Inheritance diagram for MCSideTrackingValidationModule:

Public Member Functions
def	__init__ (self, name, contact, output_file_name=None, reco_tracks_name='RecoTracks', mc_reco_tracks_name='MCRecoTracks', expert_level=None)

def	initialize (self)

def	prepare (self)

def	pick (self, mc_reco_track)

def	peel (self, mc_reco_track)

def	peel_mc_to_pr_match_info (self, mc_reco_track)

def	peel_hit_efficiencies_in_all_pr_tracks (self, mc_reco_track)

Public Attributes
	reco_tracks_name
	Name of the StoreArray of the tracks from pattern recognition.

	mc_reco_tracks_name
	Name of the StoreArray of the ideal mc tracks.

	track_match_look_up
	Reference to the track match lookup object reading the relation information constructed by the MCMatcherTracksModule.

	found_det_hit_ids
	Set of all detector and hits ids contained in any pr track.

	matched_det_hit_ids
	Set of all detector and hits ids contained in matched pr tracks.

	clone_det_hit_ids
	Set of all detector and hits ids contained in clone pr tracks.

	fake_det_hit_ids
	Set of all detector and hits ids contained in background and ghost pr tracks.

Static Public Attributes
int	default_expert_level = 10
	the threshold value for the expert level

refiners	save_tree
	Save a tree of all collected variables in a sub folder.

refiners	save_tree_basic
	Save a basic tree.

refiners	save_overview_figures_of_merit
	Generate the average finding efficiencies and hit efficiencies.

refiners	save_hit_efficiency_histogram
	Save a histogram of the hit efficiency.

dict	renaming_select_for_finding_efficiency_profiles
	Rename the efficiency-profile quantities so that they display nicely in ROOT TLatex.

refiners	save_finding_efficiency_profiles
	Make profile of finding efficiency.

refiners	save_finding_efficiency_by_tan_lamba_profiles
	Make profile of finding efficiency versus tan(lambda)

refiners	save_finding_efficiency_by_tan_lamba_in_pt_groups_profiles
	Make profiles of finding efficiency versus tan(lambda) grouped by transverse momentum.

dict	renaming_select_for_hit_efficiency_profiles
	Rename the hit-profile quantities so that they display nicely in ROOT TLatex.

refiners	save_hit_efficiency_profiles
	Make profile of hit efficiency.

refiners	save_hit_efficiency_by_tan_lambda_profiles
	Make profile of finding efficiency versus tan(lambda)

refiners	save_finding_efficiency_by_pt_profiles_groupbyCharge
	Charge dependent histograms Make profile of finding efficiency versus pt, tan(lambda)

refiners	save_finding_efficiency_by_tan_lambda_profiles_groupbyCharge
	Charge dependent histograms Make profile of finding efficiency versus pt, tan(lambda)

refiners	save_hit_efficiency_by_pt_profiles_groupbyCharge
	Charge dependent histograms Make profile of finding efficiency versus pt, tan(lambda)

refiners	save_hit_efficiency_by_tan_lambda_profiles_groupbyCharge
	Charge dependent histograms Make profile of finding efficiency versus pt, tan(lambda)

refiners	save_hit_efficiency_in_all_found_hist
	This creates a histogram for all MC track displaying the ratio of hits contained in any PR track Hence this is distinctly larger than the hit efficiency to the matched PR track Usefulness under discussion.

refiners	save_missing_mc_tracks_hit_efficiency_in_all_found_hist
	This creates a histogram for each missing MC track displaying the ratio of hits contained in any PR tracks High values in this hit efficiencies means that the MC track is consumed by other PR tracks but no proper match could be established.

Detailed Description

Module to collect matching information about the found particles and to generate
   validation plots and figures of merit on the performance of track finding.

 Expert level behavior:
    expert_level = default_expert_level: all figures and plots from this module except tree entries
    expert_level > default_expert_level: everything including tree entries
    expert_level <= default_expert_level//2: only basic figures
    default_expert_level//2 < expert_level < default_expert_level: basic figures and basic tree entries

Definition at line 22 of file mc_side_module.py.

