CrossGapsAssociationAlgorithm class. More...

#include <CrossGapsAssociationAlgorithm.h>

Inheritance diagram for lar_content::CrossGapsAssociationAlgorithm:

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

Public Member Functions inherited from lar_content::ClusterAssociationAlgorithm
	ClusterAssociationAlgorithm ()
	Default constructor. More...

Private Member Functions
void	GetListOfCleanClusters (const pandora::ClusterList *const pClusterList, pandora::ClusterVector &clusterVector) const
	Populate cluster vector with subset of cluster list, containing clusters judged to be clean. More...

void	PopulateClusterAssociationMap (const pandora::ClusterVector &clusterVector, ClusterAssociationMap &clusterAssociationMap) const
	Populate the cluster association map. More...

bool	IsExtremalCluster (const bool isForward, const pandora::Cluster const pCurrentCluster, const pandora::Cluster const pTestCluster) const
	Determine which of two clusters is extremal. More...

bool	AreClustersAssociated (const TwoDSlidingFitResult &innerFitResult, const TwoDSlidingFitResult &outerFitResult) const
	Determine whether two clusters are associated. More...

bool	IsAssociated (const pandora::CartesianVector &startPosition, const pandora::CartesianVector &startDirection, const TwoDSlidingFitResult &targetFitResult) const
	Sample points along the extrapolation from a starting position to a target fit result to declare cluster association. More...

bool	IsNearCluster (const pandora::CartesianVector &samplingPoint, const TwoDSlidingFitResult &targetFitResult) const
	Whether a sampling point lies near a target 2d sliding fit result. More...

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

Private Attributes
unsigned int	m_minClusterHits
	The minimum allowed number of hits in a clean cluster. More...

unsigned int	m_minClusterLayers
	The minimum allowed number of layers for a clean cluster. More...

unsigned int	m_slidingFitWindow
	The layer window for the sliding linear fits. More...

unsigned int	m_maxSamplingPoints
	The maximum number of extension sampling points considered per association check. More...

float	m_sampleStepSize
	The sampling step size used in association checks, units cm. More...

unsigned int	m_maxUnmatchedSampleRun
	The maximum run of unmatched (and non-gap) samples to consider before stopping. More...

float	m_maxOnClusterDistance
	The maximum distance between a sampling point and sliding fit to target cluster. More...

unsigned int	m_minMatchedSamplingPoints
	Minimum number of matched sampling points to declare association. More...

float	m_minMatchedSamplingFraction
	Minimum ratio between matched sampling points and expectation to declare association. More...

float	m_gapTolerance
	The tolerance to use when querying whether a sampling point is in a gap, units cm. More...

Additional Inherited Members
Protected Types inherited from lar_content::ClusterAssociationAlgorithm
typedef std::unordered_map < const pandora::Cluster *, ClusterAssociation >	ClusterAssociationMap

Protected Member Functions inherited from lar_content::ClusterAssociationAlgorithm
virtual pandora::StatusCode	Run ()

Detailed Description

CrossGapsAssociationAlgorithm class.

Definition at line 23 of file CrossGapsAssociationAlgorithm.h.

Constructor & Destructor Documentation

lar_content::CrossGapsAssociationAlgorithm::CrossGapsAssociationAlgorithm ( )

Default constructor.

Definition at line 21 of file CrossGapsAssociationAlgorithm.cc.

                                                              :
     m_minClusterHits(10),
     m_minClusterLayers(6),
     m_slidingFitWindow(20),
     m_maxSamplingPoints(1000),
     m_sampleStepSize(0.5f),
     m_maxUnmatchedSampleRun(8),
     m_maxOnClusterDistance(1.5f),
     m_minMatchedSamplingPoints(10),
     m_minMatchedSamplingFraction(0.5f),
     m_gapTolerance(0.f)
 {
 }

Member Function Documentation

bool lar_content::CrossGapsAssociationAlgorithm::AreClustersAssociated	(	const TwoDSlidingFitResult &	innerFitResult,
		const TwoDSlidingFitResult &	outerFitResult
	)		const

private

Determine whether two clusters are associated.

Parameters

innerFitResult	two dimensional sliding fit result for the inner cluster
outerFitResult	two dimensional sliding fit result for the outer cluster

Returns: boolean

Definition at line 124 of file CrossGapsAssociationAlgorithm.cc.

