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c0b10ad4 | 1 | /************************************************************************** |
2 | * Copyright(c) 1998-1999, ALICE Experiment at CERN, All rights reserved. * | |
3 | * * | |
4 | * Author: The ALICE Off-line Project. * | |
5 | * Contributors are mentioned in the code where appropriate. * | |
6 | * * | |
7 | * Permission to use, copy, modify and distribute this software and its * | |
8 | * documentation strictly for non-commercial purposes is hereby granted * | |
9 | * without fee, provided that the above copyright notice appears in all * | |
10 | * copies and that both the copyright notice and this permission notice * | |
11 | * appear in the supporting documentation. The authors make no claims * | |
12 | * about the suitability of this software for any purpose. It is * | |
13 | * provided "as is" without express or implied warranty. * | |
14 | **************************************************************************/ | |
15 | ||
7bc6a013 | 16 | //---------------------------------------------------------------------// |
17 | // // | |
18 | // AliCFUnfolding Class // | |
19 | // Class to handle general unfolding procedure // | |
20 | // For the moment only bayesian unfolding is supported // | |
21 | // The next steps are to add chi2 minimisation and weighting methods // | |
22 | // // | |
23 | // // | |
24 | // // | |
25 | // Use : // | |
26 | // ------- // | |
27 | // The Bayesian unfolding consists of several iterations. // | |
28 | // At each iteration, an inverse response matrix is calculated, given // | |
29 | // the measured spectrum, the a priori (guessed) spectrum, // | |
30 | // the efficiency spectrum and the response matrix. // | |
c7f196bd | 31 | // // |
32 | // Then at each iteration, the unfolded spectrum is calculated using // | |
7bc6a013 | 33 | // the inverse response : the goal is to get an unfolded spectrum // |
34 | // similar (according to some criterion) to the a priori one. // | |
35 | // If the difference is too big, another iteration is performed : // | |
36 | // the a priori spectrum is updated to the unfolded one from the // | |
37 | // previous iteration, and so on so forth, until the maximum number // | |
38 | // of iterations or the similarity criterion is reached. // | |
39 | // // | |
769f5114 | 40 | // Chi2 calculation became absolute with the correlated error // |
41 | // calculation. // | |
42 | // Errors on the unfolded distribution are not known until the end // | |
43 | // Use the convergence criterion instead // | |
7bc6a013 | 44 | // // |
45 | // Currently the user has to define the max. number of iterations // | |
46 | // (::SetMaxNumberOfIterations) // | |
769f5114 | 47 | // and // |
48 | // - the chi2 below which the procedure will stop // | |
49 | // (::SetMaxChi2 or ::SetMaxChi2PerDOF) (OBSOLETE) // | |
50 | // - the convergence criterion below which the procedure will stop // | |
51 | // SetMaxConvergencePerDOF(Double_t val); // | |
52 | // // | |
53 | // Correlated error calculation can be activated by using: // | |
54 | // SetUseCorrelatedErrors(Bool_t b) in combination with convergence // | |
55 | // criterion // | |
56 | // Documentation about correlated error calculation method can be // | |
57 | // found in AliCFUnfolding::CalculateCorrelatedErrors() // | |
58 | // Author: marta.verweij@cern.ch // | |
7bc6a013 | 59 | // // |
60 | // An optional possibility is to smooth the unfolded spectrum at the // | |
61 | // end of each iteration, either using a fit function // | |
62 | // (only if #dimensions <=3) // | |
63 | // or a simple averaging using the neighbouring bins values. // | |
64 | // This is possible calling the function ::UseSmoothing // | |
65 | // If no argument is passed to this function, then the second option // | |
66 | // is used. // | |
67 | // // | |
c7f196bd | 68 | // IMPORTANT: // |
69 | //----------- // | |
70 | // With this approach, the efficiency map must be calculated // | |
71 | // with *simulated* values only, otherwise the method won't work. // | |
72 | // // | |
73 | // ex: efficiency(bin_pt) = number_rec(bin_pt) / number_sim(bin_pt) // | |
74 | // // | |
75 | // the pt bin "bin_pt" must always be the same in both the efficiency // | |
76 | // numerator and denominator. // | |
77 | // This is why the efficiency map has to be created by a method // | |
78 | // from which both reconstructed and simulated values are accessible // | |
79 | // simultaneously. // | |
80 | // // | |
81 | // // | |
7bc6a013 | 82 | //---------------------------------------------------------------------// |
83 | // Author : renaud.vernet@cern.ch // | |
84 | //---------------------------------------------------------------------// | |
c0b10ad4 | 85 | |
86 | ||
87 | #include "AliCFUnfolding.h" | |
88 | #include "TMath.h" | |
89 | #include "TAxis.h" | |
85b6bda9 | 90 | #include "TF1.h" |
91 | #include "TH1D.h" | |
92 | #include "TH2D.h" | |
93 | #include "TH3D.h" | |
769f5114 | 94 | #include "TRandom3.h" |
c0b10ad4 | 95 | |
96 | ClassImp(AliCFUnfolding) | |
97 | ||
98 | //______________________________________________________________ | |
99 | ||
