def makeNonIsoLeptons( data ):
goodLeptons = getGoodLeptons( data, collVars = leptonVars )
allExtraLeptons = sorted( \
[l for l in getLeptons( data, collVars = leptonVars ) if l not in goodLeptons] + getOtherLeptons( data , collVars = leptonVars ),
key=lambda l: -l['pt'] )
# print len(allLeptons), len(goodLeptons), len(allExtraLeptons)
assert len(goodLeptons)==2, "Analysis leptons not found!"
l1, l2 = goodLeptons
#print l1['pt'] - data.l1_pt, l2['pt'] - data.l2_pt
data.allExtraLeptons = allExtraLeptons
nonIsoMus = filter(lambda l: abs(l['pdgId'])==13 and l['miniRelIso']>0.2 and l['pt']>5, allExtraLeptons )
nonIsoEles = filter(lambda l: abs(l['pdgId'])==11 and l['miniRelIso']>0.2 and l['pt']>7, allExtraLeptons )
#print nonIsoMus, nonIsoEles
data.nonIsoMu = nonIsoMus[-1] if len(nonIsoMus)>0 else None
data.nonIsoEle = nonIsoEles[-1] if len(nonIsoEles)>0 else None
# extra ele
if data.nonIsoEle is not None:
makeSwappedMT2ll( data, l1, l2, data.nonIsoEle, verbose = verbose)
if data.nonIsoMu is not None:
makeSwappedMT2ll( data, l1, l2, data.nonIsoMu, verbose = verbose)
if verbose: print
def makeNonIsoLeptons(data):
goodLeptons = getGoodLeptons(data, collVars=leptonVars)
allExtraLeptons = sorted( \
[l for l in getLeptons( data, collVars = leptonVars ) if l not in goodLeptons] + getOtherLeptons( data , collVars = leptonVars ),
key=lambda l: -l['pt'] )
# print len(allLeptons), len(goodLeptons), len(allExtraLeptons)
assert len(goodLeptons) == 2, "Analysis leptons not found!"
l1, l2 = goodLeptons
#print l1['pt'] - data.l1_pt, l2['pt'] - data.l2_pt
data.allExtraLeptons = allExtraLeptons
nonIsoMus = filter(
lambda l: abs(l['pdgId']) == 13 and l['miniRelIso'] > 0.2 and l[
'pt'] > 5, allExtraLeptons)
nonIsoEles = filter(
lambda l: abs(l['pdgId']) == 11 and l['miniRelIso'] > 0.2 and l[
'pt'] > 7, allExtraLeptons)
#print nonIsoMus, nonIsoEles
data.nonIsoMu = nonIsoMus[-1] if len(nonIsoMus) > 0 else None
data.nonIsoEle = nonIsoEles[-1] if len(nonIsoEles) > 0 else None
# extra ele
if data.nonIsoEle is not None:
makeSwappedMT2ll(data, l1, l2, data.nonIsoEle, verbose=verbose)
if data.nonIsoMu is not None:
makeSwappedMT2ll(data, l1, l2, data.nonIsoMu, verbose=verbose)
if verbose: print
def makeLeptons( data ):
goodLeptons = getGoodLeptons( data, collVars = leptonVars + extraLepVars )
extraLeptons = sorted( \
[l for l in getLeptons( data, collVars = leptonVars + extraLepVars) if l not in goodLeptons] \
+ getOtherLeptons( data , collVars = leptonVars + extraLepVars),
key=lambda l: -l['pt'] )
#print len(goodLeptons), len(getLeptons( data, collVars = leptonVars + extraLepVars)), len(getOtherLeptons( data , collVars = leptonVars + extraLepVars)), len(extraLeptons)
if len(goodLeptons)==2:
setattr( data, "goodLeptons", goodLeptons )
setattr( data, "extraLeptons", extraLeptons )
extraMu = filter(lambda l: abs(l['pdgId'])==13 and l['relIso04']>0.5, extraLeptons )
extraEle = filter(lambda l: abs(l['pdgId'])==11 and l['relIso04']>0.5, extraLeptons )
setattr( data, "extraMu", extraMu[-1] if len(extraMu)>0 else None )
setattr( data, "extraEle", extraEle[-1] if len(extraEle)>0 else None )
# MT2ll
mt2Calc.reset()
l1, l2 = goodLeptons[0], goodLeptons[1]
mt2Calc.setLeptons(l1["pt"], l1["eta"], l1["phi"], l2["pt"], l2["eta"], l2["phi"])
mt2Calc.setMet(data.met_pt, data.met_phi)
setattr( data, "mt2ll", mt2Calc.mt2ll())
#print data.mt2ll
else:
setattr( data, "goodLeptons", None)
setattr( data, "extraLeptons", None)
setattr( data, "extraMu", None)
setattr( data, "extraEle", None)
setattr( data, "mt2ll", None)
def makeLeptons( data ):
goodLeptons = getGoodLeptons( data, collVars = leptonVars + extraLepVars )
extraLeptons = sorted( \
[l for l in getLeptons( data, collVars = leptonVars + extraLepVars) if l not in goodLeptons] \
+ getOtherLeptons( data , collVars = leptonVars + extraLepVars),
key=lambda l: -l['pt'] )
