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.
weight = reduceStat * getVarValue(
chain, "weight") * (luminosity / 1000.) if not s['isData'] else 1
mll = getVarValue(chain, "dl_mass")
mt2ll = getVarValue(chain, "dl_mt2ll")
met = getVarValue(chain, "met_pt")
nLep = getVarValue(chain, "nLepGood")
l_Pt = getVarValue(chain, "LepGood_pt")
l1_Pt = l_Pt[0]
l2_Pt = l_Pt[1]
jets = filter(
lambda j: j['pt'] > 30 and abs(j['eta']) < 2.4 and j['id'],
getJets(chain))
if nLep == 2:
if len(jets) >= 2:
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")
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
print "Found %i events in %s after preselection %s, looping over %i" % (eList.GetN(),s["name"],preselection,nEvents)
#ROOT output file
#TreeFile = ROOT.TFile("./trees/tree"+s["name"]+".root","recreate")
#Tree = ROOT.TTree("VarTree","Tree of Variables")
#Tree.Branch("MET",MET_n,"MET/D")
#Tree.Branch("MT2ee",MT2ee_n,"MT2ee/D")
#Tree.Branch("MT2emu",MT2emu_n,"MT2emu/D")
#Tree.Branch("MT2mumu",MT2mumu_n,"MT2mumu/D")
for ev in range(nEvents):
if ev%10000==0:print "At %i/%i"%(ev,nEvents)
chain.GetEntry(eList.GetEntry(ev))
mt2Calc.reset()
#event weight (L= 4fb^-1)
if s.has_key('totalweight'): weight = getWeight(chain,s, 4000) #this method for Ward's SPRING15 samples
else: weight = reduceStat*getVarValue(chain, "weight") #this method for Robert's PHYS14 samples
#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)
#GENinfo
#genparticles = getGenParts(chain)
#ROOT output file
#MET_n[0] = met
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*(luminosity/1000.)
#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
l0pt = getVarValue(chain, "l1_pt")
l1pt = getVarValue(chain, "l2_pt")
mll = getVarValue(chain,"dl_mass")
#Leptons
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.
weight = reduceStat*getVarValue(chain, "weight")*(luminosity/1000.) if not s['isData'] else 1
mll = getVarValue(chain,"dl_mass")
mt2ll = getVarValue(chain,"dl_mt2ll")
met = getVarValue(chain,"met_pt")
nLep = getVarValue(chain,"nLepGood")
l_Pt = getVarValue(chain,"LepGood_pt")
l1_Pt = l_Pt[0]
l2_Pt = l_Pt[1]
jets = filter(lambda j:j['pt']>30 and abs(j['eta'])<2.4 and j['id'], getJets(chain))
if nLep==2:
if len(jets)>=2:
plots['2l']['dl_mass']['2mj']['histo'][s['name']].Fill(mll,weight)
plots['2l']['dl_mt2ll']['2mj']['histo'][s['name']].Fill(mt2ll,weight)
def getTaus(c, collVars=tauVars):
return [
getObjDict(c, 'TauGood_', collVars, i)
for i in range(int(getVarValue(c, 'nTauGood')))
]
def getJets(c, jetVars=jetVars, jetColl="Jet"):
return [
getObjDict(c, jetColl + '_', jetVars, i)
for i in range(int(getVarValue(c, 'n' + jetColl)))
]
def getJets(c, jetVars=jetVars):
return [
getObjDict(c, 'Jet_', jetVars, i)
for i in range(int(getVarValue(c, 'nJet')))
]
counterRecoGen_recoTau[mode]={}
counterRecoGen_recoMatchedTau[mode]={}
counterRecoGen_looseMu[mode]={}
counterRecoGen_looseEle[mode]={}
counterRecofake_fakeMet50[mode]={}
counterRecofake_fakeMet100[mode]={}
counterRecofake_fakeMet200[mode]={}
counterRecoGen_failRelIso03[mode]={}
badMuonCandidates[mode]=[]
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
def getGenPartsAll(c):
return [getObjDict(c, "genPartAll_", genVars, i) for i in range(int(getVarValue(c, "ngenPartAll")))]
def getJets(c, jetVars=jetVars, jetColl="Jet"):
return [getObjDict(c, jetColl + "_", jetVars, i) for i in range(int(getVarValue(c, "n" + jetColl)))]
def getPhotons(c, collVars=photonVars, idLevel="loose"):
return [getObjDict(c, "gamma_", collVars, i) for i in range(int(getVarValue(c, "ngamma")))]
def getTaus(c, collVars=tauVars):
return [getObjDict(c, "TauGood_", collVars, i) for i in range(int(getVarValue(c, "nTauGood")))]
def getGenParts(c):
return [
getObjDict(c, "GenPart_", ["eta", "pt", "phi", "charge", "pdgId", "motherId", "grandmotherId"], i)
for i in range(int(getVarValue(c, "nGenPart")))
]
def getLeptons(c, collVars=leptonVars):
return [
getObjDict(c, 'LepGood_', collVars, i)
for i in range(int(getVarValue(c, 'nLepGood')))
]
def getJets(c, jetVars=jetVars): return [getObjDict(c, 'Jet_', jetVars, i) for i in range(int(getVarValue(c, 'nJet')))]
def getElectrons(c, collVars=leptonVars):
return [
getObjDict(c, 'LepGood_', collVars, i)
for i in range(int(getVarValue(c, 'nLepGood')))
if abs(getVarValue(c, "LepGood_pdgId", i)) == 11
]
def getLeptons(c, collVars=leptonVars): return [getObjDict(c, 'LepGood_', collVars, i) for i in range(int(getVarValue(c, 'nLepGood')))]
