Inheritance diagram for ExpertTrackingValidationModule:

Public Member Functions
def	__init__ (self, name, contact, fit=False, pulls=False, resolution=False, output_file_name=None, track_filter_object=AlwaysPassFilter(), plot_name_postfix='', plot_title_postfix='', exclude_profile_mc_parameter='', exclude_profile_pr_parameter='', use_expert_folder=True, trackCandidatesColumnName='RecoTracks', mcTrackCandidatesColumnName='MCRecoTracks', cdcHitsColumnName='CDCHits', write_tables=False)

def	initialize (self)

def	event (self)

def	examine_hits_in_event (self)

def	terminate (self)

Public Attributes
	cdcHitsColumnname
	cached name of the CDCHits StoreArray

	write_tables
	cached value of the flag to write the validation figures of merit

	number_of_total_hits
	number of all hits

	number_of_mc_hits
	number of hits on MC track

	number_of_pr_hits
	number of hits on pattern reconstructed tracks

	is_hit_found
	list of flags for [not-]found hits

	is_hit_matched
	list of flags for [not-]matched hits

	mc_missing
	list of flags where MCRecoTrack is [not] missing MCTrackCand

	ratio_hits_in_mc_tracks_and_not_in_pr_tracks
	list of fraction of number of hits in MC track but not in PR track

	ratio_hits_in_mc_tracks_and_in_pr_tracks
	list of fraction of number of hits in MC track and in PR track

	ratio_hits_in_missing_mc_tracks_and_in_pr_tracks
	list of fraction of number of hits in missing MC track and in PR track

	ratio_hits_in_mc_tracks_and_in_fake_pr_tracks
	list of fraction of number of hits in MC track and in fake PR track

	ratio_hits_in_mc_tracks_and_in_good_pr_tracks
	list of fraction of number of hits in MC track and in good PR track

	mc_is_primary
	list of flags indicating that the MC track is [not] a primary MCParticle

	mc_number_of_hits
	list of the number of MCTrackCandHits on the MC track

	number_of_connected_tracks
	This is the number of mcTrackCands sharing a hit with the track cand.

	number_of_wrong_hits
	This number gives information about the "badness" of the fake.

	pr_number_of_hits
	list of the number of pattern-reconstructed hits

	pr_number_of_matched_hits
	list of the number of pattern-reconstructed hits matched to MC track

Detailed Description

Module to collect more matching information about the found particles and to generate validation
plots and figures of merit on the performance of track finding. This module gives information on the
number of hits etc.

Definition at line 39 of file hit_module.py.

Constructor & Destructor Documentation

◆ init()

def __init__	(	self,
		name,
		contact,
		fit = `False`,
		pulls = `False`,
		resolution = `False`,
		output_file_name = `None`,
		track_filter_object = `AlwaysPassFilter()`,
		plot_name_postfix = `''`,
		plot_title_postfix = `''`,
		exclude_profile_mc_parameter = `''`,
		exclude_profile_pr_parameter = `''`,
		use_expert_folder = `True`,
		trackCandidatesColumnName = `'RecoTracks'`,
		mcTrackCandidatesColumnName = `'MCRecoTracks'`,
		cdcHitsColumnName = `'CDCHits'`,
		write_tables = `False`
	)

Constructor

Reimplemented from TrackingValidationModule.

Definition at line 45 of file hit_module.py.

            write_tables=False):
        """Constructor"""
 
        TrackingValidationModule.__init__(
            self,
            name,
            contact,
            fit,
            pulls,
            resolution,
            output_file_name,
            track_filter_object,
            plot_name_postfix,
            plot_title_postfix,
            exclude_profile_mc_parameter,
            exclude_profile_pr_parameter,
            use_expert_folder,
            trackCandidatesColumnName,
            mcTrackCandidatesColumnName)
 
        
        self.cdcHitsColumnname = cdcHitsColumnName
        
        self.write_tables = write_tables
 

Member Function Documentation

◆ event()

def event ( self )

Event method

Reimplemented from TrackingValidationModule.

