#include <NeutrinoIdTool.h>

Inheritance diagram for lar_content::NeutrinoIdTool< T >:

Classes
class	SliceFeatures
	Slice features class. More...

Public Member Functions
	NeutrinoIdTool ()
	Default constructor. More...

void	SelectOutputPfos (const pandora::Algorithm *const pAlgorithm, const SliceHypotheses &nuSliceHypotheses, const SliceHypotheses &crSliceHypotheses, pandora::PfoList &selectedPfos)
	Select which reconstruction hypotheses to use; neutrino outcomes or cosmic-ray muon outcomes for each slice. More...

Private Types
typedef std::pair< unsigned int, float >	UintFloatPair

typedef std::vector < SliceFeatures >	SliceFeaturesVector

Private Member Functions
void	GetSliceFeatures (const NeutrinoIdTool *const pTool, const SliceHypotheses &nuSliceHypotheses, const SliceHypotheses &crSliceHypotheses, SliceFeaturesVector &sliceFeaturesVector) const
	Get the features of each slice. More...

bool	GetBestMCSliceIndex (const pandora::Algorithm *const pAlgorithm, const SliceHypotheses &nuSliceHypotheses, const SliceHypotheses &crSliceHypotheses, unsigned int &bestSliceIndex) const
	Get the slice with the most neutrino induced hits using Monte-Carlo information. More...

bool	PassesQualityCuts (const pandora::Algorithm *const pAlgorithm, const float purity, const float completeness) const
	Determine if the event passes the selection cuts for training and has the required NUANCE code. More...

void	Collect2DHits (const pandora::PfoList &pfos, pandora::CaloHitList &reconstructedCaloHitList, const pandora::CaloHitSet &reconstructableCaloHitSet) const
	Collect all 2D hits in a supplied list of Pfos and push them on to an existing hit list, check so not to double count. More...

unsigned int	CountNeutrinoInducedHits (const pandora::CaloHitList &caloHitList) const
	Count the number of neutrino induced hits in a given list using MC information. More...

int	GetNuanceCode (const pandora::Algorithm *const pAlgorithm) const
	Use the current MCParticle list to get the nuance code of the neutrino in the event. More...

void	SelectAllPfos (const pandora::Algorithm *const pAlgorithm, const SliceHypotheses &hypotheses, pandora::PfoList &selectedPfos) const
	Select all pfos under the same hypothesis. More...

void	SelectPfosByProbability (const pandora::Algorithm *const pAlgorithm, const SliceHypotheses &nuSliceHypotheses, const SliceHypotheses &crSliceHypotheses, const SliceFeaturesVector &sliceFeaturesVector, pandora::PfoList &selectedPfos) const
	Select pfos based on the probability that their slice contains a neutrino interaction. More...

void	SelectPfos (const pandora::PfoList &pfos, pandora::PfoList &selectedPfos) const
	Add the given pfos to the selected Pfo list. More...

pandora::StatusCode	ReadSettings (const pandora::TiXmlHandle xmlHandle)

Private Attributes
bool	m_useTrainingMode
	Should use training mode. If true, training examples will be written to the output file. More...

std::string	m_trainingOutputFile
	Output file name for training examples. More...

bool	m_selectNuanceCode
	Should select training events by nuance code. More...

int	m_nuance
	Nuance code to select for training. More...

float	m_minPurity
	Minimum purity of the best slice to use event for training. More...

float	m_minCompleteness
	Minimum completeness of the best slice to use event for training. More...

float	m_minProbability
	Minimum probability required to classify a slice as the neutrino. More...

unsigned int	m_maxNeutrinos
	The maximum number of neutrinos to select in any one event. More...

bool	m_persistFeatures
	If true, the mva features will be persisted in the metadata. More...

T	m_mva
	The mva. More...

std::string	m_filePathEnvironmentVariable
	The environment variable providing a list of paths to mva files. More...

Detailed Description

template<typename T>
class lar_content::NeutrinoIdTool< T >

NeutrinoIdTool class.

Compares the neutrino and cosmic hypotheses of all of the slices in the event. Uses an MVA to calculate the probability of each slice containing a neutrino interaction. The N slices with the highest probabilities are identified as a neutrino (if sufficiently probable) all other slices are deemed cosmogenic.

