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LumiReweightingStandAlone.h
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1 #ifndef PhysicsTools_Utilities_interface_LumiReWeighting_h
2 #define PhysicsTools_Utilities_interface_LumiReWeighting_h
3 
19 #include "TH1F.h"
20 #include "TH3.h"
21 #include "TFile.h"
22 #include "TRandom1.h"
23 #include "TRandom2.h"
24 #include "TRandom3.h"
25 #include "TStopwatch.h"
26 #include <string>
27 #include <vector>
28 #include <cmath>
29 #include <algorithm>
30 #include <iostream>
31 
32 namespace reweight {
33 
34  // add a class to shift the mean of a poisson-like luminosity distribution by an arbitrary amount.
35  // Only valid for small (<1.5) shifts about the 2011 lumi distribution for now, because shifts are non-linear
36  // Each PoissonMeanShifter does one shift, so defining multiples can give you an arbitrary collection
37 
38  class PoissonMeanShifter {
39  public:
41 
42  PoissonMeanShifter(float Shift) {
43  // these are the polynomial or exponential coefficients for each bin of a 25-bin sequence that
44  // convert the Distribution of the 2011 luminosity to something with a lower or higher peak luminosity.
45  // The distributions aren't quite poisson because they model luminosity decreasing during a fill. This implies that
46  // they do get wider as the mean increases, so the weights are not linear with increasing mean.
47 
48  static const double p0_minus[20] = {1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
49  1., 1., 1., 1., 1., 1., 1., 1., 1., 1.};
50  static const double p1_minus[20] = {-0.677786, -0.619614, -0.49465, -0.357963, -0.238359, -0.110002, 0.0348629,
51  0.191263, 0.347648, 0.516615, 0.679646, 0.836673, 0.97764, 1.135,
52  1.29922, 1.42467, 1.55901, 1.61762, 1.67275, 1.96008};
53  static const double p2_minus[20] = {
54  0.526164, 0.251816, 0.11049, 0.026917, -0.0464692, -0.087022, -0.0931581, -0.0714295, -0.0331772, 0.0347473,
55  0.108658, 0.193048, 0.272314, 0.376357, 0.4964, 0.58854, 0.684959, 0.731063, 0.760044, 1.02386};
56 
57  static const double p1_expoM[5] = {1.63363e-03, 6.79290e-04, 3.69900e-04, 2.24349e-04, 9.87156e-06};
58 
59  static const double p2_expoM[5] = {2.64692, 3.26585, 3.53229, 4.18035, 5.64027};
60 
61  static const double p0_plus[20] = {1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
62  1., 1., 1., 1., 1., 1., 1., 1., 1., 1.};
63  static const double p1_plus[20] = {-0.739059, -0.594445, -0.477276, -0.359707, -0.233573, -0.103458, 0.0373401,
64  0.176571, 0.337617, 0.499074, 0.675126, 0.840522, 1.00917, 1.15847,
65  1.23816, 1.44271, 1.52982, 1.46385, 1.5802, 0.988689};
66  static const double p2_plus[20] = {0.208068, 0.130033, 0.0850356, 0.0448344, 0.000749832,
67  -0.0331347, -0.0653281, -0.0746009, -0.0800667, -0.0527636,
68  -0.00402649, 0.103338, 0.261261, 0.491084, 0.857966,
69  1.19495, 1.75071, 2.65559, 3.35433, 5.48835};
70 
71  static const double p1_expoP[5] = {1.42463e-01, 4.18966e-02, 1.12697e-01, 1.66197e-01, 1.50768e-01};
72 
73  static const double p2_expoP[5] = {1.98758, 2.27217, 2.26799, 2.38455, 2.52428};
74 
75  // initialize weights based on desired Shift
76 
77  for (int ibin = 0; ibin < 20; ibin++) {
78  if (Shift < .0) {
79  Pweight_[ibin] = p0_minus[ibin] + p1_minus[ibin] * Shift + p2_minus[ibin] * Shift * Shift;
80  } else {
81  Pweight_[ibin] = p0_plus[ibin] + p1_plus[ibin] * Shift + p2_plus[ibin] * Shift * Shift;
82  }
83  }
84 
85  // last few bins fit better to an exponential...
