Inheritance diagram for lar_cluster3d::ConvexHullPathFinder:

Public Member Functions
	ConvexHullPathFinder (const fhicl::ParameterSet &)
	Constructor. More...

	~ConvexHullPathFinder ()
	Destructor. More...

void	configure (fhicl::ParameterSet const &pset) override

void	initializeHistograms (art::TFileDirectory &) override
	Interface for initializing histograms if they are desired Note that the idea is to put hisgtograms in a subfolder. More...

void	ModifyClusters (reco::ClusterParametersList &) const override
	Scan an input collection of clusters and modify those according to the specific implementing algorithm. More...

float	getTimeToExecute () const override
	If monitoring, recover the time to execute a particular function. More...

virtual void	configure (const fhicl::ParameterSet &)=0
	Interface for configuring the particular algorithm tool. More...

Private Types
using	HitOrderTuple = std::tuple< float, float, reco::ProjectedPoint >

using	HitOrderTupleList = std::list< HitOrderTuple >

using	MinMaxPoints = std::pair< reco::ProjectedPoint, reco::ProjectedPoint >

using	MinMaxPointPair = std::pair< MinMaxPoints, MinMaxPoints >

using	KinkTuple = std::tuple< int, reco::ConvexHullKinkTuple, HitOrderTupleList, HitOrderTupleList >

using	KinkTupleVec = std::vector< KinkTuple >

Private Member Functions
reco::ClusterParametersList::iterator	subDivideCluster (reco::ClusterParameters &cluster, reco::PrincipalComponents &lastPCA, reco::ClusterParametersList::iterator positionItr, reco::ClusterParametersList &outputClusterList, int level=0) const
	Use PCA to try to find path in cluster. More...

bool	makeCandidateCluster (Eigen::Vector3f &, reco::ClusterParameters &, reco::HitPairListPtr::iterator, reco::HitPairListPtr::iterator, int) const

bool	makeCandidateCluster (Eigen::Vector3f &, reco::ClusterParameters &, HitOrderTupleList &, int) const

bool	completeCandidateCluster (Eigen::Vector3f &, reco::ClusterParameters &, int) const

bool	breakClusterByKinks (reco::ClusterParameters &, reco::HitPairListPtr &, reco::ClusterParametersList &, int level) const

bool	breakClusterByKinksTrial (reco::ClusterParameters &, reco::HitPairListPtr &, reco::ClusterParametersList &, int level) const

bool	breakClusterByMaxDefect (reco::ClusterParameters &, reco::HitPairListPtr &, reco::ClusterParametersList &, int level) const

bool	breakClusterInHalf (reco::ClusterParameters &, reco::HitPairListPtr &, reco::ClusterParametersList &, int level) const

bool	breakClusterAtBigGap (reco::ClusterParameters &, reco::HitPairListPtr &, reco::ClusterParametersList &, int level) const

float	closestApproach (const Eigen::Vector3f &, const Eigen::Vector3f &, const Eigen::Vector3f &, const Eigen::Vector3f &, Eigen::Vector3f &, Eigen::Vector3f &) const

void	buildConvexHull (reco::ClusterParameters &clusterParameters, int level=0) const

float	findConvexHullEndPoints (const reco::EdgeList &, const reco::ClusterHit3D , const reco::ClusterHit3D ) const

void	orderHitsAlongEdge (const reco::ProjectedPointList &, const reco::ProjectedPoint &, const Eigen::Vector2f &, HitOrderTupleList &) const

void	pruneHitOrderTupleLists (HitOrderTupleList &, HitOrderTupleList &) const

Private Attributes
bool	fEnableMonitoring
	FHICL parameters. More...

size_t	fMinTinyClusterSize
	Minimum size for a "tiny" cluster. More...

float	fConvexHullKinkAngle
	Angle to declare a kink in convex hull calc. More...

float	fConvexHullMinSep
	Min hit separation to conisder in convex hull. More...

float	fTimeToProcess

bool	fFillHistograms
	Histogram definitions. More...

TH1F *	fTopNum3DHits

TH1F *	fTopNumEdges

TH1F *	fTopEigen21Ratio

TH1F *	fTopEigen20Ratio

TH1F *	fTopEigen10Ratio

TH1F *	fTopPrimaryLength

TH1F *	fSubNum3DHits

TH1F *	fSubNumEdges

TH1F *	fSubEigen21Ratio

TH1F *	fSubEigen20Ratio

TH1F *	fSubEigen10Ratio

TH1F *	fSubPrimaryLength

TH1F *	fSubCosToPrevPCA

TH1F *	fSubCosExtToPCA

TH1F *	fSubMaxDefect

TH1F *	fSubUsedDefect

geo::Geometry *	fGeometry
	Tools. More...

std::unique_ptr< lar_cluster3d::IClusterAlg >	fClusterAlg
	Algorithm to do 3D space point clustering. More...

PrincipalComponentsAlg	fPCAAlg

Detailed Description

Definition at line 46 of file ConvexHullPathFinder_tool.cc.

Member Typedef Documentation

using lar_cluster3d::ConvexHullPathFinder::HitOrderTuple = std::tuple<float,float,reco::ProjectedPoint>

private

Definition at line 98 of file ConvexHullPathFinder_tool.cc.

using lar_cluster3d::ConvexHullPathFinder::HitOrderTupleList = std::list<HitOrderTuple>

private

Definition at line 99 of file ConvexHullPathFinder_tool.cc.

using lar_cluster3d::ConvexHullPathFinder::KinkTuple = std::tuple<int, reco::ConvexHullKinkTuple, HitOrderTupleList, HitOrderTupleList>

private

Definition at line 134 of file ConvexHullPathFinder_tool.cc.

using lar_cluster3d::ConvexHullPathFinder::KinkTupleVec = std::vector<KinkTuple>

private

Definition at line 135 of file ConvexHullPathFinder_tool.cc.

using lar_cluster3d::ConvexHullPathFinder::MinMaxPointPair = std::pair<MinMaxPoints,MinMaxPoints>

private

Definition at line 128 of file ConvexHullPathFinder_tool.cc.

using lar_cluster3d::ConvexHullPathFinder::MinMaxPoints = std::pair<reco::ProjectedPoint,reco::ProjectedPoint>

private

Definition at line 127 of file ConvexHullPathFinder_tool.cc.

Constructor & Destructor Documentation

lar_cluster3d::ConvexHullPathFinder::ConvexHullPathFinder ( const fhicl::ParameterSet & )

explicit

Constructor.

Parameters

pset

Definition at line 182 of file ConvexHullPathFinder_tool.cc.

References configure().

                                                                         :
     fPCAAlg(pset.get<fhicl::ParameterSet>("PrincipalComponentsAlg"))
 {
     this->configure(pset);
 }

lar_cluster3d::ConvexHullPathFinder::~ConvexHullPathFinder ( )

Destructor.

Definition at line 190 of file ConvexHullPathFinder_tool.cc.

191 {

192 }

Member Function Documentation

bool lar_cluster3d::ConvexHullPathFinder::breakClusterAtBigGap	(	reco::ClusterParameters &	clusterToBreak,
		reco::HitPairListPtr &	hitList,
		reco::ClusterParametersList &	outputClusterList,
		int	level
	)		const

private

Definition at line 963 of file ConvexHullPathFinder_tool.cc.

References reco::ClusterHit3D::getArclenToPoca(), reco::ClusterParameters::getFullPCA(), and makeCandidateCluster().

Referenced by subDivideCluster().

 {
     // Idea here is to scan the input hit list (assumed ordered along the current PCA) and look for "large" gaps
     // Here a gap is determined when the hits were ordered by their distance along the primary PCA to their doca to it.
     
