#######################################################
# get the TChains for each sample #
#######################################################
for s in backgrounds+signals:
for plot in plots:
plots[plot]['histo'][s['name']] = ROOT.TH1F(plot + "_" + s["name"], plot + "_" + s["name"], *(plots[plot]['binning']))
#######################################################
# Start filling in the histograms #
#######################################################
for i,s in enumerate(backgrounds+signals): #Enumerate returns a couple (index of key in array, dictionary)
chain = s["chain"]
print "Looping over %s" % s["name"]
eList = getEList(chain, preselection) #Returns a list with events that pass the selection
nEvents = eList.GetN()/reduceStat
for ev in range(nEvents):
chain.GetEntry(eList.GetEntry(ev))
m_ll = getVarValue(chain, "dl_mass")
ZVetoCut = abs(90.2-m_ll) > 15
isSF = True if (getVarValue(chain, "isEE") == 1 or getVarValue(chain, "isMuMu") == 1) else False
if (not isSF):
ZVetoCut = True
weight = reduceStat*getVarValue(chain, "weight")*lumi
if (ZVetoCut):
for plot in plots.keys():
variable = getVarValue(chain, plots[plot]['variable'])
plots[plot]['histo'][s["name"]].Fill(variable, weight)
print (plots[plot]['histo'][s['name']].GetEntries())
t.Branch("njets", njets, "njets/I")
t.Branch("nleptons", nleptons, "nleptons/I")
t.Branch("nVertices", nVertices, "nVertices/I")
t.Branch("isMC", isMC, "isMC/I")
t.Branch("isElecElec", isElecElec, "isElecElec/I")
t.Branch("isMuonMuon", isMuonMuon, "isMuonMuon/I")
t.Branch("isMuonElec", isMuonElec, "isMuonElec/I")
t.Branch("LeadingLepton","TLorentzVector",lepton1)
t.Branch("SubLeadingLepton","TLorentzVector",lepton2)
t.Branch('Process', Process, 'Process[200]/C')
chain = s["chain"]
if s['isData']==0:
eList = getEList(chain, preselectionMC)
nEvents = eList.GetN()/reduceStat
print "Found %i events in %s after preselection %s, looping over %i" % (eList.GetN(),s["name"],preselectionMC,nEvents)
else:
eList = getEList(chain, preselectionData)
nEvents = eList.GetN()/reduceStat
print "Found %i events in %s after preselection %s, looping over %i" % (eList.GetN(),s["name"],preselectionData,nEvents)
for ev in range(nEvents):
if ev%10000==0:print "At %i/%i"%(ev,nEvents)
chain.GetEntry(eList.GetEntry(ev))
mt2Calc.reset()
for s in backgrounds+data:
#construct 1D histograms
for lepton in plots.keys():
for pk in plots[lepton].keys():
for plot in plots[lepton][pk].keys():
plots[lepton][pk][plot]['histo'][s["name"]] = ROOT.TH1D(plots[lepton][pk][plot]['name']+"_"+s["name"]+"_"+pk, plots[lepton][pk][plot]['name']+"_"+s["name"]+"_"+pk, *plots[lepton][pk][plot]['binning'])
plots[lepton][pk][plot]['histo'][s["name"]].Sumw2()
for lepton in plots_cut.keys():
for pk in plots_cut[lepton].keys():
for plot in plots_cut[lepton][pk].keys():
plots_cut[lepton][pk][plot]['histo'][s["name"]] = ROOT.TH1D(plots_cut[lepton][pk][plot]['name']+"_"+s["name"]+"_"+pk, plots_cut[lepton][pk][plot]['name']+"_"+s["name"]+"_"+pk, *plots_cut[lepton][pk][plot]['binning'])
plots_cut[lepton][pk][plot]['histo'][s["name"]].Sumw2()
chain = s["chain"]
eList = getEList(chain, preselection) if not s['isData'] else getEList(chain, preselection+'&&'+datacut)
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))
#pileupweight = puReweighting(chain) if not s['isData'] else 1.