100 | AliCFUnfolding::AliCFUnfolding() : | |
101 | TNamed(), | |
5a575436 | 102 | fResponseOrig(0x0), |
103 | fPriorOrig(0x0), | |
104 | fEfficiencyOrig(0x0), | |
769f5114 | 105 | fMeasuredOrig(0x0), |
a9500e70 | 106 | fMaxNumIterations(0), |
c0b10ad4 | 107 | fNVariables(0), |
c0b10ad4 | 108 | fUseSmoothing(kFALSE), |
85b6bda9 | 109 | fSmoothFunction(0x0), |
a9500e70 | 110 | fSmoothOption("iremn"), |
769f5114 | 111 | fMaxConvergence(0), |
a9500e70 | 112 | fNRandomIterations(0), |
5a575436 | 113 | fResponse(0x0), |
114 | fPrior(0x0), | |
115 | fEfficiency(0x0), | |
116 | fMeasured(0x0), | |
c0b10ad4 | 117 | fInverseResponse(0x0), |
118 | fMeasuredEstimate(0x0), | |
119 | fConditional(0x0), | |
c0b10ad4 | 120 | fUnfolded(0x0), |
a9500e70 | 121 | fUnfoldedFinal(0x0), |
c0b10ad4 | 122 | fCoordinates2N(0x0), |
123 | fCoordinatesN_M(0x0), | |
769f5114 | 124 | fCoordinatesN_T(0x0), |
5a575436 | 125 | fRandomResponse(0x0), |
126 | fRandomEfficiency(0x0), | |
127 | fRandomMeasured(0x0), | |
769f5114 | 128 | fRandom3(0x0), |
769f5114 | 129 | fDeltaUnfoldedP(0x0), |
a9500e70 | 130 | fDeltaUnfoldedN(0x0), |
769f5114 | 131 | fNCalcCorrErrors(0), |
132 | fRandomSeed(0) | |
c0b10ad4 | 133 | { |
134 | // | |
135 | // default constructor | |
136 | // | |
137 | } | |
138 | ||
139 | //______________________________________________________________ | |
140 | ||
141 | AliCFUnfolding::AliCFUnfolding(const Char_t* name, const Char_t* title, const Int_t nVar, | |
a9500e70 | 142 | const THnSparse* response, const THnSparse* efficiency, const THnSparse* measured, const THnSparse* prior , |
143 | Double_t maxConvergencePerDOF, UInt_t randomSeed, Int_t maxNumIterations | |
144 | ) : | |
c0b10ad4 | 145 | TNamed(name,title), |
5a575436 | 146 | fResponseOrig((THnSparse*)response->Clone()), |
147 | fPriorOrig(0x0), | |
148 | fEfficiencyOrig((THnSparse*)efficiency->Clone()), | |
769f5114 | 149 | fMeasuredOrig((THnSparse*)measured->Clone()), |
a9500e70 | 150 | fMaxNumIterations(maxNumIterations), |
c0b10ad4 | 151 | fNVariables(nVar), |
c0b10ad4 | 152 | fUseSmoothing(kFALSE), |
85b6bda9 | 153 | fSmoothFunction(0x0), |
a9500e70 | 154 | fSmoothOption("iremn"), |
769f5114 | 155 | fMaxConvergence(0), |
a9500e70 | 156 | fNRandomIterations(maxNumIterations), |
5a575436 | 157 | fResponse((THnSparse*)response->Clone()), |
158 | fPrior(0x0), | |
159 | fEfficiency((THnSparse*)efficiency->Clone()), | |
160 | fMeasured((THnSparse*)measured->Clone()), | |
c0b10ad4 | 161 | fInverseResponse(0x0), |
162 | fMeasuredEstimate(0x0), | |
163 | fConditional(0x0), | |
c0b10ad4 | 164 | fUnfolded(0x0), |
a9500e70 | 165 | fUnfoldedFinal(0x0), |
c0b10ad4 | 166 | fCoordinates2N(0x0), |
167 | fCoordinatesN_M(0x0), | |
769f5114 | 168 | fCoordinatesN_T(0x0), |
5a575436 | 169 | fRandomResponse((THnSparse*)response->Clone()), |
170 | fRandomEfficiency((THnSparse*)efficiency->Clone()), | |
171 | fRandomMeasured((THnSparse*)measured->Clone()), | |
769f5114 | 172 | fRandom3(0x0), |
769f5114 | 173 | fDeltaUnfoldedP(0x0), |
a9500e70 | 174 | fDeltaUnfoldedN(0x0), |
769f5114 | 175 | fNCalcCorrErrors(0), |
a9500e70 | 176 | fRandomSeed(randomSeed) |
c0b10ad4 | 177 | { |
178 | // | |
179 | // named constructor | |
180 | // | |
181 | ||
182 | AliInfo(Form("\n\n--------------------------\nCreating an unfolder :\n--------------------------\nresponse matrix has %d dimension(s)",fResponse->GetNdimensions())); | |
a9500e70 | 183 | |
c0b10ad4 | 184 | if (!prior) CreateFlatPrior(); // if no prior distribution declared, simply use a flat distribution |
185 | else { | |
186 | fPrior = (THnSparse*) prior->Clone(); | |
5a575436 | 187 | fPriorOrig = (THnSparse*)fPrior->Clone(); |
85b6bda9 | 188 | if (fPrior->GetNdimensions() != fNVariables) |
189 | AliFatal(Form("The prior matrix should have %d dimensions, and it has actually %d",fNVariables,fPrior->GetNdimensions())); | |
c0b10ad4 | 190 | } |
85b6bda9 | 191 | |
192 | if (fEfficiency->GetNdimensions() != fNVariables) | |
193 | AliFatal(Form("The efficiency matrix should have %d dimensions, and it has actually %d",fNVariables,fEfficiency->GetNdimensions())); | |
194 | if (fMeasured->GetNdimensions() != fNVariables) | |
195 | AliFatal(Form("The measured matrix should have %d dimensions, and it has actually %d",fNVariables,fMeasured->GetNdimensions())); | |
196 | if (fResponse->GetNdimensions() != 2*fNVariables) | |
197 | AliFatal(Form("The response matrix should have %d dimensions, and it has actually %d",2*fNVariables,fResponse->GetNdimensions())); | |
c0b10ad4 | 198 | |
85b6bda9 | 199 | |
c0b10ad4 | 200 | for (Int_t iVar=0; iVar<fNVariables; iVar++) { |
201 | AliInfo(Form("prior matrix has %d bins in dimension %d",fPrior ->GetAxis(iVar)->GetNbins(),iVar)); | |
202 | AliInfo(Form("efficiency matrix has %d bins in dimension %d",fEfficiency->GetAxis(iVar)->GetNbins(),iVar)); | |
203 | AliInfo(Form("measured matrix has %d bins in dimension %d",fMeasured ->GetAxis(iVar)->GetNbins(),iVar)); | |
204 | } | |
769f5114 | 205 | |
5a575436 | 206 | fRandomResponse ->SetTitle("Randomized response matrix"); |
207 | fRandomEfficiency->SetTitle("Randomized efficiency"); | |
208 | fRandomMeasured ->SetTitle("Randomized measured"); | |
