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HGCalHistoSeedingImpl.cc
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1 #include "L1Trigger/L1THGCal/interface/backend/HGCalHistoSeedingImpl.h"
2 #include "L1Trigger/L1THGCal/interface/backend/HGCalShowerShape.h"
3 #include "DataFormats/Math/interface/deltaR.h"
4 #include "DataFormats/Math/interface/deltaPhi.h"
5 #include <numeric>
6 
7 HGCalHistoSeedingImpl::HGCalHistoSeedingImpl(const edm::ParameterSet& conf)
8  : seedingAlgoType_(conf.getParameter<std::string>("type_histoalgo")),
9  nBins1_(conf.getParameter<unsigned>("nBins_X1_histo_multicluster")),
10  nBins2_(conf.getParameter<unsigned>("nBins_X2_histo_multicluster")),
11  binsSumsHisto_(conf.getParameter<std::vector<unsigned>>("binSumsHisto")),
12  histoThreshold_(conf.getParameter<double>("threshold_histo_multicluster")),
13  neighbour_weights_(conf.getParameter<std::vector<double>>("neighbour_weights")),
14  smoothing_ecal_(conf.getParameter<std::vector<double>>("seed_smoothing_ecal")),
15  smoothing_hcal_(conf.getParameter<std::vector<double>>("seed_smoothing_hcal")),
16  kROverZMin_(conf.getParameter<double>("kROverZMin")),
17  kROverZMax_(conf.getParameter<double>("kROverZMax")) {
18  if (seedingAlgoType_ == "HistoMaxC3d") {
19  seedingType_ = HistoMaxC3d;
20  } else if (seedingAlgoType_ == "HistoSecondaryMaxC3d") {
21  seedingType_ = HistoSecondaryMaxC3d;
22  } else if (seedingAlgoType_ == "HistoThresholdC3d") {
23  seedingType_ = HistoThresholdC3d;
24  } else if (seedingAlgoType_ == "HistoInterpolatedMaxC3d") {
25  seedingType_ = HistoInterpolatedMaxC3d;
26  } else {
27  throw cms::Exception("HGCTriggerParameterError") << "Unknown Multiclustering type '" << seedingAlgoType_;
28  }
29 
30  if (conf.getParameter<std::string>("seed_position") == "BinCentre") {
31  seedingPosition_ = BinCentre;
32  } else if (conf.getParameter<std::string>("seed_position") == "TCWeighted") {
33  seedingPosition_ = TCWeighted;
34  } else {
35  throw cms::Exception("HGCTriggerParameterError")
36  << "Unknown Seed Position option '" << conf.getParameter<std::string>("seed_position");
37  }
38  if (conf.getParameter<std::string>("seeding_space") == "RPhi") {
39  seedingSpace_ = RPhi;
40  navigator_ = Navigator(nBins1_, Navigator::AxisType::Bounded, nBins2_, Navigator::AxisType::Circular);
41  } else if (conf.getParameter<std::string>("seeding_space") == "XY") {
42  seedingSpace_ = XY;
43  navigator_ = Navigator(nBins1_, Navigator::AxisType::Bounded, nBins2_, Navigator::AxisType::Bounded);
44  } else {
45  throw cms::Exception("HGCTriggerParameterError")
46  << "Unknown seeding space '" << conf.getParameter<std::string>("seeding_space");
47  }
48 
49  edm::LogInfo("HGCalMulticlusterParameters")
50  << "\nMulticluster number of X1-bins for the histo algorithm: " << nBins1_
51  << "\nMulticluster number of X2-bins for the histo algorithm: " << nBins2_
52  << "\nMulticluster MIPT threshold for histo threshold algorithm: " << histoThreshold_
53  << "\nMulticluster type of multiclustering algortihm: " << seedingAlgoType_;
