Inheritance diagram for ExpertTrackingValidationModule:

Public Member Functions
	__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)

	initialize (self)

	event (self)

	examine_hits_in_event (self)

	terminate (self)

	examine_pr_tracks (self)

	examine_mc_tracks (self)

	profiles_by_mc_parameters (self, xs, quantity_name, unit=None, parameter_names=['d_0', 'p_t', 'tan_lambda', 'multiplicity', 'phi', 'ndf',], make_hist=True, weights=None, is_asymmetry=False)

	profiles_by_pr_parameters (self, xs, quantity_name, unit=None, parameter_names=['Seed_p_t', 'Seed tan #lambda', 'Seed #phi'], make_hist=True)

	profiles_by_parameters_base (self, xs, quantity_name, parameter_names, profile_parameters, unit, make_hist, weights=None, is_asymmetry=False)

Public Attributes
	cdcHitsColumnname = cdcHitsColumnName
	cached name of the CDCHits StoreArray

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

	number_of_total_hits = collections.deque()
	number of all hits

	number_of_mc_hits = collections.deque()
	number of hits on MC track

	number_of_pr_hits = collections.deque()
	number of hits on pattern reconstructed tracks

	is_hit_found = collections.deque()
	list of flags for [not-]found hits

	is_hit_matched = collections.deque()
	list of flags for [not-]matched hits

	mc_missing = collections.deque()
	list of flags where MCRecoTrack is [not] missing MCTrackCand

	ratio_hits_in_mc_tracks_and_not_in_pr_tracks = collections.deque()
	list of fraction of number of hits in MC track but not in PR track

	ratio_hits_in_mc_tracks_and_in_pr_tracks = collections.deque()
	list of fraction of number of hits in MC track and in PR track

	ratio_hits_in_missing_mc_tracks_and_in_pr_tracks = collections.deque()
	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 = collections.deque()
	list of flags indicating that the MC track is [not] a primary MCParticle

	mc_number_of_hits = collections.deque()
	list of the number of MCTrackCandHits on the MC track

	number_of_connected_tracks = collections.deque()
	This is the number of mcTrackCands sharing a hit with the track cand.

	number_of_wrong_hits = collections.deque()
	This number gives information about the "badness" of the fake.

	pr_number_of_hits = collections.deque()
	list of the number of pattern-reconstructed hits

	pr_number_of_matched_hits = collections.deque()
	list of the number of pattern-reconstructed hits matched to MC track

	validation_name = name
	cached value of the tracking-validation name

	contact = contact
	cached value of the contact person name

	fit = fit
	cached value of the track fit

	pulls = pulls
	cached values of the track-fit pulls

	resolution = resolution
	cached value of the resolution

str	output_file_name
	cached value of the output ROOT TFile

	track_filter_object = track_filter_object
	cached value of the track-filter object

	plot_name_postfix = plot_name_postfix
	cached value of the suffix appended to the plot names

	plot_title_postfix = plot_title_postfix
	cached value of the suffix appended to the plot titles

	exclude_profile_pr_parameter = exclude_profile_pr_parameter
	cached list of perigee parameters excluded from PR side plots

	exclude_profile_mc_parameter = exclude_profile_mc_parameter
	cached list of perigee parameters excluded from MC side plots

	use_expert_folder = use_expert_folder
	cached flag to use the "expert" folder for the pull and residual plots

	trackCandidatesColumnName = trackCandidatesColumnName
	cached name of the RecoTracks StoreArray

	mcTrackCandidatesColumnName = mcTrackCandidatesColumName
	cached name of the MCRecoTracks StoreArray

	non_expert_parameters = non_expert_parameters
	list of parameters that determines which plots (all with corresponding x-axis) are marked as shifter plots

list	resolution_pt_binning = [0.05, 0.1, 0.25, 0.4, 0.6, 1., 1.5, 2., 3., 4.]
	default binning used for resolution plots over pt

	referenceFileName = None
	If this variable is set the code will open the file with same name as the file created here and will read the binning from the TH1/TProfile with same name as the one created here.

	trackMatchLookUp = Belle2.TrackMatchLookUp(self.mcTrackCandidatesColumnName, self.trackCandidatesColumnName)
	Track-match object that examines relation information from MCMatcherTracksModule.

