lar_cluster3d::VoronoiPathFinder Class Reference

Inheritance diagram for lar_cluster3d::VoronoiPathFinder:

Public Member Functions
	VoronoiPathFinder (const fhicl::ParameterSet &)
	Constructor. More...
	~VoronoiPathFinder ()
	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	Point = std::tuple< float, float, const reco::ClusterHit3D * >
using	PointList = std::list< Point >
using	MinMaxPoints = std::pair< Point, Point >
using	MinMaxPointPair = std::pair< MinMaxPoints, MinMaxPoints >

Private Member Functions
reco::ClusterParametersList::iterator	breakIntoTinyBits (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...
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
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
void	buildVoronoiDiagram (reco::ClusterParameters &clusterParameters, int level=0) const
float	findConvexHullEndPoints (const reco::EdgeList &, const reco::ClusterHit3D *, const reco::ClusterHit3D *) const

Private Attributes
bool	fEnableMonitoring
	FHICL parameters. More...
size_t	fMinTinyClusterSize
	Minimum size for a "tiny" cluster. 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
std::unique_ptr < lar_cluster3d::IClusterAlg >	fClusterAlg
	Tools. More...
PrincipalComponentsAlg	fPCAAlg

Detailed Description

Definition at line 40 of file VoronoiPathFinder_tool.cc.

Member Typedef Documentation

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

private

Definition at line 120 of file VoronoiPathFinder_tool.cc.

using lar_cluster3d::VoronoiPathFinder::MinMaxPoints = std::pair<Point,Point>

private

Definition at line 119 of file VoronoiPathFinder_tool.cc.

using lar_cluster3d::VoronoiPathFinder::Point = std::tuple<float,float,const reco::ClusterHit3D*>

private

Definition at line 117 of file VoronoiPathFinder_tool.cc.

using lar_cluster3d::VoronoiPathFinder::PointList = std::list<Point>

private

Definition at line 118 of file VoronoiPathFinder_tool.cc.

Constructor & Destructor Documentation

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

explicit

Constructor.

Parameters

pset

Definition at line 163 of file VoronoiPathFinder_tool.cc.

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

lar_cluster3d::VoronoiPathFinder::~VoronoiPathFinder

(

)

Destructor.

Definition at line 171 of file VoronoiPathFinder_tool.cc.

{
}

Member Function Documentation

reco::ClusterParametersList::iterator lar_cluster3d::VoronoiPathFinder::breakIntoTinyBits ( 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 332 of file VoronoiPathFinder_tool.cc.

{
    // This needs to be a recursive routine...
    // Idea is to take the input cluster and order 3D hits by arclength along PCA primary axis
    // If the cluster is above the minimum number of hits then we divide into two and call ourself
    // with the two halves. This means we form a new cluster with hits and PCA and then call ourself
    // If the cluster is below the minimum then we can't break any more, simply add this cluster to
    // the new output list.

    // set an indention string
    std::string pluses(level/2, '+');
    std::string indent(level/2, ' ');

    indent += pluses;

    reco::ClusterParametersList::iterator inputPositionItr = positionItr;

    // To make best use of this we'll also want the PCA for this cluster... so...
    // Recover the prime ingredients
    reco::PrincipalComponents& fullPCA     = clusterToBreak.getFullPCA();
    std::vector<double>        eigenValVec = {3. * std::sqrt(fullPCA.getEigenValues()[0]),
                                              3. * std::sqrt(fullPCA.getEigenValues()[1]),
                                              3. * std::sqrt(fullPCA.getEigenValues()[2])};
    Eigen::Vector3f            fullPrimaryVec(fullPCA.getEigenVectors().row(2));
    Eigen::Vector3f            lastPrimaryVec(lastPCA.getEigenVectors().row(2));