setattr( data, "goodLeptons", goodLeptons )
setattr( data, "extraLeptons", extraLeptons )
extraMu = filter(lambda l: abs(l['pdgId'])==13 and l['relIso04']>0.5, extraLeptons )
extraEle = filter(lambda l: abs(l['pdgId'])==11 and l['relIso04']>0.5, extraLeptons )
setattr( data, "extraMu", extraMu[-1] if len(extraMu)>0 else None )
setattr( data, "extraEle", extraEle[-1] if len(extraEle)>0 else None )
def makeLeptons(data):
goodLeptons = getGoodLeptons(data,
collVars=leptonVars + extraLepVars)
extraLeptons = sorted( \
[l for l in getLeptons( data, collVars = leptonVars + extraLepVars) if l not in goodLeptons] \
+ getOtherLeptons( data , collVars = leptonVars + extraLepVars),
key=lambda l: -l['pt'] )
setattr(data, "goodLeptons", goodLeptons)
setattr(data, "extraLeptons", extraLeptons)
extraMu = filter(
lambda l: abs(l['pdgId']) == 13 and l['relIso04'] > 0.5,
extraLeptons)
extraEle = filter(
lambda l: abs(l['pdgId']) == 11 and l['relIso04'] > 0.5,
extraLeptons)
setattr(data, "extraMu", extraMu[-1] if len(extraMu) > 0 else None)
setattr(data, "extraEle",
extraEle[-1] if len(extraEle) > 0 else None)
isElecElec[0] = 1
if not s["name"].find("DoubleMuon") and triggerMuMu :
trigger = 1
isMuonMuon[0] = 1
if not s["name"].find("DoubleMuon") and triggerMuMu==0 :
trigger = 0
isMuonMuon[0] = 1
if not s["name"].find("MuonEG") and triggerMuEle :
trigger = 1
isMuonElec[0] = 1
if not s["name"].find("MuonEG") and triggerMuEle==0 :
trigger = 0
isMuonElec[0] = 1
leptons = filter(lambda l: looseMuID(l) or looseEleID(l), getLeptons(chain))
jets = filter(lambda j:j['pt']>30 and abs(j['eta'])<2.4 and j['id'], getJets(chain))
bjets = filter(lambda j:j['btagCSV']>0.890, jets)
nbjets[0] = len(bjets)
njets[0] = len(jets)
nleptons[0] = len(leptons)
Process[:200] = s["name"]
HT[0] = sum([j['pt'] for j in jets])
PhiMetJet1 = deltaPhi(metPhi,getVarValue(chain, "Jet_phi",0))
PhiMetJet2 = deltaPhi(metPhi,getVarValue(chain, "Jet_phi",1))
if PhiMetJet1 <= PhiMetJet2: PhiMetJet_small = PhiMetJet1
else: PhiMetJet_small = PhiMetJet2
chain, "weight") * lumiScale if not s['isData'] else 1
mt2ll = getVarValue(chain, "dl_mt2ll")
met = getVarValue(chain, "met_pt")
metPhi = getVarValue(chain, "met_phi")
genMet = getVarValue(chain, "met_genPt")
genMetPhi = getVarValue(chain, "met_genPhi")
deltaMet = sqrt((met * cos(metPhi) - genMet * cos(genMetPhi))**2 +
(met * sin(metPhi) - genMet * sin(genMetPhi))**2)
jets = filter(
lambda j: j['pt'] > 30 and abs(j['eta']) < 2.4 and j['id'],
getJets(chain))
allLeptons = getLeptons(
chain,
collVars=leptonVars + [
'mcMatchId', 'mcMatchAny', 'mcMatchTau', 'mcPt', 'ip3d',
'relIso03', 'relIso04', 'jetPtRatiov1', 'jetPtRelv1',
'jetPtRelv2', 'jetPtRatiov2', 'jetBTagCSV', 'jetDR'
])
leptons = filter(lambda l: looseMuID(l) or looseEleID(l), allLeptons)
#LepGood_mcMatchId Match to source from hard scatter (pdgId of heaviest particle in chain, 25 for H, 6 for t, 23/24 for W/Z), zero if non-prompt or fake for Leptons after the preselection
#LepGood_mcMatchAny Match to any final state leptons: 0 if unmatched, 1 if light flavour (including prompt), 4 if charm, 5 if bottom for Leptons after the preselection
#LepGood_mcMatchTau True if the leptons comes from a tau for Leptons after the preselection
# for l in leptons:
# if (l['mcMatchAny']==0 and (not (l['mcMatchId']==0))) or ( (not l['mcMatchAny']==0) and (l['mcMatchId']==0)):
# print "Match?",l
#RECO
looseMu = filter(
def cutflow(sig):
#######################################################
# SELECT WHAT YOU WANT TO DO HERE #
#######################################################
reduceStat = 100 #recude the statistics, i.e. 10 is ten times less samples to look at
#######################################################
# Define cutflow you want to make #
#######################################################
mt2llcut = 80.
metcut = 40.
lumi = 10000.
