GCC Code Coverage Report

Directory: ./
File: src/iguana/algorithms/clas12/PhotonGBTFilter/Algorithm.cc
Date: 2025-10-15 15:39:33
Exec Total Coverage
Lines: 194 212 91.5%
Functions: 11 11 100.0%
Branches: 184 248 74.2%
Line Branch Exec Source
1 #include "Algorithm.h"
2 #include "iguana/algorithms/TypeDefs.h"
3 #include "models/RGA_inbending_pass1.cpp"
4 #include "models/RGA_outbending_pass1.cpp"
5 #include "models/RGA_inbending_pass2.cpp"
6 #include "models/RGA_outbending_pass2.cpp"
7 #include "models/RGC_Summer2022_pass1.cpp"
8
9 namespace iguana::clas12 {
10
11 REGISTER_IGUANA_ALGORITHM(PhotonGBTFilter);
12
13 // Map for the GBT Models to use depending on pass and run number
14 std::map<std::tuple<int, int, int>, std::function<double(std::vector<float> const &)>> const PhotonGBTFilter::modelMap = {
15 ✗ {{5032, 5332, 1}, [](std::vector<float> const &data) { return ApplyCatboostModel_RGA_inbending_pass1(data); }}, // Fall2018 RGA Inbending
16 ✗ {{5032, 5332, 2}, [](std::vector<float> const &data) { return ApplyCatboostModel_RGA_inbending_pass2(data); }}, // Fall2018 RGA Inbending
17 ✗ {{5333, 5666, 1}, [](std::vector<float> const &data) { return ApplyCatboostModel_RGA_outbending_pass1(data); }}, // Fall2018 RGA Outbending
18 ✗ {{5333, 5666, 2}, [](std::vector<float> const &data) { return ApplyCatboostModel_RGA_outbending_pass2(data); }}, // Fall2018 RGA Outbending
19 2564 {{6616, 6783, 1}, [](std::vector<float> const &data) { return ApplyCatboostModel_RGA_inbending_pass1(data); }}, // Spring2019 RGA Inbending
20 ✗ {{6616, 6783, 2}, [](std::vector<float> const &data) { return ApplyCatboostModel_RGA_inbending_pass2(data); }}, // Spring2019 RGA Inbending
21 ✗ {{6156, 6603, 1}, [](std::vector<float> const &data) { return ApplyCatboostModel_RGA_inbending_pass1(data); }}, // Spring2019 RGB Inbending
22 ✗ {{6156, 6603, 2}, [](std::vector<float> const &data) { return ApplyCatboostModel_RGA_inbending_pass2(data); }}, // Spring2019 RGB Inbending
23 ✗ {{11093, 11283, 1}, [](std::vector<float> const &data) { return ApplyCatboostModel_RGA_outbending_pass1(data); }}, // Fall2019 RGB Outbending
24 ✗ {{11093, 11283, 2}, [](std::vector<float> const &data) { return ApplyCatboostModel_RGA_outbending_pass2(data); }}, // Fall2019 RGB Outbending
25 ✗ {{11284, 11300, 1}, [](std::vector<float> const &data) { return ApplyCatboostModel_RGA_inbending_pass1(data); }}, // Fall2019 RGB BAND Inbending
26 ✗ {{11284, 11300, 2}, [](std::vector<float> const &data) { return ApplyCatboostModel_RGA_inbending_pass2(data); }}, // Fall2019 RGB BAND Inbending
27 ✗ {{11323, 11571, 1}, [](std::vector<float> const &data) { return ApplyCatboostModel_RGA_inbending_pass1(data); }}, // Spring2020 RGB Inbending
28 ✗ {{11323, 11571, 2}, [](std::vector<float> const &data) { return ApplyCatboostModel_RGA_inbending_pass2(data); }}, // Spring2020 RGB Inbending
29 ✗ {{16042, 16772, 1}, [](std::vector<float> const &data) { return ApplyCatboostModel_RGC_Summer2022_pass1(data); }}, // Summer2022 RGC Inbending
30 ✗ {{16042, 16772, 2}, [](std::vector<float> const &data) { return ApplyCatboostModel_RGC_Summer2022_pass1(data); }} // Summer2022 RGC Inbending (no pass2 currently)
31 };
32
33 2 void PhotonGBTFilter::Start(hipo::banklist& banks)
34 {
35
36 2 ParseYAMLConfig();
37
38
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 2 b_particle = GetBankIndex(banks, "REC::Particle");
39
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 2 b_calorimeter = GetBankIndex(banks, "REC::Calorimeter");
40
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 2 b_config = GetBankIndex(banks, "RUN::config");
41
42
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 4 o_pass = GetOptionScalar<int>("pass");
43
