def Create_Histograms_UnmatchedInfo(ntuple):
pl = gr.EventPlotter()
results = an.Calculate_UnmatchedSharedHits(ntuple, 100)
pl.ClassHistogram(
results,
"Maximum number of shared hits in unmatched and unclassified classes",
False, False)
def Create_Histograms_PixInfo(ntuple):
pl = gr.EventPlotter()
nPix_data, nLay_data = an.Calculate_MatchPixelHits(ntuple, 100)
pl.ClassHistogram(nPix_data, "Number of pixelhits in matches", False,
False)
pl.ClassHistogram(nLay_data, "Number of pixel layers in matches", False,
False)
def main():
'''
The main function, below are commented templates of function calls to perform analysis.
'''
ntuple = TrackingNtuple(
"/afs/cern.ch/work/j/jmaanpaa/public/trackingNtuple_810pre6_v5.root")
#ntuple = TrackingNtuple("trackingNtuple_pre9_1000ev.root") # Uncomment this for 1000 event data
pl = gr.EventPlotter()
###### USEFUL COMMANDS, UNCOMMENT TO USE ######
PlotFakes(ntuple) # Plots the fakes in 3D one by one
#Create_Histograms_MaxMatched_FakeTracks(ntuple)
#Create_Histograms_MaxMatched_RealTracks(ntuple)
#Create_Histograms_Classification(ntuple) # CREATES tHE MAIN CLASSIFICATION HISTOGRAMS
#Create_Histograms_IncludedDecayHitFrac(ntuple)
#Create_Histograms_UnmatchedInfo(ntuple)
#Create_Histograms_PixInfo(ntuple)
#Create_EndOfTrackingHistograms(ntuple)
#Create_EndOfTrackingHistogramsReal(ntuple)
#Create_EOT_DistanceHistogram(ntuple)
#Create_ResolutionHistograms(ntuple)
#print an.Calculate_AmountOfFakes(ntuple)
###### NOT THAT USEFUL COMMANDS, NOT TESTED AFTER UPDATES ######
#ntuple_iter = iter(ntuple)#__iter__()
#event = ntuple_iter.next()
#tracks = event.tracks()
#Create_PtHistograms(ntuple)
#an.Save_Normalisation_Coefficients(ntuple)
#Create_EndOfTrackingPointsPlot(ntuple)
#an.EndOfTrackingDetectorInfo(Load_File("End_list_file_layermiss.dmp"),[0],True)
#Create_Histograms_SharedHitsFracs_RealTracks(ntuple)
#Create_Histograms_SharedHitsFracs(ntuple)
#PlotEvent3DHits(event, "PSG")
#PlotXY(event, [-10,10])
#PlotZY(event,flag="P")#, [-10,10])
#PlotPixelGluedTracks3D(tracks)
#PlotTracks3D(FindFakes(event))
#PlotTracks3D(FindUntrackedParticles(event))
#EfficiencyRate(ntuple, 100)
#for track in an.FindFakes(event):
# pl.DrawTrackTest(track)
#pl.PlotEvent3DHits(event)
'''
def Create_PtHistograms(ntuple):
''' Creates pt histograms for fake and true tracks. '''
pl = gr.EventPlotter()
fake_pts, true_pts = an.TrackPt(ntuple)
fake_pts = [min(pt, 4.99) for pt in fake_pts]
true_pts = [min(pt, 4.99) for pt in true_pts]
pl.ClassHistogram(fake_pts, "Fake p_{t}", False, False)
pl.ClassHistogram(true_pts, "True p_{t}", False, False)
def Create_Histograms_MaxMatched_RealTracks(ntuple):
pl = gr.EventPlotter()
results = an.Calculate_MaxMatchedHits_RealTracks(ntuple, 100)
pl.ClassHistogram(results, "Matched Hits of real reconstructed tracks",
False, False)
results = an.Calculate_MaxMatchedConsecutiveHits_RealTracks(ntuple, 100)
pl.ClassHistogram(results,
"Matched consecutive hits of real reconstructed track",
False, False)
def Create_EOT_DistanceHistogram(ntuple):
pl = gr.EventPlotter()
end_list = an.Get_EndOfTrackingPoints(ntuple, 100, [])
#Save_To_File(end_list,"End_list_file_layermiss.dmp")
#end_list = Load_File("End_list_file_layermiss.dmp")
errors = an.Analyse_EOT_Error(end_list, True)
pl.ClassHistogram(errors, "Distance between EOT fake and particle hit",
False, False)
def PlotFakes(ntuple):
