Inheritance diagram for lar_cluster3d::MinSpanTreeAlg:

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

	~MinSpanTreeAlg ()
	Destructor. More...

void	configure (fhicl::ParameterSet const &pset) override
	Interface for configuring the particular algorithm tool. 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 &hitPairList, reco::ClusterParametersList &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...

Public Member Functions inherited from lar_cluster3d::IClusterAlg
virtual	~IClusterAlg () noexcept=default
	Virtual Destructor. 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 , float alpha, kdTree::KdTreeNode &, 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::Geometry const *	m_geometry

PrincipalComponentsAlg	m_pcaAlg

kdTree	m_kdTree

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

Additional Inherited Members
Public Types inherited from lar_cluster3d::IClusterAlg
enum	TimeValues { BUILDTHREEDHITS = 0, BUILDHITTOHITMAP = 1, RUNDBSCAN = 2, BUILDCLUSTERINFO = 3, PATHFINDING = 4, NUMTIMEVALUES }
	enumerate the possible values for time checking if monitoring timing More...

Detailed Description

Definition at line 43 of file MinSpanTreeAlg_tool.cc.

Member Typedef Documentation

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

private

Definition at line 111 of file MinSpanTreeAlg_tool.cc.

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

private

Definition at line 110 of file MinSpanTreeAlg_tool.cc.

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

private

Definition at line 131 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 130 of file MinSpanTreeAlg_tool.cc.

Constructor & Destructor Documentation

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

explicit

Constructor.

Parameters

pset

Definition at line 148 of file MinSpanTreeAlg_tool.cc.

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

lar_cluster3d::MinSpanTreeAlg::~MinSpanTreeAlg ( )

Destructor.

Definition at line 157 of file MinSpanTreeAlg_tool.cc.

158 {

159 }

Member Function Documentation

void lar_cluster3d::MinSpanTreeAlg::AStar	(	const reco::ClusterHit3D *	startNode,
		const reco::ClusterHit3D *	goalNode,
		float	alpha,
		kdTree::KdTreeNode &	topNode,
		reco::ClusterParameters &	clusterParams
	)		const

private

Algorithm to find shortest path between two 3D hits.

Definition at line 533 of file MinSpanTreeAlg_tool.cc.

 {
     // 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));
 
     alpha = 1.; //std::max(0.5,alpha);
 
     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);
             }
         }
     }
 
     return;
 }

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

private

Definition at line 901 of file MinSpanTreeAlg_tool.cc.

 {
     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++;
                 }
 
                 // How many hits in this chain?
 //                size_t numHits(std::distance(firstHitItr,lastHitItr));
 //                float  minOverlapFraction(0.);
 
 //                if (numHits > 1)
 //                {
 //                    reco::HitPairListPtr::iterator bestMinOverlapItr = std::max_element(firstHitItr,lastHitItr,[](const auto& left, const auto& right){return left->getMinOverlapFraction() < right->getMinOverlapFraction();});
 //
 //                    minOverlapFraction = std::min(0.999*(*bestMinOverlapItr)->getMinOverlapFraction(),0.90);
 //                }
 
                 while(firstHitItr != lastHitItr)
                 {
 //                    if (currentHit->getMinOverlapFraction() > minOverlapFraction) testList.push_back(currentHit); //currentHit->setStatusBit(reco::ClusterHit3D::SKELETONHIT);
 
                     currentHit = *++firstHitItr;
                 }
 
                 firstHitItr = lastHitItr;
             }
 /*
             for(const auto& hit : localHitList)
             {
                 std::cout << "- wires: ";
 
                 for(size_t idx = 0; idx < 3; idx++)
                 {
                     float viewTime = -1.;
 
                     if (hit->getHits()[closestView[idx]]) viewTime = hit->getHits()[closestView[idx]]->getTimeTicks();
 
                     std::cout << closestView[idx] << ":" << hit->getWireIDs()[closestView[idx]].Wire << " - " << viewTime << ", ";
                 }
 
                 bool isSkeleton = hit->getStatusBits() & reco::ClusterHit3D::SKELETONHIT;
 
                 std::cout << "ave time: " << hit->getAvePeakTime() << ", min/max overlap: " << hit->getMinOverlapFraction() << "/" << hit->getMaxOverlapFraction() << ", tagged: " << isSkeleton << std::endl;
 
                 if (isSkeleton) testList.push_back(hit);
             }
 */
             curCluster = testList;
         }
     }
 
     return;
 }

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 208 of file MinSpanTreeAlg_tool.cc.

 {
     /**
      *  @brief Driver for processing input 2D hits, transforming to 3D hits and building lists
      *         of associated 3D hits (candidate 3D clusters)
      */
 
     // 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;
 
     return;
 }

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 70 of file MinSpanTreeAlg_tool.cc.

71 {return;}

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

overridevirtual

Interface for configuring the particular algorithm tool.

Parameters

ParameterSet The input set of parameters for configuration

Implements lar_cluster3d::IClusterAlg.

Definition at line 163 of file MinSpanTreeAlg_tool.cc.

