def filler( event ):
# shortcut
r = reader.event
event.isData = isData
event.year = options.year
event.lumi = lumi
if isMC:
# weight
event.weight = lumiScaleFactor*r.genWeight if lumiScaleFactor is not None else 0
#print event.weight, lumiScaleFactor*r.genWeight, sample.xSection, sample.normalization
# PU reweighting
event.reweightPU = nTrueInt_puRW ( r.Pileup_nTrueInt )
event.reweightPUDown = nTrueInt_puRWDown ( r.Pileup_nTrueInt )
event.reweightPUUp = nTrueInt_puRWUp ( r.Pileup_nTrueInt )
# GEN Particles
gPart = getParticles( r, collVars=readGenVarList, coll="GenPart" )
gJets = getParticles( r, collVars=readGenJetVarList, coll="GenJet" )
gPart.sort( key = lambda p: -p['pt'] )
gJets.sort( key = lambda p: -p['pt'] )
# Overlap removal flags for ttgamma/ttbar and Zgamma/DY
GenPhoton = filterGenPhotons( gPart, status='all' )
# OR ttgamma/tt
GenIsoPhotonTTG = filter( lambda g: isIsolatedPhoton( g, gPart, coneSize=0.1, ptCut=5, excludedPdgIds=[12,-12,14,-14,16,-16] ), GenPhoton )
GenIsoPhotonNoMesonTTG = filter( lambda g: not hasMesonMother( getParentIds( g, gPart ) ), GenIsoPhotonTTG )
# OR DY/ZG, WG/WJets
GenIsoPhoton = filter( lambda g: isIsolatedPhoton( g, gPart, coneSize=0.05, ptCut=5, excludedPdgIds=[12,-12,14,-14,16,-16] ), GenPhoton )
GenIsoPhotonNoMeson = filter( lambda g: not hasMesonMother( getParentIds( g, gPart ) ), GenIsoPhoton )
# OR singleT/tG
GenIsoPhotonNoMesonTG = filter( lambda g: not photonFromTopDecay( getParentIds( g, gPart ) ), GenIsoPhotonNoMeson )
# OR GJets/QCD
GenIsoPhotonGJets = filter( lambda g: isIsolatedPhoton( g, gPart, coneSize=0.4, ptCut=5, excludedPdgIds=[12,-12,14,-14,16,-16] ), GenPhoton )
GenIsoPhotonNoMesonGJets = filter( lambda g: not hasMesonMother( getParentIds( g, gPart ) ), GenIsoPhotonGJets )
event.isTTGamma = len( filter( lambda g: genPhotonSel_TTG_OR(g), GenIsoPhotonNoMesonTTG ) ) > 0
event.isZWGamma = len( filter( lambda g: genPhotonSel_ZG_OR(g), GenIsoPhotonNoMeson ) ) > 0
event.isTGamma = len( filter( lambda g: genPhotonSel_T_OR(g), GenIsoPhotonNoMesonTG ) ) > 0
event.isGJets = len( filter( lambda g: genPhotonSel_GJ_OR(g), GenIsoPhotonNoMesonGJets ) ) > 0
# new OR flag: Apply overlap removal directly in pp to better handle the plots
if options.flagTTGamma:
event.overlapRemoval = event.isTTGamma #good TTgamma event
elif options.flagTTBar:
event.overlapRemoval = not event.isTTGamma #good TTbar event
elif options.flagZWGamma:
event.overlapRemoval = event.isZWGamma #good Zgamma, Wgamma event
elif options.flagDYWJets:
event.overlapRemoval = not event.isZWGamma #good DY, WJets event
elif options.flagTGamma:
event.overlapRemoval = event.isTGamma #good TGamma event
elif options.flagSingleTopTch:
event.overlapRemoval = not event.isTGamma #good singleTop t-channel event
elif options.flagGJets:
event.overlapRemoval = event.isGJets #good gamma+jets event
elif options.flagQCD:
event.overlapRemoval = not event.isGJets #good QCD event
else:
event.overlapRemoval = 1 # all other events
event.jsonPassed = 1
elif isData:
event.isTTGamma = 1
event.isZWGamma = 1
event.isTGamma = 1
event.isGJets = 1
event.overlapRemoval = 1 # all other events
event.weight = 1.
