monitoring.py

#!/usr/bin/env python
 

 
"""
 Contains classes to read in the monitoring output
 and some simple plotting routines.
 
 This is used by printReporting.py and latexReporting.py
 to create summaries for a FEI training or application.
"""
 
try:
    from generators import get_default_decayfile
except ModuleNotFoundError:
    print("MonitoringBranchingFractions won't work.")
from basf2_mva_evaluation import plotting
import basf2_mva_util
import pickle
import copy
import math
import os
import numpy as np
import pdg
import fei
 
 
def removeJPsiSlash(string):
    """ Remove slashes in a string, which is not allowed for filenames. """
    return string.replace('/', '')
 
 
def load_config():
    """ Load the FEI configuration from the Summary.pickle file. """
    if not os.path.isfile('Summary.pickle'):
        raise RuntimeError("""Could not find Summary.pickle!
                              This file is automatically created by the FEI training.
                              But you can also create it yourself using:
                              pickle.dump((particles, configuration), open('Summary.pickle', 'wb'))""")
    return pickle.load(open('Summary.pickle', 'rb'))
 
 
class Statistic:
    """
    This class provides the efficiency, purity and other quantities for a
    given number of true signal candidates, signal candidates and background candidates
    """
 
    def __init__(self, nTrueSig, nSig, nBg):
        """
        Create a new Statistic object
        @param nTrueSig the number of true signal particles
        @param nSig the number of reconstructed signal candidates
        @param nBg the number of reconstructed background candidates
        """
        
        self._nTrueSig = nTrueSig
        
        self._nSig = nSig
        
        self._nBg = nBg
 
    @property
    def nSig(self):
        """ Returns the number of reconstructed signal candidates. """
        return self._nSig
 
    @property
    def nTrueSig(self):
        """ Returns the number of reconstructed true signal candidates. """
        return self._nTrueSig
 
    @property
    def nBg(self):
        """ Returns the number of reconstructed background candidates. """
        return self._nBg
 
    @property
    def nTotal(self):
        """ Returns total number of reconstructed candidates. """
        return self._nSig + self._nBg
 
    @property
    def purity(self):
        """ Returns the purity of the reconstructed candidates. """
        if self._nSig == 0:
            return 0.0
        # \cond false positive doxygen warning
        if self.nTotal == 0:
            return 0.0
        return self._nSig / float(self.nTotal)
        # \endcond
 
    @property
    def efficiency(self):
        """ Returns the efficiency of the reconstructed signal candidates with respect to the number of true signal particles. """
        if self._nSig == 0:
            return 0.0
        if self._nTrueSig == 0:
            return float('inf')
        return self._nSig / float(self._nTrueSig)
 
    @property
    def purityError(self):
        """ Returns the uncertainty of the purity. """
        # \cond false positive doxygen warning
        if self.nTotal == 0:
            return 0.0
        return self.calcStandardDeviation(self._nSig, self.nTotal)
        # \endcond
 
    @property
    def efficiencyError(self):
        """
        Returns the uncertainty of the efficiency.
        For an efficiency eps = self._nSig/self._nTrueSig, this function calculates the
        standard deviation according to http://arxiv.org/abs/physics/0701199 .
        """
        if self._nTrueSig == 0:
            return float('inf')
        return self.calcStandardDeviation(self._nSig, self._nTrueSig)
 
    def calcStandardDeviation(self, k, n):
        """ Helper method to calculate the standard deviation for efficiencies. """
        k = float(k)
        n = float(n)
        variance = (k + 1) * (k + 2) / ((n + 2) * (n + 3)) - (k + 1) ** 2 / ((n + 2) ** 2)
        if variance <= 0:
            return 0.0
        return math.sqrt(variance)
 
    def __str__(self):
        """ Returns a string representation of a Statistic object. """
        o = f"nTrueSig {self.nTrueSig}    nSig {self.nSig}    nBg {self.nBg}\n"
        o += f"Efficiency {self.efficiency:.3f} ({self.efficiencyError:.3f})\n"
        o += f"Purity {self.purity:.3f} ({self.purityError:.3f})\n"
        return o
 
    def __add__(self, a):
        """ Adds two Statistics objects and returns a new object. """
        # \cond false positive doxygen warning
        return Statistic(self.nTrueSig, self.nSig + a.nSig, self.nBg + a.nBg)
        # \endcond
 
    def __radd__(self, a):
        """
        Returns a new Statistic object if the current one is added to zero.
        Necessary to apply sum-function to Statistic objects.
        """
        if a != 0:
            return NotImplemented
        # \cond false positive doxygen warning
        return Statistic(self.nTrueSig, self.nSig, self.nBg)
        # \endcond
 