baselineamount = {
'samples': {},
'SoverB': None,
}
cutflow = {\
'metsig':{'baseline': 0.,
'cuts': {\
3:{'samples':{}, 'SoverB': None},
4:{'samples':{}, 'SoverB': None},
5:{'samples':{}, 'SoverB': None},
6:{'samples':{}, 'SoverB': None},
7:{'samples':{}, 'SoverB': None},
8:{'samples':{}, 'SoverB': None},
9:{'samples':{}, 'SoverB': None},
10:{'samples':{}, 'SoverB': None},
10:{'samples':{}, 'SoverB': None},
},
'event': None,
},
'nbjets':{'baseline': 1.,
'cuts': {\
0:{'samples':{}, 'SoverB': None},
1:{'samples':{}, 'SoverB': None},
2:{'samples':{}, 'SoverB': None},
3:{'samples':{}, 'SoverB': None},
4:{'samples':{}, 'SoverB': None},
5:{'samples':{}, 'SoverB': None},
},
'event': None,
},
'njets': {'baseline': 2.,
'cuts': {\
1:{'samples':{}, 'SoverB': None},
2:{'samples':{}, 'SoverB': None},
3:{'samples':{}, 'SoverB': None},
4:{'samples':{}, 'SoverB': None},
5:{'samples':{}, 'SoverB': None},
6:{'samples':{}, 'SoverB': None},
7:{'samples':{}, 'SoverB': None},
},
'event': None,
},
'ht': {'baseline': 0.,
'cuts': {\
50: {'samples':{}, 'SoverB': None},
100:{'samples':{}, 'SoverB': None},
200:{'samples':{}, 'SoverB': None},
300:{'samples':{}, 'SoverB': None},
400:{'samples':{}, 'SoverB': None},
500:{'samples':{}, 'SoverB': None},
},
'event': None,
},
}
#preselection: MET>40, njets>=2, n_bjets>=1, n_lep>=2
#For now see here for the Sum$ syntax: https://root.cern.ch/root/html/TTree.html#TTree:Draw@2
preselection = 'met_pt>' + str(metcut) + '&&Sum$(LepGood_pt>20)==2'
#######################################################
# load all the samples #
#######################################################
backgrounds = [singleTop_50ns, DY_50ns, TTJets_50ns]
#signals = [SMS_T2tt_2J_mStop425_mLSP325, SMS_T2tt_2J_mStop500_mLSP325, SMS_T2tt_2J_mStop650_mLSP325, SMS_T2tt_2J_mStop850_mLSP100]
signal = [sig]
#######################################################
# get the TChains for each sample #
#######################################################
for s in backgrounds + signal:
s['chain'] = getChain(s, histname="")
#######################################################
# Start filling in the histograms #
#######################################################
for s in backgrounds + signal:
for cuttype in cutflow.keys():
for cut in cutflow[cuttype]['cuts'].keys():
cutflow[cuttype]['cuts'][cut]['samples'][s["name"]] = 0
baselineamount['samples'][s['name']] = 0
chain = s["chain"]
#Using Event loop
#get EList after preselection
print "Looping over %s" % s["name"]
eList = getEList(chain, preselection)
nEvents = eList.GetN() / reduceStat
print "Found %i events in %s after preselection %s, looping over %i" % (
eList.GetN(), s["name"], preselection, nEvents)
for ev in range(nEvents):
increment = 50
if nEvents > increment and ev % (nEvents / increment) == 0:
sys.stdout.write(
'\r' + "=" * (ev / (nEvents / increment)) + " " *
((nEvents - ev) / (nEvents / increment)) + "]" +
str(round((ev + 1) / (float(nEvents) / 100), 2)) + "%")
sys.stdout.flush()
sys.stdout.write('\r')
chain.GetEntry(eList.GetEntry(ev))
mt2Calc.reset()
#event weight (L= 4fb^-1)
weight = reduceStat * getVarValue(chain, "weight")
weight = weight * (lumi / 4000.)
#MET
met = getVarValue(chain, "met_pt")
metPhi = getVarValue(chain, "met_phi")
#jetpt
leadingjetpt = getVarValue(chain, "Jet_pt", 0)
subleadingjetpt = getVarValue(chain, "Jet_pt", 1)
#Leptons
allLeptons = getLeptons(chain)
muons = filter(looseMuID, allLeptons)
electrons = filter(looseEleID, allLeptons)
nlep = len(allLeptons)
#SF and OF channels
leptons = {\
'mu': {'name': 'mumu', 'file': muons},
'e': {'name': 'ee', 'file': electrons},
'emu': {'name': 'emu', 'file': [electrons,muons]},
}
for lep in leptons.keys():
twoleptons = False
#Same Flavor
if lep != 'emu':
if len(leptons[lep]['file']) == 2 and leptons[lep]['file'][
0]['pdgId'] * leptons[lep]['file'][1]['pdgId'] < 0:
twoleptons = True
l0pt, l0eta, l0phi = leptons[lep]['file'][0][
