def create_toy_mc(input_files, sample, output_folder, n_toy, centre_of_mass, config):
from dps.utils.file_utilities import make_folder_if_not_exists
from dps.utils.toy_mc import generate_toy_MC_from_distribution, generate_toy_MC_from_2Ddistribution
from dps.utils.Unfolding import get_unfold_histogram_tuple
make_folder_if_not_exists(output_folder)
output_file_name = get_output_file_name(output_folder, sample, n_toy, centre_of_mass)
variable_bins = bin_edges_vis.copy()
with root_open(output_file_name, 'recreate') as f_out:
input_file_index = 0
for input_file in input_files:
input_file_hists = File(input_file)
for channel in config.analysis_types.keys():
if channel is 'combined':continue
for variable in variable_bins:
output_dir = f_out.mkdir(str(input_file_index) + '/' + channel + '/' + variable, recurse=True)
cd = output_dir.cd
mkdir = output_dir.mkdir
h_truth, h_measured, h_response, _ = get_unfold_histogram_tuple(input_file_hists,
variable,
channel,
centre_of_mass = centre_of_mass,
visiblePS = True,
load_fakes=False)
cd()
mkdir('Original')
cd ('Original')
h_truth.Write('truth')
h_measured.Write('measured')
h_response.Write('response')
for i in range(1, n_toy+1):
toy_id = 'toy_{0}'.format(i)
mkdir(toy_id)
cd(toy_id)
# create histograms
# add tuples (truth, measured, response) of histograms
truth = generate_toy_MC_from_distribution(h_truth)
measured = generate_toy_MC_from_distribution(h_measured)
response = generate_toy_MC_from_2Ddistribution(h_response)
truth.SetName('truth')
measured.SetName('measured')
response.SetName('response')
truth.Write()
measured.Write()
response.Write()
input_file_index += 1
def main():
parser = OptionParser()
parser.add_option('--topPtReweighting', dest='applyTopPtReweighting', type='int', default=0 )
parser.add_option('--topEtaReweighting', dest='applyTopEtaReweighting', type='int', default=0 )
parser.add_option('-c', '--centreOfMassEnergy', dest='centreOfMassEnergy', type='int', default=13 )
parser.add_option('--pdfWeight', type='int', dest='pdfWeight', default=-1 )
parser.add_option('--muFmuRWeight', type='int', dest='muFmuRWeight', default=-1 )
parser.add_option('--alphaSWeight', type='int', dest='alphaSWeight', default=-1 )
parser.add_option('--nGeneratorWeights', type='int', dest='nGeneratorWeights', default=1 )
parser.add_option('-s', '--sample', dest='sample', default='central')
parser.add_option('-d', '--debug', action='store_true', dest='debug', default=False)
parser.add_option('-n', action='store_true', dest='donothing', default=False)
parser.add_option('-e', action='store_true', dest='extraHists', default=False)
parser.add_option('-f',action='store_true', dest='fineBinned', default=False)
(options, _) = parser.parse_args()
measurement_config = XSectionConfig( options.centreOfMassEnergy )
# Input file name
file_name = 'crap.root'
if int(options.centreOfMassEnergy) == 13:
# file_name = fileNames['13TeV'][options.sample]
file_name = getFileName('13TeV', options.sample, measurement_config)
# if options.generatorWeight >= 0:
# file_name = 'localInputFile.root'
else:
print "Error: Unrecognised centre of mass energy."
pdfWeight = options.pdfWeight
muFmuRWeight = options.muFmuRWeight
alphaSWeight = options.alphaSWeight
# Output file name
outputFileName = 'crap.root'
outputFileDir = 'unfolding/%sTeV/' % options.centreOfMassEnergy
make_folder_if_not_exists(outputFileDir)
energySuffix = '%sTeV' % ( options.centreOfMassEnergy )
if options.applyTopEtaReweighting != 0:
if options.applyTopEtaReweighting == 1:
outputFileName = outputFileDir+'/unfolding_TTJets_%s_asymmetric_withTopEtaReweighting_up.root' % energySuffix
elif options.applyTopEtaReweighting == -1:
outputFileName = outputFileDir+'/unfolding_TTJets_%s_asymmetric_withTopEtaReweighting_down.root' % energySuffix
elif options.applyTopPtReweighting != 0:
if options.applyTopPtReweighting == 1:
outputFileName = outputFileDir+'/unfolding_TTJets_%s_asymmetric_withTopPtReweighting_up.root' % energySuffix
elif options.applyTopPtReweighting == -1:
outputFileName = outputFileDir+'/unfolding_TTJets_%s_asymmetric_withTopPtReweighting_down.root' % energySuffix
elif alphaSWeight == 0:
outputFileName = outputFileDir+'/unfolding_TTJets_%s_asymmetric_alphaSDown.root' % ( energySuffix )
elif alphaSWeight == 1:
outputFileName = outputFileDir+'/unfolding_TTJets_%s_asymmetric_alphaSUp.root' % ( energySuffix )
elif muFmuRWeight == 1:
outputFileName = outputFileDir+'/unfolding_TTJets_%s_asymmetric_1muR2muF.root' % ( energySuffix )
elif muFmuRWeight == 2:
outputFileName = outputFileDir+'/unfolding_TTJets_%s_asymmetric_1muR05muF.root' % ( energySuffix )
elif muFmuRWeight == 3:
outputFileName = outputFileDir+'/unfolding_TTJets_%s_asymmetric_2muR1muF.root' % ( energySuffix )
elif muFmuRWeight == 4:
outputFileName = outputFileDir+'/unfolding_TTJets_%s_asymmetric_2muR2muF.root' % ( energySuffix )
elif muFmuRWeight == 6:
outputFileName = outputFileDir+'/unfolding_TTJets_%s_asymmetric_05muR1muF.root' % ( energySuffix )
elif muFmuRWeight == 8:
outputFileName = outputFileDir+'/unfolding_TTJets_%s_asymmetric_05muR05muF.root' % ( energySuffix )
elif pdfWeight >= 0 and pdfWeight <= 99:
outputFileName = outputFileDir+'/unfolding_TTJets_%s_asymmetric_pdfWeight_%i.root' % ( energySuffix, pdfWeight )
elif options.sample != 'central':
outputFileName = outputFileDir+'/unfolding_TTJets_%s_%s_asymmetric.root' % ( energySuffix, options.sample )
elif options.fineBinned :
outputFileName = outputFileDir+'/unfolding_TTJets_%s.root' % ( energySuffix )
else:
outputFileName = outputFileDir+'/unfolding_TTJets_%s_asymmetric.root' % energySuffix
with root_open( file_name, 'read' ) as f, root_open( outputFileName, 'recreate') as out:
# Get the tree
treeName = "TTbar_plus_X_analysis/Unfolding/Unfolding"
if options.sample == "jesup":
treeName += "_JESUp"
elif options.sample == "jesdown":
treeName += "_JESDown"
elif options.sample == "jerup":
treeName += "_JERUp"
elif options.sample == "jerdown":
treeName += "_JERDown"
tree = f.Get(treeName)
nEntries = tree.GetEntries()
# weightTree = f.Get('TTbar_plus_X_analysis/Unfolding/GeneratorSystematicWeights')
# if meWeight >= 0 :
# tree.AddFriend('TTbar_plus_X_analysis/Unfolding/GeneratorSystematicWeights')
# tree.SetBranchStatus('genWeight_*',1)
# tree.SetBranchStatus('genWeight_%i' % meWeight, 1)
# For variables where you want bins to be symmetric about 0, use abs(variable) (but also make plots for signed variable)
allVariablesBins = bin_edges_vis.copy()
for variable in bin_edges_vis:
if 'Rap' in variable:
allVariablesBins['abs_%s' % variable] = [0,bin_edges_vis[variable][-1]]
recoVariableNames = {}
genVariable_particle_names = {}
genVariable_parton_names = {}
histograms = {}
outputDirs = {}
for variable in allVariablesBins:
if options.debug and variable != 'HT' : continue
if options.sample in measurement_config.met_systematics and variable not in ['MET', 'ST', 'WPT']:
continue
outputDirs[variable] = {}
histograms[variable] = {}
#
# Variable names
#
recoVariableName = branchNames[variable]
sysIndex = None
if variable in ['MET', 'ST', 'WPT']:
if options.sample == "jesup":
recoVariableName += '_METUncertainties'
sysIndex = 2
elif options.sample == "jesdown":
recoVariableName += '_METUncertainties'
sysIndex = 3
elif options.sample == "jerup":
recoVariableName += '_METUncertainties'
sysIndex = 0
elif options.sample == "jerdown":
recoVariableName+= '_METUncertainties'
sysIndex = 1
elif options.sample in measurement_config.met_systematics:
recoVariableName += '_METUncertainties'
sysIndex = measurement_config.met_systematics[options.sample]
genVariable_particle_name = None
genVariable_parton_name = None
if variable in genBranchNames_particle:
genVariable_particle_name = genBranchNames_particle[variable]
if variable in genBranchNames_parton:
genVariable_parton_name = genBranchNames_parton[variable]
recoVariableNames[variable] = recoVariableName
genVariable_particle_names[variable] = genVariable_particle_name
genVariable_parton_names[variable] = genVariable_parton_name
for channel in channels:
# Make dir in output file
outputDirName = variable+'_'+channel.outputDirName
outputDir = out.mkdir(outputDirName)
outputDirs[variable][channel.channelName] = outputDir
#
# Book histograms
#
# 1D histograms
histograms[variable][channel.channelName] = {}
h = histograms[variable][channel.channelName]
h['truth'] = Hist( allVariablesBins[variable], name='truth')
h['truthVis'] = Hist( allVariablesBins[variable], name='truthVis')
h['truth_parton'] = Hist( allVariablesBins[variable], name='truth_parton')
h['measured'] = Hist( reco_bin_edges_vis[variable], name='measured')
h['measuredVis'] = Hist( reco_bin_edges_vis[variable], name='measuredVis')
h['measured_without_fakes'] = Hist( reco_bin_edges_vis[variable], name='measured_without_fakes')
h['measuredVis_without_fakes'] = Hist( reco_bin_edges_vis[variable], name='measuredVis_without_fakes')
h['fake'] = Hist( reco_bin_edges_vis[variable], name='fake')
h['fakeVis'] = Hist( reco_bin_edges_vis[variable], name='fakeVis')
# 2D histograms
h['response'] = Hist2D( reco_bin_edges_vis[variable], allVariablesBins[variable], name='response')
h['response_without_fakes'] = Hist2D( reco_bin_edges_vis[variable], allVariablesBins[variable], name='response_without_fakes')
h['responseVis_without_fakes'] = Hist2D( reco_bin_edges_vis[variable], allVariablesBins[variable], name='responseVis_without_fakes')
h['response_parton'] = Hist2D( reco_bin_edges_vis[variable], allVariablesBins[variable], name='response_parton')
h['response_without_fakes_parton'] = Hist2D( reco_bin_edges_vis[variable], allVariablesBins[variable], name='response_without_fakes_parton')
if options.fineBinned:
minVar = trunc( allVariablesBins[variable][0] )
maxVar = trunc( max( tree.GetMaximum(genVariable_particle_names[variable]), tree.GetMaximum( recoVariableNames[variable] ) ) * 1.2 )
nBins = int(maxVar - minVar)
if variable is 'lepton_eta' or variable is 'bjets_eta':
maxVar = 2.5
minVar = -2.5
nBins = 1000
elif 'abs' in variable and 'eta' in variable:
maxVar = 3.0
minVar = 0.
