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
	Interface for configuring the particular algorithm tool. More...

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...

Public Member Functions inherited from lar_cluster3d::IClusterModAlg
virtual	~IClusterModAlg () noexcept=default
	Virtual Destructor. 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::ClusterParametersList &, int level) const

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

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

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

bool	breakClusterAtBigGap (reco::ClusterParameters &, 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

void	fillConvexHullHists (reco::ClusterParameters &, bool) const

Private Attributes
bool	fEnableMonitoring
	FHICL parameters. More...

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

float	fMinGapSize
	Minimum gap size to break at gaps. More...

float	fMinEigen0To1Ratio
	Minimum ratio of eigen 0 to 1 to continue breaking. 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 *	fTopExtremeSep

TH1F *	fTopConvexCosEdge

TH1F *	fTopConvexEdgeLen

TH1F *	fSubNum3DHits

TH1F *	fSubNumEdges

TH1F *	fSubEigen21Ratio

TH1F *	fSubEigen20Ratio

TH1F *	fSubEigen10Ratio

TH1F *	fSubPrimaryLength

TH1F *	fSubCosToPrevPCA

TH1F *	fSubCosExtToPCA

TH1F *	fSubConvexCosEdge

TH1F *	fSubConvexEdgeLen

TH1F *	fSubMaxDefect

TH1F *	fSubUsedDefect

std::unique_ptr < lar_cluster3d::IClusterAlg >	fClusterAlg
	Tools. More...

PrincipalComponentsAlg	fPCAAlg

Detailed Description

Definition at line 39 of file ConvexHullPathFinder_tool.cc.

Member Typedef Documentation

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

private

Definition at line 91 of file ConvexHullPathFinder_tool.cc.

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

private

Definition at line 92 of file ConvexHullPathFinder_tool.cc.

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

private

Definition at line 127 of file ConvexHullPathFinder_tool.cc.

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

private

Definition at line 128 of file ConvexHullPathFinder_tool.cc.

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

private

Definition at line 121 of file ConvexHullPathFinder_tool.cc.

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

private

Definition at line 120 of file ConvexHullPathFinder_tool.cc.

Constructor & Destructor Documentation

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

explicit

Constructor.

Parameters

pset

Definition at line 182 of file ConvexHullPathFinder_tool.cc.

                                                                         :
     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::ClusterParametersList &	outputClusterList,
		int	level
	)		const

private

Definition at line 1018 of file ConvexHullPathFinder_tool.cc.

 {
     // 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.
     reco::PrincipalComponents& fullPCA(clusterToBreak.getFullPCA());
 
     // Recover our hits
     reco::HitPairListPtr& hitList = clusterToBreak.getHitPairListPtr();
 
     // 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(hitList, fullPCA);
 
     // Sort the hits along the PCA
     hitList.sort([](const auto& left, const auto& right){return left->getArclenToPoca() < right->getArclenToPoca();});
 
     // Loop through the input hit list and keep track of first hit of largest gap
     reco::HitPairListPtr::iterator bigGapHitItr = hitList.begin();
     float                                biggestGap   = 0.;
 
     reco::HitPairListPtr::iterator lastHitItr = hitList.begin();
 
     for(reco::HitPairListPtr::iterator hitItr = hitList.begin(); hitItr != hitList.end(); hitItr++)
     {
         float currentGap = std::abs((*hitItr)->getArclenToPoca() - (*lastHitItr)->getArclenToPoca());
 
         if (currentGap > biggestGap)
         {
             bigGapHitItr = hitItr;      // Note that this is an iterator and will be the "end" going from begin, and "begin" for second half
             biggestGap   = currentGap;
         }
 
         lastHitItr = hitItr;
     }
 
     // Require some minimum gap size...
     if (biggestGap > fMinGapSize)
     {
         outputClusterList.push_back(reco::ClusterParameters());
 
         reco::ClusterParameters& clusterParams1  = outputClusterList.back();
 
         reco::PrincipalComponents& fullPCA(clusterToBreak.getFullPCA());
         Eigen::Vector3f            fullPrimaryVec(fullPCA.getEigenVectors().row(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::ClusterParametersList &	outputClusterList,
		int	level
	)		const

private

Definition at line 651 of file ConvexHullPathFinder_tool.cc.