86 
87  for (int ibin = 20; ibin < 25; ibin++) {
88  if (Shift < 0.) {
89  Pweight_[ibin] = p1_expoM[ibin - 20] * exp(p2_expoM[ibin - 20] * Shift);
90  } else {
91  Pweight_[ibin] = p1_expoP[ibin - 20] * exp(p2_expoP[ibin - 20] * Shift);
92  }
93  }
94  };
95 
96  double ShiftWeight(int ibin) {
97  if (ibin < 25 && ibin >= 0) {
98  return Pweight_[ibin];
99  } else {
100  return 0;
101  }
102  };
103 
104  double ShiftWeight(float pvnum) {
105  int ibin = int(pvnum);
106 
107  if (ibin < 25 && ibin >= 0) {
108  return Pweight_[ibin];
109  } else {
110  return 0;
111  }
112  };
113 
114  private:
115  double Pweight_[25];
116  };
117 
119  public:
121 
122  LumiReWeighting(std::string generatedFile, std::string dataFile, std::string GenHistName, std::string DataHistName)
123  : generatedFileName_(generatedFile),
124  dataFileName_(dataFile),
125  GenHistName_(GenHistName),
126  DataHistName_(DataHistName) {
127  generatedFile_ = new TFile(generatedFileName_.c_str()); //MC distribution
128  dataFile_ = new TFile(dataFileName_.c_str()); //Data distribution
129 
130  Data_distr_ = new TH1F(*(static_cast<TH1F*>(dataFile_->Get(DataHistName_.c_str())->Clone())));
131  MC_distr_ = new TH1F(*(static_cast<TH1F*>(generatedFile_->Get(GenHistName_.c_str())->Clone())));
132 
133  // normalize both histograms first
134 
135  Data_distr_->Scale(1.0 / Data_distr_->Integral());
136  MC_distr_->Scale(1.0 / MC_distr_->Integral());
137 
138  weights_ = new TH1F(*(Data_distr_));
139 
140  // MC * data/MC = data, so the weights are data/MC:
141 
142  weights_->SetName("lumiWeights");
143 
144  TH1F* den = new TH1F(*(MC_distr_));
145 
146  weights_->Divide(den); // so now the average weight should be 1.0
147 
148  std::cout << " Lumi/Pileup Reweighting: Computed Weights per In-Time Nint " << std::endl;
149 
150  int NBins = weights_->GetNbinsX();
151 
152  for (int ibin = 1; ibin < NBins + 1; ++ibin) {
153  std::cout << " " << ibin - 1 << " " << weights_->GetBinContent(ibin) << std::endl;
154  }
155 
156  weightOOT_init();
157 
158  FirstWarning_ = true;
159  }
160 
161  LumiReWeighting(const std::vector<float>& MC_distr, const std::vector<float>& Lumi_distr) {
162  // no histograms for input: use vectors
163 
164  // now, make histograms out of them:
165 
166  // first, check they are the same size...
167 
168  if (MC_distr.size() != Lumi_distr.size()) {
169  std::cerr << "ERROR: LumiReWeighting: input vectors have different sizes. Quitting... \n";
170  return;
171  }
172 
173  Int_t NBins = MC_distr.size();
174 
175  MC_distr_ = new TH1F("MC_distr", "MC dist", NBins, -0.5, float(NBins) - 0.5);
176  Data_distr_ = new TH1F("Data_distr", "Data dist", NBins, -0.5, float(NBins) - 0.5);
177 
178  weights_ = new TH1F("luminumer", "luminumer", NBins, -0.5, float(NBins) - 0.5);
179  TH1F* den = new TH1F("lumidenom", "lumidenom", NBins, -0.5, float(NBins) - 0.5);
180 
181  for (int ibin = 1; ibin < NBins + 1; ++ibin) {
182  weights_->SetBinContent(ibin, Lumi_distr[ibin - 1]);
183  Data_distr_->SetBinContent(ibin, Lumi_distr[ibin - 1]);
184  den->SetBinContent(ibin, MC_distr[ibin - 1]);
185  MC_distr_->SetBinContent(ibin, MC_distr[ibin - 1]);
186  }
187 
188  // check integrals, make sure things are normalized
189 
190  float deltaH = weights_->Integral();
191  if (fabs(1.0 - deltaH) > 0.02) { //*OOPS*...
