Public Member Functions
	__init__ (self, particle)

	calculateStatistic (self, hist, target)

	calculateUniqueStatistic (self, tree)

Public Attributes
	particle = particle
	Particle containing its configuration.

	mc_count = MonitoringMCCount(particle)
	Dictionary with 'sum', 'std', 'mean', 'min' and 'max' of the MC counts.

	module_statistic = MonitoringModuleStatistics(particle)
	Module statistics.

	time_per_channel = self.module_statistic.channel_time
	time per channel

	time_per_channel_per_module = self.module_statistic.channel_time_per_module
	time per channel per module

	total_time = self.module_statistic.particle_time + sum(self.time_per_channel.values())
	total time

	total_number_of_channels = len(self.particle.channels)
	Total number of channels.

int	reconstructed_number_of_channels = 0
	Reconstructed number of channels.

	branching_fractions = MonitoringBranchingFractions()
	Branching fractions.

	exc_br_per_channel = self.branching_fractions.getExclusive(particle)
	Exclusive branching fractions per channel.

	inc_br_per_channel = self.branching_fractions.getInclusive(particle)
	Inclusive branching fraction per channel.

dict	before_ranking = {}
	Monitoring histogram in PreReconstruction before the ranking-cut.

dict	after_ranking = {}
	Monitoring histogram in PreReconstruction after the ranking-cut.

dict	after_vertex = {}
	Monitoring histogram in PreReconstruction after the vertex fit.

dict	after_classifier = {}
	Monitoring histogram in PostReconstruction after the mva application.

dict	training_data = {}
	Monitoring histogram for TrainingData Generation only available if Monitoring runs on the training monitoring data.

dict	ignored_channels = {}
	Dictionary containing whether the channel reconstructed at least one candidate or not.

	before_postcut = self.calculateStatistic(hist, self.particle.mvaConfig.target)
	Monitoring histogram in PostReconstruction before the postcut.

	before_ranking_postcut = self.calculateStatistic(hist, self.particle.mvaConfig.target)
	Monitoring histogram in PostReconstruction before the ranking postcut.

	after_ranking_postcut = self.calculateStatistic(hist, self.particle.mvaConfig.target)
	Monitoring histogram in PostReconstruction after the ranking postcut.

	before_tag = self.calculateStatistic(hist, self.particle.mvaConfig.target)
	Statistic object before unique tagging of signals.

	final_ntuple = MonitoringNTuple('Monitor_Final.root', f'{plist}')
	Reference to the final ntuple.

	after_tag = self.calculateUniqueStatistic(self.final_ntuple.tree)
	Statistic object after unique tagging of signals.

Detailed Description

Monitoring object containing all the monitoring information
about a single particle

Definition at line 611 of file monitoring.py.

Constructor & Destructor Documentation

◆ init()

__init__	(		self,
			particle )

Read the monitoring information of the given particle
@param particle the particle for which the information is read

Definition at line 617 of file monitoring.py.

    def __init__(self, particle):
        """
        Read the monitoring information of the given particle
        @param particle the particle for which the information is read
        """
        
        self.particle = particle
        particlesInStages = fei.core.get_stages_from_particles([particle])
        stage = 0
        for i in range(len(particlesInStages)):
            for iparticle in particlesInStages[i]:
                if iparticle.identifier == self.particle.identifier:
                    stage = i+1
                    break
        if stage == 0:
            raise RuntimeError(f"Could not find particle {self.particle.identifier} in the list of stages.")
 