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 4 o_threshold = GetOptionScalar<double>("threshold");
44 2 }
45
46
47
48 2000 void PhotonGBTFilter::Run(hipo::banklist& banks) const
49 {
50
51
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 2000 auto& particleBank = GetBank(banks, b_particle, "REC::Particle");
52
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 2000 auto& caloBank = GetBank(banks, b_calorimeter, "REC::Calorimeter");
53
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 2000 auto& configBank = GetBank(banks,b_config,"RUN::config");
54 2000 int runnum = configBank.getInt("run",0);
55
56 // Get CaloMap for the event
57 2000 auto calo_map = GetCaloMap(caloBank);
58
59 // dump the bank
60
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 4000 ShowBank(particleBank, Logger::Header("INPUT PARTICLES"));
61
62 // Loop over each photon in the particleBank to classify it
63 // Here we loop over the particleBank RowList
64 // This ensures we are only concerned with filtering photons that passed upstream filters
65
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 2000 particleBank.getMutableRowList().filter([this, &caloBank, &calo_map, runnum](auto bank, auto row) {
66 13986 auto pid = bank.getInt("pid", row);
67
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 13986 if (pid != 22) return true;
68
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 7356 return Filter(bank, caloBank, calo_map, row, runnum);
69 });
70
71 // dump the modified bank
72
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 6000 ShowBank(particleBank, Logger::Header("OUTPUT PARTICLES"));
73
74 2000 }
75
76 9240 bool PhotonGBTFilter::PidPurityPhotonFilter(float const E, float const Epcal, float const theta) const
77 {
78 // Apply standard pid cuts on the photon, compatible to how the models were trained
79 // 1. Minimum photon energy cut of 200 MeV
80 // 2. Photon must have deposited energy in the PCal
81 // 3. Photon must be in the Forward Detector
82
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 9240 if( E<0.2 || Epcal <= 0 || !(ForwardDetectorFilter(theta))) return false;
83 return true;
84 }
85
86 20669 bool PhotonGBTFilter::ForwardDetectorFilter(float const theta) const
87 {
88 // Apply forward detector cut
89
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 20669 if( theta*180/M_PI<5 || theta*180/M_PI>35 ) return false;
90 return true;
91 }
92
93 3678 bool PhotonGBTFilter::Filter(hipo::bank const &particleBank, hipo::bank const &caloBank, std::map<int, PhotonGBTFilter::calo_row_data> calo_map, int const row, int const runnum) const
94 {
95
96 // Set variables native to the photon we are classifying
97 3678 double gPx = particleBank.getFloat("px",row);
98 3678 double gPy = particleBank.getFloat("py",row);
99 3678 double gPz = particleBank.getFloat("pz",row);
100
101 // Set ML features intrinsic to the photon of interest
102 3678 double gE = sqrt(gPx*gPx+gPy*gPy+gPz*gPz);
103 3678 double gTheta = acos(gPz / gE);
104 3678 double gEpcal = calo_map[row].pcal_e;
105 3678 double gm2u = calo_map[row].pcal_m2u;
106 3678 double gm2v = calo_map[row].pcal_m2v;
107
108 // Apply PID purity cuts on the photon
109 // If they do not pass, then these photons are incompatible with the trained GBT model
110
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 3678 if( PidPurityPhotonFilter(gE,gEpcal,gTheta)==false ) return false;
111
112 //Define the variables "m_g" , "m_ch" , "m_nh"
113 //Should not be changed because the model was trained with this specific set of inputs
114 int const m_g = 3; // Number of neighboring gammas
115 int const m_ch = 2; // Number of neighboring charged hadrons (protons, pions, kaons)
116 int const m_nh = 2; // Number of neighboring neutral hadrons (neutrons)
117
118 double R_e = 0;
119 double dE_e = 0;
