def findBinning(variables=variables, selection=('(1)', []), prefix=""):
#take first variable
var = variables[0]
remainder = variables[1:]
#descend the list of thresholds
upperCut=-1
for t in reversed(var['thresholds']):
bin = ( t, upperCut )
# print selection
print prefix+"%s (cut: %s). Now Looking into %s %s."%(", ".join([s['name']+":"+str(s['cut']) for s in selection[1]]), selection[0], var['name'], repr(bin))
cut_str = cutString(var['name'], bin)
cut = "&&".join([lowestCuts, preselection, selection[0], cut_str])
signalYields = {s['name']:lumiFac*getYieldFromChain(s['chain'], cut, 'weight') for s in signals}
bkgYield = max(0, lumiFac*getYieldFromChain(c_bkg, cut, 'weight'))
thisSelection=(selection[0]+"&&"+cut_str,selection[1]+[{'name':var['name'], 'cut': (t, upperCut)}])
#Is the region large enough?
# print " "*len(prefix)+"Cut:", cut
print " "*len(prefix)+"Found bkg",bkgYield, "sig.:", signalYields.values()
if regionCondition(bkgYield, signalYields):
#Yes -> split it with the next variable
if len(remainder)>0:
print prefix+"Splitting up-->"
findBinning(variables=remainder, selection = thisSelection, prefix="--> "+prefix)
else:
print " "*len(prefix)+"Adding: "+", ".join([s['name']+":"+str(s['cut']) for s in thisSelection[1]])
regions.append({'cuts':thisSelection[1], 'sigYields':signalYields,'bkgYield':bkgYield})
upperCut = t
else:
#No->merge
pass
if not regionCondition(bkgYield, signalYields):
print " "*len(prefix)+"Loop done. Adding region: "+", ".join([s['name']+":"+str(s['cut']) for s in thisSelection[1]])
regions.append({'cuts':thisSelection[1], 'sigYields':signalYields,'bkgYield':bkgYield})
def findBinning(variables=variables, selection=('(1)', []), prefix=""):
#take first variable
var = variables[0]
remainder = variables[1:]
#descend the list of thresholds
upperCut = -1
for t in reversed(var['thresholds']):
bin = (t, upperCut)
# print selection
print prefix + "%s (cut: %s). Now Looking into %s %s." % (", ".join(
[s['name'] + ":" + str(s['cut'])
for s in selection[1]]), selection[0], var['name'], repr(bin))
cut_str = cutString(var['name'], bin)
cut = "&&".join([lowestCuts, preselection, selection[0], cut_str])
signalYields = {
s['name']: lumiFac * getYieldFromChain(s['chain'], cut, 'weight')
for s in signals
}
bkgYield = max(0, lumiFac * getYieldFromChain(c_bkg, cut, 'weight'))
thisSelection = (selection[0] + "&&" + cut_str,
selection[1] + [{
'name': var['name'],
'cut': (t, upperCut)
}])
#Is the region large enough?
# print " "*len(prefix)+"Cut:", cut
print " " * len(
prefix) + "Found bkg", bkgYield, "sig.:", signalYields.values()
if regionCondition(bkgYield, signalYields):
#Yes -> split it with the next variable
if len(remainder) > 0:
print prefix + "Splitting up-->"
findBinning(variables=remainder,
selection=thisSelection,
prefix="--> " + prefix)
else:
print " " * len(prefix) + "Adding: " + ", ".join([
s['name'] + ":" + str(s['cut']) for s in thisSelection[1]
])
regions.append({
'cuts': thisSelection[1],
'sigYields': signalYields,
'bkgYield': bkgYield
})
upperCut = t
else:
#No->merge
pass
if not regionCondition(bkgYield, signalYields):
print " " * len(prefix) + "Loop done. Adding region: " + ", ".join(
[s['name'] + ":" + str(s['cut']) for s in thisSelection[1]])
regions.append({
'cuts': thisSelection[1],
'sigYields': signalYields,
'bkgYield': bkgYield
})
def _estimate(self, region, channel, setup):
printHeader("DD DY prediction for %s channel %s" %(self.name, channel))
#Sum of all channels for 'all'
if channel=='all':
return sum( [ self.cachedEstimate(region, c, setup) for c in ['MuMu', 'EE', 'EMu'] ], u_float(0.,0.) )
#MC based for 'EMu'
elif channel=='EMu':
preSelection = setup.preselection('MC', zWindow="allZ", channel=channel)
cut = "&&".join([region.cutString(setup.sys['selectionModifier']), preSelection['cut'] ])
weight = preSelection['weightStr']
if setup.verbose:
print "Using cut %s and weight %s"%(cut, weight)
return setup.lumi[channel]/1000. * u_float( getYieldFromChain(setup.sample['DY'][channel]['chain'], cutString = cut, weight=weight, returnError = True) )
#Data driven for EE and MuMu
else:
preSelection = setup.preselection('MC', zWindow="offZ", channel=channel)
weight = preSelection['weightStr']
assert abs(1.-setup.lumi[channel]/setup.sample['Data'][channel]['lumi'])<0.01, "Lumi specified in setup %f does not match lumi in data sample %f in channel %s"%(setup.lumi[channel], setup.sample['Data'][channel]['lumi'], channel)
cut_offZ_1b = "&&".join([region.cutString(setup.sys['selectionModifier']), setup.selection('MC', channel=channel, zWindow = 'offZ', **setup.defaultParameters(update={'nBTags':(1,-1)}))['cut'] ])
cut_onZ_1b = "&&".join([region.cutString(setup.sys['selectionModifier']), setup.selection('MC', channel=channel, zWindow = 'onZ', **setup.defaultParameters(update={'nBTags':(1,-1)}))['cut'] ])
cut_onZ_0b = "&&".join([region.cutString(setup.sys['selectionModifier']), setup.selection('MC', channel=channel, zWindow = 'onZ', **setup.defaultParameters(update={'nBTags':(0,0)}))['cut'] ])
cut_data_onZ_0b = "&&".join([region.cutString(setup.sys['selectionModifier']), setup.selection('Data', channel=channel, zWindow = 'onZ', **setup.defaultParameters(update={'nBTags':(0,0)}) )['cut'] ])
# R1 = DY-MC (offZ, 1b) / DY-MC (onZ, 1b)
# R2 = DY-MC (onZ, 1b) / DY-MC (onZ, 0b)
# DY-est = R1*R2*(Data(2l, onZ, 0b) - EWK(onZ, 0b)) = DY-MC (offZ, 1b) / DY-MC (onZ, 0b) *( Data(2l, onZ, 0b) - EWK(onZ, 0b))
yield_offZ_1b = setup.lumi[channel]/1000.*u_float( getYieldFromChain(setup.sample['DY'][channel]['chain'], cutString = cut_offZ_1b, weight=weight, returnError = True))
if setup.verbose: print "yield_offZ_1b: %s"%yield_offZ_1b
yield_onZ_0b = setup.lumi[channel]/1000.*u_float( getYieldFromChain(setup.sample['DY'][channel]['chain'], cutString = cut_onZ_0b, weight=weight, returnError = True))
if setup.verbose: print "yield_onZ_0b: %s"%yield_onZ_0b
yield_data = u_float( getYieldFromChain(setup.sample['Data'][channel]['chain'], cutString = cut_data_onZ_0b, weight=weight, returnError = True))
if setup.verbose: print "yield_data: %s (for cut: %s \n with weight: %s)"%(yield_data, cut_data_onZ_0b, weight)
#electroweak subtraction
print "\n Substracting electroweak backgrounds from data: \n"
yield_other = u_float(0., 0.)
