.SetPtEtaPhiM( event.genZ_pt, event.genZ_eta, event.genZ_phi, 0)
response = .99
Z = ROOT.TLorentzVector()
Z.SetPtEtaPhiM( event.Z1_pt_4l*response, event.Z1_eta_4l, event.Z1_phi_4l, 0) #mass missing?!
# smear the Z with some resolution. 20% resolution assumed for now.
#if Z.Pt()>200:
# rd = rand.Gaus(0,0.25)
#elif Z.Pt()>100:
# rd = rand.Gaus(0,0.25)
#else:
# rd = rand.Gaus(0,0.25)
#rd = rand.Gaus(0,0.20)
#Z_pt = (1+rd)*Z.Pt()*0.95
rd = rand.Gaus(0,20)
Z_pt = (rd+Z.Pt())*0.95
#Z_px = Z_pt*math.cos(Z.Phi())
#Z_py = Z_pt*math.sin(Z.Phi())
smearZ = ROOT.TLorentzVector()
smearZ.SetPtEtaPhiM( Z_pt, event.Z1_eta_4l, event.Z1_phi_4l, 0)
#print met.Phi(), Z.Phi()
newMet = Z + met
newMetSmear = met + smearZ
mt2Calc.setMet(newMet.Pt(), newMet.Phi())
dl_mt2ll_Z = mt2Calc.mt2ll()
mt2Calc.setMet(newMetSmear.Pt(), newMet.Phi()) # phi doesn't change anyway
dl_mt2ll_Z_smear = mt2Calc.mt2ll()
event.newMet_pt = newMet.Pt()
event.newMet_phi = newMet.Phi()
event.newMetSmeared_pt = newMetSmear.Pt()
event.dl_mt2ll_Z = dl_mt2ll_Z
event.dl_mt2ll_Z_smear = dl_mt2ll_Z_smear
event.dPhi_nonZ_l1_newMet = deltaPhi(newMet.Phi(), l1.Phi())
event.dPhi_nonZ_l2_newMet = deltaPhi(newMet.Phi(), l2.Phi())
return 1#newMet.Pt(), newMetSmear.Pt(), dl_mt2ll_Z, dl_mt2ll_Z_smear, newMet.Phi()
def getBJetDR( event, sample ):
jets = getJets(event, jetVars=jetVars, jetColl="jet")
trueBJets = [ j for j in jets if abs(j['hadronFlavour'])==5 ]
bjets = [ j for j in jets if j['btagDeepCSV']>0.6324 and j['pt']>30 and abs(j['eta'])<2.4 ]
minDR = -1
mindPhi = -1
if len(bjets)>1:
minDR = 999.
mindPhi = 4.
comb = itertools.combinations(bjets, 2)
for c in comb:
dR = deltaR(c[0], c[1])
dPhi = deltaPhi(c[0]['phi'], c[1]['phi'])
dEta = abs(c[0]['eta'] - c[1]['eta'])
if dR < minDR:
minDR = dR
mindPhi = dPhi
mindEta = dEta
l1 = ROOT.TLorentzVector()
l2 = ROOT.TLorentzVector()
l1.SetPtEtaPhiM( c[0]['pt'], c[0]['eta'], c[0]['phi'], 0)
l2.SetPtEtaPhiM( c[1]['pt'], c[1]['eta'], c[1]['phi'], 0)
M = (l1 + l2).M()
event.bjet_dR = minDR
event.bjet_dPhi = mindPhi
event.bjet_invMass = M
event.bjet_dEta = mindEta
event.b1_pt = l1.Pt()
event.b1_phi = l1.Phi()
event.b1_eta = l1.Eta()
event.b2_pt = l2.Pt()
event.b2_phi = l2.Phi()
event.b2_eta = l2.Eta()
return
def getDPhiZLep(event, sample):
event.dPhiZLep = deltaPhi(event.lep_phi[event.nonZ_l1_index], event.Z_phi)
def filler(event):
# shortcut
r = reader.event
if isMC: gPart = getGenPartsAll(r)
# weight
if isMC:
event.weight = lumiScaleFactor * r.genWeight if lumiScaleFactor is not None else 1
elif isData:
event.weight = 1
else:
raise NotImplementedError("isMC %r isData %r " % (isMC, isData))
# lumi lists and vetos
if isData:
#event.vetoPassed = vetoList.passesVeto(r.run, r.lumi, r.evt)
event.jsonPassed = lumiList.contains(r.run, r.lumi)
# store decision to use after filler has been executed
event.jsonPassed_ = event.jsonPassed
if isMC:
event.reweightPU36fb = nTrueInt36fb_puRW(r.nTrueInt)
event.reweightPU36fbDown = nTrueInt36fb_puRWDown(r.nTrueInt)
event.reweightPU36fbUp = nTrueInt36fb_puRWUp(r.nTrueInt)
# top pt reweighting
if isMC and options.doTopPtReweighting:
event.reweightTopPt = topPtReweightingFunc(getTopPtsForReweighting(
r)) / topScaleF if doTopPtReweighting else 1.