 {
     // Set up container to keep track of edges
     using HitKinkTuple    = std::tuple<int, reco::HitPairListPtr::iterator>;
     using HitKinkTupleVec = std::vector<HitKinkTuple>;
 
     // Recover our hits
     reco::HitPairListPtr& hitList = clusterToBreak.getHitPairListPtr();
 
     // 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().row(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 (fFillHistograms)
             {
                 fillConvexHullHists(clusterParams1, false);
                 fillConvexHullHists(clusterParams2, false);
             }
         }
 
         // 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::ClusterParametersList &	outputClusterList,
		int	level
	)		const

private

Definition at line 719 of file ConvexHullPathFinder_tool.cc.

 {
     // 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().row(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 (fFillHistograms)
             {
                 fillConvexHullHists(clusterParams1, false);
                 fillConvexHullHists(clusterParams2, false);
             }
         }
 
         // 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::ClusterParametersList &	outputClusterList,
		int	level
	)		const

private

Definition at line 879 of file ConvexHullPathFinder_tool.cc.

 {
     // 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().row(2));
 
     // Recover our hits
     reco::HitPairListPtr& hitList = clusterToBreak.getHitPairListPtr();
 
     // 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(hitList, fullPCA);
 
     // Sort the hits along the PCA
     hitList.sort([](const auto& left, const auto& right){return left->getArclenToPoca() < right->getArclenToPoca();});
 
     // 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::ClusterParametersList &	outputClusterList,
		int	level
	)		const

private

Definition at line 981 of file ConvexHullPathFinder_tool.cc.

 {
     reco::PrincipalComponents& fullPCA(clusterToBreak.getFullPCA());
     Eigen::Vector3f            fullPrimaryVec(fullPCA.getEigenVectors().row(2));
 
     // Recover our hits
     reco::HitPairListPtr& hitList = clusterToBreak.getHitPairListPtr();
 
     // 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(hitList, fullPCA);
 
     // Sort the hits along the PCA
     hitList.sort([](const auto& left, const auto& right){return left->getArclenToPoca() < right->getArclenToPoca();});
 
     reco::HitPairListPtr::iterator vertexItr = hitList.begin();
 
     std::advance(vertexItr, hitList.size()/2);
 
     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();
 
         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 1079 of file ConvexHullPathFinder_tool.cc.

 {
     // 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
     const Eigen::Vector3f&    pcaCenter  = pca.getAvePosition();
     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 = pca.getEigenVectors() * pcaToHitVec;
 
         pointList.emplace_back(pcaToHit(1),pcaToHit(2),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::ProjectedPointList& convexHullPointList = convexHull.getConvexHullPointList();
         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);
 
             convexHullPointList.push_back(curPoint);
             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 1265 of file ConvexHullPathFinder_tool.cc.

 {
     // 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 616 of file ConvexHullPathFinder_tool.cc.

 {
     // 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().row(2));
 
         // If the PCA's are opposite the flip the axes
         if (primaryPCA.dot(newPrimaryVec) < 0.)
         {
             for(size_t vecIdx = 0; vecIdx < 3; vecIdx++) newFullPCA.flipAxis(vecIdx);
         }
 
         // 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;
 }

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

overridevirtual

Interface for configuring the particular algorithm tool.

Parameters

ParameterSet The input set of parameters for configuration

Implements lar_cluster3d::IClusterModAlg.

Definition at line 196 of file ConvexHullPathFinder_tool.cc.

 {
     fEnableMonitoring     = pset.get<bool>  ("EnableMonitoring",   true );
     fMinTinyClusterSize   = pset.get<size_t>("MinTinyClusterSize", 40   );
     fMinGapSize           = pset.get<float >("MinClusterGapSize",   2.0 );
     fMinEigen0To1Ratio    = pset.get<float >("MinEigen0To1Ratio",  10.0 );
     fConvexHullKinkAngle  = pset.get<float >("ConvexHullKinkAgle",  0.95);
     fConvexHullMinSep     = pset.get<float >("ConvexHullMinSep",    0.65);
     fClusterAlg           = art::make_tool<lar_cluster3d::IClusterAlg>(pset.get<fhicl::ParameterSet>("ClusterAlg"));
 
     fTimeToProcess = 0.;
 
     return;
 }

void lar_cluster3d::ConvexHullPathFinder::fillConvexHullHists	(	reco::ClusterParameters &	clusterParameters,
		bool	top
	)		const

private

Definition at line 1213 of file ConvexHullPathFinder_tool.cc.