54 
55  if (seedingAlgoType_.find("Histo") != std::string::npos && seedingSpace_ == RPhi &&
56  nBins1_ != binsSumsHisto_.size()) {
57  throw cms::Exception("Inconsistent bin size")
58  << "Inconsistent nBins_X1_histo_multicluster ( " << nBins1_ << " ) and binSumsHisto ( " << binsSumsHisto_.size()
59  << " ) size in HGCalMulticlustering\n";
60  }
61 
62  if (neighbour_weights_.size() != neighbour_weights_size_) {
63  throw cms::Exception("Inconsistent vector size")
64  << "Inconsistent size of neighbour weights vector in HGCalMulticlustering ( " << neighbour_weights_.size()
65  << " ). Should be " << neighbour_weights_size_ << "\n";
66  }
67 }
68 
69 HGCalHistoSeedingImpl::Histogram HGCalHistoSeedingImpl::fillHistoClusters(
70  const std::vector<edm::Ptr<l1t::HGCalCluster>>& clustersPtrs) {
71  Histogram histoClusters(nBins1_, nBins2_);
72  std::array<double, 4> bounds = boundaries();
73  double minx1 = std::get<0>(bounds);
74  double maxx1 = std::get<1>(bounds);
75  double minx2 = std::get<2>(bounds);
76  double maxx2 = std::get<3>(bounds);
77 
78  for (auto& clu : clustersPtrs) {
79  float x1 = 0., x2 = 0;
80  switch (seedingSpace_) {
81  case RPhi:
82  x1 = sqrt(pow(clu->centreProj().x(), 2) + pow(clu->centreProj().y(), 2));
83  x2 = reco::reduceRange(clu->phi());
84  break;
85  case XY:
86  x1 = clu->centreProj().x();
87  x2 = clu->centreProj().y();
88  break;
89  };
90  if (x1 < minx1 || x1 >= maxx1) {
91  throw cms::Exception("OutOfBound") << "TC X1 = " << x1 << " out of the seeding histogram bounds " << minx1
92  << " - " << maxx1;
93  }
94  if (x2 < minx2 || x2 >= maxx2) {
95  throw cms::Exception("OutOfBound") << "TC X2 = " << x2 << " out of the seeding histogram bounds " << minx2
96  << " - " << maxx2;
97  }
98  unsigned bin1 = unsigned((x1 - minx1) * nBins1_ / (maxx1 - minx1));
99  unsigned bin2 = unsigned((x2 - minx2) * nBins2_ / (maxx2 - minx2));
100 
101  auto& bin = histoClusters.at(triggerTools_.zside(clu->detId()), bin1, bin2);
102  bin.values[Bin::Content::Sum] += clu->mipPt();
103  if (triggerTools_.isEm(clu->detId())) {
104  bin.values[Bin::Content::Ecal] += clu->mipPt();
105  } else {
106  bin.values[Bin::Content::Hcal] += clu->mipPt();
107  }
108  bin.weighted_x += (clu->centreProj().x()) * clu->mipPt();
109  bin.weighted_y += (clu->centreProj().y()) * clu->mipPt();
110  }
111 
112  for (auto& bin : histoClusters) {
113  bin.weighted_x /= bin.values[Bin::Content::Sum];
114  bin.weighted_y /= bin.values[Bin::Content::Sum];
115  }
116 
117  return histoClusters;
118 }
119 
120 HGCalHistoSeedingImpl::Histogram HGCalHistoSeedingImpl::fillSmoothHistoClusters(const Histogram& histoClusters,
121  const vector<double>& kernel,
122  Bin::Content binContent) {
123  Histogram histoSmooth(histoClusters);
124 
125  unsigned kernel_size = std::sqrt(kernel.size());
126  if (kernel_size * kernel_size != kernel.size()) {
127  throw cms::Exception("HGCTriggerParameterError") << "Only square kernels can be used.";
128  }
129  if (kernel_size % 2 != 1) {
