def getAllLeptons(c, collVars=leptonVars, collection="LepGood"):
n = int(getVarValue(c, 'n%s' % collection))
res = [getObjDict(c, '%s_' % collection, collVars, i) for i in range(n)]
# recall intial index
for i in range(n):
res[i]['i%s' % collection] = i
return res
def getJets(event, sample):
jetVars = ['eta', 'pt', 'phi', 'btagCSV']
event.jets_sortbtag = [
getObjDict(event, 'jet_', jetVars, i)
for i in range(int(getVarValue(event, 'nJetSelected')))
] #nJetSelected
event.jets_sortbtag.sort(key=lambda l: -l['btagCSV'])
def getSelectedJets(event, sample):
jetVars = ['eta', 'pt', 'phi', 'btagCSV', 'DFbb', 'DFb', 'id']
event.selectedJets = [
getObjDict(event, 'jet_', jetVars, i)
for i in range(int(getVarValue(event, 'njet')))
if (abs(event.jet_eta[i]) <= 2.4 and event.jet_pt[i] > 30
and event.jet_id[i])
] #nJetSelected
def getLooseLeptonMult(event, sample):
leptons = [
getObjDict(event, 'lep_', [
'eta', 'pt', 'phi', 'charge', 'pdgId', 'sourceId', 'mediumMuonId'
], i) for i in range(len(event.lep_pt))
]
lepLoose = [
l for l in leptons
if l['pt'] > 10 and ((l['mediumMuonId'] and abs(l['pdgId']) == 13)
or abs(l['pdgId']) == 11)
]
event.nLepLoose = len(lepLoose)
def getJets(event, sample):
jetVars = ['eta', 'pt', 'phi', 'btagCSV', 'id']
event.jets = filter(lambda j: j['pt'] > 30 and j['id'], [
getObjDict(event, 'jet_', jetVars, i)
for i in range(int(getVarValue(event, 'njet')))
])
b_jets = filter(lambda j: isAnalysisJet(j) and isBJet(j), event.jets)
leading_bjet = b_jets[0] if len(b_jets) > 0 else None
if leading_bjet is not None:
v_leading_bjet = ROOT.TLorentzVector()
v_leading_bjet.SetPtEtaPhiM(leading_bjet['pt'], leading_bjet['eta'],
leading_bjet['phi'], 0.)
untagged_jets = filter(lambda j: not isBJet(j), event.jets)
event.leading_untagged_jet = untagged_jets[0] if len(
untagged_jets) > 0 else None
if event.leading_untagged_jet is not None and leading_bjet is not None:
v_leading_untagged_jet = ROOT.TLorentzVector()
v_leading_untagged_jet.SetPtEtaPhiM(event.leading_untagged_jet['pt'],
event.leading_untagged_jet['eta'],
event.leading_untagged_jet['phi'],
0.)
event.m_leadingUntagged_bJet = (v_leading_bjet +
v_leading_untagged_jet).M()
else:
event.m_leadingUntagged_bJet = float('nan')
untagged_jets.sort(key=lambda j: -abs(j['eta']))
event.mostForward_untagged_jet = untagged_jets[0] if len(
untagged_jets) > 0 else None
if event.mostForward_untagged_jet is not None and leading_bjet is not None:
v_mostForward_untagged_jet = ROOT.TLorentzVector()
v_mostForward_untagged_jet.SetPtEtaPhiM(
event.mostForward_untagged_jet['pt'],
event.mostForward_untagged_jet['eta'],
event.mostForward_untagged_jet['phi'], 0.)
event.m_mostForwardUntagged_bJet = (v_leading_bjet +
v_mostForward_untagged_jet).M()
else:
event.m_mostForwardUntagged_bJet = float('nan')
event.jets = filter(isAnalysisJet, event.jets)
def _estimate(self, region, channel, setup):
if not setup.nonprompt:
raise (NotImplementedError, "Need a nonprompt setup")
''' Concrete implementation of abstract method 'estimate' as defined in Systematic
'''
logger.info("Prediction for %s channel %s" % (self.name, channel))
if channel.name == 'all':
# estimate fake contribution from events with at least three loose leptons, and less than 3 tight leptons
# take loose leptons with same pT requirements like analysis leptons
tmpSample = self.sample
variables = map(TreeVariable.fromString, [
"run/I", "lumi/I", "evt/I", "Z_pt/F", "cosThetaStar/F",
"weight/F", "met_pt/F", "Z_mass/F", "nJetSelected/I",
"nBTag/I", 'Z_l1_index/I', 'Z_l2_index/I', 'nonZ_l1_index/I',
'nonZ_l2_index/I', "nLeptons_FO_3l/I", "nLeptons_tight_3l/I",
"nLeptons_tight_4l/I"
])
if not self.sample.isData:
logger.info("Adding weights to be read.")