If training mode is switched on, then the tool will write MVA training exmples to the specified output file. The events selected for training must pass (user configurable) slicing quality cuts. Users may also select events based on their interaction type (nuance code).

Definition at line 32 of file NeutrinoIdTool.h.

Member Typedef Documentation

template<typename T>

typedef std::vector<SliceFeatures> lar_content::NeutrinoIdTool< T >::SliceFeaturesVector

private

Definition at line 162 of file NeutrinoIdTool.h.

template<typename T>

typedef std::pair<unsigned int, float> lar_content::NeutrinoIdTool< T >::UintFloatPair

private

Definition at line 161 of file NeutrinoIdTool.h.

Constructor & Destructor Documentation

template<typename T >

lar_content::NeutrinoIdTool< T >::NeutrinoIdTool ( )

Default constructor.

Definition at line 29 of file NeutrinoIdTool.cc.

                                   :
     m_useTrainingMode(false),
     m_selectNuanceCode(false),
     m_nuance(-std::numeric_limits<int>::max()),
     m_minPurity(0.9f),
     m_minCompleteness(0.9f),
     m_minProbability(0.0f),
     m_maxNeutrinos(1),
     m_persistFeatures(false),
     m_filePathEnvironmentVariable("FW_SEARCH_PATH")
 {
 }

Member Function Documentation

template<typename T>

void lar_content::NeutrinoIdTool< T >::Collect2DHits	(	const pandora::PfoList &	pfos,
		pandora::CaloHitList &	reconstructedCaloHitList,
		const pandora::CaloHitSet &	reconstructableCaloHitSet
	)		const

private

Collect all 2D hits in a supplied list of Pfos and push them on to an existing hit list, check so not to double count.

Parameters

pfos	input list of pfos
reconstructedCaloHitList	output list of all 2d hits in the input pfos
reconstructableCaloHitSet	set of reconstructable calo hits

Definition at line 165 of file NeutrinoIdTool.cc.

 {
     CaloHitList collectedHits;
     LArPfoHelper::GetCaloHits(pfos, TPC_VIEW_U, collectedHits);
     LArPfoHelper::GetCaloHits(pfos, TPC_VIEW_V, collectedHits);
     LArPfoHelper::GetCaloHits(pfos, TPC_VIEW_W, collectedHits);
 
     for (const CaloHit *const pCaloHit : collectedHits)
     {
         const CaloHit *const pParentHit = static_cast<const CaloHit *>(pCaloHit->GetParentAddress());
         if (!reconstructableCaloHitSet.count(pParentHit))
             continue;
 
         // Ensure no hits have been double counted
         if (std::find(reconstructedCaloHitList.begin(), reconstructedCaloHitList.end(), pParentHit) == reconstructedCaloHitList.end())
             reconstructedCaloHitList.push_back(pParentHit);
     }
 }

template<typename T>

unsigned int lar_content::NeutrinoIdTool< T >::CountNeutrinoInducedHits ( const pandora::CaloHitList & caloHitList ) const

private

Count the number of neutrino induced hits in a given list using MC information.

Parameters

caloHitSet input list of calo hits

Returns: the number of neutrino induced hits in the input list

Definition at line 187 of file NeutrinoIdTool.cc.

 {
     unsigned int nNuHits(0);
     for (const CaloHit *const pCaloHit : caloHitList)
     {
         try
         {
             if (LArMCParticleHelper::IsNeutrino(LArMCParticleHelper::GetParentMCParticle(MCParticleHelper::GetMainMCParticle(pCaloHit))))
                 nNuHits++;
         }
         catch (const StatusCodeException &)
         {
         }
     }
 
     return nNuHits;
 }

template<typename T >

bool lar_content::NeutrinoIdTool< T >::GetBestMCSliceIndex	(	const pandora::Algorithm *const	pAlgorithm,
		const SliceHypotheses &	nuSliceHypotheses,
		const SliceHypotheses &	crSliceHypotheses,
		unsigned int &	bestSliceIndex
	)		const

private

Get the slice with the most neutrino induced hits using Monte-Carlo information.