a9500e70 | 209 | SetMaxConvergencePerDOF(maxConvergencePerDOF) ; |
c0b10ad4 | 210 | Init(); |
211 | } | |
212 | ||
c0b10ad4 | 213 | //______________________________________________________________ |
214 | ||
215 | AliCFUnfolding::~AliCFUnfolding() { | |
216 | // | |
217 | // destructor | |
218 | // | |
219 | if (fResponse) delete fResponse; | |
220 | if (fPrior) delete fPrior; | |
c0b10ad4 | 221 | if (fEfficiency) delete fEfficiency; |
5a575436 | 222 | if (fEfficiencyOrig) delete fEfficiencyOrig; |
c0b10ad4 | 223 | if (fMeasured) delete fMeasured; |
769f5114 | 224 | if (fMeasuredOrig) delete fMeasuredOrig; |
85b6bda9 | 225 | if (fSmoothFunction) delete fSmoothFunction; |
5a575436 | 226 | if (fPriorOrig) delete fPriorOrig; |
c0b10ad4 | 227 | if (fInverseResponse) delete fInverseResponse; |
228 | if (fMeasuredEstimate) delete fMeasuredEstimate; | |
229 | if (fConditional) delete fConditional; | |
a9500e70 | 230 | if (fUnfolded) delete fUnfolded; |
231 | if (fUnfoldedFinal) delete fUnfoldedFinal; | |
c0b10ad4 | 232 | if (fCoordinates2N) delete [] fCoordinates2N; |
233 | if (fCoordinatesN_M) delete [] fCoordinatesN_M; | |
234 | if (fCoordinatesN_T) delete [] fCoordinatesN_T; | |
5a575436 | 235 | if (fRandomResponse) delete fRandomResponse; |
236 | if (fRandomEfficiency) delete fRandomEfficiency; | |
237 | if (fRandomMeasured) delete fRandomMeasured; | |
769f5114 | 238 | if (fRandom3) delete fRandom3; |
769f5114 | 239 | if (fDeltaUnfoldedP) delete fDeltaUnfoldedP; |
a9500e70 | 240 | if (fDeltaUnfoldedN) delete fDeltaUnfoldedN; |
c0b10ad4 | 241 | } |
242 | ||
243 | //______________________________________________________________ | |
244 | ||
245 | void AliCFUnfolding::Init() { | |
246 | // | |
247 | // initialisation function : creates internal settings | |
248 | // | |
249 | ||
769f5114 | 250 | fRandom3 = new TRandom3(fRandomSeed); |
251 | ||
c0b10ad4 | 252 | fCoordinates2N = new Int_t[2*fNVariables]; |
253 | fCoordinatesN_M = new Int_t[fNVariables]; | |
254 | fCoordinatesN_T = new Int_t[fNVariables]; | |
255 | ||
256 | // create the matrix of conditional probabilities P(M|T) | |
a9500e70 | 257 | CreateConditional(); //done only once at initialization |
c0b10ad4 | 258 | |
259 | // create the frame of the inverse response matrix | |
260 | fInverseResponse = (THnSparse*) fResponse->Clone(); | |
261 | // create the frame of the unfolded spectrum | |
262 | fUnfolded = (THnSparse*) fPrior->Clone(); | |
a9500e70 | 263 | fUnfolded->SetTitle("Unfolded"); |
c0b10ad4 | 264 | // create the frame of the measurement estimate spectrum |
265 | fMeasuredEstimate = (THnSparse*) fMeasured->Clone(); | |
a9500e70 | 266 | |
5a575436 | 267 | // create the frame of the delta profiles |
a9500e70 | 268 | fDeltaUnfoldedP = (THnSparse*)fPrior->Clone(); |
269 | fDeltaUnfoldedP->SetTitle("#Delta unfolded"); | |
270 | fDeltaUnfoldedP->Reset(); | |
271 | fDeltaUnfoldedN = (THnSparse*)fPrior->Clone(); | |
5a575436 | 272 | fDeltaUnfoldedN->SetTitle(""); |
a9500e70 | 273 | fDeltaUnfoldedN->Reset(); |
c0b10ad4 | 274 | } |
275 | ||
a9500e70 | 276 | |
c0b10ad4 | 277 | //______________________________________________________________ |
278 | ||
279 | void AliCFUnfolding::CreateEstMeasured() { | |
280 | // | |
281 | // This function creates a estimate (M) of the reconstructed spectrum | |
7bc6a013 | 282 | // given the a priori distribution (T), the efficiency (E) and the conditional matrix (COND) |
c0b10ad4 | 283 | // |
284 | // --> P(M) = SUM { P(M|T) * P(T) } | |
7bc6a013 | 285 | // --> M(i) = SUM_k { COND(i,k) * T(k) * E (k)} |
c0b10ad4 | 286 | // |
287 | // This is needed to calculate the inverse response matrix | |
288 | // | |
289 | ||
290 | ||
291 | // clean the measured estimate spectrum | |
85f9f9e1 | 292 | fMeasuredEstimate->Reset(); |
293 | ||
85b6bda9 | 294 | THnSparse* priorTimesEff = (THnSparse*) fPrior->Clone(); |
295 | priorTimesEff->Multiply(fEfficiency); | |
296 | ||
c0b10ad4 | 297 | // fill it |
7bc6a013 | 298 | for (Long_t iBin=0; iBin<fConditional->GetNbins(); iBin++) { |
c0b10ad4 | 299 | Double_t conditionalValue = fConditional->GetBinContent(iBin,fCoordinates2N); |
300 | GetCoordinates(); | |
85b6bda9 | 301 | Double_t priorTimesEffValue = priorTimesEff->GetBinContent(fCoordinatesN_T); |
85b6bda9 | 302 | Double_t fill = conditionalValue * priorTimesEffValue ; |
303 | ||
304 | if (fill>0.) { | |
12e419d5 | 305 | fMeasuredEstimate->AddBinContent(fCoordinatesN_M,fill); |
769f5114 | 306 | fMeasuredEstimate->SetBinError(fCoordinatesN_M,0.); |
85b6bda9 | 307 | } |
c0b10ad4 | 308 | } |
85b6bda9 | 309 | delete priorTimesEff ; |
c0b10ad4 | 310 | } |
311 | ||
312 | //______________________________________________________________ | |
313 | ||
314 | void AliCFUnfolding::CreateInvResponse() { | |
315 | // | |
316 | // Creates the inverse response matrix (INV) with Bayesian method | |
317 | // : uses the conditional matrix (COND), the prior probabilities (T) and the efficiency map (E) | |
318 | // | |
319 | // --> P(T|M) = P(M|T) * P(T) * eff(T) / SUM { P(M|T) * P(T) } | |
320 | // --> INV(i,j) = COND(i,j) * T(j) * E(j) / SUM_k { COND(i,k) * T(k) } | |
321 | // | |
322 | ||