def getGenParts(c):
return [
getObjDict(c, 'GenPart_', [
'eta', 'pt', 'phi', 'charge', 'pdgId', 'motherId', 'grandmotherId'
], i) for i in range(int(getVarValue(c, 'nGenPart')))
]
def getElectrons(c, collVars=leptonVars): return [getObjDict(c, 'LepGood_', collVars, i) for i in range(int(getVarValue(c, 'nLepGood'))) if abs(getVarValue(c,"LepGood_pdgId",i))==11]
def loopAndFill(stacks, mode="loop"):
allSamples = []
allSampleNames = []
allPlots = []
usedBranches = []
for s in stacks:
usedBranches = list(set(usedBranches + s.usedBranches))
for l in s.plotLists:
for p in l:
allPlots.append(p)
if p.leaf:
if not p.leaf in usedBranches:
usedBranches.append(p.leaf)
if p.weightString and not p.weightString in usedBranches:
usedBranches.append(p.weightString)
else:
usedBranches = list(set(usedBranches + p.usedBranches))
if not p.sample in allSamples:
assert p.sample.has_key('dir') or p.sample.has_key(
'dirname'
), "Missing key dir or dirname in sample %s" % repr(
p.sample)
allSamples.append(p.sample)
if mode == 'loop':
assert not any([
p.string for p in allPlots
]), "Loop mode is %s but specified 'string' for: %s" % (
mode, ", ".join([p.name for p in allPlots if p.string]))
if mode == 'draw':
assert all([
p.string for p in allPlots
]), "Loop mode is %s but specified no 'string' for: %s" % (
mode, ", ".join([p.name for p in allPlots if not p.string]))
print "Found", len(allSamples), 'different samples:', ", ".join(
s['name'] for s in allSamples)
for s in allSamples:
cutStringForSample = []
plotsPerCutForSample = {}
# print s['name'], s.has_key('isData'), s.has_key('isData') and s['isData'], s.has_key('isData') and s['isData'] and s.has_key('dataCut')
for p in allPlots:
if p.sample == s:
cut = p.cut['string'] if not (
s.has_key('isData') and s['isData']
and p.cut.has_key('dataCut')
) else "(" + p.cut['string'] + ")&&(" + p.cut['dataCut'] + ")"
if not cut in cutStringForSample:
cutStringForSample.append(cut)
plotsPerCutForSample[cut] = []
if not p in plotsPerCutForSample[cut]:
plotsPerCutForSample[cut].append(p)
s['plotsPerCutForSample'] = plotsPerCutForSample
for s in allSamples:
sampleScaleFac = 1 if not s.has_key('scale') else s['scale']
if sampleScaleFac != 1:
print "Using sampleScaleFac", sampleScaleFac, "for sample", s[
"name"]
bins = s['bins'] if s.has_key('bins') else ['default']
for b in bins:
treeName = 'Events' if not s.has_key('treeName') else s['treeName']
maxN = -1 if not (s.has_key('small') and s['small']) else 1
c = ROOT.TChain(treeName)
counter = 0
dir = s['dirname'] if s.has_key('dirname') else s['dir']
fileList = getFileList(
dir + '/' + b, maxN=maxN,
histname="") if s.has_key('bins') else [s['file']]
for f in fileList:
if not f[-5:] == '.root': continue
# counter+=1
# c.Add(f)
if checkRootFile(f, checkForObjects=[treeName]):
counter += 1
c.Add(f)
else:
print "File %s looks broken." % f
ntot = c.GetEntries()
print "Added ", counter, 'files from sample', s[
'name'], 'dir', dir, 'bin', b, 'ntot', ntot
switchOnBranches(c, usedBranches)
if ntot == 0:
print "Warning! Found zero events in", s[
'name'], 'bin', b, " -> do nothing"
continue
if mode.lower() == 'loop':
for ics, cutString in enumerate(
s['plotsPerCutForSample'].keys()):
plotsToFill = s['plotsPerCutForSample'][cutString]
elistName = "eList_" + s['name'] + '_' + b + '_' + str(ics)
elist = ROOT.TEventList(elistName)
c.Draw(">>" + elistName, cutString)
# print "elist",elist,elist.GetN(),cutString,'plots',plotsToFill
number_events = elist.GetN(
) # if not (s.has_key('small') and s['small']) else min(elist.GetN(), 100)
print "Reading: ", s[
"name"], b, "with", number_events, "events passing cutString", cutString, 'and will fill', len(
[p.name for p in plotsToFill]), 'vars.'
for p in plotsToFill:
if not (p.cut.has_key('func') and p.cut['func']):
p.cut['func'] = None
if p.TTreeFormula:
assert p.TTreeFormula and not (
p.TTreeFormula == ""
), "Problem in TTreeFormula %s" % p.TTreeFormula
fString = 'ROOT.TTreeFormula("' + p.name + '","' + p.TTreeFormula + '",c)'
exec('p.ttreeFormula=' + fString)
print "Created TTreeFormula:", fString
for i in range(0, number_events):
if (i % 10000 == 0) and i > 0:
print i
c.GetEntry(elist.GetEntry(i))
for p in plotsToFill:
# print p.cut['func'], p.cut['func'](c)
if (not p.cut['func']) or p.cut['func'](c):
weight = c.GetLeaf(p.weightString).GetValue(
) if p.weightString else 1.
reWeight = p.weightFunc(
c) if p.weightFunc else 1.