variables += map(
TreeVariable.fromString,
['reweightPU36fb/F', 'reweightBTagDeepCSV_SF/F'])
variables += [
VectorTreeVariable.fromString(
'lep[pt/F,ptCorr/F,eta/F,phi/F,FO_3l/I,tight_3l/I,FO_SS/I,tight_SS/I,jetPtRatiov2/F,pdgId/I]'
)
]
tmpSample.setSelectionString([
setup.preselection(self.dataMC,
nElectrons=channel.nE,
nMuons=channel.nM)['cut'],
region.cutString()
])
reader = tmpSample.treeReader(allBranchesActive=True,
variables=variables)
reader.start()
fakeYield = u_float(0)
nEvents = u_float(tmpSample.getYieldFromDraw())
logger.info("Runing over %s events.", nEvents.val)
while reader.run():
nLep = len([l for l in reader.event.lep_pt if l > 0])
lep = [
getObjDict(reader.event, "lep" + '_', [
"pt", "ptCorr", "eta", "phi", "FO_3l", "FO_SS",
"tight_3l", "tight_SS", "pdgId", "jetPtRatiov2"
], i) for i in range(nLep)
]
# get the relevant leptons
lep = [l for l in lep if l[setup.leptonId]]
# get tight and loose separately
looseNotTight = [l for l in lep if not l[setup.tight_ID]]
tight = [l for l in lep if l[setup.tight_ID]]
nLooseNotTight = len(looseNotTight)
nTight = len(tight)
# Really get ALL possible combinations.
allCombinations = itertools.combinations(
tight + looseNotTight, setup.nLeptons)
for comb in allCombinations:
FR = 1.
nLooseNotTight = 0
for l in comb:
if l[setup.tight_ID]:
continue
else:
if abs(l['pdgId']) == 11: FRmap = self.elMap
elif abs(l['pdgId']) == 13: FRmap = self.muMap
else: raise NotImplementedError
# we run out of stats in data at higher pt, hence we cut at a lower value
ptCut = 45. if self.sample.isData else 99.
ptCorrected = l[
'ptCorr'] if l['ptCorr'] < ptCut else (ptCut -
1)
FR_from_map = FRmap.GetBinContent(
FRmap.FindBin(ptCorrected, abs(l['eta'])))
if self.sample.isData:
FR *= FR_from_map / (1 - FR_from_map)
else:
FR *= FR_from_map
nLooseNotTight += 1
FR *= (-1)**(nLooseNotTight + 1)
allweights = [setup.sys['weight']] + setup.sys['reweight']
if self.sample.isData:
weight = 1
else:
weights = [
getattr(reader.event, w) for w in allweights
]
weight = reduce(mul, weights, 1)
fakeYield += (weight * FR)
# apply the statistical uncertainty to the result
result = u_float(0.) if nEvents.val == 0 else u_float(
fakeYield, (nEvents.sigma / nEvents.val) * fakeYield)
return result if self.sample.isData else (result * setup.lumi /
1000.)