Definition at line 136 of file hit_module.py.

    def event(self):
        """Event method"""
 
        TrackingValidationModule.event(self)
        self.examine_hits_in_event()
 

◆ examine_hits_in_event()

def examine_hits_in_event ( self )

Classify all of the hits in the event according to the parent track(s)

Definition at line 142 of file hit_module.py.

    def examine_hits_in_event(self):
        """Classify all of the hits in the event according to the parent track(s)"""
 
        trackCands = Belle2.PyStoreArray(self.trackCandidatesColumnName)
        mcTrackCands = Belle2.PyStoreArray(self.mcTrackCandidatesColumnName)
        if self.cdcHitsColumnname not in Belle2.PyStoreArray.list():
            # No CDC hits available, hit analysis incomplete, don't perform
            # hit analysis
            return
        cdcHits = Belle2.PyStoreArray(self.cdcHitsColumnname)
 
        # # CDC Hits in MC tracks
        totalHitListMC = []
        for mcTrackCand in mcTrackCands:
            cdcHitIDs = [cdcHit.getArrayIndex() for cdcHit in getObjectList(mcTrackCand.getCDCHitList())]  # Checked
            # Working around a bug in ROOT where you should not access empty std::vectors
            if len(cdcHitIDs) == 0:
                cdcHitIDs = set()
            else:
                cdcHitIDs = set(cdcHitIDs)
            totalHitListMC.extend(cdcHitIDs)
 
        # Make the ids unique
        totalHitListMC = set(totalHitListMC)
 
        # # CDC Hits in PR tracks
        totalHitListPR = []
        totalHitListPRGood = []
        totalHitListPRClone = []
        totalHitListPRFake = []
        for trackCand in trackCands:
            if trackCand.getNumberOfTotalHits() == 0:
                basf2.B2WARNING("Encountered a pattern recognition track with no hits")
                continue
 
            cdcHitIDs = [cdcHit.getArrayIndex() for cdcHit in getObjectList(trackCand.getCDCHitList())]  # Checked
            # Working around a bug in ROOT where you should not access empty std::vectors
            if len(cdcHitIDs) == 0:
                cdcHitIDs = set()
            else:
                cdcHitIDs = set(cdcHitIDs)
 
            totalHitListPR.extend(cdcHitIDs)
            if self.trackMatchLookUp.isAnyChargeMatchedPRRecoTrack(trackCand):
                totalHitListPRGood.extend(cdcHitIDs)
 
            if self.trackMatchLookUp.isAnyChargeClonePRRecoTrack(trackCand):
                totalHitListPRClone.extend(cdcHitIDs)
 
            if (self.trackMatchLookUp.isBackgroundPRRecoTrack(trackCand) or
                    self.trackMatchLookUp.isGhostPRRecoTrack(trackCand)):
                totalHitListPRFake.extend(cdcHitIDs)
 
        # Make the ids unique
        totalHitListPR = set(totalHitListPR)
        totalHitListPRGood = set(totalHitListPRGood)
        totalHitListPRClone = set(totalHitListPRClone)
        totalHitListPRFake = set(totalHitListPRFake)
 
        # # All CDC Hits
        totalHitList = {cdcHit.getArrayIndex() for cdcHit in cdcHits}
 
        number_of_mc_hits = len(totalHitListMC)
        number_of_pr_hits = len(totalHitListPR)
        number_of_all_hits = len(totalHitList)
 
        is_hit_matched = 0
        is_hit_found = len(totalHitListMC & totalHitListPR)
 
        for trackCand in trackCands:
 
            is_matched = self.trackMatchLookUp.isAnyChargeMatchedPRRecoTrack(trackCand)
            is_clone = self.trackMatchLookUp.isAnyChargeClonePRRecoTrack(trackCand)
 
            trackCandHits = [cdcHit.getArrayIndex() for cdcHit in getObjectList(trackCand.getCDCHitList())]
            # Working around a bug in ROOT where you should not access empty std::vectors
            if len(trackCandHits) == 0:
                trackCandHits = set()
            else:
                trackCandHits = set(trackCandHits)
 
