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File indexing completed on 2024-09-07 04:37:37

 #include "RecoLocalCalo/HGCalRecProducers/interface/HGCalImagingAlgo.h"
 
 // Geometry
 #include "DataFormats/HcalDetId/interface/HcalSubdetector.h"
 #include "Geometry/CaloGeometry/interface/CaloCellGeometry.h"
 #include "Geometry/CaloGeometry/interface/CaloSubdetectorGeometry.h"
 #include "Geometry/Records/interface/IdealGeometryRecord.h"
 
 #include "RecoEcal/EgammaCoreTools/interface/PositionCalc.h"
 //
 #include "DataFormats/CaloRecHit/interface/CaloID.h"
 #include "oneapi/tbb/task_arena.h"
 #include "oneapi/tbb.h"
 
 using namespace hgcal_clustering;
 
 void HGCalImagingAlgo::getEventSetupPerAlgorithm(const edm::EventSetup &es) {
   points_.clear();
   minpos_.clear();
   maxpos_.clear();
   points_.resize(2 * (maxlayer_ + 1));
   minpos_.resize(2 * (maxlayer_ + 1), {{0.0f, 0.0f}});
   maxpos_.resize(2 * (maxlayer_ + 1), {{0.0f, 0.0f}});
 }
 
 void HGCalImagingAlgo::populate(const HGCRecHitCollection &hits) {
   // 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 = 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
 }
 // Create a vector of Hexels associated to one cluster from a collection of
 // HGCalRecHits - this can be used directly to make the final cluster list -
 // this method can be invoked multiple times for the same event with different
 // input (reset should be called between events)
 void HGCalImagingAlgo::makeClusters() {
   layerClustersPerLayer_.resize(2 * maxlayer_ + 2);
   // assign all hits in each layer to a cluster core or halo
   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);
   }
 }
 
 std::vector<reco::BasicCluster> HGCalImagingAlgo::getClusters(bool doSharing) {
   reco::CaloID caloID = reco::CaloID::DET_HGCAL_ENDCAP;
   std::vector<std::pair<DetId, float>> thisCluster;
   for (auto &clsOnLayer : layerClustersPerLayer_) {
     for (unsigned int i = 0; i < clsOnLayer.size(); ++i) {
       double energy = 0;
       Point position;
 
       //Will save the maximum density hit of the cluster
       size_t rsmax = max_index(clsOnLayer[i]);
 
       if (doSharing) {
         std::vector<unsigned> seeds = findLocalMaximaInCluster(clsOnLayer[i]);
         // sharing found seeds.size() sub-cluster seeds in cluster i
 
         std::vector<std::vector<double>> fractions;
         // first pass can have noise it in
         shareEnergy(clsOnLayer[i], seeds, fractions);
 
         // reset and run second pass after vetoing seeds
         // that result in trivial clusters (less than 2 effective cells)
 
         for (unsigned isub = 0; isub < fractions.size(); ++isub) {
           double effective_hits = 0.0;
           double energy = calculateEnergyWithFraction(clsOnLayer[i], fractions[isub]);
           Point position = calculatePositionWithFraction(clsOnLayer[i], fractions[isub]);
 
           for (unsigned ihit = 0; ihit < fractions[isub].size(); ++ihit) {
             const double fraction = fractions[isub][ihit];
             if (fraction > 1e-7) {
               effective_hits += fraction;
               thisCluster.emplace_back(clsOnLayer[i][ihit].data.detid, fraction);
             }
           }
 