'pt'], leptons[lep]['file'][0]['eta'], leptons[
lep]['file'][0]['phi']
l1pt, l1eta, l1phi = leptons[lep]['file'][1][
'pt'], leptons[lep]['file'][1]['eta'], leptons[
lep]['file'][1]['phi']
leadingleptonpt = l0pt
subleadingleptonpt = l1pt
mll = sqrt(2. * l0pt * l1pt *
(cosh(l0eta - l1eta) - cos(l0phi - l1phi)))
zveto = True
#Opposite Flavor
if lep == 'emu':
if len(leptons[lep]['file'][0]) == 1 and len(
leptons[lep]['file']
[1]) == 1 and leptons[lep]['file'][0][0][
'pdgId'] * leptons[lep]['file'][1][0]['pdgId'] < 0:
twoleptons = True
l0pt, l0eta, l0phi = leptons[lep]['file'][0][0][
'pt'], leptons[lep]['file'][0][0]['eta'], leptons[
lep]['file'][0][0]['phi']
l1pt, l1eta, l1phi = leptons[lep]['file'][1][0][
'pt'], leptons[lep]['file'][1][0]['eta'], leptons[
lep]['file'][1][0]['phi']
if l1pt > l0pt:
leadingleptonpt = l1pt
subleadingleptonpt = l0pt
else:
leadingleptonpt = l0pt
subleadingleptonpt = l1pt
mll = sqrt(2. * l0pt * l1pt *
(cosh(l0eta - l1eta) - cos(l0phi - l1phi)))
zveto = False
if (twoleptons and mll > 20
and not zveto) or (twoleptons and mll > 20 and zveto
and abs(mll - 91.2) > 15):
mt2Calc.setMet(met, metPhi)
mt2Calc.setLeptons(l0pt, l0eta, l0phi, l1pt, l1eta, l1phi)
mt2ll = mt2Calc.mt2ll()
jets = filter(
lambda j: j['pt'] > 30 and abs(j['eta']) < 2.4 and j[
'id'], getJets(chain))
bjetspt = filter(lambda j: j['btagCSV'] > 0.89, jets)
nobjets = filter(lambda j: j['btagCSV'] <= 0.89, jets)
nmuons = len(muons)
nelectrons = len(electrons)
ht = sum([j['pt'] for j in jets])
if len(jets) < 1: continue
if mt2ll < mt2llcut: continue
cutflow['metsig']['event'] = met / sqrt(ht)
cutflow['nbjets']['event'] = len(bjetspt)
cutflow['njets']['event'] = len(jets)
cutflow['ht']['event'] = ht
for cuttype in cutflow.keys():
eventisgood = True
for cuttype2 in cutflow.keys():
if cuttype2 != cuttype:
if cutflow[cuttype2]['event'] < cutflow[
cuttype2]['baseline']:
eventisgood = False
if eventisgood:
for cut in cutflow[cuttype]['cuts']:
if cutflow[cuttype]['event'] >= cut:
cutflow[cuttype]['cuts'][cut]['samples'][
s['name']] += weight
baselineisgood = True
for cuttype in cutflow.keys():
if cutflow[cuttype]['event'] < cutflow[cuttype][
'baseline']:
baselineisgood = False
if baselineisgood:
baselineamount['samples'][s['name']] += weight
del eList
sigtot = baselineamount['samples'][signal[0]['name']]
bkgtot = sum(baselineamount['samples'].values()) - sigtot
if bkgtot > 0: baselineamount['SoverB'] = 100 * (sigtot / sqrt(bkgtot))
else: baselineamount['SoverB'] = 0.
for cuttype in cutflow.keys():
for cut in cutflow[cuttype]['cuts']:
sigtot = cutflow[cuttype]['cuts'][cut]['samples'][signal[0]
['name']]
bkgtot = sum(
cutflow[cuttype]["cuts"][cut]['samples'].values()) - sigtot
if bkgtot > 0:
cutflow[cuttype]['cuts'][cut]['SoverB'] = 100 * (sigtot /
sqrt(bkgtot))
else:
cutflow[cuttype]['cuts'][cut]['SoverB'] = 0.
maketable(baselineamount, cutflow, mt2llcut, metcut, signal, lumi)
isElecElec[0] = 1
if not s["name"].find("DoubleMuon") and triggerMuMu:
trigger = 1
isMuonMuon[0] = 1
if not s["name"].find("DoubleMuon") and triggerMuMu == 0:
trigger = 0
isMuonMuon[0] = 1
if not s["name"].find("MuonEG") and triggerMuEle:
trigger = 1
isMuonElec[0] = 1
if not s["name"].find("MuonEG") and triggerMuEle == 0:
trigger = 0
isMuonElec[0] = 1
leptons = filter(lambda l: looseMuID(l) or looseEleID(l),
getLeptons(chain))
jets = filter(
lambda j: j['pt'] > 30 and abs(j['eta']) < 2.4 and j['id'],
getJets(chain))
bjets = filter(lambda j: j['btagCSV'] > 0.890, jets)
nbjets[0] = len(bjets)
njets[0] = len(jets)
nleptons[0] = len(leptons)
Process[:200] = s["name"]
HT[0] = sum([j['pt'] for j in jets])
METSqrtHT[0] = met / HT[0]
ptLeadingBJet[0] = bjets[0]['pt']
etaLeadingBJet[0] = bjets[0]['eta']
PhiMetJet1 = deltaPhi(metPhi, getVarValue(chain, "Jet_phi", 0))
isElecElec[0] = 1
if not s["name"].find("DoubleMuon") and triggerMuMu :
trigger = 1
isMuonMuon[0] = 1