     // Loop through the input hit list and keep track of first hit of largest gap
     reco::HitPairListPtr::iterator bigGapHitItr = hitList.begin();
     float                                biggestGap   = 0.;
     
     const reco::ClusterHit3D* lastHit = *hitList.begin();
     
     for(reco::HitPairListPtr::iterator hitItr = hitList.begin(); hitItr != hitList.end(); hitItr++)
     {
         const reco::ClusterHit3D* currentHit = *hitItr;
         
         float currentGap = std::abs(currentHit->getArclenToPoca() - lastHit->getArclenToPoca());
         
         if (currentGap > biggestGap)
         {
             bigGapHitItr = hitItr;
             biggestGap   = currentGap;
         }
         
         lastHit = currentHit;
     }
     
     // Require some minimum gap size...
     if (biggestGap > 2.)
     {
         outputClusterList.push_back(reco::ClusterParameters());
         
         reco::ClusterParameters& clusterParams1  = outputClusterList.back();
         
         reco::PrincipalComponents& fullPCA(clusterToBreak.getFullPCA());
         Eigen::Vector3f            fullPrimaryVec(fullPCA.getEigenVectors()[0][0],fullPCA.getEigenVectors()[0][1],fullPCA.getEigenVectors()[0][2]);
         
         if (makeCandidateCluster(fullPrimaryVec, clusterParams1, hitList.begin(), bigGapHitItr, level))
         {
             outputClusterList.push_back(reco::ClusterParameters());
             
             reco::ClusterParameters& clusterParams2  = outputClusterList.back();
             
             makeCandidateCluster(fullPrimaryVec, clusterParams2, bigGapHitItr, hitList.end(), level);
         }
         
         if (outputClusterList.size() != 2) outputClusterList.clear();
     }
     
     return !outputClusterList.empty();
 }

bool lar_cluster3d::ConvexHullPathFinder::breakClusterByKinks	(	reco::ClusterParameters &	clusterToBreak,
		reco::HitPairListPtr &	hitList,
		reco::ClusterParametersList &	outputClusterList,
		int	level
	)		const

private

Definition at line 630 of file ConvexHullPathFinder_tool.cc.

References fMinTinyClusterSize, reco::ClusterParameters::getConvexHull(), reco::ConvexHull::getConvexHullKinkPoints(), reco::ClusterParameters::getFullPCA(), art::left(), makeCandidateCluster(), min, and art::right().

Referenced by subDivideCluster().

 {
     // Set up container to keep track of edges
     using HitKinkTuple    = std::tuple<int, reco::HitPairListPtr::iterator>;
     using HitKinkTupleVec = std::vector<HitKinkTuple>;
     
     // Set up container to keep track of edges
     HitKinkTupleVec kinkTupleVec;
     
     reco::ConvexHull&              convexHull    = clusterToBreak.getConvexHull();
     reco::ConvexHullKinkTupleList& kinkPointList = convexHull.getConvexHullKinkPoints();
     
     for(auto& kink : kinkPointList)
     {
         const reco::ClusterHit3D* hit3D = std::get<2>(std::get<0>(kink));
         
         reco::HitPairListPtr::iterator kinkItr = std::find(hitList.begin(),hitList.end(),hit3D);
         
         if (kinkItr == hitList.end()) continue;
         
         int numStartToKink = std::distance(hitList.begin(),kinkItr);
         int numKinkToEnd   = std::distance(kinkItr, hitList.end());
         int minNumHits     = std::min(numStartToKink,numKinkToEnd);
         
         if (minNumHits > int(fMinTinyClusterSize)) kinkTupleVec.emplace_back(minNumHits,kinkItr);
     }
     
     // No work if the list is empty
     if (!kinkTupleVec.empty())
     {
         std::sort(kinkTupleVec.begin(),kinkTupleVec.end(),[](const auto& left,const auto& right){return std::get<0>(left) > std::get<0>(right);});
         
         // Recover the kink point
         reco::HitPairListPtr::iterator kinkItr = std::get<1>(kinkTupleVec.front());
         
         // Set up to split the input cluster
         outputClusterList.push_back(reco::ClusterParameters());
         
         reco::ClusterParameters& clusterParams1  = outputClusterList.back();
         
         reco::PrincipalComponents& fullPCA(clusterToBreak.getFullPCA());
         Eigen::Vector3f            fullPrimaryVec(fullPCA.getEigenVectors()[0][0],fullPCA.getEigenVectors()[0][1],fullPCA.getEigenVectors()[0][2]);
         
         if (makeCandidateCluster(fullPrimaryVec, clusterParams1, hitList.begin(), kinkItr, level))
         {
             outputClusterList.push_back(reco::ClusterParameters());
             
             reco::ClusterParameters& clusterParams2  = outputClusterList.back();
             
             makeCandidateCluster(fullPrimaryVec, clusterParams2, kinkItr, hitList.end(), level);
         }
         
         // If we did not make 2 clusters then be sure to clear the output list
         if (outputClusterList.size() != 2) outputClusterList.clear();
     }
     
     return !outputClusterList.empty();
 }

bool lar_cluster3d::ConvexHullPathFinder::breakClusterByKinksTrial	(	reco::ClusterParameters &	clusterToBreak,
		reco::HitPairListPtr &	hitList,
		reco::ClusterParametersList &	outputClusterList,
		int	level
	)		const

private

Definition at line 689 of file ConvexHullPathFinder_tool.cc.

References fMinTinyClusterSize, reco::ClusterParameters::getConvexHull(), reco::ConvexHull::getConvexHullKinkPoints(), reco::ClusterParameters::getFullPCA(), reco::ConvexHull::getProjectedPointList(), art::left(), makeCandidateCluster(), min, orderHitsAlongEdge(), pruneHitOrderTupleLists(), and art::right().

 {
     // Set up container to keep track of edges
     KinkTupleVec kinkTupleVec;
     
     reco::ConvexHull&              convexHull    = clusterToBreak.getConvexHull();
     reco::ProjectedPointList&      pointList     = convexHull.getProjectedPointList();
     reco::ConvexHullKinkTupleList& kinkPointList = convexHull.getConvexHullKinkPoints();
     
     for(auto& kink : kinkPointList)
     {
         // Make an instance of the vec value to avoid copying if we can...
         kinkTupleVec.push_back(KinkTuple());
         
         KinkTuple& kinkTuple = kinkTupleVec.back();
         
         std::get<1>(kinkTuple) = kink;
         
         // Recover vectors, want them pointing away from intersection point
         Eigen::Vector2f    firstEdge  = -std::get<1>(kink);
         HitOrderTupleList& firstList  = std::get<2>(kinkTuple);
         HitOrderTupleList& secondList = std::get<3>(kinkTuple);
         
         orderHitsAlongEdge(pointList, std::get<0>(kink), firstEdge, firstList);
         
         if (firstList.size() > fMinTinyClusterSize)
         {
             Eigen::Vector2f secondEdge = std::get<2>(kink);
         
             orderHitsAlongEdge(pointList, std::get<0>(kink), secondEdge, secondList);
         
             if (secondList.size() > fMinTinyClusterSize)
                 std::get<0>(kinkTuple) = std::min(firstList.size(),secondList.size());
         }
         
         // Special handling...
         if (firstList.size() + secondList.size() > pointList.size())
         {
             if (firstList.size() > secondList.size()) pruneHitOrderTupleLists(firstList,secondList);
             else                                      pruneHitOrderTupleLists(secondList,firstList);
             
             std::get<0>(kinkTuple) = std::min(firstList.size(),secondList.size());
         }
         
         if (std::get<0>(kinkTuple) < int(fMinTinyClusterSize)) kinkTupleVec.pop_back();
     }
     
     // No work if the list is empty
     if (!kinkTupleVec.empty())
     {
         // If more than one then want the kink with the most elements both sizes
         std::sort(kinkTupleVec.begin(),kinkTupleVec.end(),[](const auto& left,const auto& right){return std::get<0>(left) > std::get<0>(right);});
         
         // Recover the kink point
         KinkTuple& kinkTuple = kinkTupleVec.front();
         
         // Set up to split the input cluster
         outputClusterList.push_back(reco::ClusterParameters());
         
         reco::ClusterParameters& clusterParams1  = outputClusterList.back();
         
         reco::PrincipalComponents& fullPCA(clusterToBreak.getFullPCA());
         Eigen::Vector3f            fullPrimaryVec(fullPCA.getEigenVectors()[0][0],fullPCA.getEigenVectors()[0][1],fullPCA.getEigenVectors()[0][2]);
 
         if (makeCandidateCluster(fullPrimaryVec, clusterParams1, std::get<2>(kinkTuple), level))
         {
             outputClusterList.push_back(reco::ClusterParameters());
             
             reco::ClusterParameters& clusterParams2  = outputClusterList.back();
             
             makeCandidateCluster(fullPrimaryVec, clusterParams2, std::get<3>(kinkTuple), level);
         }
         
         // If we did not make 2 clusters then be sure to clear the output list
         if (outputClusterList.size() != 2) outputClusterList.clear();
     }
 
     return !outputClusterList.empty();
 }

bool lar_cluster3d::ConvexHullPathFinder::breakClusterByMaxDefect	(	reco::ClusterParameters &	clusterToBreak,
		reco::HitPairListPtr &	hitList,
		reco::ClusterParametersList &	outputClusterList,
		int	level
	)		const

private

Definition at line 843 of file ConvexHullPathFinder_tool.cc.

