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