85b6bda9 | 323 | THnSparse* priorTimesEff = (THnSparse*) fPrior->Clone(); |
324 | priorTimesEff->Multiply(fEfficiency); | |
325 | ||
7bc6a013 | 326 | for (Long_t iBin=0; iBin<fConditional->GetNbins(); iBin++) { |
c0b10ad4 | 327 | Double_t conditionalValue = fConditional->GetBinContent(iBin,fCoordinates2N); |
328 | GetCoordinates(); | |
85b6bda9 | 329 | Double_t estMeasuredValue = fMeasuredEstimate->GetBinContent(fCoordinatesN_M); |
85b6bda9 | 330 | Double_t priorTimesEffValue = priorTimesEff ->GetBinContent(fCoordinatesN_T); |
85b6bda9 | 331 | Double_t fill = (estMeasuredValue>0. ? conditionalValue * priorTimesEffValue / estMeasuredValue : 0. ) ; |
85b6bda9 | 332 | if (fill>0. || fInverseResponse->GetBinContent(fCoordinates2N)>0.) { |
333 | fInverseResponse->SetBinContent(fCoordinates2N,fill); | |
769f5114 | 334 | fInverseResponse->SetBinError (fCoordinates2N,0.); |
85b6bda9 | 335 | } |
336 | } | |
337 | delete priorTimesEff ; | |
c0b10ad4 | 338 | } |
339 | ||
340 | //______________________________________________________________ | |
341 | ||
342 | void AliCFUnfolding::Unfold() { | |
343 | // | |
344 | // Main routine called by the user : | |
769f5114 | 345 | // it calculates the unfolded spectrum from the response matrix, measured spectrum and efficiency |
346 | // several iterations are performed until a reasonable chi2 or convergence criterion is reached | |
c0b10ad4 | 347 | // |
348 | ||
769f5114 | 349 | Int_t iIterBayes = 0 ; |
350 | Double_t convergence = 0.; | |
c0b10ad4 | 351 | |
352 | for (iIterBayes=0; iIterBayes<fMaxNumIterations; iIterBayes++) { // bayes iterations | |
a9500e70 | 353 | |
354 | CreateEstMeasured(); // create measured estimate from prior | |
355 | CreateInvResponse(); // create inverse response from prior | |
356 | CreateUnfolded(); // create unfoled spectrum from measured and inverse response | |
357 | ||
358 | convergence = GetConvergence(); | |
359 | AliDebug(0,Form("convergence at iteration %d is %e",iIterBayes,convergence)); | |
360 | ||
361 | if (fMaxConvergence>0. && convergence<fMaxConvergence && fNCalcCorrErrors == 0) { | |
362 | fNRandomIterations = iIterBayes; | |
363 | AliDebug(0,Form("convergence is met at iteration %d",iIterBayes)); | |
c0b10ad4 | 364 | break; |
365 | } | |
769f5114 | 366 | |
85b6bda9 | 367 | if (fUseSmoothing) { |
368 | if (Smooth()) { | |
369 | AliError("Couldn't smooth the unfolded spectrum!!"); | |
a9500e70 | 370 | if (fNCalcCorrErrors>0) { |
371 | AliInfo(Form("=======================\nUnfold of randomized distribution finished at iteration %d with convergence %e \n",iIterBayes,convergence)); | |
372 | } | |
373 | else { | |
374 | AliInfo(Form("\n\n=======================\nFinish at iteration %d : convergence is %e and you required it to be < %e\n=======================\n\n",iIterBayes,convergence,fMaxConvergence)); | |
769f5114 | 375 | } |
85b6bda9 | 376 | return; |
377 | } | |
378 | } | |
769f5114 | 379 | |
a9500e70 | 380 | // update the prior distribution |
381 | if (fPrior) delete fPrior ; | |
382 | fPrior = (THnSparse*)fUnfolded->Clone() ; | |
383 | fPrior->SetTitle("Prior"); | |
384 | ||
385 | } // end bayes iteration | |
386 | ||
387 | if (fNCalcCorrErrors==0) fUnfoldedFinal = (THnSparse*) fUnfolded->Clone() ; | |
388 | ||
389 | // | |
390 | //for (Long_t iBin=0; iBin<fUnfoldedFinal->GetNbins(); iBin++) AliDebug(2,Form("%e\n",fUnfoldedFinal->GetBinError(iBin))); | |
391 | // | |
392 | ||
393 | if (fNCalcCorrErrors == 0) { | |
394 | AliInfo("\n================================================\nFinished bayes iteration, now calculating errors...\n================================================\n"); | |
395 | fNCalcCorrErrors = 1; | |
769f5114 | 396 | CalculateCorrelatedErrors(); |
397 | } | |
398 | ||
a9500e70 | 399 | if (fNCalcCorrErrors >1 ) { |
400 | AliInfo(Form("\n\n=======================\nFinished at iteration %d : convergence is %e and you required it to be < %e\n=======================\n\n",iIterBayes,convergence,fMaxConvergence)); | |
401 | } | |
402 | else if(fNCalcCorrErrors>0) { | |
403 | AliInfo(Form("=======================\nUnfolding of randomized distribution finished at iteration %d with convergence %e \n",iIterBayes,convergence)); | |
c0b10ad4 | 404 | } |
c0b10ad4 | 405 | } |
406 | ||
407 | //______________________________________________________________ | |
408 | ||
409 | void AliCFUnfolding::CreateUnfolded() { | |
410 | // | |
411 | // Creates the unfolded (T) spectrum from the measured spectrum (M) and the inverse response matrix (INV) | |
412 | // We have P(T) = SUM { P(T|M) * P(M) } | |
413 | // --> T(i) = SUM_k { INV(i,k) * M(k) } | |
414 | // | |
415 | ||
416 | ||
417 | // clear the unfolded spectrum | |
a9500e70 | 418 | // if in the process of error calculation, the random unfolded spectrum is created |
419 | // otherwise the normal unfolded spectrum is created | |
420 | ||
421 | fUnfolded->Reset(); | |
c0b10ad4 | 422 | |
7bc6a013 | 423 | for (Long_t iBin=0; iBin<fInverseResponse->GetNbins(); iBin++) { |
c0b10ad4 | 424 | Double_t invResponseValue = fInverseResponse->GetBinContent(iBin,fCoordinates2N); |
425 | GetCoordinates(); | |
85b6bda9 | 426 | Double_t effValue = fEfficiency->GetBinContent(fCoordinatesN_T); |