References fFillHistograms, fMinTinyClusterSize, fSubMaxDefect, fSubUsedDefect, reco::ClusterParameters::getBestEdgeList(), reco::ClusterParameters::getConvexHull(), reco::ConvexHull::getConvexHullExtremePoints(), reco::ClusterParameters::getFullPCA(), reco::ClusterHit3D::getPosition(), art::left(), makeCandidateCluster(), and art::right().

Referenced by subDivideCluster().

 {
     // Set up container to keep track of edges
     using DistEdgeTuple    = std::tuple<float, const reco::EdgeTuple*>;
     using DistEdgeTupleVec = std::vector<DistEdgeTuple>;
     
     DistEdgeTupleVec distEdgeTupleVec;
     
     reco::ProjectedPointList::const_iterator extremePointListItr = clusterToBreak.getConvexHull().getConvexHullExtremePoints().begin();
     
     const reco::ClusterHit3D*  firstEdgeHit  = std::get<2>(*extremePointListItr++);
     const reco::ClusterHit3D*  secondEdgeHit = std::get<2>(*extremePointListItr);
     Eigen::Vector3f            edgeVec(secondEdgeHit->getPosition()[0] - firstEdgeHit->getPosition()[0],
                                        secondEdgeHit->getPosition()[1] - firstEdgeHit->getPosition()[1],
                                        secondEdgeHit->getPosition()[2] - firstEdgeHit->getPosition()[2]);
     double                     edgeLen       = edgeVec.norm();
     
     // normalize it
     edgeVec.normalize();
     
 
     // Now loop through all the edges and search for the furthers point
     for(const auto& edge : clusterToBreak.getBestEdgeList())
     {
         const reco::ClusterHit3D* nextEdgeHit = std::get<0>(edge);  // recover the first point
         
         // Create vector to this point from the longest edge
         Eigen::Vector3f hitToEdgeVec(nextEdgeHit->getPosition()[0] - firstEdgeHit->getPosition()[0],
                                      nextEdgeHit->getPosition()[1] - firstEdgeHit->getPosition()[1],
                                      nextEdgeHit->getPosition()[2] - firstEdgeHit->getPosition()[2]);
         
         // Get projection
         float hitProjection = hitToEdgeVec.dot(edgeVec);
         
         // Require that the point is really "opposite" the longest edge
         if (hitProjection > 0. && hitProjection < edgeLen)
         {
             Eigen::Vector3f distToHitVec = hitToEdgeVec - hitProjection * edgeVec;
             float           distToHit    = distToHitVec.norm();
             
             distEdgeTupleVec.emplace_back(distToHit,&edge);
         }
     }
     
     std::sort(distEdgeTupleVec.begin(),distEdgeTupleVec.end(),[](const auto& left,const auto& right){return std::get<0>(left) > std::get<0>(right);});
     
     reco::PrincipalComponents& fullPCA(clusterToBreak.getFullPCA());
     Eigen::Vector3f            fullPrimaryVec(fullPCA.getEigenVectors()[0][0],fullPCA.getEigenVectors()[0][1],fullPCA.getEigenVectors()[0][2]);
 
     // Get a temporary  container to hol
     float usedDefectDist(0.);
     
     for(const auto& distEdgeTuple : distEdgeTupleVec)
     {
         const reco::EdgeTuple&    edgeTuple = *std::get<1>(distEdgeTuple);
         const reco::ClusterHit3D* edgeHit   = std::get<0>(edgeTuple);
         
         usedDefectDist = std::get<0>(distEdgeTuple);
         
         // Now find the hit identified above as furthest away
         reco::HitPairListPtr::iterator vertexItr = std::find(hitList.begin(),hitList.end(),edgeHit);
         
         // Make sure enough hits either side, otherwise we just keep the current cluster
         if (vertexItr == hitList.end() || std::distance(hitList.begin(),vertexItr) < int(fMinTinyClusterSize) || std::distance(vertexItr,hitList.end()) < int(fMinTinyClusterSize)) continue;
         
         outputClusterList.push_back(reco::ClusterParameters());
         
         reco::ClusterParameters& clusterParams1  = outputClusterList.back();
         
         if (makeCandidateCluster(fullPrimaryVec, clusterParams1, hitList.begin(), vertexItr, level))
         {
             outputClusterList.push_back(reco::ClusterParameters());
             
             reco::ClusterParameters& clusterParams2  = outputClusterList.back();
             
             if (makeCandidateCluster(fullPrimaryVec, clusterParams2, vertexItr, hitList.end(), level))
             {
                 if (fFillHistograms)
                 {
                     fSubMaxDefect->Fill(std::get<0>(distEdgeTupleVec.front()), 1.);
                     fSubUsedDefect->Fill(usedDefectDist, 1.);
                 }
                 break;
             }
         }
         
         // If here then we could not make two valid clusters and so we try again
         outputClusterList.clear();
     }
     
    return !outputClusterList.empty();
 }

bool lar_cluster3d::ConvexHullPathFinder::breakClusterInHalf	(	reco::ClusterParameters &	clusterToBreak,
		reco::HitPairListPtr &	hitList,
		reco::ClusterParametersList &	outputClusterList,
		int	level
	)		const

private

Definition at line 936 of file ConvexHullPathFinder_tool.cc.

References reco::ClusterParameters::getFullPCA(), and makeCandidateCluster().

Referenced by subDivideCluster().

 {
     reco::HitPairListPtr::iterator vertexItr = hitList.begin();
     
     std::advance(vertexItr, hitList.size()/2);
     
     outputClusterList.push_back(reco::ClusterParameters());
     
     reco::ClusterParameters& clusterParams1  = outputClusterList.back();
     
     reco::PrincipalComponents& fullPCA(clusterToBreak.getFullPCA());
     Eigen::Vector3f            fullPrimaryVec(fullPCA.getEigenVectors()[0][0],fullPCA.getEigenVectors()[0][1],fullPCA.getEigenVectors()[0][2]);
 
     if (makeCandidateCluster(fullPrimaryVec, clusterParams1, hitList.begin(), vertexItr, level))
     {
         outputClusterList.push_back(reco::ClusterParameters());
         
         reco::ClusterParameters& clusterParams2  = outputClusterList.back();
         
         makeCandidateCluster(fullPrimaryVec, clusterParams2, vertexItr, hitList.end(), level);
     }
     
     if (outputClusterList.size() != 2) outputClusterList.clear();
     
     return !outputClusterList.empty();
 }

void lar_cluster3d::ConvexHullPathFinder::buildConvexHull	(	reco::ClusterParameters &	clusterParameters,
		int	level = `0`
	)		const

private

Definition at line 1015 of file ConvexHullPathFinder_tool.cc.

References fConvexHullKinkAngle, fConvexHullMinSep, reco::PrincipalComponents::getAvePosition(), lar_cluster3d::ConvexHull::getConvexHull(), reco::ClusterParameters::getConvexHull(), lar_cluster3d::ConvexHull::getConvexHullArea(), reco::ConvexHull::getConvexHullEdgeList(), reco::ConvexHull::getConvexHullEdgeMap(), reco::ConvexHull::getConvexHullExtremePoints(), reco::ConvexHull::getConvexHullKinkPoints(), reco::PrincipalComponents::getEigenVectors(), reco::ClusterParameters::getFullPCA(), reco::ClusterParameters::getHitPairListPtr(), reco::ClusterHit3D::getPosition(), reco::ConvexHull::getProjectedPointList(), art::detail::indent(), art::left(), max, and art::right().

