kdTree class definiton More...

#include "kdTree.h"

Classes
class	KdTreeNode
	define a kd tree node More...

Public Types
using	KdTreeNodeVec = std::vector< KdTreeNode >

using	KdTreeNodeList = std::list< KdTreeNode >

using	Hit3DVec = std::vector< const reco::ClusterHit3D * >

using	CandPair = std::pair< double, const reco::ClusterHit3D * >

using	CandPairList = std::list< CandPair >

Public Member Functions
	kdTree ()
	Default Constructor. More...

	kdTree (fhicl::ParameterSet const &pset)
	Constructor. More...

	~kdTree ()
	Destructor. More...

void	configure (fhicl::ParameterSet const &pset)
	Configure our kdTree... More...

KdTreeNode &	BuildKdTree (Hit3DVec::iterator, Hit3DVec::iterator, KdTreeNodeList &, int depth=0) const
	Given an input set of ClusterHit3D objects, build a kd tree structure. More...

size_t	FindNearestNeighbors (const reco::ClusterHit3D *, const KdTreeNode &, CandPairList &, float &) const

bool	FindEntry (const reco::ClusterHit3D *, const KdTreeNode &, CandPairList &, float &, bool &, int) const

bool	FindEntryBrute (const reco::ClusterHit3D *, const KdTreeNode &, int) const

KdTreeNode	BuildKdTree (const reco::HitPairList &, KdTreeNodeList &) const
	Given an input HitPairList, build out the map of nearest neighbors. More...

KdTreeNode	BuildKdTree (const reco::HitPairListPtr &, KdTreeNodeList &) const
	Given an input HitPairListPtr, build out the map of nearest neighbors. More...

float	getTimeToExecute () const

Private Member Functions
bool	consistentPairs (const reco::ClusterHit3D pair1, const reco::ClusterHit3D pair2, float &hitSeparation) const
	The bigger question: are two pairs of hits consistent? More...

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

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

Private Attributes
bool	fEnableMonitoring

float	fTimeToBuild

float	fPairSigmaPeakTime
	Consider hits consistent if "significance" less than this. More...

float	fRefLeafBestDist
	Set neighborhood distance to this when ref leaf found. More...

int	fMaxWireDeltas
	Maximum total number of delta wires. More...

Detailed Description

kdTree class definiton

Definition at line 31 of file kdTree.h.

Member Typedef Documentation

using lar_cluster3d::kdTree::CandPair = std::pair<double, const reco::ClusterHit3D*>

Definition at line 83 of file kdTree.h.

using lar_cluster3d::kdTree::CandPairList = std::list<CandPair>

Definition at line 84 of file kdTree.h.

using lar_cluster3d::kdTree::Hit3DVec = std::vector<const reco::ClusterHit3D*>

Definition at line 70 of file kdTree.h.

using lar_cluster3d::kdTree::KdTreeNodeList = std::list<KdTreeNode>

Definition at line 69 of file kdTree.h.

using lar_cluster3d::kdTree::KdTreeNodeVec = std::vector<KdTreeNode>

Definition at line 68 of file kdTree.h.

Constructor & Destructor Documentation

lar_cluster3d::kdTree::kdTree ( )

inline

Default Constructor.

Definition at line 36 of file kdTree.h.

       : fEnableMonitoring(false)
       , fTimeToBuild(0.)
       , fPairSigmaPeakTime(0.)
       , fRefLeafBestDist(0.)
       , fMaxWireDeltas(0)
     {}

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

Constructor.

Parameters

pset

Definition at line 24 of file kdTree.cxx.

References configure().

   {
     this->configure(pset);
   }

lar_cluster3d::kdTree::~kdTree ( )

Destructor.

Definition at line 31 of file kdTree.cxx.

31 {}

Member Function Documentation

kdTree::KdTreeNode & lar_cluster3d::kdTree::BuildKdTree	(	Hit3DVec::iterator	first,
		Hit3DVec::iterator	last,
		KdTreeNodeList &	kdTreeNodeContainer,
		int	depth = `0`
	)		const

Given an input set of ClusterHit3D objects, build a kd tree structure.