Parameters

pAlgorithm	address of the master algorithm
nuSliceHypotheses	the input neutrino slice hypotheses
crSliceHypotheses	the input cosmic slice hypotheses
bestSliceIndex	the index of the slice with the most neutrino hits

Returns: does the best slice pass the quality cuts for training?

Definition at line 97 of file NeutrinoIdTool.cc.

 {
     unsigned int nHitsInBestSlice(0), nNuHitsInBestSlice(0);
 
     // Get all hits in all slices to find true number of mc hits
     const CaloHitList *pAllReconstructedCaloHitList(nullptr);
     PANDORA_THROW_RESULT_IF(STATUS_CODE_SUCCESS, !=, PandoraContentApi::GetCurrentList(*pAlgorithm, pAllReconstructedCaloHitList));
 
     const MCParticleList *pMCParticleList(nullptr);
     PANDORA_THROW_RESULT_IF(STATUS_CODE_SUCCESS, !=, PandoraContentApi::GetCurrentList(*pAlgorithm, pMCParticleList));
 
     // Obtain map: [mc particle -> primary mc particle]
     LArMCParticleHelper::MCRelationMap mcToPrimaryMCMap;
     LArMCParticleHelper::GetMCPrimaryMap(pMCParticleList, mcToPrimaryMCMap);
 
     // Remove non-reconstructable hits, e.g. those downstream of a neutron
     CaloHitList reconstructableCaloHitList;
     LArMCParticleHelper::PrimaryParameters parameters;
     LArMCParticleHelper::SelectCaloHits(pAllReconstructedCaloHitList, mcToPrimaryMCMap, reconstructableCaloHitList,
         parameters.m_selectInputHits, parameters.m_maxPhotonPropagation);
 
     const int nuNHitsTotal(this->CountNeutrinoInducedHits(reconstructableCaloHitList));
     const CaloHitSet reconstructableCaloHitSet(reconstructableCaloHitList.begin(), reconstructableCaloHitList.end());
 
     for (unsigned int sliceIndex = 0, nSlices = nuSliceHypotheses.size(); sliceIndex < nSlices; ++sliceIndex)
     {
         CaloHitList reconstructedCaloHitList;
         this->Collect2DHits(crSliceHypotheses.at(sliceIndex), reconstructedCaloHitList, reconstructableCaloHitSet);
 
         for (const ParticleFlowObject *const pNeutrino : nuSliceHypotheses.at(sliceIndex))
         {
             const PfoList &nuFinalStates(pNeutrino->GetDaughterPfoList());
             this->Collect2DHits(nuFinalStates, reconstructedCaloHitList, reconstructableCaloHitSet);
         }
 
         const unsigned int nNuHits(this->CountNeutrinoInducedHits(reconstructedCaloHitList));
 
         if (nNuHits > nNuHitsInBestSlice)
         {
             nNuHitsInBestSlice = nNuHits;
             nHitsInBestSlice = reconstructedCaloHitList.size();
             bestSliceIndex = sliceIndex;
         }
     }
 
     // ATTN for events with no neutrino induced hits, default neutrino purity and completeness to zero
     const float purity(nHitsInBestSlice > 0 ? static_cast<float>(nNuHitsInBestSlice) / static_cast<float>(nHitsInBestSlice) : 0.f);
     const float completeness(nuNHitsTotal > 0 ? static_cast<float>(nNuHitsInBestSlice) / static_cast<float>(nuNHitsTotal) : 0.f);
     return this->PassesQualityCuts(pAlgorithm, purity, completeness);
 }

template<typename T >

int lar_content::NeutrinoIdTool< T >::GetNuanceCode ( const pandora::Algorithm *const pAlgorithm ) const

private

Use the current MCParticle list to get the nuance code of the neutrino in the event.

Parameters

pAlgorithm address of the master algorithm

Returns: the nuance code of the event

Definition at line 208 of file NeutrinoIdTool.cc.