Constructor & Destructor Documentation

◆ init()

def __init__	(	self,
		name,
		contact,
		output_file_name = `None`,
		reco_tracks_name = `'RecoTracks'`,
		mc_reco_tracks_name = `'MCRecoTracks'`,
		expert_level = `None`
	)

Constructor

Definition at line 35 of file mc_side_module.py.

            expert_level=None):
        """Constructor"""
 
        output_file_name = output_file_name or name + 'TrackingValidation.root'
 
        super().__init__(foreach=mc_reco_tracks_name,
                         name=name,
                         output_file_name=output_file_name,
                         contact=contact,
                         expert_level=expert_level)
 
        self.reco_tracks_name = reco_tracks_name
 
        self.mc_reco_tracks_name = mc_reco_tracks_name
 
        self.track_match_look_up = None
 
        if self.expert_level >= self.default_expert_level:
            
            self.found_det_hit_ids = set()
 
            self.matched_det_hit_ids = set()
 
            self.clone_det_hit_ids = set()
 
            self.fake_det_hit_ids = set()

Member Function Documentation

◆ initialize()

def initialize ( self )

Initialization signal at the start of the event processing

Definition at line 75 of file mc_side_module.py.

    def initialize(self):
        """Initialization signal at the start of the event processing"""
        super().initialize()
        self.track_match_look_up = Belle2.TrackMatchLookUp(self.mc_reco_tracks_name, self.reco_tracks_name)
 

◆ peel()

def peel	(	self,
		mc_reco_track
	)

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

Definition at line 119 of file mc_side_module.py.

    def peel(self, mc_reco_track):
        """Looks at the individual Monte Carlo tracks and store information about them"""
        track_match_look_up = self.track_match_look_up
 
        # Analyse from the Monte Carlo reference side
        mc_reco_tracks = Belle2.PyStoreArray(self.foreach)
        multiplicity = mc_reco_tracks.getEntries()
 
        mc_particle = track_match_look_up.getRelatedMCParticle(mc_reco_track)
        is_primary = bool(mc_particle.hasStatus(Belle2.MCParticle.c_PrimaryParticle))
        mc_to_pr_match_info_crops = self.peel_mc_to_pr_match_info(mc_reco_track)
        mc_store_array_crops = peelers.peel_store_array_info(mc_reco_track, key="mc_{part_name}")
 
        crops = dict(is_primary=is_primary,
                     multiplicity=multiplicity,
                     **mc_to_pr_match_info_crops,
                     **mc_store_array_crops
                     )
 
        if self.expert_level >= self.default_expert_level:
            reco_track = track_match_look_up.getRelatedPRRecoTrack(mc_reco_track)
            mc_particle_crops = peelers.peel_mc_particle(mc_particle)
            hit_content_crops = peelers.peel_reco_track_hit_content(mc_reco_track)
 
            # Custom peel function to get single detector hit purities
            subdetector_hit_efficiency_crops = peelers.peel_subdetector_hit_efficiency(mc_reco_track, reco_track)
 
            mc_hit_efficiencies_in_all_pr_tracks_crops = self.peel_hit_efficiencies_in_all_pr_tracks(mc_reco_track)
 
            # Event Info
            event_meta_data = Belle2.PyStoreObj("EventMetaData")
            event_crops = peelers.peel_event_info(event_meta_data)
 
            # Store Array for easier joining
            store_array_crops = peelers.peel_store_array_info(reco_track, key="pr_{part_name}")
 
            # Information on PR reco track
            pr_purity_information = {
                "pr_hit_purity": track_match_look_up.getRelatedPurity(reco_track) if reco_track else float("nan"),
                **peelers.peel_subdetector_hit_purity(reco_track=reco_track, mc_reco_track=mc_reco_track,
                                                      key="pr_{part_name}")
            }
 
            # Information on TrackFinders
            trackfinder_crops = peelers.peel_trackfinder(reco_track)
 
            # Basic peel function to get Quality Indicators
            qualityindicator_crops = peelers.peel_quality_indicators(reco_track)
 
            crops.update(dict(**hit_content_crops,
                              **mc_particle_crops,
                              **subdetector_hit_efficiency_crops,
                              **mc_hit_efficiencies_in_all_pr_tracks_crops,
                              **event_crops,
                              **store_array_crops,
                              **pr_purity_information,
                              **trackfinder_crops,
                              **qualityindicator_crops
                              ))
 
        return crops
 

◆ peel_hit_efficiencies_in_all_pr_tracks()

def peel_hit_efficiencies_in_all_pr_tracks	(	self,
		mc_reco_track
	)

Extracts hit efficiencies

Definition at line 195 of file mc_side_module.py.

