def create_toy_mc(input_file, output_folder, n_toy, centre_of_mass, ttbar_xsection, met_type, start_at=1):
from tools.file_utilities import make_folder_if_not_exists
from tools.toy_mc import generate_toy_MC_from_distribution, generate_toy_MC_from_2Ddistribution
from tools.Unfolding import get_unfold_histogram_tuple
make_folder_if_not_exists(output_folder)
input_file_hists = File(input_file)
output_file_name = get_output_file_name(
output_folder, start_at, n_toy, centre_of_mass)
variable_bins = bin_edges.copy()
with root_open(output_file_name, 'recreate') as f_out:
for channel in ['electron', 'muon']:
for variable in variable_bins:
output_dir = f_out.mkdir(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,
met_type,
centre_of_mass,
ttbar_xsection,
load_fakes=False)
cd()
for i in range(start_at, start_at + n_toy):
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()
def generate_toy(n_toy, n_input_mc, config, output_folder, start_at=0, split=1):
from progressbar import Percentage, Bar, ProgressBar, ETA
set_root_defaults()
genWeight = '( EventWeight * {0})'.format(config.luminosity_scale)
file_name = config.ttbar_category_templates_trees['central']
make_folder_if_not_exists(output_folder)
outfile = get_output_file_name(
output_folder, n_toy, start_at, n_input_mc, config.centre_of_mass_energy)
variable_bins = bin_edges.copy()
widgets = ['Progress: ', Percentage(), ' ', Bar(),
' ', ETA()]
with root_open(file_name, 'read') as f_in, root_open(outfile, 'recreate') as f_out:
tree = f_in.Get("TTbar_plus_X_analysis/Unfolding/Unfolding")
n_events = tree.GetEntries()
print("Number of entries in tree : ", n_events)
for channel in ['electron', 'muon']:
print('Channel :', channel)
gen_selection, gen_selection_vis = '', ''
if channel is 'muon':
gen_selection = '( isSemiLeptonicMuon == 1 )'
gen_selection_vis = '( isSemiLeptonicMuon == 1 && passesGenEventSelection )'
else:
gen_selection = '( isSemiLeptonicElectron == 1 )'
gen_selection_vis = '( isSemiLeptonicElectron == 1 && passesGenEventSelection )'
selection = '( {0} ) * ( {1} )'.format(genWeight, gen_selection)
selection_vis = '( {0} ) * ( {1} )'.format(genWeight,
gen_selection_vis)
weighted_entries = get_weighted_entries(tree, selection)
weighted_entries_vis = get_weighted_entries(tree, selection_vis)
pbar = ProgressBar(widgets=widgets, maxval=n_input_mc).start()
toy_mc_sets = []
for variable in ['MET', 'HT', 'ST', 'WPT']: # variable_bins:
toy_mc = ToySet(f_out, variable, channel, n_toy)
toy_mc_sets.append(toy_mc)
count = 0
for event in tree:
# generate 300 weights for each event
mc_weights = get_mc_weight(weighted_entries, n_toy)
mc_weights_vis = get_mc_weight(weighted_entries_vis, n_toy)
if count >= n_input_mc:
break
count += 1
if count < start_at:
continue
# weight = event.EventWeight * config.luminosity_scale
# # rescale to N input events
# weight *= n_events / n_input_mc / split
weight = 1
for toy_mc in toy_mc_sets:
toy_mc.fill(event, weight, mc_weights, mc_weights_vis)
if count % 1000 == 1:
pbar.update(count)
print('Processed {0} events'.format(count))
pbar.finish()
for toy_mc in toy_mc_sets:
toy_mc.write()
print('Toy MC was saved to file:', outfile)
def main():
parser = OptionParser()
parser.add_option('--topPtReweighting', action='store_true', dest='applyTopPtReweighting', default=False )
parser.add_option('-c', '--centreOfMassEnergy', dest='centreOfMassEnergy', type='int', default=13 )
parser.add_option('--generatorWeight', type='int', dest='generatorWeight', 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."