    double cosNewToLast     = std::abs(fullPrimaryVec.dot(lastPrimaryVec));
    double eigen2To1Ratio   = eigenValVec[2] / eigenValVec[1];
    double eigen1To0Ratio   = eigenValVec[1] / eigenValVec[2];
    double eigen2To0Ratio   = eigenValVec[0] / eigenValVec[2];
    double eigen2And1Ave    = 0.5 * (eigenValVec[1] + eigenValVec[0]);
    double eigenAveTo0Ratio = eigen2And1Ave / eigenValVec[2];

    bool storeCurrentCluster(true);
    int  minimumClusterSize(fMinTinyClusterSize);

    std::cout << indent << ">>> breakIntoTinyBits with " << clusterToBreak.getHitPairListPtr().size() << " input hits, " << clusterToBreak.getBestEdgeList().size() << " edges, rat21: " << eigen2To1Ratio << ", rat20: " << eigen2To0Ratio << ", rat10: " << eigen1To0Ratio << ", ave0: " << eigenAveTo0Ratio <<  std::endl;
    std::cout << indent << "   --> eigen 0/1/2: " << eigenValVec[0] << "/" << eigenValVec[1] << "/" << eigenValVec[2] << ", cos: " << cosNewToLast << std::endl;

    // Create a rough cut intended to tell us when we've reached the land of diminishing returns
    if (clusterToBreak.getBestEdgeList().size()   > 5 && cosNewToLast > 0.25 && eigen2To1Ratio < 0.9 && eigen2To0Ratio > 0.001 &&
        clusterToBreak.getHitPairListPtr().size() > size_t(2 * minimumClusterSize))
    {
        // 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::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();

        // Recover the list of 3D hits associated to this cluster
        reco::HitPairListPtr& clusHitPairVector = 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(clusHitPairVector, fullPCA);

        // Sort the hits along the PCA
        clusHitPairVector.sort([](const auto& left, const auto& right){return left->getArclenToPoca() < right->getArclenToPoca();});

        // Set up container to keep track of edges
        using DistEdgeTuple    = std::tuple<float, const reco::EdgeTuple*>;
        using DistEdgeTupleVec = std::vector<DistEdgeTuple>;

        DistEdgeTupleVec distEdgeTupleVec;

        // 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);});

        for(const auto& distEdgeTuple : distEdgeTupleVec)
        {
            const reco::EdgeTuple&    edgeTuple = *std::get<1>(distEdgeTuple);
            const reco::ClusterHit3D* edgeHit   = std::get<0>(edgeTuple);

            // Now find the hit identified above as furthest away
            reco::HitPairListPtr::iterator vertexItr = std::find(clusHitPairVector.begin(),clusHitPairVector.end(),edgeHit);

            // Make sure enough hits either side, otherwise we just keep the current cluster
            if (vertexItr == clusHitPairVector.end() || std::distance(clusHitPairVector.begin(),vertexItr) < minimumClusterSize || std::distance(vertexItr,clusHitPairVector.end()) < minimumClusterSize) continue;

            // Now we create a list of pairs of iterators to the start and end of each subcluster
            using Hit3DItrPair   = std::pair<reco::HitPairListPtr::iterator,reco::HitPairListPtr::iterator>;
            using VertexPairList = std::list<Hit3DItrPair>;

            VertexPairList vertexPairList;

            vertexPairList.emplace_back(Hit3DItrPair(clusHitPairVector.begin(),vertexItr));
            vertexPairList.emplace_back(Hit3DItrPair(vertexItr,clusHitPairVector.end()));

            storeCurrentCluster = false;

            // Ok, now loop through our pairs
            for(auto& hit3DItrPair : vertexPairList)
            {
                reco::ClusterParameters clusterParams;
                reco::HitPairListPtr&   hitPairListPtr = clusterParams.getHitPairListPtr();

                std::cout << indent << "+>    -- building new cluster, size: " << std::distance(hit3DItrPair.first,hit3DItrPair.second) << std::endl;

                // size the container...
                hitPairListPtr.resize(std::distance(hit3DItrPair.first,hit3DItrPair.second));

                // and copy the hits into the container
                std::copy(hit3DItrPair.first,hit3DItrPair.second,hitPairListPtr.begin());