            # this is not very efficient...
            list_of_connected_mc_tracks = set()
            list_of_numbers_of_hits_for_connected_tracks = collections.deque()
            # number_of_connected_tracks = 0
            # number_of_wrong_hits = 0
 
            for mcTrackCand in mcTrackCands:
                mcTrackCandHits = [cdcHit.getArrayIndex() for cdcHit in getObjectList(mcTrackCand.getCDCHitList())]
                # Working around a bug in ROOT where you should not access empty std::vectors
                if len(mcTrackCandHits) == 0:
                    mcTrackCandHits = set()
                else:
                    mcTrackCandHits = set(mcTrackCandHits)
 
                length_of_intersection = len(mcTrackCandHits & trackCandHits)
                if length_of_intersection > 0:
                    list_of_connected_mc_tracks.add(mcTrackCand)
                    list_of_numbers_of_hits_for_connected_tracks.append(length_of_intersection)
 
            if len(list_of_numbers_of_hits_for_connected_tracks) == 0:
                self.number_of_wrong_hits.append(0)
                self.pr_number_of_matched_hits.append(0)
            else:
                maximum_intersection = \
                    max(list_of_numbers_of_hits_for_connected_tracks)
                self.pr_number_of_matched_hits.append(sum(list_of_numbers_of_hits_for_connected_tracks))
                self.number_of_wrong_hits.append(sum(list_of_numbers_of_hits_for_connected_tracks) -
                                                 maximum_intersection)
 
            self.number_of_connected_tracks.append(len(list_of_connected_mc_tracks))
 
            if is_matched or is_clone:
                mcTrackCand = \
                    self.trackMatchLookUp.getRelatedMCRecoTrack(trackCand)
                mcTrackCandHits = [cdcHit.getArrayIndex() for cdcHit in getObjectList(mcTrackCand.getCDCHitList())]  # Checked
                # Working around a bug in ROOT where you should not access empty std::vectors
                if len(mcTrackCandHits) == 0:
                    mcTrackCandHits = set()
                else:
                    mcTrackCandHits = set(mcTrackCandHits)
 
                is_hit_matched += len(trackCandHits & mcTrackCandHits)
 
            self.pr_number_of_hits.append(len(trackCandHits))
 
        for mcTrackCand in mcTrackCands:
            is_missing = \
                self.trackMatchLookUp.isMissingMCRecoTrack(mcTrackCand)
 
            mcTrackCandHits = [cdcHit.getArrayIndex() for cdcHit in getObjectList(mcTrackCand.getCDCHitList())]  # Checked
 
            # Working around a bug in ROOT where you should not access empty std::vectors
            if len(mcTrackCandHits) == 0:
                continue
            else:
                mcTrackCandHits = set(mcTrackCandHits)
 
            ratio = 1.0 * len(mcTrackCandHits & totalHitListPR) / len(mcTrackCandHits)
 
            self.ratio_hits_in_mc_tracks_and_not_in_pr_tracks.append(1.0 - ratio)
            self.ratio_hits_in_mc_tracks_and_in_pr_tracks.append(ratio)
            if is_missing:
                self.ratio_hits_in_missing_mc_tracks_and_in_pr_tracks.append(ratio)
            self.ratio_hits_in_mc_tracks_and_in_good_pr_tracks.append(
                1.0 * len(mcTrackCandHits & totalHitListPRGood) / len(mcTrackCandHits))
            self.ratio_hits_in_mc_tracks_and_in_fake_pr_tracks.append(
                1.0 * len(mcTrackCandHits & totalHitListPRFake) / len(mcTrackCandHits))
 