    def peel_hit_efficiencies_in_all_pr_tracks(self, mc_reco_track):
        """Extracts hit efficiencies"""
        mc_det_hit_ids = utilities.get_det_hit_ids(mc_reco_track)
 
        hit_efficiency_in_all_found = utilities.calc_hit_efficiency(self.found_det_hit_ids,
                                                                    mc_det_hit_ids)
 
        unfound_hit_efficiency = 1.0 - hit_efficiency_in_all_found
 
        hit_efficiency_in_all_matched = utilities.calc_hit_efficiency(self.matched_det_hit_ids,
                                                                      mc_det_hit_ids)
 
        hit_efficiency_in_all_fake = utilities.calc_hit_efficiency(self.fake_det_hit_ids,
                                                                   mc_det_hit_ids)
 
        hit_efficiency_crops = dict(
            hit_efficiency_in_all_found=hit_efficiency_in_all_found,
            unfound_hit_efficiency=unfound_hit_efficiency,
            hit_efficiency_in_all_matched=hit_efficiency_in_all_matched,
            hit_efficiency_in_all_fake=hit_efficiency_in_all_fake,
        )
        return hit_efficiency_crops
 

◆ peel_mc_to_pr_match_info()

def peel_mc_to_pr_match_info	(	self,
		mc_reco_track
	)

Extracts track-match information from the MCMatcherTracksModule results

Definition at line 181 of file mc_side_module.py.

    def peel_mc_to_pr_match_info(self, mc_reco_track):
        """Extracts track-match information from the MCMatcherTracksModule results"""
        track_match_look_up = self.track_match_look_up
        return dict(
            is_matched=track_match_look_up.isAnyChargeMatchedMCRecoTrack(mc_reco_track),
            is_matched_correct_charge=track_match_look_up.isCorrectChargeMatchedMCRecoTrack(mc_reco_track),
            is_matched_wrong_charge=track_match_look_up.isWrongChargeMatchedMCRecoTrack(mc_reco_track),
            is_merged=track_match_look_up.isAnyChargeMergedMCRecoTrack(mc_reco_track),
            is_merged_correct_charge=track_match_look_up.isCorrectChargeMergedMCRecoTrack(mc_reco_track),
            is_merged_wrong_charge=track_match_look_up.isWrongChargeMergedMCRecoTrack(mc_reco_track),
            is_missing=track_match_look_up.isMissingMCRecoTrack(mc_reco_track),
            hit_efficiency=track_match_look_up.getRelatedEfficiency(mc_reco_track),
        )
 

◆ pick()

def pick	(	self,
		mc_reco_track
	)

Pick every MCRecoTrack

Definition at line 115 of file mc_side_module.py.

    def pick(self, mc_reco_track):
        """Pick every MCRecoTrack"""
        return True
 

◆ prepare()

def prepare ( self )

Collect some statistics about the pattern recognition tracks used for comparision to the MC tracks

Executed once at the start of each event.

Definition at line 80 of file mc_side_module.py.

    def prepare(self):
        """Collect some statistics about the pattern recognition tracks used for comparision to the MC tracks
 
        Executed once at the start of each event.
        """
        super().prepare()
        if self.expert_level >= self.default_expert_level:
            reco_tracks = Belle2.PyStoreArray(self.reco_tracks_name)
            track_match_look_up = self.track_match_look_up
 
            found_det_hit_ids = set()
            matched_det_hit_ids = set()
            clone_det_hit_ids = set()
            fake_det_hit_ids = set()
 
            for reco_track in reco_tracks:
                det_hit_ids = utilities.get_det_hit_ids(reco_track)
 
                found_det_hit_ids |= det_hit_ids
 
                if track_match_look_up.isAnyChargeMatchedPRRecoTrack(reco_track):
                    matched_det_hit_ids |= det_hit_ids
 
                if track_match_look_up.isAnyChargeClonePRRecoTrack(reco_track):
                    clone_det_hit_ids |= det_hit_ids
 
                if (track_match_look_up.isGhostPRRecoTrack(reco_track) or
                        track_match_look_up.isBackgroundPRRecoTrack(reco_track)):
                    fake_det_hit_ids |= det_hit_ids
 
            self.found_det_hit_ids = found_det_hit_ids
            self.matched_det_hit_ids = matched_det_hit_ids
            self.clone_det_hit_ids = clone_det_hit_ids
            self.fake_det_hit_ids = fake_det_hit_ids
 

Member Data Documentation

◆ clone_det_hit_ids

clone_det_hit_ids

Set of all detector and hits ids contained in clone pr tracks.