Referenced by completeCandidateCluster(), and ModifyClusters().

 {
     // set an indention string
     std::string minuses(level/2, '-');
     std::string indent(level/2, ' ');
     
     indent += minuses;
 
     // The plan is to build the enclosing 2D polygon around the points in the PCA plane of most spread for this cluster
     // To do so we need to start by building a list of 2D projections onto the plane of most spread...
     reco::PrincipalComponents& pca = clusterParameters.getFullPCA();
 
     // Recover the parameters from the Principal Components Analysis that we need to project and accumulate
     Eigen::Vector3f pcaCenter(pca.getAvePosition()[0],pca.getAvePosition()[1],pca.getAvePosition()[2]);
     Eigen::Vector3f planeVec0(pca.getEigenVectors()[0][0],pca.getEigenVectors()[0][1],pca.getEigenVectors()[0][2]);
     Eigen::Vector3f planeVec1(pca.getEigenVectors()[1][0],pca.getEigenVectors()[1][1],pca.getEigenVectors()[1][2]);
     Eigen::Vector3f pcaPlaneNrml(pca.getEigenVectors()[2][0],pca.getEigenVectors()[2][1],pca.getEigenVectors()[2][2]);
 
     // Let's get the rotation matrix from the standard coordinate system to the PCA system.
     Eigen::Matrix3f rotationMatrix;
     
     rotationMatrix << planeVec0(0),    planeVec0(1),    planeVec0(2),
                       planeVec1(0),    planeVec1(1),    planeVec1(2),
                       pcaPlaneNrml(0), pcaPlaneNrml(1), pcaPlaneNrml(2);
     
     reco::ConvexHull&         convexHull = clusterParameters.getConvexHull();
     reco::ProjectedPointList& pointList  = convexHull.getProjectedPointList();
 
     // Loop through hits and do projection to plane
     for(const auto& hit3D : clusterParameters.getHitPairListPtr())
     {
         Eigen::Vector3f pcaToHitVec(hit3D->getPosition()[0] - pcaCenter(0),
                                     hit3D->getPosition()[1] - pcaCenter(1),
                                     hit3D->getPosition()[2] - pcaCenter(2));
         Eigen::Vector3f pcaToHit = rotationMatrix * pcaToHitVec;
 
         pointList.emplace_back(dcel2d::Point(pcaToHit(0),pcaToHit(1),hit3D));
     }
     
     // Sort the point vec by increasing x, then increase y
     pointList.sort([](const auto& left, const auto& right){return (std::abs(std::get<0>(left) - std::get<0>(right)) > std::numeric_limits<float>::epsilon()) ? std::get<0>(left) < std::get<0>(right) : std::get<1>(left) < std::get<1>(right);});
     
     // containers for finding the "best" hull...
     std::vector<ConvexHull>               convexHullVec;
     std::vector<reco::ProjectedPointList> rejectedListVec;
     bool                                  increaseDepth(pointList.size() > 3);
     float                                 lastArea(std::numeric_limits<float>::max());
 
     while(increaseDepth)
     {
         // Get another convexHull container
         convexHullVec.push_back(ConvexHull(pointList, fConvexHullKinkAngle, fConvexHullMinSep));
         rejectedListVec.push_back(reco::ProjectedPointList());
 
         const ConvexHull&               convexHull       = convexHullVec.back();
         reco::ProjectedPointList&       rejectedList     = rejectedListVec.back();
         const reco::ProjectedPointList& convexHullPoints = convexHull.getConvexHull();
         
         increaseDepth = false;
         
         if (convexHull.getConvexHullArea() > 0.)
         {
             if (convexHullVec.size() < 2 || convexHull.getConvexHullArea() < 0.8 * lastArea)
             {
                 for(auto& point : convexHullPoints)
                 {
                     pointList.remove(point);
                     rejectedList.emplace_back(point);
                 }
                 lastArea      = convexHull.getConvexHullArea();
 //                increaseDepth = true;
             }
         }
     }
     
     // do we have a valid convexHull?
     while(!convexHullVec.empty() && convexHullVec.back().getConvexHullArea() < 0.5)
     {
         convexHullVec.pop_back();
         rejectedListVec.pop_back();
     }
 
     // If we found the convex hull then build edges around the region
     if (!convexHullVec.empty())
     {
         size_t               nRejectedTotal(0);
         reco::HitPairListPtr hitPairListPtr = clusterParameters.getHitPairListPtr();
         
         for(const auto& rejectedList : rejectedListVec)
         {
             nRejectedTotal += rejectedList.size();
             
             for(const auto& rejectedPoint : rejectedList)
             {
                 if (convexHullVec.back().findNearestDistance(rejectedPoint) > 0.5)
                     hitPairListPtr.remove(std::get<2>(rejectedPoint));
             }
         }
         
         // Now add "edges" to the cluster to describe the convex hull (for the display)
         reco::Hit3DToEdgeMap& edgeMap  = convexHull.getConvexHullEdgeMap();
         reco::EdgeList&       edgeList = convexHull.getConvexHullEdgeList();
 
         reco::ProjectedPoint lastPoint = convexHullVec.back().getConvexHull().front();
     
         for(auto& curPoint : convexHullVec.back().getConvexHull())
         {
             if (curPoint == lastPoint) continue;
         
             const reco::ClusterHit3D* lastPoint3D = std::get<2>(lastPoint);
             const reco::ClusterHit3D* curPoint3D  = std::get<2>(curPoint);
         
             float distBetweenPoints = (curPoint3D->getPosition()[0] - lastPoint3D->getPosition()[0]) * (curPoint3D->getPosition()[0] - lastPoint3D->getPosition()[0])
                                     + (curPoint3D->getPosition()[1] - lastPoint3D->getPosition()[1]) * (curPoint3D->getPosition()[1] - lastPoint3D->getPosition()[1])
                                     + (curPoint3D->getPosition()[2] - lastPoint3D->getPosition()[2]) * (curPoint3D->getPosition()[2] - lastPoint3D->getPosition()[2]);
         
             distBetweenPoints = std::sqrt(distBetweenPoints);
         
             reco::EdgeTuple edge(lastPoint3D,curPoint3D,distBetweenPoints);
         
             edgeMap[lastPoint3D].push_back(edge);
             edgeMap[curPoint3D].push_back(edge);
             edgeList.emplace_back(edge);
         
             lastPoint = curPoint;
         }
         
         // Store the "extreme" points
         const ConvexHull::PointList& extremePoints    = convexHullVec.back().getExtremePoints();
         reco::ProjectedPointList&    extremePointList = convexHull.getConvexHullExtremePoints();
         
         for(const auto& point : extremePoints) extremePointList.push_back(point);
         
         // Store the "kink" points
         const reco::ConvexHullKinkTupleList& kinkPoints    = convexHullVec.back().getKinkPoints();
         reco::ConvexHullKinkTupleList&       kinkPointList = convexHull.getConvexHullKinkPoints();
         
         for(const auto& kink : kinkPoints) kinkPointList.push_back(kink);
     }
 
     return;
 }

float lar_cluster3d::ConvexHullPathFinder::closestApproach	(	const Eigen::Vector3f &	P0,
		const Eigen::Vector3f &	u0,
		const Eigen::Vector3f &	P1,
		const Eigen::Vector3f &	u1,
		Eigen::Vector3f &	poca0,
		Eigen::Vector3f &	poca1
	)		const

private

Definition at line 1158 of file ConvexHullPathFinder_tool.cc.

References d, den, and e.

 {
     // Technique is to compute the arclength to each point of closest approach
     Eigen::Vector3f w0 = P0 - P1;
     float a(1.);
     float b(u0.dot(u1));
     float c(1.);
     float d(u0.dot(w0));
     float e(u1.dot(w0));
     float den(a * c - b * b);
     
     float arcLen0 = (b * e - c * d) / den;
     float arcLen1 = (a * e - b * d) / den;
     
     poca0 = P0 + arcLen0 * u0;
     poca1 = P1 + arcLen1 * u1;
     
     return (poca0 - poca1).norm();
 }

bool lar_cluster3d::ConvexHullPathFinder::completeCandidateCluster	(	Eigen::Vector3f &	primaryPCA,
		reco::ClusterParameters &	candCluster,
		int	level
	)		const

private

Definition at line 577 of file ConvexHullPathFinder_tool.cc.