	pr_clones_and_matches = collections.deque()
	Use dequeues in favour of lists to prevent repeated memory allocation of cost O(n)

	pr_matches = collections.deque()
	list of PR-track matches

	pr_fakes = collections.deque()
	list of PR-track fakes

	pr_seed_pt = collections.deque()
	list of PR-track seed pt values

	pr_seed_tan_lambdas = collections.deque()
	list of PR-track seed tan(lambda) values

	pr_seed_phi = collections.deque()
	list of PR-track seed phi values

	pr_omega_truths = collections.deque()
	list of PR-track seed omega-truth values

	pr_omega_estimates = collections.deque()
	list of PR-track seed omega-estimate values

	pr_omega_variances = collections.deque()
	list of PR-track seed omega-variance values

	pr_tan_lambda_truths = collections.deque()
	list of PR-track seed tan(lambda)-truth values

	pr_tan_lambda_estimates = collections.deque()
	list of PR-track seed tan(lambda)-estimate values

	pr_tan_lambda_variances = collections.deque()
	list of PR-track seed tan(lambda)-variance values

	pr_d0_truths = collections.deque()
	list of PR-track seed d0-truth values

	pr_d0_estimates = collections.deque()
	list of PR-track seed d0-estimate values

	pr_d0_variances = collections.deque()
	list of PR-track seed d0-variance values

	pr_z0_truths = collections.deque()
	list of PR-track seed z0-truth values

	pr_z0_estimates = collections.deque()
	list of PR-track seed z0-estimate values

	pr_pt_truths = collections.deque()
	list of PR-track seed pt-truth values

	pr_pt_estimates = collections.deque()
	list of PR-track seed pt-estimate values

	pr_bining_pt = collections.deque()
	list of PR-track binning values

	mc_matches = collections.deque()
	list of MC-track matches

	mc_charge_matches = collections.deque()
	list of MC-track matches, including matched charge

	mc_charge_asymmetry = collections.deque()
	list of MC-track matches charge asymmetry

	mc_charge_asymmetry_weights = collections.deque()
	list of MC-track matches charge asymmetry weights

	mc_primaries = collections.deque()
	list of MC-track primaries

	mc_d0s = collections.deque()
	list of MC-track d0 values

	mc_tan_lambdas = collections.deque()
	list of MC-track tan(lambda) values

	mc_phi = collections.deque()
	direction of the track in phi

	mc_pts = collections.deque()
	list of MC-track pt values

	mc_hit_efficiencies = collections.deque()
	list of MC-track hit efficiencies

	mc_multiplicities = collections.deque()
	list of MC-track multiplicities

	mc_ndf = collections.deque()
	list of MC-track number of degrees of freedom

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()

__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

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)
 
        ## cached name of the CDCHits StoreArray
        self.cdcHitsColumnname = cdcHitsColumnName
        ## cached value of the flag to write the validation figures of merit
        self.write_tables = write_tables
 

Member Function Documentation

◆ event()

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()

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)
 

◆ examine_mc_tracks()

examine_mc_tracks ( self )

inherited

Looks at the individual Monte Carlo tracks and store information about them

Definition at line 381 of file module.py.

    def examine_mc_tracks(self):
        """Looks at the individual Monte Carlo tracks and store information about them"""
 
        trackMatchLookUp = self.trackMatchLookUp
 
        # Analyse from the Monte Carlo reference side
        mcTrackCands = Belle2.PyStoreArray(self.mcTrackCandidatesColumnName)
        mcParticles = Belle2.PyStoreArray('MCParticles')
        if not mcTrackCands:
            return
 
        multiplicity = mcTrackCands.getEntries()
        multiplicity_primaries = multiplicity
 
        # measure the charge asymmetry
        n_matched_plus = 0
        n_matched_minus = 0
 
        for mcTrackCand in mcTrackCands:
            is_matched = trackMatchLookUp.isAnyChargeMatchedMCRecoTrack(mcTrackCand)
 
            relatedPRtrackCand = trackMatchLookUp.getRelatedPRRecoTrack(mcTrackCand)
            if relatedPRtrackCand:
                is_chargeMatched = trackMatchLookUp.isChargeMatched(relatedPRtrackCand)
            else:
                is_chargeMatched = False
 
            if is_chargeMatched:
                if mcTrackCand.getChargeSeed() > 0:
                    n_matched_plus += 1
                else:
                    n_matched_minus += 1
 
            hit_efficiency = trackMatchLookUp.getRelatedEfficiency(mcTrackCand)
            if math.isnan(hit_efficiency):
                hit_efficiency = 0
 
            mcParticle = trackMatchLookUp.getRelatedMCParticle(mcTrackCand)
            mcHelix = getHelixFromMCParticle(mcParticle)
 