# Leptons: Reading LepGood and LepOther and fill new LepGood collection in the output tree
mu_selector = muonSelector(isoVar="relIso04",
barrelIso=0.25,
endcapIso=0.25,
absEtaCut=2.4,
dxy=0.05,
dz=0.1)
ele_selector = eleSelector(isoVar="relIso03",
barrelIso=0.1,
endcapIso=0.1,
absEtaCut=2.5,
dxy=0.05,
dz=0.1,
eleId="M",
noMissingHits=False)
leptons = getGoodAndOtherLeptons(r,
ptCut=10,
mu_selector=mu_selector,
ele_selector=ele_selector)
leptons.sort(key=lambda p: -p['pt'])
# Store leptons
event.nlep = len(leptons)
for iLep, lep in enumerate(leptons):
lep['index'] = iLep # Index wrt to the output collection!
lep['relIso'] = lep["relIso04"] if abs(
lep['pdgId']) == 13 else lep["relIso03"]
for b in lepton_vars_store:
getattr(event, "lep_" + b)[iLep] = lep[b]
# Storing lepton counters
event.nGoodMuons = len(filter(lambda l: abs(l['pdgId']) == 13, leptons))
event.nGoodElectrons = len(filter(lambda l: abs(l['pdgId']) == 11,
leptons))
event.nGoodLeptons = len(leptons)
# Lepton convinience
if options.leptonConvinience:
for i in range(min(4, len(leptons))):
for var in lep_convinience_vars:
setattr(event, "l{n}_{var}".format(n=i + 1, var=var),
leptons[i][var])
# Identify best Z
(event.Z_mass, event.Z_l1_index,
event.Z_l2_index) = closestOSDLMassToMZ(leptons)
nonZ_lepton_indices = [
i for i in range(len(leptons))
if i not in [event.Z_l1_index, event.Z_l2_index]
]
event.nonZ_l1_index = nonZ_lepton_indices[0] if len(
nonZ_lepton_indices) > 0 else -1
event.nonZ_l2_index = nonZ_lepton_indices[1] if len(
nonZ_lepton_indices) > 1 else -1
# Store Z information
if event.Z_mass >= 0:
if leptons[event.Z_l1_index]['pdgId'] * leptons[
event.Z_l2_index]['pdgId'] > 0 or abs(
leptons[event.Z_l1_index]['pdgId']) != abs(
leptons[event.Z_l2_index]['pdgId']):
raise RuntimeError("not a Z! Should never happen")
Z_l1 = ROOT.TLorentzVector()
Z_l1.SetPtEtaPhiM(leptons[event.Z_l1_index]['pt'],
leptons[event.Z_l1_index]['eta'],
leptons[event.Z_l1_index]['phi'], 0)
Z_l2 = ROOT.TLorentzVector()
Z_l2.SetPtEtaPhiM(leptons[event.Z_l2_index]['pt'],
leptons[event.Z_l2_index]['eta'],
leptons[event.Z_l2_index]['phi'], 0)
Z = Z_l1 + Z_l2
event.Z_pt = Z.Pt()
event.Z_eta = Z.Eta()
event.Z_phi = Z.Phi()
event.Z_lldPhi = deltaPhi(leptons[event.Z_l1_index]['phi'],
leptons[event.Z_l2_index]['phi'])
event.Z_lldR = deltaR(leptons[event.Z_l1_index],
leptons[event.Z_l2_index])
## get the information for cos(theta*)
# get the Z and lepton (negative charge) vectors
lm_index = event.Z_l1_index if event.lep_pdgId[
event.Z_l1_index] > 0 else event.Z_l2_index
event.cosThetaStar = cosThetaStar(event.Z_mass, event.Z_pt,
event.Z_eta, event.Z_phi,
event.lep_pt[lm_index],
event.lep_eta[lm_index],
event.lep_phi[lm_index])
# Jets and lepton jet cross-cleaning
allJets = getAllJets(r,
leptons,
ptCut=0,
jetVars=jetVarNames,
absEtaCut=99,
jetCollections=["Jet", "DiscJet"]) #JetId is required
selected_jets, other_jets = [], []
for j in allJets:
if isAnalysisJet(j, ptCut=30, absEtaCut=2.4):
selected_jets.append(j)
else:
other_jets.append(j)
# Don't change analysis jets even if we keep all jets, hence, apply abs eta cut
bJets = filter(
lambda j: isBJet(j, tagger='CSVv2') and abs(j['eta']) <= 2.4,
selected_jets)
bJetsDeepCSV = filter(
lambda j: isBJet(j, tagger='DeepCSV') and abs(j['eta']) <= 2.4,
selected_jets)
nonBJets = filter(
lambda j: not (isBJet(j, tagger='CSVv2') and abs(j['eta']) <= 2.4),
selected_jets)
nonBJetsDeepCSV = filter(
lambda j: not (isBJet(j, tagger='DeepCSV') and abs(j['eta']) <= 2.4),
selected_jets)
# Store jets
event.nJetSelected = len(selected_jets)
jets_stored = allJets
event.njet = len(jets_stored)
for iJet, jet in enumerate(jets_stored):
for b in jetVarNames:
getattr(event, "jet_" + b)[iJet] = jet[b]
# ETmiss
event.met_pt = r.met_pt
event.met_phi = r.met_phi
# Analysis observables
event.ht = sum([j['pt'] for j in selected_jets])
event.metSig = event.met_pt / sqrt(event.ht) if event.ht > 0 else float(
'nan')
event.nBTag = len(bJets)
event.nBTagDeepCSV = len(bJetsDeepCSV)
# Systematics
jets_sys = {}
bjets_sys = {}
nonBjets_sys = {}
metVariants = [''] # default
if options.keepPhotons:
# Keep photons and estimate met including (leading pt) photon
photons = getGoodPhotons(r, ptCut=20, idLevel="loose", isData=isData)
event.nPhotonGood = len(photons)
if event.nPhotonGood > 0:
metVariants += [
'_photonEstimated'
] # do all met calculations also for the photonEstimated variant
event.photon_pt = photons[0]['pt']