 
class MonitoringHist:
    """
    Reads all TH1F and TH2F from a ROOT file
    and puts them into a more accessible format.
    """
 
    def __init__(self, filename, dirname):
        """
        Reads histograms from the given file
        @param filename the name of the ROOT file
        """
        # Always avoid the top-level 'import ROOT'.
        import ROOT  # noqa
        
        self.values = {}
        
        self.centers = {}
        
        self.two_dimensional = {}
        
        self.nbins = {}
        
        self.valid = os.path.isfile(filename)
 
        if not self.valid:
            return
 
        f = ROOT.TFile.Open(filename, 'read')
        d = f.Get(ROOT.Belle2.MakeROOTCompatible.makeROOTCompatible(dirname))
 
        for key in d.GetListOfKeys():
            name = ROOT.Belle2.MakeROOTCompatible.invertMakeROOTCompatible(key.GetName())
            hist = key.ReadObj()
            if not (isinstance(hist, ROOT.TH1D) or isinstance(hist, ROOT.TH1F) or
                    isinstance(hist, ROOT.TH2D) or isinstance(hist, ROOT.TH2F)):
                continue
            self.two_dimensional[name] = isinstance(hist, ROOT.TH2D) or isinstance(hist, ROOT.TH2F)
            if self.two_dimensional[name]:
                nbins = (hist.GetNbinsX(), hist.GetNbinsY())
                self.centers[name] = [[hist.GetXaxis().GetBinCenter(i) for i in range(nbins[0] + 2)],
                                      [hist.GetYaxis().GetBinCenter(i) for i in range(nbins[1] + 2)]]
                self.values[name] = [[hist.GetBinContent(i, j) for i in range(nbins[0] + 2)] for j in range(nbins[1] + 2)]
                self.nbins[name] = nbins
            else:
                nbins = hist.GetNbinsX()
                self.centers[name] = np.array([hist.GetBinCenter(i) for i in range(nbins + 2)])
                self.values[name] = np.array([hist.GetBinContent(i) for i in range(nbins + 2)])
                self.nbins[name] = nbins
 
    def sum(self, name):
        """
        Calculates the sum of a given histogram (== sum of all entries)
        @param name key of the histogram
        """
        if name not in self.centers:
            return np.nan
        if self.two_dimensional[name]:
            tempsum = 0
            for i in range(len(self.values[name])):
                for j in range(len(self.values[name][i])):
                    tempsum += self.values[name][i][j]
            return tempsum
        return np.sum(self.values[name])
 
    def mean(self, name):
        """
        Calculates the mean of a given histogram
        @param name key of the histogram
        """
        if name not in self.centers:
            return np.nan
        if self.two_dimensional[name]:
            tempsum = 0
            for i in range(len(self.values[name])):
                for j in range(len(self.values[name][i])):
                    tempsum += self.centers[name][i][j] * self.values[name][i][j]
            return tempsum / self.sum(name)
        return np.average(self.centers[name], weights=self.values[name])
 
    def std(self, name):
        """
        Calculates the standard deviation of a given histogram
        @param name key of the histogram
        """
        if name not in self.centers:
            return np.nan
        if self.two_dimensional[name]:
            avg = self.mean(name)
            tempsum = 0
            for i in range(len(self.values[name])):
                for j in range(len(self.values[name][i])):
                    tempsum += self.values[name][i][j] * (self.centers[name][i][j] - avg)**2
            return np.sqrt(tempsum / self.sum(name))
        avg = np.average(self.centers[name], weights=self.values[name])
        return np.sqrt(np.average((self.centers[name] - avg)**2, weights=self.values[name]))
 
    def min(self, name):
        """
        Calculates the minimum of a given histogram
        @param name key of the histogram
        """
        if name not in self.centers:
            return np.nan
        if self.two_dimensional[name]:
            tempmin = np.inf
            for i in range(len(self.values[name])):
                for j in range(len(self.values[name][i])):
                    if self.values[name][i][j] < tempmin:
                        tempmin = self.centers[name][i][j]
            return tempmin
        nonzero = np.nonzero(self.values[name])[0]
        if len(nonzero) == 0:
            return np.nan
        return self.centers[name][nonzero[0]]
 
