def create_toy_mc(input_file, sample, output_folder, n_toy, centre_of_mass, ttbar_xsection):
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, sample, n_toy, centre_of_mass)
variable_bins = bin_edges_vis.copy()
with root_open(output_file_name, 'recreate') as f_out:
for channel in ['combined']:
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,
centre_of_mass = centre_of_mass,
ttbar_xsection = ttbar_xsection,
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()
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("--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 = []
# nGeneratorWeights = How many PDF weights do you want to run in 1 job (specified in runJobsCrab.py)
if options.nGeneratorWeights > 1:
for i in range(0, options.nGeneratorWeights):
generatorWeightsToRun.append(options.generatorWeight + i)
# generatorWeights = 1 for PDF Variations
# generatorWeights either 4 or 8 for alpha_s, renormalisation, hadronisation
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.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 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:
# 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 = 0.0
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 == 100000: break
# # #
# # # Weights and selection
# # #
# Pileup weight
# Don't apply if calculating systematic
pileupWeight = event.PUWeight
if options.sample == "pileupSystematic":
pileupWeight = 1
# 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 meWeight >= 0:
genWeight *= branch("genWeight_%i" % meWeight)
offlineWeight *= branch("genWeight_%i" % meWeight)
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 = event.passSelection == 1
elif channel.channelName is "ePlusJets":
offlineSelection = 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