References buildConvexHull(), fPCAAlg, reco::PrincipalComponents::getAveHitDoca(), reco::PrincipalComponents::getAvePosition(), reco::PrincipalComponents::getEigenValues(), reco::PrincipalComponents::getEigenVectors(), reco::ClusterParameters::getFullPCA(), reco::ClusterParameters::getHitPairListPtr(), reco::PrincipalComponents::getNumHitsUsed(), reco::ClusterParameters::getSkeletonPCA(), reco::PrincipalComponents::getSvdOK(), and lar_cluster3d::PrincipalComponentsAlg::PCAAnalysis_3D().

Referenced by makeCandidateCluster().

 {
     // First stage of feature extraction runs here
     fPCAAlg.PCAAnalysis_3D(candCluster.getHitPairListPtr(), candCluster.getFullPCA());
     
     // Recover the new fullPCA
     reco::PrincipalComponents& newFullPCA = candCluster.getFullPCA();
     
     // Will we want to store this cluster?
     bool keepThisCluster(false);
     
     // Must have a valid pca
     if (newFullPCA.getSvdOK())
     {
         // Need to check if the PCA direction has been reversed
         Eigen::Vector3f newPrimaryVec(newFullPCA.getEigenVectors()[0][0],newFullPCA.getEigenVectors()[0][1],newFullPCA.getEigenVectors()[0][2]);
         
         // If the PCA's are opposite the flip the axes
         if (primaryPCA.dot(newPrimaryVec) < 0.)
         {
             reco::PrincipalComponents::EigenVectors eigenVectors;
             
             eigenVectors.resize(3);
             
             for(size_t vecIdx = 0; vecIdx < 3; vecIdx++)
             {
                 eigenVectors[vecIdx].resize(3,0.);
                 
                 eigenVectors[vecIdx][0] = -newFullPCA.getEigenVectors()[vecIdx][0];
                 eigenVectors[vecIdx][1] = -newFullPCA.getEigenVectors()[vecIdx][1];
                 eigenVectors[vecIdx][2] = -newFullPCA.getEigenVectors()[vecIdx][2];
             }
             
             newFullPCA = reco::PrincipalComponents(true,
                                                    newFullPCA.getNumHitsUsed(),
                                                    newFullPCA.getEigenValues(),
                                                    eigenVectors,
                                                    newFullPCA.getAvePosition(),
                                                    newFullPCA.getAveHitDoca());
         }
         
         // Set the skeleton PCA to make sure it has some value
         candCluster.getSkeletonPCA() = candCluster.getFullPCA();
         
         // Be sure to compute the oonvex hull surrounding the now broken cluster
         buildConvexHull(candCluster, level+2);
         
         keepThisCluster = true;
     }
     
     return keepThisCluster;
 }

virtual void lar_cluster3d::IClusterModAlg::configure ( const fhicl::ParameterSet & )

pure virtualinherited

Interface for configuring the particular algorithm tool.

Parameters

ParameterSet The input set of parameters for configuration

void lar_cluster3d::ConvexHullPathFinder::configure ( fhicl::ParameterSet const & pset )

override

Definition at line 196 of file ConvexHullPathFinder_tool.cc.

References fClusterAlg, fConvexHullKinkAngle, fConvexHullMinSep, fEnableMonitoring, fGeometry, fMinTinyClusterSize, fTimeToProcess, and fhicl::ParameterSet::get().

Referenced by ConvexHullPathFinder().

 {
     fEnableMonitoring     = pset.get<bool>  ("EnableMonitoring",   true );
     fMinTinyClusterSize   = pset.get<size_t>("MinTinyClusterSize", 40   );
     fConvexHullKinkAngle  = pset.get<float >("ConvexHullKinkAgle",  0.92);
     fConvexHullMinSep     = pset.get<float >("ConvexHullMinSep",    0.65);
     fClusterAlg         = art::make_tool<lar_cluster3d::IClusterAlg>(pset.get<fhicl::ParameterSet>("ClusterAlg"));
 
     art::ServiceHandle<geo::Geometry> geometry;
     
     fGeometry = &*geometry;
     
     fTimeToProcess = 0.;
     
     return;
 }

float lar_cluster3d::ConvexHullPathFinder::findConvexHullEndPoints	(	const reco::EdgeList &	convexHull,
		const reco::ClusterHit3D *	first3D,
		const reco::ClusterHit3D *	last3D
	)		const

private

Definition at line 1183 of file ConvexHullPathFinder_tool.cc.

References DEFINE_ART_CLASS_TOOL.

 {
     float largestDistance(0.);
     
     // Note that edges are vectors and that the convex hull edge list will be ordered
     // The idea is that the maximum distance from a given edge is to the edge just before the edge that "turns back" towards the current edge
     // meaning that the dot product of the two edges becomes negative.
     reco::EdgeList::const_iterator firstEdgeItr = convexHull.begin();
     
     while(firstEdgeItr != convexHull.end())
     {
         reco::EdgeList::const_iterator nextEdgeItr = firstEdgeItr;
 
 //        Eigen::Vector2f firstEdgeVec(std::get<3>(*firstEdgeItr),std::get<);
 //        Eigen::Vector2f lastPrimaryVec(lastPCA.getEigenVectors()[0][0],lastPCA.getEigenVectors()[0][1],lastPCA.getEigenVectors()[0][2]);
 //        float           cosToLast = newPrimaryVec.dot(lastPrimaryVec);
 
         while(++nextEdgeItr != convexHull.end())
         {
             
         }
     }
     
     return largestDistance;
 }

float lar_cluster3d::ConvexHullPathFinder::getTimeToExecute ( ) const

inlineoverridevirtual

If monitoring, recover the time to execute a particular function.

Implements lar_cluster3d::IClusterModAlg.

Definition at line 82 of file ConvexHullPathFinder_tool.cc.

References fTimeToProcess, and subDivideCluster().

82 {return fTimeToProcess;}

lar_cluster3d::ConvexHullPathFinder::fTimeToProcess

float fTimeToProcess

Definition: ConvexHullPathFinder_tool.cc:148

void lar_cluster3d::ConvexHullPathFinder::initializeHistograms ( art::TFileDirectory & histDir )

overridevirtual

Interface for initializing histograms if they are desired Note that the idea is to put hisgtograms in a subfolder.

Parameters

TFileDirectory - the folder to store the hists in

Implements lar_cluster3d::IClusterModAlg.

Definition at line 213 of file ConvexHullPathFinder_tool.cc.

References dir, fFillHistograms, fSubCosExtToPCA, fSubCosToPrevPCA, fSubEigen10Ratio, fSubEigen20Ratio, fSubEigen21Ratio, fSubMaxDefect, fSubNum3DHits, fSubNumEdges, fSubPrimaryLength, fSubUsedDefect, fTopEigen10Ratio, fTopEigen20Ratio, fTopEigen21Ratio, fTopNum3DHits, fTopNumEdges, fTopPrimaryLength, art::TFileDirectory::make(), and art::TFileDirectory::mkdir().