130  throw cms::Exception("HGCTriggerParameterError") << "The kernel size must be an odd value.";
131  }
132  int shift_max = (kernel_size - 1) / 2;
133  double normalization = std::accumulate(kernel.begin(), kernel.end(), 0.);
134  for (int z_side : {-1, 1}) {
135  for (unsigned x1 = 0; x1 < nBins1_; x1++) {
136  for (unsigned x2 = 0; x2 < nBins2_; x2++) {
137  const auto& bin_orig = histoClusters.at(z_side, x1, x2);
138  double smooth = 0.;
139  navigator_.setHome(x1, x2);
140  for (int x1_shift = -shift_max; x1_shift <= shift_max; x1_shift++) {
141  int index1 = x1_shift + shift_max;
142  for (int x2_shift = -shift_max; x2_shift <= shift_max; x2_shift++) {
143  auto shifted = navigator_.move(x1_shift, x2_shift);
144  int index2 = x2_shift + shift_max;
145  double kernel_value = kernel.at(index1 * kernel_size + index2);
146  bool out = shifted[0] == -1 || shifted[1] == -1;
147  double content = (out ? 0. : histoClusters.at(z_side, shifted[0], shifted[1]).values[binContent]);
148  smooth += content * kernel_value;
149  }
150  }
151  auto& bin = histoSmooth.at(z_side, x1, x2);
152  bin.values[binContent] = smooth / normalization;
153  bin.weighted_x = bin_orig.weighted_x;
154  bin.weighted_y = bin_orig.weighted_y;
155  }
156  }
157  }
158 
159  return histoSmooth;
160 }
161 
162 HGCalHistoSeedingImpl::Histogram HGCalHistoSeedingImpl::fillSmoothPhiHistoClusters(const Histogram& histoClusters,
163  const vector<unsigned>& binSums) {
164  Histogram histoSumPhiClusters(nBins1_, nBins2_);
165 
166  for (int z_side : {-1, 1}) {
167  for (unsigned bin1 = 0; bin1 < nBins1_; bin1++) {
168  int nBinsSide = (binSums[bin1] - 1) / 2;
169  float R1 = kROverZMin_ + bin1 * (kROverZMax_ - kROverZMin_);
170  float R2 = R1 + (kROverZMax_ - kROverZMin_);
171  double area =
172  0.5 * (pow(R2, 2) - pow(R1, 2)) *
173  (1 +
174  0.5 *
175  (1 -
176  pow(0.5,
177  nBinsSide))); // Takes into account different area of bins in different R-rings + sum of quadratic weights used
178 
179  for (unsigned bin2 = 0; bin2 < nBins2_; bin2++) {
180  const auto& bin_orig = histoClusters.at(z_side, bin1, bin2);
181  float content = bin_orig.values[Bin::Content::Sum];
182 
183  for (int bin22 = 1; bin22 <= nBinsSide; bin22++) {
184  int binToSumLeft = bin2 - bin22;
185  if (binToSumLeft < 0)
186  binToSumLeft += nBins2_;
187  unsigned binToSumRight = bin2 + bin22;
188  if (binToSumRight >= nBins2_)
189  binToSumRight -= nBins2_;
190 
191  content += histoClusters.at(z_side, bin1, binToSumLeft).values[Bin::Content::Sum] /
192  pow(2, bin22); // quadratic kernel
193 
194  content += histoClusters.at(z_side, bin1, binToSumRight).values[Bin::Content::Sum] /
195  pow(2, bin22); // quadratic kernel
196  }
197 
198  auto& bin = histoSumPhiClusters.at(z_side, bin1, bin2);
199  bin.values[Bin::Content::Sum] = content / area;
200  bin.weighted_x = bin_orig.weighted_x;
201  bin.weighted_y = bin_orig.weighted_y;
202  }
203  }
204  }
205 
206  return histoSumPhiClusters;
207 }
208 
209 HGCalHistoSeedingImpl::Histogram HGCalHistoSeedingImpl::fillSmoothRPhiHistoClusters(const Histogram& histoClusters) {