nBins = 1000
elif 'Rap' in variable:
maxVar = 3.0
minVar = -3.0
nBins = 1000
elif 'NJets' in variable:
maxVar = 20.5
minVar = 3.5
nBins = 17
h['truth'] = Hist( nBins, minVar, maxVar, name='truth')
h['truthVis'] = Hist( nBins, minVar, maxVar, name='truthVis')
h['truth_parton'] = Hist( nBins, minVar, maxVar, name='truth_parton')
h['measured'] = Hist( nBins, minVar, maxVar, name='measured')
h['measuredVis'] = Hist( nBins, minVar, maxVar, name='measuredVis')
h['measured_without_fakes'] = Hist( nBins, minVar, maxVar, name='measured_without_fakes')
h['measuredVis_without_fakes'] = Hist( nBins, minVar, maxVar, name='measuredVis_without_fakes')
h['fake'] = Hist( nBins, minVar, maxVar, name='fake')
h['fakeVis'] = Hist( nBins, minVar, maxVar, name='fakeVis')
h['response'] = Hist2D( nBins, minVar, maxVar, nBins, minVar, maxVar, name='response')
h['response_without_fakes'] = Hist2D( nBins, minVar, maxVar, nBins, minVar, maxVar, name='response_without_fakes')
h['responseVis_without_fakes'] = Hist2D( nBins, minVar, maxVar, nBins, minVar, maxVar, name='responseVis_without_fakes')
h['response_parton'] = Hist2D( nBins, minVar, maxVar, nBins, minVar, maxVar, name='response_parton')
h['response_without_fakes_parton'] = Hist2D( nBins, minVar, maxVar, nBins, minVar, maxVar, name='response_without_fakes_parton')
# Some interesting histograms
h['puOffline'] = Hist( 20, 0, 2, name='puWeights_offline')
h['eventWeightHist'] = Hist( 100, -2, 2, name='eventWeightHist')
h['genWeightHist'] = Hist( 100, -2, 2, name='genWeightHist')
h['offlineWeightHist'] = Hist( 100, -2, 2, name='offlineWeightHist')
h['phaseSpaceInfoHist'] = Hist( 10, 0, 1, name='phaseSpaceInfoHist')
# Counters for studying phase space
nVis = {c.channelName : 0 for c in channels}
nVisNotOffline = {c.channelName : 0 for c in channels}
nOffline = {c.channelName : 0 for c in channels}
nOfflineNotVis = {c.channelName : 0 for c in channels}
nFull = {c.channelName : 0 for c in channels}
nOfflineSL = {c.channelName : 0 for c in channels}
n=0
# Event Loop
# for event, weight in zip(tree,weightTree):
for event in tree:
branch = event.__getattr__
n+=1
if not n%100000: print 'Processing event %.0f Progress : %.2g %%' % ( n, float(n)/nEntries*100 )
# if n == 10000: break
# # #
# # # Weights and selection
# # #
# Pileup weight
# Don't apply if calculating systematic
pileupWeight = event.PUWeight
# print event.PUWeight,event.PUWeight_up,event.PUWeight_down
if options.sample == "pileupUp":
pileupWeight = event.PUWeight_up
elif options.sample == "pileupDown":
pileupWeight = event.PUWeight_down
# Generator level weight
genWeight = event.EventWeight * measurement_config.luminosity_scale
# Offline level weights
offlineWeight = pileupWeight
# Lepton weight
leptonWeight = event.LeptonEfficiencyCorrection
if options.sample == 'leptonup':
leptonWeight = event.LeptonEfficiencyCorrectionUp
elif options.sample == 'leptondown':
leptonWeight == event.LeptonEfficiencyCorrectionDown
# B Jet Weight
bjetWeight = event.BJetWeight
if options.sample == "bjetup":
bjetWeight = event.BJetUpWeight
elif options.sample == "bjetdown":
bjetWeight = event.BJetDownWeight
elif options.sample == "lightjetup":
bjetWeight = event.LightJetUpWeight
elif options.sample == "lightjetdown":
bjetWeight = event.LightJetDownWeight
offlineWeight = event.EventWeight * measurement_config.luminosity_scale
offlineWeight *= pileupWeight
offlineWeight *= bjetWeight
offlineWeight *= leptonWeight
# Generator weight
# Scale up/down, pdf
if pdfWeight >= 0:
genWeight *= branch('pdfWeight_%i' % pdfWeight)
offlineWeight *= branch('pdfWeight_%i' % pdfWeight)
pass
if muFmuRWeight >= 0:
genWeight *= branch('muFmuRWeight_%i' % muFmuRWeight)
offlineWeight *= branch('muFmuRWeight_%i' % muFmuRWeight)
pass
if alphaSWeight >= 0:
genWeight *= branch('alphaSWeight_%i' % alphaSWeight)
offlineWeight *= branch('alphaSWeight_%i' % alphaSWeight)
pass
if options.applyTopPtReweighting != 0:
ptWeight = calculateTopPtWeight( branch('lepTopPt_parton'), branch('hadTopPt_parton'), options.applyTopPtReweighting)
offlineWeight *= ptWeight
genWeight *= ptWeight
if options.applyTopEtaReweighting != 0:
etaWeight = calculateTopEtaWeight( branch('lepTopRap_parton'), branch('hadTopRap_parton'), options.applyTopEtaReweighting)
offlineWeight *= etaWeight
genWeight *= etaWeight
for channel in channels:
# Generator level selection
genSelection = ''
genSelectionVis = ''
if channel.channelName is 'muPlusJets' :
genSelection = event.isSemiLeptonicMuon == 1
genSelectionVis = event.isSemiLeptonicMuon == 1 and event.passesGenEventSelection == 1
elif channel.channelName is 'ePlusJets' :
genSelection = event.isSemiLeptonicElectron == 1
genSelectionVis = event.isSemiLeptonicElectron == 1 and event.passesGenEventSelection == 1
# Offline level selection
offlineSelection = 0
if channel.channelName is 'muPlusJets' :
offlineSelection = int(event.passSelection) == 1
elif channel.channelName is 'ePlusJets' :
offlineSelection = int(event.passSelection) == 2
# Fake selection
fakeSelection = offlineSelection and not genSelection
fakeSelectionVis = offlineSelection and not genSelectionVis
# Phase space info
if genSelection:
nFull[channel.channelName] += genWeight
if offlineSelection:
nOfflineSL[channel.channelName] += genWeight
if genSelectionVis:
nVis[channel.channelName] += genWeight
if not offlineSelection:
nVisNotOffline[channel.channelName] += genWeight
if offlineSelection:
nOffline[channel.channelName] += offlineWeight
if not genSelectionVis:
nOfflineNotVis[channel.channelName] += offlineWeight
for variable in allVariablesBins:
if options.sample in measurement_config.met_systematics and variable not in ['MET', 'ST', 'WPT']:
continue
# # #
# # # Variable to plot
# # #
recoVariable = branch(recoVariableNames[variable])
if variable in ['MET', 'ST', 'WPT'] and \
sysIndex != None and ( offlineSelection or fakeSelection or fakeSelectionVis ) :
recoVariable = recoVariable[sysIndex]
if 'abs' in variable:
recoVariable = abs(recoVariable)
# With TUnfold, reco variable never goes in the overflow (or underflow)
# if recoVariable > allVariablesBins[variable][-1]:
# print 'Big reco variable : ',recoVariable
# print 'Setting to :',min( recoVariable, allVariablesBins[variable][-1] - 0.000001 )
if not options.fineBinned:
recoVariable = min( recoVariable, allVariablesBins[variable][-1] - 0.000001 )
genVariable_particle = branch(genVariable_particle_names[variable])
if 'abs' in variable:
genVariable_particle = abs(genVariable_particle)
# #
# # Fill histograms
# #
histogramsToFill = histograms[variable][channel.channelName]
if not options.donothing:
if genSelection:
histogramsToFill['truth'].Fill( genVariable_particle, genWeight)
if genSelectionVis:
histogramsToFill['truthVis'].Fill( genVariable_particle, genWeight)
if offlineSelection:
histogramsToFill['measured'].Fill( recoVariable, offlineWeight)
histogramsToFill['measuredVis'].Fill( recoVariable, offlineWeight)
if genSelectionVis :
histogramsToFill['measuredVis_without_fakes'].Fill( recoVariable, offlineWeight)
if genSelection:
histogramsToFill['measured_without_fakes'].Fill( recoVariable, offlineWeight)
histogramsToFill['response'].Fill( recoVariable, genVariable_particle, offlineWeight )
if offlineSelection and genSelection:
histogramsToFill['response_without_fakes'].Fill( recoVariable, genVariable_particle, offlineWeight )
elif genSelection:
histogramsToFill['response_without_fakes'].Fill( allVariablesBins[variable][0]-1, genVariable_particle, genWeight )
# if genVariable_particle < 0 : print recoVariable, genVariable_particle
# if genVariable_particle < 0 : print genVariable_particle
if offlineSelection and genSelectionVis:
histogramsToFill['responseVis_without_fakes'].Fill( recoVariable, genVariable_particle, offlineWeight )
elif genSelectionVis:
histogramsToFill['responseVis_without_fakes'].Fill( allVariablesBins[variable][0]-1, genVariable_particle, genWeight )
if fakeSelection:
histogramsToFill['fake'].Fill( recoVariable, offlineWeight)
if fakeSelectionVis:
histogramsToFill['fakeVis'].Fill( recoVariable, offlineWeight)
if options.extraHists:
if genSelection:
histogramsToFill['eventWeightHist'].Fill(event.EventWeight)
histogramsToFill['genWeightHist'].Fill(genWeight)
histogramsToFill['offlineWeightHist'].Fill(offlineWeight)
#
# Output histgorams to file
#
for variable in allVariablesBins:
if options.sample in measurement_config.met_systematics and variable not in ['MET', 'ST', 'WPT']:
continue
for channel in channels:
# Fill phase space info
h = histograms[variable][channel.channelName]['phaseSpaceInfoHist']
h.SetBinContent(1, nVisNotOffline[channel.channelName] / nVis[channel.channelName])
h.SetBinContent(2, nOfflineNotVis[channel.channelName] / nOffline[channel.channelName])
h.SetBinContent(3, nVis[channel.channelName] / nFull[channel.channelName])
# Selection efficiency for SL ttbar
h.SetBinContent(4, nOfflineSL[channel.channelName] / nFull[channel.channelName])
# Fraction of offline that are SL
h.SetBinContent(5, nOfflineSL[channel.channelName] / nOffline[channel.channelName])
outputDirs[variable][channel.channelName].cd()
for h in histograms[variable][channel.channelName]:
histograms[variable][channel.channelName][h].Write()
with root_open( outputFileName, 'update') as out:
# Done all channels, now combine the two channels, and output to the same file
for path, dirs, objects in out.walk():
if 'electron' in path:
outputDir = out.mkdir(path.replace('electron','combined'))
outputDir.cd()
for h in objects:
h_e = out.Get(path+'/'+h)
h_mu = out.Get(path.replace('electron','muon')+'/'+h)
h_comb = (h_e + h_mu).Clone(h)
h_comb.Write()
pass
pass
pass
def main():
args = parse_arguments()
measurement_config = XSectionConfig( args.centreOfMassEnergy )
# Input file name
file_name = 'crap.root'
if int(args.centreOfMassEnergy) == 13:
file_name = getFileName('13TeV', args.sample, measurement_config)
else:
print "Error: Unrecognised centre of mass energy."
pdfWeight = args.pdfWeight
CT14Weight = args.CT14Weight
MMHT14Weight = args.MMHT14Weight
muFmuRWeight = args.muFmuRWeight
alphaSWeight = args.alphaSWeight
semiLepBrWeight = args.semiLepBrWeight
fragWeight = args.fragWeight
# Output file name
outputFileName = 'crap.root'
outputFileDir = 'unfolding/%sTeV/' % args.centreOfMassEnergy
make_folder_if_not_exists(outputFileDir)
energySuffix = '%sTeV' % ( args.centreOfMassEnergy )
if args.applyTopEtaReweighting != 0:
if args.applyTopEtaReweighting == 1:
outputFileName = outputFileDir+'/unfolding_TTJets_%s_asymmetric_withTopEtaReweighting_up.root' % energySuffix
elif args.applyTopEtaReweighting == -1:
outputFileName = outputFileDir+'/unfolding_TTJets_%s_asymmetric_withTopEtaReweighting_down.root' % energySuffix
elif args.applyTopPtReweighting:
if args.applyTopPtReweighting == 1:
outputFileName = outputFileDir+'/unfolding_TTJets_%s_asymmetric_withTopPtReweighting_up.root' % energySuffix
elif args.applyTopPtReweighting == -1:
outputFileName = outputFileDir+'/unfolding_TTJets_%s_asymmetric_withTopPtReweighting_down.root' % energySuffix
elif muFmuRWeight == 1:
outputFileName = outputFileDir+'/unfolding_TTJets_%s_asymmetric_1muR2muF.root' % ( energySuffix )
elif muFmuRWeight == 2:
outputFileName = outputFileDir+'/unfolding_TTJets_%s_asymmetric_1muR05muF.root' % ( energySuffix )
elif muFmuRWeight == 3:
outputFileName = outputFileDir+'/unfolding_TTJets_%s_asymmetric_2muR1muF.root' % ( energySuffix )
elif muFmuRWeight == 4:
outputFileName = outputFileDir+'/unfolding_TTJets_%s_asymmetric_2muR2muF.root' % ( energySuffix )
elif muFmuRWeight == 6:
outputFileName = outputFileDir+'/unfolding_TTJets_%s_asymmetric_05muR1muF.root' % ( energySuffix )
elif muFmuRWeight == 8:
outputFileName = outputFileDir+'/unfolding_TTJets_%s_asymmetric_05muR05muF.root' % ( energySuffix )
elif alphaSWeight == 0:
outputFileName = outputFileDir+'/unfolding_TTJets_%s_asymmetric_alphaS_down.root' % ( energySuffix )
elif alphaSWeight == 1:
outputFileName = outputFileDir+'/unfolding_TTJets_%s_asymmetric_alphaS_up.root' % ( energySuffix )
elif semiLepBrWeight == -1:
outputFileName = outputFileDir+'/unfolding_TTJets_%s_asymmetric_semiLepBr_down.root' % ( energySuffix )
elif semiLepBrWeight == 1:
outputFileName = outputFileDir+'/unfolding_TTJets_%s_asymmetric_semiLepBr_up.root' % ( energySuffix )
elif fragWeight == 1:
outputFileName = outputFileDir+'/unfolding_TTJets_%s_asymmetric_frag_down.root' % ( energySuffix )
elif fragWeight == 2:
outputFileName = outputFileDir+'/unfolding_TTJets_%s_asymmetric_frag_central.root' % ( energySuffix )
elif fragWeight == 3:
outputFileName = outputFileDir+'/unfolding_TTJets_%s_asymmetric_frag_up.root' % ( energySuffix )
elif fragWeight == 4:
outputFileName = outputFileDir+'/unfolding_TTJets_%s_asymmetric_frag_peterson.root' % ( energySuffix )
elif pdfWeight >= 0 and pdfWeight <= 99:
outputFileName = outputFileDir+'/unfolding_TTJets_%s_asymmetric_pdfWeight_%i.root' % ( energySuffix, pdfWeight )
elif CT14Weight >= 0 and CT14Weight <= 54:
outputFileName = outputFileDir+'/unfolding_TTJets_%s_asymmetric_CT14Weight_%i.root' % ( energySuffix, CT14Weight )
elif MMHT14Weight >= 0 and MMHT14Weight <= 55:
outputFileName = outputFileDir+'/unfolding_TTJets_%s_asymmetric_MMHT14Weight_%i.root' % ( energySuffix, MMHT14Weight )
elif 'central' not in args.sample:
outputFileName = outputFileDir+'/unfolding_TTJets_%s_%s_asymmetric.root' % ( energySuffix, args.sample )
elif args.fineBinned :
outputFileName = outputFileDir+'/unfolding_TTJets_%s.root' % ( energySuffix )
else:
outputFileName = outputFileDir+'/unfolding_TTJets_%s_asymmetric.root' % energySuffix
if '70pc' in args.sample or '30pc' in args.sample:
outputFileName.replace('asymmetric','asymmetric_'+args.sample.split('_')[1])
if 'firstHalf' in args.sample:
outputFileName = outputFileName.replace('asymmetric','asymmetric_firstHalf')
elif 'secondHalf' in args.sample:
outputFileName = outputFileName.replace('asymmetric','asymmetric_secondHalf')
if args.newPS :
outputFileName = outputFileName.replace('asymmetric','asymmetric_newPS')
# Get the tree/chain
treeName = "TTbar_plus_X_analysis/Unfolding/Unfolding"
print file_name
file_name = getUnmergedDirectory(file_name)
print file_name
tree = ROOT.TChain(treeName);
filenames = glob.glob( file_name )
for f in filenames:
tree.Add(f)
with root_open( outputFileName, 'recreate') as out:
nEntries = tree.GetEntries()
print 'Number of entries:',nEntries
# For variables where you want bins to be symmetric about 0, use abs(variable) (but also make plots for signed variable)
allVariablesBins = bin_edges_vis.copy()
for variable in bin_edges_vis:
if 'Rap' in variable:
allVariablesBins['abs_%s' % variable] = [0,bin_edges_vis[variable][-1]]
recoVariableNames = {}
genVariable_particle_names = {}
genVariable_parton_names = {}
histograms = {}
residuals = {}
residual_options= {}
outputDirs = {}
outputDirsRes = {}
for variable in allVariablesBins:
if args.debug and variable != 'HT' : continue
if args.sample in measurement_config.met_specific_systematics \
and variable in measurement_config.variables_no_met:
continue
outputDirs[variable] = {}
outputDirsRes[variable] = {}
histograms[variable] = {}
residuals[variable] = {}
residual_options[variable] = {}
#
# Variable names
#
recoVariableName = branchNames[variable]
sysIndex = None
if variable in ['MET', 'ST', 'WPT']:
if args.sample == "jerup":
recoVariableName += '_METUncertainties'
sysIndex = 0
elif args.sample == "jerdown":
recoVariableName+= '_METUncertainties'
sysIndex = 1
elif args.sample == "jesup":
recoVariableName += '_METUncertainties'
sysIndex = 2
elif args.sample == "jesdown":
recoVariableName += '_METUncertainties'
