def makeJetBalancing( event, sample ):
good_jets = getJets( event, jetColl="JetGood")
# leading jet
event.rawPt = good_jets[0]['rawPt']
event.eta = good_jets[0]['eta']
event.area = good_jets[0]['area']
# compute correction factors
if sample.isData:
corr = jetCorrector_L1L2L3ResData.correction(event.rawPt, event.eta, event.area, event.rho, event.run)
corr_L1 = jetCorrector_L1Data.correction(event.rawPt, event.eta, event.area, event.rho, event.run)
else:
corr = jetCorrector_L1L2L3MC.correction(event.rawPt, event.eta, event.area, event.rho, event.run)
corr_L1 = jetCorrector_L1MC.correction(event.rawPt, event.eta, event.area, event.rho, event.run)
event.deltaPU = event.rawPt*corr*(1-1./corr_L1)
event.deltaPUPerArea = event.deltaPU/event.area
if event.rho>0:
event.deltaPUPerRho = event.deltaPU/event.rho
event.deltaPUPerAreaRho = event.deltaPU/(event.rho*event.area)
else:
event.deltaPUPerRho = float('nan')
event.deltaPUPerAreaRho = float('nan')
# balance raw jet
event.r_ptbal_raw = event.rawPt / event.dl_pt
# balance jet corrected for everything EXCEPT L1, however, L2L3+L2L3res are evaluated at the L1 corrected pT
event.r_ptbal_noL1 = event.rawPt*( corr / corr_L1 ) / event.dl_pt
# balance L1 corrected jet
event.r_ptbal_L1 = event.rawPt*corr_L1 / event.dl_pt
# balance fully corrected jet
event.r_ptbal_corr = event.rawPt*corr / event.dl_pt
return
def makeJetMatchingInfo( event, sample ):
if sample.isData:
good_jets = getJets( event, jetColl="JetGood", jetVars = jetVars)
else:
good_jets = getJets( event, jetColl="JetGood", jetVars = jetVars + jetMCVars)
# leading jet
event.rawPt = good_jets[0]['rawPt']
event.eta = good_jets[0]['eta']
event.area = good_jets[0]['area']
if not sample.isData:
event.isQuark = jetHasMCMatch( good_jets[0] ) and abs(good_jets[0]['partonFlavour'])>0 and abs(good_jets[0]['partonFlavour'])<6
event.isGluon = jetHasMCMatch( good_jets[0] ) and good_jets[0]['partonFlavour'] == 21
event.isPU = good_jets[0]['mcPt']==0 and good_jets[0]['partonFlavour'] == 0
event.isOther = not (event.isQuark or event.isGluon or event.isPU)
#print sample.name, good_jets[0]['pt'], good_jets[0]['partonFlavour'], good_jets[0]['mcPt'], event.isQuark, event.isGluon, event.isPU, event.isOther
else:
event.isQuark = None
event.isGluon = None
event.isPU = None
event.isOther = None
return
def filler( event ):
# shortcut
r = reader.event
if isMC:
event.weight = r.xsec*lumiScaleFactor*r.genWeight if lumiScaleFactor is not None else 1
else:
event.weight = 1
# lumi lists and vetos
if isData:
#event.vetoPassed = vetoList.passesVeto(r.run, r.lumi, r.evt)
event.jsonPassed = lumiList.contains(r.run, r.lumi)
# store decision to use after filler has been executed
event.jsonPassed_ = event.jsonPassed
# PU weight
if isMC:
event.reweightPU = puRW ( r.nTrueInt )
event.reweightPUDown = puRWDown ( r.nTrueInt )
event.reweightPUUp = puRWUp ( r.nTrueInt )
jets = getJets( r, jetColl="Jet", jetVars = jetVarNames)
event.nJetGood = len(jets) #FIXME
for iJet, j in enumerate(jets):
# 'Corr' correction level: L1L2L3 L2res
if sample.isData:
jet_corr_factor = jetCorrector_data. correction( j['rawPt'], j['eta'], j['area'], r.rho, r.run )
jet_corr_factor_RC = jetCorrector_RC_data.correction( j['rawPt'], j['eta'], j['area'], r.rho, r.run )
else:
jet_corr_factor = jetCorrector_mc. correction( j['rawPt'], j['eta'], j['area'], r.rho, r.run )
jet_corr_factor_RC = jetCorrector_RC_mc. correction( j['rawPt'], j['eta'], j['area'], r.rho, r.run )
# corrected jet
j['pt_corr'] = jet_corr_factor * j['rawPt']
event.Jet_pt_corr[iJet] = j['pt_corr'] #FIXME
# L1RC
j['pt_corr_RC'] = jet_corr_factor_RC * j['rawPt']
tag_jet, probe_jet = jets[:2]
# randomize if both are in barrel:
if abs(tag_jet['eta'])<1.3 and abs(probe_jet['eta'])<1.3:
if random.random()>0.5:
tag_jet, probe_jet = probe_jet, tag_jet
# tag jet in barrel
if abs(tag_jet['eta'])>1.3 and abs(probe_jet['eta'])<1.3:
tag_jet, probe_jet = probe_jet, tag_jet
third_jet = jets[2] if len(jets)>=3 else null_jet
event.tag_jet_index = tag_jet['index']
event.probe_jet_index = probe_jet['index']
event.third_jet_index = third_jet['index']
event.pt_avg = 0.5*( tag_jet['pt'] + probe_jet['pt'] )
event.alpha = third_jet['pt']/event.pt_avg
event.A = (probe_jet['pt'] - tag_jet['pt']) / (probe_jet['pt'] + tag_jet['pt'])
# MET corrections
good_jets = filter( lambda j:j['pt_corr'] > 15, jets)
# compute type-1 MET shifts for chs met L1L2L3 - L1RC (if 'noL1', then L1FastJets is divided out and L1RC is not applied )
type1_met_shifts = \
{'px' :sum( ( j['pt_corr_RC'] - j['pt_corr'] )*cos(j['phi']) for j in good_jets),
'py' :sum( ( j['pt_corr_RC'] - j['pt_corr'] )*sin(j['phi']) for j in good_jets) }
# chs MET
event.chs_MEx_corr = r.met_chsPt*cos(r.met_chsPhi) + type1_met_shifts['px']
event.chs_MEy_corr = r.met_chsPt*sin(r.met_chsPhi) + type1_met_shifts['py']
event.chs_MEt_corr = sqrt( event.chs_MEx_corr**2 + event.chs_MEy_corr**2 )
event.chs_MEphi_corr = atan2( event.chs_MEy_corr, event.chs_MEx_corr )
# R(MPF)
event.B = ( event.chs_MEx_corr*tag_jet['pt']*cos(tag_jet['phi']) + event.chs_MEy_corr*tag_jet['pt']*sin(tag_jet['phi']) ) / tag_jet['pt'] / (tag_jet['pt'] + probe_jet['pt'])
migration_fraction = ROOT.TProfile("fraction", "fraction", len(eta_binning)-1, array.array('d', eta_binning))
migration_fraction_20_30 = ROOT.TProfile("fraction_20_30", "fraction_20_30", len(eta_binning)-1, array.array('d', eta_binning))
migration_fraction_30_50 = ROOT.TProfile("fraction_30_50", "fraction_30_50", len(eta_binning)-1, array.array('d', eta_binning))
migration_fraction_50_100 = ROOT.TProfile("fraction_50_100", "fraction_50_100", len(eta_binning)-1, array.array('d', eta_binning))
migration_fraction_100 = ROOT.TProfile("fraction_100", "fraction_100", len(eta_binning)-1, array.array('d', eta_binning))
pt_bins = jet_respons_genEta.keys()
def getPtBin( pt ):
for ptb in pt_bins:
if pt>=ptb[0] and (ptb[1]<0 or pt<ptb[1]):
return ptb
r = QCD_flat.treeReader( variables = map( TreeVariable.fromString, ["Jet[mcPt/F,rawPt/F,mcEta/F,eta/F]", 'nJet/I'] ) )
r.start()
while r.run():
jets = getJets( r.event, jetColl="Jet", jetVars = ['mcPt', 'rawPt', 'mcEta', 'eta'] )
for i in range( r.event.nJet ):
# find bins
reco_bin = migration_response_ratio.FindBin( r.event.Jet_eta[i] )
