print "Running over files:",inputFiles
print "plotDir:",options.plotDir
handles={k:Handle(edmCollections[k][0]) for k in edmCollections.keys()}
res={}
events = Events(inputFiles)
events.toBegin()
products={}
size=events.size() #if not small else 2000
missingCollections=[]
for nev in range(size):
if nev%1000==0:print nev,'/',size
events.to(nev)
eaux=events.eventAuxiliary()
run=eaux.run()
if options.run>0 and not run==options.run:
# print run, options.run
continue
for k in [ x for x in edmCollections.keys() if x not in missingCollections]:
try:
events.getByLabel(edmCollections[k][1:],handles[k])
products[k]=handles[k].product()
except:
products[k]=None
print "Not found:",k
missingCollections.append(k)
d={}
# print "Event",nev,'size',products['pfCandidates'].size()
tracksRef = Handle("std::vector<reco::Track>")
label = "generalTracks"
#label = "globalMuons"
quality = "highPurity"
#quality = "tight"
#quality = "loose"
def format(x) :
return '{:.2f}'.format(x)
for i in range(0, eventsRef.size()):
#for i in range(0, 200):
a= eventsRef.to(i)
ev = eventsRef.eventAuxiliary().event()
# print "Event", i
a=eventsRef.getByLabel(label, tracksRef)
trVal = []
for track in tracksRef.product():
# if (track.phi()<0) : continue
# if (track.eta()<0) : continue
if (track.pt()<1) : continue
# if (track.qoverpError()*track.p()>2) : continue
# if (not track.quality(track.qualityByName(quality))) : continue
# pattern = track.hitPattern()
# if (pattern.numberOfValidHits() != (pattern.numberOfValidPixelHits()+pattern.numberOfValidStripHits())) :
# print pattern.numberOfValidHits(),pattern.numberOfValidPixelHits(),pattern.numberOfValidStripHits(), pattern.numberOfValidPixelHits()+pattern.numberOfValidStripHits()
trVal.append([i, ev, 10*int(100*track.eta())+track.phi(), ver, format(track.eta()), format(track.phi()), format(track.pt()), format(track.qoverpError()*track.p()),
track.numberOfValidHits() , track.hitPattern().numberOfValidPixelHits(),
track.numberOfLostHits(), format(track.validFraction()), track.ndof(),format(track.chi2()),track.algo()-4,track.originalAlgo()-4,track.quality(track.qualityByName("highPurity"))])
def main(args):
MAX_FILES_TO_PROCESS = args.maxfiles
x_axis_definition = [40, 0.5, 40.5]
if args.bxaxis:
x_axis_definition = [4000, -0.5, 3999.5]
numerical_lumi = []
numerical_bn = []
bunch_to_train = [0 for x in range(4000)]
# In case the user specified LS using the range syntax X-Y, extend it in the form X, X+1, ..., Y.
if args.lumi:
numerical_lumi.extend(explicitRange(args.lumi))
# Same range extension for the BX number
if args.bn:
numerical_bn.extend(explicitRange(args.bn))
if args.alltrainsaverage:
numerical_bn.extend(explicitRange(args.alltrainsaverage, bunch_to_train))
if args.alltrains:
numerical_bn.extend([i for i in range(0,4000)])
print "\nSelecting LS: ", numerical_lumi
print "\nSelecting BN: ", numerical_bn
print "\nBunch to Train: ", bunch_to_train
f = TFile(args.output, "RECREATE")
tracks_h = Handle("std::vector<reco::Track>")
pv_h = Handle("std::vector<reco::Vertex>")
hit_type = {0:'ok', 1:'missing', 2:'inactive', 3:'bad'}
hit_category = {0:'track_hits',
1:'missing_inner_hits',
2:'missing_outer_hits'}
det = {1:'PXB',
2:'PXF',
3:'TIB',
4:'TID',
5:'TOB',
6:'TEC'}
subdet = {'PXB': { 1:'Layer1', 2:'Layer2', 3:'Layer3'},
'PXF': { 1:'Disk1', 2:'Disk2'},
