Inheritance diagram for lar_cluster3d::MinSpanTreeAlg:

Public Types
enum	TimeValues { BUILDTHREEDHITS = 0, BUILDHITTOHITMAP = 1, RUNDBSCAN = 2, BUILDCLUSTERINFO = 3, PATHFINDING = 4, NUMTIMEVALUES }
	enumerate the possible values for time checking if monitoring timing More...

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

void	Cluster3DHits (reco::HitPairList &hitPairList, reco::ClusterParametersList &clusterParametersList) const override
	Given a set of recob hits, run DBscan to form 3D clusters. More...

void	Cluster3DHits (reco::HitPairListPtr &, reco::ClusterParametersList &) const override
	Given a set of recob hits, run DBscan to form 3D clusters. More...

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

Private Types
using	BestNodeTuple = std::tuple< const reco::ClusterHit3D *, float, float >

using	BestNodeMap = std::unordered_map< const reco::ClusterHit3D *, BestNodeTuple >

using	ChannelStatusVec = std::vector< size_t >
	define data structure for keeping track of channel status More...

using	ChannelStatusByPlaneVec = std::vector< ChannelStatusVec >

Private Member Functions
void	RunPrimsAlgorithm (reco::HitPairList &, kdTree::KdTreeNode &, reco::ClusterParametersList &) const
	Driver for Prim's algorithm. More...

void	PruneAmbiguousHits (reco::ClusterParameters &, reco::Hit2DToClusterMap &) const
	Prune the obvious ambiguous hits. More...

void	FindBestPathInCluster (reco::ClusterParameters &) const
	Algorithm to find the best path through the given cluster. More...

reco::HitPairListPtr	DepthFirstSearch (const reco::EdgeTuple &, const reco::Hit3DToEdgeMap &, float &) const
	a depth first search to find longest branches More...

void	FindBestPathInCluster (reco::ClusterParameters &, kdTree::KdTreeNode &) const
	Alternative version of FindBestPathInCluster utilizing an A* algorithm. More...

void	AStar (const reco::ClusterHit3D , const reco::ClusterHit3D , reco::ClusterParameters &) const
	Algorithm to find shortest path between two 3D hits. More...

void	ReconstructBestPath (const reco::ClusterHit3D *, BestNodeMap &, reco::HitPairListPtr &, reco::EdgeList &) const

float	DistanceBetweenNodes (const reco::ClusterHit3D , const reco::ClusterHit3D ) const

void	LeastCostPath (const reco::EdgeTuple &, const reco::ClusterHit3D *, reco::ClusterParameters &, float &) const
	Find the lowest cost path between two nodes using MST edges. More...

void	CheckHitSorting (reco::ClusterParameters &clusterParams) const

Private Attributes
bool	m_enableMonitoring
	Data members to follow. More...

std::vector< float >	m_timeVector

std::vector< std::vector< float > >	m_wireDir

geo::GeometryCore const *	m_geometry

geo::WireReadoutGeom const *	m_wireReadoutGeom

PrincipalComponentsAlg	m_pcaAlg

kdTree	m_kdTree

std::unique_ptr< lar_cluster3d::IClusterParametersBuilder >	m_clusterBuilder
	Common cluster builder tool. More...

Detailed Description

Definition at line 44 of file MinSpanTreeAlg_tool.cc.

Member Typedef Documentation

using lar_cluster3d::MinSpanTreeAlg::BestNodeMap = std::unordered_map<const reco::ClusterHit3D*, BestNodeTuple>

private

Definition at line 110 of file MinSpanTreeAlg_tool.cc.

using lar_cluster3d::MinSpanTreeAlg::BestNodeTuple = std::tuple<const reco::ClusterHit3D*, float, float>

private

Definition at line 109 of file MinSpanTreeAlg_tool.cc.

using lar_cluster3d::MinSpanTreeAlg::ChannelStatusByPlaneVec = std::vector<ChannelStatusVec>

private

Definition at line 133 of file MinSpanTreeAlg_tool.cc.

using lar_cluster3d::MinSpanTreeAlg::ChannelStatusVec = std::vector<size_t>

private

define data structure for keeping track of channel status

Definition at line 132 of file MinSpanTreeAlg_tool.cc.

Member Enumeration Documentation

enum lar_cluster3d::IClusterAlg::TimeValues

inherited

enumerate the possible values for time checking if monitoring timing

Enumerator
BUILDTHREEDHITS
BUILDHITTOHITMAP
RUNDBSCAN
BUILDCLUSTERINFO
PATHFINDING
NUMTIMEVALUES

Definition at line 49 of file IClusterAlg.h.

                     {
       BUILDTHREEDHITS = 0,
       BUILDHITTOHITMAP = 1,
       RUNDBSCAN = 2,
       BUILDCLUSTERINFO = 3,
       PATHFINDING = 4,
       NUMTIMEVALUES
     };

Constructor & Destructor Documentation

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

explicit

Constructor.

Parameters

pset

Definition at line 152 of file MinSpanTreeAlg_tool.cc.

References geo::WireReadoutGeom::ChannelToWire(), geo::WireGeo::Direction(), Get, art::ServiceHandle< T, SCOPE >::get(), fhicl::ParameterSet::get(), m_clusterBuilder, m_geometry, m_kdTree, m_pcaAlg, m_timeVector, m_wireDir, m_wireReadoutGeom, lar_cluster3d::IClusterAlg::NUMTIMEVALUES, and geo::WireReadoutGeom::WirePtr().