 {
     // It is assumed that the input TFileDirectory has been set up to group histograms into a common
     // folder at the calling routine's level. Here we create one more level of indirection to keep
     // histograms made by this tool separate.
     fFillHistograms = true;
 
     std::string dirName = "ConvexHullPath";
     
     art::TFileDirectory dir = histDir.mkdir(dirName.c_str());
     
     // Divide into two sets of hists... those for the overall cluster and
     // those for the subclusters
     fTopNum3DHits     = dir.make<TH1F>("TopNum3DHits",  "Number 3D Hits",  200,    0.,   200.);
     fTopNumEdges      = dir.make<TH1F>("TopNumEdges",   "Number Edges",    200,    0.,   200.);
     fTopEigen21Ratio  = dir.make<TH1F>("TopEigen21Rat", "Eigen 2/1 Ratio", 100,    0.,     1.);
     fTopEigen20Ratio  = dir.make<TH1F>("TopEigen20Rat", "Eigen 2/0 Ratio", 100,    0.,     1.);
     fTopEigen10Ratio  = dir.make<TH1F>("TopEigen10Rat", "Eigen 1/0 Ratio", 100,    0.,     1.);
     fTopPrimaryLength = dir.make<TH1F>("TopPrimaryLen", "Primary Length",  200,    0.,   200.);
     
     fSubNum3DHits     = dir.make<TH1F>("SubNum3DHits",  "Number 3D Hits",  200,    0.,   200.);
     fSubNumEdges      = dir.make<TH1F>("SubNumEdges",   "Number Edges",    200,    0.,   200.);
     fSubEigen21Ratio  = dir.make<TH1F>("SubEigen21Rat", "Eigen 2/1 Ratio", 100,    0.,     1.);
     fSubEigen20Ratio  = dir.make<TH1F>("SubEigen20Rat", "Eigen 2/0 Ratio", 100,    0.,     1.);
     fSubEigen10Ratio  = dir.make<TH1F>("SubEigen10Rat", "Eigen 1/0 Ratio", 100,    0.,     1.);
     fSubPrimaryLength = dir.make<TH1F>("SubPrimaryLen", "Primary Length",  200,    0.,   200.);
     fSubCosToPrevPCA  = dir.make<TH1F>("SubCosToPrev",  "Cos(theta)",      101,    0.,     1.01);
     fSubCosExtToPCA   = dir.make<TH1F>("SubCosExtPCA",  "Cos(theta)",      102,   -1.01,   1.01);
     fSubMaxDefect     = dir.make<TH1F>("SubMaxDefect",  "Max Defect",      100,    0.,    50.);
     fSubUsedDefect    = dir.make<TH1F>("SubUsedDefect", "Used Defect",     100,    0.,    50.);
 
     return;
 }

bool lar_cluster3d::ConvexHullPathFinder::makeCandidateCluster	(	Eigen::Vector3f &	primaryPCA,
		reco::ClusterParameters &	candCluster,
		reco::HitPairListPtr::iterator	firstHitItr,
		reco::HitPairListPtr::iterator	lastHitItr,
		int	level
	)		const

private

Definition at line 489 of file ConvexHullPathFinder_tool.cc.

References completeCandidateCluster(), reco::ClusterParameters::getConvexHull(), reco::ConvexHull::getConvexHullEdgeList(), reco::PrincipalComponents::getEigenValues(), reco::PrincipalComponents::getEigenVectors(), reco::ClusterParameters::getFullPCA(), reco::ClusterParameters::getHitPairListPtr(), and art::detail::indent().

Referenced by breakClusterAtBigGap(), breakClusterByKinks(), breakClusterByKinksTrial(), breakClusterByMaxDefect(), and breakClusterInHalf().

 {
     std::string indent(level/2, ' ');
     
     reco::HitPairListPtr& hitPairListPtr = candCluster.getHitPairListPtr();
 
     // size the container...
     hitPairListPtr.resize(std::distance(firstHitItr,lastHitItr));
     
     // and copy the hits into the container
     std::copy(firstHitItr,lastHitItr,hitPairListPtr.begin());
    
     // Will we want to store this cluster?
     bool keepThisCluster(false);
     
     // Must have a valid pca
     if (completeCandidateCluster(primaryPCA, candCluster, level))
     {
         // Recover the new fullPCA
         reco::PrincipalComponents& newFullPCA = candCluster.getFullPCA();
         
         // Need to check if the PCA direction has been reversed
         Eigen::Vector3f newPrimaryVec(newFullPCA.getEigenVectors()[0][0],newFullPCA.getEigenVectors()[0][1],newFullPCA.getEigenVectors()[0][2]);
 
         std::vector<double> eigenValVec      = {3. * std::sqrt(newFullPCA.getEigenValues()[0]),
                                                 3. * std::sqrt(newFullPCA.getEigenValues()[1]),
                                                 3. * std::sqrt(newFullPCA.getEigenValues()[2])};
         double              cosNewToLast     = std::abs(primaryPCA.dot(newPrimaryVec));
         double              eigen2To1Ratio   = eigenValVec[2] / eigenValVec[1];
         double              eigen1To0Ratio   = eigenValVec[1] / eigenValVec[0];
         
         // Create a rough cut intended to tell us when we've reached the land of diminishing returns
 //        if (candCluster.getBestEdgeList().size() > 4 && cosNewToLast > 0.25 && eigen2To1Ratio < 0.9 && eigen2To0Ratio > 0.001)
         if (candCluster.getConvexHull().getConvexHullEdgeList().size() > 4 && cosNewToLast > 0.25 && eigen2To1Ratio > 0.01 && eigen2To1Ratio < 0.99 && eigen1To0Ratio < 0.5)
         {
             keepThisCluster = true;
         }
     }
     
     return keepThisCluster;
 }

bool lar_cluster3d::ConvexHullPathFinder::makeCandidateCluster	(	Eigen::Vector3f &	primaryPCA,
		reco::ClusterParameters &	candCluster,
		HitOrderTupleList &	orderedList,
		int	level
	)		const

private

Definition at line 535 of file ConvexHullPathFinder_tool.cc.

References completeCandidateCluster(), reco::ClusterParameters::getBestEdgeList(), reco::PrincipalComponents::getEigenValues(), reco::PrincipalComponents::getEigenVectors(), reco::ClusterParameters::getFullPCA(), reco::ClusterParameters::getHitPairListPtr(), and art::detail::indent().

 {
     std::string indent(level/2, ' ');
     
     reco::HitPairListPtr& hitPairListPtr = candCluster.getHitPairListPtr();
     
     // Fill the list the old fashioned way...
     for(const auto& tupleVal : orderedList) hitPairListPtr.emplace_back(std::get<2>(std::get<2>(tupleVal)));
     
     // Will we want to store this cluster?
     bool keepThisCluster(false);
     
     // Must have a valid pca
     if (completeCandidateCluster(primaryPCA, candCluster, level))
     {
         // Recover the new fullPCA
         reco::PrincipalComponents& newFullPCA = candCluster.getFullPCA();
         
         // Need to check if the PCA direction has been reversed
         Eigen::Vector3f newPrimaryVec(newFullPCA.getEigenVectors()[0][0],newFullPCA.getEigenVectors()[0][1],newFullPCA.getEigenVectors()[0][2]);
         
         std::vector<double> eigenValVec      = {3. * std::sqrt(newFullPCA.getEigenValues()[0]),
             3. * std::sqrt(newFullPCA.getEigenValues()[1]),
             3. * std::sqrt(newFullPCA.getEigenValues()[2])};
         double              cosNewToLast     = std::abs(primaryPCA.dot(newPrimaryVec));
         double              eigen2To1Ratio   = eigenValVec[2] / eigenValVec[1];
         double              eigen1To0Ratio   = eigenValVec[1] / eigenValVec[0];
         
         // Create a rough cut intended to tell us when we've reached the land of diminishing returns
         //        if (candCluster.getBestEdgeList().size() > 4 && cosNewToLast > 0.25 && eigen2To1Ratio < 0.9 && eigen2To0Ratio > 0.001)
         if (candCluster.getBestEdgeList().size() > 4 && cosNewToLast > 0.25 && eigen2To1Ratio > 0.01 && eigen2To1Ratio < 0.99 && eigen1To0Ratio < 0.5)
         {
             keepThisCluster = true;
         }
     }
     
     return keepThisCluster;
 }

void lar_cluster3d::ConvexHullPathFinder::ModifyClusters ( reco::ClusterParametersList & clusterParametersList ) const

overridevirtual

Scan an input collection of clusters and modify those according to the specific implementing algorithm.

Parameters

clusterParametersList A list of cluster objects (parameters from associated hits)

Top level interface for algorithm to consider pairs of clusters from the input list and determine if they are consistent with each other and, therefore, should be merged. This is done by looking at the PCA for each cluster and looking at the projection of the primary axis along the vector connecting their centers.

Implements lar_cluster3d::IClusterModAlg.