 {
     if (outerFitResult.GetCluster()->GetInnerPseudoLayer() < innerFitResult.GetCluster()->GetInnerPseudoLayer())
         throw pandora::StatusCodeException(STATUS_CODE_NOT_ALLOWED);
 
     if (outerFitResult.GetCluster()->GetInnerPseudoLayer() < innerFitResult.GetCluster()->GetOuterPseudoLayer())
         return false;
 
     return (this->IsAssociated(innerFitResult.GetGlobalMaxLayerPosition(), innerFitResult.GetGlobalMaxLayerDirection(), outerFitResult) &&
             this->IsAssociated(outerFitResult.GetGlobalMinLayerPosition(), outerFitResult.GetGlobalMinLayerDirection() * -1.f, innerFitResult));
 }

void lar_content::CrossGapsAssociationAlgorithm::GetListOfCleanClusters	(	const pandora::ClusterList *const	pClusterList,
		pandora::ClusterVector &	clusterVector
	)		const

privatevirtual

Populate cluster vector with subset of cluster list, containing clusters judged to be clean.

Parameters

pClusterList	address of the cluster list
clusterVector	to receive the populated cluster vector

Implements lar_content::ClusterAssociationAlgorithm.

Definition at line 37 of file CrossGapsAssociationAlgorithm.cc.

 {
     // ATTN May want to opt-out completely if no gap information available
     // if (PandoraContentApi::GetGeometry(*this)->GetDetectorGapList().empty())
     //     return;
 
     for (const Cluster *const pCluster : *pClusterList)
     {
         if (pCluster->GetNCaloHits() < m_minClusterHits)
             continue;
 
         if (1 + pCluster->GetOuterPseudoLayer() - pCluster->GetInnerPseudoLayer() < m_minClusterLayers)
             continue;
 
         clusterVector.push_back(pCluster);
     }
 
     std::sort(clusterVector.begin(), clusterVector.end(), LArClusterHelper::SortByInnerLayer);
 }

bool lar_content::CrossGapsAssociationAlgorithm::IsAssociated	(	const pandora::CartesianVector &	startPosition,
		const pandora::CartesianVector &	startDirection,
		const TwoDSlidingFitResult &	targetFitResult
	)		const

private

Sample points along the extrapolation from a starting position to a target fit result to declare cluster association.

Parameters

startPosition	the start position
startDirection	the start direction
targetFitResult	the target fit result

Returns: boolean

Definition at line 138 of file CrossGapsAssociationAlgorithm.cc.

 {
     const HitType hitType(LArClusterHelper::GetClusterHitType(targetFitResult.GetCluster()));
     unsigned int nSamplingPoints(0), nGapSamplingPoints(0), nMatchedSamplingPoints(0), nUnmatchedSampleRun(0);
 
     for (unsigned int iSample = 0; iSample < m_maxSamplingPoints; ++iSample)
     {
         ++nSamplingPoints;
         const CartesianVector samplingPoint(startPosition + startDirection * static_cast<float>(iSample) * m_sampleStepSize);
 
         if (LArGeometryHelper::IsInGap(this->GetPandora(), samplingPoint, hitType, m_gapTolerance))
         {
             ++nGapSamplingPoints;
             nUnmatchedSampleRun = 0; // ATTN Choose to also reset run when entering gap region
             continue;
         }
 
         if (this->IsNearCluster(samplingPoint, targetFitResult))
         {
             ++nMatchedSamplingPoints;
             nUnmatchedSampleRun = 0;
         }
         else if (++nUnmatchedSampleRun > m_maxUnmatchedSampleRun)
         {
             break;
         }
     }
 
     const float expectation((targetFitResult.GetGlobalMaxLayerPosition() - targetFitResult.GetGlobalMinLayerPosition()).GetMagnitude() / m_sampleStepSize);
     const float matchedSamplingFraction(expectation > 0.f ? static_cast<float>(nMatchedSamplingPoints) / expectation : 0.f);
 
     if ((nMatchedSamplingPoints > m_minMatchedSamplingPoints) || (matchedSamplingFraction > m_minMatchedSamplingFraction))
         return true;
 
     return false;
 }

bool lar_content::CrossGapsAssociationAlgorithm::IsExtremalCluster	(	const bool	isForward,
		const pandora::Cluster *const	pCurrentCluster,
		const pandora::Cluster *const	pTestCluster
	)		const

privatevirtual

Determine which of two clusters is extremal.