     : m_enableMonitoring{pset.get<bool>("EnableMonitoring", true)}
     , m_geometry{art::ServiceHandle<geo::Geometry const>{}.get()}
     , m_wireReadoutGeom{&art::ServiceHandle<geo::WireReadout const>()->Get()}
     , m_pcaAlg(pset.get<fhicl::ParameterSet>("PrincipalComponentsAlg"))
     , m_kdTree(pset.get<fhicl::ParameterSet>("kdTree"))
   {
     m_timeVector.resize(NUMTIMEVALUES, 0.);
 
     // Determine the unit directon and normal vectors to the wires
     m_wireDir.resize(3);
 
     raw::ChannelID_t uChannel(0);
     std::vector<geo::WireID> uWireID = m_wireReadoutGeom->ChannelToWire(uChannel);
     const geo::WireGeo* uWireGeo = m_wireReadoutGeom->WirePtr(uWireID[0]);
 
     auto const uWireDir = uWireGeo->Direction();
     m_wireDir[0] = {(float)uWireDir.X(), (float)-uWireDir.Z(), (float)uWireDir.Y()};
 
     raw::ChannelID_t vChannel(2400);
     std::vector<geo::WireID> vWireID = m_wireReadoutGeom->ChannelToWire(vChannel);
     const geo::WireGeo* vWireGeo = m_wireReadoutGeom->WirePtr(vWireID[0]);
 
     auto const vWireDir = vWireGeo->Direction();
     m_wireDir[1] = {(float)vWireDir.X(), (float)-vWireDir.Z(), (float)vWireDir.Y()};
     m_wireDir[2] = {0., 0., 1.};
 
     m_clusterBuilder = art::make_tool<lar_cluster3d::IClusterParametersBuilder>(
       pset.get<fhicl::ParameterSet>("ClusterParamsBuilder"));
   }

Member Function Documentation

void lar_cluster3d::MinSpanTreeAlg::AStar	(	const reco::ClusterHit3D *	startNode,
		const reco::ClusterHit3D *	goalNode,
		reco::ClusterParameters &	clusterParams
	)		const

private

Algorithm to find shortest path between two 3D hits.

Definition at line 505 of file MinSpanTreeAlg_tool.cc.

References DistanceBetweenNodes(), reco::ClusterParameters::getBestEdgeList(), reco::ClusterParameters::getBestHitPairListPtr(), reco::ClusterParameters::getHit3DToEdgeMap(), reco::ClusterHit3D::getStatusBits(), reco::ClusterHit3D::PATHCHECKED, ReconstructBestPath(), and reco::ClusterHit3D::setStatusBit().

Referenced by getTimeToExecute().

   {
     // Recover the list of hits and edges
     reco::HitPairListPtr& pathNodeList = clusterParams.getBestHitPairListPtr();
     reco::EdgeList& bestEdgeList = clusterParams.getBestEdgeList();
     reco::Hit3DToEdgeMap& curEdgeMap = clusterParams.getHit3DToEdgeMap();
 
     // Find the shortest path from start to goal using an A* algorithm
     // Keep track of the nodes which have already been evaluated
     reco::HitPairListPtr closedList;
 
     // Keep track of the nodes that have been "discovered" but yet to be evaluated
     reco::HitPairListPtr openList = {startNode};
 
     // Create a map which, for each node, will tell us the node it can be most efficiencly reached from.
     BestNodeMap bestNodeMap;
 
     bestNodeMap[startNode] =
       BestNodeTuple(startNode, 0., DistanceBetweenNodes(startNode, goalNode));
 
     while (!openList.empty()) {
       // The list is not empty so by def we will return something
       reco::HitPairListPtr::iterator currentNodeItr = openList.begin();
 
       // If the list contains more than one element then we need to find the one with the smallest total estimated cost to the end
       if (openList.size() > 1)
         currentNodeItr = std::min_element(
           openList.begin(), openList.end(), [bestNodeMap](const auto& next, const auto& best) {
             return std::get<2>(bestNodeMap.at(next)) < std::get<2>(bestNodeMap.at(best));
           });
 
       // Easier to deal directly with the pointer to the node
       const reco::ClusterHit3D* currentNode = *currentNodeItr;
 
       // Check to see if we have reached the goal and need to evaluate the path
       if (currentNode == goalNode) {
         // The path reconstruction will
         ReconstructBestPath(goalNode, bestNodeMap, pathNodeList, bestEdgeList);
 
         break;
       }
 
       // Otherwise need to keep evaluating
       else {
         openList.erase(currentNodeItr);
         currentNode->setStatusBit(reco::ClusterHit3D::PATHCHECKED);
 
         // Get tuple values for the current node
         const BestNodeTuple& currentNodeTuple = bestNodeMap.at(currentNode);
         float currentNodeScore = std::get<1>(currentNodeTuple);
 
         // Recover the edges associated to the current point
         const reco::EdgeList& curEdgeList = curEdgeMap[currentNode];
 
         for (const auto& curEdge : curEdgeList) {
           const reco::ClusterHit3D* candHit3D = std::get<1>(curEdge);
 
           if (candHit3D->getStatusBits() & reco::ClusterHit3D::PATHCHECKED) continue;
 
           float tentative_gScore = currentNodeScore + std::get<2>(curEdge);
 