            # fill the FilterProperties will all properties on this track
            # gathered so far
            filterProperties = FilterProperties(mcParticle=mcParticle,
                                                mcParticles=mcParticles)
 
            if not self.track_filter_object.doesMcPass(filterProperties):
                continue
 
            momentum = mcParticle.getMomentum()
            pt = momentum.Rho()
            tan_lambda = np.divide(1.0, math.tan(momentum.Theta()))  # Avoid zero division exception
            d0 = mcHelix.getD0()
            det_hit_ids = get_det_hit_ids(mcTrackCand)
            ndf = calc_ndf_from_det_hit_ids(det_hit_ids)
 
            self.mc_matches.append(is_matched)
            self.mc_charge_matches.append(is_chargeMatched and is_matched)
            self.mc_primaries.append(is_primary(mcParticle))
            self.mc_hit_efficiencies.append(hit_efficiency)
            self.mc_pts.append(pt)
            self.mc_d0s.append(d0)
            self.mc_tan_lambdas.append(tan_lambda)
            self.mc_multiplicities.append(multiplicity)
            self.mc_phi.append(momentum.Phi())
            self.mc_ndf.append(ndf)
            if not is_primary(mcParticle):
                multiplicity_primaries -= 1
 
        charge_asymmetry = (n_matched_plus - n_matched_minus)/(n_matched_plus +
                                                               n_matched_minus) if (n_matched_plus + n_matched_minus) != 0 else 0
        for mcTrackCand in mcTrackCands:
            if is_primary(mcParticle):
                self.mc_charge_asymmetry.append(charge_asymmetry)
                self.mc_charge_asymmetry_weights.append(1./multiplicity_primaries)
            else:
                self.mc_charge_asymmetry.append(0)
                self.mc_charge_asymmetry_weights.append(0)
 

◆ examine_pr_tracks()

examine_pr_tracks ( self )

inherited

Looks at the individual pattern reconstructed tracks and store information about them

Definition at line 264 of file module.py.

    def examine_pr_tracks(self):
        """Looks at the individual pattern reconstructed tracks and store information about them"""
 
        # Analyse from the pattern recognition side
        trackMatchLookUp = self.trackMatchLookUp
 
        trackCands = Belle2.PyStoreArray(self.trackCandidatesColumnName)
        mcParticles = Belle2.PyStoreArray("MCParticles")
        if not trackCands:
            return
 
        for trackCand in trackCands:
            is_matched = trackMatchLookUp.isAnyChargeMatchedPRRecoTrack(trackCand)
            is_clone = trackMatchLookUp.isAnyChargeClonePRRecoTrack(trackCand)
 
            pt_truth = float('nan')
            omega_truth = float('nan')
            tan_lambda_truth = float('nan')
            d0_truth = float('nan')
            z0_truth = float('nan')
 
            mcParticle = None
            if is_matched or is_clone:
                # Only matched and clone tracks have a related MCParticle
                mcParticle = trackMatchLookUp.getRelatedMCParticle(trackCand)
                mcHelix = getHelixFromMCParticle(mcParticle)
                omega_truth = mcHelix.getOmega()
                tan_lambda_truth = mcHelix.getTanLambda()
                pt_truth = mcParticle.getMomentum().Rho()
                d0_truth = mcHelix.getD0()
                z0_truth = mcHelix.getZ0()
 
            # fill the FilterProperties will all properties on this track
            # gathered so far
            filterProperties = FilterProperties(trackCand=trackCand,
                                                mcParticle=mcParticle, mcParticles=mcParticles)
 
            if self.fit:
                prTrackFitResult = \
                    trackMatchLookUp.getRelatedTrackFitResult(trackCand)
                filterProperties.wasFitted = prTrackFitResult is not None
                filterProperties.fitResult = prTrackFitResult
            else:
                prTrackFitResult = getSeedTrackFitResult(trackCand)
                filterProperties.seedResult = prTrackFitResult
 
