ATLAS Offline Software
AFP_NoisyPixelTool.cxx
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1 /*
2  Copyright (C) 2002-2022 CERN for the benefit of the ATLAS collaboration
3 */
4 
5 #include "AFP_Calibration/AFP_NoisyPixelTool.h"
6 
7 int AFP_NoisyPixelTool::Identify(std::shared_ptr<const TH2F> input, std::vector<TH2F>& output) const
8 {
9  if(output.size()!=1)
10  {
11  return 0;
12  }
13 
14  output.reserve(4);
15 
16  TH2F& template_output = output.at(0);
17  template_output.Reset();
18 
19  TH2F tmp_output1(template_output);
20  tmp_output1.SetNameTitle(Form("leffpixels_found_%s",template_output.GetName()), Form("low efficiency pixels, found, %s", template_output.GetTitle()));
21 
22  TH2F tmp_output2(template_output);
23  tmp_output2.SetNameTitle(Form("noisypixels_eff_%s",template_output.GetName()), Form("noisy pixels, efficiency, %s", template_output.GetTitle()));
24 
25  TH2F tmp_output3(template_output);
26  tmp_output3.SetNameTitle(Form("leffpixels_eff_%s",template_output.GetName()), Form("low efficiency pixels, efficiency, %s", template_output.GetTitle()));
27  TH2F& noisypixels_found_output = output.at(0);
28  noisypixels_found_output.SetNameTitle(Form("noisypixels_found_%s",template_output.GetName()), Form("noisy pixels, found, %s", template_output.GetTitle()));
29 
30  output.push_back(tmp_output1);
31  output.push_back(tmp_output2);
32  output.push_back(tmp_output3);
33 
34  TH2F& leffpixels_found_output = output.at(1);
35  TH2F& noisypixels_eff_output = output.at(2);
36  TH2F& leffpixels_eff_output = output.at(3);
37 
38 
39  if(input->GetMaximum()<0.5) return 0;
40 
41  std::vector<TH2F> vec_found_leff{},vec_found_noisy{},vec_eff_leff{},vec_eff_noisy{};
42 
43  for(const auto& method : m_methods)
44  {
45  TH2F tmp_eff_noisy, tmp_eff_leff;
46  TH2F tmp_found_leff, tmp_found_noisy;
47 
48  if(method!="FIT")
49  {
50  // count inactive pixels around each pixel
51  TH2F tmp_inact_pix_around=countInactivePixelsAround(input);
52 
53  // find low eff. and noisy pixels
54  std::tie(tmp_found_leff,tmp_found_noisy,tmp_eff_leff, tmp_eff_noisy) = findLEffAndNoisyPixels(input, tmp_inact_pix_around, method);
55 
56  // reconsider some pixels
57  filterLEffPixelsAroundNoisy(input, tmp_inact_pix_around, tmp_found_leff,tmp_found_noisy,tmp_eff_leff,tmp_eff_noisy, method);
58  }
59  else
60  {
61  // method is "FIT"
62 
63  tmp_eff_noisy=TH2F(*input);
64  tmp_eff_noisy.SetName("tmp_eff_noisy");
65  tmp_eff_noisy.Reset();
66  tmp_eff_leff=TH2F(*input);
67  tmp_eff_leff.SetName("tmp_eff_leff");
68  tmp_eff_leff.Reset();
69  tmp_found_noisy=TH2F(*input);
70  tmp_found_noisy.SetName("tmp_found_noisy");
71  tmp_found_noisy.Reset();
72  tmp_found_leff=TH2F(*input);
73  tmp_found_leff.SetName("tmp_found_leff");
74  tmp_found_leff.Reset();
75 
76  auto p=makeFits(input);
77 
78  const double Threshold_LEFF=5.;
79  const double Threshold_NOISY=(m_threshNoisy-1)*100;
80 
81  for(int col_ID=1; col_ID<=input->GetNbinsY(); col_ID++)
82  {
83  for(int row_ID=1; row_ID<=input->GetNbinsX(); row_ID++)
84  {
85  double ratio_leff=0, ratio_noisy=0;
86  double gauss_helper_12 = (col_ID-p[1][row_ID])/p[2][row_ID];
87  double gauss_helper_45 = (col_ID-p[4][row_ID])/p[5][row_ID];
88  double fit = p[0][row_ID]*exp(-0.5*gauss_helper_12*gauss_helper_12)
89  + p[3][row_ID]*exp(-0.5*gauss_helper_45*gauss_helper_45) + p[6][row_ID];
90  double sigma=std::sqrt(fit);
91  if(sigma<1) sigma=1;
92 
93  double bin_content=input->GetBinContent(row_ID,col_ID);
94 
