PreReconstruction Class Reference

Public Member Functions
	__init__ (self, typing.Sequence[config.Particle] particles, config.FeiConfiguration config)
pybasf2.Path	reconstruct (self)

Public Attributes
	particles = particles
	list of config.Particle objects
	config = config
	config.FeiConfiguration object

Detailed Description

Steers the reconstruction phase before the mva method was applied
It Includes:
    - The ParticleCombination (for each particle and channel we create candidates using
                               the daughter candidates from the previous stages)
    - MC Matching
    - Vertex Fitting (this is the slowest part of the whole FEI, KFit is used by default,
                      but you can use fastFit as a drop-in replacement https://github.com/thomaskeck/FastFit/,
                      this will speed up the whole FEI by a factor 2-3)

Definition at line 295 of file core.py.

Constructor & Destructor Documentation

__init__()

__init__	(		self,
		typing.Sequence[config.Particle]	particles,
		config.FeiConfiguration	config )

Create a new PreReconstruction object
@param particles list of config.Particle objects
@param config config.FeiConfiguration object

Definition at line 307 of file core.py.

    def __init__(self, particles: typing.Sequence[config.Particle], config: config.FeiConfiguration):
        """
        Create a new PreReconstruction object
        @param particles list of config.Particle objects
        @param config config.FeiConfiguration object
        """
        
        self.particles = particles
        
        self.config = config
 

Member Function Documentation

reconstruct()

pybasf2.Path reconstruct

(

self

)

Returns pybasf2.Path which reconstructs the particles and does the vertex fitting if necessary

Definition at line 318 of file core.py.

    def reconstruct(self) -> pybasf2.Path:
        """
        Returns pybasf2.Path which reconstructs the particles and does the vertex fitting if necessary
        """
        path = basf2.create_path()
 
        for particle in self.particles:
            for channel in particle.channels:
 
                if (len(channel.daughters) == 1) and (pdg.from_name(
                        channel.daughters[0].split(':')[0]) == pdg.from_name(particle.name)):
                    ma.cutAndCopyList(channel.name, channel.daughters[0], channel.preCutConfig.userCut, writeOut=True, path=path)
                    v2EI = basf2.register_module('VariablesToExtraInfo')
                    v2EI.set_name(f'VariablesToExtraInfo_{channel.name}')
                    v2EI.param('particleList', channel.name)
                    v2EI.param('variables', {f'constant({channel.decayModeID})': 'decayModeID'})
                    # suppress warning that decay mode ID won't be overwritten if it already exists
                    v2EI.set_log_level(basf2.logging.log_level.ERROR)
                    path.add_module(v2EI)
                else:
                    ma.reconstructDecay(channel.decayString, channel.preCutConfig.userCut, channel.decayModeID,
                                        writeOut=True, path=path)
                if self.config.monitor:
                    ma.matchMCTruth(channel.name, path=path)
                    bc_variable = channel.preCutConfig.bestCandidateVariable
                    if self.config.monitor == 'simple':
                        hist_variables = [channel.mvaConfig.target, 'extraInfo(decayModeID)']
                        hist_variables_2d = [(channel.mvaConfig.target, 'extraInfo(decayModeID)')]
                    else:
                        hist_variables = [bc_variable, 'mcErrors', 'mcParticleStatus',
                                          channel.mvaConfig.target] + list(channel.mvaConfig.spectators.keys())
                        hist_variables_2d = [(bc_variable, channel.mvaConfig.target),
                                             (bc_variable, 'mcErrors'),
                                             (bc_variable, 'mcParticleStatus')]
                        for specVar in channel.mvaConfig.spectators:
                            hist_variables_2d.append((bc_variable, specVar))
                            hist_variables_2d.append((channel.mvaConfig.target, specVar))
                    filename = os.path.join(self.config.monitoring_path, 'Monitor_PreReconstruction_BeforeRanking.root')
                    ma.variablesToHistogram(
                        channel.name,
                        variables=config.variables2binnings(hist_variables),
                        variables_2d=config.variables2binnings_2d(hist_variables_2d),
                        filename=filename,
                        ignoreCommandLineOverride=True,
                        directory=f'{channel.label}',
                        path=path)
 
                if channel.preCutConfig.bestCandidateMode == 'lowest':
                    ma.rankByLowest(channel.name,
                                    channel.preCutConfig.bestCandidateVariable,
                                    channel.preCutConfig.bestCandidateCut,
                                    'preCut_rank',
                                    path=path)
                elif channel.preCutConfig.bestCandidateMode == 'highest':
                    ma.rankByHighest(channel.name,
                                     channel.preCutConfig.bestCandidateVariable,
                                     channel.preCutConfig.bestCandidateCut,
                                     'preCut_rank',
                                     path=path)
                else:
                    raise RuntimeError(f'Unknown bestCandidateMode {repr(channel.preCutConfig.bestCandidateMode)}')
 
                if 'gamma' in channel.decayString and channel.pi0veto:
                    ma.buildRestOfEvent(channel.name, path=path)
                    Ddaughter_roe_path = basf2.Path()
                    deadEndPath = basf2.Path()
                    ma.signalSideParticleFilter(channel.name, '', Ddaughter_roe_path, deadEndPath)
                    ma.fillParticleList('gamma:roe', 'isInRestOfEvent == 1', path=Ddaughter_roe_path)
 