Parameters

hitPairList	The input list of 3D hits to run clustering on
kdTreeVec	Container for the nodes

Definition at line 109 of file kdTree.cxx.

References reco::ClusterHit3D::getPosition(), art::left(), art::right(), lar_cluster3d::kdTree::KdTreeNode::xPlane, lar_cluster3d::kdTree::KdTreeNode::yPlane, and lar_cluster3d::kdTree::KdTreeNode::zPlane.

Referenced by BuildKdTree(), lar_cluster3d::DBScanAlg::Cluster3DHits(), lar_cluster3d::MinSpanTreeAlg::Cluster3DHits(), and lar_cluster3d::MSTPathFinder::ModifyClusters().

   {
     // Ok, so if the input list is more than one element then we have work to do... but if less then handle end condition
     if (std::distance(first, last) < 2) {
       if (first != last)
         kdTreeNodeContainer.emplace_back(*first);
       else
         kdTreeNodeContainer.emplace_back(KdTreeNode());
     }
     // Otherwise we need to keep splitting...
     else {
       // First task is to find "d" with the largest range. We need to find the min/max for the four dimensions
       std::pair<Hit3DVec::iterator, Hit3DVec::iterator> minMaxXPair = std::minmax_element(
         first, last, [](const reco::ClusterHit3D* left, const reco::ClusterHit3D* right) {
           return left->getPosition()[0] < right->getPosition()[0];
         });
       std::pair<Hit3DVec::iterator, Hit3DVec::iterator> minMaxYPair = std::minmax_element(
         first, last, [](const reco::ClusterHit3D* left, const reco::ClusterHit3D* right) {
           return left->getPosition()[1] < right->getPosition()[1];
         });
       std::pair<Hit3DVec::iterator, Hit3DVec::iterator> minMaxZPair = std::minmax_element(
         first, last, [](const reco::ClusterHit3D* left, const reco::ClusterHit3D* right) {
           return left->getPosition()[2] < right->getPosition()[2];
         });
 
       std::vector<float> rangeVec(3, 0.);
 
       rangeVec[0] =
         (*minMaxXPair.second)->getPosition()[0] - (*minMaxXPair.first)->getPosition()[0];
       rangeVec[1] =
         (*minMaxYPair.second)->getPosition()[1] - (*minMaxYPair.first)->getPosition()[1];
       rangeVec[2] =
         (*minMaxZPair.second)->getPosition()[2] - (*minMaxZPair.first)->getPosition()[2];
 
       std::vector<float>::iterator maxRangeItr = std::max_element(rangeVec.begin(), rangeVec.end());
 
       size_t maxRangeIdx = std::distance(rangeVec.begin(), maxRangeItr);
 
       // Sort the list so we can do the split
       std::sort(first, last, [maxRangeIdx](const auto& left, const auto& right) {
         return left->getPosition()[maxRangeIdx] < right->getPosition()[maxRangeIdx];
       });
 
       size_t middleElem = std::distance(first, last) / 2;
       Hit3DVec::iterator middleItr = first;
 
       std::advance(middleItr, middleElem);
 
       // Take care of the special case where the value of the median may be repeated so we actually want to make sure we point at the first occurence
       if (std::distance(first, middleItr) > 1) {
         while (middleItr != first + 1) {
           if (!((*(middleItr - 1))->getPosition()[maxRangeIdx] <
                 (*middleItr)->getPosition()[maxRangeIdx]))
             middleItr--;
           else
             break;
         }
       }
 