            # skip this track due to the filtering rules ?
            if not self.track_filter_object.doesPrPass(filterProperties):
                continue
 
            omega_estimate = float('nan')
            omega_variance = float('nan')
            tan_lambda_estimate = float('nan')
            tan_lambda_variance = float('nan')
            d0_estimate = float('nan')
            d0_variance = float('nan')
            z0_estimate = float('nan')
            pt_estimate = float('nan')
 
            momentum = float('nan')
 
            # store seed information, they are always available from the pattern reco
            # even if the fit was no successful
            # this information can we used when plotting fake tracks, for example
            seed_momentum = trackCand.getMomentumSeed()
            # Avoid zero division exception
            seed_tan_lambda = np.divide(1.0, math.tan(seed_momentum.Theta()))
            seed_phi = seed_momentum.Phi()
            seed_pt = seed_momentum.Rho()
 
            if prTrackFitResult:
                omega_estimate = prTrackFitResult.getOmega()
                omega_variance = prTrackFitResult.getCov()[9]
 
                tan_lambda_estimate = prTrackFitResult.getCotTheta()
                tan_lambda_variance = prTrackFitResult.getCov()[14]
 
                d0_estimate = prTrackFitResult.getD0()
                d0_variance = prTrackFitResult.getCov()[0]
 
                z0_estimate = prTrackFitResult.getZ0()
 
                momentum = prTrackFitResult.getMomentum()
                pt_estimate = momentum.Rho()
 
            # store properties of the seed
            self.pr_seed_pt.append(seed_pt)
            self.pr_seed_tan_lambdas.append(seed_tan_lambda)
            self.pr_seed_phi.append(seed_phi)
 
            self.pr_bining_pt.append(pt_truth)
 
            # store properties resulting from this trackfit
            isMatchedOrIsClone = is_matched or is_clone
            self.pr_clones_and_matches.append(isMatchedOrIsClone)
            self.pr_matches.append(is_matched)
 
            self.pr_fakes.append(not isMatchedOrIsClone)
 
            self.pr_omega_estimates.append(omega_estimate)
            self.pr_omega_variances.append(omega_variance)
            self.pr_omega_truths.append(omega_truth)
 
            self.pr_tan_lambda_estimates.append(tan_lambda_estimate)
            self.pr_tan_lambda_variances.append(tan_lambda_variance)
            self.pr_tan_lambda_truths.append(tan_lambda_truth)
 
            self.pr_d0_estimates.append(d0_estimate)
            self.pr_d0_variances.append(d0_variance)
            self.pr_d0_truths.append(d0_truth)
 
            self.pr_z0_estimates.append(z0_estimate)
            self.pr_z0_truths.append(z0_truth)
 
            self.pr_pt_estimates.append(pt_estimate)
            self.pr_pt_truths.append(pt_truth)
 

◆ initialize()

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()

◆ profiles_by_mc_parameters()

profiles_by_mc_parameters	(	self,
		xs,
		quantity_name,
		unit = None,
		parameter_names = [ 'd_0', 'p_t', 'tan_lambda', 'multiplicity', 'phi', 'ndf', ],
		make_hist = True,
		weights = None,
		is_asymmetry = False )

inherited

Create profile histograms by MC-track parameters

Definition at line 708 of file module.py.

    ):
        """Create profile histograms by MC-track parameters"""
 
        # apply exclusion list
        new_parameter_names = [item for item in parameter_names if item
                               not in self.exclude_profile_mc_parameter]
 
        # Profile versus the various parameters
        profile_parameters = {
            'd_{0}': self.mc_d0s,
            'p_{t}': self.mc_pts,
            'tan #lambda': self.mc_tan_lambdas,
            '#phi': self.mc_phi,
            'multiplicity': self.mc_multiplicities,
            'ndf': self.mc_ndf,
        }
 
        return self.profiles_by_parameters_base(
            xs,
            quantity_name,
            new_parameter_names,
            profile_parameters,
            unit,
            make_hist,
            weights=weights,
            is_asymmetry=is_asymmetry
        )
 

◆ profiles_by_parameters_base()

profiles_by_parameters_base	(	self,
		xs,
		quantity_name,
		parameter_names,
		profile_parameters,
		unit,
		make_hist,
		weights = None,
		is_asymmetry = False )

inherited

Create profile histograms for generic parameters

Definition at line 780 of file module.py.