        
        self.mc_count = MonitoringMCCount(particle)
        
        self.module_statistic = MonitoringModuleStatistics(particle)
        
        self.time_per_channel = self.module_statistic.channel_time
        
        self.time_per_channel_per_module = self.module_statistic.channel_time_per_module
        
        self.total_time = self.module_statistic.particle_time + sum(self.time_per_channel.values())
 
        
        self.total_number_of_channels = len(self.particle.channels)
        
        self.reconstructed_number_of_channels = 0
 
        
        self.branching_fractions = MonitoringBranchingFractions()
        
        self.exc_br_per_channel = self.branching_fractions.getExclusive(particle)
        
        self.inc_br_per_channel = self.branching_fractions.getInclusive(particle)
 
        
        self.before_ranking = {}
        
        self.after_ranking = {}
        
        self.after_vertex = {}
        
        self.after_classifier = {}
        
        self.training_data = {}
        
        self.ignored_channels = {}
 
        for channel in self.particle.channels:
            hist = MonitoringHist('Monitor_PreReconstruction_BeforeRanking.root', f'{channel.label}')
            self.before_ranking[channel.label] = self.calculateStatistic(hist, channel.mvaConfig.target)
            hist = MonitoringHist('Monitor_PreReconstruction_AfterRanking.root', f'{channel.label}')
            self.after_ranking[channel.label] = self.calculateStatistic(hist, channel.mvaConfig.target)
            hist = MonitoringHist('Monitor_PreReconstruction_AfterVertex.root', f'{channel.label}')
            self.after_vertex[channel.label] = self.calculateStatistic(hist, channel.mvaConfig.target)
            hist = MonitoringHist('Monitor_PostReconstruction_AfterMVA.root', f'{channel.label}')
            self.after_classifier[channel.label] = self.calculateStatistic(hist, channel.mvaConfig.target)
            if hist.valid and hist.sum(channel.mvaConfig.target) > 0:
                self.reconstructed_number_of_channels += 1
                self.ignored_channels[channel.label] = False
            else:
                self.ignored_channels[channel.label] = True
            hist = MonitoringHist('Monitor_TrainingData.root', f'{channel.label}')
            self.training_data[channel.label] = hist
 
        plist = removeJPsiSlash(particle.identifier)
        hist = MonitoringHist('Monitor_PostReconstruction_BeforePostCut.root', f'{plist}')
        
        self.before_postcut = self.calculateStatistic(hist, self.particle.mvaConfig.target)
        hist = MonitoringHist('Monitor_PostReconstruction_BeforeRanking.root', f'{plist}')
        
        self.before_ranking_postcut = self.calculateStatistic(hist, self.particle.mvaConfig.target)
        hist = MonitoringHist('Monitor_PostReconstruction_AfterRanking.root', f'{plist}')
        
        self.after_ranking_postcut = self.calculateStatistic(hist, self.particle.mvaConfig.target)
        
        self.before_tag = self.calculateStatistic(hist, self.particle.mvaConfig.target)
        
        self.final_ntuple = MonitoringNTuple('Monitor_Final.root', f'{plist}')
        
        self.after_tag = self.calculateUniqueStatistic(self.final_ntuple.tree)
 

Member Function Documentation

◆ calculateStatistic()

calculateStatistic	(	self,
		hist,
		target )

Calculate Statistic object where all signal candidates are considered signal

Definition at line 704 of file monitoring.py.

    def calculateStatistic(self, hist, target):
        """
        Calculate Statistic object where all signal candidates are considered signal
        """
        nTrueSig = self.mc_count['sum']
        if not hist.valid:
            return Statistic(nTrueSig, 0, 0)
        signal_bins = (hist.centers[target] > 0.5)
        bckgrd_bins = ~signal_bins
        nSig = hist.values[target][signal_bins].sum()
        nBg = hist.values[target][bckgrd_bins].sum()
        return Statistic(nTrueSig, nSig, nBg)
 

◆ calculateUniqueStatistic()

calculateUniqueStatistic	(		self,
			tree )

Calculate Static object where only unique signal candidates are considered signal

Definition at line 717 of file monitoring.py.

    def calculateUniqueStatistic(self, tree):
        """
        Calculate Static object where only unique signal candidates are considered signal
        """
        nTrueSig = self.mc_count['sum']
        if not tree:
            return Statistic(nTrueSig, 0, 0)
 
        nSig, nBg = 0, 0
        for entry in tree:
            if getattr(entry, "extraInfo__bouniqueSignal__bc") == 1:
                nSig += 1
            else:
                nBg += 1
        return Statistic(nTrueSig, nSig, nBg)

Member Data Documentation

◆ after_classifier

dict after_classifier = {}

Monitoring histogram in PostReconstruction after the mva application.