210  Histogram histoSumRPhiClusters(nBins1_, nBins2_);
211 
212  for (int z_side : {-1, 1}) {
213  for (unsigned bin1 = 0; bin1 < nBins1_; bin1++) {
214  float weight =
215  (bin1 == 0 || bin1 == nBins1_ - 1) ? 1.5 : 2.; //Take into account edges with only one side up or down
216 
217  for (unsigned bin2 = 0; bin2 < nBins2_; bin2++) {
218  const auto& bin_orig = histoClusters.at(z_side, bin1, bin2);
219  float content = bin_orig.values[Bin::Content::Sum];
220  float contentDown = bin1 > 0 ? histoClusters.at(z_side, bin1 - 1, bin2).values[Bin::Content::Sum] : 0;
221  float contentUp = bin1 < (nBins1_ - 1) ? histoClusters.at(z_side, bin1 + 1, bin2).values[Bin::Content::Sum] : 0;
222 
223  auto& bin = histoSumRPhiClusters.at(z_side, bin1, bin2);
224  bin.values[Bin::Content::Sum] = (content + 0.5 * contentDown + 0.5 * contentUp) / weight;
225  bin.weighted_x = bin_orig.weighted_x;
226  bin.weighted_y = bin_orig.weighted_y;
227  }
228  }
229  }
230 
231  return histoSumRPhiClusters;
232 }
233 
234 void HGCalHistoSeedingImpl::setSeedEnergyAndPosition(std::vector<std::pair<GlobalPoint, double>>& seedPositionsEnergy,
235  int z_side,
236  unsigned bin1,
237  unsigned bin2,
238  const Bin& histBin) {
239  float x_seed = 0;
240  float y_seed = 0;
241  std::array<double, 4> bounds = boundaries();
242  double minx1 = std::get<0>(bounds);
243  double maxx1 = std::get<1>(bounds);
244  double minx2 = std::get<2>(bounds);
245  double maxx2 = std::get<3>(bounds);
246 
247  if (seedingPosition_ == BinCentre) {
248  float x1_seed = minx1 + (bin1 + 0.5) * (maxx1 - minx1) / nBins1_;
249  float x2_seed = minx2 + (bin2 + 0.5) * (maxx2 - minx2) / nBins2_;
250  switch (seedingSpace_) {
251  case RPhi:
252  x_seed = x1_seed * cos(x2_seed);
253  y_seed = x1_seed * sin(x2_seed);
254  break;
255  case XY:
256  x_seed = x1_seed;
257  y_seed = x2_seed;
258  };
259  } else if (seedingPosition_ == TCWeighted) {
260  x_seed = histBin.weighted_x;
261  y_seed = histBin.weighted_y;
262  }
263 
264  seedPositionsEnergy.emplace_back(GlobalPoint(x_seed, y_seed, z_side), histBin.values[Bin::Content::Sum]);
265 }
266 
267 std::vector<std::pair<GlobalPoint, double>> HGCalHistoSeedingImpl::computeMaxSeeds(const Histogram& histoClusters) {
268  std::vector<std::pair<GlobalPoint, double>> seedPositionsEnergy;
269 
270  for (int z_side : {-1, 1}) {
271  for (unsigned bin1 = 0; bin1 < nBins1_; bin1++) {
272  for (unsigned bin2 = 0; bin2 < nBins2_; bin2++) {
273  float MIPT_seed = histoClusters.at(z_side, bin1, bin2).values[Bin::Content::Sum];
274  bool isMax = MIPT_seed > histoThreshold_;
275  if (!isMax)
276  continue;
277 
278  navigator_.setHome(bin1, bin2);
279  auto pos_N = navigator_.move(1, 0);
280  auto pos_S = navigator_.move(-1, 0);
281  auto pos_W = navigator_.move(0, -1);
282  auto pos_E = navigator_.move(0, 1);
283  auto pos_NW = navigator_.move(1, -1);
284  auto pos_NE = navigator_.move(1, 1);
285  auto pos_SW = navigator_.move(-1, -1);
286  auto pos_SE = navigator_.move(-1, 1);
287 