    ):
        """Create profile histograms for generic parameters"""
 
        contact = self.contact
 
        validation_plots = []
        plot_name_prefix = self.validation_name + '_' + root_save_name(quantity_name) \
            + self.plot_name_postfix
 
        if make_hist:
            # Histogram of the quantity
            histogram = ValidationPlot(plot_name_prefix, self.referenceFileName)
            histogram.hist(xs, weights=weights)
 
            histogram.xlabel = quantity_name
            histogram.description = 'Not a serious plot yet.'
            histogram.check = ''
            histogram.contact = contact
            histogram.title = quantity_name + self.plot_title_postfix
 
            validation_plots.append(histogram)
 
        for (parameter_name, parameter_values) in list(profile_parameters.items()):
            if parameter_name in parameter_names \
                    or root_save_name(parameter_name) in parameter_names:
 
                is_expert = not (parameter_name in self.non_expert_parameters)
 
                parameter_root_name = root_save_name(parameter_name)
 
                # Apply some boundaries for the maximal tracking acceptance
                # such that the plots look more instructive
                if 'tan_lambda' in parameter_root_name:
                    lower_bound = -2.0
                    upper_bound = 5.0
                    # need different bounds for cosmics
                    if 'cosmics' in self.validation_name.lower() or \
                       'cosmics' in self.output_file_name.lower():
                        lower_bound = -1.5
                        upper_bound = 1.5
                elif 'ndf' in parameter_root_name:
                    lower_bound = 0
                    upper_bound = min(200, np.max(parameter_values))
                elif 'p_t' in parameter_root_name:
                    lower_bound = 0
                    upper_bound = 2.5
                    # need different upper_bound for cosmics
                    if 'cosmics' in self.validation_name.lower() or \
                       'cosmics' in self.output_file_name.lower():
                        upper_bound = 30
                elif 'd_0' in parameter_root_name:
                    lower_bound = -0.06
                    upper_bound = 0.06
                    # need different bounds for cosmics
                    if 'cosmics' in self.validation_name.lower() or \
                       'cosmics' in self.output_file_name.lower():
                        lower_bound = -20
                        upper_bound = 20
                else:
                    lower_bound = None
                    upper_bound = None
 
                profile_plot_name = plot_name_prefix + '_by_' \
                    + root_save_name(parameter_name)
                profile_plot = ValidationPlot(profile_plot_name, self.referenceFileName)
                profile_plot.profile(parameter_values,
                                     xs,
                                     weights=weights,
                                     outlier_z_score=10.0,
                                     lower_bound=lower_bound,
                                     upper_bound=upper_bound,
                                     y_binary=True,
                                     is_expert=is_expert,
                                     is_asymmetry=is_asymmetry)
 
                profile_plot.xlabel = compose_axis_label(parameter_name)
                profile_plot.ylabel = compose_axis_label(quantity_name, unit)
                profile_plot.title = quantity_name + ' by ' + parameter_name \
                    + ' profile' + self.plot_title_postfix
 
                profile_plot.description = \
                    'Dependence of %s of the track over the true %s' \
                    % (quantity_name, parameter_name)
                profile_plot.check = 'Variations should be low'
                profile_plot.contact = contact
                validation_plots.append(profile_plot)
 
        return validation_plots

◆ profiles_by_pr_parameters()

profiles_by_pr_parameters	(	self,
		xs,
		quantity_name,
		unit = None,
		parameter_names = ['Seed_p_t', 'Seed tan #lambda', 'Seed #phi'],
		make_hist = True )

inherited

Create profile histograms by PR-track parameters

Definition at line 752 of file module.py.

    ):
        """Create profile histograms by PR-track parameters"""
 
        # apply exclusion list
        new_parameter_names = [item for item in parameter_names if item
                               not in self.exclude_profile_pr_parameter]
 
        # Profile versus the various parameters
        profile_parameters = {'Seed p_{t}': self.pr_seed_pt,
                              'Seed tan #lambda': self.pr_seed_tan_lambdas,
                              'Seed #phi': self.pr_seed_phi}
 
        return self.profiles_by_parameters_base(
            xs,
            quantity_name,
            new_parameter_names,
            profile_parameters,
            unit,
            make_hist,
        )
 

◆ terminate()

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 = cdcHitsColumnName

cached name of the CDCHits StoreArray

Definition at line 83 of file hit_module.py.