192  weights_->Scale(1.0 / deltaH);
193  Data_distr_->Scale(1.0 / deltaH);
194  }
195  float deltaMC = den->Integral();
196  if (fabs(1.0 - deltaMC) > 0.02) {
197  den->Scale(1.0 / deltaMC);
198  MC_distr_->Scale(1.0 / deltaMC);
199  }
200 
201  weights_->Divide(den); // so now the average weight should be 1.0
202 
203  std::cout << " Lumi/Pileup Reweighting: Computed Weights per In-Time Nint " << std::endl;
204 
205  for (int ibin = 1; ibin < NBins + 1; ++ibin) {
206  std::cout << " " << ibin - 1 << " " << weights_->GetBinContent(ibin) << std::endl;
207  }
208 
209  weightOOT_init();
210 
211  FirstWarning_ = true;
212  }
213 
214  void weight3D_init(float ScaleFactor, std::string WeightOutputFile = "") {
215  //create histogram to write output weights, save pain of generating them again...
216 
217  TH3D* WHist = new TH3D("WHist", "3D weights", 50, 0., 50., 50, 0., 50., 50, 0., 50.);
218  TH3D* DHist = new TH3D("DHist", "3D weights", 50, 0., 50., 50, 0., 50., 50, 0., 50.);
219  TH3D* MHist = new TH3D("MHist", "3D weights", 50, 0., 50., 50, 0., 50., 50, 0., 50.);
220 
221  using std::min;
222 
223  if (MC_distr_->GetEntries() == 0) {
224  std::cout << " MC and Data distributions are not initialized! You must call the LumiReWeighting constructor. "
225  << std::endl;
226  }
227 
228  // arrays for storing number of interactions
229 
230  double MC_ints[50][50][50];
231  double Data_ints[50][50][50];
232 
233  for (int i = 0; i < 50; i++) {
234  for (int j = 0; j < 50; j++) {
235  for (int k = 0; k < 50; k++) {
236  MC_ints[i][j][k] = 0.;
237  Data_ints[i][j][k] = 0.;
238  }
239  }
240  }
241 
242  double factorial[50];
243  double PowerSer[50];
244  double base = 1.;
245 
246  factorial[0] = 1.;
247  PowerSer[0] = 1.;
248 
249  for (int i = 1; i < 50; ++i) {
250  base = base * float(i);
251  factorial[i] = base;
252  }
253 
254  double x;
255  double xweight;
256  double probi, probj, probk;
257  double Expval, mean;
258  int xi;
259 
260  // Get entries for Data, MC, fill arrays:
261  int NMCbin = MC_distr_->GetNbinsX();
262 
263  for (int jbin = 1; jbin < NMCbin + 1; jbin++) {
264  x = MC_distr_->GetBinCenter(jbin);
265  xweight = MC_distr_->GetBinContent(jbin); //use as weight for matrix
266 
267  //for Summer 11, we have this int feature:
268  xi = int(x);
269 
270  // Generate Poisson distribution for each value of the mean
271  mean = double(xi);
272 
273  if (mean < 0.) {
274  std::cout << "LumiReweighting:BadInputValue"
275  << " Your histogram generates MC luminosity values less than zero!"
276  << " Please Check. Terminating." << std::endl;
277  }
278 
279  if (mean == 0.) {
280  Expval = 1.;
281  } else {
282  Expval = exp(-1. * mean);
283  }
284 
285  base = 1.;
286 
287  for (int i = 1; i < 50; ++i) {
288  base = base * mean;
289  PowerSer[i] = base; // PowerSer is mean^i
290  }
291 
292  // compute poisson probability for each Nvtx in weight matrix
293  for (int i = 0; i < 50; i++) {
294  probi = PowerSer[i] / factorial[i] * Expval;
295  for (int j = 0; j < 50; j++) {
296  probj = PowerSer[j] / factorial[j] * Expval;
297  for (int k = 0; k < 50; k++) {
298  probk = PowerSer[k] / factorial[k] * Expval;
299  // joint probability is product of event weights multiplied by weight of input distribution bin
300  MC_ints[i][j][k] = MC_ints[i][j][k] + probi * probj * probk * xweight;
301  }
302  }
303  }
304  }
305 
306  int NDatabin = Data_distr_->GetNbinsX();
307 
308  for (int jbin = 1; jbin < NDatabin + 1; jbin++) {
309  mean = (Data_distr_->GetBinCenter(jbin)) * ScaleFactor;
310  xweight = Data_distr_->GetBinContent(jbin);
311 
312  // Generate poisson distribution for each value of the mean
313  if (mean < 0.) {
314  std::cout << "LumiReweighting:BadInputValue"
315  << " Your histogram generates MC luminosity values less than zero!"
