module.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
 
 
 
import math
import collections
import numpy as np
 
from tracking.root_utils import root_save_name
 
from tracking.validation.plot import ValidationPlot, compose_axis_label
 
from tracking.validation.pull import PullAnalysis
from tracking.validation.resolution import ResolutionAnalysis
from tracking.validation.fom import ValidationFiguresOfMerit
from tracking.validation.utilities import (
    getHelixFromMCParticle,
    getSeedTrackFitResult,
    is_primary,
    get_det_hit_ids,
    calc_ndf_from_det_hit_ids
)
 
import basf2
 
import ROOT
from ROOT import Belle2
import os
 
ROOT.gSystem.Load("libtracking")
 
 
class FilterProperties(object):
    """
    contains all informations necessary for track filters to decide whether
    track will be included into the processed list of tracks
    This class is used for both providing information on pattern reco and
    MC tracks
    """
 
    def __init__(
        self,
        trackCand=None,
        mcParticle=None,
        mcParticles=None,
        wasFitted=False,
        fitResult=None,
        seedResult=None,
    ):
        """Constructor"""
 
        
        self.trackCandtrackCand = trackCand
        
        self.mcParticlemcParticle = mcParticle
        
        self.mcParticlesmcParticles = mcParticles
        
        self.wasFittedwasFitted = wasFitted
        
        self.fitResultfitResult = fitResult
        
        self.seedResultseedResult = seedResult
 
 
# This class will accept all pattern reco and mctracks
# It is used as the default option for the TrackingValidationModule
#
# It can be used as a starting point to implement your own particle filter class
# and supply it to the TrackingValidationModule via the init method's
# track_filter_object property.
#
#  doesPrPass is called for all pattern reconstructed tracks
#    Objects available infilterProperties:
#    trackCand is guaranteed to be != None
#    If wasFitted == True, fitResult will be set
#        otherwise seedResult will be set
#
#  doesMcPass is called for all MC tracks
#    Objects available infilterProperties:
#    mcParticle is guaranteed to be != None
#
 
class AlwaysPassFilter(object):
    """Filter that always passes"""
 
    def doesPrPass(self, filterProperties):
        """Pattern-reconstructed track always passes"""
        return True
 
    def doesMcPass(self, filterProperties):
        """MC track always passes"""
        return True
 
 
class TrackingValidationModule(basf2.Module):
 
    """Module to collect matching information about the found particles and to
       generate validation plots and figures of merit on the performance of track finding."""
 
    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",
        mcTrackCandidatesColumName="MCRecoTracks"
    ):
        """Constructor"""
 
        super(TrackingValidationModule, self).__init__()
 
        
        self.validation_namevalidation_name = name
        
        self.contactcontact = contact
        
        self.fitfit = fit
        
        self.pullspulls = pulls
        
        self.resolutionresolution = resolution
        
        self.output_file_nameoutput_file_name = output_file_name or self.validation_namevalidation_name \
            + 'TrackingValidation.root'
        
        self.track_filter_objecttrack_filter_object = track_filter_object
        
        self.plot_name_postfixplot_name_postfix = plot_name_postfix
        
        self.plot_title_postfixplot_title_postfix = plot_title_postfix
        
        self.exclude_profile_pr_parameterexclude_profile_pr_parameter = exclude_profile_pr_parameter
        
        self.exclude_profile_mc_parameterexclude_profile_mc_parameter = exclude_profile_mc_parameter
        
        self.use_expert_folderuse_expert_folder = use_expert_folder
        
        self.trackCandidatesColumnNametrackCandidatesColumnName = trackCandidatesColumnName
        
        self.mcTrackCandidatesColumnNamemcTrackCandidatesColumnName = mcTrackCandidatesColumName
 
        
        self.resolution_pt_binningresolution_pt_binning = [0.05, 0.1, 0.25, 0.4, 0.6, 1., 1.5, 2., 3., 4.]
 