# print c, p.weightFunc, p.weightFunc(c), getVarValue(c, "nVert"), c.GetLeaf("nVert").GetValue(), c.nVert
if p.leaf:
val = getVarValue(c, p.leaf, p.ind)
# print "Fill leaf",p.leaf, p.ind, val, weight,sampleScaleFac
if p.TTreeFormula:
p.ttreeFormula.UpdateFormulaLeaves()
val = p.ttreeFormula.EvalInstance()
if p.func:
val = p.func(c)
# if val>170:print val, reWeight, weight, sampleScaleFac, p.leaf, p.ind, i, c.GetEntries(), elist.GetEntry(i),"x",c.GetLeaf('Jet_pt').GetValue(4), c.GetLeaf('met_pt').GetValue(), c.GetLeaf('lumi').GetValue(), c.GetLeaf('evt').GetValue()
if val < float('inf'):
p.histo.Fill(
val,
reWeight * weight * sampleScaleFac)
# print p.histo.GetName(), b, val, weight*sampleScaleFac, reWeight*weight*sampleScaleFac
elif mode.lower() == 'draw':
for ics, cutString in enumerate(
s['plotsPerCutForSample'].keys()):
plotsToFill = s['plotsPerCutForSample'][cutString]
print "Reading: ", s[
"name"], b, "with cutString", cutString, 'and will fill', len(
[p.name for p in plotsToFill]), 'vars.'
for p in plotsToFill:
print c, "String", p.string, p.binning, "Cut", cutString, p.weightString, p.binningIsExplicit, sampleScaleFac
tmp = getPlotFromChain(
c,
p.string,
p.binning,
cutString,
p.weightString,
binningIsExplicit=p.binningIsExplicit)
tmp.Scale(sampleScaleFac)
p.histo.Add(tmp)
# for ics, cutString in enumerate(s['plotsPerCutForSample'].keys()):
# plotsToFill = s['plotsPerCutForSample'][cutString]
# for p in plotsToFill:
# print c.GetEntries(), p.name,p.histo.Integral()
# c.GetListOfFiles().ls()
c.Reset()
del c
#do over-flow bins
for p in allPlots:
if p.overFlow and p.overFlow in ["upper", "both"]:
nbins = p.histo.GetNbinsX()
p.histo.SetBinContent(
nbins,
p.histo.GetBinContent(nbins) +
p.histo.GetBinContent(nbins + 1))
p.histo.SetBinError(
nbins,
sqrt(
p.histo.GetBinError(nbins)**2 +
p.histo.GetBinError(nbins + 1)**2))
if p.overFlow and p.overFlow in ["lower", "both"]:
p.histo.SetBinContent(
1,
p.histo.GetBinContent(0) + p.histo.GetBinContent(1))
p.histo.SetBinError(
1, sqrt(p.histo.GetBinError(0)**2 + p.histo.GetBinError(1)**2))
#sum stacks
for s in stacks:
sumStackHistos(s)
#normalize
for p in allPlots:
if p.normalizeTo:
t = p.normalizeTo.histo.Integral()
y = p.histo.Integral()
r = p.normalizeRef.histo.Integral() if p.normalizeRef else y
if r > 0:
p.histo.Scale(t / r)
#start event loop
for ev in range(nEvents):
##################################
# Processing output #
##################################
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))
#No double counting in data samples
if getVarValue(chain, "evt") in ListOfDataEvents:
continue
else:
ListOfDataEvents.append(getVarValue(chain, "evt"))
weight = reduceStat*getVarValue(chain, "weight")*(luminosity/1000.) if not s['isData'] else 1
#leptons
electrons = getGoodElectrons(chain)
muons = getGoodMuons(chain)
leptons = getGoodLeptons(chain,10)
#mt2ll for 2l
mt2ll = getVarValue(chain,"dl_mt2ll")
#met
*plots[pk][plot]['binning'])
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):
if ev % 10000 == 0: print "At %i/%i" % (ev, nEvents)
chain.GetEntry(eList.GetEntry(ev))
mt2Calc.reset()
#event weight (L= 4fb^-1)
weight = reduceStat * getVarValue(chain, "weight")
#MET
met = getVarValue(chain, "met_pt")
metPhi = getVarValue(chain, "met_phi")
#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},
}
for lep in leptons.keys():
twoleptons = False
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")
if s not in data: weight = weight*(luminosity/1000.)
#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
l0pt = getVarValue(chain, "l1_pt")
l1pt = getVarValue(chain, "l2_pt")
mll = getVarValue(chain,"dl_mass")
#Leptons
for plot in plots[pk].keys():
plots[pk][plot]['histo'][s["name"]] = ROOT.TH1F(plots[pk][plot]['name']+"_"+s["name"]+"_"+pk, plots[pk][plot]['name']+"_"+s["name"]+"_"+pk, *plots[pk][plot]['binning'])
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):
if ev%10000==0:print "At %i/%i"%(ev,nEvents)
chain.GetEntry(eList.GetEntry(ev))
mt2Calc.reset()
#event weight (L= 4fb^-1)
weight = reduceStat*getVarValue(chain, "weight")
#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)
#GENinfo
genparticles = getGenParts(chain)
#SF and OF channels
leptons = {\
def makeMETPerformanceHistos(setup):
for name, v in setup["variables"].iteritems():
v['bins']=[(v["binning"][i],v["binning"][i+1]) for i in range(len(v["binning"])-1)]
v['uperp']={}
v['upara']={}
v['uparaPlusQt']={}
v['qt']={}
v['histo']={}
for mname, mv in setup["metVariables"].iteritems():
hname = '_'.join([name,mname])
v['histo'][mname]=ROOT.TH1D('histo_'+hname, 'histo_'+hname, len(v['binning'])-1, array('d',v['binning']))
v['upara'][mname]={}
v['uparaPlusQt'][mname]={}
v['uperp'][mname]={}
v['qt'][mname]={}
v['upara'][mname]["scale"] = ROOT.TH1D(hname+'_upara_scale', hname+'_upara_scale',len(v['binning'])-1, array('d',v['binning']))
# v['upara'][mname]["scale"] = ROOT.TH1D(hname+'_upara_scale', hname+'_upara_scale',len(v['binning'])-1, array('d',v['binning']))
v['upara'][mname]["RMS"] = ROOT.TH1D(hname+'_upara_RMS', hname+'_upara_RMS',len(v['binning'])-1, array('d',v['binning']))
v['uperp'][mname]["RMS"] = ROOT.TH1D(hname+'_uperp_RMS', hname+'_uperp_RMS',len(v['binning'])-1, array('d',v['binning']))