120
121 double R_gamma[m_g]; // Angular distance between calo shower centers
122 double dE_gamma[m_g]; // Energy difference
123 double Epcal_gamma[m_g]; // Energy deposited in the pcal
124 double m2u_gamma[m_g]; // Shower shape variables
125 double m2v_gamma[m_g]; // Shower shape variables
126
127 double R_ch[m_ch]; // Angular distance between calo shower centers
128 double dE_ch[m_ch]; // Energy difference
129 double Epcal_ch[m_ch]; // Energy deposited in the pcal
130 double m2u_ch[m_ch]; // Shower shape variables
131 double m2v_ch[m_ch]; // Shower shape variables
132
133 double R_nh[m_nh]; // Angular distance between calo shower centers
134 double dE_nh[m_nh]; // Energy difference
135 double Epcal_nh[m_nh]; // Energy deposited in the pcal
136 double m2u_nh[m_nh]; // Shower shape variables
137 double m2v_nh[m_nh]; // Shower shape variables
138
139 double num_photons_0_1, num_photons_0_2, num_photons_0_35;
140
141 //Initialize the arrays
142
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 10256 for (int i=0; i<m_g; ++i) {
143 7692 R_gamma[i] = 0;
144 7692 dE_gamma[i] = 0;
145 7692 Epcal_gamma[i] = 0;
146 7692 m2u_gamma[i] = 0;
147 7692 m2v_gamma[i] = 0;
148 }
149
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 7692 for (int i=0; i<m_ch; ++i){
150 5128 R_ch[i] = 0;
151 5128 dE_ch[i] = 0;
152 5128 Epcal_ch[i] = 0;
153 5128 m2u_ch[i] = 0;
154 5128 m2v_ch[i] = 0;
155 }
156
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 7692 for (int i=0; i<m_nh; ++i){
157 5128 R_nh[i] = 0;
158 5128 dE_nh[i] = 0;
159 5128 Epcal_nh[i] = 0;
160 5128 m2u_nh[i] = 0;
161 5128 m2v_nh[i] = 0;
162 }
163
164 //Set the number of photons within R<0.1, R<0.2, R<0.35
165 num_photons_0_1 = 0;
166 num_photons_0_2 = 0;
167 num_photons_0_35 = 0;
168
169
170 // Get 3-vector that points to the photon of interest's location in the calorimeter
171 2564 auto calo_POI = calo_map.at(row);
172 2564 ROOT::Math::XYZVector vPOI = GetParticleCaloVector(calo_POI);
173
174
175 // Build nearest neighbor event structure
176 // Loop over particles in the event
177 // Here we loop over particleBank.getRows(), which ignores upstream filters
178 // This is critical as the GBTs were trained on identifying nearest neighbors for the whole REC::Particle bank
179 // Only considering nearest neighbor particles that pass upstream filters would call the accuracy of the model into question
180
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 24868 for(int inner_row = 0; inner_row < particleBank.getRows(); inner_row++) {
181 // Skip over the particle if it is photon we are trying to classify
182
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 31552 if (inner_row == row) continue;
183
184 // Check if the key exists in the calo_map
185 // This skips over REC::Particle entries without a REC::Calorimeter entry
186
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 19740 if (calo_map.find(inner_row) == calo_map.end()) continue;
187 11496 auto calo_PART = calo_map.at(inner_row);
188
189 11496 auto pid = particleBank.getInt("pid",inner_row);
190
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 11496 auto mass = particle::get(particle::mass, pid);
191
192 // Skip over particle if its mass was undefined
193
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 11496 if (!mass.has_value()) continue;
194 11426 auto px = particleBank.getFloat("px",inner_row);
195 11426 auto py = particleBank.getFloat("py",inner_row);
196 11426 auto pz = particleBank.getFloat("pz",inner_row);
197 11426 auto p = sqrt(px*px+py*py+pz*pz);
198 11426 auto E = sqrt(p*p+mass.value()*mass.value());
199 11426 auto th = acos(pz/p);
200 // Skip over particle if it is not in the forward detector (necessary for model compatibility)
201