for o in coll:
if deltaR(l, o) < dR and (l['pdgId'] == o['pdgId']
or not checkPdgId):
return True
return False
return match
for s in samples:
# for pk in plots.keys():
# plots[pk]['histo'][s['name']] = ROOT.TH1F("met_"+s["name"], "met_"+s["name"], *(plots[pk]['binning']))
chain = s["chain"]
print "Looping over %s" % s["name"]
eList = getEList(chain, preselection + "&&dl_mt2ll>140")
nEvents = eList.GetN() / reduceStat
print "Found %i events in %s after preselection %s, looping over %i" % (
eList.GetN(), s["name"], preselection, nEvents)
ntot = 0
counterReco = {}
counterRecoGen = {}
counterRecoGen_muMatched = {}
counterRecoGen_eleMatched = {}
counterRecoGen_allMatched = {}
counterRecoGen_oneMuMatchedToB = {}
counterRecoGen_oneEleMatchedToB = {}
counterRecoGen_oneMuMatchedToTau = {}
counterRecoGen_oneEleMatchedToTau = {}
counterRecoGen_recoTau = {}
counterRecoGen_recoMatchedTau = {}
return False
ele_MVAID_cuts_vloose = {(0,0.8):-0.16 , (0.8, 1.479):-0.65, (1.57, 999): -0.74}
ele_MVAID_cuts_loose = {(0,0.8):0.35 , (0.8, 1.479):0.20, (1.57, 999): -0.52}
ele_MVAID_cuts_tight = {(0,0.8):0.87 , (0.8, 1.479):0.60, (1.57, 999): 0.17}
def looseEleID(l, ptCut=10, absEtaCut=2.5):
if l["pt"]>=ptCut and abs(l["eta"])<absEtaCut and abs(l["pdgId"])==11 and l["convVeto"] and cmgMVAEleID(l, ele_MVAID_cuts_loose):
if abs(l['eta'])<1.479:
return l["relIso03"]<0.076 and abs(l["sip3d"])<4.0 and abs(l['dz'])< 0.373 and abs(l['dxy'])< 0.0118 and l["lostHits"]<=2
else:
return l["relIso03"]<0.0678 and abs(l["sip3d"])<4.0 and abs(l['dz'])< 0.602 and abs(l['dxy'])< 0.0739 and l["lostHits"]<=1
else: return False
eList = getEList(c, preselection)
nEvents = eList.GetN()
print "Found %i events after preselection %s, looping over %i" % (eList.GetN(),preselection,nEvents)
bins = [
(3,3,0,0),
(3,3,1,1),
(3,3,2,-1),
(4,-1,0,0),
(4,-1,1,1),
(4,-1,2,-1),
]
def getBinName(b):
res=""
if b[0]==b[1]:res+="=="+str(b[0])+'j'
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)
def dRMatch(coll, dR=0.4):
def match(l):
for o in coll:
if deltaR(l,o)<dR: return True
return False
return match
for s in samples:
# for pk in plots.keys():
# plots[pk]['histo'][s['name']] = ROOT.TH1F("met_"+s["name"], "met_"+s["name"], *(plots[pk]['binning']))
chain = s["chain"]
print "Looping over %s" % s["name"]
eList = getEList(chain, preselection+"&&dl_mt2ll>140")
nEvents = eList.GetN()/reduceStat
print "Found %i events in %s after preselection %s, looping over %i" % (eList.GetN(),s["name"],preselection,nEvents)
ntot=0
counterReco={}
counterRecoGen={}
counterRecoGen_muMatched={}
counterRecoGen_eleMatched={}
counterRecoGen_allMatched={}
counterRecoGen_oneMuMatchedToB={}
counterRecoGen_oneEleMatchedToB={}
counterRecoGen_oneMuMatchedToTau={}
counterRecoGen_oneEleMatchedToTau={}
for mode in ["isMuMu", "isEE", "isEMu"]:
counterReco[mode]=0
counterRecoGen[mode]={}
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)
if l["pt"] >= ptCut and abs(l["eta"]) < absEtaCut and abs(
l["pdgId"]) == 11 and l["convVeto"] and cmgMVAEleID(
l, ele_MVAID_cuts_loose):
if abs(l['eta']) < 1.479:
return l["relIso03"] < 0.076 and abs(l["sip3d"]) < 4.0 and abs(
l['dz']) < 0.373 and abs(
l['dxy']) < 0.0118 and l["lostHits"] <= 2
else:
return l["relIso03"] < 0.0678 and abs(l["sip3d"]) < 4.0 and abs(
l['dz']) < 0.602 and abs(
l['dxy']) < 0.0739 and l["lostHits"] <= 1
else:
return False
eList = getEList(c, preselection)
nEvents = eList.GetN()
print "Found %i events after preselection %s, looping over %i" % (
eList.GetN(), preselection, nEvents)
bins = [
(3, 3, 0, 0),
(3, 3, 1, 1),
(3, 3, 2, -1),
(4, -1, 0, 0),
(4, -1, 1, 1),
(4, -1, 2, -1),
]
def getBinName(b):
# Start filling in the histograms #
#######################################################
for s in sample:
#construct 1D histograms
for lepton in plots.keys():
for plot in plots[lepton].keys():
plots[lepton][plot]['histo'][s["name"]] = ROOT.TH1D(