v['upara'][mname]["RMScorr"] = ROOT.TH1D(hname+'_upara_RMScorr', hname+'_upara_RMScorr',len(v['binning'])-1, array('d',v['binning']))
v['uperp'][mname]["RMScorr"] = ROOT.TH1D(hname+'_uperp_RMScorr', hname+'_uperp_RMScorr',len(v['binning'])-1, array('d',v['binning']))
for b in v['bins']:
hname = name+'_'.join([str(x) for x in b])
v['uperp'][mname][b] = ROOT.TH1D(hname+'_'+mname+'_uperp', hname+'_'+mname+'_uperp', 800,-400,400)
v['upara'][mname][b] = ROOT.TH1D(hname+'_'+mname+'_upara', hname+'_'+mname+'_upara', 800,-600,200)
v['uparaPlusQt'][mname][b] = ROOT.TH1D(hname+'_'+mname+'_uparaPlusQt', hname+'_'+mname+'_uparaPlusQt', 800,-400,400)
v['qt'][mname][b] = ROOT.TH1D(hname+'_'+mname+'_qt', hname+'_'+mname+'_qt', 500,0,500)
for s in setup["samples"]:
for b in s['bins']:
b['chain'].Draw('>>eList', setup['preselection'])
eList = ROOT.gDirectory.Get('eList')
nEvents = eList.GetN() if not setup.has_key('maxEvents') or setup['maxEvents']<0 else setup['maxEvents']
for nev in range(nEvents):
if nev%1000==0:print "At %i / %i"%(nev, nEvents)
b['chain'].GetEntry(eList.GetEntry(nev))
muons = setup['leptons'](b['chain'])
if len(muons)!=2:continue
l0, l1 = muons
if not setup['massWindow'](l0,l1):continue
qx = l0['pt']*cos(l0['phi']) + l1['pt']*cos(l1['phi'])
qy = l0['pt']*sin(l0['phi']) + l1['pt']*sin(l1['phi'])
# qphi = atan2(qy, qx)
qt = sqrt(qx**2+qy**2)
# print l0, l1
for mname, mv in setup["metVariables"].iteritems():
mv['pt'] = mv['ptFunc'](b['chain'])
mv['phi'] = mv['phiFunc'](b['chain'])
ux = -mv['pt']*cos(mv['phi']) - qx
uy = -mv['pt']*sin(mv['phi']) - qy
upara = (ux*qx+uy*qy)/qt
uperp = (ux*qy-uy*qx)/qt
weight = getVarValue(b['chain'], 'genWeight')*b['lumiScale']
for name, v in setup["variables"].iteritems():
if v['func']=='qt':varValue=qt
else:
varValue = v['func'](b['chain'])
v['histo'][mname].Fill(varValue, weight) #Filling distribution of binning variable
varBin = findBin(v, varValue)
if varBin:
v['uperp'][mname][varBin].Fill(uperp, weight)
v['upara'][mname][varBin].Fill(upara, weight)
v['uparaPlusQt'][mname][varBin].Fill(upara+qt, weight)
v['qt'][mname][varBin].Fill(qt, weight)
del eList
for name, v in setup["variables"].iteritems():
for mname, mv in setup["metVariables"].iteritems():
for bin in v['bins']:
upara_mean = v['upara'][mname][bin].GetMean()
upara_mean_err = v['upara'][mname][bin].GetMeanError()
uperp_mean = v['uperp'][mname][bin].GetMean()
uperp_mean_err = v['uperp'][mname][bin].GetMeanError()
upara_RMS = v['uparaPlusQt'][mname][bin].GetRMS()
upara_RMS_err = v['uparaPlusQt'][mname][bin].GetRMSError()
uperp_RMS = v['uperp'][mname][bin].GetRMS()
uperp_RMS_err = v['uperp'][mname][bin].GetRMSError()
qt_mean = v['qt'][mname][bin].GetMean()
qt_mean_err = v['qt'][mname][bin].GetMeanError()
if (not qt_mean>0):continue
scale = - upara_mean / qt_mean
scale_err = upara_mean / qt_mean * sqrt(upara_mean_err**2/upara_mean**2 + qt_mean_err**2/qt_mean**2)
upara_RMS_scaleCorr = upara_RMS/scale if scale>0 else float('nan')
upara_RMS_scaleCorr_err = upara_RMS/scale*sqrt(upara_RMS_err**2/upara_RMS**2 + scale_err**2/scale**2) if upara_RMS>0 and scale>0 else float('nan')
uperp_RMS_scaleCorr = uperp_RMS/scale if scale>0 else float('nan')
uperp_RMS_scaleCorr_err = uperp_RMS/scale*sqrt(uperp_RMS_err**2/uperp_RMS**2 + scale_err**2/scale**2) if uperp_RMS>0 and scale>0 else float('nan')
val = 0.5*(bin[0]+bin[1])
nbin = v['upara'][mname]["scale"].FindBin(val)
v['upara'][mname]["scale"].SetBinContent(nbin, scale)
v['upara'][mname]["scale"].SetBinError(nbin, scale_err)
v['upara'][mname]["RMS"].SetBinContent(nbin, upara_RMS)
v['upara'][mname]["RMS"].SetBinError(nbin, upara_RMS_err)
v['upara'][mname]["RMScorr"].SetBinContent(nbin, upara_RMS_scaleCorr)
v['upara'][mname]["RMScorr"].SetBinError(nbin, upara_RMS_scaleCorr_err)
v['uperp'][mname]["RMS"].SetBinContent(nbin, uperp_RMS)
v['uperp'][mname]["RMS"].SetBinError(nbin, uperp_RMS_err)
v['uperp'][mname]["RMScorr"].SetBinContent(nbin, uperp_RMS_scaleCorr)
v['uperp'][mname]["RMScorr"].SetBinError(nbin, uperp_RMS_scaleCorr_err)
return setup
counterRecoGen_recoMatchedTau[mode]={}
counterRecoGen_looseMu[mode]={}
counterRecoGen_looseEle[mode]={}
counterRecofake_fakeMet50[mode]={}
counterRecofake_fakeMet100[mode]={}
counterRecofake_fakeMet200[mode]={}
counterRecoGen_failRelIso03[mode]={}
counterRecoGen_genLepOutOfAcceptance[mode]={}
badMuonCandidates[mode]=[]
badElectronCandidates[mode]=[]
for ev in range(nEvents):
ntot+=1
if ev%10000==0:print "At %i/%i"%(ev,nEvents)
s.chain.GetEntry(eList.GetEntry(ev))
mt2Calc.reset()
weight = getVarValue(s.chain, "weight")*lumiScale
mt2ll = getVarValue(s.chain, "dl_mt2ll")
met = getVarValue(s.chain, "met_pt")
metPhi = getVarValue(s.chain, "met_phi")
genMet = getVarValue(s.chain, "met_genPt")
genMetPhi = getVarValue(s.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(s.chain, jetColl="JetGood"))
allLeptons = getLeptons(s.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 s.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
leptoncounterTTZ = 0
neutrinocounterTTZ = 0
othercounterTTZ = 0
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*(luminosity/1000.)