           if (verbosity_ < pINFO) {
             std::cout << "\t******** NEW CLUSTER (SHARING) ********" << std::endl;
             std::cout << "\tEff. No. of cells = " << effective_hits << std::endl;
             std::cout << "\t     Energy       = " << energy << std::endl;
             std::cout << "\t     Phi          = " << position.phi() << std::endl;
             std::cout << "\t     Eta          = " << position.eta() << std::endl;
             std::cout << "\t*****************************" << std::endl;
           }
           clusters_v_.emplace_back(energy, position, caloID, thisCluster, algoId_);
           if (!clusters_v_.empty()) {
             clusters_v_.back().setSeed(clsOnLayer[i][rsmax].data.detid);
           }
           thisCluster.clear();
         }
       } else {
         position = calculatePosition(clsOnLayer[i]);  // energy-weighted position
         //   std::vector< KDNode >::iterator it;
         for (auto &it : clsOnLayer[i]) {
           energy += it.data.isHalo ? 0. : it.data.weight;
           // use fraction to store whether this is a Halo hit or not
           thisCluster.emplace_back(it.data.detid, (it.data.isHalo ? 0.f : 1.f));
         }
         if (verbosity_ < pINFO) {
           std::cout << "******** NEW CLUSTER (HGCIA) ********" << std::endl;
           std::cout << "Index          " << i << std::endl;
           std::cout << "No. of cells = " << clsOnLayer[i].size() << std::endl;
           std::cout << "     Energy     = " << energy << std::endl;
           std::cout << "     Phi        = " << position.phi() << std::endl;
           std::cout << "     Eta        = " << position.eta() << std::endl;
           std::cout << "*****************************" << std::endl;
         }
         clusters_v_.emplace_back(energy, position, caloID, thisCluster, algoId_);
         if (!clusters_v_.empty()) {
           clusters_v_.back().setSeed(clsOnLayer[i][rsmax].data.detid);
         }
         thisCluster.clear();
       }
     }
   }
   return clusters_v_;
 }
 
 math::XYZPoint HGCalImagingAlgo::calculatePosition(std::vector<KDNode> &v) const {
   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] + 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);
 }
 
 double HGCalImagingAlgo::calculateLocalDensity(std::vector<KDNode> &nd, KDTree &lp, const unsigned int layer) const {
   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 < firstLayerBH_)
     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<Hexel> 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]) < delta_c) {
         nd[i].data.rho += found[j].weight;
         maxdensity = std::max(maxdensity, nd[i].data.rho);
       }
     }  // end loop found
   }  // end loop nodes
   return maxdensity;
 }
 
 double HGCalImagingAlgo::calculateDistanceToHigher(std::vector<KDNode> &nd) const {
   // 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 (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];
       // Limit the search box
       if ((nd[i].data.x - nd[j].data.x) * (nd[i].data.x - nd[j].data.x) > dist2)
         continue;
       if ((nd[i].data.y - nd[j].data.y) * (nd[i].data.y - nd[j].data.y) > dist2)
         continue;
       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;
 }
 int HGCalImagingAlgo::findAndAssignClusters(std::vector<KDNode> &nd,
                                             KDTree &lp,
                                             double maxdensity,
                                             KDTreeBox<2> &bounds,
                                             const unsigned int layer,
                                             std::vector<std::vector<KDNode>> &clustersOnLayer) const {
   // 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 < firstLayerBH_)
     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) {
       std::cout << "Adding new cluster with index " << nClustersOnLayer << std::endl;
       std::cout << "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) {  // clusterIndex is initialised with -1 if not yet used in cluster
       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) {
     std::cout << "resizing cluster vector by " << nClustersOnLayer << std::endl;
   }
   clustersOnLayer.resize(nClustersOnLayer);
 
   // assign points closer than dc to other clusters to border region
   // and find critical border density
   std::vector<double> rho_b(nClustersOnLayer, 0.);
   lp.clear();
   lp.build(nd, bounds);
   // now loop on all hits again :( and check: if there are hits from another
   // cluster within d_c -> flag as border hit
   for (unsigned int i = 0; i < nd_size; ++i) {
     int ci = nd[i].data.clusterIndex;
     bool flag_isolated = true;
     if (ci != -1) {
       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<Hexel> found;
       lp.search(search_box, found);
 