event.reweightPU = 1
# lumi lists and vetos
event.jsonPassed = lumiList.contains( r.run, r.luminosityBlock )
# make data weight zero if JSON was not passed
if not event.jsonPassed: event.weight = 0
# store decision to use after filler has been executed
event.jsonPassed_ = event.jsonPassed
else:
raise NotImplementedError( "isMC %r isData %r " % (isMC, isData) )
# Photons
allPhotons = getParticles( r, readPhotonVarList, coll="Photon" )
allPhotons.sort( key = lambda g: -g['pt'] )
convertUnits( allPhotons )
for g in allPhotons:
g['photonCat'] = -1
g['photonCatMagic'] = -1
g['leptonMother'] = -1
g['mother'] = -1
# Photons
loosePhotons = list( filter( lambda g: g["pt"]>=10 and abs(g["eta"])<=2.5 and g["cutBased" if options.year == 2016 else "cutBasedBitmap"]>=1, allPhotons ) )
event.nPhotonLoose = len( loosePhotons )
mediumPhotons = list( filter( lambda g: g["pt"]>=10 and abs(g["eta"])<=2.5 and g["cutBased" if options.year == 2016 else "cutBasedBitmap"]>=2, allPhotons ) )
event.nPhotonMedium = len( mediumPhotons )
tightPhotons = list( filter( lambda g: g["pt"]>=10 and abs(g["eta"])<=2.5 and g["cutBased" if options.year == 2016 else "cutBasedBitmap"]>=3, allPhotons ) )
event.nPhotonTight = len( tightPhotons )
MVAPhotons = list( filter( lambda g: g["pt"]>=10 and abs(g["eta"])<=2.5 and g["mvaID_WP80"], allPhotons ) )
event.nPhotonMVA = len( MVAPhotons )
# Store analysis photons + default photons for a faster plot script
p0, p1 = ( loosePhotons + [None,None] )[:2]
fill_vector( event, "PhotonLoose0", writePhotonVarList, p0 )
p0, p1 = ( mediumPhotons + [None,None] )[:2]
fill_vector( event, "PhotonMedium0", writePhotonVarList, p0 )
p0, p1 = ( tightPhotons + [None,None] )[:2]
fill_vector( event, "PhotonTight0", writePhotonVarList, p0 )
p0, p1 = ( MVAPhotons + [None,None] )[:2]
fill_vector( event, "PhotonMVA0", writePhotonVarList, p0 )
# Leptons
allMuons = getParticles( r, readMuonVarList, coll="Muon" )
allMuons.sort( key = lambda l: -l['pt'] )
convertUnits( allMuons )
tightMuons = list( filter( lambda l: l["pt"]>=10 and abs(l["eta"])<=2.4 and l["tightId"], allMuons ) )
leadtightMuons = list( filter( lambda l: l["pt"]>=20, tightMuons ) )
event.nMuonTight = len(tightMuons)
event.nLeadMuonTight = len(leadtightMuons)
# Store analysis Muons + 2 default Muons for a faster plotscript
lt0, lt1 = ( tightMuons + [None,None] )[:2]
# Semi-leptonic analysis
fill_vector( event, "MuonTight0", writeMuonVarList, lt0 )
fill_vector( event, "MuonTight1", writeMuonVarList, lt1 )
# Jets
allJets = getParticles( r, collVars=readJetVarList, coll="Jet" )
for j in allJets:
j["isBJet"] = isBJet( j, tagger=tagger, year=options.year )
j["isGood"] = 0 #not used
j["clean"] = 0 #not used
# Loose jets w/o pt/eta requirement
goodJets = list( filter( lambda j: recoJetSel(j), allJets ) )
goodJets = deltaRCleaning( goodJets, tightMuons, dRCut=0.4 )
goodJets = deltaRCleaning( goodJets, mediumPhotons, dRCut=0.4 )
event.nJetGood = len(goodJets)
# Store analysis jets + 2 default jets for a faster plotscript
j0, j1 = ( goodJets + [None,None] )[:2]