 {
     reco::ProjectedPointList& convexHullPoints = clusterParameters.getConvexHull().getConvexHullPointList();
 
     if (convexHullPoints.size() > 2)
     {
         reco::ProjectedPointList::iterator pointItr = convexHullPoints.begin();
 
         // Advance to the second to last element
         std::advance(pointItr, convexHullPoints.size() - 2);
 
         reco::ProjectedPoint lastPoint = *pointItr++;
 
         // Reset pointer to the first element
         pointItr = convexHullPoints.begin();
 
         reco::ProjectedPoint curPoint = *pointItr++;
         Eigen::Vector2f      lastEdge(std::get<0>(curPoint) - std::get<0>(lastPoint), std::get<1>(curPoint) - std::get<1>(lastPoint));
 
         lastEdge.normalize();
 
         while(pointItr != convexHullPoints.end())
         {
             reco::ProjectedPoint& nextPoint = *pointItr++;
 
             Eigen::Vector2f nextEdge(std::get<0>(nextPoint) - std::get<0>(curPoint), std::get<1>(nextPoint) - std::get<1>(curPoint));
             float           nextEdgeLen = nextEdge.norm();
 
             nextEdge.normalize();
 
             float cosLastNextEdge = lastEdge.dot(nextEdge);
 
             if (top)
             {
                 fTopConvexCosEdge->Fill(cosLastNextEdge, 1.);
                 fTopConvexEdgeLen->Fill(std::min(nextEdgeLen,float(49.9)), 1.);
             }
             else
             {
                 fSubConvexCosEdge->Fill(cosLastNextEdge, 1.);
                 fSubConvexEdgeLen->Fill(std::min(nextEdgeLen,float(49.9)), 1.);
             }
 
             if (nextEdgeLen > fConvexHullMinSep) lastEdge = nextEdge;
 
             curPoint = nextPoint;
         }
     }
 
     return;
 }

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

private

Definition at line 1290 of file ConvexHullPathFinder_tool.cc.

 {
     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 75 of file ConvexHullPathFinder_tool.cc.

75 {return fTimeToProcess;}

lar_cluster3d::ConvexHullPathFinder::fTimeToProcess

float fTimeToProcess

Definition: ConvexHullPathFinder_tool.cc:145

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 211 of file ConvexHullPathFinder_tool.cc.

 {
     // 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.);
     fTopExtremeSep    = dir.make<TH1F>("TopExtremeSep", "Extreme Dist",    200,    0.,   200.);
     fTopConvexCosEdge = dir.make<TH1F>("TopConvexCos",  "CH Edge Cos",     100,   -1.,     1.);
     fTopConvexEdgeLen = dir.make<TH1F>("TopConvexEdge", "CH Edge Len",     200,    0.,    50.);
 
     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);
     fSubConvexCosEdge = dir.make<TH1F>("SubConvexCos",  "CH Edge Cos",     100,   -1.,     1.);
     fSubConvexEdgeLen = dir.make<TH1F>("SubConvexEdge", "CH Edge Len",     200,    0.,    50.);
     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 528 of file ConvexHullPathFinder_tool.cc.

 {
     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().row(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[0] / eigenValVec[1];
         double              eigen1To0Ratio   = eigenValVec[1] / eigenValVec[2];
 
         // 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 574 of file ConvexHullPathFinder_tool.cc.

 {
     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().row(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[0] / eigenValVec[1];
         double              eigen1To0Ratio   = eigenValVec[1] / eigenValVec[2];
 
         // 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 250 of file ConvexHullPathFinder_tool.cc.

 {
     /**
      *  @brief 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.
      */
 
     // 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(), 0);
 
                     // 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[0] / eigenValVec[1];
                         double                     eigen1To0Ratio = eigenValVec[1] / eigenValVec[2];
                         double                     eigen2To0Ratio = eigenValVec[2] / eigenValVec[2];
                         int                        num3DHits      = cluster.getHitPairListPtr().size();
                         int                        numEdges       = cluster.getConvexHull().getConvexHullEdgeList().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[2],199.), 1.);
 //                        fTopExtremeSep->Fill(std::min(edgeLen,199.), 1.);
                         fillConvexHullHists(clusterParameters, true);
                     }
                 }
             }
         }
 
         // 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 805 of file ConvexHullPathFinder_tool.cc.

 {
     // 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 847 of file ConvexHullPathFinder_tool.cc.

 {
     // 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 350 of file ConvexHullPathFinder_tool.cc.