                    matches = list(re.finditer('gamma', channel.decayString))
                    pi0lists = []
                    for igamma in range(len(matches)):
                        start, end = matches[igamma-1].span()
                        tempString = f'{channel.decayString[:start]}^gamma{channel.decayString[end:]}'
                        ma.fillSignalSideParticleList(f'gamma:sig_{igamma}', tempString, path=Ddaughter_roe_path)
                        ma.reconstructDecay(f'pi0:veto_{igamma} -> gamma:sig_{igamma} gamma:roe', '', path=Ddaughter_roe_path)
                        pi0lists.append(f'pi0:veto_{igamma}')
                    ma.copyLists('pi0:veto', pi0lists, writeOut=False, path=Ddaughter_roe_path)
                    ma.rankByLowest('pi0:veto', 'abs(dM)', 1, path=Ddaughter_roe_path)
                    ma.matchMCTruth('pi0:veto', path=Ddaughter_roe_path)
                    ma.variableToSignalSideExtraInfo(
                        'pi0:veto',
                        {
                            'InvM': 'pi0vetoMass',
                            'formula((daughter(0,E)-daughter(1,E))/(daughter(0,E)+daughter(1,E)))': 'pi0vetoEnergyAsymmetry',
                        },
                        path=Ddaughter_roe_path
                    )
                    path.for_each('RestOfEvent', 'RestOfEvents', Ddaughter_roe_path)
 
                if self.config.monitor:
                    filename = os.path.join(self.config.monitoring_path, 'Monitor_PreReconstruction_AfterRanking.root')
                    if self.config.monitor != 'simple':
                        hist_variables += ['extraInfo(preCut_rank)']
                        hist_variables_2d += [('extraInfo(preCut_rank)', channel.mvaConfig.target),
                                              ('extraInfo(preCut_rank)', 'mcErrors'),
                                              ('extraInfo(preCut_rank)', 'mcParticleStatus')]
                        for specVar in channel.mvaConfig.spectators:
                            hist_variables_2d.append(('extraInfo(preCut_rank)', specVar))
                    ma.variablesToHistogram(
                        channel.name,
                        variables=config.variables2binnings(hist_variables),
                        variables_2d=config.variables2binnings_2d(hist_variables_2d),
                        filename=filename,
                        ignoreCommandLineOverride=True,
                        directory=f'{channel.label}',
                        path=path)
                # If we are not in monitor mode we do the mc matching now,
                # otherwise we did it above already!
                elif self.config.training:
                    ma.matchMCTruth(channel.name, path=path)
 
                if b2bii.isB2BII() and particle.name in ['K_S0', 'Lambda0']:
                    pvfit = basf2.register_module('ParticleVertexFitter')
                    pvfit.set_name(f'ParticleVertexFitter_{channel.name}')
                    pvfit.param('listName', channel.name)
                    pvfit.param('confidenceLevel', channel.preCutConfig.vertexCut)
                    pvfit.param('vertexFitter', 'KFit')
                    pvfit.param('fitType', 'vertex')
                    pvfit.set_log_level(basf2.logging.log_level.ERROR)  # let's not produce gigabytes of uninteresting warnings
                    path.add_module(pvfit)
                elif re.findall(r"[\w']+", channel.decayString).count('pi0') > 1 and particle.name != 'pi0':
                    basf2.B2INFO(f"Ignoring vertex fit for {channel.name} because multiple pi0 are not supported yet.")
                elif len(channel.daughters) > 1:
                    pvfit = basf2.register_module('ParticleVertexFitter')
                    pvfit.set_name(f'ParticleVertexFitter_{channel.name}')
                    pvfit.param('listName', channel.name)
                    pvfit.param('confidenceLevel', channel.preCutConfig.vertexCut)
                    pvfit.param('vertexFitter', 'KFit')
                    if particle.name in ['pi0']:
                        pvfit.param('fitType', 'mass')
                    else:
                        pvfit.param('fitType', 'vertex')
                    pvfit.set_log_level(basf2.logging.log_level.ERROR)  # let's not produce gigabytes of uninteresting warnings
                    path.add_module(pvfit)
 
                if self.config.monitor:
                    if self.config.monitor == 'simple':
                        hist_variables = [channel.mvaConfig.target, 'extraInfo(decayModeID)']
                        hist_variables_2d = [(channel.mvaConfig.target, 'extraInfo(decayModeID)')]
                    else:
                        hist_variables = ['chiProb', 'mcErrors', 'mcParticleStatus',
                                          channel.mvaConfig.target] + list(channel.mvaConfig.spectators.keys())
                        hist_variables_2d = [('chiProb', channel.mvaConfig.target),
                                             ('chiProb', 'mcErrors'),
                                             ('chiProb', 'mcParticleStatus')]
                        for specVar in channel.mvaConfig.spectators:
                            hist_variables_2d.append(('chiProb', specVar))
                            hist_variables_2d.append((channel.mvaConfig.target, specVar))
                    filename = os.path.join(self.config.monitoring_path, 'Monitor_PreReconstruction_AfterVertex.root')
                    ma.variablesToHistogram(
                        channel.name,
                        variables=config.variables2binnings(hist_variables),
                        variables_2d=config.variables2binnings_2d(hist_variables_2d),
                        filename=filename,
                        ignoreCommandLineOverride=True,
                        directory=f'{channel.label}',
                        path=path)
 
        return path
 
 

Member Data Documentation

config

config = config

config.FeiConfiguration object

Definition at line 316 of file core.py.

particles

particles = particles

list of config.Particle objects

Definition at line 314 of file core.py.

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

• analysis/scripts/fei/core.py