for s in ['TTJets' , 'TTZ' , 'other']:
yield_other += setup.lumi[channel]/1000.*u_float(getYieldFromChain(setup.sample[s][channel]['chain'], cutString = cut_onZ_0b, weight=weight, returnError=True))
if setup.verbose: print "yield_other_onZ_0b %s added, now: %s"%(s, yield_other)
normRegYield = yield_data - yield_other
if normRegYield.val<0: print "\n !!!Warning!!! \n Negative normalization region yield data: (%s), MC: (%s) \n"%(yield_data, yield_other)
mcRatio = yield_offZ_1b / yield_onZ_0b
res = mcRatio * normRegYield
print "mcRatio is: ", mcRatio
return res
def _estimate(self, region, channel, setup):
if setup.verbose:
printHeader("MC prediction for %s channel %s" %
(self.name, channel))
if channel == 'all':
return sum([
self.cachedEstimate(region, c, setup)
for c in ['MuMu', 'EE', 'EMu']
], u_float(0., 0.))
else:
preSelection = setup.preselection('MC', channel=channel)
cut = "&&".join([
region.cutString(setup.sys['selectionModifier']),
preSelection['cut']
])
weight = preSelection['weightStr']
if setup.verbose:
print "Using cut %s and weight %s" % (cut, weight)
if not self.sample[channel].has_key('chain'):
loadChain(self.sample[channel])
return setup.lumi[channel] / 1000. * u_float(
getYieldFromChain(self.sample[channel]['chain'],
cutString=cut,
weight=weight,
returnError=True))
def _estimate(self, region, channel, setup):
if setup.verbose: printHeader("MC prediction for %s channel %s" %(self.name, channel))
if channel=='all':
return sum( [ self.cachedEstimate(region, c, setup) for c in ['MuMu', 'EE', 'EMu'] ], u_float(0., 0.) )
else:
preSelection = setup.preselection('MC', channel=channel)
cut = "&&".join([region.cutString(setup.sys['selectionModifier']), preSelection['cut']])
weight = preSelection['weightStr']
if setup.verbose:
print "Using cut %s and weight %s"%(cut, weight)
if not self.sample[channel].has_key('chain'):
loadChain(self.sample[channel])
return setup.lumi[channel]/1000.*u_float(getYieldFromChain(self.sample[channel]['chain'], cutString = cut, weight=weight, returnError = True) )
("==2 leptons", "nGoodMuons+nGoodElectrons==2"),
("opposite sign","isOS==1"),
("m(ll)>20", "dl_mass>20"),
("|m(ll) - mZ|>15 for SF","( (isMuMu==1||isEE==1)&&abs(dl_mass-91.2)>=15 || isEMu==1 )"),
(">=2 jets", "(Sum$(JetGood_pt>30&&abs(JetGood_eta)<2.4&&JetGood_id))>=2"),
(">=1 b-tags (CSVv2)", "Sum$(JetGood_pt>30&&abs(JetGood_eta)<2.4&&JetGood_id&&JetGood_btagCSV>0.890)>=1"),
("MET>80", "met_pt>80"),
("MET/sqrt(HT)>5", "met_pt/sqrt(Sum$(JetGood_pt*(JetGood_pt>30&&abs(JetGood_eta)<2.4&&JetGood_id)))>5"),
("dPhi(JetGood_1,2|MET)>0.25", "cos(met_phi-JetGood_phi[0])<cos(0.25)&&cos(met_phi-JetGood_phi[1])<cos(0.25)"),
("MT2(ll) > 140", "dl_mt2ll>140"),
# ("looseLeptonVeto", "Sum$(LepGood_pt>15&&LepGood_miniRelIso<0.4)==2"),
# ("multiIso M(Mu), T(Ele)", multiIsoWPMT),
# ("multiIso VT(Mu), VT(Ele)", multiIsoWPVTVT),
# ("filterCut", "Flag_HBHENoiseIsoFilter&&Flag_HBHENoiseFilter&&Flag_CSCTightHaloFilter&&Flag_goodVertices&&Flag_eeBadScFilter&&Flag_EcalDeadCellTriggerPrimitiveFilter" ),
# ("relIso04<0.12", relIso04sm12Cut),
# ("MT2(ll) > 240", "dl_mt2ll>240"),
]
lumiFac=10
print 30*" "+ "".join([ "%13s"%s.name for s in samples ] )
for i in reversed(range(len(cuts))):
r=[]
for s in samples:
selection = "&&".join(c[1] for c in cuts[:i+1])
if selection=="":selection="(1)"
y = lumiFac*getYieldFromChain(s.chain, selection, 'weight')
n = getYieldFromChain(s.chain, selection, '(1)')
r.append(y)
print "%30s"%cuts[i][0]+ "".join([ " %12.1f"%r[j] for j in range(len(r))] )
def _estimate(self, region, channel, setup):
printHeader("DD TTZ prediction for '%s' channel %s" %(self.name, channel))
#Sum of all channels for 'all'
if channel=='all':
return sum( [ self.cachedEstimate(region, c, channel, setup) for c in ['MuMu', 'EE', 'EMu'] ] )
else:
#Data driven for EE, EMu and MuMu.
preSelection = setup.preselection('MC', channel=channel)
#check lumi consistency
assert abs(1.-setup.lumi[channel]/setup.sample['Data'][channel]['lumi'])<0.01, "Lumi specified in setup %f does not match lumi in data sample %f in channel %s"%(setup.lumi[channel], setup.sample['Data'][channel]['lumi'], channel)
selection_MC_2l = "&&".join([region.cutString(setup.sys['selectionModifier']), preSelection['cut']])
weight = preSelection['weightStr']
yield_MC_2l = setup.lumi[channel]/1000.*u_float(getYieldFromChain(setup.sample['TTZ'][channel]['chain'], cutString = selection_MC_2l, weight=weight, returnError = True) )
if setup.verbose: print "yield_MC_2l: %s"%yield_MC_2l
muonSelection_loosePt = looseMuIDString(ptCut=10)
electronSelection_loosePt = looseEleIDString(ptCut=10)
#mu_mu_mu
MuMuMuSelection = "nGoodMuons>=2" + '&&' + muonSelection_loosePt + "==3"
if setup.parameters['useTriggers']: MuMuMuSelection += '&&HLT_3mu'
#e_e_e
EEESelection = "nGoodElectrons>=2" + '&&' + electronSelection_loosePt + "==3"
if setup.parameters['useTriggers']: EEESelection += '&&HLT_3e'
#e_e_mu
EEMuSelection = "(nGoodMuons+nGoodElectrons)>=2" + "&&" + electronSelection_loosePt + "==2&&" + muonSelection_loosePt + "==1"
if setup.parameters['useTriggers']: EEMuSelection += '&&HLT_2e1mu'
#mu_mu_e
MuMuESelection = "(nGoodMuons+nGoodElectrons)>=2" + "&&" + electronSelection_loosePt + "==1&&" + muonSelection_loosePt + "==2"
if setup.parameters['useTriggers']: MuMuESelection += '&&HLT_2mu1e'
MC_hadronSelection = setup.selection('MC', hadronicSelection = True,
**setup.defaultParameters(update={'nJets': self.nJets, 'nBTags':self.nMediumBTags, 'metMin': 0., 'metSigMin':0., 'dPhiJetMet':0. })
)['cut']
data_hadronSelection = setup.selection('Data', hadronicSelection = True,
**setup.defaultParameters(update={'nJets': self.nJets, 'nBTags':self.nMediumBTags, 'metMin': 0., 'metSigMin':0., 'dPhiJetMet':0. })
)['cut']