       KdTreeNode::SplitAxis axis[] = {KdTreeNode::xPlane, KdTreeNode::yPlane, KdTreeNode::zPlane};
       float axisVal = 0.5 * ((*middleItr)->getPosition()[maxRangeIdx] +
                              (*(middleItr - 1))->getPosition()[maxRangeIdx]);
       KdTreeNode& leftNode = BuildKdTree(first, middleItr, kdTreeNodeContainer, depth + 1);
       KdTreeNode& rightNode = BuildKdTree(middleItr, last, kdTreeNodeContainer, depth + 1);
 
       kdTreeNodeContainer.push_back(KdTreeNode(axis[maxRangeIdx], axisVal, leftNode, rightNode));
     }
 
     return kdTreeNodeContainer.back();
   }

kdTree::KdTreeNode lar_cluster3d::kdTree::BuildKdTree	(	const reco::HitPairList &	hitPairList,
		KdTreeNodeList &	kdTreeNodeContainer
	)		const

Given an input HitPairList, build out the map of nearest neighbors.

Definition at line 48 of file kdTree.cxx.

References BuildKdTree(), fEnableMonitoring, and fTimeToBuild.

   {
     // The first task is to build the kd tree
     cet::cpu_timer theClockBuildNeighborhood;
 
     if (fEnableMonitoring) theClockBuildNeighborhood.start();
 
     // The input is a list and we need to copy to a vector so we can sort ranges
     Hit3DVec hit3DVec;
 
     hit3DVec.reserve(hitPairList.size());
 
     for (const auto& hit : hitPairList)
       hit3DVec.emplace_back(&hit);
 
     KdTreeNode topNode = BuildKdTree(hit3DVec.begin(), hit3DVec.end(), kdTreeNodeContainer);
 
     if (fEnableMonitoring) {
       theClockBuildNeighborhood.stop();
       fTimeToBuild = theClockBuildNeighborhood.accumulated_real_time();
     }
 
     return topNode;
   }

kdTree::KdTreeNode lar_cluster3d::kdTree::BuildKdTree	(	const reco::HitPairListPtr &	hitPairList,
		KdTreeNodeList &	kdTreeNodeContainer
	)		const

Given an input HitPairListPtr, build out the map of nearest neighbors.

Definition at line 75 of file kdTree.cxx.

References BuildKdTree(), fEnableMonitoring, fTimeToBuild, reco::ClusterHit3D::HITINVIEW0, reco::ClusterHit3D::HITINVIEW1, and reco::ClusterHit3D::HITINVIEW2.

   {
 
     // The first task is to build the kd tree
     cet::cpu_timer theClockBuildNeighborhood;
 
     if (fEnableMonitoring) theClockBuildNeighborhood.start();
 
     // The input is a list and we need to copy to a vector so we can sort ranges
     //Hit3DVec hit3DVec{std::begin(hitPairList),std::end(hitPairList)};
     Hit3DVec hit3DVec;
 
     hit3DVec.reserve(hitPairList.size());
 
     for (const auto& hit3D : hitPairList) {
       // Make sure all the bits used by the clustering stage have been cleared
       hit3D->clearStatusBits(~(reco::ClusterHit3D::HITINVIEW0 | reco::ClusterHit3D::HITINVIEW1 |
                                reco::ClusterHit3D::HITINVIEW2));
       for (const auto& hit2D : hit3D->getHits())
         if (hit2D) hit2D->clearStatusBits(0xFFFFFFFF);
       hit3DVec.emplace_back(hit3D);
     }
 
     KdTreeNode topNode = BuildKdTree(hit3DVec.begin(), hit3DVec.end(), kdTreeNodeContainer);
 
     if (fEnableMonitoring) {
       theClockBuildNeighborhood.stop();
       fTimeToBuild = theClockBuildNeighborhood.accumulated_real_time();
     }
 
     return topNode;
   }

void lar_cluster3d::kdTree::configure ( fhicl::ParameterSet const & pset )

Configure our kdTree...

Parameters

ParameterSet The input set of parameters for configuration

Definition at line 35 of file kdTree.cxx.

References fEnableMonitoring, fMaxWireDeltas, fPairSigmaPeakTime, fRefLeafBestDist, fTimeToBuild, and fhicl::ParameterSet::get().

