def doPerformanceStudyOnMCOnly(inputFiles,
histogramForEstimation=defaultHistogram,
function='expo',
fitRanges=[(0.2, 1.6), (0.3, 1.6), (0.4, 1.6)]):
if DEBUG:
print '*' * 120
print "Estimating QCD using a fit to RelIso"
print 'Histogram = ', histogramForEstimation
print 'Fit functions = ', function
print 'Fit ranges = ', fitRanges
print '*' * 120
#get histograms
histograms = FileReader.getHistogramDictionary(histogramForEstimation,
inputFiles)
global allMC, qcd
histograms['SumMC'] = plotting.sumSamples(histograms, allMC)
histograms['QCD'] = plotting.sumSamples(histograms, qcd)
# qcdInSignalRegion = histograms['QCD'].Integral()
# qcdError = 0
# if not qcdInSignalRegion == 0:
# qcdError = qcdInSignalRegion / sqrt(qcdInSignalRegion)
import copy
results = {}
qcdInSignalRegion, qcdError = getIntegral(histograms['QCD'], (0, 0.1))
# getRelIsoCalibrationCurve(inputFiles, histogramForEstimation, function, fitRanges)
for fitRange in fitRanges:
#take all other fit ranges as systematics
fitRangesForSystematics = copy.deepcopy(fitRanges)
fitRangesForSystematics.remove(fitRange)
#instead of data use sum MC
resultFromMethod = relIsoMethodWithSystematics(
histograms['SumMC'], function, fitRange, fitRangesForSystematics,
False)
estimate, absoluteError = resultFromMethod[
'estimate'], resultFromMethod['absoluteError']
N_est = ufloat((estimate, absoluteError))
N_qcd = ufloat((qcdInSignalRegion, qcdError))
relativeDeviation = N_est / N_qcd
result = {}
result['performance'] = (relativeDeviation.nominal_value,
relativeDeviation.std_dev())
result['estimate'] = (estimate, absoluteError)
result['qcdInSignalRegion'] = (qcdInSignalRegion, qcdError)
result['fitfunction'] = function
result['fitRange'] = fitRange
result['fitRangesForSystematics'] = fitRangesForSystematics
result['fit'] = resultFromMethod['fit']
results[str(fitRange)] = result
return results
def doPerformanceStudyOnMCOnly(inputFiles,
histogramForEstimation=defaultHistogram,
function='expo',
fitRanges=[(0.2, 1.6), (0.3, 1.6), (0.4, 1.6)]):
if DEBUG:
print '*' * 120
print "Estimating QCD using a fit to RelIso"
print 'Histogram = ', histogramForEstimation
print 'Fit functions = ', function
print 'Fit ranges = ', fitRanges
print '*' * 120
#get histograms
histograms = FileReader.getHistogramDictionary(histogramForEstimation, inputFiles)
global allMC, qcd
histograms['SumMC'] = plotting.sumSamples(histograms, allMC)
histograms['QCD'] = plotting.sumSamples(histograms, qcd)
# qcdInSignalRegion = histograms['QCD'].Integral()
# qcdError = 0
# if not qcdInSignalRegion == 0:
# qcdError = qcdInSignalRegion / sqrt(qcdInSignalRegion)
import copy
results = {}
qcdInSignalRegion, qcdError = getIntegral(histograms['QCD'], (0, 0.1))
# getRelIsoCalibrationCurve(inputFiles, histogramForEstimation, function, fitRanges)
for fitRange in fitRanges:
#take all other fit ranges as systematics
fitRangesForSystematics = copy.deepcopy(fitRanges)
fitRangesForSystematics.remove(fitRange)
#instead of data use sum MC
resultFromMethod = relIsoMethodWithSystematics(histograms['SumMC'], function, fitRange, fitRangesForSystematics, False)