#loose bjet selection added here
if self.nLooseBTags[0]>=0:
MC_hadronSelection += '&&Sum$(Jet_pt>30&&abs(Jet_eta)<2.4&&Jet_id&&Jet_btagCSV>0.605)>='+str(self.nLooseBTags[0])
data_hadronSelection += '&&Sum$(Jet_pt>30&&abs(Jet_eta)<2.4&&Jet_id&&Jet_btagCSV>0.605)>='+str(self.nLooseBTags[0])
if self.nLooseBTags[1]>=0:
MC_hadronSelection += '&&Sum$(Jet_pt>30&&abs(Jet_eta)<2.4&&Jet_id&&Jet_btagCSV>0.605)<='+str(self.nLooseBTags[1])
data_hadronSelection += '&&Sum$(Jet_pt>30&&abs(Jet_eta)<2.4&&Jet_id&&Jet_btagCSV>0.605)<='+str(self.nLooseBTags[1])
MC_MuMuMu = "&&".join([
MC_hadronSelection,
MuMuMuSelection,
"abs(mlmZ_mass-91.2)<10"
])
MC_EEE = "&&".join([
MC_hadronSelection,
EEESelection,
"abs(mlmZ_mass-91.2)<10"
])
MC_EEMu = "&&".join([
MC_hadronSelection,
EEMuSelection,
"abs(mlmZ_mass-91.2)<10"
])
MC_MuMuE = "&&".join([
MC_hadronSelection,
MuMuESelection,
"abs(mlmZ_mass-91.2)<10"
])
MC_3l = "(("+MC_MuMuMu+")||("+MC_EEE+")||("+MC_EEMu+")||("+MC_MuMuE+"))"
data_MuMuMu = "&&".join([
data_hadronSelection,
MuMuMuSelection,
"abs(mlmZ_mass-91.2)<10"
])
data_EEE = "&&".join([
data_hadronSelection,
EEESelection,
"abs(mlmZ_mass-91.2)<10"
])
data_EEMu = "&&".join([
data_hadronSelection,
EEMuSelection,
"abs(mlmZ_mass-91.2)<10"
])
data_MuMuE = "&&".join([
data_hadronSelection,
MuMuESelection,
"abs(mlmZ_mass-91.2)<10"
])
######yield_MC_3l computed for ALL channels but lumi changes slightly here depending on channel
yield_MC_3l = setup.lumi[channel]/1000.*u_float( getYieldFromChain(setup.sample['TTZ'][channel]['chain'], cutString = MC_3l, weight=weight, returnError = True))
if setup.verbose: print "yield_MC_looseSelection_3l: %s"%yield_MC_3l
yield_data_MuMuMu = u_float( getYieldFromChain(setup.sample['Data']['MuMu']['chain'], cutString = data_MuMuMu, weight=weight, returnError = True))
if setup.verbose: print "yield_data_looseSelection_MuMuMu: %s"%yield_data_MuMuMu
yield_data_EEE = u_float( getYieldFromChain(setup.sample['Data']['EE']['chain'], cutString = data_EEE, weight=weight, returnError = True))
if setup.verbose: print "yield_data_looseSelection_EEE: %s"%yield_data_EEE
yield_data_EMu = u_float( getYieldFromChain(setup.sample['Data']['EMu']['chain'], cutString = "(("+data_MuMuE+')||('+data_EEMu+'))', weight=weight, returnError = True))
if setup.verbose: print "yield_data_looseSelection_EMu: %s"%yield_data_EMu
yield_data_3l = yield_data_MuMuMu+yield_data_EEE+yield_data_EMu
if setup.verbose: print "yield_data_3l: %s"%yield_data_3l
#electroweak subtraction
print "\n Substracting electroweak backgrounds from data: \n"
yield_other = u_float(0., 0.)
for s in ['TTJets' , 'DY', 'other']:
yield_other+= setup.lumi[channel]/1000.* u_float(getYieldFromChain(setup.sample[s][channel]['chain'], cutString = MC_3l, weight=weight, returnError=True))
if setup.verbose: print "yield_looseSelection_other %s added, now: %s"%(s, yield_other)
normRegYield = yield_data_3l - yield_other
if normRegYield.val<0: print "\n !!!Warning!!! \n Negative normalization region yield data: (%s), MC: (%s) \n"%(yield_data_3l, yield_other)
print "normRegYield", normRegYield
print "\n Control Region predictys ", normRegYield, " TTZ events in data; ", yield_MC_3l, " TTZ events in MC. Ratio ---> ", (normRegYield/yield_MC_3l)
print "DD-TTZ ---> ", (normRegYield/yield_MC_3l)*yield_MC_2l
return (normRegYield/yield_MC_3l)*yield_MC_2l
),
("MET>80", "met_pt>80"),
("MET/sqrt(HT)>5",
"met_pt/sqrt(Sum$(JetGood_pt*(JetGood_pt>30&&abs(JetGood_eta)<2.4&&JetGood_id)))>5"
),
("dPhi(JetGood_1,2|MET)>0.25",
"cos(met_phi-JetGood_phi[0])<cos(0.25)&&cos(met_phi-JetGood_phi[1])<cos(0.25)"
),
("MT2(ll) > 140", "dl_mt2ll>140"),
# ("looseLeptonVeto", "Sum$(LepGood_pt>15&&LepGood_miniRelIso<0.4)==2"),
# ("multiIso M(Mu), T(Ele)", multiIsoWPMT),
# ("multiIso VT(Mu), VT(Ele)", multiIsoWPVTVT),
# ("filterCut", "Flag_HBHENoiseIsoFilter&&Flag_HBHENoiseFilter&&Flag_CSCTightHaloFilter&&Flag_goodVertices&&Flag_eeBadScFilter&&Flag_EcalDeadCellTriggerPrimitiveFilter" ),
# ("relIso04<0.12", relIso04sm12Cut),
# ("MT2(ll) > 240", "dl_mt2ll>240"),
]
lumiFac = 10
print 30 * " " + "".join(["%13s" % s.name for s in samples])
for i in reversed(range(len(cuts))):
r = []
for s in samples:
selection = "&&".join(c[1] for c in cuts[:i + 1])
if selection == "": selection = "(1)"
y = lumiFac * getYieldFromChain(s.chain, selection, 'weight')
n = getYieldFromChain(s.chain, selection, '(1)')
r.append(y)
print "%30s" % cuts[i][0] + "".join(
[" %12.1f" % r[j] for j in range(len(r))])
'3l':{\
'dl_mt2ll':{'title':'MT2ll (GeV)', 'name':'MT2ll_3l', 'binning': mt2llbinning, 'histo':{'totalbkg':0.,}},
'dl_mass':{'title':'M_{ll} (GeV)', 'name':'Mll_3l', 'binning': mllbinning, 'histo':{'totalbkg':0.,}},
'met_pt':{'title':'MET (GeV)', 'name':'MET_3l', 'binning': metbinning, 'histo':{'totalbkg':0.,}},
'LepGood_pt[0]':{'title':'l1 p_{T} (GeV)', 'name':'l1pt_3l', 'binning': lepbinning, 'histo':{'totalbkg':0.,}},
'LepGood_pt[1]':{'title':'l2 p_{T} (GeV)', 'name':'l2pt_3l', 'binning': lepbinning, 'histo':{'totalbkg':0.,}},
'LepGood_pt[2]':{'title':'l3 p_{T} (GeV)', 'name':'l3pt_3l', 'binning': lepbinning, 'histo':{'totalbkg':0.,}},
},
}
weight = str(luminosity / 1000.) + '*weightPU' #+'*reweightTopPt'
MuMuMudatayield = getYieldFromChain(
getChain(data[0], histname=""),
cutString="&&".join([preselection, datacut, presel_flavour_MuMuMu]),
weight="1.")
EEEdatayield = getYieldFromChain(
getChain(data[1], histname=""),
cutString="&&".join([preselection, datacut, presel_flavour_EEE]),
weight="1.")
MuMuEdatayield = getYieldFromChain(
getChain(data[2], histname=""),
cutString="&&".join([preselection, datacut, presel_flavour_MuMuE]),
weight="1.")