#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
l0pt = getVarValue(chain, "l1_pt")
l1pt = getVarValue(chain, "l2_pt")
mll = getVarValue(chain,"dl_mass")
#Leptons
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")
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
for mode in ["isMuMu", "isEE", "isEMu"]:
counterReco[mode] = 0
counterRecoGen[mode] = {}
counterRecoGen_muMatched[mode] = {}
counterRecoGen_eleMatched[mode] = {}
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 pk in plots.keys():
plots[pk]['histo'][sample['name']] = ROOT.TH1F("plot_"+sample["name"], "plot_"+sample["name"], *(plots[pk]['binning']))
plots[pk]['histo'][sample['name']].SetLineColor(plots[pk]['color'])
plots[pk]['histo'][sample['name']].SetMarkerColor(plots[pk]['color'])
plots[pk]['histo'][sample['name']].SetMarkerSize(0)
chain = sample["chain"]
print "Looping over %s" % sample["name"]
eList = getEList(chain, preselection)
nEvents = eList.GetN()/reduceStat
print "Found %i events in %s after preselection %s, looping over %i" % (eList.GetN(),sample["name"],preselection,nEvents)
for ev in range(nEvents):
if ev%10000==0:print "At %i/%i"%(ev,nEvents)
chain.GetEntry(eList.GetEntry(ev))
mt2Calc.reset()
weight = reduceStat*getVarValue(chain, "weight")*lumiScale
met = getVarValue(chain, "met_pt")
metPhi = getVarValue(chain, "met_phi")
puppiMet = getVarValue(chain, "puppiMet_pt")
puppiMetPhi = getVarValue(chain, "puppiMet_phi")
genMet = getVarValue(chain, "met_genPt")
genMetPhi = getVarValue(chain, "met_genPhi")
leptons = getGoodLeptons(chain, leptonVars+['mcMatchId', 'mcMatchAny', 'mcPt'])
l0GenPt, l0HasMatch, l0pt, l0eta, l0phi = leptons[0]['mcPt'], abs(leptons[0]['mcMatchId'])==6, leptons[0]['pt'], leptons[0]['eta'], leptons[0]['phi']
l1GenPt, l1HasMatch, l1pt, l1eta, l1phi = leptons[1]['mcPt'], abs(leptons[1]['mcMatchId'])==6, leptons[1]['pt'], leptons[1]['eta'], leptons[1]['phi']
mll = sqrt(2.*l0pt*l1pt*(cosh(l0eta-l1eta)-cos(l0phi-l1phi)))
jets = filter(lambda j:j['pt']>30 and abs(j['eta'])<2.4 and j['id'], getJets(chain, jetVars+['mcPt', 'mcMatchId', 'mcFlavour']))
# bjets = filter(lambda j:j['btagCSV']>0.890, jets)
# print len(leptons), len(bjets), mll
mt2Calc.setMet(met,metPhi)
mt2Calc.setLeptons(l0pt, l0eta, l0phi, l1pt, l1eta, l1phi)
for plot in plots[pk].keys():
plots[pk][plot]['histo'][s["name"]] = ROOT.TH1F(plots[pk][plot]['name']+"_"+s["name"]+"_"+pk, plots[pk][plot]['name']+"_"+s["name"]+"_"+pk, *plots[pk][plot]['binning'])
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):
if ev%10000==0:print "At %i/%i"%(ev,nEvents)
chain.GetEntry(eList.GetEntry(ev))
mt2Calc.reset()
#event weight (L= 4fb^-1)
weight = reduceStat*getVarValue(chain, "weight")
#MET
met = getVarValue(chain, "met_pt")
metPhi = getVarValue(chain, "met_phi")
#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},
}
for lep in leptons.keys():
twoleptons = False
def getOtherLeptons(c, collVars=leptonVars):
return [
getObjDict(c, 'LepOther_', collVars, i)
for i in range(int(getVarValue(c, 'nLepOther')))
]
def getGenLeps(c): return [getObjDict(c, 'genLep_', ['eta','pt','phi','charge', 'pdgId', 'sourceId'], i) for i in range(int(getVarValue(c, 'ngenLep')))]
def getGenLeps(c):
return [
getObjDict(c, 'genLep_',
['eta', 'pt', 'phi', 'charge', 'pdgId', 'sourceId'], i)
for i in range(int(getVarValue(c, 'ngenLep')))
]
def getGenPartsAll(c): return [getObjDict(c, 'genPartAll_', ['eta','pt','phi','charge', 'pdgId', 'motherId', 'grandmotherId'], i) for i in range(int(getVarValue(c, 'ngenPartAll')))]
def getPhotons(c, collVars=photonVars, idLevel='loose'):
return [
getObjDict(c, 'gamma_', collVars, i)
for i in range(int(getVarValue(c, 'ngamma')))
]
def getJets(c): return [getObjDict(c, 'Jet_', ['eta','pt','phi','btagCMVA','btagCSV', 'id'], i) for i in range(int(getVarValue(c, 'nJet')))]
def getGenPartsAll(c):
return [
getObjDict(c, 'genPartAll_', genVars, i)
for i in range(int(getVarValue(c, 'ngenPartAll')))
]
