lar_cluster3d::ClusterPathFinder Class Reference

Inheritance diagram for lar_cluster3d::ClusterPathFinder:

Public Member Functions
	ClusterPathFinder (const fhicl::ParameterSet &)
	Constructor. More...
	~ClusterPathFinder ()
	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::ClusterParametersList::iterator positionItr, reco::ClusterParametersList &outputClusterList, int level=0) const
	Use PCA to try to find path in cluster. More...
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

Private Attributes
bool	m_enableMonitoring
	Data members to follow. More...
size_t	m_minTinyClusterSize
	Minimum size for a "tiny" cluster. More...
float	m_timeToProcess
std::unique_ptr < lar_cluster3d::IClusterAlg >	m_clusterAlg
	Algorithm to do 3D space point clustering. More...
PrincipalComponentsAlg	m_pcaAlg

Detailed Description

Definition at line 36 of file ClusterPathFinder_tool.cc.

Member Typedef Documentation

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

private

Definition at line 97 of file ClusterPathFinder_tool.cc.

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

private

Definition at line 96 of file ClusterPathFinder_tool.cc.

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

private

Definition at line 94 of file ClusterPathFinder_tool.cc.

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

private

Definition at line 95 of file ClusterPathFinder_tool.cc.

Constructor & Destructor Documentation

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

explicit

Constructor.

Parameters

pset

Definition at line 112 of file ClusterPathFinder_tool.cc.

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

lar_cluster3d::ClusterPathFinder::~ClusterPathFinder

(

)

Destructor.

Definition at line 120 of file ClusterPathFinder_tool.cc.

{
}

Member Function Documentation

reco::ClusterParametersList::iterator lar_cluster3d::ClusterPathFinder::breakIntoTinyBits ( reco::ClusterParameters &  cluster, 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 224 of file ClusterPathFinder_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::cout << indent << ">>> breakIntoTinyBits with " << clusterToBreak.getHitPairListPtr().size() << " input 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(clusterToBreak, level+2);

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

    // Create a rough cut intended to tell us when we've reached the land of diminishing returns
    if (clusterToBreak.getBestEdgeList().size()   > 6 &&
        clusterToBreak.getHitPairListPtr().size() > size_t(2 * minimumClusterSize))
    {
        // 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
        m_pcaAlg.PCAAnalysis_calc3DDocas(clusHitPairVector, fullPCA);

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

        // Now we use the convex hull vertex points to form split points for breaking up the incoming cluster
        reco::EdgeList&                        bestEdgeList = clusterToBreak.getBestEdgeList();
        std::vector<const reco::ClusterHit3D*> vertexHitVec;

        std::cout << indent << "+> Breaking cluster, convex hull has " << bestEdgeList.size() << " edges to work with" << std::endl;

        for(const auto& edge : bestEdgeList)
        {
            vertexHitVec.push_back(std::get<0>(edge));
            vertexHitVec.push_back(std::get<1>(edge));
        }

        // Sort this vector, we aren't worried about duplicates right now...
        std::sort(vertexHitVec.begin(),vertexHitVec.end(),[](const auto& left, const auto& right){return left->getArclenToPoca() < right->getArclenToPoca();});

        // 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;
        reco::HitPairListPtr::iterator firstHitItr = clusHitPairVector.begin();

        for(const auto& hit3D : vertexHitVec)
        {
            reco::HitPairListPtr::iterator vertexItr = std::find(firstHitItr,clusHitPairVector.end(),hit3D);

            if (vertexItr == clusHitPairVector.end())
            {
                std::cout << indent << ">>>>>>>>>>>>>>>>> Hit not found in input list, cannot happen? <<<<<<<<<<<<<<<<<<<"  << std::endl;
                break;
            }

            std::cout << indent << "+> -- Distance from first to current vertex point: " << std::distance(firstHitItr,vertexItr) << " first: " << *firstHitItr << ", vertex: " << *vertexItr;

            // Require a minimum number of points...
            if (std::distance(firstHitItr,vertexItr) > minimumClusterSize)
            {
                vertexPairList.emplace_back(Hit3DItrPair(firstHitItr,vertexItr));
                firstHitItr = vertexItr;

                std::cout << " ++ made pair ";
            }

            std::cout << std::endl;
        }

        // Not done if there is distance from first to end of list
        if (std::distance(firstHitItr,clusHitPairVector.end()) > 0)
        {
            std::cout << indent << "+> loop over vertices done, remant distance: " << std::distance(firstHitItr,clusHitPairVector.end()) << std::endl;