288  float MIPT_N = (pos_N[0] != -1 && pos_N[1] != -1)
289  ? histoClusters.at(z_side, pos_N[0], pos_N[1]).values[Bin::Content::Sum]
290  : 0;
291  float MIPT_S = (pos_S[0] != -1 && pos_S[1] != -1)
292  ? histoClusters.at(z_side, pos_S[0], pos_S[1]).values[Bin::Content::Sum]
293  : 0;
294  float MIPT_W = (pos_W[0] != -1 && pos_W[1] != -1)
295  ? histoClusters.at(z_side, pos_W[0], pos_W[1]).values[Bin::Content::Sum]
296  : 0;
297  float MIPT_E = (pos_E[0] != -1 && pos_E[1] != -1)
298  ? histoClusters.at(z_side, pos_E[0], pos_E[1]).values[Bin::Content::Sum]
299  : 0;
300  float MIPT_NW = (pos_NW[0] != -1 && pos_NW[1] != -1)
301  ? histoClusters.at(z_side, pos_NW[0], pos_NW[1]).values[Bin::Content::Sum]
302  : 0;
303  float MIPT_NE = (pos_NE[0] != -1 && pos_NE[1] != -1)
304  ? histoClusters.at(z_side, pos_NE[0], pos_NE[1]).values[Bin::Content::Sum]
305  : 0;
306  float MIPT_SW = (pos_SW[0] != -1 && pos_SW[1] != -1)
307  ? histoClusters.at(z_side, pos_SW[0], pos_SW[1]).values[Bin::Content::Sum]
308  : 0;
309  float MIPT_SE = (pos_SE[0] != -1 && pos_SE[1] != -1)
310  ? histoClusters.at(z_side, pos_SE[0], pos_SE[1]).values[Bin::Content::Sum]
311  : 0;
312 
313  isMax &= MIPT_seed >= MIPT_S && MIPT_seed > MIPT_N && MIPT_seed >= MIPT_E && MIPT_seed >= MIPT_SE &&
314  MIPT_seed >= MIPT_NE && MIPT_seed > MIPT_W && MIPT_seed > MIPT_SW && MIPT_seed > MIPT_NW;
315 
316  if (isMax) {
317  setSeedEnergyAndPosition(seedPositionsEnergy, z_side, bin1, bin2, histoClusters.at(z_side, bin1, bin2));
318  }
319  }
320  }
321  }
322 
323  return seedPositionsEnergy;
324 }
325 
326 std::vector<std::pair<GlobalPoint, double>> HGCalHistoSeedingImpl::computeInterpolatedMaxSeeds(
327  const Histogram& histoClusters) {
328  std::vector<std::pair<GlobalPoint, double>> seedPositionsEnergy;
329 
330  for (int z_side : {-1, 1}) {
331  for (unsigned bin1 = 0; bin1 < nBins1_; bin1++) {
332  for (unsigned bin2 = 0; bin2 < nBins2_; bin2++) {
333  float MIPT_seed = histoClusters.at(z_side, bin1, bin2).values[Bin::Content::Sum];
334 
335  navigator_.setHome(bin1, bin2);
336  auto pos_N = navigator_.move(1, 0);
337  auto pos_S = navigator_.move(-1, 0);
338  auto pos_W = navigator_.move(0, -1);
339  auto pos_E = navigator_.move(0, 1);
340  auto pos_NW = navigator_.move(1, -1);
341  auto pos_NE = navigator_.move(1, 1);
342  auto pos_SW = navigator_.move(-1, -1);
343  auto pos_SE = navigator_.move(-1, 1);
344 
345  float MIPT_N = (pos_N[0] != -1 && pos_N[1] != -1)
346  ? histoClusters.at(z_side, pos_N[0], pos_N[1]).values[Bin::Content::Sum]
347  : 0;
348  float MIPT_S = (pos_S[0] != -1 && pos_S[1] != -1)
349  ? histoClusters.at(z_side, pos_S[0], pos_S[1]).values[Bin::Content::Sum]
350  : 0;
351  float MIPT_W = (pos_W[0] != -1 && pos_W[1] != -1)
352  ? histoClusters.at(z_side, pos_W[0], pos_W[1]).values[Bin::Content::Sum]
353  : 0;
354  float MIPT_E = (pos_E[0] != -1 && pos_E[1] != -1)
355  ? histoClusters.at(z_side, pos_E[0], pos_E[1]).values[Bin::Content::Sum]
356  : 0;
357  float MIPT_NW = (pos_NW[0] != -1 && pos_NW[1] != -1)