if not s["name"].find("DoubleMuon") and triggerMuMu==0 :
trigger = 0
isMuonMuon[0] = 1
if not s["name"].find("MuonEG") and triggerMuEle :
trigger = 1
isMuonElec[0] = 1
if not s["name"].find("MuonEG") and triggerMuEle==0 :
trigger = 0
isMuonElec[0] = 1
leptons = filter(lambda l: looseMuID(l) or looseEleID(l), getLeptons(chain))
jets = filter(lambda j:j['pt']>30 and abs(j['eta'])<2.4 and j['id'], getJets(chain))
bjets = filter(lambda j:j['btagCSV']>0.890, jets)
nbjets[0] = len(bjets)
njets[0] = len(jets)
nleptons[0] = len(leptons)
Process[:200] = s["name"]
HT[0] = sum([j['pt'] for j in jets])
METSqrtHT[0] = met/HT[0]
ptLeadingBJet[0] = bjets[0]['pt']
etaLeadingBJet[0] = bjets[0]['eta']
PhiMetJet1 = deltaPhi(metPhi,getVarValue(chain, "Jet_phi",0))
PhiMetJet2 = deltaPhi(metPhi,getVarValue(chain, "Jet_phi",1))
counterRecoGen_allMatched[mode]={}
counterRecoGen_oneMuMatchedToB[mode]={}
counterRecoGen_oneEleMatchedToB[mode]={}
counterRecoGen_oneMuMatchedToTau[mode]={}
counterRecoGen_oneEleMatchedToTau[mode]={}
for ev in range(nEvents):
ntot+=1
if ev%10000==0:print "At %i/%i"%(ev,nEvents)
chain.GetEntry(eList.GetEntry(ev))
mt2Calc.reset()
weight = reduceStat*getVarValue(chain, "weight")*lumiScale if not s['isData'] else 1
met = getVarValue(chain, "met_pt")
metPhi = getVarValue(chain, "met_phi")
jets = filter(lambda j:j['pt']>30 and abs(j['eta'])<2.4 and j['id'], getJets(chain))
leptons = filter(lambda l: looseMuID(l) or looseEleID(l), getLeptons(chain, collVars=leptonVars+['mcMatchId','mcMatchAny','mcMatchTau','mcPt']))
#LepGood_mcMatchId Match to source from hard scatter (pdgId of heaviest particle in chain, 25 for H, 6 for t, 23/24 for W/Z), zero if non-prompt or fake for Leptons after the preselection
#LepGood_mcMatchAny Match to any final state leptons: 0 if unmatched, 1 if light flavour (including prompt), 4 if charm, 5 if bottom for Leptons after the preselection
#LepGood_mcMatchTau True if the leptons comes from a tau for Leptons after the preselection
# for l in leptons:
# if (l['mcMatchAny']==0 and (not (l['mcMatchId']==0))) or ( (not l['mcMatchAny']==0) and (l['mcMatchId']==0)):
# print "Match?",l
#RECO
mu = filter(lambda l: abs(l['pdgId'])==13, leptons)
ele = filter(lambda l: abs(l['pdgId'])==11, leptons)
#RECO mathes
muMatched = filter(lambda l: abs(l['mcMatchAny'])==1, mu)
eleMatched = filter(lambda l: abs(l['mcMatchAny'])==1, ele)
ntot += 1
if ev % 10000 == 0: print "At %i/%i" % (ev, nEvents)
chain.GetEntry(eList.GetEntry(ev))
mt2Calc.reset()
weight = reduceStat * getVarValue(
chain, "weight") * lumiScale if not s['isData'] else 1
met = getVarValue(chain, "met_pt")
metPhi = getVarValue(chain, "met_phi")
jets = filter(
lambda j: j['pt'] > 30 and abs(j['eta']) < 2.4 and j['id'],
getJets(chain))
leptons = filter(
lambda l: looseMuID(l) or looseEleID(l),
getLeptons(chain,
collVars=leptonVars +
['mcMatchId', 'mcMatchAny', 'mcMatchTau', 'mcPt']))
#LepGood_mcMatchId Match to source from hard scatter (pdgId of heaviest particle in chain, 25 for H, 6 for t, 23/24 for W/Z), zero if non-prompt or fake for Leptons after the preselection
#LepGood_mcMatchAny Match to any final state leptons: 0 if unmatched, 1 if light flavour (including prompt), 4 if charm, 5 if bottom for Leptons after the preselection
#LepGood_mcMatchTau True if the leptons comes from a tau for Leptons after the preselection
# for l in leptons:
# if (l['mcMatchAny']==0 and (not (l['mcMatchId']==0))) or ( (not l['mcMatchAny']==0) and (l['mcMatchId']==0)):
# print "Match?",l
#RECO
mu = filter(lambda l: abs(l['pdgId']) == 13, leptons)
ele = filter(lambda l: abs(l['pdgId']) == 11, leptons)
#RECO mathes
muMatched = filter(lambda l: abs(l['mcMatchAny']) == 1, mu)
# lhe[i].GetXaxis().SetBinLabel(j+1, getBinName(bins[j]))
def getLHEWeights(c):
res = {}
for i in range(110): #c.GetLeaf("nLHEweight").GetValue()):
res[int(c.GetLeaf("LHEweight_id").GetValue(i))] = c.GetLeaf(
