#include <HGCalCLUEAlgo.h>

Inheritance diagram for HGCalCLUEAlgo:

Classes
struct	Hexel

Public Types
typedef math::XYZPoint	Point
	point in the space More...

Public Types inherited from HGCalClusteringAlgoBase
enum	VerbosityLevel { pDEBUG = 0, pWARNING = 1, pINFO = 2, pERROR = 3 }

Public Member Functions
void	computeThreshold ()

std::vector< reco::BasicCluster >	getClusters (bool) override

Density	getDensity () override

	HGCalCLUEAlgo (const edm::ParameterSet &ps)

void	makeClusters () override

void	populate (const HGCRecHitCollection &hits) override

void	reset () override

	~HGCalCLUEAlgo () override

Public Member Functions inherited from HGCalClusteringAlgoBase
void	getEventSetup (const edm::EventSetup &es)

	HGCalClusteringAlgoBase (VerbosityLevel v, reco::CaloCluster::AlgoId algo)

void	setAlgoId (reco::CaloCluster::AlgoId algo)

void	setVerbosity (VerbosityLevel the_verbosity)

virtual	~HGCalClusteringAlgoBase ()

Static Public Member Functions
static void	fillPSetDescription (edm::ParameterSetDescription &iDesc)

Private Types
typedef KDTreeNodeInfoT< Hexel, 2 >	KDNode

typedef KDTreeLinkerAlgo< Hexel, 2 >	KDTree

Private Member Functions
double	calculateDistanceToHigher (std::vector< KDNode > &) const

double	calculateLocalDensity (std::vector< KDNode > &, KDTree &, const unsigned int) const

math::XYZPoint	calculatePosition (const std::vector< KDNode > &) const

double	distance (const Hexel &pt1, const Hexel &pt2) const

double	distance2 (const Hexel &pt1, const Hexel &pt2) const

int	findAndAssignClusters (std::vector< KDNode > &, KDTree &, double, KDTreeBox &, const unsigned int, std::vector< std::vector< KDNode > > &) const

void	setDensity (const std::vector< KDNode > &nd)

std::vector< size_t >	sort_by_delta (const std::vector< KDNode > &v) const

Private Attributes
std::vector< double >	dEdXweights_

Density	density_

bool	dependSensor_

double	ecut_

double	fcPerEle_

std::vector< double >	fcPerMip_

bool	initialized_

double	kappa_

std::vector< std::vector< std::vector< KDNode > > >	layerClustersPerLayer_

std::vector< std::array< float, 2 > >	maxpos_

std::vector< std::array< float, 2 > >	minpos_

double	noiseMip_

std::vector< double >	nonAgedNoises_

std::vector< std::vector< KDNode > >	points_

std::vector< double >	positionDeltaRho_c_

std::vector< double >	thicknessCorrection_

std::vector< std::vector< double > >	thresholds_

std::vector< double >	thresholdW0_

std::vector< std::vector< double > >	v_sigmaNoise_

std::vector< double >	vecDeltas_

Additional Inherited Members
Static Public Attributes inherited from HGCalClusteringAlgoBase
static const unsigned int	lastLayerEE = 28

static const unsigned int	lastLayerFH = 40

static const unsigned int	maxlayer = 52

Protected Attributes inherited from HGCalClusteringAlgoBase
reco::CaloCluster::AlgoId	algoId_

std::vector< reco::BasicCluster >	clusters_v_

hgcal::RecHitTools	rhtools_

VerbosityLevel	verbosity_

Detailed Description

Definition at line 31 of file HGCalCLUEAlgo.h.

Member Typedef Documentation

typedef KDTreeNodeInfoT<Hexel, 2> HGCalCLUEAlgo::KDNode

private

Definition at line 206 of file HGCalCLUEAlgo.h.

typedef KDTreeLinkerAlgo<Hexel, 2> HGCalCLUEAlgo::KDTree

private

Definition at line 205 of file HGCalCLUEAlgo.h.

typedef math::XYZPoint HGCalCLUEAlgo::Point

point in the space

Definition at line 120 of file HGCalCLUEAlgo.h.