estimate, absoluteError = resultFromMethod['estimate'], resultFromMethod['absoluteError']
N_est = ufloat((estimate, absoluteError))
N_qcd = ufloat((qcdInSignalRegion, qcdError))
relativeDeviation = N_est / N_qcd
result = {}
result['performance'] = (relativeDeviation.nominal_value, relativeDeviation.std_dev())
result['estimate'] = (estimate, absoluteError)
result['qcdInSignalRegion'] = (qcdInSignalRegion, qcdError)
result['fitfunction'] = function
result['fitRange'] = fitRange
result['fitRangesForSystematics'] = fitRangesForSystematics
result['fit'] = resultFromMethod['fit']
results[str(fitRange)] = result
return results
def getStuff(histogramForEstimation, inputFiles):
histograms = FileReader.getHistogramDictionary(histogramForEstimation, inputFiles)
global allMC, qcd
histograms['SumMC'] = plotting.sumSamples(histograms, allMC)
histograms['QCD'] = plotting.sumSamples(histograms, qcd)
qcdInSignalRegion, qcdError = getIntegral(histograms['QCD'], (0, 0.1))
data, dataError = getIntegral(histograms['SingleElectron'], (0, 0.1))
sumMC, sumMCError = getIntegral(histograms['SumMC'], (0, 0.1))
result = {
'N_data': data,
'N_QCD': qcdInSignalRegion,
'N_QCD_Error': qcdError,
'N_SumMC': sumMC
}
return result
def getStuff(histogramForEstimation, inputFiles):
histograms = FileReader.getHistogramDictionary(histogramForEstimation,
inputFiles)
global allMC, qcd
histograms['SumMC'] = plotting.sumSamples(histograms, allMC)
histograms['QCD'] = plotting.sumSamples(histograms, qcd)
qcdInSignalRegion, qcdError = getIntegral(histograms['QCD'], (0, 0.1))
data, dataError = getIntegral(histograms['SingleElectron'], (0, 0.1))
sumMC, sumMCError = getIntegral(histograms['SumMC'], (0, 0.1))
result = {
'N_data': data,
'N_QCD': qcdInSignalRegion,
'N_QCD_Error': qcdError,
'N_SumMC': sumMC
}
return result
hist_type0.Draw()
hist_sysshift.Draw('same')
hist_sysshift_type0.Draw('same')
hist_nominal.Draw('same')
if variable == 'MET':
hist_nominal.SetAxisRange(0, 300, "X")
hist_sysshift.SetAxisRange(0, 300, "X")
hist_type0.SetAxisRange(0, 300, "X")
hist_sysshift_type0.SetAxisRange(0, 300, "X")
hist_type0.Draw()
hist_sysshift.Draw('same')
hist_sysshift_type0.Draw('same')
hist_nominal.Draw('same')
legend = plotting.create_legend(x0=0.72,
y0=0.90,
x1=0.84,
y1=0.75)
legend.SetTextSize(0.03)
legend.AddEntry(hist_nominal, 't#bar{t} nominal', 'l')
legend.AddEntry(hist_sysshift, 't#bar{t} sys_shift', 'l')
legend.AddEntry(hist_type0, 't#bar{t} type0', 'l')
legend.AddEntry(hist_sysshift_type0,
't#bar{t} sys_shift+type0', 'l')
legend.Draw()
canvas.SaveAs(output_path + met + "_" + variable + "_" +
bjet_bin + ".pdf")
def printCutFlow(hist, analysis, outputFormat='Latex'):
scale_ttbar = 164.4 / 157.5
used_data = 'ElectronHad'
lepton = 'Electron/electron'
if 'Mu' in analysis:
used_data = 'SingleMu'
lepton = 'Muon/muon'
hist_1mBtag = 'TTbarPlusMetAnalysis/' + analysis + '/Ref selection/' + lepton + '_AbsEta_1orMoreBtag'
hist_2mBtag = 'TTbarPlusMetAnalysis/' + analysis + '/Ref selection/' + lepton + '_AbsEta_2orMoreBtags'
hist_names = [