Updated each event.

Definition at line 70 of file mc_side_module.py.

◆ default_expert_level

int default_expert_level = 10

static

the threshold value for the expert level

Definition at line 33 of file mc_side_module.py.

◆ fake_det_hit_ids

fake_det_hit_ids

Set of all detector and hits ids contained in background and ghost pr tracks.

Updated each event.

Definition at line 73 of file mc_side_module.py.

◆ found_det_hit_ids

found_det_hit_ids

Set of all detector and hits ids contained in any pr track.

Updated each event.

Definition at line 64 of file mc_side_module.py.

◆ matched_det_hit_ids

matched_det_hit_ids

Set of all detector and hits ids contained in matched pr tracks.

Updated each event.

Definition at line 67 of file mc_side_module.py.

◆ mc_reco_tracks_name

mc_reco_tracks_name

Name of the StoreArray of the ideal mc tracks.

Definition at line 57 of file mc_side_module.py.

◆ reco_tracks_name

reco_tracks_name

Name of the StoreArray of the tracks from pattern recognition.

Definition at line 54 of file mc_side_module.py.

◆ renaming_select_for_finding_efficiency_profiles

dict renaming_select_for_finding_efficiency_profiles

static

Initial value:

=  {
        'is_matched': 'finding efficiency',
        'd0_truth': 'd_{0}',
        'pt_truth': 'p_{t}',
        'multiplicity': 'multiplicity',
        'phi0_truth': '#phi',
    }

Rename the efficiency-profile quantities so that they display nicely in ROOT TLatex.

Definition at line 272 of file mc_side_module.py.

◆ renaming_select_for_hit_efficiency_profiles

dict renaming_select_for_hit_efficiency_profiles

static

Initial value:

=  {
        'hit_efficiency': 'hit efficiency',
        'd0_truth': 'd_{0}',
        'pt_truth': 'p_{t}',
        'multiplicity': 'multiplicity',
        'phi0_truth': '#phi',
    }

Rename the hit-profile quantities so that they display nicely in ROOT TLatex.

Definition at line 330 of file mc_side_module.py.

◆ save_finding_efficiency_by_pt_profiles_groupbyCharge

refiners save_finding_efficiency_by_pt_profiles_groupbyCharge

static

Initial value:

=  refiners.save_profiles(
        
    )

Charge dependent histograms Make profile of finding efficiency versus pt, tan(lambda)

Definition at line 370 of file mc_side_module.py.

◆ save_finding_efficiency_by_tan_lamba_in_pt_groups_profiles

refiners save_finding_efficiency_by_tan_lamba_in_pt_groups_profiles

static

Initial value:

=  refiners.save_profiles(
        
    )

Make profiles of finding efficiency versus tan(lambda) grouped by transverse momentum.

Definition at line 311 of file mc_side_module.py.

◆ save_finding_efficiency_by_tan_lamba_profiles

refiners save_finding_efficiency_by_tan_lamba_profiles

static

Initial value:

=  refiners.save_profiles(
        
    )

Make profile of finding efficiency versus tan(lambda)

Definition at line 294 of file mc_side_module.py.

◆ save_finding_efficiency_by_tan_lambda_profiles_groupbyCharge

refiners save_finding_efficiency_by_tan_lambda_profiles_groupbyCharge

static

Initial value:

=  refiners.save_profiles(
        
    )

Charge dependent histograms Make profile of finding efficiency versus pt, tan(lambda)

Definition at line 388 of file mc_side_module.py.

◆ save_finding_efficiency_profiles

refiners save_finding_efficiency_profiles

static

Initial value:

=  refiners.save_profiles(
        
    )

Make profile of finding efficiency.

Definition at line 281 of file mc_side_module.py.

◆ save_hit_efficiency_by_pt_profiles_groupbyCharge

refiners save_hit_efficiency_by_pt_profiles_groupbyCharge

static

Initial value:

=  refiners.save_profiles(
        
    )

Charge dependent histograms Make profile of finding efficiency versus pt, tan(lambda)

Definition at line 407 of file mc_side_module.py.

◆ save_hit_efficiency_by_tan_lambda_profiles

refiners save_hit_efficiency_by_tan_lambda_profiles

static

Initial value:

=  refiners.save_profiles(
        
    )

Make profile of finding efficiency versus tan(lambda)

Definition at line 352 of file mc_side_module.py.