event.photon_eta = photons[0]['eta']
event.photon_phi = photons[0]['phi']
event.photon_idCutBased = photons[0]['idCutBased']
if isMC:
genPhoton = getGenPhoton(gPart)
event.photon_genPt = genPhoton[
'pt'] if genPhoton is not None else float('nan')
event.photon_genEta = genPhoton[
'eta'] if genPhoton is not None else float('nan')
event.met_pt_photonEstimated, event.met_phi_photonEstimated = getMetPhotonEstimated(
r.met_pt, r.met_phi, photons[0])
event.metSig_photonEstimated = event.met_pt_photonEstimated / sqrt(
event.ht) if event.ht > 0 else float('nan')
event.photonJetdR = min(
deltaR(photons[0], j)
for j in selected_jets) if len(selected_jets) > 0 else 999
event.photonLepdR = min(
deltaR(photons[0], l)
for l in leptons) if len(leptons) > 0 else 999
if isMC:
event.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: isAnalysisJet(
j, ptCut=30, absEtaCut=2.4, ptVar='pt_' + var), allJets)
bjets_sys[var] = filter(
lambda j: isBJet(j) and abs(j['eta']) < 2.4, jets_sys[var])
nonBjets_sys[var] = filter(
lambda j: not (isBJet(j) and abs(j['eta']) < 2.4),
jets_sys[var])
setattr(event, "nJetSelected_" + var, len(jets_sys[var]))
setattr(event, "ht_" + var,
sum([j['pt_' + var] for j in jets_sys[var]]))
setattr(event, "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 or 'JECV' in var:
(met_corr_pt, met_corr_phi) = getMetJetCorrected(
getattr(event, "met_pt" + i),
getattr(event, "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(event, "met_pt" + i + "_" + var, met_corr_pt)
setattr(event, "met_phi" + i + "_" + var, met_corr_phi)
ht = getattr(event, "ht_" +
var) if 'Unclustered' not in var else event.ht
setattr(
event, "metSig" + i + "_" + var,
getattr(event, "met_pt" + i + "_" + var) /
sqrt(ht) if ht > 0 else float('nan'))
if addSystematicVariations:
# B tagging weights method 1a, first for CSVv2
for j in selected_jets:
btagEff_CSVv2.addBTagEffToJet(j)
#print "CSVv2", selected_jets[0]['beff']['SF'], selected_jets[0]['pt']
for var in btagEff_CSVv2.btagWeightNames:
if var != 'MC':
setattr(
event, 'reweightBTagCSVv2_' + var,
btagEff_CSVv2.getBTagSF_1a(
var, bJets,
filter(lambda j: abs(j['eta']) < 2.4, nonBJets)))
# B tagging weights method 1a, now for DeepCSV
for j in selected_jets:
btagEff_DeepCSV.addBTagEffToJet(j)
#print "DeepCSV", selected_jets[0]['beff']['SF'], selected_jets[0]['pt']
for var in btagEff_DeepCSV.btagWeightNames:
if var != 'MC':
setattr(
event, 'reweightBTagDeepCSV_' + var,
btagEff_DeepCSV.getBTagSF_1a(
var, bJetsDeepCSV,
filter(lambda j: abs(j['eta']) < 2.4,
nonBJetsDeepCSV)))
# gen information on extra leptons
if isMC and not options.skipGenMatching:
genSearch.init(gPart)
# Start with status 1 gen leptons
# gLep = filter( lambda p:abs(p['pdgId']) in [11, 13] and p['status']==1 and p['pt']>10 and abs(p['eta'])<2.5, gPart )
# ... no acceptance cuts
gLep = filter(
lambda p: abs(p['pdgId']) in [11, 13] and p['status'] == 1, 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) #FIXME -> fix all the gen / reco match indices!
if matched_lep:
l["lepGood2MatchIndex"] = matched_lep['index']
else:
l["lepGood2MatchIndex"] = -1
# store genleps
event.nGenLep = len(gLep)
for iLep, lep in enumerate(gLep):
for b in genLepVarNames:
getattr(event, "GenLep_" + b)[iLep] = lep[b]
# gen Z
genZs = filter(
lambda p: (abs(p['pdgId']) == 23 and genSearch.isLast(p)), gPart)
genZs.sort(key=lambda p: -p['pt'])
if len(genZs) > 0:
genZ = genZs[0]
event.genZ_pt = genZ['pt']
event.genZ_mass = genZ['mass']
event.genZ_eta = genZ['eta']
event.genZ_phi = genZ['phi']
lep_m = filter(lambda p: p['pdgId'] in [11, 13, 15],
genSearch.daughters(genZ))
event.genZ_daughter_flavor = max(
[p['pdgId'] for p in genSearch.daughters(genZ)])
if len(lep_m) == 1:
event.genZ_cosThetaStar = cosThetaStar(
event.genZ_mass, event.genZ_pt, event.genZ_eta,
event.genZ_phi, lep_m[0]['pt'], lep_m[0]['eta'],
lep_m[0]['phi'])
Zs = [
p for p in gPart
if (abs(p['pdgId']) == 23 and abs(p['motherId']) != 23)
]
ZsFromB = [z for z in Zs
if (abs(z['motherId']) == 5)] #or abs(z['motherId'])==21 )]
ZsFromQ = [z for z in Zs if (abs(z['motherId']) < 5)]
bsFromG = [b for b in bs if abs(b['motherId']) == 21]
if len(recoBJets) > 1:
minDR = 999.
comb = itertools.combinations(recoBJets, 2)
for c in comb:
#dR = deltaR(c[0], c[1])
dR = deltaPhi(c[0]['phi'], c[1]['phi'])
if dR < minDR: minDR = dR
minDR = 3.9 if minDR > 4 else minDR
h_DR_reco.Fill(minDR, r.weight)
if len(trueBJets) > 1:
minDR = 999.