EEMudatayield = getYieldFromChain(
getChain(data[2], histname=""),
cutString="&&".join([preselection, datacut, presel_flavour_EEMu]),
weight="1.")
datayield = MuMuMudatayield + EEEdatayield + MuMuEdatayield + EEMudatayield
if os.path.exists(outDir) and options.overwrite: #not options.update:
print "Directory %s exists. Delete it."%outDir
shutil.rmtree(outDir)
if not os.path.exists(outDir): os.makedirs(outDir)
if not os.path.exists(tmpDir): os.makedirs(tmpDir)
if options.signal:
signalDir = os.path.join(options.targetDir, options.skim, "T2tt")
if not os.path.exists(signalDir):
os.makedirs(signalDir)
if doTopPtReweighting:
print "Computing top pt average weight...",
c = ROOT.TChain("tree")
for chunk in chunks:
c.Add(chunk['file'])
# print getTopPtDrawString()
topScaleF = getYieldFromChain(c, cutString = "(1)", weight=getTopPtDrawString())
topScaleF/=c.GetEntries()
c.IsA().Destructor(c)
del c
print "found a top pt average correction factor of %f"%topScaleF
if options.signal:
from StopsDilepton.tools.xSecSusy import xSecSusy
xSecSusy_ = xSecSusy()
channel='stop13TeV'
signalWeight={}
c = ROOT.TChain("tree")
for chunk in chunks:
c.Add(chunk['file'])
print "Fetching signal weights..."
mMax = 1500
bStr = str(mMax)+','+str(mMax)
def _estimate(self, region, channel, setup):
printHeader("DD DY prediction for %s channel %s" %
(self.name, channel))
#Sum of all channels for 'all'
if channel == 'all':
return sum([
self.cachedEstimate(region, c, setup)
for c in ['MuMu', 'EE', 'EMu']
], u_float(0., 0.))
#MC based for 'EMu'
elif channel == 'EMu':
preSelection = setup.preselection('MC',
zWindow="allZ",
channel=channel)
cut = "&&".join([
region.cutString(setup.sys['selectionModifier']),
preSelection['cut']
])
weight = preSelection['weightStr']
if setup.verbose:
print "Using cut %s and weight %s" % (cut, weight)
return setup.lumi[channel] / 1000. * u_float(
getYieldFromChain(setup.sample['DY'][channel]['chain'],
cutString=cut,
weight=weight,
returnError=True))
#Data driven for EE and MuMu
else:
preSelection = setup.preselection('MC',
zWindow="offZ",
channel=channel)
weight = preSelection['weightStr']
assert abs(
1. -
setup.lumi[channel] / setup.sample['Data'][channel]['lumi']
) < 0.01, "Lumi specified in setup %f does not match lumi in data sample %f in channel %s" % (
setup.lumi[channel], setup.sample['Data'][channel]['lumi'],
channel)
cut_offZ_1b = "&&".join([
region.cutString(setup.sys['selectionModifier']),
setup.selection(
'MC',
channel=channel,
zWindow='offZ',
**setup.defaultParameters(update={'nBTags': (1,
-1)}))['cut']
])
cut_onZ_1b = "&&".join([
region.cutString(setup.sys['selectionModifier']),
setup.selection(
'MC',
channel=channel,
zWindow='onZ',
**setup.defaultParameters(update={'nBTags': (1,
-1)}))['cut']
])
cut_onZ_0b = "&&".join([
region.cutString(setup.sys['selectionModifier']),
setup.selection(
'MC',
channel=channel,
zWindow='onZ',
**setup.defaultParameters(update={'nBTags': (0,
0)}))['cut']
])
cut_data_onZ_0b = "&&".join([
region.cutString(setup.sys['selectionModifier']),
setup.selection(
'Data',
channel=channel,
zWindow='onZ',
**setup.defaultParameters(update={'nBTags': (0,
0)}))['cut']
])
# R1 = DY-MC (offZ, 1b) / DY-MC (onZ, 1b)
# R2 = DY-MC (onZ, 1b) / DY-MC (onZ, 0b)
# DY-est = R1*R2*(Data(2l, onZ, 0b) - EWK(onZ, 0b)) = DY-MC (offZ, 1b) / DY-MC (onZ, 0b) *( Data(2l, onZ, 0b) - EWK(onZ, 0b))
yield_offZ_1b = setup.lumi[channel] / 1000. * u_float(
getYieldFromChain(setup.sample['DY'][channel]['chain'],
cutString=cut_offZ_1b,
weight=weight,
returnError=True))
if setup.verbose: print "yield_offZ_1b: %s" % yield_offZ_1b
yield_onZ_0b = setup.lumi[channel] / 1000. * u_float(
getYieldFromChain(setup.sample['DY'][channel]['chain'],
cutString=cut_onZ_0b,
weight=weight,
returnError=True))
if setup.verbose: print "yield_onZ_0b: %s" % yield_onZ_0b
yield_data = u_float(
getYieldFromChain(setup.sample['Data'][channel]['chain'],
cutString=cut_data_onZ_0b,
weight=weight,
returnError=True))
if setup.verbose:
print "yield_data: %s (for cut: %s \n with weight: %s)" % (
yield_data, cut_data_onZ_0b, weight)
#electroweak subtraction
print "\n Substracting electroweak backgrounds from data: \n"
yield_other = u_float(0., 0.)
for s in ['TTJets', 'TTZ', 'other']:
yield_other += setup.lumi[channel] / 1000. * u_float(
getYieldFromChain(setup.sample[s][channel]['chain'],
cutString=cut_onZ_0b,
weight=weight,
returnError=True))
if setup.verbose:
print "yield_other_onZ_0b %s added, now: %s" % (
s, yield_other)
normRegYield = yield_data - yield_other
if normRegYield.val < 0:
print "\n !!!Warning!!! \n Negative normalization region yield data: (%s), MC: (%s) \n" % (
yield_data, yield_other)
mcRatio = yield_offZ_1b / yield_onZ_0b
res = mcRatio * normRegYield
print "mcRatio is: ", mcRatio
return res
# 'LepGood_pt[2]':{'title':'l3 p_{T} (GeV)', 'name':'l3pt_2l', 'binning': lepbinning, 'histo':{'totalbkg':0.,}},
# },
'3l':{\
'dl_mt2ll':{'title':'MT2ll (GeV)', 'name':'MT2ll_3l', 'binning': mt2llbinning, 'histo':{'totalbkg':0.,}},
'dl_mass':{'title':'M_{ll} (GeV)', 'name':'Mll_3l', 'binning': mllbinning, 'histo':{'totalbkg':0.,}},
'met_pt':{'title':'MET (GeV)', 'name':'MET_3l', 'binning': metbinning, 'histo':{'totalbkg':0.,}},
'LepGood_pt[0]':{'title':'l1 p_{T} (GeV)', 'name':'l1pt_3l', 'binning': lepbinning, 'histo':{'totalbkg':0.,}},
'LepGood_pt[1]':{'title':'l2 p_{T} (GeV)', 'name':'l2pt_3l', 'binning': lepbinning, 'histo':{'totalbkg':0.,}},
'LepGood_pt[2]':{'title':'l3 p_{T} (GeV)', 'name':'l3pt_3l', 'binning': lepbinning, 'histo':{'totalbkg':0.,}},
},
}
weight = str(luminosity/1000.)+'*weightPU'#+'*reweightTopPt'
MuMuMudatayield = getYieldFromChain(getChain(data[0],histname=""), cutString = "&&".join([preselection, datacut, presel_flavour_MuMuMu]), weight="1.")
EEEdatayield = getYieldFromChain(getChain(data[1],histname=""), cutString = "&&".join([preselection, datacut, presel_flavour_EEE]), weight="1.")
MuMuEdatayield = getYieldFromChain(getChain(data[2],histname=""), cutString = "&&".join([preselection, datacut, presel_flavour_MuMuE]), weight="1.")
