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 // This file is part of the ACTS project.
 //
 // Copyright (C) 2016 CERN for the benefit of the ACTS project
 //
 // This Source Code Form is subject to the terms of the Mozilla Public
 // License, v. 2.0. If a copy of the MPL was not distributed with this
 // file, You can obtain one at https://mozilla.org/MPL/2.0/.
 
 #include "ActsExamples/Digitization/ModuleClusters.hpp"
 
 #include "Acts/Clusterization/Clusterization.hpp"
 #include "Acts/Utilities/Helpers.hpp"
 #include "ActsExamples/Digitization/MeasurementCreation.hpp"
 
 #include <array>
 #include <cmath>
 #include <cstdlib>
 #include <limits>
 #include <map>
 #include <stdexcept>
 
 namespace ActsExamples {
 
 void ModuleClusters::add(DigitizedParameters params, simhit_t simhit) {
   ModuleValue mval;
   mval.paramIndices = std::move(params.indices);
   mval.paramValues = std::move(params.values);
   mval.paramVariances = std::move(params.variances);
   mval.sources = {simhit};
 
   if (m_merge && !params.cluster.channels.empty()) {
     // Break-up the cluster
     for (const auto& cell : params.cluster.channels) {
       ModuleValue mval_cell = mval;
       mval_cell.value = cell;
       m_moduleValues.push_back(std::move(mval_cell));
     }
   } else {
     // pass-through mode or smeared indices only
     mval.value = std::move(params.cluster);
     m_moduleValues.push_back(std::move(mval));
   }
 }
 
 std::vector<std::pair<DigitizedParameters, std::set<ModuleClusters::simhit_t>>>
 ModuleClusters::digitizedParameters() {
   if (m_merge) {  // (re-)build the clusters
     merge();
   }
   std::vector<std::pair<DigitizedParameters, std::set<simhit_t>>> retv;
   for (ModuleValue& mval : m_moduleValues) {
     if (std::holds_alternative<Cluster::Cell>(mval.value)) {
       // Should never happen! Either the cluster should have
       // passed-through or the cells merged into clusters in the
       // merge() step. Treat this as a bug.
       throw std::runtime_error("Invalid cluster!");
     }
     DigitizedParameters dpars;
     dpars.indices = mval.paramIndices;
     dpars.values = mval.paramValues;
     dpars.variances = mval.paramVariances;
     dpars.cluster = std::get<Cluster>(mval.value);
     retv.emplace_back(std::move(dpars), mval.sources);
   }
   return retv;
 }
 
 // Needed for clusterization
 int getCellRow(const ModuleValue& mval) {
   if (std::holds_alternative<ActsExamples::Cluster::Cell>(mval.value)) {
     return std::get<ActsExamples::Cluster::Cell>(mval.value).bin[0];
   }
   throw std::domain_error("ModuleValue does not contain cell!");
 }
 
 int getCellColumn(const ActsExamples::ModuleValue& mval) {
   if (std::holds_alternative<ActsExamples::Cluster::Cell>(mval.value)) {
     return std::get<ActsExamples::Cluster::Cell>(mval.value).bin[1];
   }
   throw std::domain_error("ModuleValue does not contain cell!");
 }
 
 void clusterAddCell(std::vector<ModuleValue>& cl, const ModuleValue& ce) {
   cl.push_back(ce);
 }
 
 std::vector<ModuleValue> ModuleClusters::createCellCollection() {
   std::map<ActsFatras::Segmentizer::Bin2D, ModuleValue> uniqueCells;
   for (const ModuleValue& mval : m_moduleValues) {
     if (!std::holds_alternative<Cluster::Cell>(mval.value)) {
       continue;
     }
     const auto& cell = std::get<ActsExamples::Cluster::Cell>(mval.value).bin;
 
     if (const auto it = uniqueCells.find(cell); it != uniqueCells.end()) {
       // Cell already exists, so merge the hit sources
       std::get<Cluster::Cell>(it->second.value).activation +=
           std::get<Cluster::Cell>(mval.value).activation;
     } else {
       // New cell
       uniqueCells[cell] = mval;
     }
   }
   std::vector<ModuleValue> cells;
   cells.reserve(uniqueCells.size());
   for (const auto& [_, mval] : uniqueCells) {
     cells.push_back(mval);
   }
   return cells;
 }
 