"LHEweight_wgt").GetValue(i)
return res
for ev in range(nEvents)[:maxN]:
if ev % 10000 == 0: print "At %i/%i" % (ev, nEvents)
c.GetEntry(eList.GetEntry(ev))
weight = c.GetLeaf('genWeight').GetValue() * lumiScaleFactor
leptons = getLeptons(c, leptonVars + ['relIso03']) + getOtherLeptons(
c, leptonVars + ['relIso03'])
ele = filter(lambda l: looseEleID(l), leptons)
mu = filter(lambda l: looseMuID(l), leptons)
ele = filter(lambda e: min([999] + [deltaR(m, e) for m in mu]) > 0.1, ele)
lep = mu + ele
lep.sort(key=lambda l: -l['pt'])
if not (len(lep) == 3 and abs(closestOSDLMassToMZ(lep) - mZ) < 10):
continue
jets = getGoodJets(c)
bTaggedJets = filter(lambda l: l['btagCSV'] > 0.605, jets)
nJets = len(jets)
nBTags = len(bTaggedJets)
nBin = getBin(nJets, nBTags)
if nBin < 0: continue
badElectronCandidates[mode]=[]
for ev in range(nEvents):
ntot+=1
if ev%10000==0:print "At %i/%i"%(ev,nEvents)
chain.GetEntry(eList.GetEntry(ev))
mt2Calc.reset()
weight = reduceStat*getVarValue(chain, "weight")*lumiScale if not s['isData'] else 1
mt2ll = getVarValue(chain, "dl_mt2ll")
met = getVarValue(chain, "met_pt")
metPhi = getVarValue(chain, "met_phi")
genMet = getVarValue(chain, "met_genPt")
genMetPhi = getVarValue(chain, "met_genPhi")
deltaMet = sqrt((met*cos(metPhi)-genMet*cos(genMetPhi))**2+(met*sin(metPhi)-genMet*sin(genMetPhi))**2)
jets = filter(lambda j:j['pt']>30 and abs(j['eta'])<2.4 and j['id'], getJets(chain))
allLeptons = getLeptons(chain, collVars=leptonVars+['mcMatchId','mcMatchAny','mcMatchTau','mcPt','ip3d', 'relIso03', 'relIso04', 'jetPtRatiov1', 'jetPtRelv1', 'jetPtRelv2', 'jetPtRatiov2', 'jetBTagCSV', 'jetDR'])
leptons = filter(lambda l: looseMuID(l) or looseEleID(l), allLeptons)
#LepGood_mcMatchId Match to source from hard scatter (pdgId of heaviest particle in chain, 25 for H, 6 for t, 23/24 for W/Z), zero if non-prompt or fake for Leptons after the preselection
#LepGood_mcMatchAny Match to any final state leptons: 0 if unmatched, 1 if light flavour (including prompt), 4 if charm, 5 if bottom for Leptons after the preselection
#LepGood_mcMatchTau True if the leptons comes from a tau for Leptons after the preselection
# for l in leptons:
# if (l['mcMatchAny']==0 and (not (l['mcMatchId']==0))) or ( (not l['mcMatchAny']==0) and (l['mcMatchId']==0)):
# print "Match?",l
#RECO
looseMu = filter(lambda l: abs(l['pdgId'])==13 and l['miniRelIso']<0.4 and l['pt']>15, allLeptons)
looseEle= filter(lambda l: abs(l['pdgId'])==11 and l['miniRelIso']<0.4 and l['pt']>15, allLeptons)
mu = filter(lambda l: abs(l['pdgId'])==13, leptons)
ele = filter(lambda l: abs(l['pdgId'])==11, leptons)
sys.stdout.write('\r')
chain.GetEntry(eList.GetEntry(ev))
mt2Calc.reset()
#event weight (L= 4fb^-1)
weight = reduceStat*getVarValue(chain, "weight")
if s not in data: weight = weight*(luminosity/4000.)
#MET
met = getVarValue(chain, "met_pt")
metPhi = getVarValue(chain, "met_phi")
#jetpt
leadingjetpt = getVarValue(chain, "Jet_pt",0)
subleadingjetpt = getVarValue(chain, "Jet_pt",1)
#Leptons
allLeptons = getLeptons(chain)
muons = filter(looseMuID, allLeptons)
electrons = filter(looseEleID, allLeptons)
#SF and OF channels
leptons = {\
'mu': {'name': 'mumu', 'file': muons},
'e': {'name': 'ee', 'file': electrons},
'emu': {'name': 'emu', 'file': [electrons,muons]},
}
for lep in leptons.keys():
twoleptons = False
#Same Flavor
if lep != 'emu':
if len(leptons[lep]['file'])==2 and leptons[lep]['file'][0]['pdgId']*leptons[lep]['file'][1]['pdgId']<0:
def cutflow(sig):
#######################################################
# SELECT WHAT YOU WANT TO DO HERE #
#######################################################
reduceStat = 100 #recude the statistics, i.e. 10 is ten times less samples to look at
#######################################################
# Define cutflow you want to make #
#######################################################
mt2llcut = 80.
metcut = 40.
lumi = 10000.