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 11426 if (ForwardDetectorFilter(th)==false) continue;
202
203 // Get 3-vector that points to the neighboring particle's location in the calorimeter
204 11254 ROOT::Math::XYZVector vPART = GetParticleCaloVector(calo_PART);
205
206 // Get angular distance between photon of interest and particle
207 double R = ROOT::Math::VectorUtil::Angle(vPOI, vPART);
208
209 // Logic for filling nearest neighbor variables
210
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 11254 if(pid==22) {//photon
211
212 // Apply Photon Purity Cuts to ensure this neighbor can be used in classification
213
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 5562 if ( PidPurityPhotonFilter(E,calo_PART.pcal_e,th)==false ) continue;
214
215
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 4800 if (R < 0.1) num_photons_0_1++;
216
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 4800 if (R < 0.2) num_photons_0_2++;
217
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 4800 if (R < 0.35) num_photons_0_35++;
218
219
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 7456 for (int i=0; i<m_g; ++i) {
220
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 7200 if (R < R_gamma[i] || R_gamma[i] == 0) {
221 int j = m_g - 1;
222
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 11744 while (j > i) {
223 7200 R_gamma[j] = R_gamma[j - 1];
224 7200 dE_gamma[j] = dE_gamma[j - 1];
225 7200 Epcal_gamma[j] = Epcal_gamma[j - 1];
226 7200 m2u_gamma[j] = m2u_gamma[j - 1];
227 7200 m2v_gamma[j] = m2v_gamma[j - 1];
228 j--;
229 }
230 4544 R_gamma[i] = R;
231 4544 dE_gamma[i] = gE - E;
232 4544 Epcal_gamma[i] = calo_PART.pcal_e;
233 4544 m2u_gamma[i] = calo_PART.pcal_m2u;
234 4544 m2v_gamma[i] = calo_PART.pcal_m2v;
235 4544 break;
236 }
237 }
238 }
239
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 5692 else if(pid==11){//electron
240
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 308 if(R<R_e || R_e==0){
241 R_e = R;
242 308 dE_e = gE - E;
243 }
244 }
245
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 5384 else if(pid==2212||pid==-2212||pid==211||pid==-211||pid==321||pid==-321){//charged hadron
246
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 3010 for (int i=0; i<m_ch; ++i) {
247
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 2946 if (R < R_ch[i] || R_ch[i] == 0) {
248 int j = m_ch - 1;
249
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 4466 while (j > i) {
250 2038 R_ch[j] = R_ch[j - 1];
251 2038 dE_ch[j] = dE_ch[j - 1];
252 2038 Epcal_ch[j] = Epcal_ch[j - 1];
253 2038 m2u_ch[j] = m2u_ch[j - 1];
254 2038 m2v_ch[j] = m2v_ch[j - 1];
255 j--;
256 }
257 2428 R_ch[i] = R;
258 2428 dE_ch[i] = gE- E;
259 2428 Epcal_ch[i] = calo_PART.pcal_e;
260 2428 m2u_ch[i] = calo_PART.pcal_m2u;
261 2428 m2v_ch[i] = calo_PART.pcal_m2v;
262 2428 break;
263 }
264 }
265 }
266
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 2892 else if(pid==2112||pid==-2112){//neutral hadron
267
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 3998 for (int i=0; i<m_nh; ++i) {
268
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 3734 if (R < R_nh[i] || R_nh[i] == 0) {
269 int j = m_nh - 1;
270
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 4678 while (j > i) {
271 2050 R_nh[j] = R_nh[j - 1];
272 2050 dE_nh[j] = dE_nh[j - 1];
273 2050 Epcal_nh[j] = Epcal_nh[j - 1];
274 2050 m2u_nh[j] = m2u_nh[j - 1];
275 2050 m2v_nh[j] = m2v_nh[j - 1];
276 j--;
277 }
278 2628 R_nh[i] = R;
279 2628 dE_nh[i] = gE - E;
280 2628 Epcal_nh[i] = calo_PART.pcal_e;
281 2628 m2u_nh[i] = calo_PART.pcal_m2u;
282 2628 m2v_nh[i] = calo_PART.pcal_m2v;
283 2628 break;
284 }
285 }
286 }
287 else{//unrecognized OR uncompatible particle type for the trained model
288 ✗ continue;
289 }
290 }
291