generatorWeightsToRun = []
if options.nGeneratorWeights > 1 :
for i in range (0, options.nGeneratorWeights):
generatorWeightsToRun.append( options.generatorWeight + i )
elif options.generatorWeight >= 0 :
generatorWeightsToRun.append(options.generatorWeight)
else: generatorWeightsToRun.append( -1 )
# Output file name
outputFileName = 'crap.root'
outputFileDir = 'unfolding/%sTeV/' % options.centreOfMassEnergy
make_folder_if_not_exists(outputFileDir)
energySuffix = '%sTeV' % ( options.centreOfMassEnergy )
for meWeight in generatorWeightsToRun :
if options.applyTopPtReweighting:
outputFileName = outputFileDir+'/unfolding_TTJets_%s_asymmetric_withTopPtReweighting.root' % energySuffix
elif meWeight == 4:
outputFileName = outputFileDir+'/unfolding_TTJets_%s_asymmetric_scaleUpWeight.root' % ( energySuffix )
elif meWeight == 8:
outputFileName = outputFileDir+'/unfolding_TTJets_%s_asymmetric_scaleDownWeight.root' % ( energySuffix )
elif meWeight >= 9 and meWeight <= 108:
outputFileName = outputFileDir+'/unfolding_TTJets_%s_asymmetric_generatorWeight_%i.root' % ( energySuffix, meWeight )
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:
for channel in channels:
if options.debug and channel.channelName != 'muPlusJets' : continue
print 'Channel : ',channel.channelName
# 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()
print "Number of entries in tree : ", nEntries
if meWeight >= 0 :
tree.AddFriend('TTbar_plus_X_analysis/Unfolding/GeneratorSystematicWeights')
tree.SetBranchStatus('genWeight_*',0)
tree.SetBranchStatus('genWeight_%i' % meWeight, 1)
# Keep record of generator weight
if meWeight >= 0:
generatorWeight = '( genWeight_%i )' % meWeight
generatorWeightHist = Hist( 50, 0.4, 1.6, name='generatorWeights_'+channel.channelName )
if not options.donothing:
tree.Draw( generatorWeight, hist=generatorWeightHist)
outputDir = 0
if not ( out.FindObject('generatorWeights') ):
outputDir = out.mkdir('generatorWeights')
else :
outputDir = out.Get('generatorWeights')
outputDir.cd()
generatorWeightHist.Write()
pass
# For variables where you want bins to be symmetric about 0, use abs(variable) (but also make plots for signed variable)
allVariablesBins = bin_edges.copy()
for variable in bin_edges:
if 'Rap' in variable:
allVariablesBins['abs_%s' % variable] = [0,bin_edges[variable][-1]]
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
print '--->Doing variable :',variable
# Output dir name
metSuffix='_patType1CorrectedPFMet'
if variable is 'HT':
metSuffix=''
pass
# Make dir in output file
outputDir = out.mkdir('unfolding_'+variable+'_analyser_'+channel.outputDirName+'_channel'+metSuffix)
#
# Variable names
#
recoVariable = branchNames[variable]
if variable in ['MET', 'ST', 'WPT']:
if options.sample == "jesup":
recoVariable += '_METUncertainties[2]'
elif options.sample == "jesdown":
recoVariable += '_METUncertainties[3]'
elif options.sample == "jerup":
recoVariable += '_METUncertainties[0]'
elif options.sample == "jerdown":
recoVariable += '_METUncertainties[1]'
elif options.sample in measurement_config.met_systematics:
recoVariable += '_METUncertainties[%i]' % measurement_config.met_systematics[options.sample]
genVariable_particle = genBranchNames_particle[variable]
genVariable_parton = None