fill_vector( event, "JetGood0", writeJetVarList, j0 )
fill_vector( event, "JetGood1", writeJetVarList, j1 )
# bJets
bJets = list( filter( lambda x: x["isBJet"], goodJets ) )
event.nBTagGood = len( bJets )
# Store bJets + 2 default bjets for a faster plot script
bj0, bj1 = ( list(bJets) + [None,None] )[:2]
fill_vector( event, "Bj0", writeBJetVarList, bj0 )
fill_vector( event, "Bj1", writeBJetVarList, bj1 )
event.ht = sum( [ j['pt'] for j in goodJets ] )
# variables w/ photons
if len(loosePhotons) > 0:
if goodJets:
event.minGLJetdR = min( deltaR( loosePhotons[0], j ) for j in goodJets )
if tightMuons:
event.minGLLepdR = min( deltaR( loosePhotons[0], l ) for l in tightMuons )
if len(tightMuons)>0:
event.l0gLDR = deltaR( loosePhotons[0], tightMuons[0] )
if len(tightMuons)>1:
event.l1gLDR = deltaR( loosePhotons[0], tightMuons[1] )
event.mllgL = ( get4DVec(tightMuons[0]) + get4DVec(tightMuons[1]) + get4DVec(loosePhotons[0]) ).M()
if len(mediumPhotons) > 0:
if goodJets:
event.minGMJetdR = min( deltaR( mediumPhotons[0], j ) for j in goodJets )
if tightMuons:
event.minGMLepdR = min( deltaR( mediumPhotons[0], l ) for l in tightMuons )
if len(tightMuons)>0:
event.l0gMDR = deltaR( mediumPhotons[0], tightMuons[0] )
if len(tightMuons)>1:
event.l1gMDR = deltaR( mediumPhotons[0], tightMuons[1] )
event.mllgM = ( get4DVec(tightMuons[0]) + get4DVec(tightMuons[1]) + get4DVec(mediumPhotons[0]) ).M()
if len(tightPhotons) > 0:
if goodJets:
event.minGTJetdR = min( deltaR( tightPhotons[0], j ) for j in goodJets )
if tightMuons:
event.minGTLepdR = min( deltaR( tightPhotons[0], l ) for l in tightMuons )
if len(tightMuons)>0:
event.l0gTDR = deltaR( tightPhotons[0], tightMuons[0] )
if len(tightMuons)>1:
event.l1gTDR = deltaR( tightPhotons[0], tightMuons[1] )
event.mllgT = ( get4DVec(tightMuons[0]) + get4DVec(tightMuons[1]) + get4DVec(tightPhotons[0]) ).M()
if len(MVAPhotons) > 0:
if goodJets:
event.minGMVAJetdR = min( deltaR( MVAPhotons[0], j ) for j in goodJets )
if tightMuons:
event.minGMVALepdR = min( deltaR( MVAPhotons[0], l ) for l in tightMuons )
if len(tightMuons)>0:
event.l0gMVADR = deltaR( MVAPhotons[0], tightMuons[0] )
if len(tightMuons)>1:
event.l1gMVADR = deltaR( MVAPhotons[0], tightMuons[1] )
event.mllgMVA = ( get4DVec(tightMuons[0]) + get4DVec(tightMuons[1]) + get4DVec(MVAPhotons[0]) ).M()
if len(tightMuons) > 1:
event.lldR = deltaR( tightMuons[0], tightMuons[1] )
event.lldPhi = deltaPhi( tightMuons[0]['phi'], tightMuons[1]['phi'] )
event.mll = ( get4DVec(tightMuons[0]) + get4DVec(tightMuons[1]) ).M()
if len(goodJets) > 0 and len(tightMuons) > 0:
event.leptonJetdR = min( deltaR( l, j ) for j in goodJets for l in tightMuons )
def filler(event):
event.run, event.lumi, event.evt = reader.evt
event.lumiweight1fb = lumiweight1fb
if reader.position % 100 == 0:
logger.info("At event %i/%i", reader.position, reader.nEvents)
if args.addReweights:
event.nrw = weightInfo.nid
lhe_weights = reader.products['lhe'].weights()
weights = []
param_points = []
for weight in lhe_weights:
# Store nominal weight (First position!)
if weight.id == 'rwgt_1': event.rw_nominal = weight.wgt
if not weight.id in weightInfo.id: continue
pos = weightInfo.data[weight.id]
event.rw_w[pos] = weight.wgt
weights.append(weight.wgt)
interpreted_weight = interpret_weight(weight.id)
for var in weightInfo.variables:
getattr(event, "rw_" + var)[pos] = interpreted_weight[var]
# weight data for interpolation
if not hyperPoly.initialized:
param_points.append(
tuple(interpreted_weight[var]
for var in weightInfo.variables))
# get list of values of ref point in specific order
ref_point_coordinates = [
weightInfo.ref_point_coordinates[var]
for var in weightInfo.variables
]
# Initialize with Reference Point
if not hyperPoly.initialized:
hyperPoly.initialize(param_points, ref_point_coordinates)
coeff = hyperPoly.get_parametrization(weights)
# = HyperPoly(weight_data, args.interpolationOrder)
event.np = hyperPoly.ndof
event.chi2_ndof = hyperPoly.chi2_ndof(coeff, weights)
#logger.debug( "chi2_ndof %f coeff %r", event.chi2_ndof, coeff )
if event.chi2_ndof > 10**-6:
logger.warning("chi2_ndof is large: %f", event.chi2_ndof)
for n in xrange(hyperPoly.ndof):
event.p_C[n] = coeff[n]
# lumi weight / w0
event.ref_lumiweight1fb = event.lumiweight1fb / coeff[0]
# All gen particles
gp = reader.products['gp']
# for searching
search = GenSearch(gp)
# find heavy objects before they decay
genTops = map(
lambda t: {var: getattr(t, var)()
for var in top_varnames},
filter(lambda p: abs(p.pdgId()) == 6 and search.isLast(p), gp))
genTops.sort(key=lambda p: -p['pt'])
fill_vector_collection(event, "genTop", top_varnames, genTops)
# generated Z's
genZs = filter(
lambda p: abs(p.pdgId()) == 23 and search.isLast(p) and abs(
p.daughter(0).pdgId()) in [11, 13], gp)
genZs.sort(key=lambda p: -p.pt())
if len(genZs) > 0:
genZ = genZs[0]
for var in boson_read_varnames:
setattr(event, "genZ_" + var, getattr(genZ, var)())
else:
genZ = None
event.signalZ = 0 #ttZ with Z from gluon or top
if genZ is not None:
if abs(search.ascend(genZ).mother(0).pdgId()) in [6, 21]:
event.signalZ = 1 #ttZ with Z from gluon or top
d1, d2 = genZ.daughter(0), genZ.daughter(1)
if d1.pdgId() > 0:
lm, lp = d1, d2
else:
lm, lp = d2, d1
event.genZ_daughterPdg = lm.pdgId()
event.genZ_cosThetaStar = cosThetaStar(genZ.mass(), genZ.pt(),
genZ.eta(), genZ.phi(), lm.pt(),
lm.eta(), lm.phi())
# generated W's
genWs = filter(lambda p: abs(p.pdgId()) == 24 and search.isLast(p), gp)
genWs.sort(key=lambda p: -p.pt())
# W can't have a top-mother - We're looking for the extra boson (there is always an extra boson)
genWs = filter(lambda p: abs(search.ascend(p).mother(0).pdgId()) != 6,
genWs)
if len(genWs) > 0:
genW = genWs[0]
for var in boson_read_varnames:
setattr(event, "genW_" + var, getattr(genW, var)())
else:
genW = None
if genW is not None:
d1, d2 = genW.daughter(0), genW.daughter(1)
if abs(d1.pdgId()) in [11, 13, 15]:
lep, neu = d1, d2
else:
lep, neu = d2, d1
event.genW_daughterPdg = lep.pdgId()
def printTopMothers():
genTopCheck = [(search.ascend(l), l, search.ancestry(search.ascend(l)))
for l in filter(lambda p: abs(p.pdgId()) == 6, gp)]
print genTopCheck
genTopCheck.sort(key=lambda p: -p[1].pt())