Parameters

isForward	whether propagation direction is forward
pCurrentCluster	current extremal cluster
pTestCluster	potential extremal cluster

Returns: boolean

Implements lar_content::ClusterAssociationAlgorithm.

Definition at line 108 of file CrossGapsAssociationAlgorithm.cc.

 {
     const unsigned int currentLayer(isForward ? pCurrentCluster->GetOuterPseudoLayer() : pCurrentCluster->GetInnerPseudoLayer());
     const unsigned int testLayer(isForward ? pTestCluster->GetOuterPseudoLayer() : pTestCluster->GetInnerPseudoLayer());
 
     if (isForward && ((testLayer > currentLayer) || ((testLayer == currentLayer) && LArClusterHelper::SortByNHits(pTestCluster, pCurrentCluster))))
         return true;
 
     if (!isForward && ((testLayer < currentLayer) || ((testLayer == currentLayer) && LArClusterHelper::SortByNHits(pTestCluster, pCurrentCluster))))
         return true;
 
     return false;
 }

bool lar_content::CrossGapsAssociationAlgorithm::IsNearCluster	(	const pandora::CartesianVector &	samplingPoint,
		const TwoDSlidingFitResult &	targetFitResult
	)		const

private

Whether a sampling point lies near a target 2d sliding fit result.

Parameters

samplingPoint	the sampling point
targetFitResult	the target fit result

Returns: boolean

Definition at line 178 of file CrossGapsAssociationAlgorithm.cc.

 {
     float rL(std::numeric_limits<float>::max()), rT(std::numeric_limits<float>::max());
     targetFitResult.GetLocalPosition(samplingPoint, rL, rT);
 
     CartesianVector fitPosition(0.f, 0.f, 0.f);
 
     if (STATUS_CODE_SUCCESS == targetFitResult.GetGlobalFitPosition(rL, fitPosition))
     {
         if ((fitPosition - samplingPoint).GetMagnitudeSquared() < m_maxOnClusterDistance * m_maxOnClusterDistance)
             return true;
     }
 
     CartesianVector fitPositionAtX(0.f, 0.f, 0.f);
 
     if (STATUS_CODE_SUCCESS == targetFitResult.GetGlobalFitPositionAtX(samplingPoint.GetX(), fitPositionAtX))
     {
         if ((fitPositionAtX - samplingPoint).GetMagnitudeSquared() < m_maxOnClusterDistance * m_maxOnClusterDistance)
             return true;
     }
 
     return false;
 }

void lar_content::CrossGapsAssociationAlgorithm::PopulateClusterAssociationMap	(	const pandora::ClusterVector &	clusterVector,
		ClusterAssociationMap &	clusterAssociationMap
	)		const

privatevirtual

Populate the cluster association map.

Parameters

clusterVector	the cluster vector
clusterAssociationMap	to receive the populated cluster association map

Implements lar_content::ClusterAssociationAlgorithm.

Definition at line 59 of file CrossGapsAssociationAlgorithm.cc.

 {
     TwoDSlidingFitResultMap slidingFitResultMap;
     const float slidingFitPitch(LArGeometryHelper::GetWireZPitch(this->GetPandora()));
 
     for (const Cluster *const pCluster : clusterVector)
     {
         try
         {
             (void)slidingFitResultMap.insert(
                 TwoDSlidingFitResultMap::value_type(pCluster, TwoDSlidingFitResult(pCluster, m_slidingFitWindow, slidingFitPitch)));
         }
         catch (StatusCodeException &)
         {
         }
     }
 
     // ATTN This method assumes that clusters have been sorted by layer
     for (ClusterVector::const_iterator iterI = clusterVector.begin(), iterIEnd = clusterVector.end(); iterI != iterIEnd; ++iterI)
     {
         const Cluster *const pInnerCluster = *iterI;
         TwoDSlidingFitResultMap::const_iterator fitIterI = slidingFitResultMap.find(pInnerCluster);
 
         if (slidingFitResultMap.end() == fitIterI)
             continue;
 
         for (ClusterVector::const_iterator iterJ = iterI, iterJEnd = clusterVector.end(); iterJ != iterJEnd; ++iterJ)
         {
             const Cluster *const pOuterCluster = *iterJ;
 
             if (pInnerCluster == pOuterCluster)
                 continue;
 