85b6bda9 | 427 | Double_t measuredValue = fMeasured ->GetBinContent(fCoordinatesN_M); |
85b6bda9 | 428 | Double_t fill = (effValue>0. ? invResponseValue * measuredValue / effValue : 0.) ; |
769f5114 | 429 | |
85b6bda9 | 430 | if (fill>0.) { |
a9500e70 | 431 | // set errors to zero |
432 | // true errors will be filled afterwards | |
769f5114 | 433 | Double_t err = 0.; |
a9500e70 | 434 | fUnfolded->SetBinError (fCoordinatesN_T,err); |
435 | fUnfolded->AddBinContent(fCoordinatesN_T,fill); | |
85b6bda9 | 436 | } |
c0b10ad4 | 437 | } |
438 | } | |
769f5114 | 439 | |
440 | //______________________________________________________________ | |
441 | ||
442 | void AliCFUnfolding::CalculateCorrelatedErrors() { | |
443 | ||
5a575436 | 444 | // Step 1: Create randomized distribution (fRandomXXXX) of each bin of |
a9500e70 | 445 | // the measured spectrum to calculate correlated errors. |
446 | // Poisson statistics: mean = measured value of bin | |
769f5114 | 447 | // Step 2: Unfold randomized distribution |
a9500e70 | 448 | // Step 3: Store difference of unfolded spectrum from measured distribution and |
449 | // unfolded distribution from randomized distribution | |
450 | // -> fDeltaUnfoldedP (TProfile with option "S") | |
769f5114 | 451 | // Step 4: Repeat Step 1-3 several times (fNRandomIterations) |
452 | // Step 5: The spread of fDeltaUnfoldedP for each bin is the error on the unfolded spectrum of that specific bin | |
453 | ||
769f5114 | 454 | |
a9500e70 | 455 | //Do fNRandomIterations = bayes iterations performed |
456 | for (int i=0; i<fNRandomIterations; i++) { | |
457 | ||
458 | // reset prior to original one | |
459 | if (fPrior) delete fPrior ; | |
5a575436 | 460 | fPrior = (THnSparse*) fPriorOrig->Clone(); |
a9500e70 | 461 | |
462 | // create randomized distribution and stick measured spectrum to it | |
463 | CreateRandomizedDist(); | |
5a575436 | 464 | |
465 | if (fResponse) delete fResponse ; | |
466 | fResponse = (THnSparse*) fRandomResponse->Clone(); | |
467 | fResponse->SetTitle("Response"); | |
468 | ||
469 | if (fEfficiency) delete fEfficiency ; | |
470 | fEfficiency = (THnSparse*) fRandomEfficiency->Clone(); | |
471 | fEfficiency->SetTitle("Efficiency"); | |
472 | ||
473 | if (fMeasured) delete fMeasured ; | |
474 | fMeasured = (THnSparse*) fRandomMeasured->Clone(); | |
a9500e70 | 475 | fMeasured->SetTitle("Measured"); |
476 | ||
5a575436 | 477 | //unfold with randomized distributions |
a9500e70 | 478 | Unfold(); |
479 | FillDeltaUnfoldedProfile(); | |
480 | } | |
769f5114 | 481 | |
a9500e70 | 482 | // Get statistical errors for final unfolded spectrum |
483 | // ie. spread of each pt bin in fDeltaUnfoldedP | |
484 | Double_t sigma = 0.; | |
a9500e70 | 485 | for (Long_t iBin=0; iBin<fUnfoldedFinal->GetNbins(); iBin++) { |
f21c7024 | 486 | fUnfoldedFinal->GetBinContent(iBin,fCoordinatesN_M); |
a9500e70 | 487 | sigma = fDeltaUnfoldedP->GetBinError(fCoordinatesN_M); |
488 | //AliDebug(2,Form("filling error %e\n",sigma)); | |
489 | fUnfoldedFinal->SetBinError(fCoordinatesN_M,sigma); | |
769f5114 | 490 | } |
a9500e70 | 491 | |
492 | // now errors are calculated | |
493 | fNCalcCorrErrors = 2; | |
769f5114 | 494 | } |
a9500e70 | 495 | |
769f5114 | 496 | //______________________________________________________________ |
497 | void AliCFUnfolding::CreateRandomizedDist() { | |
498 | // | |
a9500e70 | 499 | // Create randomized dist from original measured distribution |
500 | // This distribution is created several times, each time with a different random number | |
769f5114 | 501 | // |
502 | ||
5a575436 | 503 | for (Long_t iBin=0; iBin<fResponseOrig->GetNbins(); iBin++) { |
504 | Double_t val = fResponseOrig->GetBinContent(iBin,fCoordinatesN_M); //used as mean | |
505 | Double_t err = fResponseOrig->GetBinError(fCoordinatesN_M); //used as sigma | |
506 | Double_t ran = fRandom3->Gaus(val,err); | |
507 | // random = fRandom3->PoissonD(measuredValue); //doesn't work for normalized spectra, use Gaus (assuming raw counts in bin is large >10) | |
508 | fRandomResponse->SetBinContent(iBin,ran); | |
509 | } | |
510 | for (Long_t iBin=0; iBin<fEfficiencyOrig->GetNbins(); iBin++) { | |
511 | Double_t val = fEfficiencyOrig->GetBinContent(iBin,fCoordinatesN_M); //used as mean | |
512 | Double_t err = fEfficiencyOrig->GetBinError(fCoordinatesN_M); //used as sigma | |
513 | Double_t ran = fRandom3->Gaus(val,err); | |
514 | // random = fRandom3->PoissonD(measuredValue); //doesn't work for normalized spectra, use Gaus (assuming raw counts in bin is large >10) | |
515 | fRandomEfficiency->SetBinContent(iBin,ran); | |
516 | } | |
517 | for (Long_t iBin=0; iBin<fMeasuredOrig->GetNbins(); iBin++) { | |
518 | Double_t val = fMeasuredOrig->GetBinContent(iBin,fCoordinatesN_M); //used as mean | |
519 | Double_t err = fMeasuredOrig->GetBinError(fCoordinatesN_M); //used as sigma | |
520 | Double_t ran = fRandom3->Gaus(val,err); | |
769f5114 | 521 | // random = fRandom3->PoissonD(measuredValue); //doesn't work for normalized spectra, use Gaus (assuming raw counts in bin is large >10) |
5a575436 | 522 | fRandomMeasured->SetBinContent(iBin,ran); |
769f5114 | 523 | } |