Constructor & Destructor Documentation

HGCalCLUEAlgo::HGCalCLUEAlgo ( const edm::ParameterSet & ps )

inline

Definition at line 34 of file HGCalCLUEAlgo.h.

References HGCalClusteringAlgoBase::maxlayer, and maxpos_.

   : HGCalClusteringAlgoBase(
       (HGCalClusteringAlgoBase::VerbosityLevel)ps.getUntrackedParameter<unsigned int>("verbosity",3),
       reco::CaloCluster::undefined),
      thresholdW0_(ps.getParameter<std::vector<double> >("thresholdW0")),
      positionDeltaRho_c_(ps.getParameter<std::vector<double> >("positionDeltaRho_c")),
      vecDeltas_(ps.getParameter<std::vector<double> >("deltac")),
      kappa_(ps.getParameter<double>("kappa")),
      ecut_(ps.getParameter<double>("ecut")),
      dependSensor_(ps.getParameter<bool>("dependSensor")),
      dEdXweights_(ps.getParameter<std::vector<double> >("dEdXweights")),
      thicknessCorrection_(ps.getParameter<std::vector<double> >("thicknessCorrection")),
      fcPerMip_(ps.getParameter<std::vector<double> >("fcPerMip")),
      fcPerEle_(ps.getParameter<double>("fcPerEle")),
      nonAgedNoises_(ps.getParameter<edm::ParameterSet>("noises").getParameter<std::vector<double> >("values")),
      noiseMip_(ps.getParameter<edm::ParameterSet>("noiseMip").getParameter<double>("value")),
      initialized_(false),
      points_(2*(maxlayer+1)),
      minpos_(2*(maxlayer+1),{ {0.0f,0.0f} }),
      maxpos_(2*(maxlayer+1),{ {0.0f,0.0f} }) {}

HGCalCLUEAlgo::~HGCalCLUEAlgo ( )

inlineoverride

Definition at line 55 of file HGCalCLUEAlgo.h.

References getClusters(), hfClusterShapes_cfi::hits, makeClusters(), and populate().

55 {}

Member Function Documentation

double HGCalCLUEAlgo::calculateDistanceToHigher ( std::vector< KDNode > & nd ) const

private

Definition at line 240 of file HGCalCLUEAlgo.cc.

References data, mps_fire::i, hgcal_clustering::sorted_indices(), mathSSE::sqrt(), and tmp.

Referenced by distance().

                                                                            {
   // sort vector of Hexels by decreasing local density
   std::vector<size_t> &&rs = sorted_indices(nd);
 
   double maxdensity = 0.0;
   int nearestHigher = -1;
 
   if (!rs.empty())
     maxdensity = nd[rs[0]].data.rho;
   else
     return maxdensity;  // there are no hits
   double dist2 = 0.;
   // start by setting delta for the highest density hit to
   // the most distant hit - this is a convention
 
   for (const auto &j : nd) {
     double tmp = distance2(nd[rs[0]].data, j.data);
     if (tmp > dist2) dist2 = tmp;
   }
   nd[rs[0]].data.delta = std::sqrt(dist2);
   nd[rs[0]].data.nearestHigher = nearestHigher;
 
   // now we save the largest distance as a starting point
   const double max_dist2 = dist2;
   const unsigned int nd_size = nd.size();
 
   for (unsigned int oi = 1; oi < nd_size; ++oi) {  // start from second-highest density
     dist2 = max_dist2;
     unsigned int i = rs[oi];
     // we only need to check up to oi since hits
     // are ordered by decreasing density
     // and all points coming BEFORE oi are guaranteed to have higher rho
     // and the ones AFTER to have lower rho
     for (unsigned int oj = 0; oj < oi; ++oj) {
       unsigned int j = rs[oj];
       double tmp = distance2(nd[i].data, nd[j].data);
       if (tmp <= dist2) {  // this "<=" instead of "<" addresses the (rare) case
                            // when there are only two hits
         dist2 = tmp;
         nearestHigher = j;
       }
     }
     nd[i].data.delta = std::sqrt(dist2);
     nd[i].data.nearestHigher = nearestHigher;  // this uses the original unsorted hitlist
   }
   return maxdensity;
 }

double HGCalCLUEAlgo::calculateLocalDensity	(	std::vector< KDNode > &	nd,
		KDTree &	lp,
		const unsigned int	layer
	)		const

private

Definition at line 210 of file HGCalCLUEAlgo.cc.