 {
     const MCParticleList *pMCParticleList = nullptr;
     PANDORA_THROW_RESULT_IF(STATUS_CODE_SUCCESS, !=, PandoraContentApi::GetCurrentList(*pAlgorithm, pMCParticleList));
 
     MCParticleVector trueNeutrinos;
     LArMCParticleHelper::GetTrueNeutrinos(pMCParticleList, trueNeutrinos);
 
     if (trueNeutrinos.size() != 1)
     {
         std::cout << "NeutrinoIdTool::GetNuanceCode - Error: number of true neutrinos in event must be exactly one" << std::endl;
         throw StatusCodeException(STATUS_CODE_OUT_OF_RANGE);
     }
 
     return LArMCParticleHelper::GetNuanceCode(trueNeutrinos.front());
 }

template<typename T >

void lar_content::NeutrinoIdTool< T >::GetSliceFeatures	(	const NeutrinoIdTool< T > *const	pTool,
		const SliceHypotheses &	nuSliceHypotheses,
		const SliceHypotheses &	crSliceHypotheses,
		SliceFeaturesVector &	sliceFeaturesVector
	)		const

private

Get the features of each slice.

Parameters

pTool	the address of the this NeutrinoId tool
nuSliceHypotheses	the input neutrino slice hypotheses
crSliceHypotheses	the input cosmic slice hypotheses
sliceFeaturesVector	vector to hold the slice features

Definition at line 87 of file NeutrinoIdTool.cc.

 {
     for (unsigned int sliceIndex = 0, nSlices = nuSliceHypotheses.size(); sliceIndex < nSlices; ++sliceIndex)
         sliceFeaturesVector.push_back(SliceFeatures(nuSliceHypotheses.at(sliceIndex), crSliceHypotheses.at(sliceIndex), pTool));
 }

template<typename T >

bool lar_content::NeutrinoIdTool< T >::PassesQualityCuts	(	const pandora::Algorithm *const	pAlgorithm,
		const float	purity,
		const float	completeness
	)		const

private

Determine if the event passes the selection cuts for training and has the required NUANCE code.

Parameters

pAlgorithm	address of the master algorithm
purity	purity of best slice
completeness	completeness of best slice

Returns: does the evenr pass the quality cuts on purity and completeness and has the required NUANCE code

Definition at line 152 of file NeutrinoIdTool.cc.

 {
     if (purity < m_minPurity || completeness < m_minCompleteness)
         return false;
     if (m_selectNuanceCode && (this->GetNuanceCode(pAlgorithm) != m_nuance))
         return false;
 
     return true;
 }

template<typename T>

StatusCode lar_content::NeutrinoIdTool< T >::ReadSettings ( const pandora::TiXmlHandle xmlHandle )

private

Definition at line 593 of file NeutrinoIdTool.cc.

 {
     PANDORA_RETURN_RESULT_IF_AND_IF(STATUS_CODE_SUCCESS, STATUS_CODE_NOT_FOUND, !=, XmlHelper::ReadValue(xmlHandle, "UseTrainingMode", m_useTrainingMode));
 
     if (m_useTrainingMode)
     {
         PANDORA_RETURN_RESULT_IF(STATUS_CODE_SUCCESS, !=, XmlHelper::ReadValue(xmlHandle, "TrainingOutputFileName", m_trainingOutputFile));
     }
 
     PANDORA_RETURN_RESULT_IF_AND_IF(STATUS_CODE_SUCCESS, STATUS_CODE_NOT_FOUND, !=, XmlHelper::ReadValue(xmlHandle, "MinimumPurity", m_minPurity));
 
     PANDORA_RETURN_RESULT_IF_AND_IF(
         STATUS_CODE_SUCCESS, STATUS_CODE_NOT_FOUND, !=, XmlHelper::ReadValue(xmlHandle, "MinimumCompleteness", m_minCompleteness));
 
     PANDORA_RETURN_RESULT_IF_AND_IF(
         STATUS_CODE_SUCCESS, STATUS_CODE_NOT_FOUND, !=, XmlHelper::ReadValue(xmlHandle, "SelectNuanceCode", m_selectNuanceCode));
 
     if (m_selectNuanceCode)
     {
         PANDORA_RETURN_RESULT_IF(STATUS_CODE_SUCCESS, !=, XmlHelper::ReadValue(xmlHandle, "NuanceCode", m_nuance));
     }
 