524 | } | |
525 | ||
526 | //______________________________________________________________ | |
527 | void AliCFUnfolding::FillDeltaUnfoldedProfile() { | |
528 | // | |
a9500e70 | 529 | // Store difference of unfolded spectrum from measured distribution and unfolded spectrum from randomized distribution |
530 | // The delta profile has been set to a THnSparse to handle N dimension | |
531 | // The THnSparse contains in each bin the mean value and spread of the difference | |
532 | // This function updates the profile wrt to its previous mean and error | |
533 | // The relation between iterations (n+1) and n is as follows : | |
534 | // mean_{n+1} = (n*mean_n + value_{n+1}) / (n+1) | |
535 | // sigma_{n+1} = sqrt { 1/(n+1) * [ n*sigma_n^2 + (n^2+n)*(mean_{n+1}-mean_n)^2 ] } (can this be optimized?) | |
536 | ||
537 | for (Long_t iBin=0; iBin<fUnfolded->GetNbins(); iBin++) { | |
538 | Double_t deltaInBin = fUnfoldedFinal->GetBinContent(iBin,fCoordinatesN_M) - fUnfolded->GetBinContent(iBin); | |
539 | Double_t entriesInBin = fDeltaUnfoldedN->GetBinContent(fCoordinatesN_M); | |
540 | //AliDebug(2,Form("%e %e ==> delta = %e\n",fUnfoldedFinal->GetBinContent(iBin,fCoordinatesN_M),fUnfolded->GetBinContent(iBin),deltaInBin)); | |
541 | ||
542 | Double_t mean_n = fDeltaUnfoldedP->GetBinContent(fCoordinatesN_M) ; | |
543 | Double_t mean_nplus1 = mean_n ; | |
544 | mean_nplus1 *= entriesInBin ; | |
545 | mean_nplus1 += deltaInBin ; | |
546 | mean_nplus1 /= (entriesInBin+1) ; | |
547 | ||
548 | Double_t sigma = fDeltaUnfoldedP->GetBinContent(fCoordinatesN_M) ; | |
549 | sigma *= sigma ; | |
550 | sigma *= entriesInBin ; | |
551 | sigma += ( (entriesInBin*entriesInBin+entriesInBin) * TMath::Power(mean_nplus1 - mean_n,2) ) ; | |
552 | sigma /= (entriesInBin+1) ; | |
553 | sigma = TMath::Sqrt(sigma) ; | |
554 | ||
555 | //AliDebug(2,Form("sigma = %e\n",sigma)); | |
556 | ||
557 | fDeltaUnfoldedP->SetBinContent(fCoordinatesN_M,mean_nplus1) ; | |
558 | fDeltaUnfoldedP->SetBinError (fCoordinatesN_M,sigma) ; | |
559 | fDeltaUnfoldedN->SetBinContent(fCoordinatesN_M,entriesInBin+1); | |
769f5114 | 560 | } |
561 | } | |
562 | ||
85b6bda9 | 563 | //______________________________________________________________ |
564 | ||
c0b10ad4 | 565 | void AliCFUnfolding::GetCoordinates() { |
566 | // | |
567 | // assign coordinates in Measured and True spaces (dim=N) from coordinates in global space (dim=2N) | |
568 | // | |
569 | for (Int_t i = 0; i<fNVariables ; i++) { | |
570 | fCoordinatesN_M[i] = fCoordinates2N[i]; | |
571 | fCoordinatesN_T[i] = fCoordinates2N[i+fNVariables]; | |
572 | } | |
573 | } | |
574 | ||
575 | //______________________________________________________________ | |
576 | ||
577 | void AliCFUnfolding::CreateConditional() { | |
578 | // | |
579 | // creates the conditional probability matrix (R*) holding the P(M|T), given the reponse matrix R | |
580 | // | |
581 | // --> R*(i,j) = R(i,j) / SUM_k{ R(k,j) } | |
582 | // | |
583 | ||
5a575436 | 584 | fConditional = (THnSparse*) fResponse->Clone(); // output of this function |
a9500e70 | 585 | |
85b6bda9 | 586 | Int_t* dim = new Int_t [fNVariables]; |
587 | for (Int_t iDim=0; iDim<fNVariables; iDim++) dim[iDim] = fNVariables+iDim ; //dimensions corresponding to TRUE values (i.e. from N to 2N-1) | |
a9500e70 | 588 | |
589 | THnSparse* responseInT = fConditional->Projection(fNVariables,dim,"E"); // output denominator : | |
590 | // projection of the response matrix on the TRUE axis | |
85b6bda9 | 591 | delete [] dim; |
769f5114 | 592 | |
c0b10ad4 | 593 | // fill the conditional probability matrix |
7bc6a013 | 594 | for (Long_t iBin=0; iBin<fResponse->GetNbins(); iBin++) { |
c0b10ad4 | 595 | Double_t responseValue = fResponse->GetBinContent(iBin,fCoordinates2N); |
596 | GetCoordinates(); | |
a9500e70 | 597 | Double_t projValue = responseInT->GetBinContent(fCoordinatesN_T); |
769f5114 | 598 | |
85b6bda9 | 599 | Double_t fill = responseValue / projValue ; |
600 | if (fill>0. || fConditional->GetBinContent(fCoordinates2N)>0.) { | |
601 | fConditional->SetBinContent(fCoordinates2N,fill); | |
769f5114 | 602 | Double_t err = 0.; |
85b6bda9 | 603 | fConditional->SetBinError (fCoordinates2N,err); |
604 | } | |
c0b10ad4 | 605 | } |
a9500e70 | 606 | delete responseInT ; |
c0b10ad4 | 607 | } |
769f5114 | 608 | //______________________________________________________________ |
609 | ||
610 | Int_t AliCFUnfolding::GetDOF() { | |
611 | // | |
612 | // number of dof = number of bins | |
613 | // | |
614 | ||
615 | Int_t nDOF = 1 ; | |
616 | for (Int_t iDim=0; iDim<fNVariables; iDim++) { | |
617 | nDOF *= fPrior->GetAxis(iDim)->GetNbins(); | |
618 | } | |
619 | AliDebug(0,Form("Number of degrees of freedom = %d",nDOF)); | |
620 | return nDOF; | |
621 | } | |
c0b10ad4 | 622 | |
623 | //______________________________________________________________ | |
624 | ||
625 | Double_t AliCFUnfolding::GetChi2() { | |
626 | // | |
627 | // Returns the chi2 between unfolded and a priori spectrum | |
769f5114 | 628 | // This function became absolute with the correlated error calculation. |
629 | // Errors on the unfolded distribution are not known until the end | |