References data, SoftLeptonByDistance_cfi::distance, runEdmFileComparison::found, mps_fire::i, SiStripPI::max, and KDTreeLinkerAlgo< DATA >::search().

Referenced by distance().

                                                                             {
   double maxdensity = 0.;
   float delta_c;  // maximum search distance (critical distance) for local
                   // density calculation
   if (layer <= lastLayerEE)
     delta_c = vecDeltas_[0];
   else if (layer <= lastLayerFH)
     delta_c = vecDeltas_[1];
   else
     delta_c = vecDeltas_[2];
 
   // for each node calculate local density rho and store it
   for (unsigned int i = 0; i < nd.size(); ++i) {
     // speec up search by looking within +/- delta_c window only
     KDTreeBox search_box(nd[i].dims[0] - delta_c, nd[i].dims[0] + delta_c, nd[i].dims[1] - delta_c,
                          nd[i].dims[1] + delta_c);
     std::vector<KDNode> found;
     lp.search(search_box, found);
     const unsigned int found_size = found.size();
     for (unsigned int j = 0; j < found_size; j++) {
       if (distance(nd[i].data, found[j].data) < delta_c) {
         nd[i].data.rho += (nd[i].data.detid == found[j].data.detid ? 1. : 0.5) * found[j].data.weight;
         maxdensity = std::max(maxdensity, nd[i].data.rho);
       }
     }  // end loop found
   }    // end loop nodes
   return maxdensity;
 }

math::XYZPoint HGCalCLUEAlgo::calculatePosition ( const std::vector< KDNode > & v ) const

private

Definition at line 145 of file HGCalCLUEAlgo.cc.

References data, mps_fire::i, training_settings::idx, cmsBatch::log, and SiStripPI::max.

Referenced by distance().

                                                                               {
   float total_weight = 0.f;
   float x = 0.f;
   float y = 0.f;
 
   unsigned int v_size = v.size();
   unsigned int maxEnergyIndex = 0;
   float maxEnergyValue = 0;
 
   // loop over hits in cluster candidate
   // determining the maximum energy hit
   for (unsigned int i = 0; i < v_size; i++) {
     if (v[i].data.weight > maxEnergyValue) {
       maxEnergyValue = v[i].data.weight;
       maxEnergyIndex = i;
     }
   }
 
   // Si cell or Scintillator. Used to set approach and parameters
   int thick = rhtools_.getSiThickIndex(v[maxEnergyIndex].data.detid);
 
   // for hits within positionDeltaRho_c_ from maximum energy hit
   // build up weight for energy-weighted position
   // and save corresponding hits indices
   std::vector<unsigned int> innerIndices;
   for (unsigned int i = 0; i < v_size; i++) {
     if (thick == -1 || distance2(v[i].data, v[maxEnergyIndex].data) < positionDeltaRho_c_[thick]) {
       innerIndices.push_back(i);
 
       float rhEnergy = v[i].data.weight;
       total_weight += rhEnergy;
       // just fill x, y for scintillator
       // for Si it is overwritten later anyway
       if (thick == -1) {
         x += v[i].data.x * rhEnergy;
         y += v[i].data.y * rhEnergy;
       }
     }
   }
   // just loop on reduced vector of interesting indices
   // to compute log weighting
   if (thick != -1 && total_weight != 0.) {  // Silicon case
     float total_weight_log = 0.f;
     float x_log = 0.f;
     float y_log = 0.f;
     for (auto idx : innerIndices) {
       float rhEnergy = v[idx].data.weight;
       if (rhEnergy == 0.) continue;
       float Wi = std::max(thresholdW0_[thick] + std::log(rhEnergy / total_weight), 0.);
       x_log += v[idx].data.x * Wi;
       y_log += v[idx].data.y * Wi;
       total_weight_log += Wi;
     }
     total_weight = total_weight_log;
     x = x_log;
     y = y_log;
   }
 
   if (total_weight != 0.) {
     auto inv_tot_weight = 1. / total_weight;
     return math::XYZPoint(x * inv_tot_weight, y * inv_tot_weight, v[maxEnergyIndex].data.z);
   }
   return math::XYZPoint(0, 0, 0);
 }

void HGCalCLUEAlgo::computeThreshold ( )

Definition at line 369 of file HGCalCLUEAlgo.cc.