    def max(self, name):
        """
        Calculates the maximum of a given histogram
        @param name key of the histogram
        """
        if name not in self.centers:
            return np.nan
        if self.two_dimensional[name]:
            tempmax = -np.inf
            for i in range(len(self.values[name])):
                for j in range(len(self.values[name][i])):
                    if self.values[name][i][j] > tempmax:
                        tempmax = self.centers[name][i][j]
            return tempmax
        nonzero = np.nonzero(self.values[name])[0]
        if len(nonzero) == 0:
            return np.nan
        return self.centers[name][nonzero[-1]]
 
 
class MonitoringNTuple:
    """
    Reads the ntuple named variables from a ROOT file
    """
 
    def __init__(self, filename, treenameprefix):
        """
        Reads ntuple from the given file
        @param filename the name of the ROOT file
        """
        # Always avoid the top-level 'import ROOT'.
        import ROOT  # noqa
        
        self.valid = os.path.isfile(filename)
        print(f'FEI-monitoring: Looking for {filename}')
        if not self.valid:
            raise RuntimeError(f"Could not find {filename}: current dir is {os.getcwd()}")
            return
        
        self.f = ROOT.TFile.Open(filename, 'read')
        print(f'FEI-monitoring: Found {filename}')
        
        self.tree = self.f.Get(ROOT.Belle2.MakeROOTCompatible.makeROOTCompatible(f'{treenameprefix} variables'))
        print(f'FEI-monitoring: Found {treenameprefix} variables')
        
        self.filename = filename
 
 
class MonitoringModuleStatistics:
    """
    Reads the module statistics for a single particle from the outputted root file
    and puts them into a more accessible format
    """
 
    def __init__(self, particle):
        """
        Reads the module statistics from the file named Monitor_ModuleStatistics.root
        @param particle the particle for which the statistics are read
        """
        # Always avoid the top-level 'import ROOT'.
        import ROOT  # noqa
        root_file = ROOT.TFile.Open('Monitor_ModuleStatistics.root', 'read')
        persistentTree = root_file.Get('persistent')
        persistentTree.GetEntry(0)
        # Clone() needed so we actually own the object (original dies when tfile is deleted)
        stats = persistentTree.ProcessStatistics.Clone()
 
        # merge statistics from all persistent trees into 'stats'
        numEntries = persistentTree.GetEntriesFast()
        for i in range(1, numEntries):
            persistentTree.GetEntry(i)
            stats.merge(persistentTree.ProcessStatistics)
 
        # TODO .getTimeSum returns always 0 at the moment ?!
        statistic = {m.getName(): m.getTimeSum(m.c_Event) / 1e9 for m in stats.getAll()}
 
        
        self.channel_time = {}
        
        self.channel_time_per_module = {}
        for channel in particle.channels:
            if channel.label not in self.channel_time:
                self.channel_time[channel.label] = 0.0
                self.channel_time_per_module[channel.label] = {'ParticleCombiner': 0.0,
                                                               'BestCandidateSelection': 0.0,
                                                               'PListCutAndCopy': 0.0,
                                                               'VariablesToExtraInfo': 0.0,
                                                               'MCMatch': 0.0,
                                                               'ParticleSelector': 0.0,
                                                               'MVAExpert': 0.0,
                                                               'ParticleVertexFitter': 0.0,
                                                               'TagUniqueSignal': 0.0,
                                                               'VariablesToHistogram': 0.0,
                                                               'VariablesToNtuple': 0.0}
            for key, time in statistic.items():
                if (channel.decayString in key or channel.name in key):
                    self.channel_time[channel.label] += time
                    for k in self.channel_time_per_module[channel.label]:
                        if k in key:
                            self.channel_time_per_module[channel.label][k] += time
 
        
        self.particle_time = 0
        for key, time in statistic.items():
            if particle.identifier in key:
                self.particle_time += time
 
 
def MonitorSigProbPlot(particle, filename):
    """ Creates a Signal probability plot using ROOT. """
    if not particle.final_ntuple.valid:
        return
    df = basf2_mva_util.chain2dict(particle.final_ntuple.tree,
                                   ['extraInfo__bouniqueSignal__bc',
                                    'extraInfo__boSignalProbability__bc', particle.particle.mvaConfig.target],
                                   ['unique', 'probability', 'signal'], max_entries=int(1e8))
 
    p = plotting.VerboseDistribution(range_in_std=5.0)
    common = (df['probability'] >= 0) & (df['probability'] <= 1)
    df = df[common]
    p.add(df, 'probability', (df['signal'] == 1), label="Signal")
    p.add(df, 'probability', (df['signal'] == 0), label="Background")
    p.finish()
    p.axis.set_title("Signal probability")
    p.axis.set_xlabel("Probability")
    p.save(filename + '.png')
 