comb = itertools.combinations(trueBJets, 2)
for c in comb:
#dR = deltaR(c[0], c[1])
dR = deltaPhi(c[0]['phi'], c[1]['phi'])
if dR < minDR: minDR = dR
minDR = 3.9 if minDR > 4 else minDR
h_DR_true.Fill(minDR, r.weight)
def getDPhiZLep(event, sample):
event.dPhiZLep = deltaPhi(event.lep_phi[event.nonZ1_l1_index_4l],
event.Z2_phi_4l)
def getDPhiZJet(event, sample):
event.dPhiZJet = deltaPhi(event.jets[0]['phi'], event.Z_phi) if len(
event.jets) > 0 and event.Z_mass > 0 else float('nan')
def getDeltaPhi(event, sample=None):
# 4l
# event.nonZl1_Z1_deltaPhi = deltaPhi(event.lep_phi[event.nonZ1_l1_index_4l], event.Z1_phi_4l)
# event.nonZl2_Z1_deltaPhi = deltaPhi(event.lep_phi[event.nonZ1_l2_index_4l], event.Z1_phi_4l)
# 3l
event.nonZl1_Z_deltaPhi = deltaPhi(event.lep_phi[event.nonZ_l1_index], event.Z_phi)
def makeDeltaPhi(event, sample):
leps = getLepsFromZ(event, sample)
if len(leps) == 2 and leps[0]['pdgId'] * leps[1]['pdgId'] < 0:
event.dPhi_ll = deltaPhi(leps[0]['phi'], leps[1]['phi'])
else:
event.dPhi_ll = -1
def getDeltaPhi(event, sample=None):
event.nonZl1_Z1_deltaPhi = deltaPhi(event.lep_phi[event.nonZ1_l1_index_4l],
event.Z1_phi_4l)
event.nonZl2_Z1_deltaPhi = deltaPhi(event.lep_phi[event.nonZ1_l2_index_4l],
event.Z1_phi_4l)
def getDeltaPhi(event, sample=None):
event.nonZl1_Z_deltaPhi = deltaPhi(event.lep_phi[event.nonZ_l1_index], event.Z_phi)
def filler(event):
if reader.position % 100 == 0:
logger.info("At event %i/%i", reader.position, reader.nEvents)
event.lumiweight1fb = lumiweight1fb
# read jets
jets = filter(lambda j: isGoodDelphesJet(j), reader.jets())
jets.sort(key=lambda p: -p['pt'])
addIndex(jets)
# make b jets
# for j in jets:
# print j['pt'], j['eta'], j['bTag'], j['bTagPhys'], j['bTagAlgo']
bJets = filter(lambda j: j['bTagPhys'] >= 4, jets)
nonBJets = filter(lambda j: not (j['bTagPhys'] < 4), jets)
bj0, bj1 = (bJets + nonBJets + [None, None])[:2]
fill_vector(event, "bj0", jet_write_varnames, bj0)
fill_vector(event, "bj1", jet_write_varnames, bj1)
event.nBTag = len(bJets)
# read leptons
allLeps = reader.muonsTight() + reader.electrons()
allLeps.sort(key=lambda p: -p['pt'])
leps = filter(isGoodDelphesLepton, allLeps)
# cross-cleaning of reco-objects
# leps = filter( lambda l: (min([999]+[deltaR2(l, j) for j in jets if j['pt']>30]) > 0.3**2 ), leps )
# give index to leptons
addIndex(leps)
# Store
fill_vector_collection(event, "lep", lep_varnames, leps)
fill_vector_collection(event, "jet", jet_varnames, jets)
event.nMuons = len(filter(lambda l: abs(l['pdgId']) == 13, leps))
event.nElectrons = len(filter(lambda l: abs(l['pdgId']) == 11, leps))
# MET
met = reader.met()[0]
event.met_pt = met['pt']
event.met_phi = met['phi']
# search for Z in leptons
(event.Z_mass, Z_l1_index, Z_l2_index) = closestOSDLMassToMZ(leps)
nonZ_indices = [
i for i in range(len(leps)) if i not in [Z_l1_index, Z_l2_index]
]
event.Z_l1_index = leps[Z_l1_index]['index'] if Z_l1_index >= 0 else -1
event.Z_l2_index = leps[Z_l2_index]['index'] if Z_l2_index >= 0 else -1
event.nonZ_l1_index = leps[
nonZ_indices[0]]['index'] if len(nonZ_indices) > 0 else -1
event.nonZ_l2_index = leps[
nonZ_indices[1]]['index'] if len(nonZ_indices) > 1 else -1
# Store Z information
if event.Z_mass >= 0:
if leps[event.Z_l1_index]['pdgId'] * leps[
event.Z_l2_index]['pdgId'] > 0 or abs(
leps[event.Z_l1_index]['pdgId']) != abs(
leps[event.Z_l2_index]['pdgId']):
raise RuntimeError("not a Z! Should not happen")
Z_l1 = ROOT.TLorentzVector()
Z_l1.SetPtEtaPhiM(leps[event.Z_l1_index]['pt'],
leps[event.Z_l1_index]['eta'],
leps[event.Z_l1_index]['phi'], 0)
Z_l2 = ROOT.TLorentzVector()
Z_l2.SetPtEtaPhiM(leps[event.Z_l2_index]['pt'],
leps[event.Z_l2_index]['eta'],
leps[event.Z_l2_index]['phi'], 0)
Z = Z_l1 + Z_l2
event.Z_pt = Z.Pt()
event.Z_eta = Z.Eta()
event.Z_phi = Z.Phi()
event.Z_lldPhi = deltaPhi(leps[event.Z_l1_index]['phi'],
leps[event.Z_l2_index]['phi'])
event.Z_lldR = deltaR(leps[event.Z_l1_index], leps[event.Z_l2_index])
lm_index = event.Z_l1_index if leps[