Referenced by reset().

                                      {
   // To support the TDR geometry and also the post-TDR one (v9 onwards), we
   // need to change the logic of the vectors containing signal to noise and
   // thresholds. The first 3 indices will keep on addressing the different
   // thicknesses of the Silicon detectors, while the last one, number 3 (the
   // fourth) will address the Scintillators. This change will support both
   // geometries at the same time.
 
   if (initialized_) return;  // only need to calculate thresholds once
 
   initialized_ = true;
 
   std::vector<double> dummy;
   const unsigned maxNumberOfThickIndices = 3;
   dummy.resize(maxNumberOfThickIndices + 1, 0);  // +1 to accomodate for the Scintillators
   thresholds_.resize(maxlayer, dummy);
   v_sigmaNoise_.resize(maxlayer, dummy);
 
   for (unsigned ilayer = 1; ilayer <= maxlayer; ++ilayer) {
     for (unsigned ithick = 0; ithick < maxNumberOfThickIndices; ++ithick) {
       float sigmaNoise = 0.001f * fcPerEle_ * nonAgedNoises_[ithick] * dEdXweights_[ilayer] /
                          (fcPerMip_[ithick] * thicknessCorrection_[ithick]);
       thresholds_[ilayer - 1][ithick] = sigmaNoise * ecut_;
       v_sigmaNoise_[ilayer - 1][ithick] = sigmaNoise;
     }
     float scintillators_sigmaNoise = 0.001f * noiseMip_ * dEdXweights_[ilayer];
     thresholds_[ilayer - 1][maxNumberOfThickIndices] = ecut_ * scintillators_sigmaNoise;
     v_sigmaNoise_[ilayer - 1][maxNumberOfThickIndices] = scintillators_sigmaNoise;
   }
 }

double HGCalCLUEAlgo::distance	(	const Hexel &	pt1,
		const Hexel &	pt2
	)		const

inlineprivate

Definition at line 231 of file HGCalCLUEAlgo.h.

References calculateDistanceToHigher(), calculateLocalDensity(), calculatePosition(), distance2(), findAndAssignClusters(), setDensity(), and mathSSE::sqrt().

                                                  {  // 2-d distance on the layer (x-y)
     return std::sqrt(distance2(pt1, pt2));
   }

double HGCalCLUEAlgo::distance2	(	const Hexel &	pt1,
		const Hexel &	pt2
	)		const

inlineprivate

Definition at line 226 of file HGCalCLUEAlgo.h.

References PVValHelper::dx, PVValHelper::dy, HGCalCLUEAlgo::Hexel::x, and HGCalCLUEAlgo::Hexel::y.

Referenced by distance().

                                                                     {  // distance squared
     const double dx = pt1.x - pt2.x;
     const double dy = pt1.y - pt2.y;
     return (dx * dx + dy * dy);
   }  // distance squaredq

static void HGCalCLUEAlgo::fillPSetDescription ( edm::ParameterSetDescription & iDesc )

inlinestatic

Definition at line 87 of file HGCalCLUEAlgo.h.

References edm::ParameterSetDescription::add(), and edm::ParameterSetDescription::addUntracked().