292 // Create and populate input_data vector for the ML model
293 std::vector<float> input_data = {
294 static_cast<float>(gE), static_cast<float>(gEpcal), static_cast<float>(gTheta),
295 2564 static_cast<float>(gm2u), static_cast<float>(gm2v), static_cast<float>(R_e),
296 2564 static_cast<float>(dE_e)
297 2564 };
298
299
300
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 10256 for (int i = 0; i < m_g; ++i) input_data.push_back(static_cast<float>(R_gamma[i]));
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 10256 for (int i = 0; i < m_g; ++i) input_data.push_back(static_cast<float>(dE_gamma[i]));
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 10256 for (int i = 0; i < m_g; ++i) input_data.push_back(static_cast<float>(Epcal_gamma[i]));
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 10256 for (int i = 0; i < m_g; ++i) input_data.push_back(static_cast<float>(m2u_gamma[i]));
304
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 10256 for (int i = 0; i < m_g; ++i) input_data.push_back(static_cast<float>(m2v_gamma[i]));
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306
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308
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 7692 for (int i = 0; i < m_ch; ++i) input_data.push_back(static_cast<float>(R_ch[i]));
309
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 7692 for (int i = 0; i < m_ch; ++i) input_data.push_back(static_cast<float>(dE_ch[i]));
310
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 7692 for (int i = 0; i < m_ch; ++i) input_data.push_back(static_cast<float>(Epcal_ch[i]));
311
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 7692 for (int i = 0; i < m_ch; ++i) input_data.push_back(static_cast<float>(m2u_ch[i]));
312
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 7692 for (int i = 0; i < m_ch; ++i) input_data.push_back(static_cast<float>(m2v_ch[i]));
313
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 7692 for (int i = 0; i < m_nh; ++i) input_data.push_back(static_cast<float>(R_nh[i]));
317
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 7692 for (int i = 0; i < m_nh; ++i) input_data.push_back(static_cast<float>(dE_nh[i]));
318
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 7692 for (int i = 0; i < m_nh; ++i) input_data.push_back(static_cast<float>(Epcal_nh[i]));
319
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 7692 for (int i = 0; i < m_nh; ++i) input_data.push_back(static_cast<float>(m2u_nh[i]));
320
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 7692 for (int i = 0; i < m_nh; ++i) input_data.push_back(static_cast<float>(m2v_nh[i]));
321
322
323
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 2564 input_data.push_back(static_cast<float>(num_photons_0_1));
324
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 2564 input_data.push_back(static_cast<float>(num_photons_0_2));
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 2564 input_data.push_back(static_cast<float>(num_photons_0_35));
326
327
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 2564 return ClassifyPhoton(input_data, runnum);
328 }
329
330 2564 bool PhotonGBTFilter::ClassifyPhoton(std::vector<float> const &input_data, int const runnum) const {
331 2564 auto modelFunction = getModelFunction(runnum);
332 double sigmoid_x = modelFunction(input_data);
333 2564 double prediction = 1 / (1 + exp(-sigmoid_x));
334
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 5128 return (prediction > o_threshold);
335 }
336
337 2000 std::map<int, PhotonGBTFilter::calo_row_data> PhotonGBTFilter::GetCaloMap(hipo::bank const& bank) const
338 {
339 std::map<int, PhotonGBTFilter::calo_row_data> calo_map;
340 // Loop over REC::Calorimeter rows
341 // Here we use bank.getRows() to purposefully ignore upstream filters