EEMudatayield = getYieldFromChain(getChain(data[2],histname=""), cutString = "&&".join([preselection, datacut, presel_flavour_EEMu]), weight="1.")
datayield = MuMuMudatayield+EEEdatayield+MuMuEdatayield+EEMudatayield
bkgyield = 0.
for s in backgrounds:
bkgyield_temp = getYieldFromChain(getChain(s,histname=""), cutString = '&&'.join([preselection,"(("+presel_flavour_MuMuMu+")||("+presel_flavour_EEE+")||("+presel_flavour_EEMu+")||("+presel_flavour_MuMuE+"))"]), weight=weight)
bkgyield+= bkgyield_temp
print s['name'], ": ", bkgyield_temp
print "datayield: ", datayield, " , bkgyield: ", bkgyield
print "MuMuMu", MuMuMudatayield
print '\n', "Looping over %s" % s["name"]
#for MC
weight = str(luminosity/1000.)+'*weightPU'+'*reweightTopPt'
for cut in piechart.keys():
for flavor in piechart[cut].keys():
if flavor == "EE":
flavourcut = 'isEE==1&&nGoodElectrons==2&&nGoodMuons==0&&HLT_ee_DZ'
elif flavor == "EMu":
flavourcut = 'isEMu==1&&nGoodElectrons==1&&nGoodMuons==1&&HLT_mue'
elif flavor == "MuMu":
flavourcut = 'isMuMu==1&&nGoodElectrons==0&&nGoodMuons==2&&HLT_mumuIso'
yield_ = getYieldFromChain(getChain(s,histname=""), cutString = '&&'.join([preselection,flavourcut]),weight=weight)
yield_0j_0bj = getYieldFromChain(getChain(s,histname=""), cutString = '&&'.join([preselection,"nGoodJets==0","nBTags==0","dl_mt2ll>="+cut,flavourcut]),weight=weight)
yield_1j_0bj = getYieldFromChain(getChain(s,histname=""), cutString = '&&'.join([preselection,"nGoodJets==1","nBTags==0","dl_mt2ll>="+cut,flavourcut]),weight=weight)
yield_1j_1bj = getYieldFromChain(getChain(s,histname=""), cutString = '&&'.join([preselection,"nGoodJets==1","nBTags==1","dl_mt2ll>="+cut,flavourcut]),weight=weight)
yield_2mj_0bj = getYieldFromChain(getChain(s,histname=""), cutString = '&&'.join([preselection,"nGoodJets>=2","nBTags==0","dl_mt2ll>="+cut,flavourcut]),weight=weight)
yield_2mj_1mbj = getYieldFromChain(getChain(s,histname=""), cutString = '&&'.join([preselection,"nGoodJets>=2","nBTags>=1","dl_mt2ll>="+cut,flavourcut]),weight=weight)
piechart[cut][flavor]["(0,0)"][s["name"]] == yield_0j_0bj
piechart[cut][flavor]["(1,0)"][s["name"]] == yield_1j_0bj
piechart[cut][flavor]["(1,1)"][s["name"]] == yield_1j_1bj
piechart[cut][flavor]["(>=2,0)"][s["name"]] == yield_2mj_0bj
piechart[cut][flavor]["(>=2,>=1)"][s["name"]] == yield_2mj_1mbj
print piechart
},
'met':{\
'_onZ_0b': {'title':'MET (GeV)', 'name':'MET_onZ_b==0b', "legend":"(onZ,0 b-tag)",'binning': mllbinning, 'histo':{}},
'_offZ_0b': {'title':'MET (GeV)', 'name':'MET_offZ_b==0b', "legend":"(offZ,0 b-tag)", 'binning': mllbinning, 'histo':{}},
'_onZ_1mb': {'title':'MET (GeV)', 'name':'MET_onZ_b>=1', "legend":"(onZ,>0 b-tag)", 'binning': mllbinning, 'histo':{}},
'_offZ_1mb': {'title':'MET (GeV)', 'name':'MET_offZ_b>=1', "legend":"(offZ,>0 b-tag)", 'binning': mllbinning, 'histo':{}},
},
}
#######################################################
# Start filling in the histograms #
#######################################################
weight = str(luminosity/1000.)+'*weight'#+'*reweightTopPt'
datayield_onZ_0b = getYieldFromChain(getChain(data[0],histname=""), cutString = "&&".join([preselection, datacut,'abs(dl_mass-91.2)<=15&&nBTags==0']), weight="1.")
bkgyield_onZ_0b = 0.
print "&&".join([preselection,'abs(dl_mass-91.2)<=15&&nBTags==0'])
for s in backgrounds:
bkgyield_onZ_0b_tmp = getYieldFromChain(getChain(s,histname=""), "&&".join([preselection,'abs(dl_mass-91.2)<=15&&nBTags==0']), weight=weight)
bkgyield_onZ_0b += bkgyield_onZ_0b_tmp
print s['name'], ": ", bkgyield_onZ_0b_tmp
datayield_offZ_0b = getYieldFromChain(getChain(data[0],histname=""), cutString = "&&".join([preselection, datacut,'abs(dl_mass-91.2)>15&&nBTags==0']), weight="1.")
bkgyield_offZ_0b = 0.
for s in backgrounds:
bkgyield_offZ_0b+= getYieldFromChain(getChain(s,histname=""), "&&".join([preselection,'abs(dl_mass-91.2)>15&&nBTags==0']), weight=weight)
def _estimate(self, region, channel, setup):
#Sum of all channels for 'all'
if channel == 'all':
return sum([
self.cachedEstimate(region, c, channel, setup)
for c in ['MuMu', 'EE', 'EMu']
])
else:
#Data driven for EE, EMu and MuMu.
zWindow = 'allZ' if channel == 'EMu' else 'offZ'
preSelection = setup.preselection('MC',
zWindow=zWindow,
channel=channel)
#check lumi consistency
assert abs(
1. -
setup.lumi[channel] / setup.sample['Data'][channel]['lumi']
) < 0.01, "Lumi specified in setup %f does not match lumi in data sample %f in channel %s" % (
setup.lumi[channel], setup.sample['Data'][channel]['lumi'],
channel)
MC_2l = "&&".join([
region.cutString(setup.sys['selectionModifier']),
preSelection['cut']
])
weight = preSelection['weightStr']
logger.debug("weight: %s", weight)
yield_MC_2l = setup.lumi[channel] / 1000. * u_float(
getYieldFromChain(setup.sample['TTZ'][channel]['chain'],
cutString=selection_MC_2l,
weight=weight,
returnError=True))
if setup.verbose: print "yield_MC_2l: %s" % yield_MC_2l
# pt leptons > 30, 20, 10 GeV
useTrigger = False # setup.parameters['useTriggers'] # better not to use three lepton triggers, seems to be too inefficient
mumumuSelection = "&&".join([
getLeptonString(3, 0),
getPtThresholdString(30, 20, 10)
]) + ("&&HLT_3mu" if useTrigger else "")
mumueSelection = "&&".join([
getLeptonString(2, 1),
getPtThresholdString(30, 20, 10)
]) + ("&&HLT_2mu1e" if useTrigger else "")
mueeSelection = "&&".join([
getLeptonString(1, 2),
getPtThresholdString(30, 20, 10)
]) + ("&&HLT_2e1mu" if useTrigger else "")
eeeSelection = "&&".join([
getLeptonString(0, 3),
getPtThresholdString(30, 20, 10)
]) + ("&&HLT_3e" if useTrigger else "")
lllSelection = "((" + ")||(".join([
mumumuSelection, mumueSelection, mueeSelection, eeeSelection
]) + "))"
bJetSelectionM = "(Sum$(JetGood_pt>30&&abs(JetGood_eta)<2.4&&JetGood_id&&JetGood_btagCSV>0.890))"
bJetSelectionL = "(Sum$(JetGood_pt>30&&abs(JetGood_eta)<2.4&&JetGood_id&&JetGood_btagCSV>0.605))"
zMassSelection = "abs(mlmZ_mass-91.1876)<10"
# Start from base hadronic selection and add loose b-tag and Z-mass requirement
selection = {}
for dataOrMC in ["Data", "MC"]:
selection[dataOrMC] = setup.selection(
dataOrMC,
hadronicSelection=True,
**setup.defaultParameters(
update={
'nJets': self.nJets,
'nBTags': self.nMediumBTags,
'metMin': 0.,
'metSigMin': 0.,
'dPhiJetMet': 0.