def getElectrons(c): return [getObjDict(c, 'LepGood_', ['eta','pt','phi','charge', 'dxy', 'dz', 'relIso03','tightId', 'pdgId', 'mediumMuonId', 'miniRelIso', 'sip3d', 'mvaIdPhys14', 'convVeto', 'lostHits'], i) for i in range(int(getVarValue(c, 'nLepGood'))) if abs(getVarValue(c,"LepGood_pdgId",i))==11]
for plot in plots[pk].keys():
plots[pk][plot]['histo'][s["name"]] = ROOT.TH1F(plots[pk][plot]['name']+"_"+s["name"]+"_"+pk, plots[pk][plot]['name']+"_"+s["name"]+"_"+pk, *plots[pk][plot]['binning'])
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):
if ev%10000==0:print "At %i/%i"%(ev,nEvents)
chain.GetEntry(eList.GetEntry(ev))
mt2Calc.reset()
#event weight (L= 4fb^-1)
weight = reduceStat*getVarValue(chain, "weight")
#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)
#GENinfo
genparticles = getGenParts(chain)
#SF and OF channels
leptons = {\
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 * (luminosity / 1000.)
#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
l0pt = getVarValue(chain, "l1_pt")
l1pt = getVarValue(chain, "l2_pt")
mll = getVarValue(chain, "dl_mass")
isEE = getVarValue(chain, "isEE")
def loopAndFill(stacks, mode="loop"):
allSamples=[]
allSampleNames=[]
allPlots = []
usedBranches = []
for s in stacks:
usedBranches = list(set(usedBranches+s.usedBranches))
for l in s.stackLists:
for p in l:
allPlots.append(p)
if p.leaf:
if not p.leaf in usedBranches:
usedBranches.append(p.leaf)
if p.weightString and not p.weightString in usedBranches:
usedBranches.append(p.weightString)
else:
usedBranches = list(set(usedBranches+p.usedBranches))
if not p.sample in allSamples:
assert p.sample.has_key('dir') or p.sample.has_key('dirname'), "Missing key dir or dirname in sample %s"%repr(p.sample)
allSamples.append(p.sample)
if mode=='loop':
assert not any ([p.string for p in allPlots]), "Loop mode is %s but specified 'string' for: %s"%(mode, ", ".join([p.name for p in allPlots if p.string]))
if mode=='draw':
assert all([p.string for p in allPlots]), "Loop mode is %s but specified no 'string' for: %s"%(mode, ", ".join([p.name for p in allPlots if not p.string]))
print "Found",len(allSamples),'different samples:',", ".join(s['name'] for s in allSamples)
for s in allSamples:
cutStringForSample=[]
plotsPerCutForSample={}
# print s['name'], s.has_key('isData'), s.has_key('isData') and s['isData'], s.has_key('isData') and s['isData'] and s.has_key('dataCut')
for p in allPlots:
if p.sample==s:
cut = p.cut['string'] if not (s.has_key('isData') and s['isData'] and p.cut.has_key('dataCut')) else "("+p.cut['string']+")&&("+p.cut['dataCut']+")"
if not cut in cutStringForSample:
cutStringForSample.append(cut)
plotsPerCutForSample[cut]=[]
if not p in plotsPerCutForSample[cut]:
plotsPerCutForSample[cut].append(p)
s['plotsPerCutForSample'] = plotsPerCutForSample
for s in allSamples:
sampleScaleFac = 1 if not s.has_key('scale') else s['scale']
if sampleScaleFac!=1:
print "Using sampleScaleFac", sampleScaleFac ,"for sample",s["name"]
for b in s['bins']:
c = ROOT.TChain('Events' if not s.has_key('treeName') else s['treeName'])
counter=0
dir = s['dirname'] if s.has_key('dirname') else s['dir']
for f in getFileList(dir+'/'+b, maxN = -1 if not (s.has_key('small') and s['small']) else 1, histname=""):#, minAgeDPM, histname, xrootPrefix, maxN):
if not f[-5:]=='.root':continue
counter+=1
c.Add(f)
ntot = c.GetEntries()
print "Added ",counter,'files from sample',s['name'],'dir',dir,'bin',b,'ntot',ntot
switchOnBranches(c, usedBranches)
if ntot==0:
print "Warning! Found zero events in",s['name'],'bin',b," -> do nothing"
continue
if mode.lower()=='loop':
for ics, cutString in enumerate(s['plotsPerCutForSample'].keys()):
plotsToFill = s['plotsPerCutForSample'][cutString]
elistName = "eList_"+s['name']+'_'+b+'_'+str(ics)
elist = ROOT.TEventList(elistName)
c.Draw(">>"+elistName,cutString)
# print "elist",elist,elist.GetN(),cutString,'plots',plotsToFill
number_events = elist.GetN()# if not (s.has_key('small') and s['small']) else min(elist.GetN(), 100)
print "Reading: ", s["name"], b, "with",number_events,"events passing cutString", cutString, 'and will fill', len([p.name for p in plotsToFill]),'vars.'