        
        self.referenceFileNamereferenceFileName = None
        if "DO_NOT_READ_BINNING" not in os.environ:
            # the validity of the file will be checked later
            self.referenceFileNamereferenceFileName = Belle2.FileSystem.findFile("tracking/validation/" + self.output_file_nameoutput_file_name, True)
            basf2.B2INFO("Will read binning from: " + self.referenceFileNamereferenceFileName)
            basf2.B2INFO("If this is not wanted set the environment variable DO_NOT_READ_BINNING or remove reference files.")
        else:
            basf2.B2INFO("Will not read binning from reference files.")
 
    def initialize(self):
        """Receive signal at the start of event processing"""
 
        
        self.trackMatchLookUptrackMatchLookUp = Belle2.TrackMatchLookUp(self.mcTrackCandidatesColumnNamemcTrackCandidatesColumnName, self.trackCandidatesColumnNametrackCandidatesColumnName)
 
        
 
        
        self.pr_clones_and_matchespr_clones_and_matches = collections.deque()
        
        self.pr_matchespr_matches = collections.deque()
        
        self.pr_fakespr_fakes = collections.deque()
 
        
        self.pr_seed_tan_lambdaspr_seed_tan_lambdas = collections.deque()
        
        self.pr_seed_phipr_seed_phi = collections.deque()
        
        self.pr_seed_thetapr_seed_theta = collections.deque()
 
        
        self.pr_omega_truthspr_omega_truths = collections.deque()
        
        self.pr_omega_estimatespr_omega_estimates = collections.deque()
        
        self.pr_omega_variancespr_omega_variances = collections.deque()
 
        
        self.pr_tan_lambda_truthspr_tan_lambda_truths = collections.deque()
        
        self.pr_tan_lambda_estimatespr_tan_lambda_estimates = collections.deque()
        
        self.pr_tan_lambda_variancespr_tan_lambda_variances = collections.deque()
 
        
        self.pr_d0_truthspr_d0_truths = collections.deque()
        
        self.pr_d0_estimatespr_d0_estimates = collections.deque()
        
        self.pr_d0_variancespr_d0_variances = collections.deque()
 
        
        self.pr_z0_truthspr_z0_truths = collections.deque()
        
        self.pr_z0_estimatespr_z0_estimates = collections.deque()
 
        
        self.pr_pt_truthspr_pt_truths = collections.deque()
        
        self.pr_pt_estimatespr_pt_estimates = collections.deque()
 
        
        self.pr_bining_ptpr_bining_pt = collections.deque()
 
        
        self.mc_matchesmc_matches = collections.deque()
        
        self.mc_primariesmc_primaries = collections.deque()
        
        self.mc_d0smc_d0s = collections.deque()
        
        self.mc_tan_lambdasmc_tan_lambdas = collections.deque()
        
        self.mc_thetamc_theta = collections.deque()
        
        self.mc_phimc_phi = collections.deque()
        
        self.mc_ptsmc_pts = collections.deque()
        
        self.mc_hit_efficienciesmc_hit_efficiencies = collections.deque()
        
        self.mc_multiplicitiesmc_multiplicities = collections.deque()
        
        self.mc_ndfmc_ndf = collections.deque()
 
    def event(self):
        """Event method"""
 
        self.examine_pr_tracksexamine_pr_tracks()
        self.examine_mc_tracksexamine_mc_tracks()
 
    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.trackMatchLookUptrackMatchLookUp
 
        trackCands = Belle2.PyStoreArray(self.trackCandidatesColumnNametrackCandidatesColumnName)
        mcParticles = Belle2.PyStoreArray("MCParticles")
        if not trackCands:
            return
 
        for trackCand in trackCands:
            is_matched = trackMatchLookUp.isMatchedPRRecoTrack(trackCand)
            is_clone = trackMatchLookUp.isClonePRRecoTrack(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().Perp()
                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.fitfit:
                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_objecttrack_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_position = trackCand.getPositionSeed()
            seed_momentum = trackCand.getMomentumSeed()
            # Avoid zero division exception
            seed_tan_lambda = np.divide(1.0, math.tan(seed_momentum.Theta()))
            seed_phi = seed_position.Phi()
            seed_theta = seed_position.Theta()
 
            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.Perp()
 