316  << " Please Check. Terminating." << std::endl;
317  }
318 
319  if (mean == 0.) {
320  Expval = 1.;
321  } else {
322  Expval = exp(-1. * mean);
323  }
324 
325  base = 1.;
326 
327  for (int i = 1; i < 50; ++i) {
328  base = base * mean;
329  PowerSer[i] = base;
330  }
331 
332  // compute poisson probability for each Nvtx in weight matrix
333 
334  for (int i = 0; i < 50; i++) {
335  probi = PowerSer[i] / factorial[i] * Expval;
336  for (int j = 0; j < 50; j++) {
337  probj = PowerSer[j] / factorial[j] * Expval;
338  for (int k = 0; k < 50; k++) {
339  probk = PowerSer[k] / factorial[k] * Expval;
340  // joint probability is product of event weights multiplied by weight of input distribution bin
341  Data_ints[i][j][k] = Data_ints[i][j][k] + probi * probj * probk * xweight;
342  }
343  }
344  }
345  }
346 
347  for (int i = 0; i < 50; i++) {
348  //if(i<5) std::cout << "i = " << i << std::endl;
349  for (int j = 0; j < 50; j++) {
350  for (int k = 0; k < 50; k++) {
351  if ((MC_ints[i][j][k]) > 0.) {
352  Weight3D_[i][j][k] = Data_ints[i][j][k] / MC_ints[i][j][k];
353  } else {
354  Weight3D_[i][j][k] = 0.;
355  }
356  WHist->SetBinContent(i + 1, j + 1, k + 1, Weight3D_[i][j][k]);
357  DHist->SetBinContent(i + 1, j + 1, k + 1, Data_ints[i][j][k]);
358  MHist->SetBinContent(i + 1, j + 1, k + 1, MC_ints[i][j][k]);
359  // if(i<5 && j<5 && k<5) std::cout << Weight3D_[i][j][k] << " " ;
360  }
361  // if(i<5 && j<5) std::cout << std::endl;
362  }
363  }
364 
365  if (!WeightOutputFile.empty()) {
366  std::cout << " 3D Weight Matrix initialized! " << std::endl;
367  std::cout << " Writing weights to file " << WeightOutputFile << " for re-use... " << std::endl;
368 
369  TFile* outfile = new TFile(WeightOutputFile.c_str(), "RECREATE");
370  WHist->Write();
371  MHist->Write();
372  DHist->Write();
373  outfile->Write();
374  outfile->Close();
375  outfile->Delete();
376  }
377 
378  return;
379  }
380 
381  void weight3D_set(std::string WeightFileName) {
382  TFile* infile = new TFile(WeightFileName.c_str());
383  TH1F* WHist = (TH1F*)infile->Get("WHist");
384 
385  // Check if the histogram exists
386  if (!WHist) {
387  std::cout << " Could not find the histogram WHist in the file "
388  << "in the file " << WeightFileName << "." << std::endl;
389  return;
390  }
391 
392  for (int i = 0; i < 50; i++) {
393  for (int j = 0; j < 50; j++) {
394  for (int k = 0; k < 50; k++) {
395  Weight3D_[i][j][k] = WHist->GetBinContent(i, j, k);
396  }
397  }
398  }
399 
400  std::cout << " 3D Weight Matrix initialized! " << std::endl;
401 
402  return;
403  }
404 
405  void weightOOT_init() {
406  // The following are poisson distributions with different means, where the maximum
407  // of the function has been normalized to weight 1.0
408  // These are used to reweight the out-of-time pileup to match the in-time distribution.