358  ? histoClusters.at(z_side, pos_NW[0], pos_NW[1]).values[Bin::Content::Sum]
359  : 0;
360  float MIPT_NE = (pos_NE[0] != -1 && pos_NE[1] != -1)
361  ? histoClusters.at(z_side, pos_NE[0], pos_NE[1]).values[Bin::Content::Sum]
362  : 0;
363  float MIPT_SW = (pos_SW[0] != -1 && pos_SW[1] != -1)
364  ? histoClusters.at(z_side, pos_SW[0], pos_SW[1]).values[Bin::Content::Sum]
365  : 0;
366  float MIPT_SE = (pos_SE[0] != -1 && pos_SE[1] != -1)
367  ? histoClusters.at(z_side, pos_SE[0], pos_SE[1]).values[Bin::Content::Sum]
368  : 0;
369 
370  float MIPT_pred = neighbour_weights_.at(0) * MIPT_NW + neighbour_weights_.at(1) * MIPT_N +
371  neighbour_weights_.at(2) * MIPT_NE + neighbour_weights_.at(3) * MIPT_W +
372  neighbour_weights_.at(5) * MIPT_E + neighbour_weights_.at(6) * MIPT_SW +
373  neighbour_weights_.at(7) * MIPT_S + neighbour_weights_.at(8) * MIPT_SE;
374 
375  bool isMax = MIPT_seed >= (MIPT_pred + histoThreshold_);
376 
377  if (isMax) {
378  setSeedEnergyAndPosition(seedPositionsEnergy, z_side, bin1, bin2, histoClusters.at(z_side, bin1, bin2));
379  }
380  }
381  }
382  }
383 
384  return seedPositionsEnergy;
385 }
386 
387 std::vector<std::pair<GlobalPoint, double>> HGCalHistoSeedingImpl::computeThresholdSeeds(
388  const Histogram& histoClusters) {
389  std::vector<std::pair<GlobalPoint, double>> seedPositionsEnergy;
390 
391  for (int z_side : {-1, 1}) {
392  for (unsigned bin1 = 0; bin1 < nBins1_; bin1++) {
393  for (unsigned bin2 = 0; bin2 < nBins2_; bin2++) {
394  float MIPT_seed = histoClusters.at(z_side, bin1, bin2).values[Bin::Content::Sum];
395  bool isSeed = MIPT_seed > histoThreshold_;
396 
397  if (isSeed) {
398  setSeedEnergyAndPosition(seedPositionsEnergy, z_side, bin1, bin2, histoClusters.at(z_side, bin1, bin2));
399  }
400  }
401  }
402  }
403 
404  return seedPositionsEnergy;
405 }
406 
407 std::vector<std::pair<GlobalPoint, double>> HGCalHistoSeedingImpl::computeSecondaryMaxSeeds(
408  const Histogram& histoClusters) {
409  std::vector<std::pair<GlobalPoint, double>> seedPositionsEnergy;
410 
411  HistogramT<uint8_t> primarySeedPositions(nBins1_, nBins2_);
412  HistogramT<uint8_t> vetoPositions(nBins1_, nBins2_);
413 
414  //Search for primary seeds
415  for (int z_side : {-1, 1}) {
416  for (unsigned bin1 = 0; bin1 < nBins1_; bin1++) {
417  for (unsigned bin2 = 0; bin2 < nBins2_; bin2++) {
418  float MIPT_seed = histoClusters.at(z_side, bin1, bin2).values[Bin::Content::Sum];
419  bool isMax = MIPT_seed > histoThreshold_;
420 
421  if (!isMax)
422  continue;
423 
424  float MIPT_S = bin1 < (nBins1_ - 1) ? histoClusters.at(z_side, bin1 + 1, bin2).values[Bin::Content::Sum] : 0;
425  float MIPT_N = bin1 > 0 ? histoClusters.at(z_side, bin1 - 1, bin2).values[Bin::Content::Sum] : 0;
426 
427  int binLeft = bin2 - 1;
428  if (binLeft < 0)
429  binLeft += nBins2_;
430  unsigned binRight = bin2 + 1;
431  if (binRight >= nBins2_)
432  binRight -= nBins2_;
433 
434  float MIPT_W = histoClusters.at(z_side, bin1, binLeft).values[Bin::Content::Sum];
435  float MIPT_E = histoClusters.at(z_side, bin1, binRight).values[Bin::Content::Sum];