     PANDORA_RETURN_RESULT_IF_AND_IF(
         STATUS_CODE_SUCCESS, STATUS_CODE_NOT_FOUND, !=, XmlHelper::ReadValue(xmlHandle, "MinimumNeutrinoProbability", m_minProbability));
 
     PANDORA_RETURN_RESULT_IF_AND_IF(STATUS_CODE_SUCCESS, STATUS_CODE_NOT_FOUND, !=, XmlHelper::ReadValue(xmlHandle, "MaximumNeutrinos", m_maxNeutrinos));
 
     PANDORA_RETURN_RESULT_IF_AND_IF(STATUS_CODE_SUCCESS, STATUS_CODE_NOT_FOUND, !=, XmlHelper::ReadValue(xmlHandle, "PersistFeatures", m_persistFeatures));
 
     PANDORA_RETURN_RESULT_IF_AND_IF(STATUS_CODE_SUCCESS, STATUS_CODE_NOT_FOUND, !=,
         XmlHelper::ReadValue(xmlHandle, "FilePathEnvironmentVariable", m_filePathEnvironmentVariable));
 
     if (!m_useTrainingMode)
     {
         std::string mvaName;
         PANDORA_RETURN_RESULT_IF(STATUS_CODE_SUCCESS, !=, XmlHelper::ReadValue(xmlHandle, "MvaName", mvaName));
 
         std::string mvaFileName;
         PANDORA_RETURN_RESULT_IF(STATUS_CODE_SUCCESS, !=, XmlHelper::ReadValue(xmlHandle, "MvaFileName", mvaFileName));
 
         const std::string fullMvaFileName(LArFileHelper::FindFileInPath(mvaFileName, m_filePathEnvironmentVariable));
         m_mva.Initialize(fullMvaFileName, mvaName);
     }
 
     return STATUS_CODE_SUCCESS;
 }

template<typename T>

void lar_content::NeutrinoIdTool< T >::SelectAllPfos	(	const pandora::Algorithm *const	pAlgorithm,
		const SliceHypotheses &	hypotheses,
		pandora::PfoList &	selectedPfos
	)		const

private

Select all pfos under the same hypothesis.

Parameters

pAlgorithm	address of the master algorithm
hypotheses	the lists of slices under a certain hypothesis
selectedPfos	the list of pfos to populate

Definition at line 228 of file NeutrinoIdTool.cc.

 {
     for (const PfoList &pfos : hypotheses)
     {
         for (const ParticleFlowObject *const pPfo : pfos)
         {
             object_creation::ParticleFlowObject::Metadata metadata;
             metadata.m_propertiesToAdd["NuScore"] = -1.f;
             PANDORA_THROW_RESULT_IF(STATUS_CODE_SUCCESS, !=, PandoraContentApi::ParticleFlowObject::AlterMetadata(*pAlgorithm, pPfo, metadata));
         }
 
         this->SelectPfos(pfos, selectedPfos);
     }
 }

template<typename T>

void lar_content::NeutrinoIdTool< T >::SelectOutputPfos	(	const pandora::Algorithm *const	pAlgorithm,
		const SliceHypotheses &	nuSliceHypotheses,
		const SliceHypotheses &	crSliceHypotheses,
		pandora::PfoList &	selectedPfos
	)

virtual

Select which reconstruction hypotheses to use; neutrino outcomes or cosmic-ray muon outcomes for each slice.

Parameters

pAlgorithm	the address of the master instance, used to access MCParticles when in training mode
nuSliceHypotheses	the parent pfos representing the neutrino outcome for each slice
crSliceHypotheses	the parent pfos representing the cosmic-ray muon outcome for each slice
sliceNuPfos	to receive the list of selected pfos

Implements lar_content::SliceIdBaseTool.

Definition at line 45 of file NeutrinoIdTool.cc.