       const unsigned int found_size = found.size();
       for (unsigned int j = 0; j < found_size; j++) {  // start from 0 here instead of 1
         // check if the hit is not within d_c of another cluster
         if (found[j].clusterIndex != -1) {
           float dist = distance(found[j], nd[i].data);
           if (dist < delta_c && found[j].clusterIndex != ci) {
             // in which case we assign it to the border
             nd[i].data.isBorder = true;
             break;
           }
           // because we are using two different containers, we have to make sure
           // that we don't unflag the
           // hit when it finds *itself* closer than delta_c
           if (dist < delta_c && dist != 0. && found[j].clusterIndex == ci) {
             // in this case it is not an isolated hit
             // the dist!=0 is because the hit being looked at is also inside the
             // search box and at dist==0
             flag_isolated = false;
           }
         }
       }
       if (flag_isolated)
         nd[i].data.isBorder = true;  // the hit is more than delta_c from any of its brethren
     }
     // check if this border hit has density larger than the current rho_b and
     // update
     if (nd[i].data.isBorder && rho_b[ci] < nd[i].data.rho)
       rho_b[ci] = nd[i].data.rho;
   }  // end loop all hits
 
   // flag points in cluster with density < rho_b as halo points, then fill the
   // cluster vector
   for (unsigned int i = 0; i < nd_size; ++i) {
     int ci = nd[i].data.clusterIndex;
     if (ci != -1) {
       if (nd[i].data.rho <= rho_b[ci])
         nd[i].data.isHalo = true;
       clustersOnLayer[ci].push_back(nd[i]);
       if (verbosity_ < pINFO) {
         std::cout << "Pushing hit " << i << " into cluster with index " << ci << std::endl;
       }
     }
   }
 
   // prepare the offset for the next layer if there is one
   if (verbosity_ < pINFO) {
     std::cout << "moving cluster offset by " << nClustersOnLayer << std::endl;
   }
   return nClustersOnLayer;
 }
 
 // find local maxima within delta_c, marking the indices in the cluster
 std::vector<unsigned> HGCalImagingAlgo::findLocalMaximaInCluster(const std::vector<KDNode> &cluster) {
   std::vector<unsigned> result;
   std::vector<bool> seed(cluster.size(), true);
   float delta_c = 2.;
 
   for (unsigned i = 0; i < cluster.size(); ++i) {
     for (unsigned j = 0; j < cluster.size(); ++j) {
       if (i != j and distance(cluster[i].data, cluster[j].data) < delta_c) {
         if (cluster[i].data.weight < cluster[j].data.weight) {
           seed[i] = false;
           break;
         }
       }
     }
   }
 
   for (unsigned i = 0; i < cluster.size(); ++i) {
     if (seed[i] && cluster[i].data.weight > 5e-4) {
       // seed at i with energy cluster[i].weight
       result.push_back(i);
     }
   }
 
   // Found result.size() sub-clusters in input cluster of length cluster.size()
 
   return result;
 }
 
 math::XYZPoint HGCalImagingAlgo::calculatePositionWithFraction(const std::vector<KDNode> &hits,
                                                                const std::vector<double> &fractions) {
   double norm(0.0), x(0.0), y(0.0), z(0.0);
   for (unsigned i = 0; i < hits.size(); ++i) {
     const double weight = fractions[i] * hits[i].data.weight;
     norm += weight;
     x += weight * hits[i].data.x;
     y += weight * hits[i].data.y;
     z += weight * hits[i].data.z;
   }
   math::XYZPoint result(x, y, z);
   result /= norm;
   return result;
 }
 
 double HGCalImagingAlgo::calculateEnergyWithFraction(const std::vector<KDNode> &hits,
                                                      const std::vector<double> &fractions) {
   double result = 0.0;
   for (unsigned i = 0; i < hits.size(); ++i) {
     result += fractions[i] * hits[i].data.weight;
   }
   return result;
 }
 
 void HGCalImagingAlgo::shareEnergy(const std::vector<KDNode> &incluster,
                                    const std::vector<unsigned> &seeds,
                                    std::vector<std::vector<double>> &outclusters) {
   std::vector<bool> isaseed(incluster.size(), false);
   outclusters.clear();
   outclusters.resize(seeds.size());
   std::vector<Point> centroids(seeds.size());
   std::vector<double> energies(seeds.size());
 
   if (seeds.size() == 1) {  // short circuit the case of a lone cluster
     outclusters[0].clear();
     outclusters[0].resize(incluster.size(), 1.0);
     return;
   }
 
   // saving seeds
 
   // create quick seed lookup
   for (unsigned i = 0; i < seeds.size(); ++i) {
     isaseed[seeds[i]] = true;
   }
 