           // Have we seen the candidate node before?
           BestNodeMap::iterator candNodeItr = bestNodeMap.find(candHit3D);
 
           if (candNodeItr == bestNodeMap.end()) { openList.push_back(candHit3D); }
           else if (tentative_gScore > std::get<1>(candNodeItr->second))
             continue;
 
           // Make a guess at score to get to target...
           float guessToTarget = DistanceBetweenNodes(candHit3D, goalNode) / 0.3;
 
           bestNodeMap[candHit3D] =
             BestNodeTuple(currentNode, tentative_gScore, tentative_gScore + guessToTarget);
         }
       }
     }
   }

void lar_cluster3d::MinSpanTreeAlg::CheckHitSorting ( reco::ClusterParameters & clusterParams ) const

private

Definition at line 830 of file MinSpanTreeAlg_tool.cc.

References DEFINE_ART_CLASS_TOOL, reco::PrincipalComponents::getEigenVectors(), reco::ClusterParameters::getHitPairListPtr(), reco::ClusterHit3D::getHits(), reco::PrincipalComponents::getSvdOK(), reco::ClusterHit3D::getWireIDs(), m_pcaAlg, m_wireDir, and lar_cluster3d::PrincipalComponentsAlg::PCAAnalysis_3D().

   {
     reco::HitPairListPtr& curCluster = clusterParams.getHitPairListPtr();
 
     // Trial A* here
     if (curCluster.size() > 2) {
       // Do a quick PCA to determine our parameter "alpha"
       reco::PrincipalComponents pca;
       m_pcaAlg.PCAAnalysis_3D(curCluster, pca);
 
       if (pca.getSvdOK()) {
         const Eigen::Vector3f& pcaAxis = pca.getEigenVectors().row(2);
 
         std::vector<size_t> closestPlane = {0, 0, 0};
         std::vector<float> bestAngle = {0., 0., 0.};
 
         for (size_t plane = 0; plane < 3; plane++) {
           const std::vector<float>& wireDir = m_wireDir[plane];
 
           float dotProd =
             std::fabs(pcaAxis[0] * wireDir[0] + pcaAxis[1] * wireDir[1] + pcaAxis[2] * wireDir[2]);
 
           if (dotProd > bestAngle[0]) {
             bestAngle[2] = bestAngle[1];
             closestPlane[2] = closestPlane[1];
             bestAngle[1] = bestAngle[0];
             closestPlane[1] = closestPlane[0];
             closestPlane[0] = plane;
             bestAngle[0] = dotProd;
           }
           else if (dotProd > bestAngle[1]) {
             bestAngle[2] = bestAngle[1];
             closestPlane[2] = closestPlane[1];
             closestPlane[1] = plane;
             bestAngle[1] = dotProd;
           }
           else {
             closestPlane[2] = plane;
             bestAngle[2] = dotProd;
           }
         }
 
         // Get a copy of our 3D hits
         reco::HitPairListPtr localHitList = curCluster;
 
         // Sort the hits
         localHitList.sort(SetCheckHitOrder(closestPlane));
 
         // Ok, let's print it all and take a look
         std::cout << "*****************************************************************************"
                      "***************"
                   << std::endl;
         std::cout << "**>>>>> longest axis: " << closestPlane[0] << ", best angle: " << bestAngle[0]
                   << std::endl;
         std::cout << "**>>>>> second  axis: " << closestPlane[1] << ", best angle: " << bestAngle[1]
                   << std::endl;
         std::cout << " " << std::endl;
 
         reco::HitPairListPtr::iterator firstHitItr = localHitList.begin();
         reco::HitPairListPtr::iterator lastHitItr = localHitList.begin();
 
         size_t bestPlane = closestPlane[0];
 
         reco::HitPairListPtr testList;
 
         while (firstHitItr != localHitList.end()) {
           const reco::ClusterHit3D* currentHit = *firstHitItr;
 
           // Search for the last matching best view hit
           while (lastHitItr != localHitList.end()) {
             // If a different wire on the best view then we're certainly done
             if (currentHit->getWireIDs()[bestPlane] != (*lastHitItr)->getWireIDs()[bestPlane])
               break;
 
             // More subtle test to see if same wire but different hit (being careful of case of no hit)
             if (currentHit->getHits()[bestPlane] && (*lastHitItr)->getHits()[bestPlane] &&
                 currentHit->getHits()[bestPlane] != (*lastHitItr)->getHits()[bestPlane])
               break;
 
             // Yet event more subtle test...
             if ((!(currentHit->getHits()[bestPlane]) && (*lastHitItr)->getHits()[bestPlane]) ||
                 (currentHit->getHits()[bestPlane] && !((*lastHitItr)->getHits()[bestPlane])))
               break;
 
             // Not there yet...
             lastHitItr++;
           }
 
           while (firstHitItr != lastHitItr) {
             currentHit = *++firstHitItr;
           }
 
           firstHitItr = lastHitItr;
         }
         curCluster = testList;
       }
     }
   }

void lar_cluster3d::MinSpanTreeAlg::Cluster3DHits	(	reco::HitPairList &	hitPairList,
		reco::ClusterParametersList &	clusterParametersList
	)		const

overridevirtual

Given a set of recob hits, run DBscan to form 3D clusters.