''' Plots fakes with the PlotFakes command in the graphics module. '''
pl = gr.EventPlotter()
for event in ntuple:
# Fill the filters with corresponding class indexes or detector layers to filter everything else from the plotting
pl.PlotFakes(event,
True,
fake_filter=[],
end_filter=[],
last_filter="",
particle_end_filter="")
def Create_Histograms_MaxMatched_FakeTracks(ntuple):
pl = gr.EventPlotter()
#results = an.Calculate_MaxMatchedHits(ntuple, 100)
#pl.ClassHistogram(results, "Max Matched Hits", False, False)
results = an.Calculate_MaxMatchedConsecutiveHits(ntuple, 100)
pl.ClassHistogram(results, "Matched consecutive hits of fake", False,
False, True, 0, 20, 20, True)
results = an.Calculate_MaxMatchedConsecutiveHits(ntuple, 100, True)
pl.ClassHistogram(results, "Consecutive hit fraction of fake", False,
False, False, 0, 1.1, 11, True)
def Create_Histograms_SharedHitsFracs_RealTracks(ntuple):
pl = gr.EventPlotter()
hits_shared, hit_fractions = an.Calculate_SharedHits_RealTracks(
ntuple, 100)
pl.ClassHistogram(
hits_shared,
"Maximum number of shared hits in real reconstructed tracks", False,
False)
pl.ClassHistogram(hit_fractions,
"Shared hit fractions in real reconstructed tracks",
False, False)
def Create_EndOfTrackingHistograms(ntuple):
'''
Creates various histograms related to end of tracking
'''
pl = gr.EventPlotter()
''' Comment/uncomment these lines depending on if you have saved the end of tracking file'''
end_list = an.Get_EndOfTrackingPoints(ntuple, 100, [])
#Save_To_File(end_list,"End_list_file_layermiss_invalid4_fix_4class.dmp")
#end_list = Load_File("End_list_file_layermiss_invalid4_fix_4class.dmp") #an.Get_EndOfTrackingPoints(ntuple, 10, [])
#end_list = an.Get_EndOfTrackingPointsReal(ntuple, 100)
#Save_To_File(end_list,"End_list_file_real_invalid.dmp")
#end_list = Load_File("End_list_file_real_invalid.dmp")
'''
for end in end_list:
if end.end_class == 0 and end.last_str == end.particle_end_str and end.last_str == "BPix3":
print end.last
print end.particle_end
print end.fake_end
print end.last_str, end.particle_end_str, end.fake_end_str
print end.end_class
print "*****"
'''
an.Analyse_EOT_ParticleDoubleHit(end_list)
pl.EndOfTrackingHistogram(end_list, "end_class", [], [], False,
False) #last false will adjust the bpix3 filter
#pl.EndOfTrackingHistogram(end_list, "invalid_reason_last",[],[],False, True)
pl.EndOfTrackingHistogram(end_list, "invalid_reason_fake", [0], [], False,
False)
pl.EndOfTrackingHistogram(end_list, "particle_pdgId", [0], [], False,
False)
#pl.EndOfTrackingHistogram(end_list, "invalid_reason_particle",[],[],False, False)
#pl.EndOfTrackingHistogram(end_list, "particle_end_layer",[0],[],False, True)
pl.EndOfTrackingHistogram(end_list, "last_layer", [0], [], False,
False) # 11,13,21
pl.EndOfTrackingHistogram(end_list, "fake_end_layer", [0], [], False,
False)
pl.EndOfTrackingHistogram(end_list, "particle_end_layer", [0], [], False,
False)
errors = an.Analyse_EOT_Error(end_list, True, "same",
False) # Same detectior id
pl.ClassHistogram(errors, "Distance between EOT fake and particle hit",
False, False)
errors = an.Analyse_EOT_Error(end_list, True, "different",
False) # Different detectior id
pl.ClassHistogram(errors, "Distance between EOT fake and particle hit",
False, False)
an.EndOfTrackingDetectorInfo(end_list, [0], True)
def Create_Histograms_Classification(ntuple):
'''
Creates histograms from classification of fakes.