plots[lepton][plot]['name'] + "_" + s["name"],
plots[lepton][plot]['name'] + "_" + s["name"],
*plots[lepton][plot]['binning'])
plots[lepton][plot]['histo'][s["name"]].Sumw2()
chain = s["chain"]
if (s == DoubleMuon_Run2015D):
eList = getEList(chain,
preselection + '&&' + datacut + '&&HLT_mumuIso')
elif (s == DoubleEG_Run2015D):
eList = getEList(chain, preselection + '&&' + datacut + '&&HLT_ee_DZ')
elif (s == MuonEG_Run2015D):
eList = getEList(chain, preselection + '&&' + datacut + '&&HLT_mue')
else:
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)
#start event loop
for ev in range(nEvents):
##################################
chain.SetBranchStatus("nGoodElectrons",1)
chain.SetBranchStatus("isOS",1)
chain.SetBranchStatus("isEE",1)
chain.SetBranchStatus("isEMu",1)
chain.SetBranchStatus("isMuMu",1)
if s not in data:
chain.SetBranchStatus("genWeight",1)
chain.SetBranchStatus("Jet_mcMatchFlav",1)
chain.SetBranchStatus("xsec",1)
chain.SetBranchStatus("Jet_partonId",1)
#Using Event loop
#get EList after preselection
print '\n', "Looping over %s" % s["name"]
if s == DY_25ns: eList = getEList(chain, preselection+"&&Sum$(Jet_pt)<150")
else: 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)
#######################################################
# Start filling in the histograms #
#######################################################
for s in sample:
#construct 1D histograms
for lepton in plots.keys():
for plot in plots[lepton].keys():
plots[lepton][plot]['histo'][s["name"]] = ROOT.TH1D(plots[lepton][plot]['name']+"_"+s["name"], plots[lepton][plot]['name']+"_"+s["name"], *plots[lepton][plot]['binning'])
plots[lepton][plot]['histo'][s["name"]].Sumw2()
chain = s["chain"]
if (s == DoubleMuon_Run2015D):
eList = getEList(chain, preselection+'&&'+datacut+'&&HLT_mumuIso')
elif (s == DoubleEG_Run2015D):
eList = getEList(chain, preselection+'&&'+datacut+'&&HLT_ee_DZ')
elif (s == MuonEG_Run2015D):
eList = getEList(chain, preselection+'&&'+datacut+'&&HLT_mue')
else:
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)
#start event loop
for ev in range(nEvents):
##################################
# Processing output #
#######################################################
# Start filling in the histograms #
#######################################################
for s in backgrounds+data:
#construct 1D histograms
for lepton in plots.keys():
for plot in plots[lepton].keys():
plots[lepton][plot]['histo'][s["name"]] = ROOT.TH1D(plots[lepton][plot]['name']+"_"+s["name"], plots[lepton][plot]['name']+"_"+s["name"], *plots[lepton][plot]['binning'])
plots[lepton][plot]['histo'][s["name"]].Sumw2()
chain = s["chain"]
if (s == DoubleMuon_Run2015D):
eList = getEList(chain, preselection) if not s['isData'] else getEList(chain, preselection+'&&'+datacut+'&&HLT_mumuIso')
elif (s == DoubleEG_Run2015D):
eList = getEList(chain, preselection) if not s['isData'] else getEList(chain, preselection+'&&'+datacut+'&&HLT_ee_DZ')
elif (s == MuonEG_Run2015D):
eList = getEList(chain, preselection) if not s['isData'] else getEList(chain, preselection+'&&'+datacut+'&&HLT_mue')
else:
eList = getEList(chain, preselection) if not s['isData'] else 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)
#start event loop
for ev in range(nEvents):
##################################
# Processing output #
chain.SetBranchStatus("isOS", 1)
chain.SetBranchStatus("isEE", 1)
chain.SetBranchStatus("isEMu", 1)
chain.SetBranchStatus("isMuMu", 1)
if s not in data:
chain.SetBranchStatus("genWeight", 1)
chain.SetBranchStatus("Jet_mcMatchFlav", 1)
chain.SetBranchStatus("xsec", 1)
chain.SetBranchStatus("Jet_partonId", 1)
#Using Event loop
#get EList after preselection
print '\n', "Looping over %s" % s["name"]
if s == DY_25ns:
eList = getEList(chain, preselection + "&&Sum$(Jet_pt)<150")
else:
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)) + "%")
t.Branch("njets", njets, "njets/I")