hist, #due to b-tag scale factors these are not as simple any more
hist_1mBtag,
hist_2mBtag
]
inputfiles = {}
for sample in FILES.samplesToLoad:
inputfiles[sample] = FILES.files[sample]
hists = FileReader.getHistogramsFromFiles(hist_names, inputfiles)
for sample in hists.keys():
for histname in hists[sample].keys():
hists[sample][histname].Sumw2()
if analysis == 'EPlusJets':
hists['QCD'] = plotting.sumSamples(hists, plotting.qcd_samples)
else:
hists['QCD'] = hists['QCD_Pt-20_MuEnrichedPt-15']
hists['SingleTop'] = plotting.sumSamples(hists, plotting.singleTop_samples)
hists['Di-Boson'] = plotting.sumSamples(hists, plotting.diboson_samples)
hists['W+Jets'] = plotting.sumSamples(hists, plotting.wplusjets_samples)
# hists['SumMC'] = plotting.sumSamples(hists, plotting.allMC_samples)
header = "| Step | TTJet | W+jets | DY + Jets | single top | Di-boson | QCD | Sum MC | Data |"
row = " | %s | %d +- %d | %d +- %d | %d +- %d | %d +- %d | %d +- %d | %d +- %d | %d +- %d | %d | "
if outputFormat == 'Latex':
header = "Selection step & \\ttbar & W + Jets & Z + Jets & Single-top & Di-boson & QCD~ & Sum MC & Data\\\\"
row = " %s & $%d \pm %d$ & $%d \pm %d$ & $%d \pm %d$ & $%d \pm %d$ & $%d \pm %d$ & $%d \pm %d$ & $%d \pm %d$ & %d \\\\ "
print header
numbers, errors = getEventNumbers(hists, hist, hist_1mBtag,
hist_2mBtag) # + '_0orMoreBtag')
for step in range(len(cuts)):
nums = numbers[step]
errs = errors[step]
nums['TTJet'] = nums['TTJet'] * scale_ttbar
errs['TTJet'] = errs['TTJet'] * scale_ttbar
if analysis == 'EPlusJets' and step >= len(
cuts) - 3: #have only estimates for >= 4 jet and beyond
histForEstimation = 'TTbarPlusMetAnalysis/EPlusJets/QCD e+jets PFRelIso/Electron/electron_pfIsolation_03_0orMoreBtag'
if step == len(cuts) - 2:
histForEstimation = 'TTbarPlusMetAnalysis/EPlusJets/QCD e+jets PFRelIso/Electron/electron_pfIsolation_03_1orMoreBtag'
if step == len(cuts) - 1:
histForEstimation = 'TTbarPlusMetAnalysis/EPlusJets/QCD e+jets PFRelIso/Electron/electron_pfIsolation_03_2orMoreBtags'
estimate = QCDRateEstimation.estimateQCDWithRelIso(
FILES.files, histForEstimation)
nums['QCD'], errs['QCD'] = estimate['estimate'], estimate[
'absoluteError']
if analysis == 'MuPlusJets' and step >= len(
cuts) - 3: #have only estimates for >= 4 jet and beyond
scale = 1.21
nums['QCD'], errs['QCD'] = nums['QCD'] * scale, errs['QCD'] * scale
sumMC = nums['TTJet'] + nums['W+Jets'] + nums['DYJetsToLL'] + nums[
'SingleTop'] + nums['QCD'] + nums['Di-Boson']
sumMC_err = sqrt(errs['TTJet']**2 + errs['W+Jets']**2 +
errs['DYJetsToLL']**2 + errs['SingleTop']**2 +
errs['QCD']**2 + errs['Di-Boson']**2)
print row % (cuts[step], nums['TTJet'], errs['TTJet'], nums['W+Jets'],
errs['W+Jets'], nums['DYJetsToLL'], errs['DYJetsToLL'],
nums['SingleTop'], errs['SingleTop'], nums['Di-Boson'],
errs['Di-Boson'], nums['QCD'], errs['QCD'], sumMC,
sumMC_err, nums[used_data])
def printCutFlow(hist, analysis, outputFormat="Latex"):
scale_ttbar = 164.4 / 157.5
used_data = "ElectronHad"
lepton = "Electron/electron"
if "Mu" in analysis:
used_data = "SingleMu"