 {
     // 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 PCA for the input cluster
         reco::PrincipalComponents& fullPCA     = clusterToBreak.getFullPCA();
         Eigen::Vector3f            fullPrimaryVec(fullPCA.getEigenVectors().row(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 starting with the top level cluster then we first try to break at the kinks
         if (level == 0)
         {
             //        if (!breakClusterByMaxDefect(clusterToBreak, clusHitPairVector, tempClusterParametersList, level))
             if (!breakClusterByKinks(clusterToBreak, tempClusterParametersList, level))
             {
                 // Look to see if we can break at a gap
                 if (!breakClusterAtBigGap(clusterToBreak, tempClusterParametersList, level))
                 {
                     // It might be that we have a large deviation in the convex hull...
                     if (!breakClusterByMaxDefect(clusterToBreak, tempClusterParametersList, level))
                     {
                         std::vector<double> eigenValVec = {3. * std::sqrt(fullPCA.getEigenValues()[0]),
                                                            3. * std::sqrt(fullPCA.getEigenValues()[1]),
                                                            3. * std::sqrt(fullPCA.getEigenValues()[2])};
 
                         // Well, we don't want "flippers" so make sure the edge has some teeth to it
                         //if (edgeLen > 10.) breakClusterInHalf(clusterToBreak, clusHitPairVector, tempClusterParametersList, level);
                         if (eigenValVec[2] > fMinEigen0To1Ratio * eigenValVec[1]) breakClusterInHalf(clusterToBreak, tempClusterParametersList, level);
                     }
                 }
             }
 
         }
         // Otherwise, change the order
         else
         {
             //        if (!breakClusterByMaxDefect(clusterToBreak, clusHitPairVector, tempClusterParametersList, level))
             if (!breakClusterAtBigGap(clusterToBreak, tempClusterParametersList, level))
             {
                 // Look to see if we can break at a gap
                 if (!breakClusterByKinks(clusterToBreak, tempClusterParametersList, level))
                 {
                     // It might be that we have a large deviation in the convex hull...
                     if (!breakClusterByMaxDefect(clusterToBreak, tempClusterParametersList, level))
                     {
                         std::vector<double> eigenValVec = {3. * std::sqrt(fullPCA.getEigenValues()[0]),
                                                            3. * std::sqrt(fullPCA.getEigenValues()[1]),
                                                            3. * std::sqrt(fullPCA.getEigenValues()[2])};
 
                         // Well, we don't want "flippers" so make sure the edge has some teeth to it
                         //if (edgeLen > 10.) breakClusterInHalf(clusterToBreak, clusHitPairVector, tempClusterParametersList, level);
                         if (eigenValVec[2] > fMinEigen0To1Ratio * eigenValVec[1]) breakClusterInHalf(clusterToBreak, 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;
 
             // The current cluster was not further subdivided so we store its info here
             // 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().row(2));
                 Eigen::Vector3f lastPrimaryVec(newFullPCA.getEigenVectors().row(2));
 
                 int             num3DHits      = clusterParams.getHitPairListPtr().size();
                 int             numEdges       = clusterParams.getConvexHull().getConvexHullEdgeList().size();
                 float           cosToLast      = newPrimaryVec.dot(lastPrimaryVec);
                 double          eigen2To1Ratio = eigenValVec[0] / eigenValVec[1];
                 double          eigen1To0Ratio = eigenValVec[1] / eigenValVec[2];
                 double          eigen2To0Ratio = eigenValVec[0] / eigenValVec[2];
 
                 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[2],199.), 1.);
                 fSubCosExtToPCA->Fill(fullPrimaryVec.dot(edgeVec), 1.);
             }
 
             // The above points to the element, want the next element
             positionItr++;
         }
     }
 
     return positionItr;
 }

Member Data Documentation

std::unique_ptr<lar_cluster3d::IClusterAlg> lar_cluster3d::ConvexHullPathFinder::fClusterAlg

private

Tools.

Algorithm to do 3D space point clustering

Definition at line 178 of file ConvexHullPathFinder_tool.cc.

float lar_cluster3d::ConvexHullPathFinder::fConvexHullKinkAngle

private

Angle to declare a kink in convex hull calc.

Definition at line 143 of file ConvexHullPathFinder_tool.cc.

float lar_cluster3d::ConvexHullPathFinder::fConvexHullMinSep

private

Min hit separation to conisder in convex hull.

Definition at line 144 of file ConvexHullPathFinder_tool.cc.

bool lar_cluster3d::ConvexHullPathFinder::fEnableMonitoring

private

FHICL parameters.

Definition at line 139 of file ConvexHullPathFinder_tool.cc.

bool lar_cluster3d::ConvexHullPathFinder::fFillHistograms

private

Histogram definitions.

Definition at line 150 of file ConvexHullPathFinder_tool.cc.

float lar_cluster3d::ConvexHullPathFinder::fMinEigen0To1Ratio

private

Minimum ratio of eigen 0 to 1 to continue breaking.

Definition at line 142 of file ConvexHullPathFinder_tool.cc.

float lar_cluster3d::ConvexHullPathFinder::fMinGapSize

private

Minimum gap size to break at gaps.