                // First stage of feature extraction runs here
                fPCAAlg.PCAAnalysis_3D(hitPairListPtr, clusterParams.getFullPCA());

                // Recover the new fullPCA
                reco::PrincipalComponents& newFullPCA = clusterParams.getFullPCA();

                // Must have a valid pca
                if (newFullPCA.getSvdOK())
                {
                    std::cout << indent << "+>    -- >> cluster has a valid Full PCA" << std::endl;

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

                    // Set the skeleton PCA to make sure it has some value
                    clusterParams.getSkeletonPCA() = clusterParams.getFullPCA();

                    // Be sure to compute the oonvex hull surrounding the now broken cluster
                    buildConvexHull(clusterParams, level+2);

                    positionItr = breakIntoTinyBits(clusterParams, fullPCA, positionItr, outputClusterList, level+4);
                }
            }

            // If successful in breaking the cluster then we are done, otherwise try to loop
            // again taking the next furthest hit
            break;
        }
    }

    // First question, are we done breaking?
    if (storeCurrentCluster)
    {
        // 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 : clusterToBreak.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);
            clusterToBreak.UpdateParameters(hit2D);
        }

        std::cout << indent << "*********>>> storing new subcluster of size " << clusterToBreak.getHitPairListPtr().size() << std::endl;

        positionItr = outputClusterList.insert(positionItr,clusterToBreak);

        // Are we filling histograms
        if (fFillHistograms)
        {
            int             num3DHits = clusterToBreak.getHitPairListPtr().size();
            int             numEdges  = clusterToBreak.getBestEdgeList().size();
            Eigen::Vector3f newPrimaryVec(fullPCA.getEigenVectors().row(2));
            Eigen::Vector3f lastPrimaryVec(lastPCA.getEigenVectors().row(2));
            float           cosToLast = newPrimaryVec.dot(lastPrimaryVec);

            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.);
        }

        // The above points to the element, want the next element
        positionItr++;
    }
    else if (inputPositionItr != positionItr)
    {
        std::cout << indent << "***** DID NOT STORE A CLUSTER *****" << std::endl;
    }

    return positionItr;
}

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

private

Definition at line 862 of file VoronoiPathFinder_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
    Eigen::Vector3f pcaCenter(pca.getAvePosition()[0],pca.getAvePosition()[1],pca.getAvePosition()[2]);

    //dcel2d::PointList pointList;
    using Point     = std::tuple<float,float,const reco::ClusterHit3D*>;
    using PointList = std::list<Point>;

    reco::ConvexHull& convexHull = clusterParameters.getConvexHull();
    PointList         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(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<PointList>  rejectedListVec;
    bool                    increaseDepth(pointList.size() > 5);
    float                   lastArea(std::numeric_limits<float>::max());

    while(increaseDepth)
    {
        // Get another convexHull container
        convexHullVec.push_back(ConvexHull(pointList));
        rejectedListVec.push_back(PointList());

        const ConvexHull& convexHull       = convexHullVec.back();
        PointList&        rejectedList     = rejectedListVec.back();
        const PointList&  convexHullPoints = convexHull.getConvexHull();

        increaseDepth = false;

        if (convexHull.getConvexHullArea() > 0.)
        {
            std::cout << indent << "-> built convex hull, 3D hits: " << pointList.size() << " with " << convexHullPoints.size() << " vertices" << ", area: " << convexHull.getConvexHullArea() << std::endl;
            std::cout << indent << "-> -Points:";
            for(const auto& point : convexHullPoints)
                std::cout << " (" << std::get<0>(point) << "," << std::get<1>(point) << ")";
            std::cout << std::endl;

            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)
            {
                std::cout << indent << "-> -- Point is " << convexHullVec.back().findNearestDistance(rejectedPoint) << " from nearest edge" << std::endl;

                if (convexHullVec.back().findNearestDistance(rejectedPoint) > 0.5)
                    hitPairListPtr.remove(std::get<2>(rejectedPoint));
            }
        }

        std::cout << indent << "-> Removed " << nRejectedTotal << " leaving " << pointList.size() << "/" << hitPairListPtr.size() << " points" << std::endl;