}))['cut']
selection[dataOrMC] += bJetSelectionL + ">=" + str(
self.nLooseBTags[0])
selection[dataOrMC] += zMassSelection
MC_3l = lllSelection + "&&" + selection["MC"]
data_mumumu = mumumuSelection + "&&" + selection["Data"]
data_mumue = mumueSelection + "&&" + selection["Data"]
data_muee = mueeSelection + "&&" + selection["Data"]
data_eee = eeeSelection + "&&" + selection["Data"]
# Calculate yields (take together)
yield_ttZ_2l = setup.lumi[channel] / 1000. * u_float(
getYieldFromChain(setup.sample['TTZ'][channel]['chain'],
cutString=MC_2l,
weight=weight,
returnError=True))
yield_ttZ_3l = setup.lumi[channel] / 1000. * u_float(
getYieldFromChain(setup.sample['TTZ'][channel]['chain'],
cutString=MC_3l,
weight=weight,
returnError=True))
yield_data_mumumu = u_float(
getYieldFromChain(setup.sample['Data']['MuMu']['chain'],
cutString=data_mumumu,
weight=weight,
returnError=True))
yield_data_eee = u_float(
getYieldFromChain(setup.sample['Data']['EE']['chain'],
cutString=data_eee,
weight=weight,
returnError=True))
yield_data_mue = u_float(
getYieldFromChain(setup.sample['Data']['EMu']['chain'],
cutString="((" + data_mumue + ')||(' +
data_muee + '))',
weight=weight,
returnError=True))
yield_data_3l = yield_data_mumumu + yield_data_mue + yield_data_eee
#electroweak subtraction
yield_other = u_float(0., 0.)
for s in ['TTJets', 'DY', 'other']:
yield_other += setup.lumi[channel] / 1000. * u_float(
getYieldFromChain(setup.sample[s][channel]['chain'],
cutString=MC_3l,
weight=weight,
returnError=True))
yield_ttZ_data = yield_data_3l - yield_other
if normRegYield.val < 0:
logger.warn("Data-driven estimate is negative!")
logger.info("Control region predictions: ")
logger.info(" data: " + str(yield_data_3l))
logger.info(" MC other: " + str(yield_other))
logger.info(" TTZ (MC): " + str(yield_ttZ_3l))
logger.info(" TTZ (data): " + str(yield_ttZ_data))
logger.info(" TTZ (ratio): " + str(yield_ttZ_data / yield_ttZ_3l))
return (yield_ttZ_data / yield_ttZ_3l) * yield_MC_2l
QCDSample = QCD_Mu5
if opts.mode=="doubleEle":
cutString = "&&".join(["isEE==1&&nGoodMuons==0&&nGoodElectrons==2", triggerEleEle, getZCut(opts.zMode)] + preselCuts)
dataCut = "&&".join([filterCut])
dataSample = DoubleEG_Run2015D
QCDSample = QCD_EMbcToE
if opts.mode=="muEle":
cutString = "&&".join(["isEMu==1&&nGoodMuons==1&&nGoodElectrons==1",triggerMuEle, getZCut(opts.zMode)] + preselCuts)
dataCut = "&&".join([filterCut])
dataSample = MuonEG_Run2015D
QCDSample = QCD_Mu5EMbcToE
cutFunc = None
lumiScaleFac = dataSample["lumi"]/1000.
backgrounds = [TTJets, WJetsToLNu, DY, singleTop, QCDSample, TTX, diBoson]
data = getYieldFromChain(getChain(dataSample,histname="",maxN=maxN), cutString = "&&".join([cutString, dataCut]), weight='weight')
bkg = 0.
for s in backgrounds:
bkg+= getYieldFromChain(getChain(s,histname="", maxN=maxN), cutString, weight='weight')
scaleFac = data/(bkg*lumiScaleFac)
print "After lumiscale %3.3f there is bkg %7.1f and data %7.1f: re-normalizing scaleFac by %3.3f"%(lumiScaleFac, lumiScaleFac*bkg, data, scaleFac)
ratioOps = {'yLabel':'Data/MC', 'numIndex':1, 'denIndex':0 ,'yRange':None, 'logY':False, 'color':ROOT.kBlack, 'yRange':(0.1, 2.1)}
#ratioOps = None
def getStack(labels, var, binning, cut, options={}):
style_Data = {'legendText':dataSample['name'], 'style':"e", 'lineThickness':0, 'errorBars':True, 'color':ROOT.kBlack, 'markerStyle':20, 'markerSize':1}
style_WJets = {'legendText':'W + Jets', 'style':"f", 'lineThickness':0, 'errorBars':False, 'color':42, 'markerStyle':None, 'markerSize':None}
'dl_mass':{\
'_onZ_0b': {'title':'m_{ll} (GeV)', 'name':'Mll_onZ_b==0b', "legend":"(onZ,0 b-tag)",'binning': mllbinning, 'histo':{}},
'_offZ_0b': {'title':'m_{ll} (GeV)', 'name':'Mll_offZ_b==0b', "legend":"(offZ,0 b-tag)", 'binning': mllbinning, 'histo':{}},
'_onZ_1mb': {'title':'m_{ll} (GeV)', 'name':'Mll_onZ_b>=1', "legend":"(onZ,>0 b-tag)", 'binning': mllbinning, 'histo':{}},
'_offZ_1mb': {'title':'m_{ll} (GeV)', 'name':'Mll_offZ_b>=1', "legend":"(offZ,>0 b-tag)", 'binning': mllbinning, 'histo':{}},
},
}
#######################################################
# Start filling in the histograms #
#######################################################
weight = str(luminosity / 1000.) + '*weightPU' + '*reweightTopPt'
datayield_onZ_0b = getYieldFromChain(getChain(data[0], histname=""),
cutString="&&".join([
preselection, datacut,
'abs(dl_mass-91.2)<=15&&nBTags==0'
]),
weight="1.")
bkgyield_onZ_0b = 0.
for s in backgrounds:
bkgyield_onZ_0b += getYieldFromChain(
getChain(s, histname=""),
"&&".join([preselection, 'abs(dl_mass-91.2)<=15&&nBTags==0']),
weight=weight)
datayield_offZ_0b = getYieldFromChain(getChain(data[0], histname=""),
cutString="&&".join([
preselection, datacut,
'abs(dl_mass-91.2)>15&&nBTags==0'
]),
weight="1.")
def _estimate(self, region, channel, setup):
printHeader("DD TTZ prediction for '%s' channel %s" %
(self.name, channel))
#Sum of all channels for 'all'
if channel == 'all':
return sum([
self.cachedEstimate(region, c, channel, setup)
for c in ['MuMu', 'EE', 'EMu']
])
else:
#Data driven for EE, EMu and MuMu.