gen_bin = migration_response_ratio.FindBin( r.event.Jet_mcEta[i] )
pt_bin = getPtBin( r.event.Jet_mcPt[i] )
if not pt_bin: continue
# compute response ratios
reco_response = jet_respons_genEta[pt_bin].GetBinContent( reco_bin )
gen_response = jet_respons_genEta[pt_bin].GetBinContent( gen_bin )
try:
ratio = reco_response/gen_response
except ZeroDivisionError:
def makeL3ResObservables(event, sample):
good_jets = getJets(event, jetColl="JetGood", jetVars=jetVars)
for j in good_jets:
# 'Corr' correction level: L1L2L3 L2res
if sample.isData:
jet_corr_factor = jetCorrector_data.correction(
j['rawPt'], j['eta'], j['area'], event.rho, event.run)
jet_corr_factor_RC = jetCorrector_RC_data.correction(
j['rawPt'], j['eta'], j['area'], event.rho, event.run)
else:
jet_corr_factor = jetCorrector_mc.correction(
j['rawPt'], j['eta'], j['area'], event.rho, event.run)
jet_corr_factor_RC = jetCorrector_RC_mc.correction(
j['rawPt'], j['eta'], j['area'], event.rho, event.run)
# corrected jet
j['pt_corr'] = jet_corr_factor * j['rawPt']
# noL1 -> divide out L1FastJet, remove
if args.noL1:
if sample.isData:
jet_corr_factor_L1 = jetCorrector_L1_data.correction(
j['rawPt'], j['eta'], j['area'], event.rho, event.run)
else:
jet_corr_factor_L1 = jetCorrector_L1_mc.correction(
j['rawPt'], j['eta'], j['area'], event.rho, event.run)
# noL1 -> divide out L1FastJet, remove
j['pt_corr'] = j['pt_corr'] / jet_corr_factor_L1
# no L1RC if 'noL1'
j['pt_corr_RC'] = j['rawPt']
else:
# L1RC
j['pt_corr_RC'] = jet_corr_factor_RC * j['rawPt']
# compute type-1 MET shifts for chs met L1L2L3 - L1RC (if 'noL1', then L1FastJets is divided out and L1RC is not applied )
type1_met_shifts = \
{'px' :sum( ( j['pt_corr_RC'] - j['pt_corr'] )*cos(j['phi']) for j in good_jets),
'py' :sum( ( j['pt_corr_RC'] - j['pt_corr'] )*sin(j['phi']) for j in good_jets) }
# leading jet
event.leading_jet = good_jets[0]
# subleading jet
event.subleading_jet = good_jets[1] if len(good_jets) >= 2 else null_jet
# alpha
event.alpha = event.subleading_jet['pt_corr'] / event.dl_pt
# alpha cut flag
event.alpha_30_passed = (event.alpha < 0.3)
event.alpha_20_passed = (event.alpha < 0.2)
event.alpha_15_passed = (event.alpha < 0.15)
event.alpha_10_passed = (event.alpha < 0.1)
# chs MET
chs_MEx_corr = event.met_chsPt * cos(
event.met_chsPhi) + type1_met_shifts['px']
chs_MEy_corr = event.met_chsPt * sin(
event.met_chsPhi) + type1_met_shifts['py']
chs_MEt_corr = sqrt(chs_MEx_corr**2 + chs_MEy_corr**2)
chs_MEphi_corr = atan2(chs_MEy_corr, chs_MEx_corr)
# if sample.isData: #FIXME
# print "evt", event.evt
# for i, j in enumerate(good_jets):
# print "jet", i, "pt(raw)", j['rawPt'], "pt(L1L2L3)", j['pt_corr'], "pt(L1RC)", j['pt_corr_RC'], "phi", cos(j['phi'])
# print "cont. to type1 from jet ", i, ( j['pt_corr_RC'] - j['pt_corr'] )*cos(j['phi']), "py", ( j['pt_corr_RC'] - j['pt_corr'] )*sin(j['phi'])
# print "type1 shifts", type1_met_shifts['px'], type1_met_shifts['py']
# print "raw chs met: pt", event.met_chsPt, 'phi', event.met_chsPhi
# print "type1 chs met: px", chs_MEx_corr, 'py', chs_MEy_corr
# print " : pt",chs_MEt_corr,"phi",chs_MEphi_corr
# print
setattr(event, "met_chsPt_type1", chs_MEt_corr)