Parameters

hitPairList	The input list of 3D hits to run clustering on
hitPairClusterMap	A map of hits that have been clustered
clusterParametersList	A list of cluster objects (parameters from associated hits)

Driver for processing input 2D hits, transforming to 3D hits and building lists of associated 3D hits (candidate 3D clusters)

Implements lar_cluster3d::IClusterAlg.

Definition at line 183 of file MinSpanTreeAlg_tool.cc.

References lar_cluster3d::IClusterAlg::BUILDCLUSTERINFO, lar_cluster3d::IClusterAlg::BUILDHITTOHITMAP, lar_cluster3d::kdTree::BuildKdTree(), trkf::fill(), FindBestPathInCluster(), lar_cluster3d::kdTree::getTimeToExecute(), m_clusterBuilder, m_enableMonitoring, m_kdTree, m_timeVector, and RunPrimsAlgorithm().

   {
     // Zero the time vector
     if (m_enableMonitoring) std::fill(m_timeVector.begin(), m_timeVector.end(), 0.);
 
     // DBScan is driven of its "epsilon neighborhood". Computing adjacency within DBScan can be time
     // consuming so the idea is the prebuild the adjaceny map and then run DBScan.
     // The following call does this work
     kdTree::KdTreeNodeList kdTreeNodeContainer;
     kdTree::KdTreeNode topNode = m_kdTree.BuildKdTree(hitPairList, kdTreeNodeContainer);
 
     if (m_enableMonitoring) m_timeVector.at(BUILDHITTOHITMAP) = m_kdTree.getTimeToExecute();
 
     // Run DBScan to get candidate clusters
     RunPrimsAlgorithm(hitPairList, topNode, clusterParametersList);
 
     // 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();
 
     m_clusterBuilder->BuildClusterInfo(clusterParametersList);
 
     if (m_enableMonitoring) {
       theClockBuildClusters.stop();
 
       m_timeVector[BUILDCLUSTERINFO] = theClockBuildClusters.accumulated_real_time();
     }
 
     // Test run the path finding algorithm
     for (auto& clusterParams : clusterParametersList)
       FindBestPathInCluster(clusterParams, topNode);
 
     mf::LogDebug("MinSpanTreeAlg") << ">>>>> Cluster3DHits done, found "
                                    << clusterParametersList.size() << " clusters" << std::endl;
   }

void lar_cluster3d::MinSpanTreeAlg::Cluster3DHits	(	reco::HitPairListPtr &	hitPairList,
		reco::ClusterParametersList &	clusterParametersList
	)		const

inlineoverridevirtual

Given a set of recob hits, run DBscan to form 3D clusters.

Parameters

hitPairListPtr	The input list of 3D hits to run clustering on
clusterParametersList	A list of cluster objects (parameters from associated hits)

Implements lar_cluster3d::IClusterAlg.

Definition at line 63 of file MinSpanTreeAlg_tool.cc.

65 {}

reco::HitPairListPtr lar_cluster3d::MinSpanTreeAlg::DepthFirstSearch	(	const reco::EdgeTuple &	curEdge,
		const reco::Hit3DToEdgeMap &	hitToEdgeMap,
		float &	bestTreeQuality
	)		const

private

a depth first search to find longest branches

Definition at line 663 of file MinSpanTreeAlg_tool.cc.

Referenced by FindBestPathInCluster(), and getTimeToExecute().

   {
     reco::HitPairListPtr hitPairListPtr;
     float bestQuality(0.);
     float curEdgeWeight = std::max(0.3, std::get<2>(curEdge));
     float curEdgeProj(1. / curEdgeWeight);
 
     reco::Hit3DToEdgeMap::const_iterator edgeListItr = hitToEdgeMap.find(std::get<1>(curEdge));
 
     if (edgeListItr != hitToEdgeMap.end()) {
       // The input edge weight has quality factors applied, recalculate just the position difference
       const Eigen::Vector3f& firstHitPos = std::get<0>(curEdge)->getPosition();
       const Eigen::Vector3f& secondHitPos = std::get<1>(curEdge)->getPosition();
       float curEdgeVec[] = {secondHitPos[0] - firstHitPos[0],
                             secondHitPos[1] - firstHitPos[1],
                             secondHitPos[2] - firstHitPos[2]};
       float curEdgeMag = std::sqrt(curEdgeVec[0] * curEdgeVec[0] + curEdgeVec[1] * curEdgeVec[1] +
                                    curEdgeVec[2] * curEdgeVec[2]);
 
       curEdgeMag = std::max(float(0.1), curEdgeMag);
 
       for (const auto& edge : edgeListItr->second) {
         // skip the self reference
         if (std::get<1>(edge) == std::get<0>(curEdge)) continue;
 
         float quality(0.);
 
         reco::HitPairListPtr tempList = DepthFirstSearch(edge, hitToEdgeMap, quality);
 
         if (quality > bestQuality) {
           hitPairListPtr = tempList;
           bestQuality = quality;
           curEdgeProj = 1. / curEdgeMag;
         }
       }
     }
 
     hitPairListPtr.push_front(std::get<1>(curEdge));
 
     bestTreeQuality += bestQuality + curEdgeProj;
 
     return hitPairListPtr;
   }

float lar_cluster3d::MinSpanTreeAlg::DistanceBetweenNodes	(	const reco::ClusterHit3D *	node1,
		const reco::ClusterHit3D *	node2
	)		const

private

Definition at line 650 of file MinSpanTreeAlg_tool.cc.