 
def MonitorSpectatorPlot(particle, spectator, filename, range=(None, None)):
    """ Creates a spectator plot using ROOT. """
    if not particle.final_ntuple.valid:
        return
    df = basf2_mva_util.chain2dict(particle.final_ntuple.tree,
                                   ['extraInfo__bouniqueSignal__bc', spectator,
                                    'extraInfo__boSignalProbability__bc', particle.particle.mvaConfig.target],
                                   ['unique', spectator, 'probability', 'signal'], max_entries=int(1e8))
    for i, cut in enumerate([0.0, 0.01, 0.05, 0.1, 0.2, 0.5]):
        p = plotting.VerboseDistribution(range_in_std=5.0)
        common = (df['probability'] >= cut)
        if range[0] is not None:
            common &= (df[spectator] >= range[0])
        if range[1] is not None:
            common &= (df[spectator] <= range[1])
        df = df[common]
        p.add(df, spectator, (df['signal'] == 1), label="Signal")
        p.add(df, spectator, (df['signal'] == 0), label="Background")
        p.finish()
        p.axis.set_title(f"{spectator} for signal probability >= {cut:.2f}")
        p.axis.set_xlabel(spectator)
        p.save(f'{filename}_{i}.png')
 
 
def MonitorROCPlot(particle, filename):
    """ Creates a ROC plot using ROOT. """
    if not particle.final_ntuple.valid:
        return
    df = basf2_mva_util.chain2dict(particle.final_ntuple.tree,
                                   ['extraInfo__bouniqueSignal__bc',
                                    'extraInfo__boSignalProbability__bc', particle.particle.mvaConfig.target],
                                   ['unique', 'probability', 'signal'], max_entries=int(1e8))
    p = plotting.RejectionOverEfficiency()
    p.add(df, 'probability', df['signal'] == 1, df['signal'] == 0, label='All')
    p.finish()
    p.save(filename + '.png')
 
 
def MonitorDiagPlot(particle, filename):
    """ Creates a Diagonal plot using ROOT. """
    if not particle.final_ntuple.valid:
        return
    df = basf2_mva_util.chain2dict(particle.final_ntuple.tree,
                                   ['extraInfo__bouniqueSignal__bc',
                                    'extraInfo__boSignalProbability__bc', particle.particle.mvaConfig.target],
                                   ['unique', 'probability', 'signal'], max_entries=int(1e8))
    p = plotting.Diagonal()
    p.add(df, 'probability', df['signal'] == 1, df['signal'] == 0)
    p.finish()
    p.save(filename + '.png')
 
 
def MonitoringMCCount(particle):
    """
    Reads the MC Counts for a given particle from the ROOT file mcParticlesCount.root
    @param particle the particle for which the MC counts are read
    @return dictionary with 'sum', 'std', 'avg', 'max', and 'min'
    """
    # Always avoid the top-level 'import ROOT'.
    import ROOT  # noqa
    root_file = ROOT.TFile.Open('mcParticlesCount.root', 'read')
 
    key = f'NumberOfMCParticlesInEvent({abs(pdg.from_name(particle.name))})'
 
    key = ROOT.Belle2.MakeROOTCompatible.makeROOTCompatible(key)
    hist = root_file.Get(key)
 
    mc_counts = {'sum': 0, 'std': 0, 'avg': 0, 'min': 0, 'max': 0}
    if hist:
        mc_counts['sum'] = sum(hist.GetXaxis().GetBinCenter(bin + 1) * hist.GetBinContent(bin + 1)
                               for bin in range(hist.GetNbinsX()))
        mc_counts['std'] = hist.GetStdDev()
        mc_counts['avg'] = hist.GetMean()
        mc_counts['max'] = hist.GetXaxis().GetBinCenter(hist.FindLastBinAbove(0.0))
        mc_counts['min'] = hist.GetXaxis().GetBinCenter(hist.FindFirstBinAbove(0.0))
    return mc_counts
 
 
class MonitoringBranchingFractions:
    """ Class extracts the branching fractions of a decay channel from the DECAY.DEC file. """
    