t.Branch("nleptons", nleptons, "nleptons/I")
t.Branch("nVertices", nVertices, "nVertices/I")
t.Branch("isMC", isMC, "isMC/I")
t.Branch("isElecElec", isElecElec, "isElecElec/I")
t.Branch("isMuonMuon", isMuonMuon, "isMuonMuon/I")
t.Branch("isMuonElec", isMuonElec, "isMuonElec/I")
t.Branch("EventNumber", EventNumber, "EventNumber/I")
t.Branch("LeadingLepton", "TLorentzVector", lepton1)
t.Branch("SubLeadingLepton", "TLorentzVector", lepton2)
t.Branch('Process', Process, 'Process[200]/C')
chain = s["chain"]
if s['isData'] == 0:
eList = getEList(chain, preselectionMC)
nEvents = eList.GetN() / reduceStat
print "Found %i events in %s after preselection %s, looping over %i" % (
eList.GetN(), s["name"], preselectionMC, nEvents)
else:
eList = getEList(chain, preselectionData)
nEvents = eList.GetN() / reduceStat
print "Found %i events in %s after preselection %s, looping over %i" % (
eList.GetN(), s["name"], preselectionData, nEvents)
for ev in range(nEvents):
if ev % 10000 == 0: print "At %i/%i" % (ev, nEvents)
chain.GetEntry(eList.GetEntry(ev))
chain.SetBranchStatus("Jet_pt",1)
chain.SetBranchStatus("Jet_phi",1)
chain.SetBranchStatus("Jet_btagCMVA",1)
chain.SetBranchStatus("Jet_btagCSV",1)
chain.SetBranchStatus("Jet_id",1)
chain.SetBranchStatus("weight",1)
if s not in data:
chain.SetBranchStatus("genWeight",1)
chain.SetBranchStatus("Jet_mcMatchFlav",1)
chain.SetBranchStatus("xsec",1)
chain.SetBranchStatus("Jet_partonId",1)
#Using Event loop
#get EList after preselection
print '\n', "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")
pk, plots[lepton][pk][plot]['name'] + "_" + s["name"] +
"_" + pk, *plots[lepton][pk][plot]['binning'])
plots[lepton][pk][plot]['histo'][s["name"]].Sumw2()
for lepton in plots_cut.keys():
for pk in plots_cut[lepton].keys():
for plot in plots_cut[lepton][pk].keys():
plots_cut[lepton][pk][plot]['histo'][s["name"]] = ROOT.TH1D(
plots_cut[lepton][pk][plot]['name'] + "_" + s["name"] +
"_" + pk, plots_cut[lepton][pk][plot]['name'] + "_" +
s["name"] + "_" + pk,
*plots_cut[lepton][pk][plot]['binning'])
plots_cut[lepton][pk][plot]['histo'][s["name"]].Sumw2()
chain = s["chain"]
eList = getEList(chain, preselection) if not s['isData'] else getEList(
chain, preselection + '&&' + datacut)
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()
# Start filling in the histograms #
#######################################################
for s in backgrounds + data:
#construct 1D histograms
for lepton in plots.keys():
for plot in plots[lepton].keys():
plots[lepton][plot]['histo'][s["name"]] = ROOT.TH1D(
plots[lepton][plot]['name'] + "_" + s["name"],
plots[lepton][plot]['name'] + "_" + s["name"],
*plots[lepton][plot]['binning'])
plots[lepton][plot]['histo'][s["name"]].Sumw2()
chain = s["chain"]
if (s == DoubleMuon_Run2015D):
eList = getEList(chain, preselection) if not s['isData'] else getEList(
chain, preselection + '&&' + datacut + '&&HLT_mumuIso')
elif (s == DoubleEG_Run2015D):
eList = getEList(chain, preselection) if not s['isData'] else getEList(
chain, preselection + '&&' + datacut + '&&HLT_ee_DZ')
elif (s == MuonEG_Run2015D):
eList = getEList(chain, preselection) if not s['isData'] else getEList(
chain, preselection + '&&' + datacut + '&&HLT_mue')
else:
eList = getEList(chain, preselection) if not s['isData'] else 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)
if s in data:
chain.SetBranchStatus("HLT_mumuIso",1)
chain.SetBranchStatus("HLT_ee_DZ",1)
chain.SetBranchStatus("HLT_mue",1)
if s not in data:
chain.SetBranchStatus("genWeight",1)
chain.SetBranchStatus("Jet_mcMatchFlav",1)
chain.SetBranchStatus("xsec",1)
chain.SetBranchStatus("Jet_partonId",1)
chain.SetBranchStatus("puWeight",1)
#Using Event loop
#get EList after preselection
print '\n', "Looping over %s" % s["name"]
if s['isData'] : eList = getEList(chain, preselection+'&&'+dataCut)
else: eList = 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)