             TwoDSlidingFitResultMap::const_iterator fitIterJ = slidingFitResultMap.find(pOuterCluster);
 
             if (slidingFitResultMap.end() == fitIterJ)
                 continue;
 
             if (!this->AreClustersAssociated(fitIterI->second, fitIterJ->second))
                 continue;
 
             clusterAssociationMap[pInnerCluster].m_forwardAssociations.insert(pOuterCluster);
             clusterAssociationMap[pOuterCluster].m_backwardAssociations.insert(pInnerCluster);
         }
     }
 }

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

privatevirtual

Reimplemented from lar_content::ClusterAssociationAlgorithm.

Definition at line 204 of file CrossGapsAssociationAlgorithm.cc.

 {
     PANDORA_RETURN_RESULT_IF_AND_IF(STATUS_CODE_SUCCESS, STATUS_CODE_NOT_FOUND, !=, XmlHelper::ReadValue(xmlHandle, "MinClusterHits", m_minClusterHits));
 
     PANDORA_RETURN_RESULT_IF_AND_IF(
         STATUS_CODE_SUCCESS, STATUS_CODE_NOT_FOUND, !=, XmlHelper::ReadValue(xmlHandle, "MinClusterLayers", m_minClusterLayers));
 
     PANDORA_RETURN_RESULT_IF_AND_IF(
         STATUS_CODE_SUCCESS, STATUS_CODE_NOT_FOUND, !=, XmlHelper::ReadValue(xmlHandle, "SlidingFitWindow", m_slidingFitWindow));
 
     PANDORA_RETURN_RESULT_IF_AND_IF(
         STATUS_CODE_SUCCESS, STATUS_CODE_NOT_FOUND, !=, XmlHelper::ReadValue(xmlHandle, "MaxSamplingPoints", m_maxSamplingPoints));
 
     PANDORA_RETURN_RESULT_IF_AND_IF(STATUS_CODE_SUCCESS, STATUS_CODE_NOT_FOUND, !=, XmlHelper::ReadValue(xmlHandle, "SampleStepSize", m_sampleStepSize));
 
     if (m_sampleStepSize < std::numeric_limits<float>::epsilon())
     {
         std::cout << "CrossGapsAssociationAlgorithm: Invalid value for SampleStepSize " << m_sampleStepSize << std::endl;
         throw StatusCodeException(STATUS_CODE_INVALID_PARAMETER);
     }
 
     PANDORA_RETURN_RESULT_IF_AND_IF(
         STATUS_CODE_SUCCESS, STATUS_CODE_NOT_FOUND, !=, XmlHelper::ReadValue(xmlHandle, "MaxUnmatchedSampleRun", m_maxUnmatchedSampleRun));
 
     PANDORA_RETURN_RESULT_IF_AND_IF(
         STATUS_CODE_SUCCESS, STATUS_CODE_NOT_FOUND, !=, XmlHelper::ReadValue(xmlHandle, "MaxOnClusterDistance", m_maxOnClusterDistance));
 
     PANDORA_RETURN_RESULT_IF_AND_IF(STATUS_CODE_SUCCESS, STATUS_CODE_NOT_FOUND, !=,
         XmlHelper::ReadValue(xmlHandle, "MinMatchedSamplingPoints", m_minMatchedSamplingPoints));
 
     PANDORA_RETURN_RESULT_IF_AND_IF(STATUS_CODE_SUCCESS, STATUS_CODE_NOT_FOUND, !=,
         XmlHelper::ReadValue(xmlHandle, "MinMatchedSamplingFraction", m_minMatchedSamplingFraction));
 
     PANDORA_RETURN_RESULT_IF_AND_IF(STATUS_CODE_SUCCESS, STATUS_CODE_NOT_FOUND, !=, XmlHelper::ReadValue(xmlHandle, "GapTolerance", m_gapTolerance));
 
     return ClusterAssociationAlgorithm::ReadSettings(xmlHandle);
 }