Definition at line 664 of file monitoring.py.

◆ after_ranking

dict after_ranking = {}

Monitoring histogram in PreReconstruction after the ranking-cut.

Definition at line 660 of file monitoring.py.

◆ after_ranking_postcut

after_ranking_postcut = self.calculateStatistic(hist, self.particle.mvaConfig.target)

Monitoring histogram in PostReconstruction after the ranking postcut.

Definition at line 696 of file monitoring.py.

◆ after_tag

after_tag = self.calculateUniqueStatistic(self.final_ntuple.tree)

Statistic object after unique tagging of signals.

Definition at line 702 of file monitoring.py.

◆ after_vertex

dict after_vertex = {}

Monitoring histogram in PreReconstruction after the vertex fit.

Definition at line 662 of file monitoring.py.

◆ before_postcut

before_postcut = self.calculateStatistic(hist, self.particle.mvaConfig.target)

Monitoring histogram in PostReconstruction before the postcut.

Definition at line 690 of file monitoring.py.

◆ before_ranking

dict before_ranking = {}

Monitoring histogram in PreReconstruction before the ranking-cut.

Definition at line 658 of file monitoring.py.

◆ before_ranking_postcut

before_ranking_postcut = self.calculateStatistic(hist, self.particle.mvaConfig.target)

Monitoring histogram in PostReconstruction before the ranking postcut.

Definition at line 693 of file monitoring.py.

◆ before_tag

before_tag = self.calculateStatistic(hist, self.particle.mvaConfig.target)

Statistic object before unique tagging of signals.

Definition at line 698 of file monitoring.py.

◆ branching_fractions

branching_fractions = MonitoringBranchingFractions()

Branching fractions.

Definition at line 651 of file monitoring.py.

◆ exc_br_per_channel

exc_br_per_channel = self.branching_fractions.getExclusive(particle)

Exclusive branching fractions per channel.

Definition at line 653 of file monitoring.py.

◆ final_ntuple

final_ntuple = MonitoringNTuple('Monitor_Final.root', f'{plist}')

Reference to the final ntuple.

Definition at line 700 of file monitoring.py.

◆ ignored_channels

dict ignored_channels = {}

Dictionary containing whether the channel reconstructed at least one candidate or not.

Definition at line 668 of file monitoring.py.

◆ inc_br_per_channel

inc_br_per_channel = self.branching_fractions.getInclusive(particle)

Inclusive branching fraction per channel.

Definition at line 655 of file monitoring.py.

◆ mc_count

mc_count = MonitoringMCCount(particle)

Dictionary with 'sum', 'std', 'mean', 'min' and 'max' of the MC counts.

Definition at line 635 of file monitoring.py.

◆ module_statistic

module_statistic = MonitoringModuleStatistics(particle)

Module statistics.

Definition at line 637 of file monitoring.py.

◆ particle

particle = particle

Particle containing its configuration.

Definition at line 623 of file monitoring.py.

◆ reconstructed_number_of_channels

int reconstructed_number_of_channels = 0

Reconstructed number of channels.

Definition at line 648 of file monitoring.py.

◆ time_per_channel

time_per_channel = self.module_statistic.channel_time

time per channel

Definition at line 639 of file monitoring.py.

◆ time_per_channel_per_module

time_per_channel_per_module = self.module_statistic.channel_time_per_module

time per channel per module

Definition at line 641 of file monitoring.py.

◆ total_number_of_channels

total_number_of_channels = len(self.particle.channels)

Total number of channels.

Definition at line 646 of file monitoring.py.

◆ total_time

total_time = self.module_statistic.particle_time + sum(self.time_per_channel.values())

total time

Definition at line 643 of file monitoring.py.

◆ training_data

dict training_data = {}

Monitoring histogram for TrainingData Generation only available if Monitoring runs on the training monitoring data.

Definition at line 666 of file monitoring.py.

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

analysis/scripts/fei/monitoring.py

Public Member Functions

Public Attributes