lepton = "Muon/muon"
hist_1mBtag = "TTbarPlusMetAnalysis/" + analysis + "/Ref selection/" + lepton + "_AbsEta_1orMoreBtag"
hist_2mBtag = "TTbarPlusMetAnalysis/" + analysis + "/Ref selection/" + lepton + "_AbsEta_2orMoreBtags"
hist_names = [hist, hist_1mBtag, hist_2mBtag] # due to b-tag scale factors these are not as simple any more
inputfiles = {}
for sample in FILES.samplesToLoad:
inputfiles[sample] = FILES.files[sample]
hists = FileReader.getHistogramsFromFiles(hist_names, inputfiles)
for sample in hists.keys():
for histname in hists[sample].keys():
hists[sample][histname].Sumw2()
if analysis == "EPlusJets":
hists["QCD"] = plotting.sumSamples(hists, plotting.qcd_samples)
else:
hists["QCD"] = hists["QCD_Pt-20_MuEnrichedPt-15"]
hists["SingleTop"] = plotting.sumSamples(hists, plotting.singleTop_samples)
hists["Di-Boson"] = plotting.sumSamples(hists, plotting.diboson_samples)
hists["W+Jets"] = plotting.sumSamples(hists, plotting.wplusjets_samples)
# hists['SumMC'] = plotting.sumSamples(hists, plotting.allMC_samples)
header = "| Step | TTJet | W+jets | DY + Jets | single top | Di-boson | QCD | Sum MC | Data |"
row = " | %s | %d +- %d | %d +- %d | %d +- %d | %d +- %d | %d +- %d | %d +- %d | %d +- %d | %d | "
if outputFormat == "Latex":
header = "Selection step & \\ttbar & W + Jets & Z + Jets & Single-top & Di-boson & QCD~ & Sum MC & Data\\\\"
row = " %s & $%d \pm %d$ & $%d \pm %d$ & $%d \pm %d$ & $%d \pm %d$ & $%d \pm %d$ & $%d \pm %d$ & $%d \pm %d$ & %d \\\\ "
print header
numbers, errors = getEventNumbers(hists, hist, hist_1mBtag, hist_2mBtag) # + '_0orMoreBtag')
for step in range(len(cuts)):
nums = numbers[step]
errs = errors[step]
nums["TTJet"] = nums["TTJet"] * scale_ttbar
errs["TTJet"] = errs["TTJet"] * scale_ttbar
if analysis == "EPlusJets" and step >= len(cuts) - 3: # have only estimates for >= 4 jet and beyond
histForEstimation = (
"TTbarPlusMetAnalysis/EPlusJets/QCD e+jets PFRelIso/Electron/electron_pfIsolation_03_0orMoreBtag"
)
if step == len(cuts) - 2:
histForEstimation = (
"TTbarPlusMetAnalysis/EPlusJets/QCD e+jets PFRelIso/Electron/electron_pfIsolation_03_1orMoreBtag"
)
if step == len(cuts) - 1:
histForEstimation = (
"TTbarPlusMetAnalysis/EPlusJets/QCD e+jets PFRelIso/Electron/electron_pfIsolation_03_2orMoreBtags"
)
estimate = QCDRateEstimation.estimateQCDWithRelIso(FILES.files, histForEstimation)
nums["QCD"], errs["QCD"] = estimate["estimate"], estimate["absoluteError"]
if analysis == "MuPlusJets" and step >= len(cuts) - 3: # have only estimates for >= 4 jet and beyond
scale = 1.21
nums["QCD"], errs["QCD"] = nums["QCD"] * scale, errs["QCD"] * scale
sumMC = nums["TTJet"] + nums["W+Jets"] + nums["DYJetsToLL"] + nums["SingleTop"] + nums["QCD"] + nums["Di-Boson"]
sumMC_err = sqrt(
errs["TTJet"] ** 2
+ errs["W+Jets"] ** 2
+ errs["DYJetsToLL"] ** 2
+ errs["SingleTop"] ** 2
+ errs["QCD"] ** 2
+ errs["Di-Boson"] ** 2
)
print row % (
cuts[step],
nums["TTJet"],
errs["TTJet"],
nums["W+Jets"],
errs["W+Jets"],
nums["DYJetsToLL"],
errs["DYJetsToLL"],
nums["SingleTop"],
errs["SingleTop"],
nums["Di-Boson"],