if len(genTopCheck) > 0:
topPdg_ids = filter(
lambda p: p != 2212,
[abs(particle.pdgId()) for particle in genTopCheck[0][2]])
print 'TOP 1'
print topPdg_ids
previous = genTopCheck[0][0].mother(1)
print 'first', genTopCheck[0][0].pdgId()
for i, pdg in enumerate(topPdg_ids):
try:
print 'mother', i, previous.pdgId()
print 'mothers', i, [
p.pdgId() for p in search.ancestry(previous)
]
previous = previous.mother(0)
except Exception as val:
print val
break
if len(genTopCheck) > 1:
topPdg_ids = filter(
lambda p: p != 2212,
[abs(particle.pdgId()) for particle in genTopCheck[1][2]])
print 'TOP 2'
print topPdg_ids
previous = genTopCheck[1][0].mother(1)
print 'first', genTopCheck[1][0].pdgId()
for i, pdg in enumerate(topPdg_ids):
try:
print 'mother', i, previous.pdgId()
print 'mothers', i, [
p.pdgId() for p in search.ancestry(previous)
]
previous = previous.mother(0)
except Exception as val:
print val
break
# MET
genMet = {
'pt': reader.products['genMET'][0].pt(),
'phi': reader.products['genMET'][0].phi()
}
event.genMet_pt = genMet['pt']
event.genMet_phi = genMet['phi']
# for ttgamma and tt events, categorize events:
# a1) ttgamma vertex, gamma from t/g, isolated, must be in ttgamma sample = ttgamma signal (photonSignal == 1)
# -> gamma isolated
# -> gamma with only tops/gluons in ancestry
# a2) ttgamma, gamma from ISR, must be in ttgamma sample = tt ISR bg (photonISR == 1)
# -> gamma isolated
# -> gamma with no top in ancestry
# -> gamma with only gluons and light quarks in ancestry
# -> direct gamma mother != gluon!!! (this is a1)
# b) tt + isolated gamma from W, l, tau, must be in tt sample = ttgamma (W,l,tau) bg (photonLep == 1)
# -> gamma isolated
# -> gamma with direct abs mother being 11, 13, 15 or 24
# c1) tt + non isolated gamma from anything including mesons = tt bg (ttBg == 1)
# -> gamma non isolated or meson in ancestry
# c2) tt + isolated gamma from bottom quark (from t decay) or jets (from W) = tt bottom bg (ttBg == 1)
# -> gamma isolated from b or j (from t/W decay)
# ATTENTION: gammas from bottoms with off-shell tops where the
# top decay is done by pythia are currently labeled as ISR!
# However this case is currently not simulated in any sample
# d) tt + gamma fake = ttgamma fake (photonFake == 1)
# -> everything else does not contain a photon
# -> if it still passes selection: it is a photon fake
genPhotonsSignalCheck = [
(search.ascend(l), l, search.ancestry(search.ascend(l)))
for l in filter(
lambda p: abs(p.pdgId()) == 22 and p.pt() > 10 and abs(p.eta()) <
2.5 and search.isLast(p) and p.status() == 1, gp)
]
genPhotonsSignalCheck.sort(key=lambda p: -p[1].pt())
photonSignal = 0 #a1
photonISR = 0 #a2
photonLep = 0 #b
ttBg = 0 #c1
photonJets = 0 #c2
photonFake = 0 #d
if len(genPhotonsSignalCheck) > 0:
# check hardest photon with pT>13 and abs(eta)<2.5
first, last, ancestry = genPhotonsSignalCheck[0]
# get abs pdgIDs of ancestry
pdg_ids = filter(lambda p: p != 2212,
[abs(particle.pdgId()) for particle in ancestry])
# check if particles are close by
close_particles = filter(
lambda p: p != last and p.pt() > 5 and deltaR2(
{
'phi': last.phi(),
'eta': last.eta()
}, {