sysIndex = 3
# Dont need this?
elif args.sample in measurement_config.met_systematics:
recoVariableName += '_METUncertainties'
sysIndex = measurement_config.met_systematics[args.sample]
genVariable_particle_name = None
genVariable_parton_name = None
if variable in genBranchNames_particle:
genVariable_particle_name = genBranchNames_particle[variable]
if args.newPS and variable in ['HT', 'ST', 'NJets']:
genVariable_particle_name += '_20GeVLastJet'
if variable in genBranchNames_parton:
genVariable_parton_name = genBranchNames_parton[variable]
recoVariableNames[variable] = recoVariableName
genVariable_particle_names[variable] = genVariable_particle_name
genVariable_parton_names[variable] = genVariable_parton_name
reco_bin_edges_vis_to_use = reco_bin_edges_vis[variable]
for channel in channels:
# Make dir in output file
outputDirName = variable+'_'+channel.outputDirName
outputDir = out.mkdir(outputDirName)
outputDirs[variable][channel.channelName] = outputDir
if args.fineBinned:
outputDirResName = outputDirName + '/residuals/'
outputDirRes = out.mkdir(outputDirResName)
outputDirsRes[variable][channel.channelName] = outputDirRes
#
# Book histograms
#
# 1D histograms
histograms[variable][channel.channelName] = {}
h = histograms[variable][channel.channelName]
h['truth'] = Hist( allVariablesBins[variable], name='truth', type='D')
h['truthVis'] = Hist( allVariablesBins[variable], name='truthVis', type='D')
h['truthVis_noWeight'] = Hist( allVariablesBins[variable], name='truthVis_noWeight', type='D')
h['truth_parton'] = Hist( allVariablesBins[variable], name='truth_parton', type='D')
h['measured'] = Hist( reco_bin_edges_vis_to_use, name='measured', type='D')
h['measuredVis'] = Hist( reco_bin_edges_vis_to_use, name='measuredVis', type='D')
h['measured_without_fakes'] = Hist( reco_bin_edges_vis_to_use, name='measured_without_fakes', type='D')
h['measuredVis_without_fakes'] = Hist( reco_bin_edges_vis_to_use, name='measuredVis_without_fakes', type='D')
h['fake'] = Hist( reco_bin_edges_vis_to_use, name='fake', type='D')
h['fakeVis'] = Hist( reco_bin_edges_vis_to_use, name='fakeVis', type='D')
# 2D histograms
h['response'] = Hist2D( reco_bin_edges_vis_to_use, allVariablesBins[variable], name='response', type='D')
h['response_without_fakes'] = Hist2D( reco_bin_edges_vis_to_use, allVariablesBins[variable], name='response_without_fakes', type='D')
h['responseVis_without_fakes'] = Hist2D( reco_bin_edges_vis_to_use, allVariablesBins[variable], name='responseVis_without_fakes', type='D')
h['response_parton'] = Hist2D( reco_bin_edges_vis_to_use, allVariablesBins[variable], name='response_parton', type='D')
h['response_without_fakes_parton'] = Hist2D( reco_bin_edges_vis_to_use, allVariablesBins[variable], name='response_without_fakes_parton', type='D')
if args.fineBinned:
minVar = trunc( allVariablesBins[variable][0] )
maxVar = trunc( max( tree.GetMaximum(genVariable_particle_names[variable]), tree.GetMaximum( reco_bin_edges_vis_to_use ) ) * 1.2 )
nBins = int(maxVar - minVar)
if variable is 'lepton_eta' or variable is 'bjets_eta':
maxVar = 2.4
minVar = -2.4
nBins = 1000
# nBins = 960 so that small bin width is usable in 00. [0.0025]
elif 'abs' in variable and 'eta' in variable:
maxVar = 2.4
minVar = 0.
nBins = 960
elif 'Rap' in variable:
maxVar = 2.4
minVar = -2.4
nBins = 1000
elif 'NJets' in variable:
maxVar = 20.5
minVar = 3.5
nBins = 17
h['truth'] = Hist( nBins, minVar, maxVar, name='truth')
h['truthVis'] = Hist( nBins, minVar, maxVar, name='truthVis')
h['truthVis_noWeight'] = Hist( nBins, minVar, maxVar, name='truthVis_noWeight')
h['truth_parton'] = Hist( nBins, minVar, maxVar, name='truth_parton')
h['measured'] = Hist( nBins, minVar, maxVar, name='measured')
h['measuredVis'] = Hist( nBins, minVar, maxVar, name='measuredVis')
h['measured_without_fakes'] = Hist( nBins, minVar, maxVar, name='measured_without_fakes')
h['measuredVis_without_fakes'] = Hist( nBins, minVar, maxVar, name='measuredVis_without_fakes')
h['fake'] = Hist( nBins, minVar, maxVar, name='fake')
h['fakeVis'] = Hist( nBins, minVar, maxVar, name='fakeVis')
h['response'] = Hist2D( nBins, minVar, maxVar, nBins, minVar, maxVar, name='response')
h['response_without_fakes'] = Hist2D( nBins, minVar, maxVar, nBins, minVar, maxVar, name='response_without_fakes')
h['responseVis_without_fakes'] = Hist2D( nBins, minVar, maxVar, nBins, minVar, maxVar, name='responseVis_without_fakes')
h['response_parton'] = Hist2D( nBins, minVar, maxVar, nBins, minVar, maxVar, name='response_parton')
h['response_without_fakes_parton'] = Hist2D( nBins, minVar, maxVar, nBins, minVar, maxVar, name='response_without_fakes_parton')
residuals[variable][channel.channelName] = {}
r = residuals[variable][channel.channelName]
for i in range (1, nBins+1):
r[i] = Hist(100, 0, maxVar*0.1, name='Residuals_Bin_'+str(i))
residual_options[variable][channel.channelName] = {}
o = residual_options[variable][channel.channelName]
o['min'] = minVar
o['max'] = maxVar
o['nbins'] = nBins
o['step'] = (maxVar-minVar)/nBins
o['bin_edges'] = np.arange(minVar, maxVar, (maxVar-minVar)/nBins)
# Some interesting histograms
h['puOffline'] = Hist( 20, 0, 2, name='puWeights_offline')
h['eventWeightHist'] = Hist( 100, -2, 2, name='eventWeightHist')
h['genWeightHist'] = Hist( 1000, 0, 1, name='genWeightHist')
h['offlineWeightHist'] = Hist( 100, -2, 2, name='offlineWeightHist')
h['phaseSpaceInfoHist'] = Hist( 10, 0, 1, name='phaseSpaceInfoHist')
print("Initialisation of Histograms Complete")
# Counters for studying phase space
nPassGenSelection = 0
nVis = {c.channelName : 0 for c in channels}
nVisNotOffline = {c.channelName : 0 for c in channels}
nOffline = {c.channelName : 0 for c in channels}
nOfflineNotVis = {c.channelName : 0 for c in channels}
nFull = {c.channelName : 0 for c in channels}
nOfflineSL = {c.channelName : 0 for c in channels}
n=0
maxEvents = -1
halfOfEvents = 0
if '70pc' in args.sample or '30pc' in args.sample:
print 'Only processing fraction of total events for sample :',args.sample
totalEvents = tree.GetEntries()
if '70pc' in args.sample:
maxEvents = int( totalEvents * 0.7 )
elif '30pc' in args.sample:
maxEvents = int( totalEvents * 0.3 )
print 'Will process ',maxEvents,'out of',totalEvents,'events'
if 'firstHalf' in args.sample or 'secondHalf' in args.sample:
totalEvents = tree.GetEntries()
halfOfEvents = int( totalEvents / 2 )
# Event Loop
# for event, weight in zip(tree,weightTree):
for event in tree:
branch = event.__getattr__
n+=1
if not n%100000: print 'Processing event %.0f Progress : %.2g %%' % ( n, float(n)/nEntries*100 )
# if n > 100000: break
if maxEvents > 0 and n > maxEvents: break
if 'firstHalf' in args.sample and n >= halfOfEvents: break
elif 'secondHalf' in args.sample and n < halfOfEvents: continue
# # #
# # # Weights and selection
# # #
# Pileup weight
# Don't apply if calculating systematic
pileupWeight = event.PUWeight
# print event.PUWeight,event.PUWeight_up,event.PUWeight_down
if args.sample == "pileupUp":
pileupWeight = event.PUWeight_up
elif args.sample == "pileupDown":
pileupWeight = event.PUWeight_down
# Generator level weight
genWeight = event.EventWeight * measurement_config.luminosity_scale
# B Jet Weight
# bjetWeight = event.BJetWeight
bjetWeight = event.BJetAlternativeWeight
if 'fsr' in args.sample:
# bjetWeight = event.BJetAlternativeWeight * event.BJetEfficiencyCorrectionWeight
bjetWeight = event.BJetWeight * event.BJetEfficiencyCorrectionWeight
if args.sample == "bjetup":
bjetWeight = event.BJetUpWeight
elif args.sample == "bjetdown":
bjetWeight = event.BJetDownWeight
elif args.sample == "lightjetup":
bjetWeight = event.LightJetUpWeight
elif args.sample == "lightjetdown":
bjetWeight = event.LightJetDownWeight
# Top pt systematic weight
topPtSystematicWeight = 1
if args.sample == 'topPtSystematic':
topPtSystematicWeight = calculateTopPtSystematicWeight( branch('lepTopPt_parton'), branch('hadTopPt_parton'))
# Offline level weights
offlineWeight = 1
offlineWeight *= pileupWeight
offlineWeight *= bjetWeight
offlineWeight_forLeptonEta = offlineWeight
genWeight *= topPtSystematicWeight
# Generator weight
# Scale up/down, pdf
if pdfWeight >= 0:
genWeight *= branch('pdfWeight_%i' % pdfWeight)
pass
if MMHT14Weight >= 0:
genWeight *= branch('MMHT14Weight_%i' % MMHT14Weight)
pass
if CT14Weight >= 0:
genWeight *= branch('CT14Weight_%i' % CT14Weight)
pass
if muFmuRWeight >= 0:
genWeight *= branch('muFmuRWeight_%i' % muFmuRWeight)
pass
if alphaSWeight == 0 or alphaSWeight == 1:
genWeight *= branch('alphaSWeight_%i' % alphaSWeight)
pass
if semiLepBrWeight == -1:
genWeight *= branch('semilepbrDown')
elif semiLepBrWeight == 1:
genWeight *= branch('semilepbrUp')
pass
if fragWeight == 1:
genWeight *= branch('downFrag')
elif fragWeight == 2:
genWeight *= branch('centralFrag')
elif fragWeight == 3:
genWeight *= branch('upFrag')
elif fragWeight == 4:
genWeight *= branch('petersonFrag')
pass
if args.applyTopPtReweighting != 0:
ptWeight = calculateTopPtWeight( branch('lepTopPt_parton'), branch('hadTopPt_parton'), args.applyTopPtReweighting)
genWeight *= ptWeight
if args.applyTopEtaReweighting != 0:
etaWeight = calculateTopEtaWeight( branch('lepTopRap_parton'), branch('hadTopRap_parton'), args.applyTopEtaReweighting)
genWeight *= etaWeight
for channel in channels:
# Generator level selection
genSelection = ''
genSelectionVis = ''
if channel.channelName is 'muPlusJets' :
genSelection = event.isSemiLeptonicMuon == 1
genSelectionVis = event.passesGenEventSelection == 1 and event.pseudoLepton_pdgId == 13
if args.newPS:
genSelectionVis = event.passesGenEventSelection_20GeVLastJet == 1 and event.pseudoLepton_pdgId == 13
elif channel.channelName is 'ePlusJets' :
genSelection = event.isSemiLeptonicElectron == 1
genSelectionVis = event.passesGenEventSelection == 1 and event.pseudoLepton_pdgId == 11
if args.newPS:
genSelectionVis = event.passesGenEventSelection_20GeVLastJet == 1 and event.pseudoLepton_pdgId == 11
# Lepton weight
# Channel specific
leptonWeight = 1
leptonWeight_forLeptonEta = 1
if channel.channelName is 'muPlusJets' :
leptonWeight = event.MuonEfficiencyCorrection
leptonWeight_forLeptonEta = event.MuonEfficiencyCorrection_etaBins
if args.sample == 'muonup':
leptonWeight = event.MuonEfficiencyCorrectionUp
leptonWeight_forLeptonEta = event.MuonEfficiencyCorrectionUp_etaBins
elif args.sample == 'muondown':
leptonWeight = event.MuonEfficiencyCorrectionDown
leptonWeight_forLeptonEta = event.MuonEfficiencyCorrectionDown_etaBins
elif channel.channelName is 'ePlusJets' :
leptonWeight = event.ElectronEfficiencyCorrection
leptonWeight_forLeptonEta = event.ElectronEfficiencyCorrection_etaBins
if args.sample == 'electronup':
leptonWeight = event.ElectronEfficiencyCorrectionUp
leptonWeight_forLeptonEta = event.ElectronEfficiencyCorrectionUp_etaBins
elif args.sample == 'electrondown':
leptonWeight = event.ElectronEfficiencyCorrectionDown
leptonWeight_forLeptonEta = event.ElectronEfficiencyCorrectionDown_etaBins
offlineWeight_withLeptonWeight = offlineWeight * leptonWeight
offlineWeight_withLeptonWeight_forLeptonEta = offlineWeight_forLeptonEta * leptonWeight_forLeptonEta
# Offline level selection
offlineSelection = 0
if channel.channelName is 'muPlusJets' :
offlineSelection = int(event.passSelection) == 1
elif channel.channelName is 'ePlusJets' :
offlineSelection = int(event.passSelection) == 2
# Fake selection
fakeSelection = offlineSelection and not genSelection
fakeSelectionVis = offlineSelection and not genSelectionVis
# Phase space info
if genSelection:
nFull[channel.channelName] += genWeight
if offlineSelection:
nOfflineSL[channel.channelName] += genWeight
if genSelectionVis:
nPassGenSelection += 1
nVis[channel.channelName] += genWeight
if not offlineSelection:
nVisNotOffline[channel.channelName] += genWeight
if offlineSelection:
nOffline[channel.channelName] += offlineWeight * genWeight
if not genSelectionVis:
nOfflineNotVis[channel.channelName] += offlineWeight * genWeight
for variable in allVariablesBins:
if args.debug and variable != 'HT' : continue
if args.sample in measurement_config.met_specific_systematics and \
variable in measurement_config.variables_no_met:
continue
offlineWeight_toUse = offlineWeight_withLeptonWeight
if 'abs_lepton_eta' in variable:
offlineWeight_toUse = offlineWeight_withLeptonWeight_forLeptonEta
# # #
# # # Variable to plot
# # #
recoVariable = branch(recoVariableNames[variable])
if variable in ['MET', 'ST', 'WPT'] and \
sysIndex != None and ( offlineSelection or fakeSelection or fakeSelectionVis ) :
recoVariable = recoVariable[sysIndex]
if 'abs' in variable:
recoVariable = abs(recoVariable)
# With TUnfold, reco variable never goes in the overflow (or underflow)
if not args.fineBinned:
recoVariable = min( recoVariable, allVariablesBins[variable][-1] - 0.000001 )
genVariable_particle = branch(genVariable_particle_names[variable])
if 'abs' in variable:
genVariable_particle = abs(genVariable_particle)
# #
# # Fill histograms
# #
histogramsToFill = histograms[variable][channel.channelName]
if not args.donothing:
if genSelection:
histogramsToFill['truth'].Fill( genVariable_particle, genWeight)
if genSelectionVis:
filledTruth = True
histogramsToFill['truthVis'].Fill( genVariable_particle, genWeight)
histogramsToFill['truthVis_noWeight'].Fill( genVariable_particle, 1)
if offlineSelection:
histogramsToFill['measured'].Fill( recoVariable, offlineWeight_toUse * genWeight )
histogramsToFill['measuredVis'].Fill( recoVariable, offlineWeight_toUse * genWeight )
if genSelectionVis :
histogramsToFill['measuredVis_without_fakes'].Fill( recoVariable, offlineWeight_toUse * genWeight )
if genSelection:
histogramsToFill['measured_without_fakes'].Fill( recoVariable, offlineWeight_toUse * genWeight )
histogramsToFill['response'].Fill( recoVariable, genVariable_particle, offlineWeight_toUse * genWeight )
if offlineSelection and genSelection:
histogramsToFill['response_without_fakes'].Fill( recoVariable, genVariable_particle, offlineWeight_toUse * genWeight )
histogramsToFill['response_without_fakes'].Fill( allVariablesBins[variable][0]-1, genVariable_particle, ( 1 - offlineWeight_toUse ) * genWeight )
elif genSelection:
histogramsToFill['response_without_fakes'].Fill( allVariablesBins[variable][0]-1, genVariable_particle, genWeight )
if offlineSelection and genSelectionVis:
histogramsToFill['responseVis_without_fakes'].Fill( recoVariable, genVariable_particle, offlineWeight_toUse * genWeight )
histogramsToFill['responseVis_without_fakes'].Fill( allVariablesBins[variable][0]-1, genVariable_particle, ( 1 - offlineWeight_toUse ) * genWeight )
filledResponse = True
elif genSelectionVis:
histogramsToFill['responseVis_without_fakes'].Fill( allVariablesBins[variable][0]-1, genVariable_particle, genWeight )
filledResponse = True
if fakeSelection:
histogramsToFill['fake'].Fill( recoVariable, offlineWeight_toUse * genWeight )
if fakeSelectionVis:
histogramsToFill['fakeVis'].Fill( recoVariable, offlineWeight_toUse * genWeight )
if args.extraHists:
if genSelectionVis:
histogramsToFill['eventWeightHist'].Fill(event.EventWeight)
histogramsToFill['genWeightHist'].Fill(genWeight)
histogramsToFill['offlineWeightHist'].Fill(offlineWeight_toUse )
if args.fineBinned:
# Bin reco var is in
options = residual_options[variable][channel.channelName]
if offlineSelection and genSelection:
# i will be fine bin number the reco var resides in
for i, edge in enumerate(options['bin_edges']):
if recoVariable > edge: continue
else: break
residual = abs(recoVariable - genVariable_particle)
if not residual > options['max']*0.1:
residuals[variable][channel.channelName][i].Fill(residual, offlineWeight_toUse*genWeight)
#
# Output histgorams to file
#
for variable in allVariablesBins:
if args.debug and variable != 'HT' : continue
if args.sample in measurement_config.met_systematics and variable not in ['MET', 'ST', 'WPT']:
continue
for channel in channels:
if nOffline[channel.channelName] != 0 :
# Fill phase space info
h = histograms[variable][channel.channelName]['phaseSpaceInfoHist']
h.SetBinContent(1, nVisNotOffline[channel.channelName] / nVis[channel.channelName])
# h.GetXaxis().SetBinLabel(1, "nVisNotOffline/nVis")
h.SetBinContent(2, nOfflineNotVis[channel.channelName] / nOffline[channel.channelName])
# h.GetXaxis().SetBinLabel(2, "nOfflineNotVis/nOffline")
h.SetBinContent(3, nVis[channel.channelName] / nFull[channel.channelName])
# h.GetXaxis().SetBinLabel(3, "nVis/nFull")
# Selection efficiency for SL ttbar
h.SetBinContent(4, nOfflineSL[channel.channelName] / nFull[channel.channelName])
# h.GetXaxis().SetBinLabel(4, "nOfflineSL/nFull")
# Fraction of offline that are SL
h.SetBinContent(5, nOfflineSL[channel.channelName] / nOffline[channel.channelName])
# h.GetXaxis().SetBinLabel(5, "nOfflineSL/nOffline")
outputDirs[variable][channel.channelName].cd()
for h in histograms[variable][channel.channelName]:
histograms[variable][channel.channelName][h].Write()
if args.fineBinned:
outputDirsRes[variable][channel.channelName].cd()
for r in residuals[variable][channel.channelName]:
residuals[variable][channel.channelName][r].Write()
print 'N events passing gen selection : ',nPassGenSelection
with root_open( outputFileName, 'update') as out:
# Done all channels, now combine the two channels, and output to the same file
for path, dirs, objects in out.walk():
if 'electron' in path:
if 'coarse' in path: continue
outputDir = out.mkdir(path.replace('electron','combined'))
outputDir.cd()
for h in objects:
h_e = out.Get(path+'/'+h)
h_mu = out.Get(path.replace('electron','muon')+'/'+h)
h_comb = (h_e + h_mu).Clone(h)
h_comb.Write()
pass
pass
pass
def main():
args = parse_arguments()
measurement_config = XSectionConfig(args.centreOfMassEnergy)
# Input file name
file_name = 'crap.root'
if int(args.centreOfMassEnergy) == 13:
file_name = getFileName('13TeV', args.sample, measurement_config)
else:
print "Error: Unrecognised centre of mass energy."