        // Now add "edges" to the cluster to describe the convex hull (for the display)
        reco::Hit3DToEdgeMap& edgeMap      = convexHull.getConvexHullEdgeMap();
        reco::EdgeList&       bestEdgeList = convexHull.getConvexHullEdgeList();

        Point 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);
            bestEdgeList.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);
    }

    return;
}

void lar_cluster3d::VoronoiPathFinder::buildVoronoiDiagram ( reco::ClusterParameters &  clusterParameters, int  level = 0  ) const

private

Definition at line 1004 of file VoronoiPathFinder_tool.cc.

{
    // 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]);

    dcel2d::PointList pointList;

    // 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(dcel2d::Point(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);});

    std::cout << "  ==> Build V diagram, sorted point list contains " << pointList.size() << " hits" << std::endl;

    // Set up the voronoi diagram builder
    voronoi2d::VoronoiDiagram voronoiDiagram(clusterParameters.getHalfEdgeList(),clusterParameters.getVertexList(),clusterParameters.getFaceList());

    // And make the diagram
    voronoiDiagram.buildVoronoiDiagram(pointList);

    // Recover the voronoi diagram vertex list and the container to store them in
    dcel2d::VertexList& vertexList = clusterParameters.getVertexList();

    // Now get the inverse of the rotation matrix so we can get the vertex positions,
    // which lie in the plane of the two largest PCA axes, in the standard coordinate system
    Eigen::Matrix3f rotationMatrixInv = pca.getEigenVectors().inverse();

    // Translate and fill
    for(auto& vertex : vertexList)
    {
        Eigen::Vector3f coords = rotationMatrixInv * vertex.getCoords();

        coords += pcaCenter;

        vertex.setCoords(coords);
    }

    // Now do the Convex Hull
    // Now add "edges" to the cluster to describe the convex hull (for the display)
    reco::Hit3DToEdgeMap& edgeMap      = clusterParameters.getHit3DToEdgeMap();
    reco::EdgeList&       bestEdgeList = clusterParameters.getBestEdgeList();

//    const dcel2d::PointList& edgePoints = voronoiDiagram.getConvexHull();
//    PointList                localList;
//
//    for(const auto& edgePoint : edgePoints) localList.emplace_back(std::get<0>(edgePoint),std::get<1>(edgePoint),std::get<2>(edgePoint));
//
//    // Sort the point vec by increasing x, then increase y
//    localList.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);});
//
//    // Why do I need to do this?
//    ConvexHull convexHull(localList);
//
//    Point lastPoint = convexHull.getConvexHull().front();
    dcel2d::Point lastPoint = voronoiDiagram.getConvexHull().front();

//    std::cout << "@@@@>> Build convex hull, voronoi handed " << edgePoints.size() << " points, convexHull cut to " << convexHull.getConvexHull().size() << std::endl;

//    for(auto& curPoint : convexHull.getConvexHull())
    for(auto& curPoint : voronoiDiagram.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);
        bestEdgeList.emplace_back(edge);

        lastPoint = curPoint;
    }

    std::cout << "****> vertexList containted " << vertexList.size() << " vertices for " << clusterParameters.getHitPairListPtr().size() << " hits" << std::endl;

    return;
}

float lar_cluster3d::VoronoiPathFinder::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 1104 of file VoronoiPathFinder_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();
}

void lar_cluster3d::VoronoiPathFinder::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 177 of file VoronoiPathFinder_tool.cc.

{
    fEnableMonitoring   = pset.get<bool>  ("EnableMonitoring",  true  );
    fMinTinyClusterSize = pset.get<size_t>("MinTinyClusterSize",40);
    fClusterAlg         = art::make_tool<lar_cluster3d::IClusterAlg>(pset.get<fhicl::ParameterSet>("ClusterAlg"));

    fTimeToProcess = 0.;

    return;
}

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

private

Definition at line 1129 of file VoronoiPathFinder_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::VoronoiPathFinder::getTimeToExecute ( ) const

inlineoverridevirtual

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

Implements lar_cluster3d::IClusterModAlg.