            # store properties of the seed
            self.pr_seed_tan_lambdaspr_seed_tan_lambdas.append(seed_tan_lambda)
            self.pr_seed_phipr_seed_phi.append(seed_phi)
            self.pr_seed_thetapr_seed_theta.append(seed_theta)
 
            self.pr_bining_ptpr_bining_pt.append(pt_truth)
 
            # store properties resulting from this trackfit
            isMatchedOrIsClone = is_matched or is_clone
            self.pr_clones_and_matchespr_clones_and_matches.append(isMatchedOrIsClone)
            self.pr_matchespr_matches.append(is_matched)
            self.pr_fakespr_fakes.append(not isMatchedOrIsClone)
 
            self.pr_omega_estimatespr_omega_estimates.append(omega_estimate)
            self.pr_omega_variancespr_omega_variances.append(omega_variance)
            self.pr_omega_truthspr_omega_truths.append(omega_truth)
 
            self.pr_tan_lambda_estimatespr_tan_lambda_estimates.append(tan_lambda_estimate)
            self.pr_tan_lambda_variancespr_tan_lambda_variances.append(tan_lambda_variance)
            self.pr_tan_lambda_truthspr_tan_lambda_truths.append(tan_lambda_truth)
 
            self.pr_d0_estimatespr_d0_estimates.append(d0_estimate)
            self.pr_d0_variancespr_d0_variances.append(d0_variance)
            self.pr_d0_truthspr_d0_truths.append(d0_truth)
 
            self.pr_z0_estimatespr_z0_estimates.append(z0_estimate)
            self.pr_z0_truthspr_z0_truths.append(z0_truth)
 
            self.pr_pt_estimatespr_pt_estimates.append(pt_estimate)
            self.pr_pt_truthspr_pt_truths.append(pt_truth)
 
    def examine_mc_tracks(self):
        """Looks at the individual Monte Carlo tracks and store information about them"""
 
        trackMatchLookUp = self.trackMatchLookUptrackMatchLookUp
 
        # Analyse from the Monte Carlo reference side
        mcTrackCands = Belle2.PyStoreArray(self.mcTrackCandidatesColumnNamemcTrackCandidatesColumnName)
        mcParticles = Belle2.PyStoreArray('MCParticles')
        if not mcTrackCands:
            return
 
        multiplicity = mcTrackCands.getEntries()
 
        for mcTrackCand in mcTrackCands:
            is_matched = trackMatchLookUp.isMatchedMCRecoTrack(mcTrackCand)
 
            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_objecttrack_filter_object.doesMcPass(filterProperties):
                continue
 
            momentum = mcParticle.getMomentum()
            pt = momentum.Perp()
            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_matchesmc_matches.append(is_matched)
            self.mc_primariesmc_primaries.append(is_primary(mcParticle))
            self.mc_hit_efficienciesmc_hit_efficiencies.append(hit_efficiency)
            self.mc_ptsmc_pts.append(pt)
            self.mc_d0smc_d0s.append(d0)
            self.mc_tan_lambdasmc_tan_lambdas.append(tan_lambda)
            self.mc_multiplicitiesmc_multiplicities.append(multiplicity)
            self.mc_thetamc_theta.append(momentum.Theta())
            self.mc_phimc_phi.append(momentum.Phi())
            self.mc_ndfmc_ndf.append(ndf)
 
    def terminate(self):
        """Receive signal at the end of event processing"""
        name = self.validation_namevalidation_name
        contact = self.contactcontact
 
        # Overall figures of merit #
        
        finding_efficiency = np.average(self.mc_matchesmc_matches, weights=self.mc_primariesmc_primaries)
        fake_rate = 1.0 - np.mean(self.pr_clones_and_matchespr_clones_and_matches)
        # can only be computed if there are entries
        if len(self.pr_clones_and_matchespr_clones_and_matches) > 0 and sum(self.pr_clones_and_matchespr_clones_and_matches) > 0:
            clone_rate = 1.0 - np.average(self.pr_matchespr_matches,
                                          weights=self.pr_clones_and_matchespr_clones_and_matches)
        else:
            clone_rate = float('nan')
 
        mc_matched_primaries = np.logical_and(self.mc_primariesmc_primaries, self.mc_matchesmc_matches)
        hit_efficiency = np.average(self.mc_hit_efficienciesmc_hit_efficiencies, weights=mc_matched_primaries)
 