   // initialize clusters to be shared
   // centroids start off at seed positions
   // seeds always have fraction 1.0, to stabilize fit
   // initializing fit
   for (unsigned i = 0; i < seeds.size(); ++i) {
     outclusters[i].resize(incluster.size(), 0.0);
     for (unsigned j = 0; j < incluster.size(); ++j) {
       if (j == seeds[i]) {
         outclusters[i][j] = 1.0;
         centroids[i] = math::XYZPoint(incluster[j].data.x, incluster[j].data.y, incluster[j].data.z);
         energies[i] = incluster[j].data.weight;
       }
     }
   }
 
   // run the fit while we are less than max iterations, and clusters are still
   // moving
   const double minFracTot = 1e-20;
   unsigned iter = 0;
   const unsigned iterMax = 50;
   double diff = std::numeric_limits<double>::max();
   const double stoppingTolerance = 1e-8;
   const auto numberOfSeeds = seeds.size();
   auto toleranceScaling = numberOfSeeds > 2 ? (numberOfSeeds - 1) * (numberOfSeeds - 1) : 1;
   std::vector<Point> prevCentroids;
   std::vector<double> frac(numberOfSeeds), dist2(numberOfSeeds);
   while (iter++ < iterMax && diff > stoppingTolerance * toleranceScaling) {
     for (unsigned i = 0; i < incluster.size(); ++i) {
       const Hexel &ihit = incluster[i].data;
       double fracTot(0.0);
       for (unsigned j = 0; j < numberOfSeeds; ++j) {
         double fraction = 0.0;
         double d2 = (std::pow(ihit.x - centroids[j].x(), 2.0) + std::pow(ihit.y - centroids[j].y(), 2.0) +
                      std::pow(ihit.z - centroids[j].z(), 2.0)) /
                     sigma2_;
         dist2[j] = d2;
         // now we set the fractions up based on hit type
         if (i == seeds[j]) {  // this cluster's seed
           fraction = 1.0;
         } else if (isaseed[i]) {
           fraction = 0.0;
         } else {
           fraction = energies[j] * std::exp(-0.5 * d2);
         }
         fracTot += fraction;
         frac[j] = fraction;
       }
       // now that we have calculated all fractions for all hits
       // assign the new fractions
       for (unsigned j = 0; j < numberOfSeeds; ++j) {
         if (fracTot > minFracTot || (i == seeds[j] && fracTot > 0.0)) {
           outclusters[j][i] = frac[j] / fracTot;
         } else {
           outclusters[j][i] = 0.0;
         }
       }
     }
 
     // save previous centroids
     prevCentroids = std::move(centroids);
     // finally update the position of the centroids from the last iteration
     centroids.resize(numberOfSeeds);
     double diff2 = 0.0;
     for (unsigned i = 0; i < numberOfSeeds; ++i) {
       centroids[i] = calculatePositionWithFraction(incluster, outclusters[i]);
       energies[i] = calculateEnergyWithFraction(incluster, outclusters[i]);
       // calculate convergence parameters
       const double delta2 = (prevCentroids[i] - centroids[i]).perp2();
       diff2 = std::max(delta2, diff2);
     }
     // update convergance parameter outside loop
     diff = std::sqrt(diff2);
   }
 }
 
 void HGCalImagingAlgo::computeThreshold() {
   // 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);
   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_;
       sigmaNoise_[ilayer - 1][ithick] = sigmaNoise;
     }
     float scintillators_sigmaNoise = 0.001f * noiseMip_ * dEdXweights_[ilayer];
     thresholds_[ilayer - 1][maxNumberOfThickIndices] = ecut_ * scintillators_sigmaNoise;
     sigmaNoise_[ilayer - 1][maxNumberOfThickIndices] = scintillators_sigmaNoise;
   }
 }
 
 void HGCalImagingAlgo::setDensity(const std::vector<KDNode> &nd) {
   // for each node calculate local density rho and store it
   for (auto &i : nd) {
     density_[i.data.detid] = i.data.rho;
   }  // end loop nodes
 }
 
 //Density
 Density HGCalImagingAlgo::getDensity() { return density_; }

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