436  float MIPT_NW = bin1 > 0 ? histoClusters.at(z_side, bin1 - 1, binLeft).values[Bin::Content::Sum] : 0;
437  float MIPT_NE = bin1 > 0 ? histoClusters.at(z_side, bin1 - 1, binRight).values[Bin::Content::Sum] : 0;
438  float MIPT_SW =
439  bin1 < (nBins1_ - 1) ? histoClusters.at(z_side, bin1 + 1, binLeft).values[Bin::Content::Sum] : 0;
440  float MIPT_SE =
441  bin1 < (nBins1_ - 1) ? histoClusters.at(z_side, bin1 + 1, binRight).values[Bin::Content::Sum] : 0;
442 
443  isMax &= MIPT_seed >= MIPT_S && MIPT_seed > MIPT_N && MIPT_seed >= MIPT_E && MIPT_seed >= MIPT_SE &&
444  MIPT_seed >= MIPT_NE && MIPT_seed > MIPT_W && MIPT_seed > MIPT_SW && MIPT_seed > MIPT_NW;
445 
446  if (isMax) {
447  setSeedEnergyAndPosition(seedPositionsEnergy, z_side, bin1, bin2, histoClusters.at(z_side, bin1, bin2));
448 
449  primarySeedPositions.at(z_side, bin1, bin2) = true;
450 
451  vetoPositions.at(z_side, bin1, binLeft) = true;
452  vetoPositions.at(z_side, bin1, binRight) = true;
453  if (bin1 > 0) {
454  vetoPositions.at(z_side, bin1 - 1, bin2) = true;
455  vetoPositions.at(z_side, bin1 - 1, binRight) = true;
456  vetoPositions.at(z_side, bin1 - 1, binLeft) = true;
457  }
458  if (bin1 < (nBins1_ - 1)) {
459  vetoPositions.at(z_side, bin1 + 1, bin2) = true;
460  vetoPositions.at(z_side, bin1 + 1, binRight) = true;
461  vetoPositions.at(z_side, bin1 + 1, binLeft) = true;
462  }
463  }
464  }
465  }
466  }
467 
468  //Search for secondary seeds
469 
470  for (int z_side : {-1, 1}) {
471  for (unsigned bin1 = 0; bin1 < nBins1_; bin1++) {
472  for (unsigned bin2 = 0; bin2 < nBins2_; bin2++) {
473  //Cannot be a secondary seed if already a primary seed, or adjacent to primary seed
474  if (primarySeedPositions.at(z_side, bin1, bin2) || vetoPositions.at(z_side, bin1, bin2))
475  continue;
476 
477  float MIPT_seed = histoClusters.at(z_side, bin1, bin2).values[Bin::Content::Sum];
478  bool isMax = MIPT_seed > histoThreshold_;
479 
480  float MIPT_S = bin1 < (nBins1_ - 1) ? histoClusters.at(z_side, bin1 + 1, bin2).values[Bin::Content::Sum] : 0;
481  float MIPT_N = bin1 > 0 ? histoClusters.at(z_side, bin1 - 1, bin2).values[Bin::Content::Sum] : 0;
482 
483  int binLeft = bin2 - 1;
484  if (binLeft < 0)
485  binLeft += nBins2_;
486  unsigned binRight = bin2 + 1;
487  if (binRight >= nBins2_)
488  binRight -= nBins2_;
489 
490  float MIPT_W = histoClusters.at(z_side, bin1, binLeft).values[Bin::Content::Sum];
491  float MIPT_E = histoClusters.at(z_side, bin1, binRight).values[Bin::Content::Sum];
492  float MIPT_NW = bin1 > 0 ? histoClusters.at(z_side, bin1 - 1, binLeft).values[Bin::Content::Sum] : 0;
493  float MIPT_NE = bin1 > 0 ? histoClusters.at(z_side, bin1 - 1, binRight).values[Bin::Content::Sum] : 0;
494  float MIPT_SW =
495  bin1 < (nBins1_ - 1) ? histoClusters.at(z_side, bin1 + 1, binLeft).values[Bin::Content::Sum] : 0;
496  float MIPT_SE =
497  bin1 < (nBins1_ - 1) ? histoClusters.at(z_side, bin1 + 1, binRight).values[Bin::Content::Sum] : 0;
498 
499  isMax &= (((bin1 < nBins1_ - 1) && vetoPositions.at(z_side, bin1 + 1, bin2)) or MIPT_seed >= MIPT_S) &&