630 | // Use the convergence criterion instead | |
c0b10ad4 | 631 | // |
632 | ||
769f5114 | 633 | Double_t chi2 = 0. ; |
634 | Double_t error_unf = 0.; | |
7bc6a013 | 635 | for (Long_t iBin=0; iBin<fPrior->GetNbins(); iBin++) { |
85f9f9e1 | 636 | Double_t priorValue = fPrior->GetBinContent(iBin,fCoordinatesN_T); |
769f5114 | 637 | error_unf = fUnfolded->GetBinError(fCoordinatesN_T); |
85f9f9e1 | 638 | chi2 += (error_unf > 0. ? TMath::Power((fUnfolded->GetBinContent(fCoordinatesN_T) - priorValue)/error_unf,2) / priorValue : 0.) ; |
c0b10ad4 | 639 | } |
640 | return chi2; | |
641 | } | |
642 | ||
643 | //______________________________________________________________ | |
644 | ||
769f5114 | 645 | Double_t AliCFUnfolding::GetConvergence() { |
646 | // | |
647 | // Returns convergence criterion = \sum_t ((U_t^{n-1}-U_t^n)/U_t^{n-1})^2 | |
648 | // U is unfolded spectrum, t is the bin, n = current, n-1 = previous | |
649 | // | |
650 | Double_t convergence = 0.; | |
651 | Double_t priorValue = 0.; | |
652 | Double_t currentValue = 0.; | |
653 | for (Long_t iBin=0; iBin < fPrior->GetNbins(); iBin++) { | |
654 | priorValue = fPrior->GetBinContent(iBin,fCoordinatesN_T); | |
a9500e70 | 655 | currentValue = fUnfolded->GetBinContent(fCoordinatesN_T); |
769f5114 | 656 | |
657 | if (priorValue > 0.) | |
658 | convergence += ((priorValue-currentValue)/priorValue)*((priorValue-currentValue)/priorValue); | |
a9500e70 | 659 | else |
769f5114 | 660 | AliWarning(Form("priorValue = %f. Adding 0 to convergence criterion.",priorValue)); |
769f5114 | 661 | } |
662 | return convergence; | |
663 | } | |
664 | ||
665 | //______________________________________________________________ | |
666 | ||
667 | void AliCFUnfolding::SetMaxConvergencePerDOF(Double_t val) { | |
c0b10ad4 | 668 | // |
769f5114 | 669 | // Max. convergence criterion per degree of freedom : user setting |
670 | // convergence criterion = DOF*val; DOF = number of bins | |
671 | // In Jan-Fiete's multiplicity note: Convergence criterion = DOF*0.001^2 | |
c0b10ad4 | 672 | // |
673 | ||
769f5114 | 674 | Int_t nDOF = GetDOF() ; |
675 | fMaxConvergence = val * nDOF ; | |
676 | AliInfo(Form("MaxConvergence = %e. Number of degrees of freedom = %d",fMaxConvergence,nDOF)); | |
c0b10ad4 | 677 | } |
678 | ||
679 | //______________________________________________________________ | |
680 | ||
85b6bda9 | 681 | Short_t AliCFUnfolding::Smooth() { |
c0b10ad4 | 682 | // |
683 | // Smoothes the unfolded spectrum | |
85b6bda9 | 684 | // |
685 | // By default each cell content is replaced by the average with the neighbouring bins (but not diagonally-neighbouring bins) | |
686 | // However, if a specific function fcn has been defined in UseSmoothing(fcn), the unfolded will be fit and updated using fcn | |
c0b10ad4 | 687 | // |
688 | ||
85b6bda9 | 689 | if (fSmoothFunction) { |
7bc6a013 | 690 | AliDebug(2,Form("Smoothing spectrum with fit function %p",fSmoothFunction)); |
85b6bda9 | 691 | return SmoothUsingFunction(); |
692 | } | |
7036630f | 693 | else return SmoothUsingNeighbours(fUnfolded); |
85b6bda9 | 694 | } |
695 | ||
696 | //______________________________________________________________ | |
697 | ||
7036630f | 698 | Short_t AliCFUnfolding::SmoothUsingNeighbours(THnSparse* hist) { |
85b6bda9 | 699 | // |
700 | // Smoothes the unfolded spectrum using neighouring bins | |
701 | // | |
702 | ||
7036630f | 703 | Int_t const nDimensions = hist->GetNdimensions() ; |
704 | Int_t* coordinates = new Int_t[nDimensions]; | |
705 | ||
706 | Int_t* numBins = new Int_t[nDimensions]; | |
707 | for (Int_t iVar=0; iVar<nDimensions; iVar++) numBins[iVar] = hist->GetAxis(iVar)->GetNbins(); | |
c0b10ad4 | 708 | |
7036630f | 709 | //need a copy because hist will be updated during the loop, and this creates problems |
710 | THnSparse* copy = (THnSparse*)hist->Clone(); | |
c0b10ad4 | 711 | |
7bc6a013 | 712 | for (Long_t iBin=0; iBin<copy->GetNbins(); iBin++) { //loop on non-empty bins |
7036630f | 713 | Double_t content = copy->GetBinContent(iBin,coordinates); |
7bc6a013 | 714 | Double_t error2 = TMath::Power(copy->GetBinError(iBin),2); |
c0b10ad4 | 715 | |
716 | // skip the under/overflow bins... | |
717 | Bool_t isOutside = kFALSE ; | |
7036630f | 718 | for (Int_t iVar=0; iVar<nDimensions; iVar++) { |
719 | if (coordinates[iVar]<1 || coordinates[iVar]>numBins[iVar]) { | |
c0b10ad4 | 720 | isOutside=kTRUE; |
721 | break; | |
722 | } | |
723 | } | |
724 | if (isOutside) continue; | |
725 | ||
726 | Int_t neighbours = 0; // number of neighbours to average with | |
727 | ||
7036630f | 728 | for (Int_t iVar=0; iVar<nDimensions; iVar++) { |
729 | if (coordinates[iVar] > 1) { // must not be on low edge border | |
730 | coordinates[iVar]-- ; //get lower neighbouring bin | |
731 | content += copy->GetBinContent(coordinates); | |
732 | error2 += TMath::Power(copy->GetBinError(coordinates),2); | |
c0b10ad4 | 733 | neighbours++; |
7036630f | 734 | coordinates[iVar]++ ; //back to initial coordinate |
c0b10ad4 | 735 | } |
7036630f | 736 | if (coordinates[iVar] < numBins[iVar]) { // must not be on up edge border |
737 | coordinates[iVar]++ ; //get upper neighbouring bin | |