References reco::ClusterHit3D::getPosition().

Referenced by AStar(), FindBestPathInCluster(), and ReconstructBestPath().

   {
     const Eigen::Vector3f& node1Pos = node1->getPosition();
     const Eigen::Vector3f& node2Pos = node2->getPosition();
     float deltaNode[] = {
       node1Pos[0] - node2Pos[0], node1Pos[1] - node2Pos[1], node1Pos[2] - node2Pos[2]};
 
     // Standard euclidean distance
     return std::sqrt(deltaNode[0] * deltaNode[0] + deltaNode[1] * deltaNode[1] +
                      deltaNode[2] * deltaNode[2]);
   }

void lar_cluster3d::MinSpanTreeAlg::FindBestPathInCluster ( reco::ClusterParameters & curCluster ) const

private

Algorithm to find the best path through the given cluster.

Definition at line 351 of file MinSpanTreeAlg_tool.cc.

References DepthFirstSearch(), util::empty(), reco::ClusterParameters::getBestEdgeList(), reco::ClusterParameters::getHit3DToEdgeMap(), reco::ClusterParameters::getHitPairListPtr(), m_enableMonitoring, m_timeVector, lar_cluster3d::IClusterAlg::PATHFINDING, and util::size().

Referenced by Cluster3DHits(), and getTimeToExecute().

   {
     reco::HitPairListPtr longestCluster;
     float bestQuality(0.);
     float aveNumEdges(0.);
     size_t maxNumEdges(0);
     size_t nIsolatedHits(0);
 
     // Now proceed with building the clusters
     cet::cpu_timer theClockPathFinding;
 
     // Start clocks if requested
     if (m_enableMonitoring) theClockPathFinding.start();
 
     reco::HitPairListPtr& hitPairList = curCluster.getHitPairListPtr();
     reco::Hit3DToEdgeMap& curEdgeMap = curCluster.getHit3DToEdgeMap();
     reco::EdgeList& bestEdgeList = curCluster.getBestEdgeList();
 
     // Do some spelunking...
     for (const auto& hit : hitPairList) {
       if (!curEdgeMap[hit].empty() && curEdgeMap[hit].size() == 1) {
         float quality(0.);
 
         reco::HitPairListPtr tempList =
           DepthFirstSearch(curEdgeMap[hit].front(), curEdgeMap, quality);
 
         tempList.push_front(std::get<0>(curEdgeMap[hit].front()));
 
         if (quality > bestQuality) {
           longestCluster = tempList;
           bestQuality = quality;
         }
 
         nIsolatedHits++;
       }
 
       aveNumEdges += float(curEdgeMap[hit].size());
       maxNumEdges = std::max(maxNumEdges, curEdgeMap[hit].size());
     }
 
     aveNumEdges /= float(hitPairList.size());
     std::cout << "----> # isolated hits: " << nIsolatedHits
               << ", longest branch: " << longestCluster.size()
               << ", cluster size: " << hitPairList.size() << ", ave # edges: " << aveNumEdges
               << ", max: " << maxNumEdges << std::endl;
 
     if (!longestCluster.empty()) {
       hitPairList = longestCluster;
       for (const auto& hit : hitPairList) {
         for (const auto& edge : curEdgeMap[hit])
           bestEdgeList.emplace_back(edge);
       }
 
       std::cout << "        ====> new cluster size: " << hitPairList.size() << std::endl;
     }
 
     if (m_enableMonitoring) {
       theClockPathFinding.stop();
 
       m_timeVector[PATHFINDING] += theClockPathFinding.accumulated_real_time();
     }
   }

void lar_cluster3d::MinSpanTreeAlg::FindBestPathInCluster	(	reco::ClusterParameters &	clusterParams,
		kdTree::KdTreeNode &
	)		const

private

Alternative version of FindBestPathInCluster utilizing an A* algorithm.

Definition at line 414 of file MinSpanTreeAlg_tool.cc.

References util::abs(), DistanceBetweenNodes(), util::empty(), reco::PrincipalComponents::getAvePosition(), reco::ClusterParameters::getBestEdgeList(), reco::ClusterParameters::getBestHitPairListPtr(), reco::PrincipalComponents::getEigenValues(), reco::PrincipalComponents::getEigenVectors(), reco::ClusterParameters::getHit3DToEdgeMap(), reco::ClusterParameters::getHitPairListPtr(), reco::PrincipalComponents::getSvdOK(), LeastCostPath(), art::left(), m_enableMonitoring, m_pcaAlg, m_timeVector, lar_cluster3d::IClusterAlg::PATHFINDING, lar_cluster3d::PrincipalComponentsAlg::PCAAnalysis_3D(), art::right(), and util::size().

