def calculateEfficiency(deltaR=-1, eThr=-1): nMatches = 0 nTotal = 0 eventCounter = 0 print 'Efficiency analysis:' for event in file.dataTree: #Tell us about the progress eventCounter += 1 if (eventCounter % 1000 == 0): sys.stdout.write('\rprocessing event %7d ==> %6.2f%% done.' % (eventCounter, eventCounter / float(tree.GetEntriesFast()) * 100)) sys.stdout.flush() l1DataVector = event.l1MuonData genDataVector = event.genMuonData for genObject in genDataVector: l1Match = findBestL1Match(genObject, l1DataVector, 0.3) if l1Match != None: nTotal += 1 hoMatch = findBestHoMatch(l1Match, event.hoRecHitData, deltaR, eThr) if (hoMatch != None): nMatches += 1 print print 'Done.' return nMatches, nTotal, eventCounter
def analyze(deltaR=-1, eThr=-1): deltaTimes = [] eventCounter = 0 header = '| Delta R = %4.3f. E Thr = %4.3fGeV |' % (deltaR, eThr) print len(header) * '-' print header print len(header) * '-' for event in file.dataTree: #Tell us about the progress eventCounter += 1 if (eventCounter % 1000 == 0): sys.stdout.write('\rprocessing event %7d ==> %6.2f%% done.' % (eventCounter, eventCounter / float(tree.GetEntriesFast()) * 100)) sys.stdout.flush() l1DataVector = event.l1MuonData for l1Object in l1DataVector: hoMatch = findBestHoMatch(l1Object, event.hoRecHitData, deltaR, eThr) if (hoMatch != None): deltaTimes.append(l1Object.bx * 25. - hoMatch.time) print histDeltaTime = getTH1D("histDeltaTimes", "#DeltaTime;E_{Thr} / GeV;#DeltaTime / ns", 200, -100, 100) for deltaTime in deltaTimes: histDeltaTime.Fill(deltaTime) canvas = TCanvas('canvasDeltaTimesEThr', 'Delta Time vs. E_{Thr}', 1200, 1200) histDeltaTime.SetStats(0) histDeltaTime.Draw() canvas.Update() print 'Done.' filenameTrunk = 'results/plots/analyzeFull-DeltaR%01d_%03d-EThr%01d_%03d_%s' % ( int(deltaR), int(deltaR * 1000), int(eThr), int( eThr * 1000), options.instance) canvas.SaveAs(filenameTrunk + '.png') canvas.SaveAs(filenameTrunk + '.pdf') canvas.SaveAs(filenameTrunk + '.root') return deltaTimes, deltaR, eThr
def analyze(deltaR = -1, eThr = -1): deltaTimes = [] eventCounter = 0 header = '| Delta R = %4.3f. E Thr = %4.3fGeV |' % (deltaR,eThr) print len(header)*'-' print header print len(header)*'-' for event in file.dataTree: #Tell us about the progress eventCounter += 1 if(eventCounter%1000 == 0): sys.stdout.write( '\rprocessing event %7d ==> %6.2f%% done.' % (eventCounter,eventCounter/float(tree.GetEntriesFast())*100)) sys.stdout.flush() l1DataVector = event.l1MuonData for l1Object in l1DataVector: hoMatch = findBestHoMatch(l1Object,event.hoRecHitData,deltaR,eThr) if(hoMatch != None): deltaTimes.append(l1Object.bx*25. - hoMatch.time) print histDeltaTime = getTH1D("histDeltaTimes","#DeltaTime;E_{Thr} / GeV;#DeltaTime / ns", 200,-100,100) for deltaTime in deltaTimes: histDeltaTime.Fill(deltaTime) canvas = TCanvas('canvasDeltaTimesEThr','Delta Time vs. E_{Thr}',1200,1200) histDeltaTime.SetStats(0) histDeltaTime.Draw() canvas.Update() print 'Done.' filenameTrunk = 'results/plots/analyzeFull-DeltaR%01d_%03d-EThr%01d_%03d_%s' % (int(deltaR),int(deltaR*1000), int(eThr),int(eThr*1000),options.instance) canvas.SaveAs(filenameTrunk + '.png') canvas.SaveAs(filenameTrunk + '.pdf') canvas.SaveAs(filenameTrunk + '.root') return deltaTimes,deltaR,eThr
def calculateEfficiency(deltaR = -1, eThr = -1): nMatches = 0 nTotal = 0 eventCounter = 0 print 'Efficiency analysis:' for event in file.dataTree: #Tell us about the progress eventCounter += 1 if(eventCounter%1000 == 0): sys.stdout.write( '\rprocessing event %7d ==> %6.2f%% done.' % (eventCounter,eventCounter/float(tree.GetEntriesFast())*100)) sys.stdout.flush() l1DataVector = event.l1MuonData genDataVector = event.genMuonData for genObject in genDataVector: l1Match = findBestL1Match(genObject, l1DataVector,0.3) if l1Match != None: nTotal += 1 hoMatch = findBestHoMatch(l1Match,event.hoRecHitData,deltaR,eThr) if(hoMatch != None): nMatches += 1 print print 'Done.' return nMatches, nTotal,eventCounter