409  // The total event weight is the product of the in-time weight, the out-of-time weight,
410  // and a residual correction to fix the distortions caused by the fact that the out-of-time
411  // distribution is not flat.
412 
413  static const double weight_24[25] = {0, 0, 0, 0, 2.46277e-06,
414  2.95532e-05, 0.000104668, 0.000401431, 0.00130034, 0.00342202,
415  0.00818132, 0.0175534, 0.035784, 0.0650836, 0.112232,
416  0.178699, 0.268934, 0.380868, 0.507505, 0.640922,
417  0.768551, 0.877829, 0.958624, 0.99939, 1};
418 
419  static const double weight_23[25] = {0, 1.20628e-06, 1.20628e-06, 2.41255e-06, 1.20628e-05,
420  6.39326e-05, 0.000252112, 0.000862487, 0.00244995, 0.00616527,
421  0.0140821, 0.0293342, 0.0564501, 0.100602, 0.164479,
422  0.252659, 0.36268, 0.491427, 0.627979, 0.75918,
423  0.873185, 0.957934, 0.999381, 1, 0.957738};
424 
425  static const double weight_22[25] = {
426  0, 0, 0, 5.88636e-06, 3.0609e-05, 0.000143627, 0.000561558, 0.00173059, 0.00460078,
427  0.0110616, 0.0238974, 0.0475406, 0.0875077, 0.148682, 0.235752, 0.343591, 0.473146, 0.611897,
428  0.748345, 0.865978, 0.953199, 0.997848, 1, 0.954245, 0.873688};
429 
430  static const double weight_21[25] = {
431  0, 0, 1.15381e-06, 8.07665e-06, 7.1536e-05, 0.000280375, 0.00107189, 0.00327104, 0.00809396,
432  0.0190978, 0.0401894, 0.0761028, 0.13472, 0.216315, 0.324649, 0.455125, 0.598241, 0.739215,
433  0.861866, 0.953911, 0.998918, 1, 0.956683, 0.872272, 0.76399};
434 
435  static const double weight_20[25] = {
436  0, 0, 1.12532e-06, 2.58822e-05, 0.000145166, 0.000633552, 0.00215048, 0.00592816, 0.0145605,
437  0.0328367, 0.0652649, 0.11893, 0.19803, 0.305525, 0.436588, 0.581566, 0.727048, 0.8534,
438  0.949419, 0.999785, 1, 0.953008, 0.865689, 0.753288, 0.62765};
439  static const double weight_19[25] = {
440  0, 0, 1.20714e-05, 5.92596e-05, 0.000364337, 0.00124994, 0.00403953, 0.0108149, 0.025824,
441  0.0544969, 0.103567, 0.17936, 0.283532, 0.416091, 0.562078, 0.714714, 0.846523, 0.947875,
442  1, 0.999448, 0.951404, 0.859717, 0.742319, 0.613601, 0.48552};
443 
444  static const double weight_18[25] = {
445  0, 3.20101e-06, 2.88091e-05, 0.000164319, 0.000719161, 0.00250106, 0.00773685, 0.0197513, 0.0443693,
446  0.0885998, 0.159891, 0.262607, 0.392327, 0.543125, 0.69924, 0.837474, 0.943486, 0.998029,
447  1, 0.945937, 0.851807, 0.729309, 0.596332, 0.467818, 0.350434};
448 
449  static const double weight_17[25] = {
450  1.03634e-06, 7.25437e-06, 4.97443e-05, 0.000340956, 0.00148715, 0.00501485, 0.0143067, 0.034679, 0.0742009,
451  0.140287, 0.238288, 0.369416, 0.521637, 0.682368, 0.828634, 0.939655, 1, 0.996829,
452  0.94062, 0.841575, 0.716664, 0.582053, 0.449595, 0.331336, 0.234332};
453 
454  static const double weight_16[25] = {
455  4.03159e-06, 2.41895e-05, 0.000141106, 0.00081942, 0.00314565, 0.00990662, 0.026293, 0.0603881, 0.120973,
456  0.214532, 0.343708, 0.501141, 0.665978, 0.820107, 0.938149, 1, 0.99941, 0.940768,
457  0.837813, 0.703086, 0.564023, 0.42928, 0.312515, 0.216251, 0.14561};
458 
459  static const double weight_15[25] = {