   {
     // Set up for timing the function
     cet::cpu_timer theClockPathFinding;
 
     // Start clocks if requested
     if (m_enableMonitoring) theClockPathFinding.start();
 
     // Trial A* here
     if (clusterParams.getHitPairListPtr().size() > 2) {
       // Get references to what we need....
       reco::HitPairListPtr& curCluster = clusterParams.getHitPairListPtr();
       reco::Hit3DToEdgeMap& curEdgeMap = clusterParams.getHit3DToEdgeMap();
 
       // Do a quick PCA to determine our parameter "alpha"
       reco::PrincipalComponents pca;
       m_pcaAlg.PCAAnalysis_3D(curCluster, pca);
 
       // The chances of a failure are remote, still we should check
       if (pca.getSvdOK()) {
         float pcaLen = 3.0 * sqrt(pca.getEigenValues()[2]);
         float pcaWidth = 3.0 * sqrt(pca.getEigenValues()[1]);
         float pcaHeight = 3.0 * sqrt(pca.getEigenValues()[0]);
         const Eigen::Vector3f& pcaCenter = pca.getAvePosition();
         float alpha = std::min(float(1.), std::max(float(0.001), pcaWidth / pcaLen));
 
         // Create a temporary container for the isolated points
         reco::ProjectedPointList isolatedPointList;
 
         // Go through and find the isolated points, for those get the projection to the plane of maximum spread
         for (const auto& hit3D : curCluster) {
           // the definition of an isolated hit is that it only has one associated edge
           if (!curEdgeMap[hit3D].empty() && curEdgeMap[hit3D].size() == 1) {
             Eigen::Vector3f pcaToHitVec(hit3D->getPosition()[0] - pcaCenter(0),
                                         hit3D->getPosition()[1] - pcaCenter(1),
                                         hit3D->getPosition()[2] - pcaCenter(2));
             Eigen::Vector3f pcaToHit = pca.getEigenVectors() * pcaToHitVec;
 
             // This sets x,y where x is the longer spread, y the shorter
             isolatedPointList.emplace_back(pcaToHit(2), pcaToHit(1), hit3D);
           }
         }
 
         std::cout << "************* Finding best path with A* in cluster *****************"
                   << std::endl;
         std::cout << "**> There are " << curCluster.size() << " hits, " << isolatedPointList.size()
                   << " isolated hits, the alpha parameter is " << alpha << std::endl;
         std::cout << "**> PCA len: " << pcaLen << ", wid: " << pcaWidth << ", height: " << pcaHeight
                   << ", ratio: " << pcaHeight / pcaWidth << std::endl;
 
         // If no isolated points then nothing to do...
         if (isolatedPointList.size() > 1) {
           // Sort the point vec by increasing x, if same then by increasing y.
           isolatedPointList.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);
           });
 
           // Ok, get the two most distance points...
           const reco::ClusterHit3D* startHit = std::get<2>(isolatedPointList.front());
           const reco::ClusterHit3D* stopHit = std::get<2>(isolatedPointList.back());
 
           std::cout << "**> Sorted " << isolatedPointList.size()
                     << " hits, longest distance: " << DistanceBetweenNodes(startHit, stopHit)
                     << std::endl;
 
           float cost(std::numeric_limits<float>::max());
 
           LeastCostPath(curEdgeMap[startHit].front(), stopHit, clusterParams, cost);
 
           clusterParams.getBestHitPairListPtr().push_front(startHit);
 
           std::cout << "**> Best path has " << clusterParams.getBestHitPairListPtr().size()
                     << " hits, " << clusterParams.getBestEdgeList().size() << " edges" << std::endl;
         }
       }
       else {
         std::cout << "++++++>>> PCA failure! # hits: " << curCluster.size() << std::endl;
       }
     }
 
     if (m_enableMonitoring) {
       theClockPathFinding.stop();
 
       m_timeVector[PATHFINDING] += theClockPathFinding.accumulated_real_time();
     }
   }

float lar_cluster3d::MinSpanTreeAlg::getTimeToExecute ( TimeValues index ) const

inlineoverridevirtual

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

Implements lar_cluster3d::IClusterAlg.

Definition at line 70 of file MinSpanTreeAlg_tool.cc.

References AStar(), DepthFirstSearch(), FindBestPathInCluster(), m_timeVector, PruneAmbiguousHits(), and RunPrimsAlgorithm().

70 { return m_timeVector.at(index); }

lar_cluster3d::MinSpanTreeAlg::m_timeVector

std::vector< float > m_timeVector

Definition: MinSpanTreeAlg_tool.cc:139

void lar_cluster3d::MinSpanTreeAlg::LeastCostPath	(	const reco::EdgeTuple &	curEdge,
		const reco::ClusterHit3D *	goalNode,
		reco::ClusterParameters &	clusterParams,
		float &	showMeTheMoney
	)		const

private

Find the lowest cost path between two nodes using MST edges.

Definition at line 604 of file MinSpanTreeAlg_tool.cc.

References reco::ClusterParameters::getBestEdgeList(), reco::ClusterParameters::getBestHitPairListPtr(), and reco::ClusterParameters::getHit3DToEdgeMap().

Referenced by FindBestPathInCluster().