'phi': p.phi(),
'eta': p.eta()
}) < 0.2**2, search.final_state_particles_no_neutrinos)
# print 'mothers pdg', pdg_ids
# print 'close', [p.pdgId() for p in close_particles]
# print 'first mother pdg', first.mother(0).pdgId()
# if len(pdg_ids) < 999:
# previous = first.mother(0)
# for i, pdg in enumerate(pdg_ids):
# try:
# print 'mother', i, previous.pdgId()
# previous = previous.mother(0)
# except Exception as val:
# print val
# break
# deside the categories
if max(pdg_ids) > 100 or len(close_particles) != 0:
#photon with meson in ancestry or non isolated -> cat c1)
ttBg = 1
elif abs(first.mother(0).pdgId()) in [11, 13, 15, 24]:
#isolated photon with W, l or tau direct mother -> cat b)
photonLep = 1
# elif all( [ p in [ 6, 21 ] for p in pdg_ids ] ) or abs(first.mother(0).pdgId()) == 21: # not working for photons from top, as the gluons can come from light quarks
elif abs(first.mother(0).pdgId()) in [6, 21]:
#isolated photon with photon from top or gluon -> cat a1)
photonSignal = 1
# printTopMothers()
elif all([p in [1, 2, 3, 4, 5, 21] for p in pdg_ids]):
#isolated photon with photon ancestry only containing light quarks or gluons (ISR) -> cat a1)
photonISR = 1
else:
#isolated gammas from bottoms originating from the top decay or jets from W -> cat c2)
photonJets = 1
else:
# if events with photonFake == 1 pass selection: fake gamma -> cat d)
photonFake = 1
# if all flags are 0, it is an isolated gamma from a process I havn't thought of!
# should not be there! - check!
event.signalPhoton = photonSignal
event.isrPhoton = photonISR
event.lepPhoton = photonLep
event.nonIsoPhoton = ttBg
event.jetPhoton = photonJets
event.fakePhoton = photonFake
# gen photons: particle-level isolated gen photons
genPhotons = [(search.ascend(l), l) for l in filter(
lambda p: abs(p.pdgId()) == 22 and p.pt() > 15 and search.isLast(p) and
p.status() == 1, gp)]
genPhotons.sort(key=lambda p: -p[1].pt())
genPhotons_ = []
for first, last in genPhotons[:100]:
mother_pdgId = first.mother(
0).pdgId() if first.numberOfMothers() > 0 else 0
genPhoton_ = {var: getattr(last, var)() for var in boson_read_varnames}
# kinematic photon selection
if not isGoodGenPhoton(genPhoton_): continue
genPhoton_['motherPdgId'] = mother_pdgId
genPhoton_['status'] = last.status()
close_particles = filter(
lambda p: p != last and deltaR2(
{
'phi': last.phi(),
'eta': last.eta()
}, {
'phi': p.phi(),
'eta': p.eta()
}) < 0.4**2, search.final_state_particles_no_neutrinos)
genPhoton_['relIso04'] = sum([p.pt() for p in close_particles],
0) / last.pt()
# require isolation
if genPhoton_['relIso04'] < 0.4:
genPhotons_.append(genPhoton_)
# genLeptons: prompt gen-leptons
genLeptons = [(search.ascend(l), l) for l in filter(
lambda p: abs(p.pdgId()) in [11, 13] and search.isLast(p) and p.pt() >=
0 and p.status() == 1, gp)]
promptGenLeps = []
allGenLeps = []
for first, last in genLeptons:
mother = first.mother(0) if first.numberOfMothers() > 0 else None
if mother is not None:
mother_pdgId = mother.pdgId()
mother_ascend = search.ascend(mother)
grandmother = mother_ascend.mother(
0) if mother.numberOfMothers() > 0 else None
grandmother_pdgId = grandmother.pdgId(