event.Z_l1_index]['pdgId'] > 0 else event.Z_l2_index
event.Z_cosThetaStar = cosThetaStar(event.Z_mass, event.Z_pt,
event.Z_eta, event.Z_phi,
leps[lm_index]['pt'],
leps[lm_index]['eta'],
leps[lm_index]['phi'])
def ttG2lPlots(index):
''' return plot list for ttG 2l plots
'''
plots = []
plots.append(
Plot(
name='yield',
texX='yield',
texY='Number of Events',
attribute=lambda event, sample: 0.5 + index,
binning=[3, 0, 3],
))
plots.append(
Plot(
name='nVtxs',
texX='vertex multiplicity',
texY='Number of Events',
attribute=TreeVariable.fromString("nVert/I"),
binning=[50, 0, 50],
))
plots.append(
Plot(
name='MET',
texX='E_{T}^{miss} (GeV)',
texY='Number of Events / 20 GeV',
attribute=TreeVariable.fromString("met_pt/F"),
binning=[400 / 20, 0, 400],
))
plots.append(
Plot(
name='MET_phi',
texX='#phi(E_{T}^{miss})',
texY='Number of Events / 20 GeV',
attribute=TreeVariable.fromString("met_phi/F"),
binning=[10, -pi, pi],
))
# plots.append(Plot(
# name = 'Gamma_pt', texX = 'p_{T}(#gamma) (GeV)', texY = 'Number of Events / 20 GeV',
# attribute = TreeVariable.fromString( "gamma_pt/F" ),
# binning=[20,0,400],
# ))
# plots.append(Plot(
# name = 'Gamma_pt_coarse', texX = 'p_{T}(#gamma) (GeV)', texY = 'Number of Events / 50 GeV',
# attribute = TreeVariable.fromString( "gamma_pt/F" ),
# binning=[16,0,800],
# ))
# plots.append(Plot(
# name = 'Gamma_pt_superCoarse', texX = 'p_{T}(#gamma) (GeV)', texY = 'Number of Events',
# attribute = TreeVariable.fromString( "gamma_pt/F" ),
# binning=[3,0,600],
# ))
# plots.append(Plot(
# name = 'Gamma_pt_analysis', texX = 'p_{T}(#gamma) (GeV)', texY = 'Number of Events / 100 GeV',
# attribute = TreeVariable.fromString( "gamma_pt/F" ),
# binning=[4,0,400],
# ))
# plots.append(Plot(
# name = 'l0gammaDPhi',
# texX = '#Delta#Phi(l_{0},#gamma)', texY = 'Number of Events',
# attribute = lambda event, sample: deltaPhi(event.selectedLeps[0]['phi'], event.gammas[0]['phi']),
# binning=[20,0,pi],
# ))
# plots.append(Plot(
# name = 'l0gammaDR',
# texX = '#Delta R(l_{0},#gamma)', texY = 'Number of Events',
# attribute = lambda event, sample: deltaR(event.selectedLeps[0], event.gammas[0]),
# binning=[20,0.3,3],
# ))
plots.append(
Plot(
name="M3",
texX='M_{3} (GeV)',
texY='Number of Events',
attribute=lambda event, sample: m3(event.selectedJets)[0]
if len(event.selectedJets) > 2 else float('nan'),
binning=[25, 0, 500],
))
plots.append(
Plot(
name="dRll",
texX='#Delta R(ll)',
texY='Number of Events',
attribute=lambda event, sample: deltaR(event.selectedJets[0], event
.selectedJets[1]),
binning=[20, 0.3, 3],
))
plots.append(
Plot(
name="dPhill",
texX='#Delta#phi(ll)',
texY='Number of Events',
attribute=lambda event, sample: deltaPhi(
event.selectedJets[0]['phi'], event.selectedJets[1]['phi']),
binning=[10, 0, pi],
))
plots.append(
Plot(
name="nForwardJet_Pt30_eta3",
texX='nJet p_{T}(j)>30 GeV',
texY='Number of Events',
attribute=lambda event, sample: len(event.jetForward30_eta3),
binning=[10, 0, 10],
))
plots.append(
Plot(
name="nForwardJet_Pt40_eta3",
texX='nJet p_{T}(j)>40 GeV',
texY='Number of Events',
attribute=lambda event, sample: len(event.jetForward40_eta3),
binning=[10, 0, 10],
))
plots.append(
Plot(
name="nForwardJet_Pt50_eta3",
texX='nJet p_{T}(j)>50 GeV',
texY='Number of Events',
attribute=lambda event, sample: len(event.jetForward50_eta3),
binning=[10, 0, 10],
))
plots.append(
Plot(
name="nForwardJet_Pt60_eta3",
texX='nJet p_{T}(j)>60 GeV',
texY='Number of Events',
attribute=lambda event, sample: len(event.jetForward60_eta3),
binning=[10, 0, 10],
))
plots.append(
Plot(
name="nForwardJet_Pt70_eta3",
texX='nJet p_{T}(j)>70 GeV',
texY='Number of Events',
attribute=lambda event, sample: len(event.jetForward70_eta3),
binning=[10, 0, 10],
))
plots.append(
Plot(
name="M_ll",
texX='M(ll) (GeV)',
texY='Number of Events / 10 GeV',
attribute=lambda event, sample: event.mll,
binning=[20, 0, 200],
))
plots.append(
Plot(
name="M_ll_coarse",
texX='M(ll) (GeV)',
texY='Number of Events / 20 GeV',
attribute=lambda event, sample: event.mll,
binning=[10, 0, 200],