            mcParticle = \
                self.trackMatchLookUp.getRelatedMCParticle(mcTrackCand)
            is_primary = \
                mcParticle.hasStatus(Belle2.MCParticle.c_PrimaryParticle)
            self.mc_is_primary.append(is_primary)
            self.mc_number_of_hits.append(len(mcTrackCandHits))
 
            self.mc_missing.append(is_missing)
 
        self.number_of_total_hits.append(number_of_all_hits)
        self.number_of_mc_hits.append(number_of_mc_hits)
        self.number_of_pr_hits.append(number_of_pr_hits)
 
        self.is_hit_found.append(is_hit_found)
        self.is_hit_matched.append(is_hit_matched)
 

◆ initialize()

def initialize ( self )

Receive signal at the start of event processing

Reimplemented from TrackingValidationModule.

Definition at line 87 of file hit_module.py.

    def initialize(self):
        """Receive signal at the start of event processing"""
        TrackingValidationModule.initialize(self)
 
        # Use dequeues in favour of lists to prevent repeated memory allocation of cost O(n)
        
        self.number_of_total_hits = collections.deque()
        
        self.number_of_mc_hits = collections.deque()
        
        self.number_of_pr_hits = collections.deque()
        
        self.is_hit_found = collections.deque()
        
        self.is_hit_matched = collections.deque()
 
        # MC information
        
        self.mc_missing = collections.deque()
        
        self.ratio_hits_in_mc_tracks_and_not_in_pr_tracks = collections.deque()
        
        self.ratio_hits_in_mc_tracks_and_in_pr_tracks = collections.deque()
        
        self.ratio_hits_in_missing_mc_tracks_and_in_pr_tracks = collections.deque()
        
        self.ratio_hits_in_mc_tracks_and_in_fake_pr_tracks = \
            collections.deque()
        
        self.ratio_hits_in_mc_tracks_and_in_good_pr_tracks = \
            collections.deque()
        
        self.mc_is_primary = collections.deque()
        
        self.mc_number_of_hits = collections.deque()
 
        # PT information
        
        self.number_of_connected_tracks = collections.deque()
        
        self.number_of_wrong_hits = collections.deque()
        # It is calculated by going through all hits of the fake track and the connected mc track cands and counting the number.
        # These numbers are than summed up and subtracted by the biggest number
        # of hits this candidates shares with the mc track cands.
        
        self.pr_number_of_hits = collections.deque()
        
        self.pr_number_of_matched_hits = collections.deque()

◆ terminate()

def terminate ( self )

Receive signal at the end of event processing

Reimplemented from TrackingValidationModule.

Definition at line 307 of file hit_module.py.

    def terminate(self):
        """Receive signal at the end of event processing"""
        TrackingValidationModule.terminate(self)
 
        output_tfile = ROOT.TFile(self.output_file_name, 'update')
 
        validation_plots = []
 
        # Hit ratios #
        
        all_tracks_plot = self.profiles_by_parameters_base(
            xs=self.ratio_hits_in_mc_tracks_and_in_pr_tracks,
            quantity_name="ratio of hits in MCTracks found by the track finder",
            make_hist=True,
            parameter_names=[],
            profile_parameters={},
            unit=None)
 
        validation_plots.extend(all_tracks_plot)
 
        missing_tracks_plot = self.profiles_by_parameters_base(
            xs=self.ratio_hits_in_missing_mc_tracks_and_in_pr_tracks,
            quantity_name="ratio of hits in missing MCTracks found by the track finder",
            make_hist=True,
            parameter_names=[],
            profile_parameters={},
            unit=None)
 
        validation_plots.extend(missing_tracks_plot)
 
        for validation_plot in validation_plots:
            validation_plot.write()
 
        if self.write_tables:
            # MC Figures of merit
            mc_figures_of_merit = \
                ValidationManyFiguresOfMerit(f'{self.validation_name}_mc_figures_of_merit')
 