if variable in genBranchNames_parton:
genVariable_parton = genBranchNames_parton[variable]
#
# Weights and selection
#
pileupWeight = "PUWeight"
if options.sample == "pileupSystematic":
pileupWeight = "1"
# Generator level
genWeight = '( EventWeight * %.4f)' % ( measurement_config.luminosity_scale)
# genWeight = '( unfolding.puWeight )'
genSelection = ''
genSelectionVis = ''
if channel.channelName is 'muPlusJets' :
genSelection = '( isSemiLeptonicMuon == 1 )'
genSelectionVis = '( isSemiLeptonicMuon == 1 && passesGenEventSelection )'
elif channel.channelName is 'ePlusJets' :
genSelection = '( isSemiLeptonicElectron == 1 )'
genSelectionVis = '( isSemiLeptonicElectron == 1 && passesGenEventSelection )'
# Offline level
# offlineWeight = '( unfolding.bTagWeight * unfolding.puWeight )'
leptonWeight = 'LeptonEfficiencyCorrection'
if options.sample == 'leptonup':
leptonWeight = 'LeptonEfficiencyCorrectionUp'
elif options.sample == 'leptondown':
leptonWeight == 'LeptonEfficiencyCorrectionDown'
bjetWeight = "BJetWeight"
if options.sample == "bjetup":
bjetWeight = "BJetUpWeight"
elif options.sample == "bjetdown":
bjetWeight = "BJetDownWeight"
# lightjetWeight = "LightJetWeight"
# if options.sample == "lightjetup":
# lightjetWeight = "LightJetUpWeight"
# elif options.sample == "lightjetdown":
# lightjetWeight = "LightJetDownWeight"
# offlineWeight = '( EventWeight * %s * %s * %s * %s * %.4f)' % ( pileupWeight, bjetWeight, lightjetWeight, leptonWeight, measurement_config.luminosity_scale )
offlineWeight = '( EventWeight * %s * %s * %s * %.4f)' % ( pileupWeight, bjetWeight, leptonWeight, measurement_config.luminosity_scale )
offlineSelection = ''
if channel.channelName is 'muPlusJets' :
offlineSelection = '( passSelection == 1 )'
elif channel.channelName is 'ePlusJets' :
offlineSelection = '( passSelection == 2 )'
# Fake selection
fakeSelection = '( ' + offlineSelection+"&&!"+genSelection +' ) '
fakeSelectionVis = '( ' + offlineSelection+"&&!"+genSelectionVis +' ) '
# Weights derived from variables in tree
if options.applyTopPtReweighting:
ptWeight = topPtWeight( int(options.centreOfMassEnergy) )
offlineWeight += ' * '+ptWeight
genWeight += ' * '+ptWeight
pass
# Apply generator weight
if meWeight >= 0:
genWeight = '( EventWeight * %.4f * genWeight_%s )' % (measurement_config.luminosity_scale, meWeight)
offlineWeight = '( EventWeight * %s * %s * %s * %.4f * genWeight_%s )' % ( pileupWeight, bjetWeight, leptonWeight, measurement_config.luminosity_scale, meWeight )
nEntries = 1000000
print 'Changing nEntries to ',nEntries, "<---- DOES NOT SEEM TO DO ANYTHING"
pass
# Scale factors
# scaleFactor = getScaleFactor( options.centreOfMassEnergy, channel.channelName )
# scaleFactor = '( unfolding.leptonWeight )'
# offlineWeight += ' * '+scaleFactor
#
# Histograms to fill
#
# 1D histograms
truth = Hist( allVariablesBins[variable], name='truth')
truthVis = Hist( bin_edges_vis[variable], name='truthVis')
truth_parton = Hist( allVariablesBins[variable], name='truth_parton')
measured = Hist( allVariablesBins[variable], name='measured')
measuredVis = Hist( bin_edges_vis[variable], name='measuredVis')
fake = Hist( allVariablesBins[variable], name='fake')
fakeVis = Hist( allVariablesBins[variable], name='fakeVis')