pdfWeight = args.pdfWeight
CT14Weight = args.CT14Weight
MMHT14Weight = args.MMHT14Weight
muFmuRWeight = args.muFmuRWeight
alphaSWeight = args.alphaSWeight
semiLepBrWeight = args.semiLepBrWeight
fragWeight = args.fragWeight
# Output file name
outputFileName = 'crap.root'
outputFileDir = 'unfolding/%sTeV/' % args.centreOfMassEnergy
make_folder_if_not_exists(outputFileDir)
energySuffix = '%sTeV' % (args.centreOfMassEnergy)
if args.applyTopEtaReweighting != 0:
if args.applyTopEtaReweighting == 1:
outputFileName = outputFileDir + '/unfolding_TTJets_%s_asymmetric_withTopEtaReweighting_up.root' % energySuffix
elif args.applyTopEtaReweighting == -1:
outputFileName = outputFileDir + '/unfolding_TTJets_%s_asymmetric_withTopEtaReweighting_down.root' % energySuffix
elif args.applyTopPtReweighting:
if args.applyTopPtReweighting == 1:
outputFileName = outputFileDir + '/unfolding_TTJets_%s_asymmetric_withTopPtReweighting_up.root' % energySuffix
elif args.applyTopPtReweighting == -1:
outputFileName = outputFileDir + '/unfolding_TTJets_%s_asymmetric_withTopPtReweighting_down.root' % energySuffix
elif muFmuRWeight == 1:
outputFileName = outputFileDir + '/unfolding_TTJets_%s_asymmetric_1muR2muF.root' % (
energySuffix)
elif muFmuRWeight == 2:
outputFileName = outputFileDir + '/unfolding_TTJets_%s_asymmetric_1muR05muF.root' % (
energySuffix)
elif muFmuRWeight == 3:
outputFileName = outputFileDir + '/unfolding_TTJets_%s_asymmetric_2muR1muF.root' % (
energySuffix)
elif muFmuRWeight == 4:
outputFileName = outputFileDir + '/unfolding_TTJets_%s_asymmetric_2muR2muF.root' % (
energySuffix)
elif muFmuRWeight == 6:
outputFileName = outputFileDir + '/unfolding_TTJets_%s_asymmetric_05muR1muF.root' % (
energySuffix)
elif muFmuRWeight == 8:
outputFileName = outputFileDir + '/unfolding_TTJets_%s_asymmetric_05muR05muF.root' % (
energySuffix)
elif alphaSWeight == 0:
outputFileName = outputFileDir + '/unfolding_TTJets_%s_asymmetric_alphaS_down.root' % (
energySuffix)
elif alphaSWeight == 1:
outputFileName = outputFileDir + '/unfolding_TTJets_%s_asymmetric_alphaS_up.root' % (
energySuffix)
elif semiLepBrWeight == -1:
outputFileName = outputFileDir + '/unfolding_TTJets_%s_asymmetric_semiLepBr_down.root' % (
energySuffix)
elif semiLepBrWeight == 1:
outputFileName = outputFileDir + '/unfolding_TTJets_%s_asymmetric_semiLepBr_up.root' % (
energySuffix)
elif fragWeight == 1:
outputFileName = outputFileDir + '/unfolding_TTJets_%s_asymmetric_frag_down.root' % (
energySuffix)
elif fragWeight == 2:
outputFileName = outputFileDir + '/unfolding_TTJets_%s_asymmetric_frag_central.root' % (
energySuffix)
elif fragWeight == 3:
outputFileName = outputFileDir + '/unfolding_TTJets_%s_asymmetric_frag_up.root' % (
energySuffix)
elif fragWeight == 4:
outputFileName = outputFileDir + '/unfolding_TTJets_%s_asymmetric_frag_peterson.root' % (
energySuffix)
elif pdfWeight >= 0 and pdfWeight <= 99:
outputFileName = outputFileDir + '/unfolding_TTJets_%s_asymmetric_pdfWeight_%i.root' % (
energySuffix, pdfWeight)
elif CT14Weight >= 0 and CT14Weight <= 54:
outputFileName = outputFileDir + '/unfolding_TTJets_%s_asymmetric_CT14Weight_%i.root' % (
energySuffix, CT14Weight)
elif MMHT14Weight >= 0 and MMHT14Weight <= 55:
outputFileName = outputFileDir + '/unfolding_TTJets_%s_asymmetric_MMHT14Weight_%i.root' % (
energySuffix, MMHT14Weight)
elif 'central' not in args.sample:
outputFileName = outputFileDir + '/unfolding_TTJets_%s_%s_asymmetric.root' % (
energySuffix, args.sample)
elif args.fineBinned:
outputFileName = outputFileDir + '/unfolding_TTJets_%s.root' % (
energySuffix)
else:
outputFileName = outputFileDir + '/unfolding_TTJets_%s_asymmetric.root' % energySuffix
if '70pc' in args.sample or '30pc' in args.sample:
outputFileName.replace('asymmetric',
'asymmetric_' + args.sample.split('_')[1])
if 'firstHalf' in args.sample:
outputFileName = outputFileName.replace('asymmetric',
'asymmetric_firstHalf')
elif 'secondHalf' in args.sample:
outputFileName = outputFileName.replace('asymmetric',
'asymmetric_secondHalf')
if args.newPS:
outputFileName = outputFileName.replace('asymmetric',
'asymmetric_newPS')
# Get the tree/chain
treeName = "TTbar_plus_X_analysis/Unfolding/Unfolding"
print file_name
file_name = getUnmergedDirectory(file_name)
print file_name
tree = ROOT.TChain(treeName)
filenames = glob.glob(file_name)
for f in filenames:
tree.Add(f)
with root_open(outputFileName, 'recreate') as out:
nEntries = tree.GetEntries()
print 'Number of entries:', nEntries
# For variables where you want bins to be symmetric about 0, use abs(variable) (but also make plots for signed variable)
allVariablesBins = bin_edges_vis.copy()
for variable in bin_edges_vis:
if 'Rap' in variable:
allVariablesBins['abs_%s' %
variable] = [0, bin_edges_vis[variable][-1]]
recoVariableNames = {}
genVariable_particle_names = {}
genVariable_parton_names = {}
histograms = {}
residuals = {}
residual_options = {}
outputDirs = {}
outputDirsRes = {}
for variable in allVariablesBins:
if args.debug and variable != 'HT': continue
if args.sample in measurement_config.met_specific_systematics \
and variable in measurement_config.variables_no_met:
continue
outputDirs[variable] = {}
outputDirsRes[variable] = {}
histograms[variable] = {}
residuals[variable] = {}
residual_options[variable] = {}
#
# Variable names
#
recoVariableName = branchNames[variable]
sysIndex = None
if variable in ['MET', 'ST', 'WPT']:
if args.sample == "jerup":
recoVariableName += '_METUncertainties'
sysIndex = 0
elif args.sample == "jerdown":
recoVariableName += '_METUncertainties'
sysIndex = 1
elif args.sample == "jesup":
recoVariableName += '_METUncertainties'
sysIndex = 2
elif args.sample == "jesdown":
recoVariableName += '_METUncertainties'