                                                                      {
     iDesc.add<std::vector<double>>("thresholdW0", {
         2.9,
         2.9,
         2.9
         });
     iDesc.add<std::vector<double>>("positionDeltaRho_c", {
         1.3,
         1.3,
         1.3
         });
     iDesc.add<std::vector<double>>("deltac", {
         1.3,
         1.3,
         5.0,
         });
     iDesc.add<bool>("dependSensor", true);
     iDesc.add<double>("ecut", 3.0);
     iDesc.add<double>("kappa", 9.0);
     iDesc.addUntracked<unsigned int>("verbosity", 3);
     iDesc.add<std::vector<double>>("dEdXweights",{});
     iDesc.add<std::vector<double>>("thicknessCorrection",{});
     iDesc.add<std::vector<double>>("fcPerMip",{});
     iDesc.add<double>("fcPerEle",0.0);
     edm::ParameterSetDescription descNestedNoises;
     descNestedNoises.add<std::vector<double> >("values", {});
     iDesc.add<edm::ParameterSetDescription>("noises", descNestedNoises);
     edm::ParameterSetDescription descNestedNoiseMIP;
     descNestedNoiseMIP.add<double>("value", 0 );
     iDesc.add<edm::ParameterSetDescription>("noiseMip", descNestedNoiseMIP);
   }

int HGCalCLUEAlgo::findAndAssignClusters	(	std::vector< KDNode > &	,
		KDTree &	,
		double	,
		KDTreeBox &	,
		const unsigned int	,
		std::vector< std::vector< KDNode > > &
	)		const

private

Definition at line 287 of file HGCalCLUEAlgo.cc.

References data, mps_fire::i, LogDebug, and hgcal_clustering::sorted_indices().

Referenced by distance().

                                                                                               {
   // this is called once per layer and endcap...
   // so when filling the cluster temporary vector of Hexels we resize each time
   // by the number  of clusters found. This is always equal to the number of
   // cluster centers...
 
   unsigned int nClustersOnLayer = 0;
   float delta_c;  // critical distance
   if (layer <= lastLayerEE)
     delta_c = vecDeltas_[0];
   else if (layer <= lastLayerFH)
     delta_c = vecDeltas_[1];
   else
     delta_c = vecDeltas_[2];
 
   std::vector<size_t> rs = sorted_indices(nd);  // indices sorted by decreasing rho
   std::vector<size_t> ds = sort_by_delta(nd);   // sort in decreasing distance to higher
 
   const unsigned int nd_size = nd.size();
   for (unsigned int i = 0; i < nd_size; ++i) {
     if (nd[ds[i]].data.delta < delta_c) break;  // no more cluster centers to be looked at
     if (dependSensor_) {
       float rho_c = kappa_ * nd[ds[i]].data.sigmaNoise;
       if (nd[ds[i]].data.rho < rho_c) continue;  // set equal to kappa times noise threshold
 
     } else if (nd[ds[i]].data.rho * kappa_ < maxdensity)
       continue;
 
     nd[ds[i]].data.clusterIndex = nClustersOnLayer;
     if (verbosity_ < pINFO) {
       LogDebug("HGCalCLUEAlgo")
         << "Adding new cluster with index " << nClustersOnLayer
         << "Cluster center is hit " << ds[i] << std::endl;
     }
     nClustersOnLayer++;
   }
 
   // at this point nClustersOnLayer is equal to the number of cluster centers -
   // if it is zero we are  done
   if (nClustersOnLayer == 0) return nClustersOnLayer;
 
   // assign remaining points to clusters, using the nearestHigher set from
   // previous step (always set except
   // for top density hit that is skipped...)
   for (unsigned int oi = 1; oi < nd_size; ++oi) {
     unsigned int i = rs[oi];
     int ci = nd[i].data.clusterIndex;
     if (ci == -1 && nd[i].data.delta < 2. * delta_c) {
       nd[i].data.clusterIndex = nd[nd[i].data.nearestHigher].data.clusterIndex;
     }
   }
 
   // make room in the temporary cluster vector for the additional clusterIndex
   // clusters
   // from this layer
   if (verbosity_ < pINFO) {
     LogDebug("HGCalCLUEAlgo")
       << "resizing cluster vector by " << nClustersOnLayer << std::endl;
   }
   clustersOnLayer.resize(nClustersOnLayer);
 