preSelection = setup.preselection('MC', channel=channel)
#check lumi consistency
assert abs(
1. -
setup.lumi[channel] / setup.sample['Data'][channel]['lumi']
) < 0.01, "Lumi specified in setup %f does not match lumi in data sample %f in channel %s" % (
setup.lumi[channel], setup.sample['Data'][channel]['lumi'],
channel)
selection_MC_2l = "&&".join([
region.cutString(setup.sys['selectionModifier']),
preSelection['cut']
])
weight = preSelection['weightStr']
yield_MC_2l = setup.lumi[channel] / 1000. * u_float(
getYieldFromChain(setup.sample['TTZ'][channel]['chain'],
cutString=selection_MC_2l,
weight=weight,
returnError=True))
if setup.verbose: print "yield_MC_2l: %s" % yield_MC_2l
muonSelection_loosePt = looseMuIDString(ptCut=10)
electronSelection_loosePt = looseEleIDString(ptCut=10)
#mu_mu_mu
MuMuMuSelection = "nGoodMuons>=2" + '&&' + muonSelection_loosePt + "==3"
if setup.parameters['useTriggers']: MuMuMuSelection += '&&HLT_3mu'
#e_e_e
EEESelection = "nGoodElectrons>=2" + '&&' + electronSelection_loosePt + "==3"
if setup.parameters['useTriggers']: EEESelection += '&&HLT_3e'
#e_e_mu
EEMuSelection = "(nGoodMuons+nGoodElectrons)>=2" + "&&" + electronSelection_loosePt + "==2&&" + muonSelection_loosePt + "==1"
if setup.parameters['useTriggers']: EEMuSelection += '&&HLT_2e1mu'
#mu_mu_e
MuMuESelection = "(nGoodMuons+nGoodElectrons)>=2" + "&&" + electronSelection_loosePt + "==1&&" + muonSelection_loosePt + "==2"
if setup.parameters['useTriggers']: MuMuESelection += '&&HLT_2mu1e'
MC_hadronSelection = setup.selection(
'MC',
hadronicSelection=True,
**setup.defaultParameters(
update={
'nJets': self.nJets,
'nBTags': self.nMediumBTags,
'metMin': 0.,
'metSigMin': 0.,
'dPhiJetMet': 0.
}))['cut']
data_hadronSelection = setup.selection(
'Data',
hadronicSelection=True,
**setup.defaultParameters(
update={
'nJets': self.nJets,
'nBTags': self.nMediumBTags,
'metMin': 0.,
'metSigMin': 0.,
'dPhiJetMet': 0.
}))['cut']
#loose bjet selection added here
if self.nLooseBTags[0] >= 0:
MC_hadronSelection += '&&Sum$(Jet_pt>30&&abs(Jet_eta)<2.4&&Jet_id&&Jet_btagCSV>0.605)>=' + str(
self.nLooseBTags[0])
data_hadronSelection += '&&Sum$(Jet_pt>30&&abs(Jet_eta)<2.4&&Jet_id&&Jet_btagCSV>0.605)>=' + str(
self.nLooseBTags[0])
if self.nLooseBTags[1] >= 0:
MC_hadronSelection += '&&Sum$(Jet_pt>30&&abs(Jet_eta)<2.4&&Jet_id&&Jet_btagCSV>0.605)<=' + str(
self.nLooseBTags[1])
data_hadronSelection += '&&Sum$(Jet_pt>30&&abs(Jet_eta)<2.4&&Jet_id&&Jet_btagCSV>0.605)<=' + str(
self.nLooseBTags[1])
MC_MuMuMu = "&&".join([
MC_hadronSelection, MuMuMuSelection, "abs(mlmZ_mass-91.2)<10"
])
MC_EEE = "&&".join(
[MC_hadronSelection, EEESelection, "abs(mlmZ_mass-91.2)<10"])
MC_EEMu = "&&".join(
[MC_hadronSelection, EEMuSelection, "abs(mlmZ_mass-91.2)<10"])
MC_MuMuE = "&&".join(
[MC_hadronSelection, MuMuESelection, "abs(mlmZ_mass-91.2)<10"])
MC_3l = "((" + MC_MuMuMu + ")||(" + MC_EEE + ")||(" + MC_EEMu + ")||(" + MC_MuMuE + "))"
data_MuMuMu = "&&".join([
data_hadronSelection, MuMuMuSelection, "abs(mlmZ_mass-91.2)<10"
])
data_EEE = "&&".join(
[data_hadronSelection, EEESelection, "abs(mlmZ_mass-91.2)<10"])
data_EEMu = "&&".join([
data_hadronSelection, EEMuSelection, "abs(mlmZ_mass-91.2)<10"
])
data_MuMuE = "&&".join([
data_hadronSelection, MuMuESelection, "abs(mlmZ_mass-91.2)<10"
])
######yield_MC_3l computed for ALL channels but lumi changes slightly here depending on channel
yield_MC_3l = setup.lumi[channel] / 1000. * u_float(
getYieldFromChain(setup.sample['TTZ'][channel]['chain'],
cutString=MC_3l,
weight=weight,
returnError=True))
if setup.verbose:
print "yield_MC_looseSelection_3l: %s" % yield_MC_3l
yield_data_MuMuMu = u_float(
getYieldFromChain(setup.sample['Data']['MuMu']['chain'],
cutString=data_MuMuMu,
weight=weight,
returnError=True))
if setup.verbose:
print "yield_data_looseSelection_MuMuMu: %s" % yield_data_MuMuMu
yield_data_EEE = u_float(
getYieldFromChain(setup.sample['Data']['EE']['chain'],
cutString=data_EEE,
weight=weight,
returnError=True))
if setup.verbose:
print "yield_data_looseSelection_EEE: %s" % yield_data_EEE
yield_data_EMu = u_float(
getYieldFromChain(setup.sample['Data']['EMu']['chain'],
cutString="((" + data_MuMuE + ')||(' +
data_EEMu + '))',
weight=weight,
returnError=True))
if setup.verbose:
print "yield_data_looseSelection_EMu: %s" % yield_data_EMu
yield_data_3l = yield_data_MuMuMu + yield_data_EEE + yield_data_EMu
if setup.verbose: print "yield_data_3l: %s" % yield_data_3l
#electroweak subtraction
print "\n Substracting electroweak backgrounds from data: \n"
yield_other = u_float(0., 0.)
for s in ['TTJets', 'DY', 'other']:
yield_other += setup.lumi[channel] / 1000. * u_float(
getYieldFromChain(setup.sample[s][channel]['chain'],
cutString=MC_3l,
weight=weight,
returnError=True))
if setup.verbose:
print "yield_looseSelection_other %s added, now: %s" % (
s, yield_other)
normRegYield = yield_data_3l - yield_other
if normRegYield.val < 0:
print "\n !!!Warning!!! \n Negative normalization region yield data: (%s), MC: (%s) \n" % (
yield_data_3l, yield_other)
print "normRegYield", normRegYield
print "\n Control Region predictys ", normRegYield, " TTZ events in data; ", yield_MC_3l, " TTZ events in MC. Ratio ---> ", (
normRegYield / yield_MC_3l)
print "DD-TTZ ---> ", (normRegYield / yield_MC_3l) * yield_MC_2l
return (normRegYield / yield_MC_3l) * yield_MC_2l
print '\n', "Looping over %s" % s["name"]
#for MC
weight = str(luminosity / 1000.) + '*weightPU' + '*reweightTopPt'
for cut in piechart.keys():
for flavor in piechart[cut].keys():
if flavor == "EE":
flavourcut = 'isEE==1&&nGoodElectrons==2&&nGoodMuons==0&&HLT_ee_DZ'
elif flavor == "EMu":
flavourcut = 'isEMu==1&&nGoodElectrons==1&&nGoodMuons==1&&HLT_mue'
elif flavor == "MuMu":
flavourcut = 'isMuMu==1&&nGoodElectrons==0&&nGoodMuons==2&&HLT_mumuIso'
yield_ = getYieldFromChain(getChain(s, histname=""),
cutString='&&'.join(
[preselection, flavourcut]),
weight=weight)
yield_0j_0bj = getYieldFromChain(getChain(s, histname=""),
cutString='&&'.join([
preselection, "nGoodJets==0",
"nBTags==0",
"dl_mt2ll>=" + cut, flavourcut
]),
weight=weight)
yield_1j_0bj = getYieldFromChain(getChain(s, histname=""),
cutString='&&'.join([
preselection, "nGoodJets==1",
"nBTags==0",
"dl_mt2ll>=" + cut, flavourcut
]),
SMS_T2tt_2J_mStop650_mLSP325,