Referenced by kdTree().

   {
     fEnableMonitoring = pset.get<bool>("EnableMonitoring", true);
     fPairSigmaPeakTime = pset.get<float>("PairSigmaPeakTime", 3.);
     fRefLeafBestDist = pset.get<float>("RefLeafBestDist", 0.5);
     fMaxWireDeltas = pset.get<int>("MaxWireDeltas", 3);
 
     fTimeToBuild = 0;
 
     return;
   }

bool lar_cluster3d::kdTree::consistentPairs	(	const reco::ClusterHit3D *	pair1,
		const reco::ClusterHit3D *	pair2,
		float &	hitSeparation
	)		const

private

The bigger question: are two pairs of hits consistent?

Definition at line 297 of file kdTree.cxx.

References util::abs(), DistanceBetweenNodesYZ(), fPairSigmaPeakTime, reco::ClusterHit3D::getAvePeakTime(), reco::ClusterHit3D::getSigmaPeakTime(), and reco::ClusterHit3D::getWireIDs().

Referenced by FindEntry(), and FindNearestNeighbors().

   {
     // Strategy: We consider comparing "hit pairs" which may consist of 2 or 3 actual hits.
     //           Also, if only pairs, they can be U-V, U-W or V-W so we can't assume which views we have
     //           So do a simplified comparison:
     //           1) compare the pair times and require "overlap" (in the sense of hit pair production)
     //           2) look at distance between pairs in each of the wire directions
 
     bool consistent(false);
 
     if (bestDist < std::numeric_limits<float>::max() &&
         pair1->getWireIDs()[0].Cryostat == pair2->getWireIDs()[0].Cryostat &&
         pair1->getWireIDs()[0].TPC == pair2->getWireIDs()[0].TPC) {
       // Loose constraint to weed out the obviously bad combinations
       // So this is not strictly correct but is close enough and should save computation time...
       if (std::fabs(pair1->getAvePeakTime() - pair2->getAvePeakTime()) <
           fPairSigmaPeakTime * (pair1->getSigmaPeakTime() + pair2->getSigmaPeakTime())) {
         int wireDeltas[] = {0, 0, 0};
 
         // Now go through the hits and compare view by view to look for delta wire and tigher constraint on delta t
         for (size_t idx = 0; idx < 3; idx++)
           wireDeltas[idx] =
             std::abs(int(pair1->getWireIDs()[idx].Wire) - int(pair2->getWireIDs()[idx].Wire));
 
         // put wire deltas in order...
         std::sort(wireDeltas, wireDeltas + 3);
 
         // Requirement to be considered a nearest neighbor
         if (wireDeltas[2] < 3) {
           float hitSeparation = std::max(float(0.0001), DistanceBetweenNodesYZ(pair1, pair2));
 
           // Final cut...
           if (hitSeparation < bestDist) {
             bestDist = hitSeparation;
             consistent = true;
           }
         }
       }
     }
 
     return consistent;
   }

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

private

Definition at line 355 of file kdTree.cxx.

References reco::ClusterHit3D::getPosition().

   {
     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]};
     float yzDist2 = deltaNode[1] * deltaNode[1] + deltaNode[2] * deltaNode[2];
     float xDist2 = deltaNode[0] * deltaNode[0];
 
     // Standard euclidean distance
     return std::sqrt(xDist2 + yzDist2);
 
     // 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]));
      */
   }

float lar_cluster3d::kdTree::DistanceBetweenNodesYZ	(	const reco::ClusterHit3D *	node1,
		const reco::ClusterHit3D *	node2
	)		const

private

Definition at line 342 of file kdTree.cxx.

References reco::ClusterHit3D::getPosition().

Referenced by consistentPairs().