            mc_figures_of_merit['mc_pts'] = self.mc_pts
            mc_figures_of_merit['mc_d0s'] = self.mc_d0s
            mc_figures_of_merit['mc_matches'] = self.mc_matches
            mc_figures_of_merit['mc_hit_efficiencies'] = self.mc_hit_efficiencies
            mc_figures_of_merit['mc_multiplicities'] = self.mc_multiplicities
            mc_figures_of_merit['mc_phis'] = self.mc_phi
            mc_figures_of_merit['mc_tan_lambdas'] = self.mc_tan_lambdas
            mc_figures_of_merit['mc_missing'] = self.mc_missing
            mc_figures_of_merit['mc_is_primary'] = self.mc_is_primary
            mc_figures_of_merit['mc_number_of_hits'] = self.mc_number_of_hits
            mc_figures_of_merit['ratio_hits_in_mc_tracks_and_in_good_pr_tracks'] = \
                self.ratio_hits_in_mc_tracks_and_in_good_pr_tracks
            mc_figures_of_merit['ratio_hits_in_mc_tracks_and_in_fake_pr_tracks'] = \
                self.ratio_hits_in_mc_tracks_and_in_fake_pr_tracks
            mc_figures_of_merit['ratio_hits_in_mc_tracks_and_not_in_pr_tracks'] = \
                self.ratio_hits_in_mc_tracks_and_not_in_pr_tracks
 
            mc_figures_of_merit.write()
 
            # PR Figures of merit
            pr_figures_of_merit = \
                ValidationManyFiguresOfMerit(f'{self.validation_name}_pr_figures_of_merit')
 
            pr_figures_of_merit['pr_clones_and_matches'] = \
                self.pr_clones_and_matches
            pr_figures_of_merit['pr_matches'] = self.pr_matches
            pr_figures_of_merit['pr_fakes'] = self.pr_fakes
            pr_figures_of_merit['pr_number_of_hits'] = self.pr_number_of_hits
            pr_figures_of_merit['pr_number_of_matched_hits'] = \
                self.pr_number_of_matched_hits
            pr_figures_of_merit['pr_seed_tan_lambdas'] = self.pr_seed_tan_lambdas
            pr_figures_of_merit['pr_seed_phi'] = self.pr_seed_phi
 
            pr_figures_of_merit['number_of_connected_tracks'] = \
                self.number_of_connected_tracks
            pr_figures_of_merit['number_of_wrong_hits'] = self.number_of_wrong_hits
 
            pr_figures_of_merit.write()
 
            # Hit Figures of merit
            hit_figures_of_merit = \
                ValidationFiguresOfMerit(f'{self.validation_name}_hit_figures_of_merit')
 
            hit_figures_of_merit['number_of_total_hits'] = \
                np.sum(self.number_of_total_hits)
            hit_figures_of_merit['number_of_mc_hits'] = \
                np.sum(self.number_of_mc_hits)
            hit_figures_of_merit['number_of_pr_hits'] = \
                np.sum(self.number_of_pr_hits)
            hit_figures_of_merit['is_hit_found'] = np.sum(self.is_hit_found)
            hit_figures_of_merit['is_hit_matched'] = np.sum(self.is_hit_matched)
 
            print(hit_figures_of_merit)
            hit_figures_of_merit.write()
 
        output_tfile.Close()

Member Data Documentation

◆ cdcHitsColumnname

cdcHitsColumnname

cached name of the CDCHits StoreArray

Definition at line 83 of file hit_module.py.

◆ is_hit_found

is_hit_found

list of flags for [not-]found hits

Definition at line 99 of file hit_module.py.

◆ is_hit_matched

is_hit_matched

list of flags for [not-]matched hits

Definition at line 101 of file hit_module.py.

◆ mc_is_primary

mc_is_primary

list of flags indicating that the MC track is [not] a primary MCParticle

Definition at line 119 of file hit_module.py.