   // Fill the cluster vector
   for (unsigned int i = 0; i < nd_size; ++i) {
     int ci = nd[i].data.clusterIndex;
     if (ci != -1) {
       clustersOnLayer[ci].push_back(nd[i]);
       if (verbosity_ < pINFO) {
         LogDebug("HGCalCLUEAlgo")
           << "Pushing hit " << i << " into cluster with index " << ci << std::endl;
       }
     }
   }
 
   // prepare the offset for the next layer if there is one
   if (verbosity_ < pINFO) {
     LogDebug("HGCalCLUEAlgo") << "moving cluster offset by " << nClustersOnLayer << std::endl;
   }
   return nClustersOnLayer;
 }

std::vector< reco::BasicCluster > HGCalCLUEAlgo::getClusters ( bool )

overridevirtual

Implements HGCalClusteringAlgoBase.

Definition at line 109 of file HGCalCLUEAlgo.cc.

References AlignmentPI::calculatePosition(), GetRecoTauVFromDQM_MC_cff::cl, data, reco::CaloID::DET_HGCAL_ENDCAP, LogDebug, hgcal_clustering::max_index(), and position.

Referenced by ~HGCalCLUEAlgo().

                                                            {
   reco::CaloID caloID = reco::CaloID::DET_HGCAL_ENDCAP;
   std::vector<std::pair<DetId, float>> thisCluster;
   for (const auto &clsOnLayer : layerClustersPerLayer_) {
     int index = 0;
     for (const auto &cl : clsOnLayer) {
       double energy = 0;
       Point position;
       // Will save the maximum density hit of the cluster
       size_t rsmax = max_index(cl);
       position = calculatePosition(cl);  // energy-weighted position
       for (const auto &it : cl) {
         energy += it.data.weight;
         thisCluster.emplace_back(it.data.detid, 1.f);
       }
       if (verbosity_ < pINFO) {
         LogDebug("HGCalCLUEAlgo")
           << "******** NEW CLUSTER (HGCIA) ********"
           << "Index          " << index
           << "No. of cells = " << cl.size()
           << "     Energy     = " << energy
           << "     Phi        = " << position.phi()
           << "     Eta        = " << position.eta()
           << "*****************************" << std::endl;
       }
       clusters_v_.emplace_back(energy, position, caloID, thisCluster, algoId_);
       if (!clusters_v_.empty()) {
         clusters_v_.back().setSeed(cl[rsmax].data.detid);
       }
       thisCluster.clear();
       index++;
     }
   }
   return clusters_v_;
 }

Density HGCalCLUEAlgo::getDensity ( )

overridevirtual

Implements HGCalClusteringAlgoBase.

Definition at line 408 of file HGCalCLUEAlgo.cc.

Referenced by reset().

                                   {
   return density_;
 }

void HGCalCLUEAlgo::makeClusters ( )

overridevirtual

Implements HGCalClusteringAlgoBase.

Definition at line 82 of file HGCalCLUEAlgo.cc.

References KDTreeLinkerAlgo< DATA >::build(), mps_fire::i, and AlCaHLTBitMon_ParallelJobs::p.

Referenced by ~HGCalCLUEAlgo().

                                  {
   layerClustersPerLayer_.resize(2 * maxlayer + 2);
   // assign all hits in each layer to a cluster core
   tbb::this_task_arena::isolate([&] {
     tbb::parallel_for(size_t(0), size_t(2 * maxlayer + 2), [&](size_t i) {
       KDTreeBox bounds(minpos_[i][0], maxpos_[i][0], minpos_[i][1], maxpos_[i][1]);
       KDTree hit_kdtree;
       hit_kdtree.build(points_[i], bounds);
 
       unsigned int actualLayer = i > maxlayer
                                      ? (i - (maxlayer + 1))
                                      : i;  // maps back from index used for KD trees to actual layer
 
       double maxdensity = calculateLocalDensity(points_[i], hit_kdtree,
                                                 actualLayer);  // also stores rho (energy
                                                                // density) for each point (node)
       // calculate distance to nearest point with higher density storing
       // distance (delta) and point's index
       calculateDistanceToHigher(points_[i]);
       findAndAssignClusters(points_[i], hit_kdtree, maxdensity, bounds, actualLayer,
                             layerClustersPerLayer_[i]);
     });
   });
   //Now that we have the density per point we can store it
   for(auto const& p: points_) { setDensity(p); }
 }

void HGCalCLUEAlgo::populate ( const HGCRecHitCollection & hits )

overridevirtual

Implements HGCalClusteringAlgoBase.