# 2D histograms
response = Hist2D( allVariablesBins[variable], allVariablesBins[variable], name='response')
response_without_fakes = Hist2D( allVariablesBins[variable], allVariablesBins[variable], name='response_without_fakes')
response_only_fakes = Hist2D( allVariablesBins[variable], allVariablesBins[variable], name='response_only_fakes')
responseVis_without_fakes = Hist2D( bin_edges_vis[variable], bin_edges_vis[variable], name='responseVis_without_fakes')
responseVis_only_fakes = Hist2D( bin_edges_vis[variable], bin_edges_vis[variable], name='responseVis_only_fakes')
response_parton = Hist2D( allVariablesBins[variable], allVariablesBins[variable], name='response_parton')
response_without_fakes_parton = Hist2D( allVariablesBins[variable], allVariablesBins[variable], name='response_without_fakes_parton')
response_only_fakes_parton = Hist2D( allVariablesBins[variable], allVariablesBins[variable], name='response_only_fakes_parton')
if options.fineBinned:
minVar = trunc( allVariablesBins[variable][0] )
maxVar = trunc( max( tree.GetMaximum(genVariable_particle), tree.GetMaximum( recoVariable ) ) * 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 = -0.5
nBins = 21
truth = Hist( nBins, minVar, maxVar, name='truth')
truthVis = Hist( nBins, minVar, maxVar, name='truthVis')
truth_parton = Hist( nBins, minVar, maxVar, name='truth_parton')
measured = Hist( nBins, minVar, maxVar, name='measured')
measuredVis = Hist( nBins, minVar, maxVar, name='measuredVis')
fake = Hist( nBins, minVar, maxVar, name='fake')
fakeVis = Hist( nBins, minVar, maxVar, name='fakeVis')
response = Hist2D( nBins, minVar, maxVar, nBins, minVar, maxVar, name='response')
response_without_fakes = Hist2D( nBins, minVar, maxVar, nBins, minVar, maxVar, name='response_without_fakes')
response_only_fakes = Hist2D( nBins, minVar, maxVar, nBins, minVar, maxVar, name='response_only_fakes')
responseVis_without_fakes = Hist2D( nBins, minVar, maxVar, nBins, minVar, maxVar, name='responseVis_without_fakes')
responseVis_only_fakes = Hist2D( nBins, minVar, maxVar, nBins, minVar, maxVar, name='responseVis_only_fakes')
response_parton = Hist2D( nBins, minVar, maxVar, nBins, minVar, maxVar, name='response_parton')
response_without_fakes_parton = Hist2D( nBins, minVar, maxVar, nBins, minVar, maxVar, name='response_without_fakes_parton')
response_only_fakes_parton = Hist2D( nBins, minVar, maxVar, nBins, minVar, maxVar, name='response_only_fakes_parton')
# Some interesting histograms
puOffline = Hist( 20, 0, 2, name='puWeights_offline')
eventWeightHist = Hist( 100, -2, 2, name='eventWeightHist')
genWeightHist = Hist( 100, -2, 2, name='genWeightHist')
offlineWeightHist = Hist( 100, -2, 2, name='offlineWeightHist')
phaseSpaceInfoHist = Hist( 10, 0, 1, name='phaseSpaceInfoHist')
if not options.donothing:
nVis = ( tree.Draw( '1', genWeight+'*'+genSelectionVis ) ).Integral()
nVisNotOffline = ( tree.Draw( '1', genWeight+'* ( '+genSelectionVis+'&& !'+offlineSelection+')' ) ).Integral()
nOffline = ( tree.Draw( '1', offlineWeight+'*'+offlineSelection ) ).Integral()
nOfflineNotVis = ( tree.Draw( '1', offlineWeight+'* ( '+offlineSelection+'&& !'+genSelectionVis+')' ) ).Integral()
nFull = ( tree.Draw( '1', genWeight+'*'+genSelection ) ).Integral()