   {
     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]};
     float yzDist2 = deltaNode[1] * deltaNode[1] + deltaNode[2] * deltaNode[2];
 
     // Standard euclidean distance
     return std::sqrt(yzDist2);
   }

bool lar_cluster3d::kdTree::FindEntry	(	const reco::ClusterHit3D *	refHit,
		const KdTreeNode &	node,
		CandPairList &	CandPairList,
		float &	bestDist,
		bool &	selfNotFound,
		int	depth
	)		const

Definition at line 224 of file kdTree.cxx.

References consistentPairs(), lar_cluster3d::kdTree::KdTreeNode::getAxisValue(), lar_cluster3d::kdTree::KdTreeNode::getClusterHit3D(), reco::ClusterHit3D::getPosition(), lar_cluster3d::kdTree::KdTreeNode::getSplitAxis(), lar_cluster3d::kdTree::KdTreeNode::isLeafNode(), lar_cluster3d::kdTree::KdTreeNode::leftTree(), and lar_cluster3d::kdTree::KdTreeNode::rightTree().

   {
     bool foundEntry(false);
 
     // If at a leaf then time to decide to add hit or not
     if (node.isLeafNode()) {
       float hitSeparation(0.);
 
       // Is this the droid we are looking for?
       if (refHit == node.getClusterHit3D()) selfNotFound = false;
 
       // This is the tight constraint on the hits
       if (consistentPairs(refHit, node.getClusterHit3D(), hitSeparation)) {
         CandPairList.emplace_back(hitSeparation, node.getClusterHit3D());
 
         if (bestDist < std::numeric_limits<float>::max())
           bestDist = std::max(bestDist, hitSeparation);
         else
           bestDist = std::max(float(0.5), hitSeparation);
       }
 
       foundEntry = !selfNotFound;
     }
     // Otherwise we need to keep searching
     else {
       float refPosition = refHit->getPosition()[node.getSplitAxis()];
 
       if (refPosition < node.getAxisValue()) {
         foundEntry =
           FindEntry(refHit, node.leftTree(), CandPairList, bestDist, selfNotFound, depth + 1);
 
         if (!foundEntry && refPosition + bestDist > node.getAxisValue())
           foundEntry =
             FindEntry(refHit, node.rightTree(), CandPairList, bestDist, selfNotFound, depth + 1);
       }
       else {
         foundEntry =
           FindEntry(refHit, node.rightTree(), CandPairList, bestDist, selfNotFound, depth + 1);
 
         if (!foundEntry && refPosition - bestDist < node.getAxisValue())
           foundEntry =
             FindEntry(refHit, node.leftTree(), CandPairList, bestDist, selfNotFound, depth + 1);
       }
     }
 
     return foundEntry;
   }

bool lar_cluster3d::kdTree::FindEntryBrute	(	const reco::ClusterHit3D *	refHit,
		const KdTreeNode &	node,
		int	depth
	)		const

Definition at line 277 of file kdTree.cxx.

References lar_cluster3d::kdTree::KdTreeNode::getClusterHit3D(), lar_cluster3d::kdTree::KdTreeNode::isLeafNode(), lar_cluster3d::kdTree::KdTreeNode::leftTree(), and lar_cluster3d::kdTree::KdTreeNode::rightTree().

   {
     // If at a leaf then time to decide to add hit or not
     if (node.isLeafNode()) {
       // This is the tight constraint on the hits
       if (refHit == node.getClusterHit3D()) return true;
     }
     // Otherwise we need to keep searching
     else {
       if (FindEntryBrute(refHit, node.leftTree(), depth + 1)) return true;
       if (FindEntryBrute(refHit, node.rightTree(), depth + 1)) return true;
     }
 
     return false;
   }

size_t lar_cluster3d::kdTree::FindNearestNeighbors	(	const reco::ClusterHit3D *	refHit,
		const KdTreeNode &	node,
		CandPairList &	CandPairList,
		float &	bestDist
	)		const

Definition at line 183 of file kdTree.cxx.