'TIB': { 1:'Layer1', 2:'Layer2', 3:'Layer3', 4:'Layer4'},
'TID': { 1:'wheel1', 2:'wheel2', 3:'wheel3'},
'TOB': { 1:'Layer1', 2:'Layer2', 3:'Layer3', 4:'Layer4', 5:'Layer5', 6:'Layer6'},
'TEC': { 1:'wheel1', 2:'wheel2', 3:'wheel3', 4:'wheel4', 5:'wheel5', 6:'wheel6', 7:'wheel7', 8:'wheel8', 9:'wheel9'}}
histograms = {}
histograms_barrel = {}
histograms_endcap = {}
for d in subdet.keys():
for sd in subdet[d].keys():
name = d+subdet[d][sd]
histograms.setdefault(name, {}).setdefault(0, TH1F('Hits_ok_%s' % name, 'Hits_ok_%s' % name, *x_axis_definition)).Sumw2
histograms.setdefault(name, {}).setdefault(1, TH1F('Hits_missing_%s' % name, 'Hits_missing_%s' % name, *x_axis_definition)).Sumw2
histograms.setdefault(name, {}).setdefault(2, TH1F('Hits_inactive_%s' % name, 'Hits_inactive_%s' % name, *x_axis_definition)).Sumw2
histograms.setdefault(name, {}).setdefault(3, TH1F('Hits_bad_%s' % name, 'Hits_bad_%s' % name, *x_axis_definition)).Sumw2
histograms.setdefault(name, {}).setdefault(4, TH1F('Hits_ok_and_missing_%s' % name, 'Hits_ok_and_missing_%s' % name, *x_axis_definition)).Sumw2
histograms_barrel.setdefault(name, {}).setdefault(0, TH1F('Hits_ok_%s_barrel' % name, 'Hits_ok_%s_barrel' % name, *x_axis_definition)).Sumw2
histograms_barrel.setdefault(name, {}).setdefault(1, TH1F('Hits_missing_%s_barrel' % name, 'Hits_missing_%s_barrel' % name, *x_axis_definition)).Sumw2
histograms_barrel.setdefault(name, {}).setdefault(2, TH1F('Hits_inactive_%s_barrel' % name, 'Hits_inactive_%s_barrel' % name, *x_axis_definition)).Sumw2
histograms_barrel.setdefault(name, {}).setdefault(3, TH1F('Hits_bad_%s_barrel' % name, 'Hits_bad_%s_barrel' % name, *x_axis_definition)).Sumw2
histograms_barrel.setdefault(name, {}).setdefault(4, TH1F('Hits_ok_and_missing_%s_barrel' % name, 'Hits_ok_and_missing_%s_barrel' % name, *x_axis_definition)).Sumw2
histograms_endcap.setdefault(name, {}).setdefault(0, TH1F('Hits_ok_%s_endcap' % name, 'Hits_ok_%s_endcap' % name, *x_axis_definition)).Sumw2
histograms_endcap.setdefault(name, {}).setdefault(1, TH1F('Hits_missing_%s_endcap' % name, 'Hits_missing_%s_endcap' % name, *x_axis_definition)).Sumw2
histograms_endcap.setdefault(name, {}).setdefault(2, TH1F('Hits_inactive_%s_endcap' % name, 'Hits_inactive_%s_endcap' % name, *x_axis_definition)).Sumw2
histograms_endcap.setdefault(name, {}).setdefault(3, TH1F('Hits_bad_%s_endcap' % name, 'Hits_bad_%s_endcap' % name, *x_axis_definition)).Sumw2
histograms_endcap.setdefault(name, {}).setdefault(4, TH1F('Hits_ok_and_missing_%s_endcap' % name, 'Hits_ok_and_missing_%s_endcap' % name, *x_axis_definition)).Sumw2
files = []
if args.input:
files.extend(args.input)
elif args.eosdir:
files.extend(map(lambda x: 'root://eoscms/'+ x, getFileListFromEOS(args.eosdir)) )
else:
print 'No input given, quitting'
sys.exit(1)
total_files = 0
if MAX_FILES_TO_PROCESS < 0:
total_files = len(files)
else:
total_files = min(MAX_FILES_TO_PROCESS, len(files))
analyzed_files = 0
start_cumulative_time = time()
start_time = start_cumulative_time
end_time = start_cumulative_time
file_count = -1
for input_file in files:
file_count += 1
analyzed_files += 1
if MAX_FILES_TO_PROCESS > 0 and analyzed_files > MAX_FILES_TO_PROCESS:
break
print "\n", input_file
events = Events(input_file)
total_events = float(events.size())
analized_events = 0.