◆ save_hit_efficiency_by_tan_lambda_profiles_groupbyCharge

refiners save_hit_efficiency_by_tan_lambda_profiles_groupbyCharge

static

Initial value:

=  refiners.save_profiles(
        
    )

Charge dependent histograms Make profile of finding efficiency versus pt, tan(lambda)

Definition at line 425 of file mc_side_module.py.

◆ save_hit_efficiency_histogram

refiners save_hit_efficiency_histogram

static

Initial value:

=  refiners.save_histograms(
        
    )

Save a histogram of the hit efficiency.

Definition at line 262 of file mc_side_module.py.

◆ save_hit_efficiency_in_all_found_hist

refiners save_hit_efficiency_in_all_found_hist

static

Initial value:

=  refiners.save_histograms(
        
    )

This creates a histogram for all MC track displaying the ratio of hits contained in any PR track Hence this is distinctly larger than the hit efficiency to the matched PR track Usefulness under discussion.

Definition at line 445 of file mc_side_module.py.

◆ save_hit_efficiency_profiles

refiners save_hit_efficiency_profiles

static

Initial value:

=  refiners.save_profiles(
        
    )

Make profile of hit efficiency.

Definition at line 339 of file mc_side_module.py.

◆ save_missing_mc_tracks_hit_efficiency_in_all_found_hist

refiners save_missing_mc_tracks_hit_efficiency_in_all_found_hist

static

Initial value:

=  refiners.save_histograms(
        
    )

This creates a histogram for each missing MC track displaying the ratio of hits contained in any PR tracks High values in this hit efficiencies means that the MC track is consumed by other PR tracks but no proper match could be established.

Definition at line 456 of file mc_side_module.py.

◆ save_overview_figures_of_merit

refiners save_overview_figures_of_merit

static

Initial value:

=  refiners.save_fom(
        
    )

Generate the average finding efficiencies and hit efficiencies.

Definition at line 243 of file mc_side_module.py.

◆ save_tree

refiners save_tree

static

Initial value:

=  refiners.save_tree(
        # using cond to suppress false doxygen warnings
        
    )

Save a tree of all collected variables in a sub folder.

Definition at line 222 of file mc_side_module.py.

◆ save_tree_basic

refiners save_tree_basic

static

Initial value:

=  refiners.save_tree(
        # using cond to suppress false doxygen warnings
        
    )

Save a basic tree.

Definition at line 232 of file mc_side_module.py.

◆ track_match_look_up

track_match_look_up

Reference to the track match lookup object reading the relation information constructed by the MCMatcherTracksModule.

Definition at line 60 of file mc_side_module.py.

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

tracking/scripts/tracking/harvesting_validation/mc_side_module.py

Public Member Functions

Public Attributes

Static Public Attributes

Detailed Description

Constructor & Destructor Documentation

◆ __init__()

Member Function Documentation

◆ initialize()

◆ peel()

◆ peel_hit_efficiencies_in_all_pr_tracks()

◆ peel_mc_to_pr_match_info()

◆ pick()

◆ prepare()

Member Data Documentation

◆ clone_det_hit_ids

◆ default_expert_level

◆ fake_det_hit_ids

◆ found_det_hit_ids

◆ matched_det_hit_ids

◆ mc_reco_tracks_name

◆ reco_tracks_name

◆ renaming_select_for_finding_efficiency_profiles

◆ renaming_select_for_hit_efficiency_profiles

◆ save_finding_efficiency_by_pt_profiles_groupbyCharge

◆ save_finding_efficiency_by_tan_lamba_in_pt_groups_profiles

◆ save_finding_efficiency_by_tan_lamba_profiles

◆ save_finding_efficiency_by_tan_lambda_profiles_groupbyCharge

◆ save_finding_efficiency_profiles

◆ save_hit_efficiency_by_pt_profiles_groupbyCharge

◆ save_hit_efficiency_by_tan_lambda_profiles

◆ save_hit_efficiency_by_tan_lambda_profiles_groupbyCharge

◆ save_hit_efficiency_histogram

◆ save_hit_efficiency_in_all_found_hist

◆ save_hit_efficiency_profiles

◆ save_missing_mc_tracks_hit_efficiency_in_all_found_hist

◆ save_overview_figures_of_merit

◆ save_tree

◆ save_tree_basic

◆ track_match_look_up

◆ init()