'''
pl = gr.EventPlotter()
'''
results = an.ClassifyEventFakes(ntuple, 10, False)
pl.ClassHistogram(results, "Classification of all fakes", an.classes, True)
results = an.Calculate_MaxMatchedConsecutiveHits(ntuple, 100)
pl.ClassHistogram(results, "Max matched consecutive hits", False, False)
results = an.Calculate_MaxMatchedHits(ntuple, 100)
pl.ClassHistogram(results, "Max Matched Hits", False, False)
results = an.Calculate_MaxMatchedConsecutiveHits(ntuple, 100, True)
pl.ClassHistogram(results, "Hit fraction of fake", False, False) #"Max matched consecutive hits with respect to fake", False, False)
'''
results = an.ClassifyEventFakes(ntuple, 100,
False) # ORIGINAL MATCH CRITERION
#results = an.ClassifyEventFakes(ntuple, 100, False, real_criterion = ["NLay", 6]) # SHARED LAYERS (NLay) MATCH CRITERION
#pl.ClassHistogram(results, "Classification of all fakes", an.classes, True)
all_classified = OrderedDict()
all_classified["No matches"] = results[0]
all_classified["One match"] = sum([results[i] for i in [10, 11, 12, 13]])
all_classified["Multiple matches"] = sum(
[results[i] for i in [20, 21, 22, 23]])
all_classified["Loose match"] = results[-1]
pl.ClassHistogram(all_classified, "Classification of all fakes", False,
True)
single_classified = OrderedDict()
single_classified["Decayed and reconstructed"] = results[11]
single_classified["Only reconstructed"] = results[12]
single_classified["Only decayed"] = results[13]
single_classified["Neither"] = results[10]
pl.ClassHistogram(single_classified,
"Classification of fakes with single match", False, True)
multi_classified = OrderedDict()
multi_classified["Includes a decay interaction"] = results[21]
multi_classified["All matches reconstructed"] = results[22]
multi_classified["Some matches reconstructed"] = results[23]
multi_classified["Other"] = results[20]
pl.ClassHistogram(multi_classified,
"Classification of fakes with multiple matches", False,
True)
def Create_Histograms_IncludedDecayHitFrac(ntuple):
'''
Creates plots related to the "multiple matches including a decay interaction" -class.
'''
pl = gr.EventPlotter()
daughter_frac, parent_frac, total_frac = an.Calculate_IndludedDecayHitFractions(
ntuple, 100)
pl.ClassHistogram(daughter_frac, "Daughter share of hits from fake", False,
False)
pl.ClassHistogram(parent_frac, "Parent share of hits from fake", False,
False)
pl.ClassHistogram(total_frac, "Total share of hits from fake", False,
False)
def Create_Histograms_SharedHitsFracs(ntuple):
pl = gr.EventPlotter()
hits_shared, hit_fractions = an.Calculate_SharedHits(ntuple, 100, [])
pl.ClassHistogram(hits_shared,
"Maximum number of shared hits in all classes", False,
False)
pl.ClassHistogram(hit_fractions, "Shared hit fractions in all classes",
False, False)
hits_shared, hit_fractions = an.Calculate_SharedHits(
ntuple, 100, [10, 11, 12, 13])
pl.ClassHistogram(hits_shared,
"Maximum number of shared hits in single match classes",
False, False)
pl.ClassHistogram(hit_fractions,
"Shared hit fractions in single match classes", False,
False)
hits_shared, hit_fractions = an.Calculate_SharedHits(
ntuple, 100, [20, 21, 22, 23])
pl.ClassHistogram(
hits_shared, "Maximum number of shared hits in multiple match classes",
False, False)
pl.ClassHistogram(hit_fractions,
"Shared hit fractions in multiple match classes", False,
False)
hits_shared, hit_fractions = an.Calculate_SharedHits(ntuple, 100, [10])
pl.ClassHistogram(
hits_shared,
"Maximum number of shared hits in other single match class", False,
False)
pl.ClassHistogram(hit_fractions,
"Shared hit fractions in other single match class",
False, False)
hits_shared, hit_fractions = an.Calculate_SharedHits(ntuple, 100, [13])
pl.ClassHistogram(
hits_shared,
"Maximum number of shared hits in single decay match class", False,
False)
pl.ClassHistogram(hit_fractions,
"Shared hit fractions in single decay match class",
False, False)
def Create_ResolutionHistograms(ntuple):
'''
Performs resolution analysis and plots histograms from the results.
Comment and uncomment lines depending on if you have saved the files.