phaseSpaceInfoHist.SetBinContent(1, nVisNotOffline / nVis)
phaseSpaceInfoHist.SetBinContent(2, nOfflineNotVis / nOffline)
phaseSpaceInfoHist.SetBinContent(3, nVis / nFull)
nOfflineSL = ( tree.Draw( '1', offlineWeight+'* ( '+offlineSelection+'&& '+genSelection+')' ) ).Integral()
nSL = ( tree.Draw( '1', offlineWeight+'* ( '+genSelection+')' ) ).Integral()
# Selection efficiency for SL ttbar
phaseSpaceInfoHist.SetBinContent(4, nOfflineSL / nSL)
# Fraction of offline that are SL
phaseSpaceInfoHist.SetBinContent(5, nOfflineSL / nOffline)
#
# Fill histograms
#
if not options.donothing:
# 1D
tree.Draw(genVariable_particle,genWeight+'*'+genSelection,hist=truth, nentries=nEntries)
tree.Draw(genVariable_particle,genWeight+'*'+genSelectionVis,hist=truthVis, nentries=nEntries)
# if genVariable_parton != None:
# tree.Draw(genVariable_parton,genWeight+'*'+genSelection,hist=truth_parton, nentries=nEntries)
tree.Draw(recoVariable,offlineWeight+'*'+offlineSelection,hist=measured, nentries=nEntries)
tree.Draw(recoVariable,offlineWeight+'*'+offlineSelection,hist=measuredVis, nentries=nEntries)
tree.Draw(recoVariable,offlineWeight+'*'+fakeSelection,hist=fake, nentries=nEntries)
tree.Draw(recoVariable,offlineWeight+'*'+fakeSelectionVis,hist=fakeVis, nentries=nEntries)
# 2D
tree.Draw(recoVariable+':'+genVariable_particle,offlineWeight+'*'+offlineSelection,hist=response, nentries=nEntries)
tree.Draw(recoVariable+':'+genVariable_particle,offlineWeight+'* ('+offlineSelection+'&&'+genSelection +')',hist=response_without_fakes, nentries=nEntries)
tree.Draw(recoVariable+':'+genVariable_particle,offlineWeight+'*'+fakeSelection,hist=response_only_fakes, nentries=nEntries)
tree.Draw(recoVariable+':'+genVariable_particle,offlineWeight+'* ('+offlineSelection+'&&'+genSelectionVis +')',hist=responseVis_without_fakes, nentries=nEntries)
tree.Draw(recoVariable+':'+genVariable_particle,offlineWeight+'*'+fakeSelectionVis,hist=responseVis_only_fakes, nentries=nEntries)
# if genVariable_parton != None:
# tree.Draw(recoVariable+':'+genVariable_parton,offlineWeight+'*'+offlineSelection,hist=response_parton)
# tree.Draw(recoVariable+':'+genVariable_parton,offlineWeight+'* ('+offlineSelection+'&&'+genSelection +')',hist=response_without_fakes_parton, nentries=nEntries)
# tree.Draw(recoVariable+':'+genVariable_parton,offlineWeight+'*'+fakeSelection,hist=response_only_fakes_parton, nentries=nEntries)
if options.extraHists:
tree.Draw('EventWeight',genSelection,hist=eventWeightHist)
tree.Draw(genWeight,genSelection,hist=genWeightHist)
tree.Draw( offlineWeight,offlineSelection,hist=offlineWeightHist)
pass
#
# Output histgorams to file
#
outputDir.cd()
truth.Write()
truthVis.Write()
truth_parton.Write()
measured.Write()
measuredVis.Write()
fake.Write()
fakeVis.Write()
response.Write()
response_without_fakes.Write()
response_only_fakes.Write()
response_parton.Write()
response_without_fakes_parton.Write()
response_only_fakes_parton.Write()
responseVis_without_fakes.Write()
responseVis_only_fakes.Write()
phaseSpaceInfoHist.Write()
genWeightHist.Write()
offlineWeightHist.Write()
eventWeightHist.Write()
if options.extraHists:
puOffline.Write()
pass
pass
pass
pass
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