◆ mc_missing

mc_missing

list of flags where MCRecoTrack is [not] missing MCTrackCand

Definition at line 105 of file hit_module.py.

◆ mc_number_of_hits

mc_number_of_hits

list of the number of MCTrackCandHits on the MC track

Definition at line 121 of file hit_module.py.

◆ number_of_connected_tracks

number_of_connected_tracks

This is the number of mcTrackCands sharing a hit with the track cand.

Definition at line 125 of file hit_module.py.

◆ number_of_mc_hits

number_of_mc_hits

number of hits on MC track

Definition at line 95 of file hit_module.py.

◆ number_of_pr_hits

number_of_pr_hits

number of hits on pattern reconstructed tracks

Definition at line 97 of file hit_module.py.

◆ number_of_total_hits

number_of_total_hits

number of all hits

Definition at line 93 of file hit_module.py.

◆ number_of_wrong_hits

number_of_wrong_hits

This number gives information about the "badness" of the fake.

Definition at line 127 of file hit_module.py.

◆ pr_number_of_hits

pr_number_of_hits

list of the number of pattern-reconstructed hits

Definition at line 132 of file hit_module.py.

◆ pr_number_of_matched_hits

pr_number_of_matched_hits

list of the number of pattern-reconstructed hits matched to MC track

Definition at line 134 of file hit_module.py.

◆ ratio_hits_in_mc_tracks_and_in_fake_pr_tracks

ratio_hits_in_mc_tracks_and_in_fake_pr_tracks

list of fraction of number of hits in MC track and in fake PR track

Definition at line 113 of file hit_module.py.

◆ ratio_hits_in_mc_tracks_and_in_good_pr_tracks

ratio_hits_in_mc_tracks_and_in_good_pr_tracks

list of fraction of number of hits in MC track and in good PR track

Definition at line 116 of file hit_module.py.

◆ ratio_hits_in_mc_tracks_and_in_pr_tracks

ratio_hits_in_mc_tracks_and_in_pr_tracks

list of fraction of number of hits in MC track and in PR track

Definition at line 109 of file hit_module.py.

◆ ratio_hits_in_mc_tracks_and_not_in_pr_tracks

ratio_hits_in_mc_tracks_and_not_in_pr_tracks

list of fraction of number of hits in MC track but not in PR track

Definition at line 107 of file hit_module.py.

◆ ratio_hits_in_missing_mc_tracks_and_in_pr_tracks

ratio_hits_in_missing_mc_tracks_and_in_pr_tracks

list of fraction of number of hits in missing MC track and in PR track

Definition at line 111 of file hit_module.py.

◆ write_tables

write_tables

cached value of the flag to write the validation figures of merit

Definition at line 85 of file hit_module.py.

The documentation for this class was generated from the following file:

tracking/scripts/tracking/validation/hit_module.py

Public Member Functions

Public Attributes

Detailed Description

Constructor & Destructor Documentation

◆ __init__()

Member Function Documentation

◆ event()

◆ examine_hits_in_event()

◆ initialize()

◆ terminate()

Member Data Documentation

◆ cdcHitsColumnname

◆ is_hit_found

◆ is_hit_matched

◆ mc_is_primary

◆ mc_missing

◆ mc_number_of_hits

◆ number_of_connected_tracks

◆ number_of_mc_hits

◆ number_of_pr_hits

◆ number_of_total_hits

◆ number_of_wrong_hits

◆ pr_number_of_hits

◆ pr_number_of_matched_hits

◆ ratio_hits_in_mc_tracks_and_in_fake_pr_tracks

◆ ratio_hits_in_mc_tracks_and_in_good_pr_tracks

◆ ratio_hits_in_mc_tracks_and_in_pr_tracks

◆ ratio_hits_in_mc_tracks_and_not_in_pr_tracks

◆ ratio_hits_in_missing_mc_tracks_and_in_pr_tracks

◆ write_tables

◆ init()