))
# plots.append(Plot(
# name = "M_llgamma", texX = 'M(ll#gamma) (GeV)', texY = 'Number of Events / 10 GeV',
# attribute = lambda event, sample:event.mllgamma,
# binning=[20,0,200],
# ))
# plots.append(Plot(
# name = "M_llgamma_coarse", texX = 'M(ll#gamma) (GeV)', texY = 'Number of Events / 20 GeV',
# attribute = lambda event, sample:event.mllgamma,
# binning=[10,0,200],
# ))
plots.append(
Plot(
name="nJets",
texX='N_{jets}',
texY='Number of Events',
attribute=TreeVariable.fromString("nJetSelected/I"), #nJetSelected
binning=[8, -0.5, 7.5],
))
plots.append(
Plot(
name="nBJets",
texX='N_{b-tag}',
texY='Number of Events',
attribute=TreeVariable.fromString("nBTag/I"), #nJetSelected
binning=[4, -0.5, 3.5],
))
plots.append(
Plot(
name="nLepLoose",
texX='N_{l, loose}',
texY='Number of Events',
attribute=lambda event, sample: len(event.lepLoose),
binning=[3, -0.5, 2.5],
))
plots.append(
Plot(
name="nLep",
texX='N_{lep}',
texY='Number of Events',
attribute=lambda event, sample: len(event.selectedLeps),
binning=[3, -0.5, 2.5],
))
plots.append(
Plot(
texX='p_{T}(leading l) (GeV)',
texY='Number of Events / 20 GeV',
name='lep1_pt',
attribute=lambda event, sample: event.selectedLeps[0]['pt'],
binning=[400 / 20, 0, 400],
))
plots.append(
Plot(
texX='p_{T}(subleading l) (GeV)',
texY='Number of Events / 10 GeV',
name='lep2_pt',
attribute=lambda event, sample: event.selectedLeps[1]['pt'],
binning=[200 / 10, 0, 200],
))
plots.append(
Plot(
texX='p_{T}(leading jet) (GeV)',
texY='Number of Events / 30 GeV',
name='jet1_pt',
attribute=lambda event, sample: event.selectedJets[0]['pt'],
binning=[600 / 30, 0, 600],
))
plots.append(
Plot(
texX='p_{T}(2nd leading jet) (GeV)',
texY='Number of Events / 30 GeV',
name='jet2_pt',
attribute=lambda event, sample: event.selectedJets[1]['pt'],
binning=[600 / 30, 0, 600],
))
plots.append(
Plot(
texX='p_{T}(leading b-jet cand) (GeV)',
texY='Number of Events / 20 GeV',
name='bjet1_pt',
attribute=lambda event, sample: event.jets_sortbtag[0]['pt'],
binning=[20, 0, 400],
))
plots.append(
Plot(
texX='p_{T}(2nd leading b-jet cand) (GeV)',
texY='Number of Events / 20 GeV',
name='bjet2_pt',
attribute=lambda event, sample: event.jets_sortbtag[1]['pt'],
binning=[20, 0, 400],
))
plots.append(
Plot(
name='deltaPhi_bb',
texX='#Delta#phi(bb)',
texY='Number of Events',
attribute=lambda event, sample: deltaPhi(
event.jets_sortbtag[0]['phi'], event.jets_sortbtag[1]['phi']),
binning=[20, 0, pi],
))
plots.append(
Plot(
name='deltaR_bb',
texX='#DeltaR(bb)',
texY='Number of Events',
attribute=lambda event, sample: deltaR(event.jets_sortbtag[0],
event.jets_sortbtag[1]),
binning=[20, 0, 6],
))
return plots
def getW(event, sample):
# get the W boson candidate
event.W = {'vec2D': event.selectedLeps[0]['vec2D'] + event.MET['vec2D']}
event.W['pt'] = event.W['vec2D'].Mod()
event.W['phi'] = acos(event.W['vec2D'].X() / event.W['pt'])
event.deltaPhi_Wl = deltaPhi(event.W['phi'], event.selectedLeps[0]['phi'])
def getDPhiZJet(event, sample):
event.dPhiZJet = deltaPhi(
event.jet_phi[0],
event.Z_phi) if event.njet > 0 and event.Z_mass > 0 else float(
'nan') #nJetSelected
def reconstructLeptonicTop(event, sample):
#print
#print "Next event"
lepton = ROOT.TLorentzVector()
met = ROOT.TLorentzVector()
n1 = ROOT.TLorentzVector()
n2 = ROOT.TLorentzVector()
b1 = ROOT.TLorentzVector()
b2 = ROOT.TLorentzVector()
MW = 80.385
Mt = 172.5
lepton.SetPtEtaPhiM(event.lep_pt[event.nonZ_l1_index],
event.lep_eta[event.nonZ_l1_index],
event.lep_phi[event.nonZ_l1_index], 0)
met.SetPtEtaPhiM(event.met_pt, 0, event.met_phi, 0)
# get the b from the top decay, assume back-to-back of the tops
t1 = lepton + met + b1
t2 = lepton + met + b2
if t2.Pt() > t1.Pt(): b1, b2 = b2, b1
a = lepton.Pz() * (2 * (lepton.Px() * met.Px() + lepton.Py() * met.Py()) -
lepton.M()**2 + MW**2)
arg = lepton.E()**2 * ((2 *
(lepton.Px() * met.Px() + lepton.Py() * met.Py()) -
lepton.M()**2 + MW**2)**2 - 4 * met.Pt()**2 *
(lepton.E()**2 - lepton.Pz()**2))
b = sqrt(
arg
) if arg > 0 else 0. # need this for events with MET mismeasurements