 {
     if (nuSliceHypotheses.size() != crSliceHypotheses.size())
         throw StatusCodeException(STATUS_CODE_INVALID_PARAMETER);
 
     const unsigned int nSlices(nuSliceHypotheses.size());
     if (nSlices == 0)
         return;
 
     SliceFeaturesVector sliceFeaturesVector;
     this->GetSliceFeatures(this, nuSliceHypotheses, crSliceHypotheses, sliceFeaturesVector);
 
     if (m_useTrainingMode)
     {
         // ATTN in training mode, just return everything as a cosmic-ray
         this->SelectAllPfos(pAlgorithm, crSliceHypotheses, selectedPfos);
 
         unsigned int bestSliceIndex(std::numeric_limits<unsigned int>::max());
         if (!this->GetBestMCSliceIndex(pAlgorithm, nuSliceHypotheses, crSliceHypotheses, bestSliceIndex))
             return;
 
         for (unsigned int sliceIndex = 0; sliceIndex < nSlices; ++sliceIndex)
         {
             const SliceFeatures &features(sliceFeaturesVector.at(sliceIndex));
             if (!features.IsFeatureVectorAvailable())
                 continue;
 
             LArMvaHelper::MvaFeatureVector featureVector;
             features.GetFeatureVector(featureVector);
             LArMvaHelper::ProduceTrainingExample(m_trainingOutputFile, sliceIndex == bestSliceIndex, featureVector);
         }
 
         return;
     }
 
     this->SelectPfosByProbability(pAlgorithm, nuSliceHypotheses, crSliceHypotheses, sliceFeaturesVector, selectedPfos);
 }

template<typename T>

void lar_content::NeutrinoIdTool< T >::SelectPfos	(	const pandora::PfoList &	pfos,
		pandora::PfoList &	selectedPfos
	)		const

private

Add the given pfos to the selected Pfo list.

Parameters

pfos	the pfos to select
selectedPfos	the list of pfos to populate

Definition at line 320 of file NeutrinoIdTool.cc.

 {
     selectedPfos.insert(selectedPfos.end(), pfos.begin(), pfos.end());
 }

template<typename T>

void lar_content::NeutrinoIdTool< T >::SelectPfosByProbability	(	const pandora::Algorithm *const	pAlgorithm,
		const SliceHypotheses &	nuSliceHypotheses,
		const SliceHypotheses &	crSliceHypotheses,
		const SliceFeaturesVector &	sliceFeaturesVector,
		pandora::PfoList &	selectedPfos
	)		const

private

Select pfos based on the probability that their slice contains a neutrino interaction.

Parameters

pAlgorithm	address of the master algorithm
nuSliceHypotheses	the input neutrino slice hypotheses
crSliceHypotheses	the input cosmic slice hypotheses
sliceFeaturesVector	vector holding the slice features
selectedPfos	the list of pfos to populate

Definition at line 246 of file NeutrinoIdTool.cc.

 {
     // Calculate the probability of each slice that passes the minimum probability cut
     std::vector<UintFloatPair> sliceIndexProbabilityPairs;
     for (unsigned int sliceIndex = 0, nSlices = nuSliceHypotheses.size(); sliceIndex < nSlices; ++sliceIndex)
     {
         const float nuProbability(sliceFeaturesVector.at(sliceIndex).GetNeutrinoProbability(m_mva));
 
         for (const ParticleFlowObject *const pPfo : crSliceHypotheses.at(sliceIndex))
         {
             object_creation::ParticleFlowObject::Metadata metadata;
             metadata.m_propertiesToAdd["NuScore"] = nuProbability;
 
             if (m_persistFeatures)
             {
                 LArMvaHelper::DoubleMap featureMap;
                 sliceFeaturesVector.at(sliceIndex).GetFeatureMap(featureMap);
 
                 for (auto const &[name, value] : featureMap)
                     metadata.m_propertiesToAdd[name] = value;
             }
 
             PANDORA_THROW_RESULT_IF(STATUS_CODE_SUCCESS, !=, PandoraContentApi::ParticleFlowObject::AlterMetadata(*pAlgorithm, pPfo, metadata));
         }
 
         for (const ParticleFlowObject *const pPfo : nuSliceHypotheses.at(sliceIndex))
         {
             object_creation::ParticleFlowObject::Metadata metadata;
             metadata.m_propertiesToAdd["NuScore"] = nuProbability;
 
             if (m_persistFeatures)
             {
                 LArMvaHelper::DoubleMap featureMap;
                 sliceFeaturesVector.at(sliceIndex).GetFeatureMap(featureMap);
 
                 for (auto const &[name, value] : featureMap)
                     metadata.m_propertiesToAdd[name] = value;
             }
 