SMS_T2tt_2J_mStop850_mLSP100,
]
for s in samples:
s["chain"] = getChain(s, histname="")
cuts = [
("lepVeto", "nGoodMuons+nGoodElectrons==2"),
("njet2", "(Sum$(Jet_pt>30&&abs(Jet_eta)<2.4&&Jet_id))>=2"),
("nbtag1", "Sum$(Jet_pt>30&&abs(Jet_eta)<2.4&&Jet_id&&Jet_btagCSV>0.890)>=1"),
("mll20", "dl_mass>20"),
("met80", "met_pt>80"),
("metSig5", "met_pt/sqrt(Sum$(Jet_pt*(Jet_pt>30&&abs(Jet_eta)<2.4&&Jet_id)))>5"),
("dPhiJet0-dPhiJet1", "cos(met_phi-Jet_phi[0])<cos(0.25)&&cos(met_phi-Jet_phi[1])<cos(0.25)"),
("isOS", "isOS==1"),
("SFZVeto", "( (isMuMu==1||isEE==1)&&abs(dl_mass-90.2)>=15 || isEMu==1 )"),
]
lumiFac = 10
for s in samples:
print "\nSample: %s" % s["name"]
for i in range(len(cuts) + 1):
selection = "&&".join(c[1] for c in cuts[:i])
if selection == "":
selection = "(1)"
name = "-".join(c[0] for c in cuts[:i])
y = lumiFac * getYieldFromChain(s["chain"], selection, "weight")
n = getYieldFromChain(s["chain"], selection, "(1)")
print "%10.3f %10i %s" % (y, n, name)
# print "%10.3f %10i %s %s"%(y,n,name,selection)
print "Directory %s exists. Delete it." % outDir
shutil.rmtree(outDir)
if not os.path.exists(outDir): os.makedirs(outDir)
if not os.path.exists(tmpDir): os.makedirs(tmpDir)
if options.signal:
signalDir = os.path.join(options.targetDir, options.skim, "T2tt")
if not os.path.exists(signalDir):
os.makedirs(signalDir)
if doTopPtReweighting:
print "Computing top pt average weight...",
c = ROOT.TChain("tree")
for chunk in chunks:
c.Add(chunk['file'])
# print getTopPtDrawString()
topScaleF = getYieldFromChain(c,
cutString="(1)",
weight=getTopPtDrawString())
topScaleF /= c.GetEntries()
c.IsA().Destructor(c)
del c
print "found a top pt average correction factor of %f" % topScaleF
if options.signal:
from StopsDilepton.tools.xSecSusy import xSecSusy
xSecSusy_ = xSecSusy()
channel = 'stop13TeV'
signalWeight = {}
c = ROOT.TChain("tree")
for chunk in chunks:
c.Add(chunk['file'])
print "Fetching signal weights..."
mMax = 1500
def _estimate(self, region, channel, setup):
#Sum of all channels for 'all'
if channel=='all':
return sum( [ self.cachedEstimate(region, c, channel, setup) for c in ['MuMu', 'EE', 'EMu'] ] )
else:
#Data driven for EE, EMu and MuMu.
zWindow= 'allZ' if channel=='EMu' else 'offZ'
preSelection = setup.preselection('MC', zWindow=zWindow, channel=channel)
#check lumi consistency
assert abs(1.-setup.lumi[channel]/setup.sample['Data'][channel]['lumi'])<0.01, "Lumi specified in setup %f does not match lumi in data sample %f in channel %s"%(setup.lumi[channel], setup.sample['Data'][channel]['lumi'], channel)
MC_2l = "&&".join([region.cutString(setup.sys['selectionModifier']), preSelection['cut']])
weight = preSelection['weightStr']
logger.debug("weight: %s", weight)
yield_MC_2l = setup.lumi[channel]/1000.*u_float(getYieldFromChain(setup.sample['TTZ'][channel]['chain'], cutString = selection_MC_2l, weight=weight, returnError = True) )
if setup.verbose: print "yield_MC_2l: %s"%yield_MC_2l
# pt leptons > 30, 20, 10 GeV
useTrigger = False # setup.parameters['useTriggers'] # better not to use three lepton triggers, seems to be too inefficient
mumumuSelection = "&&".join([getLeptonString(3, 0), getPtThresholdString(30, 20, 10)]) + ("&&HLT_3mu" if useTrigger else "")
mumueSelection = "&&".join([getLeptonString(2, 1), getPtThresholdString(30, 20, 10)]) + ("&&HLT_2mu1e" if useTrigger else "")
mueeSelection = "&&".join([getLeptonString(1, 2), getPtThresholdString(30, 20, 10)]) + ("&&HLT_2e1mu" if useTrigger else "")
eeeSelection = "&&".join([getLeptonString(0, 3), getPtThresholdString(30, 20, 10)]) + ("&&HLT_3e" if useTrigger else "")
lllSelection = "((" + ")||(".join([mumumuSelection, mumueSelection, mueeSelection, eeeSelection]) + "))"
bJetSelectionM = "(Sum$(JetGood_pt>30&&abs(JetGood_eta)<2.4&&JetGood_id&&JetGood_btagCSV>0.890))"
bJetSelectionL = "(Sum$(JetGood_pt>30&&abs(JetGood_eta)<2.4&&JetGood_id&&JetGood_btagCSV>0.605))"
zMassSelection = "abs(mlmZ_mass-91.1876)<10"
# Start from base hadronic selection and add loose b-tag and Z-mass requirement
selection = {}
for dataOrMC in ["Data", "MC"]:
selection[dataOrMC] = setup.selection(dataOrMC, hadronicSelection = True, **setup.defaultParameters(update={'nJets': self.nJets, 'nBTags':self.nMediumBTags, 'metMin': 0., 'metSigMin':0., 'dPhiJetMet':0. }))['cut']
selection[dataOrMC] += bJetSelectionL+">="+str(self.nLooseBTags[0])
selection[dataOrMC] += zMassSelection
MC_3l = lllSelection + "&&" + selection["MC"]
data_mumumu = mumumuSelection + "&&" + selection["Data"]
data_mumue = mumueSelection + "&&" + selection["Data"]
data_muee = mueeSelection + "&&" + selection["Data"]
data_eee = eeeSelection + "&&" + selection["Data"]
# Calculate yields (take together)
yield_ttZ_2l = setup.lumi[channel]/1000.*u_float(getYieldFromChain(setup.sample['TTZ'][channel]['chain'], cutString = MC_2l, weight=weight, returnError = True))
yield_ttZ_3l = setup.lumi[channel]/1000.*u_float(getYieldFromChain(setup.sample['TTZ'][channel]['chain'], cutString = MC_3l, weight=weight, returnError = True))
yield_data_mumumu = u_float(getYieldFromChain(setup.sample['Data']['MuMu']['chain'], cutString = data_mumumu, weight=weight, returnError = True))
yield_data_eee = u_float(getYieldFromChain(setup.sample['Data']['EE']['chain'], cutString = data_eee, weight=weight, returnError = True))
yield_data_mue = u_float(getYieldFromChain(setup.sample['Data']['EMu']['chain'], cutString = "(("+data_mumue+')||('+data_muee+'))', weight=weight, returnError = True))
yield_data_3l = yield_data_mumumu + yield_data_mue + yield_data_eee
#electroweak subtraction
yield_other = u_float(0., 0.)
for s in ['TTJets' , 'DY', 'other']:
yield_other+= setup.lumi[channel]/1000.* u_float(getYieldFromChain(setup.sample[s][channel]['chain'], cutString = MC_3l, weight=weight, returnError=True))
yield_ttZ_data = yield_data_3l - yield_other
if normRegYield.val<0: logger.warn("Data-driven estimate is negative!")
logger.info("Control region predictions: ")
logger.info(" data: " + str(yield_data_3l))
logger.info(" MC other: " + str(yield_other))
logger.info(" TTZ (MC): " + str(yield_ttZ_3l))
logger.info(" TTZ (data): " + str(yield_ttZ_data))
logger.info(" TTZ (ratio): " + str(yield_ttZ_data/yield_ttZ_3l))
return (yield_ttZ_data/yield_ttZ_3l)*yield_MC_2l