) if grandmother is not None else 0
else:
mother_pdgId = 0
grandmother_pdgId = 0
genLep = {var: getattr(last, var)() for var in lep_varnames}
genLep['motherPdgId'] = mother_pdgId
genLep['grandmotherPdgId'] = grandmother_pdgId
allGenLeps.append(genLep)
if abs(genLep['motherPdgId']) in [11, 13, 15, 23, 24, 25]:
promptGenLeps.append(genLep)
# filter gen leptons
promptGenLeps = list(filter(lambda l: isGoodGenLepton(l), promptGenLeps))
promptGenLeps.sort(key=lambda p: -p['pt'])
addIndex(promptGenLeps)
## removing photons in dR cone leptons (radiation photons)
for genPhoton in genPhotons_:
genPhoton['minLeptonDR'] = min(
[999] + [deltaR(genPhoton, l) for l in allGenLeps])
genPhotons_ = list(filter(lambda g: g['minLeptonDR'] > 0.4, genPhotons_))
addIndex(genPhotons_)
# jets
fwlite_genJets = filter(genJetId, reader.products['genJets'])
genJets = map(
lambda t: {var: getattr(t, var)()
for var in jet_read_varnames},
filter(lambda j: j.pt() > 30, fwlite_genJets))
# filter genJets
genJets = list(filter(lambda j: isGoodGenJet(j), genJets))
# cleaning of jets with isolated photons
genJets = list(
filter(
lambda j: min([999] + [deltaR2(j, p)
for p in genPhotons_]) > 0.4**2, genJets))
# store minimum DR to jets
for genPhoton in genPhotons_:
genPhoton['minJetDR'] = min([999] +
[deltaR(genPhoton, j) for j in genJets])
# find b's from tops:
b_partons = [
b for b in filter(
lambda p: abs(p.pdgId()) == 5 and p.numberOfMothers() == 1 and abs(
p.mother(0).pdgId()) == 6, gp)
]
# store if gen-jet is DR matched to a B parton
for genJet in genJets:
genJet['matchBParton'] = (min([999] + [
deltaR2(genJet, {
'eta': b.eta(),
'phi': b.phi()
}) for b in b_partons
]) < 0.2**2)
genJets = filter(
lambda j:
(min([999] + [deltaR2(j, l) for l in promptGenLeps
if l['pt'] > 10]) > 0.3**2), genJets)
genJets.sort(key=lambda p: -p['pt'])
addIndex(genJets)
# gen b jets
trueBjets = list(filter(lambda j: j['matchBParton'], genJets))
trueNonBjets = list(filter(lambda j: not j['matchBParton'], genJets))
# Mimick b reconstruction ( if the trailing b fails acceptance, we supplement with the leading non-b jet )
genBj0, genBj1 = (trueBjets + trueNonBjets + [None, None])[:2]
if genBj0: fill_vector(event, "genBj0", jet_write_varnames, genBj0)
if genBj1: fill_vector(event, "genBj1", jet_write_varnames, genBj1)
# reco-bjet/leading lepton association
if len(promptGenLeps) > 0 and genBj0 and genBj1:
if vecSumPt(genBj0, promptGenLeps[0], genMet) > vecSumPt(
genBj1, promptGenLeps[0], genMet):
event.genBjLeadlep_index, event.genBjLeadhad_index = genBj0[
'index'], genBj1['index']
else:
event.genBjLeadlep_index, event.genBjLeadhad_index = genBj1[
'index'], genBj0['index']
# find Z in genLep
(event.genLepZ_mass, genLepZ_l1_index,
genLepZ_l2_index) = closestOSDLMassToMZ(promptGenLeps)
genLepNonZ_indices = [
i for i in range(len(promptGenLeps))
if i not in [genLepZ_l1_index, genLepZ_l2_index]
]
event.genLepZ_l1_index = promptGenLeps[genLepZ_l1_index][
'index'] if genLepZ_l1_index >= 0 else -1
event.genLepZ_l2_index = promptGenLeps[genLepZ_l2_index][
'index'] if genLepZ_l2_index >= 0 else -1
event.genLepNonZ_l1_index = promptGenLeps[
genLepNonZ_indices[0]]['index'] if len(genLepNonZ_indices) > 0 else -1