    _shared = None
 
    def __init__(self):
        """
        Create a new MonitoringBranchingFraction object.
        The extracted branching fractions are cached, hence creating more than one object does not do anything.
        """
        if MonitoringBranchingFractions._shared is None:
            decay_file = get_default_decayfile()
            
            self.exclusive_branching_fractions = self.loadExclusiveBranchingFractions(decay_file)
            
            self.inclusive_branching_fractions = self.loadInclusiveBranchingFractions(self.exclusive_branching_fractions)
            MonitoringBranchingFractions._shared = (self.exclusive_branching_fractions, self.inclusive_branching_fractions)
        else:
            self.exclusive_branching_fractions, self.inclusive_branching_fractions = MonitoringBranchingFractions._shared
 
    def getExclusive(self, particle):
        """ Returns the exclusive (i.e. without the branching fractions of the daughters) branching fraction of a particle. """
        return self.getBranchingFraction(particle, self.exclusive_branching_fractions)
 
    def getInclusive(self, particle):
        """ Returns the inclusive (i.e. including all branching fractions of the daughters) branching fraction of a particle. """
        return self.getBranchingFraction(particle, self.inclusive_branching_fractions)
 
    def getBranchingFraction(self, particle, branching_fractions):
        """ Returns the branching fraction of a particle given a branching_fraction table. """
        result = {c.label: 0.0 for c in particle.channels}
        name = particle.name
        channels = [tuple(sorted(d.split(':')[0] for d in channel.daughters)) for channel in particle.channels]
        if name not in branching_fractions:
            name = pdg.conjugate(name)
            channels = [tuple(pdg.conjugate(d) for d in channel) for channel in channels]
            if name not in branching_fractions:
                return result
        for c, key in zip(particle.channels, channels):
            if key in branching_fractions[name]:
                result[c.label] = branching_fractions[name][key]
        return result
 
    def loadExclusiveBranchingFractions(self, filename):
        """
        Load branching fraction from MC decay-file.
        """
 
        def isFloat(element):
            """ Checks if element is a convertible to float"""
            try:
                float(element)
                return True
            except ValueError:
                return False
 
        def isValidParticle(element):
            """ Checks if element is a valid pdg name for a particle"""
            try:
                pdg.from_name(element)
                return True
            except LookupError:
                return False
 
        branching_fractions = {'UNKOWN': {}}
 
        mother = 'UNKOWN'
        with open(filename) as f:
            for line in f:
                fields = line.split(' ')
                fields = [x for x in fields if x != '']
                if len(fields) < 2 or fields[0][0] == '#':
                    continue
                if fields[0] == 'Decay':
                    mother = fields[1].strip()
                    if not isValidParticle(mother):
                        mother = 'UNKOWN'
                    continue
                if fields[0] == 'Enddecay':
                    mother = 'UNKOWN'
                    continue
                if mother == 'UNKOWN':
                    continue
                fields = fields[:-1]
                if len(fields) < 1 or not isFloat(fields[0]):
                    continue
                while len(fields) > 1:
                    if isValidParticle(fields[-1]):
                        break
                    fields = fields[:-1]
                if len(fields) < 1 or not all(isValidParticle(p) for p in fields[1:]):
                    continue
                neutrinoTag_list = ['nu_e', 'nu_mu', 'nu_tau', 'anti-nu_e', 'anti-nu_mu', 'anti-nu_tau']
                daughters = tuple(sorted(p for p in fields[1:] if p not in neutrinoTag_list))
                if mother not in branching_fractions:
                    branching_fractions[mother] = {}
                if daughters not in branching_fractions[mother]:
                    branching_fractions[mother][daughters] = 0.0
                branching_fractions[mother][daughters] += float(fields[0])
 
        del branching_fractions['UNKOWN']
        return branching_fractions
 
    def loadInclusiveBranchingFractions(self, exclusive_branching_fractions):
        """
        Get covered branching fraction of a particle using a recursive algorithm
        and the given exclusive branching_fractions (given as Hashable List)
        @param particle identifier of the particle
        @param branching_fractions
        """
        particles = set(exclusive_branching_fractions.keys())
        particles.update({pdg.conjugate(p) for p in particles if p != pdg.conjugate(p)})
        particles = sorted(particles, key=lambda x: pdg.get(x).Mass())
        inclusive_branching_fractions = copy.deepcopy(exclusive_branching_fractions)
 
        for p in particles:
            if p in inclusive_branching_fractions:
                br = sum(inclusive_branching_fractions[p].values())
            else:
                br = sum(inclusive_branching_fractions[pdg.conjugate(p)].values())
            for p_br in inclusive_branching_fractions.values():
                for c in p_br:
                    for i in range(c.count(p)):
                        p_br[c] *= br
        return inclusive_branching_fractions
 
 
class MonitoringParticle:
    """
    Monitoring object containing all the monitoring information
    about a single particle
    """
 
    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)
 
    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)
 
    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)