sysIndex = 3
# Dont need this?
elif args.sample in measurement_config.met_systematics:
recoVariableName += '_METUncertainties'
sysIndex = measurement_config.met_systematics[args.sample]
genVariable_particle_name = None
genVariable_parton_name = None
if variable in genBranchNames_particle:
genVariable_particle_name = genBranchNames_particle[variable]
if args.newPS and variable in ['HT', 'ST', 'NJets']:
genVariable_particle_name += '_20GeVLastJet'
if variable in genBranchNames_parton:
genVariable_parton_name = genBranchNames_parton[variable]
recoVariableNames[variable] = recoVariableName
genVariable_particle_names[variable] = genVariable_particle_name
genVariable_parton_names[variable] = genVariable_parton_name
reco_bin_edges_vis_to_use = reco_bin_edges_vis[variable]
for channel in channels:
# Make dir in output file
outputDirName = variable + '_' + channel.outputDirName
outputDir = out.mkdir(outputDirName)
outputDirs[variable][channel.channelName] = outputDir
if args.fineBinned:
outputDirResName = outputDirName + '/residuals/'
outputDirRes = out.mkdir(outputDirResName)
outputDirsRes[variable][channel.channelName] = outputDirRes
#
# Book histograms
#
# 1D histograms
histograms[variable][channel.channelName] = {}
h = histograms[variable][channel.channelName]
h['truth'] = Hist(allVariablesBins[variable],
name='truth',
type='D')
h['truthVis'] = Hist(allVariablesBins[variable],
name='truthVis',
type='D')
h['truthVis_noWeight'] = Hist(allVariablesBins[variable],
name='truthVis_noWeight',
type='D')
h['truth_parton'] = Hist(allVariablesBins[variable],
name='truth_parton',
type='D')
h['measured'] = Hist(reco_bin_edges_vis_to_use,
name='measured',
type='D')
h['measuredVis'] = Hist(reco_bin_edges_vis_to_use,
name='measuredVis',
type='D')
h['measured_without_fakes'] = Hist(
reco_bin_edges_vis_to_use,
name='measured_without_fakes',
type='D')
h['measuredVis_without_fakes'] = Hist(
reco_bin_edges_vis_to_use,
name='measuredVis_without_fakes',
type='D')
h['fake'] = Hist(reco_bin_edges_vis_to_use,
name='fake',
type='D')
h['fakeVis'] = Hist(reco_bin_edges_vis_to_use,
name='fakeVis',
type='D')
# 2D histograms
h['response'] = Hist2D(reco_bin_edges_vis_to_use,
allVariablesBins[variable],
name='response',
type='D')
h['response_without_fakes'] = Hist2D(
reco_bin_edges_vis_to_use,
allVariablesBins[variable],
name='response_without_fakes',
type='D')
h['responseVis_without_fakes'] = Hist2D(
reco_bin_edges_vis_to_use,
allVariablesBins[variable],
name='responseVis_without_fakes',
type='D')
h['response_parton'] = Hist2D(reco_bin_edges_vis_to_use,
allVariablesBins[variable],
name='response_parton',
type='D')
h['response_without_fakes_parton'] = Hist2D(
reco_bin_edges_vis_to_use,
allVariablesBins[variable],
name='response_without_fakes_parton',
type='D')
if args.fineBinned:
minVar = trunc(allVariablesBins[variable][0])
maxVar = trunc(
max(
tree.GetMaximum(
genVariable_particle_names[variable]),
tree.GetMaximum(reco_bin_edges_vis_to_use)) * 1.2)
nBins = int(maxVar - minVar)
if variable is 'lepton_eta' or variable is 'bjets_eta':
maxVar = 2.4
minVar = -2.4
nBins = 1000
# nBins = 960 so that small bin width is usable in 00. [0.0025]
elif 'abs' in variable and 'eta' in variable:
maxVar = 2.4
minVar = 0.
nBins = 960
elif 'Rap' in variable:
maxVar = 2.4
minVar = -2.4
nBins = 1000
elif 'NJets' in variable:
maxVar = 20.5
minVar = 3.5
nBins = 17
h['truth'] = Hist(nBins, minVar, maxVar, name='truth')
h['truthVis'] = Hist(nBins,
minVar,
maxVar,
name='truthVis')
h['truthVis_noWeight'] = Hist(nBins,
minVar,
maxVar,
name='truthVis_noWeight')
h['truth_parton'] = Hist(nBins,
minVar,
maxVar,
name='truth_parton')
h['measured'] = Hist(nBins,
minVar,
maxVar,
name='measured')
h['measuredVis'] = Hist(nBins,
minVar,
maxVar,
name='measuredVis')
h['measured_without_fakes'] = Hist(
nBins, minVar, maxVar, name='measured_without_fakes')
h['measuredVis_without_fakes'] = Hist(
nBins,
minVar,
maxVar,
name='measuredVis_without_fakes')
h['fake'] = Hist(nBins, minVar, maxVar, name='fake')
h['fakeVis'] = Hist(nBins, minVar, maxVar, name='fakeVis')
h['response'] = Hist2D(nBins,
minVar,
maxVar,
nBins,
minVar,
maxVar,
name='response')
h['response_without_fakes'] = Hist2D(
nBins,
minVar,
maxVar,
nBins,
minVar,
maxVar,
name='response_without_fakes')
h['responseVis_without_fakes'] = Hist2D(
nBins,
minVar,
maxVar,
nBins,
minVar,
maxVar,
name='responseVis_without_fakes')
h['response_parton'] = Hist2D(nBins,
minVar,
maxVar,
nBins,
minVar,
maxVar,
name='response_parton')
h['response_without_fakes_parton'] = Hist2D(
nBins,
minVar,
maxVar,
nBins,
minVar,
maxVar,
name='response_without_fakes_parton')
residuals[variable][channel.channelName] = {}
r = residuals[variable][channel.channelName]
for i in range(1, nBins + 1):
r[i] = Hist(100,
0,
maxVar * 0.1,
name='Residuals_Bin_' + str(i))
residual_options[variable][channel.channelName] = {}
o = residual_options[variable][channel.channelName]
o['min'] = minVar
o['max'] = maxVar
o['nbins'] = nBins
o['step'] = (maxVar - minVar) / nBins
o['bin_edges'] = np.arange(minVar, maxVar,
(maxVar - minVar) / nBins)
# Some interesting histograms
h['puOffline'] = Hist(20, 0, 2, name='puWeights_offline')
h['eventWeightHist'] = Hist(100, -2, 2, name='eventWeightHist')
h['genWeightHist'] = Hist(1000, 0, 1, name='genWeightHist')
h['offlineWeightHist'] = Hist(100,
-2,
2,
name='offlineWeightHist')
h['phaseSpaceInfoHist'] = Hist(10,
0,
1,
name='phaseSpaceInfoHist')
print("Initialisation of Histograms Complete")
# Counters for studying phase space
nPassGenSelection = 0
nVis = {c.channelName: 0 for c in channels}
nVisNotOffline = {c.channelName: 0 for c in channels}
nOffline = {c.channelName: 0 for c in channels}
nOfflineNotVis = {c.channelName: 0 for c in channels}
nFull = {c.channelName: 0 for c in channels}
nOfflineSL = {c.channelName: 0 for c in channels}
n = 0
maxEvents = -1
halfOfEvents = 0
if '70pc' in args.sample or '30pc' in args.sample:
print 'Only processing fraction of total events for sample :', args.sample
totalEvents = tree.GetEntries()
if '70pc' in args.sample:
maxEvents = int(totalEvents * 0.7)
elif '30pc' in args.sample:
maxEvents = int(totalEvents * 0.3)
print 'Will process ', maxEvents, 'out of', totalEvents, 'events'
if 'firstHalf' in args.sample or 'secondHalf' in args.sample:
totalEvents = tree.GetEntries()
halfOfEvents = int(totalEvents / 2)
# Event Loop
# for event, weight in zip(tree,weightTree):
for event in tree:
branch = event.__getattr__
n += 1
if not n % 100000:
print 'Processing event %.0f Progress : %.2g %%' % (
n, float(n) / nEntries * 100)
# if n > 100000: break
if maxEvents > 0 and n > maxEvents: break
if 'firstHalf' in args.sample and n >= halfOfEvents: break
elif 'secondHalf' in args.sample and n < halfOfEvents: continue
# # #
# # # Weights and selection
# # #
# Pileup weight
# Don't apply if calculating systematic
pileupWeight = event.PUWeight
# print event.PUWeight,event.PUWeight_up,event.PUWeight_down
if args.sample == "pileupUp":
pileupWeight = event.PUWeight_up
elif args.sample == "pileupDown":
pileupWeight = event.PUWeight_down
# Generator level weight
genWeight = event.EventWeight * measurement_config.luminosity_scale
# B Jet Weight
# bjetWeight = event.BJetWeight
bjetWeight = event.BJetAlternativeWeight
if 'fsr' in args.sample:
# bjetWeight = event.BJetAlternativeWeight * event.BJetEfficiencyCorrectionWeight
bjetWeight = event.BJetWeight * event.BJetEfficiencyCorrectionWeight
if args.sample == "bjetup":
bjetWeight = event.BJetUpWeight
elif args.sample == "bjetdown":
bjetWeight = event.BJetDownWeight
elif args.sample == "lightjetup":
bjetWeight = event.LightJetUpWeight
elif args.sample == "lightjetdown":
bjetWeight = event.LightJetDownWeight
# Top pt systematic weight
topPtSystematicWeight = 1
if args.sample == 'topPtSystematic':
topPtSystematicWeight = calculateTopPtSystematicWeight(
branch('lepTopPt_parton'), branch('hadTopPt_parton'))
# Offline level weights
offlineWeight = 1
offlineWeight *= pileupWeight
offlineWeight *= bjetWeight
offlineWeight_forLeptonEta = offlineWeight
genWeight *= topPtSystematicWeight
# Generator weight
# Scale up/down, pdf
if pdfWeight >= 0:
genWeight *= branch('pdfWeight_%i' % pdfWeight)
pass
if MMHT14Weight >= 0:
genWeight *= branch('MMHT14Weight_%i' % MMHT14Weight)
pass
if CT14Weight >= 0:
genWeight *= branch('CT14Weight_%i' % CT14Weight)
pass
if muFmuRWeight >= 0:
genWeight *= branch('muFmuRWeight_%i' % muFmuRWeight)
pass
if alphaSWeight == 0 or alphaSWeight == 1:
genWeight *= branch('alphaSWeight_%i' % alphaSWeight)
pass
if semiLepBrWeight == -1:
genWeight *= branch('semilepbrDown')
elif semiLepBrWeight == 1:
genWeight *= branch('semilepbrUp')
pass
if fragWeight == 1:
genWeight *= branch('downFrag')
elif fragWeight == 2:
genWeight *= branch('centralFrag')
elif fragWeight == 3:
genWeight *= branch('upFrag')
elif fragWeight == 4:
genWeight *= branch('petersonFrag')
pass
if args.applyTopPtReweighting != 0:
ptWeight = calculateTopPtWeight(branch('lepTopPt_parton'),
branch('hadTopPt_parton'),
args.applyTopPtReweighting)
genWeight *= ptWeight
if args.applyTopEtaReweighting != 0:
etaWeight = calculateTopEtaWeight(branch('lepTopRap_parton'),
branch('hadTopRap_parton'),
args.applyTopEtaReweighting)
genWeight *= etaWeight
for channel in channels:
# Generator level selection
genSelection = ''
genSelectionVis = ''
if channel.channelName is 'muPlusJets':
genSelection = event.isSemiLeptonicMuon == 1
genSelectionVis = event.passesGenEventSelection == 1 and event.pseudoLepton_pdgId == 13
if args.newPS:
genSelectionVis = event.passesGenEventSelection_20GeVLastJet == 1 and event.pseudoLepton_pdgId == 13
elif channel.channelName is 'ePlusJets':
genSelection = event.isSemiLeptonicElectron == 1
genSelectionVis = event.passesGenEventSelection == 1 and event.pseudoLepton_pdgId == 11
if args.newPS:
genSelectionVis = event.passesGenEventSelection_20GeVLastJet == 1 and event.pseudoLepton_pdgId == 11
# Lepton weight
# Channel specific
leptonWeight = 1
leptonWeight_forLeptonEta = 1
if channel.channelName is 'muPlusJets':
leptonWeight = event.MuonEfficiencyCorrection
leptonWeight_forLeptonEta = event.MuonEfficiencyCorrection_etaBins
if args.sample == 'muonup':
leptonWeight = event.MuonEfficiencyCorrectionUp
leptonWeight_forLeptonEta = event.MuonEfficiencyCorrectionUp_etaBins
elif args.sample == 'muondown':
leptonWeight = event.MuonEfficiencyCorrectionDown
leptonWeight_forLeptonEta = event.MuonEfficiencyCorrectionDown_etaBins
elif channel.channelName is 'ePlusJets':
leptonWeight = event.ElectronEfficiencyCorrection
leptonWeight_forLeptonEta = event.ElectronEfficiencyCorrection_etaBins
if args.sample == 'electronup':
leptonWeight = event.ElectronEfficiencyCorrectionUp
leptonWeight_forLeptonEta = event.ElectronEfficiencyCorrectionUp_etaBins
elif args.sample == 'electrondown':
leptonWeight = event.ElectronEfficiencyCorrectionDown
leptonWeight_forLeptonEta = event.ElectronEfficiencyCorrectionDown_etaBins
offlineWeight_withLeptonWeight = offlineWeight * leptonWeight
offlineWeight_withLeptonWeight_forLeptonEta = offlineWeight_forLeptonEta * leptonWeight_forLeptonEta
# Offline level selection
offlineSelection = 0
if channel.channelName is 'muPlusJets':
offlineSelection = int(event.passSelection) == 1
elif channel.channelName is 'ePlusJets':
offlineSelection = int(event.passSelection) == 2
# Fake selection
fakeSelection = offlineSelection and not genSelection
fakeSelectionVis = offlineSelection and not genSelectionVis
# Phase space info
if genSelection:
nFull[channel.channelName] += genWeight
if offlineSelection:
nOfflineSL[channel.channelName] += genWeight
if genSelectionVis:
nPassGenSelection += 1
nVis[channel.channelName] += genWeight
if not offlineSelection:
nVisNotOffline[channel.channelName] += genWeight
if offlineSelection:
nOffline[channel.channelName] += offlineWeight * genWeight
if not genSelectionVis:
nOfflineNotVis[
channel.channelName] += offlineWeight * genWeight
for variable in allVariablesBins:
if args.debug and variable != 'HT': continue
if args.sample in measurement_config.met_specific_systematics and \
variable in measurement_config.variables_no_met:
continue
offlineWeight_toUse = offlineWeight_withLeptonWeight
if 'abs_lepton_eta' in variable:
offlineWeight_toUse = offlineWeight_withLeptonWeight_forLeptonEta
# # #
# # # Variable to plot
# # #
recoVariable = branch(recoVariableNames[variable])
if variable in ['MET', 'ST', 'WPT'] and \
sysIndex != None and ( offlineSelection or fakeSelection or fakeSelectionVis ) :
recoVariable = recoVariable[sysIndex]
if 'abs' in variable:
recoVariable = abs(recoVariable)
# With TUnfold, reco variable never goes in the overflow (or underflow)
if not args.fineBinned:
recoVariable = min(
recoVariable,
allVariablesBins[variable][-1] - 0.000001)
genVariable_particle = branch(
genVariable_particle_names[variable])
if 'abs' in variable:
genVariable_particle = abs(genVariable_particle)
# #
# # Fill histograms
# #
histogramsToFill = histograms[variable][
channel.channelName]
if not args.donothing:
if genSelection:
histogramsToFill['truth'].Fill(
genVariable_particle, genWeight)
if genSelectionVis:
filledTruth = True
histogramsToFill['truthVis'].Fill(
genVariable_particle, genWeight)
histogramsToFill['truthVis_noWeight'].Fill(
genVariable_particle, 1)
if offlineSelection:
histogramsToFill['measured'].Fill(
recoVariable, offlineWeight_toUse * genWeight)
histogramsToFill['measuredVis'].Fill(
recoVariable, offlineWeight_toUse * genWeight)
if genSelectionVis:
histogramsToFill[
'measuredVis_without_fakes'].Fill(
recoVariable,
offlineWeight_toUse * genWeight)
if genSelection:
histogramsToFill[
'measured_without_fakes'].Fill(
recoVariable,
offlineWeight_toUse * genWeight)
histogramsToFill['response'].Fill(
recoVariable, genVariable_particle,
offlineWeight_toUse * genWeight)
if offlineSelection and genSelection:
histogramsToFill['response_without_fakes'].Fill(
recoVariable, genVariable_particle,
offlineWeight_toUse * genWeight)
histogramsToFill['response_without_fakes'].Fill(
allVariablesBins[variable][0] - 1,
genVariable_particle,
(1 - offlineWeight_toUse) * genWeight)
elif genSelection:
histogramsToFill['response_without_fakes'].Fill(
allVariablesBins[variable][0] - 1,
genVariable_particle, genWeight)
if offlineSelection and genSelectionVis:
histogramsToFill['responseVis_without_fakes'].Fill(
recoVariable, genVariable_particle,
offlineWeight_toUse * genWeight)
histogramsToFill['responseVis_without_fakes'].Fill(
allVariablesBins[variable][0] - 1,
genVariable_particle,
(1 - offlineWeight_toUse) * genWeight)
filledResponse = True
elif genSelectionVis:
histogramsToFill['responseVis_without_fakes'].Fill(
allVariablesBins[variable][0] - 1,
genVariable_particle, genWeight)
filledResponse = True
if fakeSelection:
histogramsToFill['fake'].Fill(
recoVariable, offlineWeight_toUse * genWeight)
if fakeSelectionVis:
histogramsToFill['fakeVis'].Fill(
recoVariable, offlineWeight_toUse * genWeight)
if args.extraHists:
if genSelectionVis:
histogramsToFill['eventWeightHist'].Fill(
event.EventWeight)
histogramsToFill['genWeightHist'].Fill(
genWeight)
histogramsToFill['offlineWeightHist'].Fill(
offlineWeight_toUse)
if args.fineBinned:
# Bin reco var is in
options = residual_options[variable][
channel.channelName]
if offlineSelection and genSelection:
# i will be fine bin number the reco var resides in
for i, edge in enumerate(options['bin_edges']):
if recoVariable > edge: continue
else: break
residual = abs(recoVariable -
genVariable_particle)
if not residual > options['max'] * 0.1:
residuals[variable][
channel.channelName][i].Fill(
residual,
offlineWeight_toUse * genWeight)
#
# Output histgorams to file
#
for variable in allVariablesBins:
if args.debug and variable != 'HT': continue
if args.sample in measurement_config.met_systematics and variable not in [
'MET', 'ST', 'WPT'
]:
continue
for channel in channels:
if nOffline[channel.channelName] != 0:
# Fill phase space info
h = histograms[variable][
channel.channelName]['phaseSpaceInfoHist']
h.SetBinContent(
1, nVisNotOffline[channel.channelName] /
nVis[channel.channelName])
# h.GetXaxis().SetBinLabel(1, "nVisNotOffline/nVis")
h.SetBinContent(
2, nOfflineNotVis[channel.channelName] /
nOffline[channel.channelName])
# h.GetXaxis().SetBinLabel(2, "nOfflineNotVis/nOffline")
h.SetBinContent(
3,
nVis[channel.channelName] / nFull[channel.channelName])
# h.GetXaxis().SetBinLabel(3, "nVis/nFull")
# Selection efficiency for SL ttbar
h.SetBinContent(
4, nOfflineSL[channel.channelName] /
nFull[channel.channelName])
# h.GetXaxis().SetBinLabel(4, "nOfflineSL/nFull")
# Fraction of offline that are SL
h.SetBinContent(
5, nOfflineSL[channel.channelName] /
nOffline[channel.channelName])
# h.GetXaxis().SetBinLabel(5, "nOfflineSL/nOffline")
outputDirs[variable][channel.channelName].cd()
for h in histograms[variable][channel.channelName]:
histograms[variable][channel.channelName][h].Write()
if args.fineBinned:
outputDirsRes[variable][channel.channelName].cd()
for r in residuals[variable][channel.channelName]:
residuals[variable][channel.channelName][r].Write()
print 'N events passing gen selection : ', nPassGenSelection
with root_open(outputFileName, 'update') as out:
# Done all channels, now combine the two channels, and output to the same file
for path, dirs, objects in out.walk():
if 'electron' in path:
if 'coarse' in path: continue
outputDir = out.mkdir(path.replace('electron', 'combined'))
outputDir.cd()
for h in objects:
h_e = out.Get(path + '/' + h)
h_mu = out.Get(path.replace('electron', 'muon') + '/' + h)
h_comb = (h_e + h_mu).Clone(h)
h_comb.Write()
pass
pass
pass