event.genLepNonZ_l2_index = promptGenLeps[
genLepNonZ_indices[1]]['index'] if len(genLepNonZ_indices) > 1 else -1
# store genLepZ stuff
if event.genLepZ_mass > 0:
genLepZ_l1 = ROOT.TLorentzVector()
genLepZ_l1.SetPtEtaPhiM(promptGenLeps[event.genLepZ_l1_index]['pt'],
promptGenLeps[event.genLepZ_l1_index]['eta'],
promptGenLeps[event.genLepZ_l1_index]['phi'],
0)
genLepZ_l2 = ROOT.TLorentzVector()
genLepZ_l2.SetPtEtaPhiM(promptGenLeps[event.genLepZ_l2_index]['pt'],
promptGenLeps[event.genLepZ_l2_index]['eta'],
promptGenLeps[event.genLepZ_l2_index]['phi'],
0)
genLepZ = genLepZ_l1 + genLepZ_l2
event.genLepZ_pt = genLepZ.Pt()
event.genLepZ_eta = genLepZ.Eta()
event.genLepZ_phi = genLepZ.Phi()
event.genLepZ_lldPhi = deltaPhi(
promptGenLeps[event.genLepZ_l1_index]['phi'],
promptGenLeps[event.genLepZ_l2_index]['phi'])
event.genLepZ_lldR = deltaR(promptGenLeps[event.genLepZ_l1_index],
promptGenLeps[event.genLepZ_l2_index])
genLepMinus_index = event.genLepZ_l1_index if promptGenLeps[
event.genLepZ_l1_index]['pdgId'] > 0 else event.genLepZ_l2_index
event.genLepZ_cosThetaStar = cosThetaStar(
event.genLepZ_mass, event.genLepZ_pt, event.genLepZ_eta,
event.genLepZ_phi, promptGenLeps[genLepMinus_index]['pt'],
promptGenLeps[genLepMinus_index]['eta'],
promptGenLeps[genLepMinus_index]['phi'])
# reco-bjet/nonZ lepton association
if event.genLepNonZ_l1_index >= 0 and genBj0 and genBj1:
if vecSumPt(genBj0, promptGenLeps[event.genLepNonZ_l1_index],
genMet) > vecSumPt(
genBj1, promptGenLeps[event.genLepNonZ_l1_index],
genMet):
event.genBjNonZlep_index, event.genBjNonZhad_index = genBj0[
'index'], genBj1['index']
else:
event.genBjNonZlep_index, event.genBjNonZhad_index = genBj1[
'index'], genBj0['index']
#for jet in genJets:
# print jet['isMuon'], jet['isElectron'], jet['isPhoton'], min([999]+[deltaR2(jet, l) for l in promptGenLeps if l['pt']>10]), jet
# jet/lepton disambiguation -> remove jets, because gen-jets cluster all leptons
#if args.logLevel == 'DEBUG':
# for jet in filter( lambda j: not (min([999]+[deltaR2(j, l) for l in promptGenLeps if l['pt']>10]) > 0.3**2 ), genJets ):
# logger.debug( "Filtered gen %f jet %r lep %r", sqrt((min([999]+[deltaR2(jet, l) for l in promptGenLeps if l['pt']>10]))), jet, [ (l['eta'], jet['pt']/l['pt']) for l in promptGenLeps] )
# assert False, ""
fill_vector_collection(event, "genPhoton", gen_photon_varnames,
genPhotons_)
fill_vector_collection(event, "genLep", lep_all_varnames, promptGenLeps)
fill_vector_collection(event, "genJet", jet_write_varnames, genJets)
plots.append(
Plot(
name='eta',
texX='#eta',
texY='Number of Events',
attribute=lambda event, sample: event.lep_eta,
binning=[30, -3, 3],
addOverFlowBin='upper',
))
plots.append(
Plot(
name='dPhi_lep_met',
texX='#Delta#phi(lep,met)',
texY='Number of Events',
attribute=lambda event, sample: deltaPhi(event.lep_phi, event.met_phi),
binning=[30, -3, 3],
addOverFlowBin='upper',
))
plots.append(
Plot(
name='dPhi_j0_met',
texX='#Delta#phi(jet,met)',
texY='Number of Events',
attribute=lambda event, sample: deltaPhi(event.JetGood_phi[0], event.
met_phi),
binning=[30, -3, 3],
addOverFlowBin='upper',
))