   {
     // Recover the mapping between hits and edges
     reco::Hit3DToEdgeMap& curEdgeMap = clusterParams.getHit3DToEdgeMap();
 
     reco::Hit3DToEdgeMap::const_iterator edgeListItr = curEdgeMap.find(std::get<1>(curEdge));
 
     showMeTheMoney = std::numeric_limits<float>::max();
 
     if (edgeListItr != curEdgeMap.end() && !edgeListItr->second.empty()) {
       reco::HitPairListPtr& bestNodeList = clusterParams.getBestHitPairListPtr();
       reco::EdgeList& bestEdgeList = clusterParams.getBestEdgeList();
 
       for (const auto& edge : edgeListItr->second) {
         // skip the self reference
         if (std::get<1>(edge) == std::get<0>(curEdge)) continue;
 
         // Have we found the droid we are looking for?
         if (std::get<1>(edge) == goalNode) {
           bestNodeList.push_back(goalNode);
           bestEdgeList.push_back(edge);
           showMeTheMoney = std::get<2>(edge);
           break;
         }
 
         // Keep searching, it is out there somewhere...
         float currentCost(0.);
 
         LeastCostPath(edge, goalNode, clusterParams, currentCost);
 
         if (currentCost < std::numeric_limits<float>::max()) {
           showMeTheMoney = std::get<2>(edge) + currentCost;
           break;
         }
       }
     }
 
     if (showMeTheMoney < std::numeric_limits<float>::max()) {
       clusterParams.getBestHitPairListPtr().push_front(std::get<1>(curEdge));
       clusterParams.getBestEdgeList().push_front(curEdge);
     }
   }

void lar_cluster3d::MinSpanTreeAlg::PruneAmbiguousHits	(	reco::ClusterParameters &	clusterParams,
		reco::Hit2DToClusterMap &	hit2DToClusterMap
	)		const

private

Prune the obvious ambiguous hits.

Definition at line 709 of file MinSpanTreeAlg_tool.cc.

References reco::ClusterParameters::getHitPairListPtr(), and util::size().

Referenced by getTimeToExecute().

   {
 
     // Recover the HitPairListPtr from the input clusterParams (which will be the
     // only thing that has been provided)
     reco::HitPairListPtr& hitPairVector = clusterParams.getHitPairListPtr();
 
     size_t nStartedWith(hitPairVector.size());
     size_t nRejectedHits(0);
 
     reco::HitPairListPtr goodHits;
 
     // Loop through the hits and try to week out the clearly ambiguous ones
     for (const auto& hit3D : hitPairVector) {
       // Loop to try to remove ambiguous hits
       size_t n2DHitsIn3DHit(0);
       size_t nThisClusterOnly(0);
       size_t nOtherCluster(0);
 
       const std::set<const reco::ClusterHit3D*>* otherClusterHits = 0;
 
       for (const auto& hit2D : hit3D->getHits()) {
         if (!hit2D) continue;
 
         n2DHitsIn3DHit++;
 
         if (hit2DToClusterMap[hit2D].size() < 2)
           nThisClusterOnly = hit2DToClusterMap[hit2D][&clusterParams].size();
         else {
           for (const auto& clusterHitMap : hit2DToClusterMap[hit2D]) {
             if (clusterHitMap.first == &clusterParams) continue;
 
             if (clusterHitMap.second.size() > nOtherCluster) {
               nOtherCluster = clusterHitMap.second.size();
               otherClusterHits = &clusterHitMap.second;
             }
           }
         }
       }
 
       if (n2DHitsIn3DHit < 3 && nThisClusterOnly > 1 && nOtherCluster > 0) {
         bool skip3DHit(false);
 
         for (const auto& otherHit3D : *otherClusterHits) {
           size_t nOther2DHits(0);
 
           for (const auto& otherHit2D : otherHit3D->getHits()) {
             if (!otherHit2D) continue;
 
             nOther2DHits++;
           }
 
           if (nOther2DHits > 2) {
             skip3DHit = true;
             nRejectedHits++;
             break;
           }
         }
 
         if (skip3DHit) continue;
       }
 
       goodHits.emplace_back(hit3D);
     }
 
     std::cout << "###>> Input " << nStartedWith << " hits, rejected: " << nRejectedHits
               << std::endl;
 
     hitPairVector.resize(goodHits.size());
     std::copy(goodHits.begin(), goodHits.end(), hitPairVector.begin());
   }

void lar_cluster3d::MinSpanTreeAlg::ReconstructBestPath	(	const reco::ClusterHit3D *	goalNode,
		BestNodeMap &	bestNodeMap,
		reco::HitPairListPtr &	pathNodeList,
		reco::EdgeList &	bestEdgeList
	)		const

private

Definition at line 585 of file MinSpanTreeAlg_tool.cc.

References DistanceBetweenNodes().

Referenced by AStar().

   {
     while (std::get<0>(bestNodeMap.at(goalNode)) != goalNode) {
       const reco::ClusterHit3D* nextNode = std::get<0>(bestNodeMap[goalNode]);
       reco::EdgeTuple bestEdge =
         reco::EdgeTuple(goalNode, nextNode, DistanceBetweenNodes(goalNode, nextNode));
 
       pathNodeList.push_front(goalNode);
       bestEdgeList.push_front(bestEdge);
 
       goalNode = nextNode;
     }
 
     pathNodeList.push_front(goalNode);
   }

void lar_cluster3d::MinSpanTreeAlg::RunPrimsAlgorithm	(	reco::HitPairList &	hitPairList,
		kdTree::KdTreeNode &	topNode,
		reco::ClusterParametersList &	clusterParametersList
	)		const

private

Driver for Prim's algorithm.

Definition at line 228 of file MinSpanTreeAlg_tool.cc.