95  if(fit>=bin_content) ratio_leff = std::abs(fit-bin_content)/sigma;
96  else ratio_noisy = std::abs(fit-bin_content)/sigma;
97 
98  if(bin_content!=0)
99  {
100  if( ratio_leff > Threshold_LEFF) tmp_found_leff.SetBinContent(row_ID,col_ID,1);
101  if( ratio_noisy > Threshold_NOISY) tmp_found_noisy.SetBinContent(row_ID,col_ID,1);
102  }
103  tmp_eff_leff.SetBinContent(row_ID,col_ID,100.*ratio_leff);
104  tmp_eff_noisy.SetBinContent(row_ID,col_ID,100.*ratio_noisy);
105  }
106  }
107  }
108 
109  // save output for this method
110  vec_found_leff.push_back(tmp_found_leff);
111  vec_found_noisy.push_back(tmp_found_noisy);
112  vec_eff_leff.push_back(tmp_eff_leff);
113  vec_eff_noisy.push_back(tmp_eff_noisy);
114  }
115 
116  // sum found pixels for all methods, set efficiency to the maximum of all methods
117  for(unsigned int m=0; m<m_methods.size(); m++)
118  {
119  for(int col_ID=1; col_ID<=input->GetNbinsY(); col_ID++)
120  {
121  for(int row_ID=1; row_ID<=input->GetNbinsX(); row_ID++)
122  {
123  noisypixels_found_output.Fill(row_ID,col_ID, vec_found_noisy[m].GetBinContent(row_ID,col_ID));
124  leffpixels_found_output.Fill(row_ID,col_ID, vec_found_leff[m].GetBinContent(row_ID,col_ID));
125 
126  if(m==0)
127  {
128  noisypixels_eff_output.SetBinContent(row_ID,col_ID, vec_eff_noisy[m].GetBinContent(row_ID,col_ID));
129  leffpixels_eff_output.SetBinContent(row_ID,col_ID, vec_eff_leff[m].GetBinContent(row_ID,col_ID));
130  }
131  else
132  {
133  if(noisypixels_eff_output.GetBinContent(row_ID,col_ID)<vec_eff_noisy[m].GetBinContent(row_ID,col_ID))
134  {
135  noisypixels_eff_output.SetBinContent(row_ID,col_ID, vec_eff_noisy[m].GetBinContent(row_ID,col_ID));
136  }
137  if(leffpixels_eff_output.GetBinContent(row_ID,col_ID)>vec_eff_leff[m].GetBinContent(row_ID,col_ID))
138  {
139  leffpixels_eff_output.SetBinContent(row_ID,col_ID, vec_eff_leff[m].GetBinContent(row_ID,col_ID));
140  }
141  }
142  }
143  }
144  }
145 
146  // report only pixels that are identified by all methods
147  for(int col_ID=1; col_ID<=input->GetNbinsY(); col_ID++)
148  {
149  for(int row_ID=1; row_ID<=input->GetNbinsX(); row_ID++)
150  {
151  if(noisypixels_found_output.GetBinContent(row_ID,col_ID)==m_methods.size()) noisypixels_found_output.SetBinContent(row_ID,col_ID,1);
152  else noisypixels_found_output.SetBinContent(row_ID,col_ID,0);
153 
154  if(leffpixels_found_output.GetBinContent(row_ID,col_ID)==m_methods.size()) leffpixels_found_output.SetBinContent(row_ID,col_ID,1);
155  else leffpixels_found_output.SetBinContent(row_ID,col_ID,0);
156  }
157  }
158 
159  return 0;
160 }
161 
162 
163 std::vector<std::pair<int,int>> AFP_NoisyPixelTool::getLegitPixels(std::shared_ptr<const TH2F> input, const int col_ID, const int row_ID, const std::string& method="8_PIX") const
164 {
165  // method="8_PIX" means to investigate all pixels around
166 
167  std::vector<std::pair<int,int>> legit_pixels={};
168 
169  for(int r=-1; r<2; r++)
170  {
171  for(int c=-1; c<2; c++)
172  {
173  if( (row_ID+r)>=1 && (col_ID+c)>=1 && (col_ID+c)<=input->GetNbinsY() && (row_ID+r)<=input->GetNbinsX() && (r!=0 || c!=0))
174  {
175  if( (method=="2_ROW" && c==0) || (method=="2_COL" && r==0)
176  || (method=="4_PIX" && (c==0 || r==0)) || (method=="8_PIX") )
177  {
178  legit_pixels.push_back(std::pair<int,int>(row_ID+r,col_ID+c));
179  }
180  }
181  }
182  }
183 
184  return legit_pixels;
185 }
186 
187 
188 TH2F AFP_NoisyPixelTool::countInactivePixelsAround(std::shared_ptr<const TH2F> input) const
189 {
190  TH2F tmp_inact_pix_around(*input);