Definition at line 247 of file ConvexHullPathFinder_tool.cc.

References buildConvexHull(), reco::ClusterParameters::daughterList(), fEnableMonitoring, fFillHistograms, fMinTinyClusterSize, fTimeToProcess, fTopEigen10Ratio, fTopEigen20Ratio, fTopEigen21Ratio, fTopNum3DHits, fTopNumEdges, fTopPrimaryLength, reco::PrincipalComponents::getEigenValues(), reco::ClusterParameters::getHitPairListPtr(), min, and subDivideCluster().

 {
     // Initial clustering is done, now trim the list and get output parameters
     cet::cpu_timer theClockBuildClusters;
     
     // Start clocks if requested
     if (fEnableMonitoring) theClockBuildClusters.start();
 
     // This is the loop over candidate 3D clusters
     // Note that it might be that the list of candidate clusters is modified by splitting
     // So we use the following construct to make sure we get all of them
     reco::ClusterParametersList::iterator clusterParametersListItr = clusterParametersList.begin();
     
     while(clusterParametersListItr != clusterParametersList.end())
     {
         // Dereference to get the cluster paramters
         reco::ClusterParameters& clusterParameters = *clusterParametersListItr;
         
         // It turns out that computing the convex hull surrounding the points in the 2D projection onto the
         // plane of largest spread in the PCA is a good way to break up the cluster... and we do it here since
         // we (currently) want this to be part of the standard output
         buildConvexHull(clusterParameters);
 
         // Make sure our cluster has enough hits...
         if (clusterParameters.getHitPairListPtr().size() > fMinTinyClusterSize)
         {
             // Get an interim cluster list
             reco::ClusterParametersList reclusteredParameters;
             
             // Call the main workhorse algorithm for building the local version of candidate 3D clusters
             //******** Remind me why we need to call this at this point when the same hits will be used? ********
             //fClusterAlg->Cluster3DHits(clusterParameters.getHitPairListPtr(), reclusteredParameters);
             reclusteredParameters.push_back(clusterParameters);
             
             // Only process non-empty results
             if (!reclusteredParameters.empty())
             {
                 // Loop over the reclustered set
                 for (auto& cluster : reclusteredParameters)
                 {
                     // It turns out that computing the convex hull surrounding the points in the 2D projection onto the
                     // plane of largest spread in the PCA is a good way to break up the cluster... and we do it here since
                     // we (currently) want this to be part of the standard output
                     buildConvexHull(cluster, 2);
 
                     // Break our cluster into smaller elements...
                     subDivideCluster(cluster, cluster.getFullPCA(), cluster.daughterList().end(), cluster.daughterList(), 4);
             
                     // Add the daughters to the cluster
                     clusterParameters.daughterList().insert(clusterParameters.daughterList().end(),cluster); 
                     
                     // If filling histograms we do the main cluster here
                     if (fFillHistograms)
                     {
                         reco::PrincipalComponents& fullPCA        = cluster.getFullPCA();
                         std::vector<double>        eigenValVec    = {3. * std::sqrt(fullPCA.getEigenValues()[0]),
                                                                      3. * std::sqrt(fullPCA.getEigenValues()[1]),
                                                                      3. * std::sqrt(fullPCA.getEigenValues()[2])};
                         double                     eigen2To1Ratio = eigenValVec[2] / eigenValVec[1];
                         double                     eigen1To0Ratio = eigenValVec[1] / eigenValVec[0];
                         double                     eigen2To0Ratio = eigenValVec[2] / eigenValVec[0];
                         int                        num3DHits      = cluster.getHitPairListPtr().size();
                         int                        numEdges       = cluster.getBestEdgeList().size();
                         
                         fTopNum3DHits->Fill(std::min(num3DHits,199), 1.);
                         fTopNumEdges->Fill(std::min(numEdges,199),   1.);
                         fTopEigen21Ratio->Fill(eigen2To1Ratio, 1.);
                         fTopEigen20Ratio->Fill(eigen2To0Ratio, 1.);
                         fTopEigen10Ratio->Fill(eigen1To0Ratio, 1.);
                         fTopPrimaryLength->Fill(std::min(eigenValVec[0],199.), 1.);
                     }
                 }
             }
         }
     
         // Go to next cluster parameters object
         clusterParametersListItr++;
     }
 
     if (fEnableMonitoring)
     {
         theClockBuildClusters.stop();
         
         fTimeToProcess = theClockBuildClusters.accumulated_real_time();
     }
     
     mf::LogDebug("Cluster3D") << ">>>>> Cluster Path finding done" << std::endl;
     
     return;
 }

void lar_cluster3d::ConvexHullPathFinder::orderHitsAlongEdge	(	const reco::ProjectedPointList &	hitList,
		const reco::ProjectedPoint &	point,
		const Eigen::Vector2f &	edge,
		HitOrderTupleList &	orderedList
	)		const

private

Definition at line 769 of file ConvexHullPathFinder_tool.cc.

References art::left(), and art::right().

Referenced by breakClusterByKinksTrial().

 {
     // Use the input kink point as the start point of the edge
     Eigen::Vector2f kinkPos(std::get<0>(point),std::get<1>(point));
     
     // Loop over the input hits
     for (const auto& hit : hitList)
     {
         // Now we need to calculate the doca and poca...
         // Start by getting this hits position
         Eigen::Vector2f hitPos(std::get<0>(hit),std::get<1>(hit));
         
         // Form a TVector from this to the cluster average position
         Eigen::Vector2f hitToKinkVec = hitPos - kinkPos;
         
         // With this we can get the arclength to the doca point
         float arcLenToPoca = hitToKinkVec.dot(edge);
         
         // Require the hit to not be past the kink point
         if (arcLenToPoca < 0.) continue;
         
         // Get the coordinates along the axis for this point
         Eigen::Vector2f pocaPos = kinkPos + arcLenToPoca * edge;
         
         // Now get doca and poca
         Eigen::Vector2f pocaPosToHitPos = hitPos - pocaPos;
         float           pocaToAxis      = pocaPosToHitPos.norm();
         
         std::cout << "-- arcLenToPoca: " << arcLenToPoca << ", doca: " << pocaToAxis << std::endl;
         
         orderedList.emplace_back(arcLenToPoca,pocaToAxis,hit);
     }
 
     // Sort the list in order of ascending distance from the kink point
     orderedList.sort([](const auto& left,const auto& right){return std::get<0>(left) < std::get<0>(right);});
 
     return;
 }

void lar_cluster3d::ConvexHullPathFinder::pruneHitOrderTupleLists	(	HitOrderTupleList &	shortList,
		HitOrderTupleList &	longList
	)		const

private

Definition at line 811 of file ConvexHullPathFinder_tool.cc.

Referenced by breakClusterByKinksTrial().

 {
     // Assume the first list is the short one, so we loop through the elements of that list..
     HitOrderTupleList::iterator shortItr = shortList.begin();
     
     while(shortItr != shortList.end())
     {
         // Recover the search key
         const reco::ClusterHit3D* hit3D = std::get<2>(std::get<2>(*shortItr));
         
         // Ok, find the corresponding point in the other list...
         HitOrderTupleList::iterator longItr = std::find_if(longList.begin(),longList.end(),[&hit3D](const auto& elem){return hit3D == std::get<2>(std::get<2>(elem));});
         
         if (longItr != longList.end())
         {
             if (std::get<1>(*longItr) < std::get<1>(*shortItr))
             {
                 shortItr = shortList.erase(shortItr);
             }
             else
             {
                 longItr = longList.erase(longItr);
                 shortItr++;
             }
         }
         else shortItr++;
     }
     
     
     return;
 }

reco::ClusterParametersList::iterator lar_cluster3d::ConvexHullPathFinder::subDivideCluster	(	reco::ClusterParameters &	cluster,
		reco::PrincipalComponents &	lastPCA,
		reco::ClusterParametersList::iterator	positionItr,
		reco::ClusterParametersList &	outputClusterList,
		int	level = `0`
	)		const

private

Use PCA to try to find path in cluster.

Parameters

clusterParameters	The given cluster parameters object to try to split
clusterParametersList	The list of clusters

Definition at line 345 of file ConvexHullPathFinder_tool.cc.