738 | content += copy->GetBinContent(coordinates); | |
739 | error2 += TMath::Power(copy->GetBinError(coordinates),2); | |
c0b10ad4 | 740 | neighbours++; |
7036630f | 741 | coordinates[iVar]-- ; //back to initial coordinate |
c0b10ad4 | 742 | } |
743 | } | |
85b6bda9 | 744 | // make an average |
7036630f | 745 | hist->SetBinContent(coordinates,content/(1.+neighbours)); |
746 | hist->SetBinError (coordinates,TMath::Sqrt(error2)/(1.+neighbours)); | |
c0b10ad4 | 747 | } |
748 | delete [] numBins; | |
7036630f | 749 | delete [] coordinates ; |
c0b10ad4 | 750 | delete copy; |
85b6bda9 | 751 | return 0; |
c0b10ad4 | 752 | } |
753 | ||
85b6bda9 | 754 | //______________________________________________________________ |
755 | ||
756 | Short_t AliCFUnfolding::SmoothUsingFunction() { | |
757 | // | |
758 | // Fits the unfolded spectrum using the function fSmoothFunction | |
759 | // | |
760 | ||
7bc6a013 | 761 | AliDebug(0,Form("Smooth function is a %s with option \"%s\" and has %d parameters : ",fSmoothFunction->ClassName(),fSmoothOption,fSmoothFunction->GetNpar())); |
85b6bda9 | 762 | |
7bc6a013 | 763 | for (Int_t iPar=0; iPar<fSmoothFunction->GetNpar(); iPar++) AliDebug(0,Form("par[%d]=%e",iPar,fSmoothFunction->GetParameter(iPar))); |
764 | ||
765 | Int_t fitResult = 0; | |
85b6bda9 | 766 | |
767 | switch (fNVariables) { | |
7bc6a013 | 768 | case 1 : fitResult = fUnfolded->Projection(0) ->Fit(fSmoothFunction,fSmoothOption); break; |
769 | case 2 : fitResult = fUnfolded->Projection(1,0) ->Fit(fSmoothFunction,fSmoothOption); break; // (1,0) instead of (0,1) -> TAxis issue | |
770 | case 3 : fitResult = fUnfolded->Projection(0,1,2)->Fit(fSmoothFunction,fSmoothOption); break; | |
771 | default: AliFatal(Form("Cannot handle such fit in %d dimensions",fNVariables)) ; return 1; | |
772 | } | |
773 | ||
774 | if (fitResult != 0) { | |
775 | AliWarning(Form("Fit failed with status %d, stopping the loop",fitResult)); | |
776 | return 1; | |
85b6bda9 | 777 | } |
778 | ||
779 | Int_t nDim = fNVariables; | |
780 | Int_t* bins = new Int_t[nDim]; // number of bins for each variable | |
781 | Long_t nBins = 1; // used to calculate the total number of bins in the THnSparse | |
782 | ||
783 | for (Int_t iVar=0; iVar<nDim; iVar++) { | |
784 | bins[iVar] = fUnfolded->GetAxis(iVar)->GetNbins(); | |
785 | nBins *= bins[iVar]; | |
786 | } | |
787 | ||
788 | Int_t *bin = new Int_t[nDim]; // bin to fill the THnSparse (holding the bin coordinates) | |
789 | Double_t x[3] = {0,0,0} ; // value in bin center (max dimension is 3 (TF3)) | |
790 | ||
791 | // loop on the bins and update of fUnfolded | |
792 | // THnSparse::Multiply(TF1*) doesn't exist, so let's do it bin by bin | |
793 | for (Long_t iBin=0; iBin<nBins; iBin++) { | |
794 | Long_t bin_tmp = iBin ; | |
795 | for (Int_t iVar=0; iVar<nDim; iVar++) { | |
796 | bin[iVar] = 1 + bin_tmp % bins[iVar] ; | |
797 | bin_tmp /= bins[iVar] ; | |
798 | x[iVar] = fUnfolded->GetAxis(iVar)->GetBinCenter(bin[iVar]); | |
799 | } | |
800 | Double_t functionValue = fSmoothFunction->Eval(x[0],x[1],x[2]) ; | |
12e419d5 | 801 | fUnfolded->SetBinError (bin,fUnfolded->GetBinError(bin)*functionValue/fUnfolded->GetBinContent(bin)); |
85b6bda9 | 802 | fUnfolded->SetBinContent(bin,functionValue); |
85b6bda9 | 803 | } |
700a1189 | 804 | delete [] bins; |
805 | delete [] bin ; | |
85b6bda9 | 806 | return 0; |
807 | } | |
c0b10ad4 | 808 | |
809 | //______________________________________________________________ | |
810 | ||
811 | void AliCFUnfolding::CreateFlatPrior() { | |
812 | // | |
813 | // Creates a flat prior distribution | |
814 | // | |
815 | ||
816 | AliInfo("Creating a flat a priori distribution"); | |
817 | ||
818 | // create the frame of the THnSparse given (for example) the one from the efficiency map | |
819 | fPrior = (THnSparse*) fEfficiency->Clone(); | |
a9500e70 | 820 | fPrior->SetTitle("Prior"); |
c0b10ad4 | 821 | |
85b6bda9 | 822 | if (fNVariables != fPrior->GetNdimensions()) |
823 | AliFatal(Form("The prior matrix should have %d dimensions, and it has actually %d",fNVariables,fPrior->GetNdimensions())); | |
824 | ||
c0b10ad4 | 825 | Int_t nDim = fNVariables; |
826 | Int_t* bins = new Int_t[nDim]; // number of bins for each variable | |
827 | Long_t nBins = 1; // used to calculate the total number of bins in the THnSparse | |
828 | ||
829 | for (Int_t iVar=0; iVar<nDim; iVar++) { | |
830 | bins[iVar] = fPrior->GetAxis(iVar)->GetNbins(); | |
831 | nBins *= bins[iVar]; | |
832 | } | |
833 | ||
834 | Int_t *bin = new Int_t[nDim]; // bin to fill the THnSparse (holding the bin coordinates) | |
835 | ||
836 | // loop that sets 1 in each bin | |
837 | for (Long_t iBin=0; iBin<nBins; iBin++) { | |
838 | Long_t bin_tmp = iBin ; | |
839 | for (Int_t iVar=0; iVar<nDim; iVar++) { | |
840 | bin[iVar] = 1 + bin_tmp % bins[iVar] ; | |
841 | bin_tmp /= bins[iVar] ; | |
842 | } | |
843 | fPrior->SetBinContent(bin,1.); // put 1 everywhere | |
85b6bda9 | 844 | fPrior->SetBinError (bin,0.); // put 0 everywhere |
c0b10ad4 | 845 | } |
846 | ||
5a575436 | 847 | fPriorOrig = (THnSparse*)fPrior->Clone(); |
c0b10ad4 | 848 | |
849 | delete [] bin; | |
850 | delete [] bins; | |
851 | } |