191  tmp_inact_pix_around.SetName("tmp_inact_pix_around");
192  tmp_inact_pix_around.Reset();
193 
194  for(int col_ID=1; col_ID<=input->GetNbinsY(); col_ID++)
195  {
196  for(int row_ID=1; row_ID<=input->GetNbinsX(); row_ID++)
197  {
198  int inactive_pixels_around=0;
199  auto legit_pixels=getLegitPixels(input, col_ID, row_ID);
200 
201  for(auto legpix : legit_pixels)
202  {
203  if(input->GetBinContent(legpix.first, legpix.second)<1) ++inactive_pixels_around;
204  }
205 
206  tmp_inact_pix_around.SetBinContent(row_ID,col_ID, inactive_pixels_around+0.01);
207  }
208  }
209 
210  return tmp_inact_pix_around;
211 }
212 
213 
214 double AFP_NoisyPixelTool::getNeighbours(std::shared_ptr<const TH2F> input, int row_ID, int col_ID) const
215 {
216  if( row_ID!=input->GetNbinsX() && row_ID!=1 && col_ID!=1 && col_ID!=input->GetNbinsY()) return 8.;
217  else if( (row_ID==input->GetNbinsX() || row_ID==1) && (col_ID==1 || col_ID==input->GetNbinsY())) return 3.;
218  else return 5;
219 }
220 
221 
222 std::tuple<TH2F,TH2F,TH2F,TH2F> AFP_NoisyPixelTool::findLEffAndNoisyPixels(std::shared_ptr<const TH2F> input, const TH2F& tmp_inact_pix_around, const std::string& method) const
223 {
224  TH2F tmp_found_leff(*input);
225  tmp_found_leff.SetName("tmp_found_leff");
226  tmp_found_leff.Reset();
227 
228  TH2F tmp_found_noisy(*input);
229  tmp_found_noisy.SetName("tmp_found_noisy");
230  tmp_found_noisy.Reset();
231 
232  TH2F tmp_eff_leff(*input);
233  tmp_eff_leff.SetName("tmp_eff_leff");
234  tmp_eff_leff.Reset();
235 
236  TH2F tmp_eff_noisy(*input);
237  tmp_eff_noisy.SetName("tmp_eff_noisy");
238  tmp_eff_noisy.Reset();
239 
240  for(int col_ID=1; col_ID<=input->GetNbinsY(); col_ID++)
241  {
242  for(int row_ID=1; row_ID<=input->GetNbinsX(); row_ID++)
243  {
244  if(input->GetBinContent(row_ID, col_ID)!=0 && tmp_inact_pix_around.GetBinContent(row_ID, col_ID)<0.1)
245  {
246  double npixels=getNeighbours(input, row_ID, col_ID);
247 
248  double sum=0.;
249  auto legit_pixels=getLegitPixels(input, col_ID, row_ID, method);
250  for(auto legpix : legit_pixels)
251  {
252  sum+=input->GetBinContent(legpix.first,legpix.second);
253  }
254 
255  double av_noisy=0;
256  double av_leff=0;
257 
258  // TODO: something about edges?
259  if(method=="2_ROW" || method=="2_COL")
260  {
261  av_noisy=sum/(std::abs(npixels-4)*0.5);
262  av_leff=sum/2.;
263  }
264  else if(method=="4_PIX")
265  {
266  av_noisy=sum/(std::ceil(npixels*0.5));
267  av_leff=sum/4.;
268  }
269  else if(method=="8_PIX")
270  {
271  av_noisy=sum/(npixels);
272  av_leff=sum/8.;
273  }
274 
275 
276  double ratio_leff = input->GetBinContent(row_ID,col_ID)/av_leff;
277  double ratio_noisy = input->GetBinContent(row_ID,col_ID)/av_noisy;
278  tmp_eff_leff.SetBinContent(row_ID,col_ID,100.*ratio_leff);
279  tmp_eff_noisy.SetBinContent(row_ID,col_ID,100.*ratio_noisy);
280 
281  if(ratio_leff < m_threshLEff) tmp_found_leff.SetBinContent(row_ID,col_ID,1.);
282  if(ratio_noisy > m_threshNoisy) tmp_found_noisy.SetBinContent(row_ID,col_ID,1.);
283  }
284  }
285  }
286 
287  return {tmp_found_leff,tmp_found_noisy,tmp_eff_leff,tmp_eff_noisy};
288 }
289 
290 
291 void AFP_NoisyPixelTool::filterLEffPixelsAroundNoisy(std::shared_ptr<const TH2F> input, const TH2F& tmp_inact_pix_around, TH2F& tmp_found_leff, TH2F& tmp_found_noisy, TH2F& tmp_eff_leff, TH2F& tmp_eff_noisy, const std::string& method) const
292 {
293  for(int col_ID=1; col_ID<=input->GetNbinsY(); col_ID++)
294  {
295  for(int row_ID=1; row_ID<=input->GetNbinsX(); row_ID++)