342
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 14258 for(int row = 0; row < bank.getRows(); row++){
343 12258 auto pindex = bank.getShort("pindex",row);
344 12258 auto x = bank.getFloat("x",row);
345 12258 auto y = bank.getFloat("y",row);
346 12258 auto z = bank.getFloat("z",row);
347 12258 auto m2u = bank.getFloat("m2u",row);
348 12258 auto m2v = bank.getFloat("m2v",row);
349 12258 auto layer = bank.getInt("layer",row);
350 12258 auto e = bank.getFloat("energy",row);
351
352 // Ensure an entry exists in the map for the given pindex
353
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 12258 if (calo_map.find(pindex) == calo_map.end()) {
354
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 7572 calo_map[pindex] = PhotonGBTFilter::calo_row_data();
355 }
356
357
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 12258 switch(layer){
358 6292 case 1: // pcal
359
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 6292 calo_map[pindex].pcal_x = x;
360
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 6292 calo_map[pindex].pcal_y = y;
361
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 6292 calo_map[pindex].pcal_z = z;
362
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 6292 calo_map[pindex].pcal_e = e;
363
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 6292 calo_map[pindex].pcal_m2u = m2u;
364
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 6292 calo_map[pindex].pcal_m2v = m2v;
365 6292 break;
366 3706 case 4: // ecin
367
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 3706 calo_map[pindex].ecin_x = x;
368
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 3706 calo_map[pindex].ecin_y = y;
369
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 3706 calo_map[pindex].ecin_z = z;
370 3706 break;
371 2260 case 7: // ecout
372
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 2260 calo_map[pindex].ecout_x = x;
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 2260 calo_map[pindex].ecout_y = y;
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 2260 calo_map[pindex].ecout_z = z;
375 2260 break;
376 }
377 }
378 2000 return calo_map;
379 }
380
381 13818 ROOT::Math::XYZVector PhotonGBTFilter::GetParticleCaloVector(PhotonGBTFilter::calo_row_data calo_row) const {
382 // Determine the 3-vector location of where the photon of interest's calo deposition is
383 // First we check the pcal coords, then ecin, then ecout
384 ROOT::Math::XYZVector v;
385
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 13818 if (calo_row.pcal_x == 0) {
386
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 2004 if (calo_row.ecin_x == 0) {
387 756 v.SetXYZ(calo_row.ecout_x, calo_row.ecout_y, calo_row.ecout_z);
388 } else {
389 1248 v.SetXYZ(calo_row.ecin_x, calo_row.ecin_y, calo_row.ecin_z);
390 }
391 } else {
392 11814 v.SetXYZ(calo_row.pcal_x, calo_row.pcal_y, calo_row.pcal_z);
393 }
394 13818 return v;
395 }
396
397 2564 std::function<double(std::vector<float> const &)> PhotonGBTFilter::getModelFunction(int runnum) const {
398
399
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 17948 for (const auto &entry : modelMap) {
400
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 17948 if (runnum >= std::get<0>(entry.first) && runnum <= std::get<1>(entry.first) && o_pass == std::get<2>(entry.first)) {
401 2564 return entry.second;
402 }
403 }
404
405 // Default to RGA inbending pass1 if no match found
406 ✗ m_log->Warn("Run Number {} with pass {} has no matching PhotonGBT model...Defaulting to RGA inbending pass1...", runnum, o_pass);
407 ✗ return [](std::vector<float> const &data) { return ApplyCatboostModel_RGA_inbending_pass1(data); };
408 }
409
410 1 void PhotonGBTFilter::Stop()
411 {
412 1 }
413
414 }
415