c = 2 * (lepton.E()**2 - lepton.Pz()**2)
MET_z_1 = (a + b) / c
MET_z_2 = (a - b) / c
E_n1 = sqrt(met.Px()**2 + met.Py()**2 + MET_z_1**2)
E_n2 = sqrt(met.Px()**2 + met.Py()**2 + MET_z_2**2)
n1.SetPxPyPzE(met.Px(), met.Py(), MET_z_1, E_n1)
n2.SetPxPyPzE(met.Px(), met.Py(), MET_z_2, E_n2)
W1 = lepton + n1
W2 = lepton + n2
t1 = lepton + n1 + b1
t2 = lepton + n2 + b1
# get top candidate with mass closer to Mt
if abs(t2.M() - Mt) < abs(t1.M() - Mt): t1, t2 = t2, t1
#print t1.M(), t2.M()
event.mt_1 = t1.Mt()
event.mt_2 = t2.Mt()
event.top1_mass = t1.M()
event.top1_pt = t1.Pt()
event.top1_phi = t1.Phi()
event.top2_mass = t2.M()
event.top2_pt = t2.Pt()
event.top2_phi = t2.Phi()
event.b1_pt = b1.Pt()
event.b1_phi = b1.Phi()
event.b2_pt = b2.Pt()
event.b2_phi = b2.Phi()
event.deltaPhi_tl = acos(
(t1.Px() * lepton.Px() + t1.Py() * lepton.Py()) / sqrt(
(t1.Px()**2 + t1.Py()**2) * (lepton.Px()**2 + lepton.Py()**2)))
lepton.Boost(-t1.BoostVector())
event.deltaPhi_tl_topRF = deltaPhi(lepton.Phi(), t1.Phi())
event.deltaR_tl_topRF = deltaR({
'eta': lepton.Eta(),
'phi': lepton.Phi()
}, {
'eta': t1.Eta(),
'phi': t1.Phi()
})
event.deltaEta_tl_topRF = abs(lepton.Eta() - t1.Eta())
dilepS.setSelectionString([dilepSelection])
dilepS.texName = texNameDilepTTZ
reader2 = dilepS.treeReader( variables = variables )
reader2.start()
while reader2.run():
r = reader2.event
weight = r.weight * r.reweightLeptonSF_tight_4l * r.reweightTrigger_tight_4l
met_pt = r.met_pt
met_phi = r.met_phi
l1_phi = r.lep_phi[r.nonZ1_l1_index_4l]
l2_phi = r.lep_phi[r.nonZ1_l2_index_4l]
dilep_dPhi_metL1.Fill(deltaPhi(met_phi, l1_phi), weight*150)
dilep_dPhi_metL2.Fill(deltaPhi(met_phi, l2_phi), weight*150)
dilep_mt2ll.style = styles.lineStyle( ROOT.kRed+1, width=2, errors=True )
quadlep_mt2ll.style = styles.lineStyle( ROOT.kGreen+1, width=2, errors=True )
dilep_mt2ll.legendText = texNameDilepTTZ
quadlep_mt2ll.legendText = texNameQuadlepTTZ
dilep_met.style = styles.lineStyle( ROOT.kRed+1, width=2, errors=True )
quadlep_met.style = styles.lineStyle( ROOT.kGreen+1, width=2, errors=True )
quadlep_metOrig.style = styles.lineStyle( ROOT.kBlue+1, width=2, errors=True )
dilep_met.legendText = texNameDilepTTZ
def extra_observables(event, sample):
G_2D = ROOT.TVector2(event.photon_pt * cos(event.photon_phi),
event.photon_pt * sin(event.photon_phi))
n_G_2D = ROOT.TVector2(cos(event.photon_phi), sin(event.photon_phi))
#G_3D = ROOT.TVector2( event.photon_pt*cos(event.photon_phi), event.photon_pt*sin(event.photon_phi) )
#n_G_3D = ROOT.TVector2( cos(event.photon_phi), sin(event.photon_phi) )
# me_x,y
met_2D = ROOT.TVector2(event.met_pt * cos(event.met_phi),
event.met_pt * sin(event.met_phi))
event.deltaPhi_ll = deltaPhi(event.lep_phi[0], event.lep_phi[1])
## get jets
#jetVars = ['eta','pt','phi','btagCSV','id']
#jets = filter( isAnalysisJet, [getObjDict(event, 'jet_', jetVars, i) for i in range(int(getVarValue(event, 'njet')))] )
#jets.sort( key = lambda l:-l['pt'] )
#bJets = filter(lambda j: isBJet(j, tagger = 'CSVv2') and abs(j['eta'])<=2.4, jets )
#nonBJets = filter(lambda j: not ( isBJet(j, tagger = 'CSVv2') and abs(j['eta'])<=2.4 ), jets)
## take highest-pT b-jet, supplement by non-bjets if there are less than two
#bj0, bj1 = (bJets+nonBJets)[:2]
#bj0_3D = ROOT.TVector3( bj0['pt']*cos(bj0['phi']), bj0['pt']*sin(bj0['phi']), bj0['pt']*sinh(bj0['eta']) )
#bj1_3D = ROOT.TVector3( bj1['pt']*cos(bj1['phi']), bj1['pt']*sin(bj1['phi']), bj1['pt']*sinh(bj1['eta']) )
#bj0_2D = ROOT.TVector2( bj0_3D[0], bj0_3D[1] )
#bj1_2D = ROOT.TVector2( bj1_3D[0], bj1_3D[1] )
## resolve pairing ambiguity of bjets to top candidates by maximising the
#if (bj0_2D + l3_2D + met_2D).Mod2() > ( bj1_2D + l3_2D + met_2D).Mod2():
# blep, bhad = bj0, bj1
# blep_2D, bhad_2D = bj0_2D, bj1_2D
# blep_3D, bhad_3D = bj0_3D, bj1_3D
#else:
# blep, bhad = bj1, bj0
# blep_2D, bhad_2D = bj1_2D, bj0_2D
# blep_3D, bhad_3D = bj1_3D, bj0_3D
#event.blep_pt = blep['pt']
## ST observables
#event.ST = l3_pt + event.met_pt # traditional (1l SUS)