setattr(event, "met_chsPhi_type1", chs_MEphi_corr)
# PT-bal
event.r_ptbal = event.leading_jet['pt_corr'] / event.dl_pt
# PT-bal raw
event.r_ptbalRaw = event.leading_jet['rawPt'] / event.dl_pt
# MPF
event.r_mpf = 1. + chs_MEt_corr * cos(chs_MEphi_corr -
event.dl_phi) / event.dl_pt
# MPF no type-1
event.r_mpfNoType1 = 1. + event.met_chsPt * cos(event.met_chsPhi -
event.dl_phi) / event.dl_pt
# gen
if not sample.isData and event.leading_jet['mcPt'] > 0:
event.r_gen = event.leading_jet['pt_corr'] / event.leading_jet['mcPt']
#event.r_gen = event.leading_jet['pt']/event.leading_jet['mcPt']
else:
event.r_gen = None
def filler( event ):
# shortcut
r = reader.event
if isMC:
event.weight = r.xsec*lumiScaleFactor*r.genWeight if lumiScaleFactor is not None else 1
else:
event.weight = 1
# lumi lists and vetos
if isData:
#event.vetoPassed = vetoList.passesVeto(r.run, r.lumi, r.evt)
event.jsonPassed = lumiList.contains(r.run, r.lumi)
# store decision to use after filler has been executed
event.jsonPassed_ = event.jsonPassed
# PU weight
if isMC:
event.reweightPU = puRW ( r.nTrueInt )
event.reweightPUDown = puRWDown ( r.nTrueInt )
event.reweightPUUp = puRWUp ( r.nTrueInt )
jets = getJets( r, jetColl="Jet", jetVars = jetVarNames)
event.nJetGood = len(jets)
for iJet, j in enumerate(jets):
# 'Corr' correction level: L1L2L3 L2res
if isData:
jet_corr_factor = jetCorrector_data. correction( j['rawPt'], j['eta'], j['area'], r.rho, r.run )
jet_corr_factor_RC = jetCorrector_RC_data.correction( j['rawPt'], j['eta'], j['area'], r.rho, r.run )
else:
jet_corr_factor = jetCorrector_mc. correction( j['rawPt'], j['eta'], j['area'], r.rho, r.run )
jet_corr_factor_RC = jetCorrector_RC_mc. correction( j['rawPt'], j['eta'], j['area'], r.rho, r.run )
# corrected jet
j['pt_corr'] = jet_corr_factor * j['rawPt']
# L1RC
j['pt_corr_RC'] = jet_corr_factor_RC * j['rawPt']
# JER
if isData:
jet_corr_factor_jer, jet_corr_factor_jer_up, jet_corr_factor_jer_down = (1., 1., 1.)
else:
jet_corr_factor_jer, jet_corr_factor_jer_up, jet_corr_factor_jer_down = smearer_mc.hybrid_correction( pt = j['pt_corr'], mcPt = j['mcPt'], eta = j['eta'], rho = r.rho )
j['pt_corr_jer'] = jet_corr_factor_jer * j['pt_corr']
j['pt_corr_jer_up'] = jet_corr_factor_jer_up * j['pt_corr']
j['pt_corr_jer_down'] = jet_corr_factor_jer_down * j['pt_corr']
# write new ntuple information
event.Jet_pt_corr[iJet] = j['pt_corr']
event.Jet_pt_corr_jer[iJet] = j['pt_corr_jer']
event.Jet_pt_corr_jer_up[iJet] = j['pt_corr_jer']
event.Jet_pt_corr_jer_down[iJet]= j['pt_corr_jer']
event.Jet_isHot[iJet] = not default_hotJetVeto.passVeto(eta = j['eta'], phi = j['phi'])
## keep correction factors for type-1 MET shifts below
#j['corr_jer'] = jet_corr_factor_jer
#j['corr_jer_up'] = jet_corr_factor_jer_up
#j['corr_jer_down'] = jet_corr_factor_jer_down
randomSwap = ( random.random()>0.5 )
for jer in ['', 'jer', 'jer_up', 'jer_down']:
postfix = '' if jer == '' else '_'+jer
pt_corr = 'pt_corr' + postfix
# sorting after JER
jets.sort( key = lambda j: -j[pt_corr] )