        figures_of_merit = ValidationFiguresOfMerit('%s_figures_of_merit'
                                                    % name)
        figures_of_merit['finding_efficiency'] = finding_efficiency
        figures_of_merit['fake_rate'] = fake_rate
        figures_of_merit['clone_rate'] = clone_rate
        figures_of_merit['hit_efficiency'] = hit_efficiency
 
        figures_of_merit.description = \
            """
finding_efficiency - the ratio of matched Monte Carlo tracks to all Monte Carlo tracks <br/>
fake_rate - ratio of pattern recognition tracks that are not related to a particle
            (background, ghost) to all pattern recognition tracks <br/>
clone_rate - ratio of clones divided the number of tracks that are related to a particle (clones and matches) <br/>
"""
        figures_of_merit.check = 'Compare for degradations with respect to the reference'
        figures_of_merit.contact = contact
        print(figures_of_merit)
 
        # Validation plots #
        
        validation_plots = []
        pull_analyses = []
 
        # Finding efficiency #
        
        plots = self.profiles_by_mc_parametersprofiles_by_mc_parameters(self.mc_matchesmc_matches,
                                               'finding efficiency',
                                               make_hist=False,
                                               weights=self.mc_primariesmc_primaries)
 
        validation_plots.extend(plots)
 
        # Fake rate (all tracks not matched or clone            #
        # use TrackCand seeds for the fake track plotting       #
        # as the fit (if successful) is probably not meaningful #
        
        print('fake list: ' + str(len(self.pr_fakespr_fakes)))
        plots = self.profiles_by_pr_parametersprofiles_by_pr_parameters(self.pr_fakespr_fakes, 'fake rate',
                                               make_hist=False)
        validation_plots.extend(plots)
 
        # Hit efficiency #
        
        plots = self.profiles_by_mc_parametersprofiles_by_mc_parameters(self.mc_hit_efficienciesmc_hit_efficiencies,
                                               'hit efficiency with matched tracks',
                                               weights=mc_matched_primaries)
        validation_plots.extend(plots)
 
        # Fit quality #
        
        if self.pullspulls:
            all_but_diagonal_plots = list(PullAnalysis.default_which_plots)
            all_but_diagonal_plots.remove("diag_profile")
            all_but_diagonal_plots.remove("diag_scatter")
 
            plot_name_prefix = name + self.plot_name_postfixplot_name_postfix
            if not self.fitfit:
                plot_name_prefix += '_seed'
 
            # Omega / curvature pull
            pr_omega_truths = np.array(self.pr_omega_truthspr_omega_truths)
            pr_omega_estimates = np.array(self.pr_omega_estimatespr_omega_estimates)
            pr_omega_variances = np.array(self.pr_omega_variancespr_omega_variances)
 
            curvature_pull_analysis = PullAnalysis('#omega', unit='1/cm',
                                                   plot_name_prefix=plot_name_prefix + '_omega',
                                                   plot_title_postfix=self.plot_title_postfixplot_title_postfix,
                                                   referenceFileName=self.referenceFileNamereferenceFileName)
 
            curvature_pull_analysis.analyse(pr_omega_truths,
                                            pr_omega_estimates,
                                            pr_omega_variances,
                                            which_plots=all_but_diagonal_plots)
 
            curvature_pull_analysis.contact = contact
            pull_analyses.append(curvature_pull_analysis)
 
            # Tan lambda pull
            pr_tan_lambda_truths = np.array(self.pr_tan_lambda_truthspr_tan_lambda_truths)
            pr_tan_lambda_estimates = np.array(self.pr_tan_lambda_estimatespr_tan_lambda_estimates)
            pr_tan_lambda_variances = np.array(self.pr_tan_lambda_variancespr_tan_lambda_variances)
 
            curvature_pull_analysis = PullAnalysis('tan #lambda',
                                                   plot_name_prefix=plot_name_prefix + '_tan_lambda',
                                                   plot_title_postfix=self.plot_title_postfixplot_title_postfix,
                                                   referenceFileName=self.referenceFileNamereferenceFileName)
 
            curvature_pull_analysis.analyse(pr_tan_lambda_truths,
                                            pr_tan_lambda_estimates,
                                            pr_tan_lambda_variances,
                                            which_plots=all_but_diagonal_plots)
 
            curvature_pull_analysis.contact = contact
            pull_analyses.append(curvature_pull_analysis)
 