References consistentPairs(), fRefLeafBestDist, lar_cluster3d::kdTree::KdTreeNode::getAxisValue(), lar_cluster3d::kdTree::KdTreeNode::getClusterHit3D(), reco::ClusterHit3D::getPosition(), lar_cluster3d::kdTree::KdTreeNode::getSplitAxis(), lar_cluster3d::kdTree::KdTreeNode::isLeafNode(), lar_cluster3d::kdTree::KdTreeNode::leftTree(), and lar_cluster3d::kdTree::KdTreeNode::rightTree().

Referenced by lar_cluster3d::DBScanAlg::Cluster3DHits(), lar_cluster3d::DBScanAlg::expandCluster(), lar_cluster3d::MinSpanTreeAlg::RunPrimsAlgorithm(), and lar_cluster3d::MSTPathFinder::RunPrimsAlgorithm().

   {
     // If at a leaf then time to decide to add hit or not
     if (node.isLeafNode()) {
       // Is this the droid we are looking for?
       if (refHit == node.getClusterHit3D())
         bestDist =
           fRefLeafBestDist; // This distance will grab neighbors with delta wire # = 1 in all three planes
       // This is the tight constraint on the hits
       else if (consistentPairs(refHit, node.getClusterHit3D(), bestDist)) {
         CandPairList.emplace_back(bestDist, node.getClusterHit3D());
 
         bestDist = std::max(
           fRefLeafBestDist,
           bestDist); // This insures we will always consider neighbors with wire # changing in 2 planes
       }
     }
     // Otherwise we need to keep searching
     else {
       float refPosition = refHit->getPosition()[node.getSplitAxis()];
 
       if (refPosition < node.getAxisValue()) {
         FindNearestNeighbors(refHit, node.leftTree(), CandPairList, bestDist);
 
         if (refPosition + bestDist > node.getAxisValue())
           FindNearestNeighbors(refHit, node.rightTree(), CandPairList, bestDist);
       }
       else {
         FindNearestNeighbors(refHit, node.rightTree(), CandPairList, bestDist);
 
         if (refPosition - bestDist < node.getAxisValue())
           FindNearestNeighbors(refHit, node.leftTree(), CandPairList, bestDist);
       }
     }
 
     return CandPairList.size();
   }

float lar_cluster3d::kdTree::getTimeToExecute ( ) const

inline

Definition at line 104 of file kdTree.h.

Referenced by lar_cluster3d::DBScanAlg::Cluster3DHits(), lar_cluster3d::MinSpanTreeAlg::Cluster3DHits(), and lar_cluster3d::MSTPathFinder::ModifyClusters().

104 { return fTimeToBuild; }

lar_cluster3d::kdTree::fTimeToBuild

float fTimeToBuild

Definition: kdTree.h:118

Member Data Documentation

bool lar_cluster3d::kdTree::fEnableMonitoring

private

Definition at line 117 of file kdTree.h.

Referenced by BuildKdTree(), and configure().

int lar_cluster3d::kdTree::fMaxWireDeltas

private

Maximum total number of delta wires.

Definition at line 121 of file kdTree.h.

Referenced by configure().

float lar_cluster3d::kdTree::fPairSigmaPeakTime

private

Consider hits consistent if "significance" less than this.

Definition at line 119 of file kdTree.h.

Referenced by configure(), and consistentPairs().

float lar_cluster3d::kdTree::fRefLeafBestDist

private

Set neighborhood distance to this when ref leaf found.

Definition at line 120 of file kdTree.h.

Referenced by configure(), and FindNearestNeighbors().

float lar_cluster3d::kdTree::fTimeToBuild

mutableprivate

Definition at line 118 of file kdTree.h.

Referenced by BuildKdTree(), and configure().

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

larreco/v10_01_12/source/larreco/RecoAlg/Cluster3DAlgs/kdTree.h
larreco/v10_01_12/source/larreco/RecoAlg/Cluster3DAlgs/kdTree.cxx

Classes

Public Types

Public Member Functions

Private Member Functions

Private Attributes

Detailed Description

Member Typedef Documentation

Constructor & Destructor Documentation

Member Function Documentation

Member Data Documentation