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
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},