◆ contact

contact = contact

inherited

cached value of the contact person name

Definition at line 132 of file module.py.

◆ exclude_profile_mc_parameter

exclude_profile_mc_parameter = exclude_profile_mc_parameter

inherited

cached list of perigee parameters excluded from MC side plots

Definition at line 151 of file module.py.

◆ exclude_profile_pr_parameter

exclude_profile_pr_parameter = exclude_profile_pr_parameter

inherited

cached list of perigee parameters excluded from PR side plots

Definition at line 149 of file module.py.

◆ fit

fit = fit

inherited

cached value of the track fit

Definition at line 134 of file module.py.

◆ is_hit_found

is_hit_found = collections.deque()

list of flags for [not-]found hits

Definition at line 99 of file hit_module.py.

◆ is_hit_matched

is_hit_matched = collections.deque()

list of flags for [not-]matched hits

Definition at line 101 of file hit_module.py.

◆ mc_charge_asymmetry

mc_charge_asymmetry = collections.deque()

inherited

list of MC-track matches charge asymmetry

Definition at line 238 of file module.py.

◆ mc_charge_asymmetry_weights

mc_charge_asymmetry_weights = collections.deque()

inherited

list of MC-track matches charge asymmetry weights

Definition at line 240 of file module.py.

◆ mc_charge_matches

mc_charge_matches = collections.deque()

inherited

list of MC-track matches, including matched charge

Definition at line 236 of file module.py.

◆ mc_d0s

mc_d0s = collections.deque()

inherited

list of MC-track d0 values

Definition at line 244 of file module.py.

◆ mc_hit_efficiencies

mc_hit_efficiencies = collections.deque()

inherited

list of MC-track hit efficiencies

Definition at line 252 of file module.py.

◆ mc_is_primary

mc_is_primary = collections.deque()

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

Definition at line 119 of file hit_module.py.

◆ mc_matches

mc_matches = collections.deque()

inherited

list of MC-track matches

Definition at line 234 of file module.py.

◆ mc_missing

mc_missing = collections.deque()

list of flags where MCRecoTrack is [not] missing MCTrackCand

Definition at line 105 of file hit_module.py.

◆ mc_multiplicities

mc_multiplicities = collections.deque()

inherited

list of MC-track multiplicities

Definition at line 254 of file module.py.

◆ mc_ndf

mc_ndf = collections.deque()

inherited

list of MC-track number of degrees of freedom

Definition at line 256 of file module.py.

◆ mc_number_of_hits

mc_number_of_hits = collections.deque()

list of the number of MCTrackCandHits on the MC track

Definition at line 121 of file hit_module.py.

◆ mc_phi

mc_phi = collections.deque()

inherited

direction of the track in phi

Definition at line 248 of file module.py.

◆ mc_primaries

mc_primaries = collections.deque()

inherited

list of MC-track primaries

Definition at line 242 of file module.py.

◆ mc_pts

mc_pts = collections.deque()

inherited

list of MC-track pt values

Definition at line 250 of file module.py.

◆ mc_tan_lambdas

mc_tan_lambdas = collections.deque()

inherited

list of MC-track tan(lambda) values

Definition at line 246 of file module.py.

◆ mcTrackCandidatesColumnName

mcTrackCandidatesColumnName = mcTrackCandidatesColumName

inherited

cached name of the MCRecoTracks StoreArray

Definition at line 157 of file module.py.

◆ non_expert_parameters

non_expert_parameters = non_expert_parameters

inherited

list of parameters that determines which plots (all with corresponding x-axis) are marked as shifter plots

Definition at line 159 of file module.py.

◆ number_of_connected_tracks

number_of_connected_tracks = collections.deque()

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 = collections.deque()

number of hits on MC track

Definition at line 95 of file hit_module.py.

◆ number_of_pr_hits

number_of_pr_hits = collections.deque()

number of hits on pattern reconstructed tracks

Definition at line 97 of file hit_module.py.