Definition at line 17 of file HGCalCLUEAlgo.cc.

References CaloRecHit::detid(), CaloRecHit::energy(), mps_fire::i, createfilelist::int, SiStripPI::max, min(), position, and edm::SortedCollection< T, SORT >::size().

Referenced by ~HGCalCLUEAlgo().

                                                             {
   // loop over all hits and create the Hexel structure, skip energies below ecut
 
   if (dependSensor_) {
     // for each layer and wafer calculate the thresholds (sigmaNoise and energy)
     // once
     computeThreshold();
   }
 
   std::vector<bool> firstHit(2 * (maxlayer + 1), true);
   for (unsigned int i = 0; i < hits.size(); ++i) {
     const HGCRecHit &hgrh = hits[i];
     DetId detid = hgrh.detid();
     unsigned int layer = rhtools_.getLayerWithOffset(detid);
     float thickness = 0.f;
     // set sigmaNoise default value 1 to use kappa value directly in case of
     // sensor-independent thresholds
     float sigmaNoise = 1.f;
     if (dependSensor_) {
       thickness = rhtools_.getSiThickness(detid);
       int thickness_index = rhtools_.getSiThickIndex(detid);
       if (thickness_index == -1) thickness_index = 3;
       double storedThreshold = thresholds_[layer - 1][thickness_index];
       sigmaNoise = v_sigmaNoise_[layer - 1][thickness_index];
 
       if (hgrh.energy() < storedThreshold)
         continue;  // this sets the ZS threshold at ecut times the sigma noise
                    // for the sensor
     }
     if (!dependSensor_ && hgrh.energy() < ecut_) continue;
 
     // map layers from positive endcap (z) to layer + maxlayer+1 to prevent
     // mixing up hits from different sides
     layer += int(rhtools_.zside(detid) > 0) * (maxlayer + 1);
 
     // determine whether this is a half-hexagon
     bool isHalf = rhtools_.isHalfCell(detid);
     const GlobalPoint position(rhtools_.getPosition(detid));
 
     // here's were the KDNode is passed its dims arguments - note that these are
     // *copied* from the Hexel
     points_[layer].emplace_back(Hexel(hgrh, detid, isHalf, sigmaNoise, thickness, &rhtools_),
                                 position.x(), position.y());
 
     // for each layer, store the minimum and maximum x and y coordinates for the
     // KDTreeBox boundaries
     if (firstHit[layer]) {
       minpos_[layer][0] = position.x();
       minpos_[layer][1] = position.y();
       maxpos_[layer][0] = position.x();
       maxpos_[layer][1] = position.y();
       firstHit[layer] = false;
     } else {
       minpos_[layer][0] = std::min((float)position.x(), minpos_[layer][0]);
       minpos_[layer][1] = std::min((float)position.y(), minpos_[layer][1]);
       maxpos_[layer][0] = std::max((float)position.x(), maxpos_[layer][0]);
       maxpos_[layer][1] = std::max((float)position.y(), maxpos_[layer][1]);
     }
   }  // end loop hits
 }

void HGCalCLUEAlgo::reset ( void )

inlineoverridevirtual

Implements HGCalClusteringAlgoBase.

Definition at line 68 of file HGCalCLUEAlgo.h.

References HGCalClusteringAlgoBase::clusters_v_, computeThreshold(), getDensity(), mps_fire::i, layerClustersPerLayer_, maxpos_, minpos_, points_, and edm::swap().

                         {
     clusters_v_.clear();
     layerClustersPerLayer_.clear();
     for (auto &it : points_) {
       it.clear();
       std::vector<KDNode>().swap(it);
     }
     for (unsigned int i = 0; i < minpos_.size(); i++) {
       minpos_[i][0] = 0.;
       minpos_[i][1] = 0.;
       maxpos_[i][0] = 0.;
       maxpos_[i][1] = 0.;
     }
   }

void HGCalCLUEAlgo::setDensity ( const std::vector< KDNode > & nd )

private

Definition at line 400 of file HGCalCLUEAlgo.cc.