 {
     m_enableMonitoring         = pset.get<bool>  ("EnableMonitoring",  true  );
 
     art::ServiceHandle<geo::Geometry const> geometry;
 
     m_geometry = &*geometry;
 
     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_geometry->ChannelToWire(uChannel);
     const geo::WireGeo*      uWireGeo = m_geometry->WirePtr(uWireID[0]);
 
     TVector3 uWireDir = uWireGeo->Direction();
 
     m_wireDir[0].resize(3);
     m_wireDir[0][0] =  uWireDir[0];
     m_wireDir[0][1] = -uWireDir[2];
     m_wireDir[0][2] =  uWireDir[1];
 
     raw::ChannelID_t vChannel(2400);
     std::vector<geo::WireID> vWireID = m_geometry->ChannelToWire(vChannel);
     const geo::WireGeo* vWireGeo = m_geometry->WirePtr(vWireID[0]);
 
     TVector3 vWireDir = vWireGeo->Direction();
 
     m_wireDir[1].resize(3);
     m_wireDir[1][0] =  vWireDir[0];
     m_wireDir[1][1] = -vWireDir[2];
     m_wireDir[1][2] =  vWireDir[1];
 
     m_wireDir[2].resize(3);
     m_wireDir[2][0] = 0.;
     m_wireDir[2][1] = 0.;
     m_wireDir[2][2] = 1.;
 
     m_clusterBuilder = art::make_tool<lar_cluster3d::IClusterParametersBuilder>(pset.get<fhicl::ParameterSet>("ClusterParamsBuilder"));
 
     return;
 }

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 722 of file MinSpanTreeAlg_tool.cc.

 {
     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 694 of file MinSpanTreeAlg_tool.cc.

 {
     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]);
 
     // Manhatten distance
     //return std::fabs(deltaNode[0]) + std::fabs(deltaNode[1]) + std::fabs(deltaNode[2]);
 /*
     // Chebyshev distance
     // We look for maximum distance by wires...
 
     // Now go through the hits and compare view by view to look for delta wire and tigher constraint on delta t
     int wireDeltas[] = {0,0,0};
 
     for(size_t idx = 0; idx < 3; idx++)
         wireDeltas[idx] = std::abs(int(node1->getWireIDs()[idx].Wire) - int(node2->getWireIDs()[idx].Wire));
 
     // put wire deltas in order...
     std::sort(wireDeltas, wireDeltas + 3);
 
     return std::sqrt(deltaNode[0]*deltaNode[0] + 0.09 * float(wireDeltas[2]*wireDeltas[2]));
  */
 }

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

private

Algorithm to find the best path through the given cluster.

Definition at line 376 of file MinSpanTreeAlg_tool.cc.

 {
     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();
     }
 
     return;
 }

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

private

Alternative version of FindBestPathInCluster utilizing an A* algorithm.

Definition at line 442 of file MinSpanTreeAlg_tool.cc.

 {
     // 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;
     
                 // Call the AStar function to try to find the best path...
 //                AStar(startHit,stopHit,alpha,topNode,clusterParams);
     
                 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();
     }
 
     return;
 }

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 76 of file MinSpanTreeAlg_tool.cc.

76 {return m_timeVector.at(index);}

lar_cluster3d::MinSpanTreeAlg::m_timeVector

std::vector< float > m_timeVector

Definition: MinSpanTreeAlg_tool.cc:137

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 641 of file MinSpanTreeAlg_tool.cc.

 {
     // 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);
     }
 
     return;
 }

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

private

Prune the obvious ambiguous hits.

Definition at line 768 of file MinSpanTreeAlg_tool.cc.

 {
 
     // 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);
 
         //        reco::ClusterParameters* otherCluster;
         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();
                         //                        otherCluster     = clusterHitMap.first;
                         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());
 
     return;
 }

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

private

Definition at line 620 of file MinSpanTreeAlg_tool.cc.

 {
     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);
 
     return;
 }

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

private

Driver for Prim's algorithm.

Definition at line 254 of file MinSpanTreeAlg_tool.cc.

 {
     // 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)) curEdgeList.push_back(reco::EdgeTuple(lastAddedHit,pair.second,pair.first));
         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();
     }
 
     return;
 }

Member Data Documentation

std::unique_ptr<lar_cluster3d::IClusterParametersBuilder> lar_cluster3d::MinSpanTreeAlg::m_clusterBuilder

private

Common cluster builder tool.

Definition at line 145 of file MinSpanTreeAlg_tool.cc.

bool lar_cluster3d::MinSpanTreeAlg::m_enableMonitoring

private

Data members to follow.

Definition at line 136 of file MinSpanTreeAlg_tool.cc.

geo::Geometry const* lar_cluster3d::MinSpanTreeAlg::m_geometry

private

Definition at line 140 of file MinSpanTreeAlg_tool.cc.

kdTree lar_cluster3d::MinSpanTreeAlg::m_kdTree

private

Definition at line 143 of file MinSpanTreeAlg_tool.cc.

PrincipalComponentsAlg lar_cluster3d::MinSpanTreeAlg::m_pcaAlg

private

Definition at line 142 of file MinSpanTreeAlg_tool.cc.

std::vector<float> lar_cluster3d::MinSpanTreeAlg::m_timeVector

mutableprivate

Definition at line 137 of file MinSpanTreeAlg_tool.cc.

std::vector<std::vector<float> > lar_cluster3d::MinSpanTreeAlg::m_wireDir

private

Definition at line 138 of file MinSpanTreeAlg_tool.cc.

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

MinSpanTreeAlg_tool.cc

Public Member Functions

Private Types

Private Member Functions

Private Attributes

Additional Inherited Members

Detailed Description

Member Typedef Documentation

Constructor & Destructor Documentation

Member Function Documentation

Member Data Documentation