◆ number_of_total_hits

number_of_total_hits = collections.deque()

number of all hits

Definition at line 93 of file hit_module.py.

◆ number_of_wrong_hits

number_of_wrong_hits = collections.deque()

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

Definition at line 127 of file hit_module.py.

◆ output_file_name

str output_file_name

inherited

Initial value:

= output_file_name or self.validation_name \

+ 'TrackingValidation.root'

cached value of the output ROOT TFile

Definition at line 140 of file module.py.

◆ plot_name_postfix

plot_name_postfix = plot_name_postfix

inherited

cached value of the suffix appended to the plot names

Definition at line 145 of file module.py.

◆ plot_title_postfix

plot_title_postfix = plot_title_postfix

inherited

cached value of the suffix appended to the plot titles

Definition at line 147 of file module.py.

◆ pr_bining_pt

pr_bining_pt = collections.deque()

inherited

list of PR-track binning values

Definition at line 231 of file module.py.

◆ pr_clones_and_matches

pr_clones_and_matches = collections.deque()

inherited

Use dequeues in favour of lists to prevent repeated memory allocation of cost O(n)

list of PR-track clones and matches

Definition at line 186 of file module.py.

◆ pr_d0_estimates

pr_d0_estimates = collections.deque()

inherited

list of PR-track seed d0-estimate values

Definition at line 216 of file module.py.

◆ pr_d0_truths

pr_d0_truths = collections.deque()

inherited

list of PR-track seed d0-truth values

Definition at line 214 of file module.py.

◆ pr_d0_variances

pr_d0_variances = collections.deque()

inherited

list of PR-track seed d0-variance values

Definition at line 218 of file module.py.

◆ pr_fakes

pr_fakes = collections.deque()

inherited

list of PR-track fakes

Definition at line 190 of file module.py.

◆ pr_matches

pr_matches = collections.deque()

inherited

list of PR-track matches

Definition at line 188 of file module.py.

◆ pr_number_of_hits

pr_number_of_hits = collections.deque()

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 = collections.deque()

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

Definition at line 134 of file hit_module.py.

◆ pr_omega_estimates

pr_omega_estimates = collections.deque()

inherited

list of PR-track seed omega-estimate values

Definition at line 202 of file module.py.

◆ pr_omega_truths

pr_omega_truths = collections.deque()

inherited

list of PR-track seed omega-truth values

Definition at line 200 of file module.py.

◆ pr_omega_variances

pr_omega_variances = collections.deque()

inherited

list of PR-track seed omega-variance values

Definition at line 204 of file module.py.

◆ pr_pt_estimates

pr_pt_estimates = collections.deque()

inherited

list of PR-track seed pt-estimate values

Definition at line 228 of file module.py.

◆ pr_pt_truths

pr_pt_truths = collections.deque()

inherited

list of PR-track seed pt-truth values

Definition at line 226 of file module.py.

◆ pr_seed_phi

pr_seed_phi = collections.deque()

inherited

list of PR-track seed phi values

Definition at line 197 of file module.py.

◆ pr_seed_pt

pr_seed_pt = collections.deque()

inherited

list of PR-track seed pt values

Definition at line 193 of file module.py.

◆ pr_seed_tan_lambdas

pr_seed_tan_lambdas = collections.deque()

inherited

list of PR-track seed tan(lambda) values

Definition at line 195 of file module.py.

◆ pr_tan_lambda_estimates

pr_tan_lambda_estimates = collections.deque()

inherited

list of PR-track seed tan(lambda)-estimate values

Definition at line 209 of file module.py.

◆ pr_tan_lambda_truths

pr_tan_lambda_truths = collections.deque()

inherited

list of PR-track seed tan(lambda)-truth values

Definition at line 207 of file module.py.

◆ pr_tan_lambda_variances

pr_tan_lambda_variances = collections.deque()

inherited

list of PR-track seed tan(lambda)-variance values

Definition at line 211 of file module.py.

◆ pr_z0_estimates

pr_z0_estimates = collections.deque()

inherited

list of PR-track seed z0-estimate values

Definition at line 223 of file module.py.

◆ pr_z0_truths

pr_z0_truths = collections.deque()

inherited

list of PR-track seed z0-truth values

Definition at line 221 of file module.py.