def extra_observables(event, sample):
# get the negative-charge lepton from Z
l_m_index = event.Z_l1_index if event.lep_pdgId[
event.Z_l1_index] > 0 else event.Z_l2_index
# beta and gamma from Z
gamma = sqrt(1 + event.Z_pt**2 / event.Z_mass**2 * cosh(event.Z_eta)**2)
beta = sqrt(1 - 1 / gamma**2)
# Z boson and l3 (convinience)
Z_3D = ROOT.TVector3(event.Z_pt * cos(event.Z_phi),
event.Z_pt * sin(event.Z_phi),
event.Z_pt * sinh(event.Z_eta))
l_m_3D = ROOT.TVector3(
event.lep_pt[l_m_index] * cos(event.lep_phi[l_m_index]),
event.lep_pt[l_m_index] * sin(event.lep_phi[l_m_index]),
event.lep_pt[l_m_index] * sinh(event.lep_eta[l_m_index]))
# cos(theta) ... cos(Z, l^-) in lab frame
cosTheta = Z_3D * l_m_3D / (sqrt(Z_3D * Z_3D) * sqrt(l_m_3D * l_m_3D))
event.cosThetaStar = (-beta + cosTheta) / (1 - beta * cosTheta)
l3_3D = ROOT.TVector3(
event.lep_pt[event.nonZ_l1_index] *
cos(event.lep_phi[event.nonZ_l1_index]),
event.lep_pt[event.nonZ_l1_index] *
sin(event.lep_phi[event.nonZ_l1_index]),
event.lep_pt[event.nonZ_l1_index] *
sinh(event.lep_eta[event.nonZ_l1_index]))
l3_pt = event.lep_pt[event.nonZ_l1_index]
l3_phi = event.lep_phi[event.nonZ_l1_index]
l3_eta = event.lep_eta[event.nonZ_l1_index]
Z_2D = ROOT.TVector2(Z_3D[0], Z_3D[1])
n_Z_2D = ROOT.TVector2(cos(event.Z_phi), sin(event.Z_phi))
n_orth_Z_2D = ROOT.TVector2(-sin(event.Z_phi), cos(event.Z_phi))
l3_2D = ROOT.TVector2(l3_3D[0], l3_3D[1])
# me_x,y
met_2D = ROOT.TVector2(event.met_pt * cos(event.met_phi),
event.met_pt * sin(event.met_phi))
# 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 resulting sum-pT (the miracle of M3)
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))
def getGenLeps(c):
return [
getObjDict(c, 'genLep_',
['eta', 'pt', 'phi', 'charge', 'pdgId', 'sourceId'], i)
for i in range(int(getVarValue(c, 'ngenLep')))
]
def getTaus(c, collVars=tauVars):
return [
getObjDict(c, 'TauGood_', collVars, i)
for i in range(int(getVarValue(c, 'nTauGood')))
]
def getAllLeptons(c, collVars=leptonVars, collection="LepGood"):
return [
getObjDict(c, '%s_' % collection, collVars, i)
for i in range(int(getVarValue(c, 'n%s' % collection)))
]
def getJets(c, jetVars=jetVars, jetColl="Jet"):
return [
getObjDict(c, jetColl + '_', jetVars, i)
for i in range(int(getVarValue(c, 'n' + jetColl)))
]
def getGenPartsAll(c, collection="genPartAll", genVars=genVars):
return [
getObjDict(c, '%s_' % collection, genVars, i)
for i in range(getattr(c, 'n%s' % collection))
]
def getGenParts(c):
return [
getObjDict(c, 'GenPart_', [
'eta', 'pt', 'phi', 'charge', 'pdgId', 'motherId', 'grandmotherId'
], i) for i in range(int(getVarValue(c, 'nGenPart')))
]
hists["lep_pt_trail"] = sample.get1DHistoFromDraw("lep_pt[2]",
selectionString=sel_string,
binning=[10, 0, 100],
addOverFlowBin='upper',
weightString=weight_central)
# Run the tree reader for cases with more complicated plots
hists["lep_pt_trail"].Reset()
sample.setSelectionString(sel_string)
reader = sample.treeReader(variables=variables)
reader.start()
while reader.run():
nLep = len([l for l in reader.event.lep_pt if l > 0])
lep = [
getObjDict(reader.event, "lep" + '_', [
"pt", "ptCorr", "eta", "phi", "FO_3l", "FO_SS", "tight_3l",
"tight_SS", "pdgId", "jetPtRatiov2"
], i) for i in range(nLep)
]
# get the relevant leptons
lep = [l for l in lep if l[tight_ID]]
if len(lep) != nLeptons: print "bug"
allweights = ["weight", "reweightPU36fb", "reweightBTagDeepCSV_SF"]
if sample.isData:
weight = 1
else:
weights = [getattr(reader.event, w) for w in allweights]
weight = reduce(mul, weights, 1)
def getAllJets(event, sample):
jetVars = ['eta', 'pt', 'btagCSV']
event.allJets = [
getObjDict(event, 'jet_', jetVars, i)
for i in range(int(getVarValue(event, 'njet')))
]
def getPhotons(c, collVars=photonVars, idLevel='loose'):
return [
getObjDict(c, 'gamma_', collVars, i)
for i in range(int(getVarValue(c, 'ngamma')))
]
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