# tag probe jet selection
tag_jet, probe_jet = jets[:2]
# randomize if both are in barrel:
if abs(tag_jet['eta'])<1.3 and abs(probe_jet['eta'])<1.3:
if randomSwap:
tag_jet, probe_jet = probe_jet, tag_jet
# tag jet in barrel
if abs(tag_jet['eta'])>1.3 and abs(probe_jet['eta'])<1.3:
tag_jet, probe_jet = probe_jet, tag_jet
third_jet = jets[2] if len(jets)>=3 else null_jet
setattr( event, "tag_jet_index"+postfix , tag_jet['index'] )
setattr( event, "probe_jet_index"+postfix , probe_jet['index'] )
setattr( event, "third_jet_index"+postfix , third_jet['index'] )
# PT avg
pt_avg = 0.5*( tag_jet[pt_corr] + probe_jet[pt_corr] )
setattr( event, "pt_avg"+postfix, pt_avg )
setattr( event, 'alpha'+postfix, third_jet[pt_corr]/pt_avg )
setattr( event, 'A'+postfix, (probe_jet[pt_corr] - tag_jet[pt_corr]) / (probe_jet[pt_corr] + tag_jet[pt_corr]) )
# MET corrections
type1_jets = filter( lambda j:j[pt_corr] > 15 and ( j['eEF'] + j['phEF'] )<0.9, jets)
# compute type-1 MET shifts for chs met L1L2L3 - L1RC
# jer_corr = 1 if jer=='' else j['corr' + postfix]
type1_met_shifts = \
{'px' : sum( ( j['pt_corr_RC'] - j['pt_corr'+postfix] )*cos(j['phi']) for j in type1_jets),
'py' : sum( ( j['pt_corr_RC'] - j['pt_corr'+postfix] )*sin(j['phi']) for j in type1_jets) }
# chs MET
chs_MEx_corr = r.met_chsPt*cos(r.met_chsPhi) + type1_met_shifts['px']
chs_MEy_corr = r.met_chsPt*sin(r.met_chsPhi) + type1_met_shifts['py']
setattr( event, "chs_MEx_corr"+postfix, chs_MEx_corr )
setattr( event, "chs_MEy_corr"+postfix, chs_MEy_corr )
chs_MEt_corr = sqrt( chs_MEx_corr**2 + chs_MEy_corr**2 )
chs_MEphi_corr = atan2( chs_MEy_corr, chs_MEx_corr )
setattr( event, "chs_MEt_corr"+postfix, chs_MEt_corr )
setattr( event, "chs_MEphi_corr"+postfix, chs_MEphi_corr )
# R(MPF)
setattr( event,
'B'+postfix,
( chs_MEx_corr*tag_jet[pt_corr]*cos(tag_jet['phi']) + chs_MEy_corr*tag_jet[pt_corr]*sin(tag_jet['phi']) ) / tag_jet[pt_corr] / (tag_jet[pt_corr] + probe_jet[pt_corr])
)
def makeL3ResObservables( event, sample ):
good_jets = getJets( event, jetColl="JetGood", jetVars = jetVars)
for j in good_jets:
# 'Corr' correction level: L1L2L3 L2res
if sample.isData:
jet_corr_factor = jetCorrector_data. correction( j['rawPt'], j['eta'], j['area'], event.rho, event.run )
jet_corr_factor_RC = jetCorrector_RC_data.correction( j['rawPt'], j['eta'], j['area'], event.rho, event.run )
else:
jet_corr_factor = jetCorrector_mc. correction( j['rawPt'], j['eta'], j['area'], event.rho, event.run )
jet_corr_factor_RC = jetCorrector_RC_mc. correction( j['rawPt'], j['eta'], j['area'], event.rho, event.run )
# corrected jet
j['pt_corr'] = jet_corr_factor * j['rawPt']
# noL1 -> divide out L1FastJet, remove
if args.noL1:
if sample.isData:
jet_corr_factor_L1 = jetCorrector_L1_data.correction( j['rawPt'], j['eta'], j['area'], event.rho, event.run )
else:
jet_corr_factor_L1 = jetCorrector_L1_mc. correction( j['rawPt'], j['eta'], j['area'], event.rho, event.run )
# noL1 -> divide out L1FastJet, remove
j['pt_corr'] = j['pt_corr']/jet_corr_factor_L1
# no L1RC if 'noL1'
j['pt_corr_RC'] = j['rawPt']
else:
# L1RC
j['pt_corr_RC'] = jet_corr_factor_RC * j['rawPt']
# compute type-1 MET shifts for chs met L1L2L3 - L1RC (if 'noL1', then L1FastJets is divided out and L1RC is not applied )
type1_met_shifts = \
{'px' :sum( ( j['pt_corr_RC'] - j['pt_corr'] )*cos(j['phi']) for j in good_jets),
'py' :sum( ( j['pt_corr_RC'] - j['pt_corr'] )*sin(j['phi']) for j in good_jets) }
# leading jet
event.leading_jet = good_jets[0]
# subleading jet
event.subleading_jet = good_jets[1] if len(good_jets)>=2 else null_jet
# alpha
event.alpha = event.subleading_jet['pt_corr'] / event.dl_pt
# alpha cut flag
event.alpha_30_passed = ( event.alpha < 0.3)
event.alpha_20_passed = ( event.alpha < 0.2)
event.alpha_15_passed = ( event.alpha < 0.15)
event.alpha_10_passed = ( event.alpha < 0.1)
# chs MET
chs_MEx_corr = event.met_chsPt*cos(event.met_chsPhi) + type1_met_shifts['px']
chs_MEy_corr = event.met_chsPt*sin(event.met_chsPhi) + type1_met_shifts['py']
chs_MEt_corr = sqrt( chs_MEx_corr**2 + chs_MEy_corr**2 )
chs_MEphi_corr = atan2( chs_MEy_corr, chs_MEx_corr )
# if sample.isData: #FIXME
# print "evt", event.evt
# for i, j in enumerate(good_jets):
# print "jet", i, "pt(raw)", j['rawPt'], "pt(L1L2L3)", j['pt_corr'], "pt(L1RC)", j['pt_corr_RC'], "phi", cos(j['phi'])
# print "cont. to type1 from jet ", i, ( j['pt_corr_RC'] - j['pt_corr'] )*cos(j['phi']), "py", ( j['pt_corr_RC'] - j['pt_corr'] )*sin(j['phi'])
# print "type1 shifts", type1_met_shifts['px'], type1_met_shifts['py']
# print "raw chs met: pt", event.met_chsPt, 'phi', event.met_chsPhi
# print "type1 chs met: px", chs_MEx_corr, 'py', chs_MEy_corr
# print " : pt",chs_MEt_corr,"phi",chs_MEphi_corr
# print
setattr( event, "met_chsPt_type1", chs_MEt_corr )
setattr( event, "met_chsPhi_type1", chs_MEphi_corr )
# PT-bal
event.r_ptbal = event.leading_jet['pt_corr'] / event.dl_pt
# PT-bal raw
event.r_ptbalRaw = event.leading_jet['rawPt'] / event.dl_pt
# MPF
event.r_mpf = 1. + chs_MEt_corr * cos(chs_MEphi_corr - event.dl_phi) / event.dl_pt
# MPF no type-1
event.r_mpfNoType1 = 1. + event.met_chsPt * cos(event.met_chsPhi - event.dl_phi) / event.dl_pt
# gen
if not sample.isData and event.leading_jet['mcPt']>0:
event.r_gen = event.leading_jet['pt_corr']/event.leading_jet['mcPt']
#event.r_gen = event.leading_jet['pt']/event.leading_jet['mcPt']
else:
event.r_gen = None
pt_bins = jet_respons_genEta.keys()
def getPtBin(pt):
for ptb in pt_bins:
if pt >= ptb[0] and (ptb[1] < 0 or pt < ptb[1]):
return ptb
r = QCD_flat.treeReader(variables=map(
TreeVariable.fromString, ["Jet[mcPt/F,rawPt/F,mcEta/F,eta/F]", 'nJet/I']))
r.start()
while r.run():
jets = getJets(r.event,
jetColl="Jet",
jetVars=['mcPt', 'rawPt', 'mcEta', 'eta'])
for i in range(r.event.nJet):
# find bins
reco_bin = migration_response_ratio.FindBin(r.event.Jet_eta[i])
gen_bin = migration_response_ratio.FindBin(r.event.Jet_mcEta[i])
pt_bin = getPtBin(r.event.Jet_mcPt[i])
if not pt_bin: continue
# compute response ratios
reco_response = jet_respons_genEta[pt_bin].GetBinContent(reco_bin)
gen_response = jet_respons_genEta[pt_bin].GetBinContent(gen_bin)
try:
ratio = reco_response / gen_response