References reco::ClusterHit3D::CLUSTERATTACHED, lar_cluster3d::kdTree::FindNearestNeighbors(), reco::ClusterHit3D::getHitChiSquare(), art::left(), m_enableMonitoring, m_kdTree, m_timeVector, art::right(), lar_cluster3d::IClusterAlg::RUNDBSCAN, and reco::ClusterHit3D::setStatusBit().

Referenced by Cluster3DHits(), and getTimeToExecute().

   {
     // If no hits then no work
     if (hitPairList.empty()) return;
 
     // Now proceed with building the clusters
     cet::cpu_timer theClockDBScan;
 
     // Start clocks if requested
     if (m_enableMonitoring) theClockDBScan.start();
 
     // Initialization
     size_t clusterIdx(0);
 
     // This will contain our list of edges
     reco::EdgeList curEdgeList;
 
     // Get the first point
     reco::HitPairList::iterator freeHitItr = hitPairList.begin();
     const reco::ClusterHit3D* lastAddedHit = &(*freeHitItr++);
 
     lastAddedHit->setStatusBit(reco::ClusterHit3D::CLUSTERATTACHED);
 
     // Make a cluster...
     clusterParametersList.push_back(reco::ClusterParameters());
 
     // Get an iterator to the first cluster
     reco::ClusterParametersList::iterator curClusterItr = --clusterParametersList.end();
 
     // We use pointers here because the objects they point to will change in the loop below
     reco::Hit3DToEdgeMap* curEdgeMap = &(*curClusterItr).getHit3DToEdgeMap();
     reco::HitPairListPtr* curCluster = &(*curClusterItr).getHitPairListPtr();
 
     // Loop until all hits have been associated to a cluster
     while (1) {
       // and the 3D hit status bits
       lastAddedHit->setStatusBit(reco::ClusterHit3D::CLUSTERATTACHED);
 
       // Purge the current list to get rid of edges which point to hits already in the cluster
       for (reco::EdgeList::iterator curEdgeItr = curEdgeList.begin();
            curEdgeItr != curEdgeList.end();) {
         if (std::get<1>(*curEdgeItr)->getStatusBits() & reco::ClusterHit3D::CLUSTERATTACHED)
           curEdgeItr = curEdgeList.erase(curEdgeItr);
         else
           curEdgeItr++;
       }
 
       // Add the lastUsedHit to the current cluster
       curCluster->push_back(lastAddedHit);
 
       // Set up to find the list of nearest neighbors to the last used hit...
       kdTree::CandPairList CandPairList;
       float bestDistance(1.5); //std::numeric_limits<float>::max());
 
       // And find them... result will be an unordered list of neigbors
       m_kdTree.FindNearestNeighbors(lastAddedHit, topNode, CandPairList, bestDistance);
 
       // Copy edges to the current list (but only for hits not already in a cluster)
       for (auto& pair : CandPairList) {
         if (!(pair.second->getStatusBits() & reco::ClusterHit3D::CLUSTERATTACHED)) {
           double edgeWeight = lastAddedHit->getHitChiSquare() * pair.second->getHitChiSquare();
 
           curEdgeList.push_back(reco::EdgeTuple(lastAddedHit, pair.second, edgeWeight));
         }
       }
 
       // If the edge list is empty then we have a complete cluster
       if (curEdgeList.empty()) {
         std::cout << "-----------------------------------------------------------------------------"
                      "------------"
                   << std::endl;
         std::cout << "**> Cluster idx: " << clusterIdx++ << " has " << curCluster->size() << " hits"
                   << std::endl;
 
         // Look for the next "free" hit
         freeHitItr = std::find_if(freeHitItr, hitPairList.end(), [](const auto& hit) {
           return !(hit.getStatusBits() & reco::ClusterHit3D::CLUSTERATTACHED);
         });
 
         // If at end of input list we are done with all hits
         if (freeHitItr == hitPairList.end()) break;
 
         std::cout << "##################################################################>"
                      "Processing another cluster"
                   << std::endl;
 
         // Otherwise, get a new cluster and set up
         clusterParametersList.push_back(reco::ClusterParameters());
 
         curClusterItr = --clusterParametersList.end();
 
         curEdgeMap = &(*curClusterItr).getHit3DToEdgeMap();
         curCluster = &(*curClusterItr).getHitPairListPtr();
         lastAddedHit = &(*freeHitItr++);
       }
       // Otherwise we are still processing the current cluster
       else {
         // Sort the list of edges by distance
         curEdgeList.sort([](const auto& left, const auto& right) {
           return std::get<2>(left) < std::get<2>(right);
         });
 
         // Populate the map with the edges...
         reco::EdgeTuple& curEdge = curEdgeList.front();
 
         (*curEdgeMap)[std::get<0>(curEdge)].push_back(curEdge);
         (*curEdgeMap)[std::get<1>(curEdge)].push_back(
           reco::EdgeTuple(std::get<1>(curEdge), std::get<0>(curEdge), std::get<2>(curEdge)));
 
         // Update the last hit to be added to the collection
         lastAddedHit = std::get<1>(curEdge);
       }
     }
 
     if (m_enableMonitoring) {
       theClockDBScan.stop();
 
       m_timeVector[RUNDBSCAN] = theClockDBScan.accumulated_real_time();
     }
   }