296  {
297  if(input->GetBinContent(row_ID,col_ID)==0) continue;
298 
299  int inactive_pixels_around=tmp_inact_pix_around.GetBinContent(row_ID, col_ID);
300  int leff_pixels_around=0;
301  auto legit_pixels=getLegitPixels(input, col_ID,row_ID);
302  for(auto legpix : legit_pixels)
303  {
304  if(tmp_found_leff.GetBinContent(legpix.first,legpix.second)>0) leff_pixels_around++;
305  }
306 
307  if(leff_pixels_around!=0 && inactive_pixels_around==0)
308  {
309  //if low eff. pixels around > 0, recalculate avarage
310  double re_av=0.;
311  int npix=0;
312 
313  auto legit_pixels=getLegitPixels(input, col_ID,row_ID, method);
314  for(auto legpix : legit_pixels)
315  {
316  if(tmp_found_leff.GetBinContent(legpix.first,legpix.second)==0)
317  {
318  re_av+=input->GetBinContent(legpix.first,legpix.second);
319  npix++;
320  }
321  }
322 
323  if(npix==0)
324  {
325  re_av=0;
326  for(auto legpix : legit_pixels)
327  {
328  re_av+=input->GetBinContent(legpix.first,legpix.second);
329  }
330 
331  if(re_av/input->GetBinContent(row_ID,col_ID)<1.5 && re_av/input->GetBinContent(row_ID,col_ID)>0.5) re_av=1;
332  }
333  else
334  {
335  re_av/=npix;
336  }
337 
338  double ratio_noisy = m_sensitivity+input->GetBinContent(row_ID,col_ID)/re_av;
339  double ratio_leff = input->GetBinContent(row_ID,col_ID)/re_av;
340  if(re_av==1)
341  {
342  tmp_eff_leff.SetBinContent(row_ID,col_ID,1);
343  tmp_eff_noisy.SetBinContent(row_ID,col_ID,1);
344  }
345 
346  if(row_ID!=input->GetNbinsX() && row_ID!=1 && col_ID!=1 && col_ID!=input->GetNbinsY())
347  {
348  if( ((ratio_noisy>m_threshNoisy && re_av!=1) || re_av==1) )
349  {
350  tmp_found_noisy.SetBinContent(row_ID,col_ID,1);
351  if(re_av!=1) tmp_eff_noisy.SetBinContent(row_ID,col_ID,100.*ratio_noisy);
352  }
353  if( ((ratio_leff<m_threshLEff && re_av!=1) || re_av==1) )
354  {
355  tmp_found_leff.SetBinContent(row_ID,col_ID,1);
356  if(re_av!=1) tmp_eff_leff.SetBinContent(row_ID,col_ID,100.*ratio_leff);
357  }
358  }
359  }
360  }
361  }
362 
363  return;
364 }
365 
366 
367 std::vector<std::vector<double>> AFP_NoisyPixelTool::makeFits(std::shared_ptr<const TH2F> input) const
368 {
369  const int nParams=7;
370  std::vector<std::vector<double>> params(nParams, std::vector<double>(input->GetNbinsX()+1, 0.));
371 
372  for(int row_ID=0; row_ID<=input->GetNbinsX(); row_ID++)
373  {
374  std::string srow_ID = std::to_string(row_ID);
375  std::unique_ptr<TH1F> hist(new TH1F(("ROW"+srow_ID).c_str(), ("ROW"+srow_ID).c_str(), input->GetNbinsY(), 0.5, input->GetNbinsX()+0.5));
376 
377  for(int col_ID=1; col_ID<=input->GetNbinsY(); col_ID++)
378  {
379  hist->Fill(col_ID, 1.0*input->GetBinContent(row_ID,col_ID));
380  }
381 
382  //Set double gaussian fit with starting parameters
383  std::shared_ptr<TF1> FIT_2(new TF1("gaus","gaus(0)+gaus(3)+[6]",0,input->GetNbinsY(),nParams));
384  if(row_ID==1)
385  {
386  FIT_2->SetParameters(25000,hist->GetMean(),hist->GetStdDev()/std::sqrt(2),
387  5000,hist->GetMean(),hist->GetStdDev()/std::sqrt(2),
388  std::max(hist->GetBinContent(2),std::max(hist->GetBinContent(3),hist->GetBinContent(4))) );
389  }
390  else
391  {
392  for(int par=0;par<nParams;++par)
393  {
394  FIT_2->SetParameter(par,params.at(par).at(row_ID-1));
395  }
396  }
397 
398  //fit
399  hist->Fit(FIT_2.get(),"Q");
400 
401  for(int par=0;par<nParams;++par)
402  {
403  params.at(par).at(row_ID-1)=FIT_2->GetParameter(par);
404  }
405  }
406 
407  return params;
408 }
409 
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