            // In the event we don't have the minimum number of hits we simply extend the last pair
            if (!vertexPairList.empty() && std::distance(firstHitItr,clusHitPairVector.end()) < minimumClusterSize)
                vertexPairList.back().second = clusHitPairVector.end();
            else
                vertexPairList.emplace_back(Hit3DItrPair(firstHitItr,clusHitPairVector.end()));
        }

        std::cout << indent << "+> ---> breaking cluster into " << vertexPairList.size() << " subclusters" << std::endl;

        if (vertexPairList.size() > 1)
        {
            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
                m_pcaAlg.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;

                    // Need to check if the PCA direction has been reversed
                    Eigen::Vector3f fullPrimaryVec(fullPCA.getEigenVectors().row(0));
                    Eigen::Vector3f newPrimaryVec(newFullPCA.getEigenVectors().row(0));

                    // If the PCA's are opposite the flip the axes
                    if (fullPrimaryVec.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
                    clusterParams.getSkeletonPCA() = clusterParams.getFullPCA();

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

    // 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);

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

private

Definition at line 416 of file ClusterPathFinder_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();

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

    // 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      = clusterParameters.getHit3DToEdgeMap();
        reco::EdgeList&       bestEdgeList = clusterParameters.getBestEdgeList();

        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;
        }
    }

    return;
}

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

private

Definition at line 550 of file ClusterPathFinder_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
    const Eigen::Vector3f& pcaCenter = pca.getAvePosition();
    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);});

    // 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" << std::endl;

    return;
}

float lar_cluster3d::ClusterPathFinder::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 647 of file ClusterPathFinder_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::ClusterPathFinder::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 126 of file ClusterPathFinder_tool.cc.

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

    m_timeToProcess = 0.;

    return;
}

float lar_cluster3d::ClusterPathFinder::getTimeToExecute ( ) const

inlineoverridevirtual

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

Implements lar_cluster3d::IClusterModAlg.

Definition at line 72 of file ClusterPathFinder_tool.cc.

{return m_timeToProcess;}

void lar_cluster3d::ClusterPathFinder::initializeHistograms ( art::TFileDirectory &  )

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 137 of file ClusterPathFinder_tool.cc.

{
    return;
}

void lar_cluster3d::ClusterPathFinder::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 142 of file ClusterPathFinder_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 (m_enableMonitoring) 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++ << 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() > m_minTinyClusterSize)
        {
            // Get an interim cluster list
            reco::ClusterParametersList reclusteredParameters;

            // Call the main workhorse algorithm for building the local version of candidate 3D clusters
            m_clusterAlg->Cluster3DHits(clusterParameters.getHitPairListPtr(), reclusteredParameters);

            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" << std::endl;

                    // Break our cluster into smaller elements...
                    breakIntoTinyBits(cluster, 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);
                }
            }
        }

        // Go to next cluster parameters object
        clusterParametersListItr++;
    }

    if (m_enableMonitoring)
    {
        theClockBuildClusters.stop();

        m_timeToProcess = theClockBuildClusters.accumulated_real_time();
    }

    mf::LogDebug("Cluster3D") << ">>>>> Cluster Path finding done" << std::endl;

    return;
}

Member Data Documentation

std::unique_ptr<lar_cluster3d::IClusterAlg> lar_cluster3d::ClusterPathFinder::m_clusterAlg

private

Algorithm to do 3D space point clustering.

Definition at line 108 of file ClusterPathFinder_tool.cc.

bool lar_cluster3d::ClusterPathFinder::m_enableMonitoring

private

Data members to follow.

Definition at line 104 of file ClusterPathFinder_tool.cc.

size_t lar_cluster3d::ClusterPathFinder::m_minTinyClusterSize

private

Minimum size for a "tiny" cluster.

Definition at line 105 of file ClusterPathFinder_tool.cc.

PrincipalComponentsAlg lar_cluster3d::ClusterPathFinder::m_pcaAlg

private

Definition at line 109 of file ClusterPathFinder_tool.cc.

float lar_cluster3d::ClusterPathFinder::m_timeToProcess

mutableprivate

Definition at line 106 of file ClusterPathFinder_tool.cc.

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

• ClusterPathFinder_tool.cc