#event.St = l3_pt + event.met_pt + event.blep_pt # scalar sum pt of the leptonic top candidate
## MT observables
#event.mT = sqrt( 2*event.met_pt*l3_pt*(1 - cos(event.met_phi - l3_phi))) # transverse mass of leptonic W candidate
#event.mt = sqrt( 2*event.met_pt*l3_pt*(1 - cos(event.met_phi - l3_phi)) \
# + 2*blep['pt']*l3_pt*(cosh(blep['eta'] - l3_eta)-cos(blep['phi'] - l3_phi))
# + 2*event.met_pt*blep['pt']*(1-cos(event.met_phi - blep['phi'])) ) # transverse mass of the leptonic top
## variables with 2D vectors
#for fname, f in [\
# #[ 'dPhiZ', lambda vec: ROOT.TVector2.DeltaPhi( vec, Z_2D )],
# [ 'absDPhiZ', lambda vec: abs(ROOT.TVector2.DeltaPhi( vec, Z_2D ))],
# [ 'projZ', lambda vec: vec* n_Z_2D ],
# #[ 'dotZ', lambda vec: vec.Dot( Z_2D ) )],
# [ 'projorthZ',lambda vec: vec*n_orth_Z_2D ],
# ]:
# for vname, v in [
# [ "met", met_2D],
# [ "met_blep", met_2D + blep_2D],
# [ "met_l3", met_2D + l3_2D],
# [ "l3", l3_2D],
# [ "blep", blep_2D],
# [ "l3_blep", l3_2D+blep_2D],
# [ "met_l3_blep", l3_2D+blep_2D+met_2D],
# ]:
# #varname = "%s_%s"%( fname, vname )
# setattr( event, "%s_%s"%( fname, vname ), f(v) )
## variables with 3D vectors
#for fname, f in [
# [ 'dRZ', lambda vec: ROOT.TVector3.DeltaR( vec, Z_3D )],
# ]:
# for vname, v in [
# [ "l3", l3_3D],
# [ "blep", blep_3D],
# [ "l3_blep", l3_3D+blep_3D],
# ]:
# #varname = "%s_%s"%( fname, vname )
# setattr( event, "%s_%s"%( fname, vname ), f(v) )
return
def extra_observables( event, sample ):
G = {'pt':event.photon_pt, 'eta':event.photon_eta, 'phi':event.photon_phi}
G_2D = ROOT.TVector2( event.photon_pt*cos(event.photon_phi), event.photon_pt*sin(event.photon_phi) )
n_G_2D = ROOT.TVector2( cos(event.photon_phi), sin(event.photon_phi) )
#G_3D = ROOT.TVector2( event.photon_pt*cos(event.photon_phi), event.photon_pt*sin(event.photon_phi) )
#n_G_3D = ROOT.TVector2( cos(event.photon_phi), sin(event.photon_phi) )
# me_x,y
met_2D = ROOT.TVector2( event.met_pt*cos(event.met_phi), event.met_pt*sin(event.met_phi) )
event.deltaPhi_G_MET = deltaPhi( G['phi'], event.met_phi )
event.deltaPhi_ll = deltaPhi(event.lep_phi[0], event.lep_phi[1])
event.deltaR_ll = deltaR({'phi':event.lep_phi[0], 'eta':event.lep_eta[0]}, {'phi':event.lep_phi[1], 'eta':event.lep_eta[1]})
## get jets
jetVars = ['eta','pt','phi','btagCSV','id']
jets = filter( isAnalysisJet, [getObjDict(event, 'jet_', jetVars, i) for i in range(int(getVarValue(event, 'njet')))] )
jets.sort( key = lambda l:-l['pt'] )
bJets = filter(lambda j: isBJet(j, tagger = 'CSVv2') and abs(j['eta'])<=2.4, jets )
nonBJets = filter(lambda j: not ( isBJet(j, tagger = 'CSVv2') and abs(j['eta'])<=2.4 ), jets)
# take highest-pT b-jet, supplement by non-bjets if there are less than two
bj1, bj2 = (bJets+nonBJets)[:2]
l_bj1 = ROOT.TLorentzVector()
l_bj1.SetPtEtaPhiM( bj1['pt'], bj1['eta'], bj1['phi'], 0 )
l_bj2 = ROOT.TLorentzVector()
l_bj2.SetPtEtaPhiM( bj2['pt'], bj2['eta'], bj2['phi'], 0 )
# leptons
l_lep1 = ROOT.TLorentzVector()
l_lep1.SetPtEtaPhiM( event.lep_pt[0], event.lep_eta[0], event.lep_phi[0], 0 )
lep1 = {'pt':event.lep_pt[0], 'eta':event.lep_eta[0], 'phi':event.lep_phi[0]}
l_lep2 = ROOT.TLorentzVector()
l_lep2.SetPtEtaPhiM( event.lep_pt[1], event.lep_eta[1], event.lep_phi[1], 0 )
lep2 = {'pt':event.lep_pt[1], 'eta':event.lep_eta[1], 'phi':event.lep_phi[1]}
# resolve pairing ambiguity
max1 = max([(l_lep1 + l_bj1).M(), (l_lep2 + l_bj2).M()])
max2 = max([(l_lep1 + l_bj2).M(), (l_lep2 + l_bj1).M()])
if max1<max2: #Choose pairing with smaller invariant mass
pass
else:
# switch
bj1, bj2 = bj2, bj1
l_bj1, l_bj2 = l_bj2, l_bj1
event.M_lb_max = max(max1, max2)
event.M_lb_min = min(max1, max2)
# G & leps
dPhi_G_l1 = deltaPhi( G['phi'], lep1['phi'] )
dPhi_G_l2 = deltaPhi( G['phi'], lep2['phi'] )
event.dPhi_G_l_max = max(dPhi_G_l1, dPhi_G_l2)
event.dPhi_G_l_min = min(dPhi_G_l1, dPhi_G_l2)
dR_G_l1 = deltaR( G, lep1 )
dR_G_l2 = deltaR( G, lep2 )
event.dR_G_l_max = max(dR_G_l1, dR_G_l2)
event.dR_G_l_min = min(dR_G_l1, dR_G_l2)
return