baselineamount = { 'samples':{}, 'SoverB': None,}
cutflow = {\
'metsig':{'baseline': 0.,
'cuts': {\
3:{'samples':{}, 'SoverB': None},
4:{'samples':{}, 'SoverB': None},
5:{'samples':{}, 'SoverB': None},
6:{'samples':{}, 'SoverB': None},
7:{'samples':{}, 'SoverB': None},
8:{'samples':{}, 'SoverB': None},
9:{'samples':{}, 'SoverB': None},
10:{'samples':{}, 'SoverB': None},
10:{'samples':{}, 'SoverB': None},
},
'event': None,
},
'nbjets':{'baseline': 1.,
'cuts': {\
0:{'samples':{}, 'SoverB': None},
1:{'samples':{}, 'SoverB': None},
2:{'samples':{}, 'SoverB': None},
3:{'samples':{}, 'SoverB': None},
4:{'samples':{}, 'SoverB': None},
5:{'samples':{}, 'SoverB': None},
},
'event': None,
},
'njets': {'baseline': 2.,
'cuts': {\
1:{'samples':{}, 'SoverB': None},
2:{'samples':{}, 'SoverB': None},
3:{'samples':{}, 'SoverB': None},
4:{'samples':{}, 'SoverB': None},
5:{'samples':{}, 'SoverB': None},
6:{'samples':{}, 'SoverB': None},
7:{'samples':{}, 'SoverB': None},
},
'event': None,
},
'ht': {'baseline': 0.,
'cuts': {\
50: {'samples':{}, 'SoverB': None},
100:{'samples':{}, 'SoverB': None},
200:{'samples':{}, 'SoverB': None},
300:{'samples':{}, 'SoverB': None},
400:{'samples':{}, 'SoverB': None},
500:{'samples':{}, 'SoverB': None},
},
'event': None,
},
}
#preselection: MET>40, njets>=2, n_bjets>=1, n_lep>=2
#For now see here for the Sum$ syntax: https://root.cern.ch/root/html/TTree.html#TTree:Draw@2
preselection = 'met_pt>'+str(metcut)+'&&Sum$(LepGood_pt>20)==2'
#######################################################
# load all the samples #
#######################################################
backgrounds = [singleTop_50ns,DY_50ns,TTJets_50ns]
#signals = [SMS_T2tt_2J_mStop425_mLSP325, SMS_T2tt_2J_mStop500_mLSP325, SMS_T2tt_2J_mStop650_mLSP325, SMS_T2tt_2J_mStop850_mLSP100]
signal = [sig]
#######################################################
# get the TChains for each sample #
#######################################################
for s in backgrounds+signal:
s['chain'] = getChain(s,histname="")
#######################################################
# Start filling in the histograms #
#######################################################
for s in backgrounds+signal:
for cuttype in cutflow.keys():
for cut in cutflow[cuttype]['cuts'].keys():
cutflow[cuttype]['cuts'][cut]['samples'][s["name"]] = 0
baselineamount['samples'][s['name']] = 0
chain = s["chain"]
#Using Event loop
#get EList after preselection
print "Looping over %s" % s["name"]
eList = getEList(chain, preselection)
nEvents = eList.GetN()/reduceStat
print "Found %i events in %s after preselection %s, looping over %i" % (eList.GetN(),s["name"],preselection,nEvents)
for ev in range(nEvents):
increment = 50
if nEvents>increment and ev%(nEvents/increment)==0:
sys.stdout.write('\r' + "=" * (ev / (nEvents/increment)) + " " * ((nEvents - ev)/ (nEvents/increment)) + "]" + str(round((ev+1) / (float(nEvents)/100),2)) + "%")
sys.stdout.flush()
sys.stdout.write('\r')
chain.GetEntry(eList.GetEntry(ev))
mt2Calc.reset()
#event weight (L= 4fb^-1)
weight = reduceStat*getVarValue(chain, "weight")
weight = weight*(lumi/4000.)
#MET
met = getVarValue(chain, "met_pt")
metPhi = getVarValue(chain, "met_phi")
#jetpt
leadingjetpt = getVarValue(chain, "Jet_pt",0)
subleadingjetpt = getVarValue(chain, "Jet_pt",1)
#Leptons
allLeptons = getLeptons(chain)
muons = filter(looseMuID, allLeptons)
electrons = filter(looseEleID, allLeptons)
nlep = len(allLeptons)
#SF and OF channels
leptons = {\
'mu': {'name': 'mumu', 'file': muons},
'e': {'name': 'ee', 'file': electrons},
'emu': {'name': 'emu', 'file': [electrons,muons]},
}
for lep in leptons.keys():
twoleptons = False
#Same Flavor
if lep != 'emu':
if len(leptons[lep]['file'])==2 and leptons[lep]['file'][0]['pdgId']*leptons[lep]['file'][1]['pdgId']<0:
twoleptons = True
l0pt, l0eta, l0phi = leptons[lep]['file'][0]['pt'], leptons[lep]['file'][0]['eta'], leptons[lep]['file'][0]['phi']