◆ pulls

pulls = pulls

inherited

cached values of the track-fit pulls

Definition at line 136 of file module.py.

◆ ratio_hits_in_mc_tracks_and_in_fake_pr_tracks

ratio_hits_in_mc_tracks_and_in_fake_pr_tracks

Initial value:

= \

collections.deque()

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

Initial value:

= \

collections.deque()

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 = collections.deque()

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 = collections.deque()

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 = collections.deque()

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

Definition at line 111 of file hit_module.py.

◆ referenceFileName

referenceFileName = None

inherited

If this variable is set the code will open the file with same name as the file created here and will read the binning from the TH1/TProfile with same name as the one created here.

If you do not want this feature either remove the corresponding root files from the validation directory (this will trigger the default behaviour) or set the environmental variable DO_NOT_READ_BINNING

Definition at line 168 of file module.py.

◆ resolution

resolution = resolution

inherited

cached value of the resolution

Definition at line 138 of file module.py.

◆ resolution_pt_binning

list resolution_pt_binning = [0.05, 0.1, 0.25, 0.4, 0.6, 1., 1.5, 2., 3., 4.]

inherited

default binning used for resolution plots over pt

Definition at line 162 of file module.py.

◆ track_filter_object

track_filter_object = track_filter_object

inherited

cached value of the track-filter object

Definition at line 143 of file module.py.

◆ trackCandidatesColumnName

trackCandidatesColumnName = trackCandidatesColumnName

inherited

cached name of the RecoTracks StoreArray

Definition at line 155 of file module.py.

◆ trackMatchLookUp

trackMatchLookUp = Belle2.TrackMatchLookUp(self.mcTrackCandidatesColumnName, self.trackCandidatesColumnName)

inherited

Track-match object that examines relation information from MCMatcherTracksModule.

Definition at line 181 of file module.py.

◆ use_expert_folder

use_expert_folder = use_expert_folder

inherited

cached flag to use the "expert" folder for the pull and residual plots

Definition at line 153 of file module.py.

◆ validation_name

validation_name = name

inherited

cached value of the tracking-validation name

Definition at line 130 of file module.py.

◆ write_tables

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()

◆ examine_mc_tracks()

◆ examine_pr_tracks()

◆ initialize()

◆ profiles_by_mc_parameters()

◆ profiles_by_parameters_base()

◆ profiles_by_pr_parameters()

◆ terminate()

Member Data Documentation

◆ cdcHitsColumnname

◆ contact

◆ exclude_profile_mc_parameter

◆ exclude_profile_pr_parameter

◆ fit

◆ is_hit_found

◆ is_hit_matched

◆ mc_charge_asymmetry

◆ mc_charge_asymmetry_weights

◆ mc_charge_matches

◆ mc_d0s

◆ mc_hit_efficiencies

◆ mc_is_primary

◆ mc_matches

◆ mc_missing

◆ mc_multiplicities

◆ mc_ndf

◆ mc_number_of_hits

◆ mc_phi

◆ mc_primaries

◆ mc_pts

◆ mc_tan_lambdas

◆ mcTrackCandidatesColumnName

◆ non_expert_parameters

◆ number_of_connected_tracks

◆ number_of_mc_hits

◆ number_of_pr_hits

◆ number_of_total_hits

◆ number_of_wrong_hits

◆ output_file_name

◆ plot_name_postfix

◆ plot_title_postfix

◆ pr_bining_pt

◆ pr_clones_and_matches

◆ pr_d0_estimates

◆ pr_d0_truths

◆ pr_d0_variances

◆ pr_fakes

◆ pr_matches

◆ pr_number_of_hits

◆ pr_number_of_matched_hits

◆ pr_omega_estimates

◆ pr_omega_truths

◆ pr_omega_variances

◆ pr_pt_estimates

◆ pr_pt_truths

◆ pr_seed_phi

◆ pr_seed_pt

◆ pr_seed_tan_lambdas

◆ pr_tan_lambda_estimates

◆ pr_tan_lambda_truths

◆ pr_tan_lambda_variances

◆ pr_z0_estimates

◆ pr_z0_truths

◆ pulls

◆ 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

◆ referenceFileName

◆ resolution

◆ resolution_pt_binning

◆ track_filter_object

◆ init()