Definition at line 76 of file VoronoiPathFinder_tool.cc.

{return fTimeToProcess;}

void lar_cluster3d::VoronoiPathFinder::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 188 of file VoronoiPathFinder_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 = "VoronoiPath";

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

private

Definition at line 788 of file VoronoiPathFinder_tool.cc.

{
    std::string indent(level/2, ' ');

    reco::HitPairListPtr& hitPairListPtr = candCluster.getHitPairListPtr();

    std::cout << indent << "+>    -- building new cluster, size: " << std::distance(firstHitItr,lastHitItr) << std::endl;

    // size the container...
    hitPairListPtr.resize(std::distance(firstHitItr,lastHitItr));

    // and copy the hits into the container
    std::copy(firstHitItr,lastHitItr,hitPairListPtr.begin());

    // First stage of feature extraction runs here
    fPCAAlg.PCAAnalysis_3D(hitPairListPtr, 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())
    {
        std::cout << indent << "+>    -- >> cluster has a valid Full PCA" << std::endl;

        // 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);

            newPrimaryVec = Eigen::Vector3f(newFullPCA.getEigenVectors().row(2));
        }

        // 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);

        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[2];
        double              eigen2To0Ratio   = eigenValVec[0] / eigenValVec[2];
        double              eigen2And1Ave    = 0.5 * (eigenValVec[1] + eigenValVec[0]);
        double              eigenAveTo0Ratio = eigen2And1Ave / eigenValVec[2];

        std::cout << indent << ">>> subDivideClusters with " << candCluster.getHitPairListPtr().size() << " input hits, " << candCluster.getBestEdgeList().size() << " edges, rat21: " << eigen2To1Ratio << ", rat20: " << eigen2To0Ratio << ", rat10: " << eigen1To0Ratio << ", ave0: " << eigenAveTo0Ratio <<  std::endl;
        std::cout << indent << "   --> eigen 0/1/2: " << eigenValVec[0] << "/" << eigenValVec[1] << "/" << eigenValVec[2] << ", cos: " << cosNewToLast << std::endl;

        // 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::VoronoiPathFinder::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 222 of file VoronoiPathFinder_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();

    int countClusters(0);

    // 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;

        std::cout << "**> Looking at Cluster " << countClusters++ << ", # hits: " << clusterParameters.getHitPairListPtr().size() << std::endl;

        // 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
        buildVoronoiDiagram(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);

            std::cout << ">>>>>>>>>>> Reclustered to " << reclusteredParameters.size() << " Clusters <<<<<<<<<<<<<<<" << std::endl;

            // Only process non-empty results
            if (!reclusteredParameters.empty())
            {
                // Loop over the reclustered set
                for (auto& cluster : reclusteredParameters)
                {
                    std::cout << "****> Calling breakIntoTinyBits with " << cluster.getHitPairListPtr().size() << " hits" << std::endl;

                    // 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);

                    std::cout << "****> Broke Cluster with " << cluster.getHitPairListPtr().size() << " into " << cluster.daughterList().size() << " sub clusters";
                    for(auto& clus : cluster.daughterList()) std::cout << ", " << clus.getHitPairListPtr().size();
                    std::cout << std::endl;

                    // 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[0] / eigenValVec[2];
                        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;
}

reco::ClusterParametersList::iterator lar_cluster3d::VoronoiPathFinder::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 564 of file VoronoiPathFinder_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;

        // To make best use of this we'll also want the PCA for this cluster... so...
        // Recover the prime ingredients
        reco::PrincipalComponents& fullPCA     = clusterToBreak.getFullPCA();
        std::vector<double>        eigenValVec = {3. * std::sqrt(fullPCA.getEigenValues()[0]),
                                                  3. * std::sqrt(fullPCA.getEigenValues()[1]),
                                                  3. * std::sqrt(fullPCA.getEigenValues()[2])};
        Eigen::Vector3f            fullPrimaryVec(fullPCA.getEigenVectors().row(2));