             PANDORA_THROW_RESULT_IF(STATUS_CODE_SUCCESS, !=, PandoraContentApi::ParticleFlowObject::AlterMetadata(*pAlgorithm, pPfo, metadata));
         }
 
         if (nuProbability < m_minProbability)
         {
             this->SelectPfos(crSliceHypotheses.at(sliceIndex), selectedPfos);
             continue;
         }
 
         sliceIndexProbabilityPairs.push_back(UintFloatPair(sliceIndex, nuProbability));
     }
 
     // Sort the slices by probability
     std::sort(sliceIndexProbabilityPairs.begin(), sliceIndexProbabilityPairs.end(),
         [](const UintFloatPair &a, const UintFloatPair &b) { return (a.second > b.second); });
 
     // Select the first m_maxNeutrinos as neutrinos, and the rest as cosmic
     unsigned int nNuSlices(0);
     for (const UintFloatPair &slice : sliceIndexProbabilityPairs)
     {
         if (nNuSlices < m_maxNeutrinos)
         {
             this->SelectPfos(nuSliceHypotheses.at(slice.first), selectedPfos);
             nNuSlices++;
             continue;
         }
 
         this->SelectPfos(crSliceHypotheses.at(slice.first), selectedPfos);
     }
 }

Member Data Documentation

template<typename T>

std::string lar_content::NeutrinoIdTool< T >::m_filePathEnvironmentVariable

private

The environment variable providing a list of paths to mva files.

Definition at line 273 of file NeutrinoIdTool.h.

template<typename T>

unsigned int lar_content::NeutrinoIdTool< T >::m_maxNeutrinos

private

The maximum number of neutrinos to select in any one event.

Definition at line 268 of file NeutrinoIdTool.h.

template<typename T>

float lar_content::NeutrinoIdTool< T >::m_minCompleteness

private

Minimum completeness of the best slice to use event for training.

Definition at line 264 of file NeutrinoIdTool.h.

template<typename T>

float lar_content::NeutrinoIdTool< T >::m_minProbability

private

Minimum probability required to classify a slice as the neutrino.

Definition at line 267 of file NeutrinoIdTool.h.

template<typename T>

float lar_content::NeutrinoIdTool< T >::m_minPurity

private

Minimum purity of the best slice to use event for training.

Definition at line 263 of file NeutrinoIdTool.h.

template<typename T>

T lar_content::NeutrinoIdTool< T >::m_mva

private

The mva.

Definition at line 272 of file NeutrinoIdTool.h.

template<typename T>

int lar_content::NeutrinoIdTool< T >::m_nuance

private

Nuance code to select for training.

Definition at line 262 of file NeutrinoIdTool.h.

template<typename T>

bool lar_content::NeutrinoIdTool< T >::m_persistFeatures

private

If true, the mva features will be persisted in the metadata.

Definition at line 270 of file NeutrinoIdTool.h.

template<typename T>

bool lar_content::NeutrinoIdTool< T >::m_selectNuanceCode

private

Should select training events by nuance code.

Definition at line 261 of file NeutrinoIdTool.h.

template<typename T>

std::string lar_content::NeutrinoIdTool< T >::m_trainingOutputFile

private

Output file name for training examples.

Definition at line 260 of file NeutrinoIdTool.h.

template<typename T>

bool lar_content::NeutrinoIdTool< T >::m_useTrainingMode

private

Should use training mode. If true, training examples will be written to the output file.

Definition at line 259 of file NeutrinoIdTool.h.

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

Classes

Public Member Functions

Private Types

Private Member Functions

Private Attributes

Detailed Description

template<typename T> class lar_content::NeutrinoIdTool< T >

Member Typedef Documentation

Constructor & Destructor Documentation

Member Function Documentation

Member Data Documentation

template<typename T>
class lar_content::NeutrinoIdTool< T >