'''
pl = gr.EventPlotter()
res_data_fakes = an.Resolution_Analysis_Fakes(ntuple, 100)
#Save_To_File(res_data_fakes, "resolutions_fake_matches1000ev.dmp")
#res_data_fakes = Load_File("resolutions_fake_matches1000ev.dmp")
#pl.ResolutionHistograms(res_data_fakes)
#pl.HistogramSigmaPt(res_data_fakes)
res_data_trues = an.Resolution_Analysis_Trues(ntuple, 100)
#Save_To_File(res_data_trues, "resolutions_trues1000ev.dmp")
#res_data_trues = Load_File("resolutions_trues1000ev.dmp")
res_data_fakes_NLay = an.Resolution_Analysis_Fakes(
ntuple, 100, real_criterion=["NLay", 6])
#Save_To_File(res_data_fakes_NLay, "resolutions_fake_NLay_matches1000ev.dmp")
#res_data_fakes_NLay = Load_File("resolutions_fake_NLay_matches1000ev.dmp")
#pl.ResolutionHistograms(res_data_fakes)
#pl.HistogramSigmaPt(res_data_fakes)
#pl.ResolutionHistograms(res_data_trues)
#pl.HistogramSigmaPt(res_data_trues)
#pl.ResolutionHistogramsCombine(res_data_trues, res_data_fakes)
pl.ResolutionHistogramsNormalised(res_data_trues, res_data_fakes)
pl.ResolutionHistogramsNormalisedCumulative(res_data_trues, res_data_fakes)
#pl.HistogramSigmaPtCombined(res_data_trues, res_data_fakes)
#pl.HistogramSigmaPtDual(res_data_trues, res_data_fakes)
pl.ResolutionsSigmaVsPt(res_data_trues, res_data_fakes)
pl.ResolutionHistogramsNormalised(res_data_trues, res_data_fakes_NLay)
pl.ResolutionHistogramsNormalisedCumulative(res_data_trues,
res_data_fakes_NLay)
pl.ResolutionsSigmaVsPt(res_data_trues, res_data_fakes_NLay)
def Create_EndOfTrackingPointsPlot(ntuple):
# NOT TESTED AFTER UPDATE
pl = gr.EventPlotter()
end_list = an.Get_EndOfTrackingPoints(ntuple, 100, [])
last = [], fake_ends = [], particle_ends = [], end_classes = [], fake_classes = []
for end in end_list:
last.append(end.last)
fake_ends.append(end.fake_end)
particle_ends.append(end.particle_end)
end_classes.append(end.end_class)
fake_classes.append(end.fake_class)
'''
pl.PlotEndOfTrackingPoints3D(last,[])#, fail)
pl.PlotEndOfTrackingPoints3D(fake_ends,[],[],2)
pl.PlotEndOfTrackingPoints3D(particle_ends,[],[],3)
#pl.PlotDetectorRange()
pl.Draw()
'''
pl.PlotEndOfTracking3D(last, fake_ends, particle_ends, end_classes,
fake_classes)
pl.PlotDetectorRange()
pl.Draw()
pl.Reset()
pl.PlotEndOfTracking3D(last, fake_ends, particle_ends, end_classes,
fake_classes, [0], [])
pl.PlotDetectorRange()
pl.Draw()
pl.Reset()
pl.PlotEndOfTracking3D(last, fake_ends, particle_ends, end_classes,
fake_classes, [], [10])
pl.PlotDetectorRange()
pl.Draw()
pl.Reset()
pl.PlotEndOfTracking3D(last, fake_ends, particle_ends, end_classes,
fake_classes, [], [13])
pl.PlotDetectorRange()
pl.Draw()
def Create_EndOfTrackingHistogramsReal(ntuple):
'''
Create end of tracking histograms for true tracks.
'''
pl = gr.EventPlotter()
''' Comment/uncomment these lines depending on if you have saved the end of tracking file'''
end_list = an.Get_EndOfTrackingPointsReal(ntuple, 100)
#Save_To_File(end_list,"End_list_file_real_fixed.dmp")
#end_list = Load_File("End_list_file_real_fixed.dmp") #an.Get_EndOfTrackingPoints(ntuple, 10, [])
'''
for end in end_list:
if end.end_class == 0 and end.last_str == end.particle_end_str and end.last_str == "BPix3":
print end.last
print end.particle_end
print end.fake_end
print end.last_str, end.particle_end_str, end.fake_end_str
print end.end_class
print "*****"
'''
pl.EndOfTrackingHistogram(end_list, "end_class", [], [])
pl.EndOfTrackingHistogram(end_list, "last_layer", [0], [], False)
pl.EndOfTrackingHistogram(end_list, "fake_end_layer", [0], [], False)
pl.EndOfTrackingHistogram(end_list, "particle_end_layer", [0], [], False)