for p in plotsToFill:
if not (p.cut.has_key('func') and p.cut['func']):
p.cut['func']=None
if p.TTreeFormula:
assert p.TTreeFormula and not (p.TTreeFormula==""), "Problem in TTreeFormula %s" % p.TTreeFormula
fString='ROOT.TTreeFormula("'+p.name+'","'+p.TTreeFormula+'",c)'
exec('p.ttreeFormula='+fString)
print "Created TTreeFormula:",fString
for i in range(0,number_events):
if (i%10000 == 0) and i>0 :
print i
c.GetEntry(elist.GetEntry(i))
for p in plotsToFill:
# print p.cut['func'], p.cut['func'](c)
if (not p.cut['func']) or p.cut['func'](c):
weight = c.GetLeaf(p.weightString).GetValue() if p.weightString else 1.
reWeight = p.weightFunc(c) if p.weightFunc else 1.
# print c, p.weightFunc, p.weightFunc(c), getVarValue(c, "nVert"), c.GetLeaf("nVert").GetValue(), c.nVert
if p.leaf:
val = getVarValue(c, p.leaf, p.ind)
# print "Fill leaf",p.leaf, p.ind, val, weight,sampleScaleFac
if p.TTreeFormula:
p.ttreeFormula.UpdateFormulaLeaves()
val = p.ttreeFormula.EvalInstance()
if p.func:
val = p.func(c)
# if val>170:print val, reWeight, weight, sampleScaleFac, p.leaf, p.ind, i, c.GetEntries(), elist.GetEntry(i),"x",c.GetLeaf('Jet_pt').GetValue(4), c.GetLeaf('met_pt').GetValue(), c.GetLeaf('lumi').GetValue(), c.GetLeaf('evt').GetValue()
if val<float('inf'):
p.histo.Fill(val, reWeight*weight*sampleScaleFac)
# print p.histo.GetName(), b, val, weight*sampleScaleFac, reWeight*weight*sampleScaleFac
elif mode.lower()=='draw':
for ics, cutString in enumerate(s['plotsPerCutForSample'].keys()):
plotsToFill = s['plotsPerCutForSample'][cutString]
print "Reading: ", s["name"], b, "with cutString", cutString, 'and will fill', len([p.name for p in plotsToFill]),'vars.'
for p in plotsToFill:
# print c, p.string, p.binning, cutString, p.weightString, p.binningIsExplicit, sampleScaleFac
tmp = getPlotFromChain(c, p.string, p.binning, cutString, p.weightString, binningIsExplicit=p.binningIsExplicit)
tmp.Scale(sampleScaleFac)
p.histo.Add(tmp)
# for ics, cutString in enumerate(s['plotsPerCutForSample'].keys()):
# plotsToFill = s['plotsPerCutForSample'][cutString]
# for p in plotsToFill:
# print c.GetEntries(), p.name,p.histo.Integral()
# c.GetListOfFiles().ls()
c.Reset()
del c
#do over-flow bins
for p in allPlots:
if p.overFlow and p.overFlow in [ "upper", "both"]:
nbins = p.histo.GetNbinsX()
p.histo.SetBinContent(nbins , p.histo.GetBinContent(nbins) + p.histo.GetBinContent(nbins + 1))
p.histo.SetBinError(nbins , sqrt(p.histo.GetBinError(nbins)**2 + p.histo.GetBinError(nbins + 1)**2))
if p.overFlow and p.overFlow in [ "lower", "both"]:
p.histo.SetBinContent(1 , p.histo.GetBinContent(0) + p.histo.GetBinContent(1))
p.histo.SetBinError(1 , sqrt(p.histo.GetBinError(0)**2 + p.histo.GetBinError(1)**2))
for s in stacks:
sumStackHistos(s)
##################################
# Processing output #
##################################
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))
#No double counting in data samples
if getVarValue(chain, "evt") in ListOfDataEvents:
continue
else:
ListOfDataEvents.append(getVarValue(chain, "evt"))
weight = reduceStat * getVarValue(
chain, "weight") * (luminosity / 1000.) if not s['isData'] else 1
#leptons
electrons = getGoodElectrons(chain)
muons = getGoodMuons(chain)
leptons = getGoodLeptons(chain, 10)
#mt2ll for 2l
mt2ll = getVarValue(chain, "dl_mt2ll")
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)
#######################################################
# 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(91.2 - 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())
print("I finished a background: " + s["name"])
processtime = datetime.now()
print "Time to process chains: ", processtime - start
*(plots[pk]['binning']))
plots[pk]['histo'][sample['name']].SetLineColor(plots[pk]['color'])
plots[pk]['histo'][sample['name']].SetMarkerColor(plots[pk]['color'])
plots[pk]['histo'][sample['name']].SetMarkerSize(0)
chain = sample["chain"]
print "Looping over %s" % sample["name"]
eList = getEList(chain, preselection)
nEvents = eList.GetN() / reduceStat
print "Found %i events in %s after preselection %s, looping over %i" % (
eList.GetN(), sample["name"], preselection, nEvents)
for ev in range(nEvents):
if ev % 10000 == 0: print "At %i/%i" % (ev, nEvents)
chain.GetEntry(eList.GetEntry(ev))
mt2Calc.reset()
weight = reduceStat * getVarValue(chain, "weight") * lumiScale
met = getVarValue(chain, "met_pt")
metPhi = getVarValue(chain, "met_phi")