References mps_fire::i.

Referenced by distance().

                                                          {
 
   // for each node store the computer local density
   for (auto &i : nd){
     density_[ i.data.detid ] =  i.data.rho ;
   }
 }

std::vector<size_t> HGCalCLUEAlgo::sort_by_delta ( const std::vector< KDNode > & v ) const

inlineprivate

Definition at line 210 of file HGCalCLUEAlgo.h.

References begin, end, training_settings::idx, jetUpdater_cfi::sort, and findQualityFiles::v.

                                                                     {
     std::vector<size_t> idx(v.size());
     std::iota(std::begin(idx), std::end(idx), 0);
     sort(idx.begin(), idx.end(),
          [&v](size_t i1, size_t i2) { return v[i1].data.delta > v[i2].data.delta; });
     return idx;
   }

Member Data Documentation

std::vector<double> HGCalCLUEAlgo::dEdXweights_

private

Definition at line 140 of file HGCalCLUEAlgo.h.

Density HGCalCLUEAlgo::density_

private

Definition at line 135 of file HGCalCLUEAlgo.h.

bool HGCalCLUEAlgo::dependSensor_

private

Definition at line 139 of file HGCalCLUEAlgo.h.

double HGCalCLUEAlgo::ecut_

private

Definition at line 132 of file HGCalCLUEAlgo.h.

double HGCalCLUEAlgo::fcPerEle_

private

Definition at line 143 of file HGCalCLUEAlgo.h.

std::vector<double> HGCalCLUEAlgo::fcPerMip_

private

Definition at line 142 of file HGCalCLUEAlgo.h.

bool HGCalCLUEAlgo::initialized_

private

Definition at line 150 of file HGCalCLUEAlgo.h.

double HGCalCLUEAlgo::kappa_

private

Definition at line 129 of file HGCalCLUEAlgo.h.

std::vector<std::vector<std::vector<KDNode> > > HGCalCLUEAlgo::layerClustersPerLayer_

private

Definition at line 208 of file HGCalCLUEAlgo.h.

Referenced by reset().

std::vector<std::array<float, 2> > HGCalCLUEAlgo::maxpos_

private

Definition at line 223 of file HGCalCLUEAlgo.h.

Referenced by HGCalCLUEAlgo(), and reset().

std::vector<std::array<float, 2> > HGCalCLUEAlgo::minpos_

private

Definition at line 222 of file HGCalCLUEAlgo.h.

Referenced by reset().

double HGCalCLUEAlgo::noiseMip_

private

Definition at line 145 of file HGCalCLUEAlgo.h.

std::vector<double> HGCalCLUEAlgo::nonAgedNoises_

private

Definition at line 144 of file HGCalCLUEAlgo.h.

std::vector<std::vector<KDNode> > HGCalCLUEAlgo::points_

private

Definition at line 218 of file HGCalCLUEAlgo.h.

Referenced by reset().

std::vector<double> HGCalCLUEAlgo::positionDeltaRho_c_

private

Definition at line 125 of file HGCalCLUEAlgo.h.

std::vector<double> HGCalCLUEAlgo::thicknessCorrection_

private

Definition at line 141 of file HGCalCLUEAlgo.h.

std::vector<std::vector<double> > HGCalCLUEAlgo::thresholds_

private

Definition at line 146 of file HGCalCLUEAlgo.h.

std::vector<double> HGCalCLUEAlgo::thresholdW0_

private

Definition at line 124 of file HGCalCLUEAlgo.h.

std::vector<std::vector<double> > HGCalCLUEAlgo::v_sigmaNoise_

private

Definition at line 147 of file HGCalCLUEAlgo.h.

std::vector<double> HGCalCLUEAlgo::vecDeltas_

private

Definition at line 128 of file HGCalCLUEAlgo.h.

Classes

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