Definition at line 141 of file ConvexHullPathFinder_tool.cc.

size_t lar_cluster3d::ConvexHullPathFinder::fMinTinyClusterSize

private

Minimum size for a "tiny" cluster.

Definition at line 140 of file ConvexHullPathFinder_tool.cc.

PrincipalComponentsAlg lar_cluster3d::ConvexHullPathFinder::fPCAAlg

private

Definition at line 179 of file ConvexHullPathFinder_tool.cc.

TH1F* lar_cluster3d::ConvexHullPathFinder::fSubConvexCosEdge

private

Definition at line 170 of file ConvexHullPathFinder_tool.cc.

TH1F* lar_cluster3d::ConvexHullPathFinder::fSubConvexEdgeLen

private

Definition at line 171 of file ConvexHullPathFinder_tool.cc.

TH1F* lar_cluster3d::ConvexHullPathFinder::fSubCosExtToPCA

private

Definition at line 169 of file ConvexHullPathFinder_tool.cc.

TH1F* lar_cluster3d::ConvexHullPathFinder::fSubCosToPrevPCA

private

Definition at line 168 of file ConvexHullPathFinder_tool.cc.

TH1F* lar_cluster3d::ConvexHullPathFinder::fSubEigen10Ratio

private

Definition at line 166 of file ConvexHullPathFinder_tool.cc.

TH1F* lar_cluster3d::ConvexHullPathFinder::fSubEigen20Ratio

private

Definition at line 165 of file ConvexHullPathFinder_tool.cc.

TH1F* lar_cluster3d::ConvexHullPathFinder::fSubEigen21Ratio

private

Definition at line 164 of file ConvexHullPathFinder_tool.cc.

TH1F* lar_cluster3d::ConvexHullPathFinder::fSubMaxDefect

private

Definition at line 172 of file ConvexHullPathFinder_tool.cc.

TH1F* lar_cluster3d::ConvexHullPathFinder::fSubNum3DHits

private

Definition at line 162 of file ConvexHullPathFinder_tool.cc.

TH1F* lar_cluster3d::ConvexHullPathFinder::fSubNumEdges

private

Definition at line 163 of file ConvexHullPathFinder_tool.cc.

TH1F* lar_cluster3d::ConvexHullPathFinder::fSubPrimaryLength

private

Definition at line 167 of file ConvexHullPathFinder_tool.cc.

TH1F* lar_cluster3d::ConvexHullPathFinder::fSubUsedDefect

private

Definition at line 173 of file ConvexHullPathFinder_tool.cc.

float lar_cluster3d::ConvexHullPathFinder::fTimeToProcess

mutableprivate

Definition at line 145 of file ConvexHullPathFinder_tool.cc.

TH1F* lar_cluster3d::ConvexHullPathFinder::fTopConvexCosEdge

private

Definition at line 159 of file ConvexHullPathFinder_tool.cc.

TH1F* lar_cluster3d::ConvexHullPathFinder::fTopConvexEdgeLen

private

Definition at line 160 of file ConvexHullPathFinder_tool.cc.

TH1F* lar_cluster3d::ConvexHullPathFinder::fTopEigen10Ratio

private

Definition at line 156 of file ConvexHullPathFinder_tool.cc.

TH1F* lar_cluster3d::ConvexHullPathFinder::fTopEigen20Ratio

private

Definition at line 155 of file ConvexHullPathFinder_tool.cc.

TH1F* lar_cluster3d::ConvexHullPathFinder::fTopEigen21Ratio

private

Definition at line 154 of file ConvexHullPathFinder_tool.cc.

TH1F* lar_cluster3d::ConvexHullPathFinder::fTopExtremeSep

private

Definition at line 158 of file ConvexHullPathFinder_tool.cc.

TH1F* lar_cluster3d::ConvexHullPathFinder::fTopNum3DHits

private

Definition at line 152 of file ConvexHullPathFinder_tool.cc.

TH1F* lar_cluster3d::ConvexHullPathFinder::fTopNumEdges

private

Definition at line 153 of file ConvexHullPathFinder_tool.cc.

TH1F* lar_cluster3d::ConvexHullPathFinder::fTopPrimaryLength

private

Definition at line 157 of file ConvexHullPathFinder_tool.cc.

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

ConvexHullPathFinder_tool.cc

Public Member Functions

Private Types

Private Member Functions

Private Attributes

Detailed Description

Member Typedef Documentation

Constructor & Destructor Documentation

Member Function Documentation

Member Data Documentation