460  9.76084e-07, 5.07564e-05, 0.000303562, 0.00174036, 0.00617959, 0.0188579, 0.047465, 0.101656, 0.189492,
461  0.315673, 0.474383, 0.646828, 0.809462, 0.934107, 0.998874, 1, 0.936163, 0.827473,
462  0.689675, 0.544384, 0.40907, 0.290648, 0.198861, 0.12951, 0.0808051};
463 
464  static const double weight_14[25] = {
465  1.13288e-05, 0.000124617, 0.000753365, 0.00345056, 0.0123909, 0.0352712, 0.0825463, 0.16413, 0.287213,
466  0.44615, 0.625826, 0.796365, 0.930624, 0.999958, 1, 0.934414, 0.816456, 0.672939,
467  0.523033, 0.386068, 0.269824, 0.180342, 0.114669, 0.0698288, 0.0406496};
468 
469  static const double weight_13[25] = {
470  2.54296e-05, 0.000261561, 0.00167018, 0.00748083, 0.0241308, 0.0636801, 0.138222, 0.255814, 0.414275,
471  0.600244, 0.779958, 0.92256, 0.999155, 1, 0.927126, 0.804504, 0.651803, 0.497534,
472  0.35976, 0.245834, 0.160904, 0.0991589, 0.0585434, 0.0332437, 0.0180159};
473 
474  static const double weight_12[25] = {
475  5.85742e-05, 0.000627706, 0.00386677, 0.0154068, 0.0465892, 0.111683, 0.222487, 0.381677, 0.5719,
476  0.765001, 0.915916, 1, 0.999717, 0.921443, 0.791958, 0.632344, 0.475195, 0.334982,
477  0.223666, 0.141781, 0.0851538, 0.048433, 0.0263287, 0.0133969, 0.00696683};
478 
479  static const double weight_11[25] = {
480  0.00015238, 0.00156064, 0.00846044, 0.0310939, 0.0856225, 0.187589, 0.343579, 0.541892, 0.74224,
481  0.909269, 0.998711, 1, 0.916889, 0.77485, 0.608819, 0.447016, 0.307375, 0.198444,
482  0.121208, 0.070222, 0.0386492, 0.0201108, 0.0100922, 0.00484937, 0.00222458};
483 
484  static const double weight_10[25] = {
485  0.000393044, 0.00367001, 0.0179474, 0.060389, 0.151477, 0.302077, 0.503113, 0.720373, 0.899568,
486  1, 0.997739, 0.909409, 0.75728, 0.582031, 0.415322, 0.277663, 0.174147, 0.102154,
487  0.0566719, 0.0298642, 0.0147751, 0.00710995, 0.00319628, 0.00140601, 0.000568796};
488 
489  static const double weight_9[25] = {
490  0.00093396, 0.00854448, 0.0380306, 0.113181, 0.256614, 0.460894, 0.690242, 0.888781, 1,
491  0.998756, 0.899872, 0.735642, 0.552532, 0.382726, 0.246114, 0.147497, 0.0825541, 0.0441199,
492  0.0218157, 0.0103578, 0.00462959, 0.0019142, 0.000771598, 0.000295893, 0.000111529};
493 
494  static const double weight_8[25] = {
495  0.00240233, 0.0192688, 0.0768653, 0.205008, 0.410958, 0.65758, 0.875657, 0.999886, 1,
496  0.889476, 0.711446, 0.517781, 0.345774, 0.212028, 0.121208, 0.0644629, 0.0324928, 0.0152492,
497  0.00673527, 0.0028547, 0.00117213, 0.000440177, 0.000168471, 5.80689e-05, 1.93563e-05};
498 
499  static const double weight_7[25] = {0.00617233, 0.0428714, 0.150018, 0.350317, 0.612535,
500  0.856525, 0.999923, 1, 0.87544, 0.679383,
501  0.478345, 0.303378, 0.176923, 0.0950103, 0.0476253,
502  0.0222211, 0.00972738, 0.00392962, 0.0015258, 0.000559168,
503  0.000183928, 6.77983e-05, 1.67818e-05, 7.38398e-06, 6.71271e-07};
504 
505  static const double weight_6[25] = {0.0154465, 0.0923472, 0.277322, 0.55552, 0.833099,
506  0.999035, 1, 0.855183, 0.641976, 0.428277,
507  0.256804, 0.139798, 0.0700072, 0.0321586, 0.0137971,