            # d0 pull
            curvature_pull_analysis = PullAnalysis('d0',
                                                   plot_name_prefix=plot_name_prefix + '_d0',
                                                   plot_title_postfix=self.plot_title_postfixplot_title_postfix,
                                                   referenceFileName=self.referenceFileNamereferenceFileName)
 
            curvature_pull_analysis.analyse(np.array(self.pr_d0_truthspr_d0_truths),
                                            np.array(self.pr_d0_estimatespr_d0_estimates),
                                            np.array(self.pr_d0_variancespr_d0_variances),
                                            which_plots=all_but_diagonal_plots)
 
            curvature_pull_analysis.contact = contact
            pull_analyses.append(curvature_pull_analysis)
 
        # Resolution plots
        
        if self.resolutionresolution:
            # d0 impact parameter resolution plot
            d0_resolution_analysis = ResolutionAnalysis('d0_res',
                                                        self.resolution_pt_binningresolution_pt_binning,
                                                        'Pt',
                                                        plot_name_prefix=plot_name_prefix + '_d0_res',
                                                        plot_title_postfix=self.plot_title_postfixplot_title_postfix,
                                                        referenceFileName=self.referenceFileNamereferenceFileName)
            d0_resolution_analysis.analyse(np.array(self.pr_bining_ptpr_bining_pt),
                                           np.array(self.pr_d0_truthspr_d0_truths),
                                           np.array(self.pr_d0_estimatespr_d0_estimates))
            d0_resolution_analysis.contact = contact
            pull_analyses.append(d0_resolution_analysis)
 
            # z0 impact parameter resolution plot
            z0_resolution_analysis = ResolutionAnalysis('z0_res',
                                                        self.resolution_pt_binningresolution_pt_binning,
                                                        "Pt",
                                                        plot_name_prefix=plot_name_prefix + '_z0_res',
                                                        plot_title_postfix=self.plot_title_postfixplot_title_postfix,
                                                        referenceFileName=self.referenceFileNamereferenceFileName)
            z0_resolution_analysis.analyse(np.array(self.pr_bining_ptpr_bining_pt),
                                           np.array(self.pr_z0_truthspr_z0_truths),
                                           np.array(self.pr_z0_estimatespr_z0_estimates))
            z0_resolution_analysis.contact = contact
            pull_analyses.append(z0_resolution_analysis)
 
            # omega curvature parameter resolution plot
            omega_resolution_analysis = ResolutionAnalysis('omega_res',
                                                           self.resolution_pt_binningresolution_pt_binning,
                                                           "Pt",
                                                           plot_name_prefix=plot_name_prefix + '_omega_res',
                                                           plot_title_postfix=self.plot_title_postfixplot_title_postfix,
                                                           referenceFileName=self.referenceFileNamereferenceFileName)
            omega_resolution_analysis.analyse(np.array(self.pr_bining_ptpr_bining_pt),
                                              np.array(self.pr_omega_truthspr_omega_truths),
                                              np.array(self.pr_omega_estimatespr_omega_estimates))
            omega_resolution_analysis.contact = contact
            pull_analyses.append(omega_resolution_analysis)
 
            # transverse momentum resolution plot
            pt_resolution_analysis = ResolutionAnalysis('pt_res',
                                                        self.resolution_pt_binningresolution_pt_binning,
                                                        "Pt",
                                                        plot_name_prefix=plot_name_prefix + '_pt_res',
                                                        plot_title_postfix=self.plot_title_postfixplot_title_postfix,
                                                        referenceFileName=self.referenceFileNamereferenceFileName)
            pt_resolution_analysis.analyse(np.array(self.pr_bining_ptpr_bining_pt),
                                           np.array(self.pr_pt_truthspr_pt_truths),
                                           np.array(self.pr_pt_estimatespr_pt_estimates))
            pt_resolution_analysis.contact = contact
            pull_analyses.append(pt_resolution_analysis)
 