for e in range(events.size()):
analized_events += 1.0
if analized_events*100./total_events == 100:
end_time = time()
if not args.debug:
sys.stdout.write("\r %d/%d --> %4.1f [%4.1f m / %6f s] ETA: %4.1f m ==> LS: %d" % (file_count, total_files,
analized_events*100./total_events,
(end_time-start_time)/60.,(end_time-start_time),
(end_time-start_cumulative_time)/(60.*analyzed_files) * (total_files - analyzed_files),
events.eventAuxiliary().luminosityBlock()))
start_time = end_time
sys.stdout.flush()
a = events.to(e)
if args.lumi:
if not events.eventAuxiliary().luminosityBlock() in numerical_lumi:
continue
if len(numerical_bn) !=0 and not events.eventAuxiliary().bunchCrossing() in numerical_bn:
continue
a = events.getByLabel("generalTracks", tracks_h)
a = events.getByLabel("offlinePrimaryVertices", pv_h)
good_vertices = getNumberOfGoodVertices(pv_h)
if good_vertices < 1:
continue
for track in range(tracks_h.product().size()):
t = tracks_h.product()[track]
if not t.quality(t.qualityByName("highPurity")):
continue
if t.pt() < 1.0 or t.dxy() > 0.1:
continue
hp = t.hitPattern()
if args.debug:
print "\n\n"
for category in hit_category.keys():
if args.debug:
print hit_category[category], "pt, eta, phi, dxy, originalAlgo-4", t.pt(), t.eta(), t.phi(), t.dxy(), t.originalAlgo()-4
for hit in range(0, hp.numberOfHits(category)):
pattern = hp.getHitPattern(category, hit)
valid = hp.validHitFilter(pattern)
missing = hp.missingHitFilter(pattern)
inactive = hp.inactiveHitFilter(pattern)
bad = hp.badHitFilter(pattern)
hit_type = -1
if valid:
hit_type = 0
elif missing:
hit_type = 1
elif inactive:
hit_type = 2
elif bad:
hit_type = 3
d = det[hp.getSubStructure(pattern)]
sd = subdet[d][hp.getSubSubStructure(pattern)]
name = d+sd
if args.overwrite:
good_vertices = args.overwrite
if args.alltrainsaverage is not None:
good_vertices = bunch_to_train[events.eventAuxiliary().bunchCrossing()]
if args.alltrains:
good_vertices = events.eventAuxiliary().bunchCrossing()
if abs(t.eta()) < 1.4:
if name in histograms_barrel.keys():
histograms_barrel[name][hit_type].Fill(good_vertices)
else:
if name in histograms_endcap.keys():
histograms_endcap[name][hit_type].Fill(good_vertices)
if name in histograms.keys():
# We don't do any selection on any category other than missing!
if hit_type != 1:
histograms[name][hit_type].Fill(good_vertices)
else:
if args.hitcategory and not category in args.hitcategory:
continue
histograms[name][hit_type].Fill(good_vertices)
if args.debug:
printPattern(hit, category, d, sd, hp, pattern)
f.cd()
for kind in histograms.keys():
histograms[kind][4].Add(histograms[kind][0]+histograms[kind][1])
histograms.setdefault(kind, {}).setdefault(5, TGraphAsymmErrors(histograms[kind][0], histograms[kind][4])).Write()
histograms_barrel[kind][4].Add(histograms_barrel[kind][0]+histograms_barrel[kind][1])
histograms_barrel.setdefault(kind, {}).setdefault(5, TGraphAsymmErrors(histograms_barrel[kind][0], histograms_barrel[kind][4])).Write()
histograms_endcap[kind][4].Add(histograms_endcap[kind][0]+histograms_endcap[kind][1])
histograms_endcap.setdefault(kind, {}).setdefault(5, TGraphAsymmErrors(histograms_endcap[kind][0], histograms_endcap[kind][4])).Write()
if args.debug:
f.ls()
f.Write()
f.Close()
tracksRef = Handle("std::vector<reco::Track>")
label = "generalTracks"
#label = "globalMuons"
quality = "highPurity"
#quality = "tight"
#quality = "loose"
def format(x):
return '{:.2f}'.format(x)
for i in range(0, eventsRef.size()):
#for i in range(0, 200):
a = eventsRef.to(i)
ev = eventsRef.eventAuxiliary().event()
# print "Event", i
a = eventsRef.getByLabel(label, tracksRef)
trVal = []
for track in tracksRef.product():
# if (track.phi()<0) : continue
# if (track.eta()<0) : continue
if (track.pt() < 1): continue
# if (track.qoverpError()*track.p()>2) : continue
# if (not track.quality(track.qualityByName(quality))) : continue
# pattern = track.hitPattern()
# if (pattern.numberOfValidHits() != (pattern.numberOfValidPixelHits()+pattern.numberOfValidStripHits())) :
# print pattern.numberOfValidHits(),pattern.numberOfValidPixelHits(),pattern.numberOfValidStripHits(), pattern.numberOfValidPixelHits()+pattern.numberOfValidStripHits()
trVal.append([
i, ev, 10 * int(100 * track.eta()) + track.phi(), ver,
format(track.eta()),