        // 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::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();

        // Recover the list of 3D hits associated to this cluster
        reco::HitPairListPtr& clusHitPairVector = 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(clusHitPairVector, fullPCA);

        // Sort the hits along the PCA
        clusHitPairVector.sort([](const auto& left, const auto& right){return left->getArclenToPoca() < right->getArclenToPoca();});

        // Set up container to keep track of edges
        using DistEdgeTuple    = std::tuple<float, const reco::EdgeTuple*>;
        using DistEdgeTupleVec = std::vector<DistEdgeTuple>;

        DistEdgeTupleVec distEdgeTupleVec;

        // 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);});

        // Get a temporary  container to hol
        reco::ClusterParametersList tempClusterParametersList;
        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(clusHitPairVector.begin(),clusHitPairVector.end(),edgeHit);

            // Make sure enough hits either side, otherwise we just keep the current cluster
            if (vertexItr == clusHitPairVector.end() || std::distance(clusHitPairVector.begin(),vertexItr) < int(fMinTinyClusterSize) || std::distance(vertexItr,clusHitPairVector.end()) < int(fMinTinyClusterSize)) continue;

            tempClusterParametersList.push_back(reco::ClusterParameters());

            reco::ClusterParameters& clusterParams1  = tempClusterParametersList.back();

            if (makeCandidateCluster(fullPrimaryVec, clusterParams1, clusHitPairVector.begin(), vertexItr, level))
            {
                tempClusterParametersList.push_back(reco::ClusterParameters());

                reco::ClusterParameters& clusterParams2  = tempClusterParametersList.back();

                if (makeCandidateCluster(fullPrimaryVec, clusterParams2, vertexItr, clusHitPairVector.end(), level)) break;
            }

            // If here then we could not make two valid clusters and so we try again
            tempClusterParametersList.clear();
        }

        // Fallback in the event of still large clusters but not defect points
        if (tempClusterParametersList.empty())
        {
            std::cout << indent << "===> no cluster cands, edgeLen: " << edgeLen << ", # hits: " << clusHitPairVector.size() << ", max defect: " << std::get<0>(distEdgeTupleVec.front()) << std::endl;

            usedDefectDist = 0.;

            if (edgeLen > 20.)
            {
                reco::HitPairListPtr::iterator vertexItr = clusHitPairVector.begin();

                std::advance(vertexItr, clusHitPairVector.size()/2);

                tempClusterParametersList.push_back(reco::ClusterParameters());

                reco::ClusterParameters& clusterParams1  = tempClusterParametersList.back();

                if (makeCandidateCluster(fullPrimaryVec, clusterParams1, clusHitPairVector.begin(), vertexItr, level))
                {
                    tempClusterParametersList.push_back(reco::ClusterParameters());

                    reco::ClusterParameters& clusterParams2  = tempClusterParametersList.back();

                    if (!makeCandidateCluster(fullPrimaryVec, clusterParams2, vertexItr, clusHitPairVector.end(), level))
                        tempClusterParametersList.clear();
                }
            }
        }

        // 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);

            std::cout << indent << "Output cluster list prev: " << curOutputClusterListSize << ", now: " << outputClusterList.size() << std::endl;

            // 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);
            }

            std::cout << indent << "*********>>> storing new subcluster of size " << clusterParams.getHitPairListPtr().size() << std::endl;

            positionItr = outputClusterList.insert(positionItr,clusterParams);

            // Are we filling histograms
            if (fFillHistograms)
            {
                // 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.getBestEdgeList().size();
                float           cosToLast      = newPrimaryVec.dot(lastPrimaryVec);
                double          eigen2To1Ratio = eigenValVec[0] / eigenValVec[1];
                double          eigen1To0Ratio = eigenValVec[1] / eigenValVec[2];
                double          eigen2To0Ratio = eigenValVec[2] / 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.);
                fSubMaxDefect->Fill(std::get<0>(distEdgeTupleVec.front()), 1.);
                fSubUsedDefect->Fill(usedDefectDist, 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::VoronoiPathFinder::fClusterAlg

private

Tools.