 void ModuleClusters::merge() {
   std::vector<ModuleValue> cells = createCellCollection();
 
   std::vector<ModuleValue> newVals;
 
   if (!cells.empty()) {
     // Case where we actually have geometric clusters
     Acts::Ccl::ClusteringData data;
     std::vector<std::vector<ModuleValue>> merged;
     Acts::Ccl::createClusters<std::vector<ModuleValue>,
                               std::vector<std::vector<ModuleValue>>>(
         data, cells, merged,
         Acts::Ccl::DefaultConnect<ModuleValue>(m_commonCorner));
 
     for (std::vector<ModuleValue>& cellv : merged) {
       // At this stage, the cellv vector contains cells that form a
       // consistent cluster based on a connected component analysis
       // only. Still have to check if they match based on the other
       // indices (a good example of this would a for a timing
       // detector).
 
       for (std::vector<ModuleValue>& remerged : mergeParameters(cellv)) {
         newVals.push_back(squash(remerged));
       }
     }
     m_moduleValues = std::move(newVals);
   } else {
     // no geo clusters
     for (std::vector<ModuleValue>& merged : mergeParameters(m_moduleValues)) {
       newVals.push_back(squash(merged));
     }
     m_moduleValues = std::move(newVals);
   }
 }
 
 // ATTN: returns vector of index into `indices'
 std::vector<std::size_t> ModuleClusters::nonGeoEntries(
     std::vector<Acts::BoundIndices>& indices) {
   std::vector<std::size_t> retv;
   for (std::size_t i = 0; i < indices.size(); i++) {
     auto idx = indices.at(i);
     if (!rangeContainsValue(m_geoIndices, idx)) {
       retv.push_back(i);
     }
   }
   return retv;
 }
 
 // Merging based on parameters
 std::vector<std::vector<ModuleValue>> ModuleClusters::mergeParameters(
     std::vector<ModuleValue> values) {
   std::vector<std::vector<ModuleValue>> retv;
 
   std::vector<bool> used(values.size(), false);
   for (std::size_t i = 0; i < values.size(); i++) {
     if (used.at(i)) {
       continue;
     }
 
     retv.emplace_back();
     std::vector<ModuleValue>& thisvec = retv.back();
 
     // Value has not yet been claimed, so claim it
     thisvec.push_back(std::move(values.at(i)));
     used.at(i) = true;
 
     // Values previously visited by index `i' have already been added
     // to a cluster or used to seed a new cluster, so start at the
     // next unseen one
     for (std::size_t j = i + 1; j < values.size(); j++) {
       // Still may have already been used, so check it
       if (used.at(j)) {
         continue;
       }
 
       // Now look for a match between current cluster and value `j' Consider
       // them matched until we find evidence to the contrary. This
       // way, merging still works when digitization is done by
       // clusters only
       bool matched = true;
 
       // The cluster to be merged into can have more than one
       // associated value at this point, so we have to consider them
       // all
       for (ModuleValue& thisval : thisvec) {
         // Loop over non-geometric dimensions
         for (auto k : nonGeoEntries(thisval.paramIndices)) {
           double p_i = thisval.paramValues.at(k);
           double p_j = values.at(j).paramValues.at(k);
           double v_i = thisval.paramVariances.at(k);
           double v_j = values.at(j).paramVariances.at(k);
 
           double left = 0, right = 0;
           if (p_i < p_j) {
             left = p_i + m_nsigma * std::sqrt(v_i);
             right = p_j - m_nsigma * std::sqrt(v_j);
           } else {
             left = p_j + m_nsigma * std::sqrt(v_j);
             right = p_i - m_nsigma * std::sqrt(v_i);
           }
           if (left < right) {
             // We know these two don't match, so break out of the
             // dimension loop
             matched = false;
             break;
           }
         }  // Loop over `k' (non-geo dimensions)
         if (matched) {
           // The value under consideration matched at least one
           // associated to the current cluster so no need to keep
           // checking others in current cluster
           break;
         }
       }  // Loop on current cluster
       if (matched) {
         // Claim value `j'
         used.at(j) = true;
         thisvec.push_back(std::move(values.at(j)));
       }
     }  // Loop on `j'
   }  // Loop on `i'
   return retv;
 }
 