errs["Di-Boson"],
nums["QCD"],
errs["QCD"],
sumMC,
sumMC_err,
nums[used_data],
)
"electron_dPhi_in": 'Events/(0.01)',
"electron_dEta_in": 'Events/(0.001)',
"electron_HadOverEM": 'Events/(0.01)',
"electron_mvaTrigV0": 'Events/(0.05)',
"electron_mvaNonTrigV0": 'Events/(0.05)',
"electron_dB": 'Events/(0.001 cm)',
'electron_sigma_ietaieta': 'Events/(0.001)'
}
histograms = [
'HLTQCDAnalyser_inclusive/' + trigger + '/' + variable
for variable in variables for trigger in triggers
]
hists = FileReader.getHistogramsFromFiles(histograms, files)
plotting.setStyle()
for variable in variables:
hists = plotting.rebin(hists, rebins[variable], '*' + variable)
hists = plotting.setXRange(hists,
limits=limits[variable],
histname='*' + variable)
hists = plotting.setYTitle(hists,
title=titles[variable],
histname='*' + variable)
labels = [
'CaloIdVT_CaloIsoT_TrkIdT_TrkIsoT', 'CaloIdVT_CaloIsoVL_TrkIdVL_TrkIsoT',
'CaloIdVL_CaloIsoT_TrkIdVL_TrkIsoT', 'CaloIdVT_TrkIdT'
]
styles = [
hist_madgraph.Sumw2()
hist_mcatnlo.Sumw2()
hist_powheg.Sumw2()
hist_pythia.Sumw2()
hist_madgraph.Scale(1/hist_madgraph.Integral())
hist_mcatnlo.Scale(1/hist_mcatnlo.Integral())
hist_powheg.Scale(1/hist_powheg.Integral())
hist_pythia.Scale(1/hist_pythia.Integral())
hist_madgraph.Draw("E1")
hist_mcatnlo.Draw('E1 same')
hist_powheg.Draw('E1 same')
hist_pythia.Draw('E1 same')
if variable == 'MET_phi':
legend = plotting.create_legend(x0=0.72, y0 = 0.90, x1=0.84, y1=0.80)
elif variable == 'deltaPhi_2bjets':
legend = plotting.create_legend(x0=0.42, y0 = 0.90, x1=0.54, y1=0.80)
else:
legend = plotting.create_legend(x0=0.72, y0 = 0.90, x1=0.84, y1=0.75)
legend.SetTextSize(0.03)
legend.AddEntry(hist_madgraph, 't#bar{t} (MADGRAPH)', 'l')
legend.AddEntry(hist_mcatnlo, 't#bar{t} (MC@NLO)', 'l')
legend.AddEntry(hist_powheg, 't#bar{t} (POWHEG)', 'l')
legend.AddEntry(hist_pythia, 't#bar{t} (PYTHIA6)', 'l')
legend.Draw()
canvas.SaveAs(output_path+variable + ".pdf")
"electron_pfIsolation_03_0orMoreBtag": "Events/(0.05)",
"electron_pfIsolation_04_0orMoreBtag": "Events/(0.05)",
"electron_pfIsolation_05_0orMoreBtag": "Events/(0.05)",
"electron_dPhi_in": "Events/(0.01)",
"electron_dEta_in": "Events/(0.001)",
"electron_HadOverEM": "Events/(0.01)",
"electron_mvaTrigV0": "Events/(0.05)",
"electron_mvaNonTrigV0": "Events/(0.05)",
"electron_dB": "Events/(0.001 cm)",
"electron_sigma_ietaieta": "Events/(0.001)",
}
histograms = ["HLTQCDAnalyser_inclusive/" + trigger + "/" + variable for variable in variables for trigger in triggers]
hists = FileReader.getHistogramsFromFiles(histograms, files)
plotting.setStyle()
for variable in variables:
hists = plotting.rebin(hists, rebins[variable], "*" + variable)
hists = plotting.setXRange(hists, limits=limits[variable], histname="*" + variable)
hists = plotting.setYTitle(hists, title=titles[variable], histname="*" + variable)
labels = [
"CaloIdVT_CaloIsoT_TrkIdT_TrkIsoT",
"CaloIdVT_CaloIsoVL_TrkIdVL_TrkIsoT",
"CaloIdVL_CaloIsoT_TrkIdVL_TrkIsoT",
"CaloIdVT_TrkIdT",
]
styles = [
{"color": kBlack, "fill": 1001},
{"color": kRed, "fill": 3004},