Algorithm to do 3D space point clustering

Definition at line 159 of file VoronoiPathFinder_tool.cc.

bool lar_cluster3d::VoronoiPathFinder::fEnableMonitoring

private

FHICL parameters.

Definition at line 129 of file VoronoiPathFinder_tool.cc.

bool lar_cluster3d::VoronoiPathFinder::fFillHistograms

private

Histogram definitions.

Definition at line 136 of file VoronoiPathFinder_tool.cc.

size_t lar_cluster3d::VoronoiPathFinder::fMinTinyClusterSize

private

Minimum size for a "tiny" cluster.

Definition at line 130 of file VoronoiPathFinder_tool.cc.

PrincipalComponentsAlg lar_cluster3d::VoronoiPathFinder::fPCAAlg

private

Definition at line 160 of file VoronoiPathFinder_tool.cc.

TH1F* lar_cluster3d::VoronoiPathFinder::fSubCosExtToPCA

private

Definition at line 152 of file VoronoiPathFinder_tool.cc.

TH1F* lar_cluster3d::VoronoiPathFinder::fSubCosToPrevPCA

private

Definition at line 151 of file VoronoiPathFinder_tool.cc.

TH1F* lar_cluster3d::VoronoiPathFinder::fSubEigen10Ratio

private

Definition at line 149 of file VoronoiPathFinder_tool.cc.

TH1F* lar_cluster3d::VoronoiPathFinder::fSubEigen20Ratio

private

Definition at line 148 of file VoronoiPathFinder_tool.cc.

TH1F* lar_cluster3d::VoronoiPathFinder::fSubEigen21Ratio

private

Definition at line 147 of file VoronoiPathFinder_tool.cc.

TH1F* lar_cluster3d::VoronoiPathFinder::fSubMaxDefect

private

Definition at line 153 of file VoronoiPathFinder_tool.cc.

TH1F* lar_cluster3d::VoronoiPathFinder::fSubNum3DHits

private

Definition at line 145 of file VoronoiPathFinder_tool.cc.

TH1F* lar_cluster3d::VoronoiPathFinder::fSubNumEdges

private

Definition at line 146 of file VoronoiPathFinder_tool.cc.

TH1F* lar_cluster3d::VoronoiPathFinder::fSubPrimaryLength

private

Definition at line 150 of file VoronoiPathFinder_tool.cc.

TH1F* lar_cluster3d::VoronoiPathFinder::fSubUsedDefect

private

Definition at line 154 of file VoronoiPathFinder_tool.cc.

float lar_cluster3d::VoronoiPathFinder::fTimeToProcess

mutableprivate

Definition at line 131 of file VoronoiPathFinder_tool.cc.

TH1F* lar_cluster3d::VoronoiPathFinder::fTopEigen10Ratio

private

Definition at line 142 of file VoronoiPathFinder_tool.cc.

TH1F* lar_cluster3d::VoronoiPathFinder::fTopEigen20Ratio

private

Definition at line 141 of file VoronoiPathFinder_tool.cc.

TH1F* lar_cluster3d::VoronoiPathFinder::fTopEigen21Ratio

private

Definition at line 140 of file VoronoiPathFinder_tool.cc.

TH1F* lar_cluster3d::VoronoiPathFinder::fTopNum3DHits

private

Definition at line 138 of file VoronoiPathFinder_tool.cc.

TH1F* lar_cluster3d::VoronoiPathFinder::fTopNumEdges

private

Definition at line 139 of file VoronoiPathFinder_tool.cc.

TH1F* lar_cluster3d::VoronoiPathFinder::fTopPrimaryLength

private

Definition at line 143 of file VoronoiPathFinder_tool.cc.

---

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

• VoronoiPathFinder_tool.cc