l1pt, l1eta, l1phi = leptons[lep]['file'][1]['pt'], leptons[lep]['file'][1]['eta'], leptons[lep]['file'][1]['phi']
leadingleptonpt = l0pt
subleadingleptonpt = l1pt
mll = sqrt(2.*l0pt*l1pt*(cosh(l0eta-l1eta)-cos(l0phi-l1phi)))
zveto = True
#Opposite Flavor
if lep == 'emu':
if len(leptons[lep]['file'][0])==1 and len(leptons[lep]['file'][1])==1 and leptons[lep]['file'][0][0]['pdgId']*leptons[lep]['file'][1][0]['pdgId']<0:
twoleptons = True
l0pt, l0eta, l0phi = leptons[lep]['file'][0][0]['pt'], leptons[lep]['file'][0][0]['eta'], leptons[lep]['file'][0][0]['phi']
l1pt, l1eta, l1phi = leptons[lep]['file'][1][0]['pt'], leptons[lep]['file'][1][0]['eta'], leptons[lep]['file'][1][0]['phi']
if l1pt > l0pt :
leadingleptonpt = l1pt
subleadingleptonpt = l0pt
else:
leadingleptonpt = l0pt
subleadingleptonpt = l1pt
mll = sqrt(2.*l0pt*l1pt*(cosh(l0eta-l1eta)-cos(l0phi-l1phi)))
zveto = False
if (twoleptons and mll>20 and not zveto) or (twoleptons and mll > 20 and zveto and abs(mll-90.2)>15):
mt2Calc.setMet(met,metPhi)
mt2Calc.setLeptons(l0pt, l0eta, l0phi, l1pt, l1eta, l1phi)
mt2ll = mt2Calc.mt2ll()
jets = filter(lambda j:j['pt']>30 and abs(j['eta'])<2.4 and j['id'], getJets(chain))
bjetspt = filter(lambda j:j['btagCSV']>0.89, jets)
nobjets = filter(lambda j:j['btagCSV']<=0.89, jets)
nmuons = len(muons)
nelectrons = len(electrons)
ht = sum([j['pt'] for j in jets])
if len(jets)<1: continue
if mt2ll<mt2llcut: continue
cutflow['metsig']['event'] = met/sqrt(ht)
cutflow['nbjets']['event'] = len(bjetspt)
cutflow['njets']['event'] = len(jets)
cutflow['ht']['event'] = ht
for cuttype in cutflow.keys():
eventisgood = True
for cuttype2 in cutflow.keys():
if cuttype2 != cuttype:
if cutflow[cuttype2]['event'] < cutflow[cuttype2]['baseline']:
eventisgood = False
if eventisgood:
for cut in cutflow[cuttype]['cuts']:
if cutflow[cuttype]['event'] >= cut:
cutflow[cuttype]['cuts'][cut]['samples'][s['name']] += weight
baselineisgood = True
for cuttype in cutflow.keys():
if cutflow[cuttype]['event'] < cutflow[cuttype]['baseline']:
baselineisgood = False
if baselineisgood:
baselineamount['samples'][s['name']] += weight
del eList
sigtot = baselineamount['samples'][signal[0]['name']]
bkgtot = sum(baselineamount['samples'].values()) - sigtot
if bkgtot > 0: baselineamount['SoverB'] = 100 * (sigtot/sqrt(bkgtot))
else: baselineamount['SoverB'] = 0.
for cuttype in cutflow.keys():
for cut in cutflow[cuttype]['cuts']:
sigtot = cutflow[cuttype]['cuts'][cut]['samples'][signal[0]['name']]
bkgtot = sum(cutflow[cuttype]["cuts"][cut]['samples'].values()) - sigtot
if bkgtot > 0: cutflow[cuttype]['cuts'][cut]['SoverB'] = 100 * (sigtot/sqrt(bkgtot))
else: cutflow[cuttype]['cuts'][cut]['SoverB'] = 0.
maketable(baselineamount, cutflow, mt2llcut, metcut,signal,lumi)
#for i in range(110):
# lhe[i] = ROOT.TH1F("lhe_"+str(i), "lhe_"+str(i), len(bins),0,len(bins))
# for j in range(len(bins)):
# lhe[i].GetXaxis().SetBinLabel(j+1, getBinName(bins[j]))
def getLHEWeights(c):
res={}
for i in range(110):#c.GetLeaf("nLHEweight").GetValue()):
res[int(c.GetLeaf("LHEweight_id").GetValue(i))] = c.GetLeaf("LHEweight_wgt").GetValue(i)
return res
for ev in range(nEvents)[:maxN]:
if ev%10000==0:print "At %i/%i"%(ev,nEvents)
c.GetEntry(eList.GetEntry(ev))
weight = c.GetLeaf('genWeight').GetValue()*lumiScaleFactor
leptons = getLeptons(c, leptonVars+['relIso03'])+getOtherLeptons(c, leptonVars+['relIso03'])
ele = filter(lambda l:looseEleID(l), leptons)
mu = filter(lambda l:looseMuID(l), leptons)
ele = filter(lambda e:min([999]+[deltaR(m, e) for m in mu])>0.1, ele)
lep = mu+ele
lep.sort(key=lambda l:-l['pt'])
if not (len(lep)==3 and abs( closestOSDLMassToMZ(lep) - mZ)< 10): continue
jets = getGoodJets(c)
bTaggedJets = filter(lambda l:l['btagCSV']>0.605, jets)
nJets = len(jets)
nBTags = len(bTaggedJets)
nBin = getBin(nJets, nBTags)
if nBin<0:continue
# print "#e %i #m %i #j %i #b %i n-bin %i"%(len(ele), len(mu), nJets, nBTags, nBin)
central.Fill(nBin, weight)