References breakClusterAtBigGap(), breakClusterByKinks(), breakClusterByMaxDefect(), breakClusterInHalf(), fFillHistograms, fMinTinyClusterSize, fPCAAlg, fSubCosExtToPCA, fSubCosToPrevPCA, fSubEigen10Ratio, fSubEigen20Ratio, fSubEigen21Ratio, fSubNum3DHits, fSubNumEdges, fSubPrimaryLength, reco::ClusterParameters::getConvexHull(), reco::ConvexHull::getConvexHullExtremePoints(), reco::PrincipalComponents::getEigenValues(), reco::PrincipalComponents::getEigenVectors(), reco::ClusterParameters::getFullPCA(), reco::ClusterParameters::getHitPairListPtr(), reco::ClusterHit3D::getPosition(), art::left(), min, lar_cluster3d::PrincipalComponentsAlg::PCAAnalysis_calc3DDocas(), art::right(), and reco::ClusterHit2D::USED.

Referenced by getTimeToExecute(), and ModifyClusters().

 {
     // This is a recursive routine to divide an input cluster, according to the maximum defect point of
     // the convex hull until we reach the point of no further improvement.
     // The assumption is that the input cluster is fully formed already, this routine then simply
     // divides, if successful division into two pieces it then stores the results
     
     // No point in doing anything if we don't have enough space points
     if (clusterToBreak.getHitPairListPtr().size() > size_t(2 * fMinTinyClusterSize))
     {
         // set an indention string
 //        std::string pluses(level/2, '+');
 //        std::string indent(level/2, ' ');
 //
 //        indent += pluses;
 
         // We want to find the convex hull vertices that lie furthest from the line to/from the extreme points
         // To find these we:
         // 1) recover the extreme points
         // 2) form the vector between them
         // 3) loop through the vertices and keep track of distance to this vector
         // 4) Sort the resulting list by furthest points and select the one we want
         reco::ConvexHull& convexHull = clusterToBreak.getConvexHull();
         
         reco::ProjectedPointList::const_iterator extremePointListItr = convexHull.getConvexHullExtremePoints().begin();
 
         const reco::ClusterHit3D*  firstEdgeHit  = std::get<2>(*extremePointListItr++);
         const reco::ClusterHit3D*  secondEdgeHit = std::get<2>(*extremePointListItr);
         Eigen::Vector3f            edgeVec(secondEdgeHit->getPosition()[0] - firstEdgeHit->getPosition()[0],
                                            secondEdgeHit->getPosition()[1] - firstEdgeHit->getPosition()[1],
                                            secondEdgeHit->getPosition()[2] - firstEdgeHit->getPosition()[2]);
         double                     edgeLen       = edgeVec.norm();
         
         // normalize it
         edgeVec.normalize();
         
         // Recover the list of 3D hits associated to this cluster
         reco::HitPairListPtr& clusHitPairVector = clusterToBreak.getHitPairListPtr();
 
         // Recover the PCA for the input cluster
         reco::PrincipalComponents& fullPCA     = clusterToBreak.getFullPCA();
         Eigen::Vector3f            fullPrimaryVec(fullPCA.getEigenVectors()[0][0],fullPCA.getEigenVectors()[0][1],fullPCA.getEigenVectors()[0][2]);
 
         // Calculate the doca to the PCA primary axis for each 3D hit
         // Importantly, this also gives us the arclength along that axis to the hit
         fPCAAlg.PCAAnalysis_calc3DDocas(clusHitPairVector, fullPCA);
         
         // Sort the hits along the PCA
         clusHitPairVector.sort([](const auto& left, const auto& right){return left->getArclenToPoca() < right->getArclenToPoca();});
 
         // Get a temporary  container to hol
         reco::ClusterParametersList tempClusterParametersList;
         
         // Try breaking clusters by finding the "maximum defect" point.
         // If this fails the fallback in the event of still large clusters is to split in half
 //        if (!breakClusterByMaxDefect(clusterToBreak, clusHitPairVector, tempClusterParametersList, level))
         if (!breakClusterByKinks(clusterToBreak, clusHitPairVector, tempClusterParametersList, level))
         {
             // Look to see if we can break at a gap
             if (!breakClusterAtBigGap(clusterToBreak, clusHitPairVector, tempClusterParametersList, level))
             {
                 // It might be that we have a large deviation in the convex hull...
                 if (!breakClusterByMaxDefect(clusterToBreak, clusHitPairVector, tempClusterParametersList, level))
                     // Well, we don't want "flippers" so make sure the edge has some teeth to it
                     if (edgeLen > 10.) breakClusterInHalf(clusterToBreak, clusHitPairVector, tempClusterParametersList, level);
             }
         }
 
         // Can only end with no candidate clusters or two so don't
         for(auto& clusterParams : tempClusterParametersList)
         {
             size_t curOutputClusterListSize = outputClusterList.size();
                 
             positionItr = subDivideCluster(clusterParams, fullPCA, positionItr, outputClusterList, level+4);
             
             // If the cluster we sent in was successfully broken then the position iterator will be shifted
             // This means we don't want to restore the current cluster here
             if (curOutputClusterListSize < outputClusterList.size()) continue;
             
             // I think this is where we fill out the rest of the parameters?
             // Start by adding the 2D hits...
             // See if we can avoid duplicates by temporarily transferring to a set
             std::set<const reco::ClusterHit2D*> hitSet;
             
             // Loop through 3D hits to get a set of unique 2D hits
             for(const auto& hit3D : clusterParams.getHitPairListPtr())
             {
                 for(const auto& hit2D : hit3D->getHits())
                 {
                     if (hit2D) hitSet.insert(hit2D);
                 }
             }
             
             // Now add these to the new cluster
             for(const auto& hit2D : hitSet)
             {
                 hit2D->setStatusBit(reco::ClusterHit2D::USED);
                 clusterParams.UpdateParameters(hit2D);
             }
 
             positionItr = outputClusterList.insert(positionItr,clusterParams);
             
             // Are we filling histograms
             if (fFillHistograms)
             {
                 std::vector<double> eigenValVec = {3. * std::sqrt(fullPCA.getEigenValues()[0]),
                                                    3. * std::sqrt(fullPCA.getEigenValues()[1]),
                                                    3. * std::sqrt(fullPCA.getEigenValues()[2])};
                 
                 // Recover the new fullPCA
                 reco::PrincipalComponents& newFullPCA = clusterParams.getFullPCA();
                 
                 Eigen::Vector3f newPrimaryVec(fullPCA.getEigenVectors()[0][0],fullPCA.getEigenVectors()[0][1],fullPCA.getEigenVectors()[0][2]);
                 Eigen::Vector3f lastPrimaryVec(newFullPCA.getEigenVectors()[0][0],newFullPCA.getEigenVectors()[0][1],newFullPCA.getEigenVectors()[0][2]);
                 
                 int             num3DHits      = clusterParams.getHitPairListPtr().size();
                 int             numEdges       = clusterParams.getBestEdgeList().size();
                 float           cosToLast      = newPrimaryVec.dot(lastPrimaryVec);
                 double          eigen2To1Ratio = eigenValVec[2] / eigenValVec[1];
                 double          eigen1To0Ratio = eigenValVec[1] / eigenValVec[0];
                 double          eigen2To0Ratio = eigenValVec[2] / eigenValVec[0];
 
                 fSubNum3DHits->Fill(std::min(num3DHits,199), 1.);
                 fSubNumEdges->Fill(std::min(numEdges,199),   1.);
                 fSubEigen21Ratio->Fill(eigen2To1Ratio, 1.);
                 fSubEigen20Ratio->Fill(eigen2To0Ratio, 1.);
                 fSubEigen10Ratio->Fill(eigen1To0Ratio, 1.);
                 fSubCosToPrevPCA->Fill(cosToLast, 1.);
                 fSubPrimaryLength->Fill(std::min(eigenValVec[0],199.), 1.);
                 fSubCosExtToPCA->Fill(fullPrimaryVec.dot(edgeVec), 1.);
             }
             
             // The above points to the element, want the next element
             positionItr++;
         }
     }
 
     return positionItr;
 }