508  0.00544756, 0.00202316, 0.000766228, 0.000259348, 8.45836e-05,
509  1.80362e-05, 8.70713e-06, 3.73163e-06, 6.21938e-07, 0};
510 
511  static const double weight_5[25] = {0.0382845, 0.191122, 0.478782, 0.797314, 1,
512  0.997148, 0.831144, 0.59461, 0.371293, 0.205903,
513  0.103102, 0.0471424, 0.0194997, 0.00749415, 0.00273709,
514  0.000879189, 0.000286049, 0.000102364, 1.70606e-05, 3.98081e-06,
515  2.27475e-06, 0, 0, 0, 0};
516 
517  static const double weight_4[25] = {0.0941305, 0.373824, 0.750094, 1, 0.997698,
518  0.800956, 0.532306, 0.304597, 0.152207, 0.0676275,
519  0.0270646, 0.00975365, 0.00326077, 0.00101071, 0.000301781,
520  7.41664e-05, 1.58563e-05, 3.58045e-06, 1.02299e-06, 0,
521  5.11493e-07, 0, 0, 0, 0};
522 
523  static const double weight_3[25] = {0.222714, 0.667015, 1, 0.999208, 0.750609,
524  0.449854, 0.224968, 0.0965185, 0.0361225, 0.012084,
525  0.00359618, 0.000977166, 0.000239269, 6.29422e-05, 1.16064e-05,
526  1.78559e-06, 0, 4.46398e-07, 0, 0,
527  0, 0, 0, 0, 0};
528 
529  static const double weight_2[25] = {
530  0.499541, 0.999607, 1, 0.666607, 0.333301, 0.13279, 0.0441871, 0.0127455, 0.00318434,
531  0.00071752, 0.000132204, 2.69578e-05, 5.16999e-06, 2.21571e-06, 0, 0, 0, 0,
532  0, 0, 0, 0, 0, 0, 0};
533 
534  static const double weight_1[25] = {
535  0.999165, 1, 0.499996, 0.166868, 0.0414266, 0.00831053, 0.00137472, 0.000198911, 2.66302e-05,
536  2.44563e-06, 2.71737e-07, 2.71737e-07, 0, 0, 0, 0, 0, 0,
537  0, 0, 0, 0, 0, 0, 0};
538 
539  static const double weight_0[25] = {1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
540 
541  //WeightOOTPU_ = {0};
542 
543  const double* WeightPtr = nullptr;
544 
545  for (int iint = 0; iint < 25; ++iint) {
546  if (iint == 0)
547  WeightPtr = weight_0;
548  if (iint == 1)
549  WeightPtr = weight_1;
550  if (iint == 2)
551  WeightPtr = weight_2;
552  if (iint == 3)
553  WeightPtr = weight_3;
554  if (iint == 4)
555  WeightPtr = weight_4;
556  if (iint == 5)
557  WeightPtr = weight_5;
558  if (iint == 6)
559  WeightPtr = weight_6;
560  if (iint == 7)
561  WeightPtr = weight_7;
562  if (iint == 8)
563  WeightPtr = weight_8;
564  if (iint == 9)
565  WeightPtr = weight_9;
566  if (iint == 10)
567  WeightPtr = weight_10;
568  if (iint == 11)
569  WeightPtr = weight_11;
570  if (iint == 12)
571  WeightPtr = weight_12;
572  if (iint == 13)
573  WeightPtr = weight_13;
574  if (iint == 14)
575  WeightPtr = weight_14;
576  if (iint == 15)
577  WeightPtr = weight_15;
578  if (iint == 16)
579  WeightPtr = weight_16;
580  if (iint == 17)
581  WeightPtr = weight_17;
582  if (iint == 18)
583  WeightPtr = weight_18;
584  if (iint == 19)
585  WeightPtr = weight_19;
586  if (iint == 20)
587  WeightPtr = weight_20;
588  if (iint == 21)
589  WeightPtr = weight_21;
590  if (iint == 22)
591  WeightPtr = weight_22;
592  if (iint == 23)
593  WeightPtr = weight_23;
594  if (iint == 24)
595  WeightPtr = weight_24;
596 
597  for (int ibin = 0; ibin < 25; ++ibin) {
598  WeightOOTPU_[iint][ibin] = *(WeightPtr + ibin);
599  }
600  }
601  }
602 