puppiMet = getVarValue(chain, "puppiMet_pt")
puppiMetPhi = getVarValue(chain, "puppiMet_phi")
genMet = getVarValue(chain, "met_genPt")
genMetPhi = getVarValue(chain, "met_genPhi")
leptons = getGoodLeptons(
chain, leptonVars + ['mcMatchId', 'mcMatchAny', 'mcPt'])
l0GenPt, l0HasMatch, l0pt, l0eta, l0phi = leptons[0]['mcPt'], abs(
leptons[0]['mcMatchId']
) == 6, leptons[0]['pt'], leptons[0]['eta'], leptons[0]['phi']
l1GenPt, l1HasMatch, l1pt, l1eta, l1phi = leptons[1]['mcPt'], abs(
leptons[1]['mcMatchId']
) == 6, leptons[1]['pt'], leptons[1]['eta'], leptons[1]['phi']
mll = sqrt(2. * l0pt * l1pt *
for mode in ["isMuMu", "isEE", "isEMu"]:
counterReco[mode]=0
counterRecoGen[mode]={}
counterRecoGen_muMatched[mode]={}
counterRecoGen_eleMatched[mode]={}
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)
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())
print("I finished a background: " + s["name"])
processtime = datetime.now()
print "Time to process chains: ", processtime - start
import itertools
small = False
from StopsDilepton.samples.cmgTuples_Spring15_50ns_postProcessed import *
from StopsDilepton.samples.cmgTuples_Spring15_25ns_postProcessed import *
from StopsDilepton.samples.cmgTuples_Data50ns_1l_postProcessed import *
from StopsDilepton.samples.cmgTuples_Data25ns_postProcessed import *
from StopsDilepton.tools.objectSelection import getLeptons, getMuons, getElectrons, getGoodMuons, getGoodElectrons, getGoodLeptons, mZ
from StopsDilepton.tools.helpers import getVarValue, getYieldFromChain, getChain
from StopsDilepton.tools.localInfo import plotDir
from simplePlotHelpers import plot, stack, loopAndFill, drawNMStacks
from StopsDilepton.tools.puReweighting import getReweightingFunction
puReweightingFunc = getReweightingFunction(era="Run2015D_205pb")
#puReweightingFunc = getReweightingFunction(era="Run2015D_205pb_doubleMu_onZ_isOS")
puReweighting = lambda c:puReweightingFunc(getVarValue(c, "nVert"))
cutBranches = ["weight", "leptonPt", "met*", "nVert",\
'Jet_pt', "Jet_id", "Jet_eta", "Jet_phi", "Jet_btagCSV",
"LepGood_pdgId", "LepGood_mediumMuonId", "LepGood_miniRelIso", "LepGood_sip3d", "LepGood_dxy", "LepGood_dz", "LepGood_convVeto", "LepGood_lostHits",
"Flag_HBHENoiseFilter", "Flag_HBHENoiseIsoFilter", "Flag_HBHENoiseFilterMinZeroPatched", "Flag_goodVertices", "Flag_CSCTightHaloFilter", "Flag_eeBadScFilter",
"HLT_mumuIso", "HLT_ee_DZ", "HLT_mue",
"is*","dl_*","l1_*","l2_*", "nGoodMuons", "nGoodElectrons"
]
subdir = "png25ns_2l"
#preprefixes = ["PUDoubleMuOnZIsOS"]
preprefixes = []
def getZCut(mode):
zstr = "abs(dl_mass - "+str(mZ)+")"
if mode.lower()=="onz": return zstr+"<15"
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() weight = reduceStat*getVarValue(chain, "weight")*lumiScale if not s['isData'] else 1 met = getVarValue(chain, "met_pt") metPhi = getVarValue(chain, "met_phi") metEta = getVarValue(chain, "met_eta") mt2ll[0] = 0 mt2bb[0] = 0 mt2blbl[0] = 0 MET[0] = met METPhi[0] = metPhi xsecWeight[0] = weight pileupWeight[0] = getVarValue(chain,"puWeight") isMC[0] = 1-s['isData'] isElecElec[0] = 0 isMuonMuon[0] = 0 isMuonElec[0] = 0
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()
weight = reduceStat * getVarValue(
chain, "weight") * lumiScale if not s['isData'] else 1
met = getVarValue(chain, "met_pt")
metPhi = getVarValue(chain, "met_phi")
metEta = getVarValue(chain, "met_eta")
EventNumber[0] = getVarValue(chain, "evt")
mt2ll[0] = 0
mt2bb[0] = 0
mt2blbl[0] = 0
MET[0] = met
METPhi[0] = metPhi
xsecWeight[0] = weight
isMC[0] = 1 - s['isData']
isElecElec[0] = 0
isMuonMuon[0] = 0
isMuonElec[0] = 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)
#TreeFile = ROOT.TFile("./trees/tree"+s["name"]+".root","recreate")
#Tree = ROOT.TTree("VarTree","Tree of Variables")
#Tree.Branch("MET",MET_n,"MET/D")
#Tree.Branch("MT2ee",MT2ee_n,"MT2ee/D")
#Tree.Branch("MT2emu",MT2emu_n,"MT2emu/D")
#Tree.Branch("MT2mumu",MT2mumu_n,"MT2mumu/D")
for ev in range(nEvents):
if ev % 10000 == 0: print "At %i/%i" % (ev, nEvents)
chain.GetEntry(eList.GetEntry(ev))
mt2Calc.reset()
#event weight (L= 4fb^-1)
if s.has_key('totalweight'):
weight = getWeight(chain, s,
4000) #this method for Ward's SPRING15 samples
else:
weight = reduceStat * getVarValue(
chain, "weight") #this method for Robert's PHYS14 samples
#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)
#GENinfo
#genparticles = getGenParts(chain)
#ROOT output file
#MET_n[0] = met