        # Saving #
        
 
        # Save everything to a ROOT file
        output_tfile = ROOT.TFile(self.output_file_nameoutput_file_name, 'recreate')
 
        # Show all parameters and the fit result in the plots
        # if viewed in the browser or the validation
        opt_fit = 0o112
        ROOT.gStyle.SetOptFit(opt_fit)
 
        figures_of_merit.write()
 
        for validation_plot in validation_plots:
            validation_plot.write()
 
        if self.use_expert_folderuse_expert_folder:
            expert_tdirectory = output_tfile.mkdir('expert', 'Expert')
            expert_tdirectory.cd()
        ROOT.gStyle.SetOptFit(opt_fit)
 
        for pull_analysis in pull_analyses:
            pull_analysis.write()
 
        output_tfile.Close()
 
    def profiles_by_mc_parameters(
        self,
        xs,
        quantity_name,
        unit=None,
        parameter_names=[
            'd_0',
            'p_t',
            'tan_lambda',
            'multiplicity',
            'phi',
            'theta',
            'ndf',
        ],
        non_expert_parameters=['p_{t}'],
        make_hist=True,
        weights=None
    ):
        """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_parameterexclude_profile_mc_parameter]
 
        # Profile versus the various parameters
        profile_parameters = {
            'd_{0}': self.mc_d0smc_d0s,
            'p_{t}': self.mc_ptsmc_pts,
            'tan #lambda': self.mc_tan_lambdasmc_tan_lambdas,
            '#phi': self.mc_phimc_phi,
            '#theta': self.mc_thetamc_theta,
            'multiplicity': self.mc_multiplicitiesmc_multiplicities,
            'ndf': self.mc_ndfmc_ndf,
        }
 
        return self.profiles_by_parameters_baseprofiles_by_parameters_base(
            xs,
            quantity_name,
            new_parameter_names,
            profile_parameters,
            unit,
            make_hist,
            non_expert_parameters=non_expert_parameters,
            weights=weights
        )
 
    def profiles_by_pr_parameters(
        self,
        xs,
        quantity_name,
        unit=None,
        parameter_names=['Seed tan #lambda', 'Seed #phi', 'Seed #theta'],
        make_hist=True,
    ):
        """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_parameterexclude_profile_pr_parameter]
 
        # Profile versus the various parameters
        profile_parameters = {'Seed tan #lambda': self.pr_seed_tan_lambdaspr_seed_tan_lambdas,
                              'Seed #phi': self.pr_seed_phipr_seed_phi,
                              'Seed #theta': self.pr_seed_thetapr_seed_theta}
 
        return self.profiles_by_parameters_baseprofiles_by_parameters_base(
            xs,
            quantity_name,
            new_parameter_names,
            profile_parameters,
            unit,
            make_hist,
        )
 
    def profiles_by_parameters_base(
        self,
        xs,
        quantity_name,
        parameter_names,
        profile_parameters,
        unit,
        make_hist,
        non_expert_parameters=[],
        weights=None,
    ):
        """Create profile histograms for generic parameters"""
 
        contact = self.contactcontact
 
        validation_plots = []
        plot_name_prefix = self.validation_namevalidation_name + '_' + root_save_name(quantity_name) \
            + self.plot_name_postfixplot_name_postfix
 
        if make_hist:
            # Histogram of the quantity
            histogram = ValidationPlot(plot_name_prefix, self.referenceFileNamereferenceFileName)
            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_postfixplot_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 non_expert_parameters)
 
                # Apply some boundaries for the maximal tracking acceptance
                # such that the plots look more instructive
                if root_save_name(parameter_name) == 'tan_lambda':
                    lower_bound = -1.73
                    upper_bound = 3.27
                elif root_save_name(parameter_name) == 'theta':
                    lower_bound = 17 * math.pi / 180
                    upper_bound = 150 * math.pi / 180
                elif root_save_name(parameter_name) == 'ndf':
                    lower_bound = 0
                    upper_bound = min(200, np.max(parameter_values))
                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.referenceFileNamereferenceFileName)
                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)
 
                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_postfixplot_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