603  double ITweight(int npv) {
604  int bin = weights_->GetXaxis()->FindBin(npv);
605  return weights_->GetBinContent(bin);
606  }
607 
608  double ITweight3BX(float ave_int) {
609  int bin = weights_->GetXaxis()->FindBin(ave_int);
610  return weights_->GetBinContent(bin);
611  }
612 
613  double weight(float n_int) {
614  int bin = weights_->GetXaxis()->FindBin(n_int);
615  return weights_->GetBinContent(bin);
616  }
617 
618  double weight3D(int pv1, int pv2, int pv3) {
619  using std::min;
620 
621  int npm1 = min(pv1, 34);
622  int np0 = min(pv2, 34);
623  int npp1 = min(pv3, 34);
624 
625  return Weight3D_[npm1][np0][npp1];
626  }
627 
628  double weightOOT(int npv_in_time, int npv_m50nsBX) {
629  static const double Correct_Weights2011[25] = {
630  // residual correction to match lumi spectrum
631  5.30031, 2.07903, 1.40729, 1.27687, 1.0702, 0.902094, 0.902345, 0.931449, 0.78202,
632  0.824686, 0.837735, 0.910261, 1.01394, 1.1599, 1.12778, 1.58423, 1.78868, 1.58296,
633  2.3291, 3.86641, 0, 0, 0, 0, 0};
634 
635  if (FirstWarning_) {
636  std::cout << " **** Warning: Out-of-time pileup reweighting appropriate only for PU_S3 **** " << std::endl;
637  std::cout << " **** will be applied **** " << std::endl;
638 
639  FirstWarning_ = false;
640  }
641 
642  // Note: for the "uncorrelated" out-of-time pileup, reweighting is only done on the 50ns
643  // "late" bunch (BX=+1), since that is basically the only one that matters in terms of
644  // energy deposition.
645 
646  if (npv_in_time < 0) {
647  std::cerr << " no in-time beam crossing found\n! ";
648  std::cerr << " Returning event weight=0\n! ";
649  return 0.;
650  }
651  if (npv_m50nsBX < 0) {
652  std::cerr << " no out-of-time beam crossing found\n! ";
653  std::cerr << " Returning event weight=0\n! ";
654  return 0.;
655  }
656 
657  int bin = weights_->GetXaxis()->FindBin(npv_in_time);
658 
659  double inTimeWeight = weights_->GetBinContent(bin);
660 
661  double TotalWeight = 1.0;
662 
663  TotalWeight = inTimeWeight * WeightOOTPU_[bin - 1][npv_m50nsBX] * Correct_Weights2011[bin - 1];
664 
665  return TotalWeight;
666  }
667 
668  protected:
674  TFile* dataFile_;
675  TH1F* weights_;
676 
677  //keep copies of normalized distributions:
678  TH1F* MC_distr_;
679  TH1F* Data_distr_;
680 
681  double WeightOOTPU_[25][25];
682  double Weight3D_[50][50][50];
683 
685  };
686 } // namespace reweight
687 
688 #endif
tuple base
Main Program
Definition: newFWLiteAna.py:92
LumiReWeighting(std::string generatedFile, std::string dataFile, std::string GenHistName, std::string DataHistName)
void weight3D_init(float ScaleFactor, std::string WeightOutputFile="")
int iint
Exp< T >::type exp(const T &t)
Definition: Exp.h:22
void weight3D_set(std::string WeightFileName)
LumiReWeighting(const std::vector< float > &MC_distr, const std::vector< float > &Lumi_distr)
T min(T a, T b)
Definition: MathUtil.h:58
double weightOOT(int npv_in_time, int npv_m50nsBX)
double weight3D(int pv1, int pv2, int pv3)
int factorial(int n)
factorial function
tuple cout
Definition: gather_cfg.py:144