500  (((bin1 > 0) && vetoPositions.at(z_side, bin1 - 1, bin2)) or MIPT_seed > MIPT_N) &&
501  ((vetoPositions.at(z_side, bin1, binRight)) or MIPT_seed >= MIPT_E) &&
502  (((bin1 < nBins1_ - 1) && vetoPositions.at(z_side, bin1 + 1, binRight)) or MIPT_seed >= MIPT_SE) &&
503  (((bin1 > 0) && vetoPositions.at(z_side, bin1 - 1, binRight)) or MIPT_seed >= MIPT_NE) &&
504  ((vetoPositions.at(z_side, bin1, binLeft)) or MIPT_seed > MIPT_W) &&
505  (((bin1 < nBins1_ - 1) && vetoPositions.at(z_side, bin1 + 1, binLeft)) or MIPT_seed > MIPT_SW) &&
506  (((bin1 > 0) && vetoPositions.at(z_side, bin1 - 1, binLeft)) or MIPT_seed > MIPT_NW);
507 
508  if (isMax) {
509  setSeedEnergyAndPosition(seedPositionsEnergy, z_side, bin1, bin2, histoClusters.at(z_side, bin1, bin2));
510  }
511  }
512  }
513  }
514 
515  return seedPositionsEnergy;
516 }
517 
518 void HGCalHistoSeedingImpl::findHistoSeeds(const std::vector<edm::Ptr<l1t::HGCalCluster>>& clustersPtrs,
519  std::vector<std::pair<GlobalPoint, double>>& seedPositionsEnergy) {
520  /* put clusters into an r/z x phi histogram */
521  Histogram histoCluster = fillHistoClusters(clustersPtrs);
522 
523  Histogram smoothHistoCluster;
524  if (seedingSpace_ == RPhi) {
525  /* smoothen along the phi direction + normalize each bin to same area */
526  Histogram smoothPhiHistoCluster = fillSmoothPhiHistoClusters(histoCluster, binsSumsHisto_);
527 
528  /* smoothen along the r/z direction */
529  smoothHistoCluster = fillSmoothRPhiHistoClusters(smoothPhiHistoCluster);
530  } else if (seedingSpace_ == XY) {
531  smoothHistoCluster = fillSmoothHistoClusters(histoCluster, smoothing_ecal_, Bin::Content::Ecal);
532  smoothHistoCluster = fillSmoothHistoClusters(smoothHistoCluster, smoothing_hcal_, Bin::Content::Hcal);
533  // Update sum with smoothen ECAL + HCAL
534  for (int z_side : {-1, 1}) {
535  for (unsigned x1 = 0; x1 < nBins1_; x1++) {
536  for (unsigned x2 = 0; x2 < nBins2_; x2++) {
537  auto& bin = smoothHistoCluster.at(z_side, x1, x2);
538  bin.values[Bin::Content::Sum] = bin.values[Bin::Content::Ecal] + bin.values[Bin::Content::Hcal];
539  }
540  }
541  }
542  }
543 
544  /* seeds determined with local maximum criteria */
545  if (seedingType_ == HistoMaxC3d)
546  seedPositionsEnergy = computeMaxSeeds(smoothHistoCluster);
547  else if (seedingType_ == HistoThresholdC3d)
548  seedPositionsEnergy = computeThresholdSeeds(smoothHistoCluster);
549  else if (seedingType_ == HistoInterpolatedMaxC3d)
550  seedPositionsEnergy = computeInterpolatedMaxSeeds(smoothHistoCluster);
551  else if (seedingType_ == HistoSecondaryMaxC3d)
552  seedPositionsEnergy = computeSecondaryMaxSeeds(smoothHistoCluster);
553 }
554 
555 std::array<double, 4> HGCalHistoSeedingImpl::boundaries() {
556  switch (seedingSpace_) {
557  case RPhi:
558  return {{kROverZMin_, kROverZMax_, -M_PI, M_PI}};
559  case XY:
560  return {{-kXYMax_, kXYMax_, -kXYMax_, kXYMax_}};
561  }
562  return {{0., 0., 0., 0.}};
563 }
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