 ModuleValue ModuleClusters::squash(std::vector<ModuleValue>& values) {
   ModuleValue mval;
   double tot = 0;
   double tot2 = 0;
   std::vector<double> weights;
 
   // First, start by computing cell weights
   for (ModuleValue& other : values) {
     if (std::holds_alternative<Cluster::Cell>(other.value)) {
       weights.push_back(std::get<Cluster::Cell>(other.value).activation);
     } else {
       weights.push_back(1);
     }
     tot += weights.back();
     tot2 += weights.back() * weights.back();
   }
 
   // Now, go over the non-geometric indices
   for (std::size_t i = 0; i < values.size(); i++) {
     ModuleValue& other = values.at(i);
     for (std::size_t j = 0; j < other.paramIndices.size(); j++) {
       auto idx = other.paramIndices.at(j);
       if (!rangeContainsValue(m_geoIndices, idx)) {
         if (!rangeContainsValue(mval.paramIndices, idx)) {
           mval.paramIndices.push_back(idx);
         }
         if (mval.paramValues.size() < (j + 1)) {
           mval.paramValues.push_back(0);
           mval.paramVariances.push_back(0);
         }
         double f = weights.at(i) / (tot > 0 ? tot : 1);
         double f2 = weights.at(i) * weights.at(i) / (tot2 > 0 ? tot2 : 1);
         mval.paramValues.at(j) += f * other.paramValues.at(j);
         mval.paramVariances.at(j) += f2 * other.paramVariances.at(j);
       }
     }
   }
 
   // Now do the geometric indices
   Cluster clus;
 
   const auto& binningData = m_segmentation.binningData();
   Acts::Vector2 pos(0., 0.);
   Acts::Vector2 var(0., 0.);
 
   std::size_t b0min = std::numeric_limits<std::size_t>::max();
   std::size_t b0max = 0;
   std::size_t b1min = std::numeric_limits<std::size_t>::max();
   std::size_t b1max = 0;
 
   for (std::size_t i = 0; i < values.size(); i++) {
     ModuleValue& other = values.at(i);
     if (!std::holds_alternative<Cluster::Cell>(other.value)) {
       continue;
     }
 
     Cluster::Cell ch = std::get<Cluster::Cell>(other.value);
     auto bin = ch.bin;
 
     std::size_t b0 = bin[0];
     std::size_t b1 = bin[1];
 
     b0min = std::min(b0min, b0);
     b0max = std::max(b0max, b0);
     b1min = std::min(b1min, b1);
     b1max = std::max(b1max, b1);
 
     float p0 = binningData[0].center(b0);
     float w0 = binningData[0].width(b0);
     float p1 = binningData[1].center(b1);
     float w1 = binningData[1].width(b1);
 
     pos += Acts::Vector2(weights.at(i) * p0, weights.at(i) * p1);
     // Assume uniform distribution to compute error
     // N.B. This will overestimate the variance
     // but it's better than nothing for now
     var += Acts::Vector2(weights.at(i) * weights.at(i) * w0 * w0 / 12,
                          weights.at(i) * weights.at(i) * w1 * w1 / 12);
 
     clus.channels.push_back(std::move(ch));
 
     // Will have the right value at last iteration Do it here to
     // avoid having bogus values when there are no clusters
     clus.sizeLoc0 = b0max - b0min + 1;
     clus.sizeLoc1 = b1max - b1min + 1;
   }
 
   if (tot > 0) {
     pos /= tot;
     var /= (tot * tot);
   }
 
   for (auto idx : m_geoIndices) {
     mval.paramIndices.push_back(idx);
     mval.paramValues.push_back(pos[idx]);
     mval.paramVariances.push_back(var[idx]);
   }
 
   mval.value = std::move(clus);
 
   // Finally do the hit association
   for (ModuleValue& other : values) {
     mval.sources.merge(other.sources);
   }
 
   return mval;
 }
 
 }  // namespace ActsExamples

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