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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 | |
d966b1e6 | 96 | |
c0b10ad4 | 97 | ClassImp(AliCFUnfolding) |
98 | ||
99 | //______________________________________________________________ | |
100 | ||
101 | AliCFUnfolding::AliCFUnfolding() : | |
102 | TNamed(), | |
5a575436 | 103 | fResponseOrig(0x0), |
104 | fPriorOrig(0x0), | |
105 | fEfficiencyOrig(0x0), | |
769f5114 | 106 | fMeasuredOrig(0x0), |
a9500e70 | 107 | fMaxNumIterations(0), |
c0b10ad4 | 108 | fNVariables(0), |
c0b10ad4 | 109 | fUseSmoothing(kFALSE), |
85b6bda9 | 110 | fSmoothFunction(0x0), |
a9500e70 | 111 | fSmoothOption("iremn"), |
769f5114 | 112 | fMaxConvergence(0), |
a9500e70 | 113 | fNRandomIterations(0), |
5a575436 | 114 | fResponse(0x0), |
115 | fPrior(0x0), | |
116 | fEfficiency(0x0), | |
117 | fMeasured(0x0), | |
c0b10ad4 | 118 | fInverseResponse(0x0), |
119 | fMeasuredEstimate(0x0), | |
120 | fConditional(0x0), | |
c0b10ad4 | 121 | fUnfolded(0x0), |
a9500e70 | 122 | fUnfoldedFinal(0x0), |
c0b10ad4 | 123 | fCoordinates2N(0x0), |
124 | fCoordinatesN_M(0x0), | |
769f5114 | 125 | fCoordinatesN_T(0x0), |
5a575436 | 126 | fRandomResponse(0x0), |
127 | fRandomEfficiency(0x0), | |
128 | fRandomMeasured(0x0), | |
769f5114 | 129 | fRandom3(0x0), |
769f5114 | 130 | fDeltaUnfoldedP(0x0), |
a9500e70 | 131 | fDeltaUnfoldedN(0x0), |
769f5114 | 132 | fNCalcCorrErrors(0), |
133 | fRandomSeed(0) | |
c0b10ad4 | 134 | { |
135 | // | |
136 | // default constructor | |
137 | // | |
138 | } | |
139 | ||
140 | //______________________________________________________________ | |
141 | ||
142 | AliCFUnfolding::AliCFUnfolding(const Char_t* name, const Char_t* title, const Int_t nVar, | |
a9500e70 | 143 | const THnSparse* response, const THnSparse* efficiency, const THnSparse* measured, const THnSparse* prior , |
144 | Double_t maxConvergencePerDOF, UInt_t randomSeed, Int_t maxNumIterations | |
145 | ) : | |
c0b10ad4 | 146 | TNamed(name,title), |
5a575436 | 147 | fResponseOrig((THnSparse*)response->Clone()), |
148 | fPriorOrig(0x0), | |
149 | fEfficiencyOrig((THnSparse*)efficiency->Clone()), | |
769f5114 | 150 | fMeasuredOrig((THnSparse*)measured->Clone()), |
a9500e70 | 151 | fMaxNumIterations(maxNumIterations), |
c0b10ad4 | 152 | fNVariables(nVar), |
c0b10ad4 | 153 | fUseSmoothing(kFALSE), |
85b6bda9 | 154 | fSmoothFunction(0x0), |
a9500e70 | 155 | fSmoothOption("iremn"), |
769f5114 | 156 | fMaxConvergence(0), |
a9500e70 | 157 | fNRandomIterations(maxNumIterations), |
5a575436 | 158 | fResponse((THnSparse*)response->Clone()), |
159 | fPrior(0x0), | |
160 | fEfficiency((THnSparse*)efficiency->Clone()), | |
161 | fMeasured((THnSparse*)measured->Clone()), | |
c0b10ad4 | 162 | fInverseResponse(0x0), |
163 | fMeasuredEstimate(0x0), | |
164 | fConditional(0x0), | |
c0b10ad4 | 165 | fUnfolded(0x0), |
a9500e70 | 166 | fUnfoldedFinal(0x0), |
c0b10ad4 | 167 | fCoordinates2N(0x0), |
168 | fCoordinatesN_M(0x0), | |
769f5114 | 169 | fCoordinatesN_T(0x0), |
5a575436 | 170 | fRandomResponse((THnSparse*)response->Clone()), |
171 | fRandomEfficiency((THnSparse*)efficiency->Clone()), | |
172 | fRandomMeasured((THnSparse*)measured->Clone()), | |
769f5114 | 173 | fRandom3(0x0), |
769f5114 | 174 | fDeltaUnfoldedP(0x0), |
a9500e70 | 175 | fDeltaUnfoldedN(0x0), |
769f5114 | 176 | fNCalcCorrErrors(0), |
a9500e70 | 177 | fRandomSeed(randomSeed) |
c0b10ad4 | 178 | { |
179 | // | |
180 | // named constructor | |
181 | // | |
182 | ||
183 | AliInfo(Form("\n\n--------------------------\nCreating an unfolder :\n--------------------------\nresponse matrix has %d dimension(s)",fResponse->GetNdimensions())); | |
a9500e70 | 184 | |
c0b10ad4 | 185 | if (!prior) CreateFlatPrior(); // if no prior distribution declared, simply use a flat distribution |
186 | else { | |
187 | fPrior = (THnSparse*) prior->Clone(); | |
5a575436 | 188 | fPriorOrig = (THnSparse*)fPrior->Clone(); |
85b6bda9 | 189 | if (fPrior->GetNdimensions() != fNVariables) |
190 | AliFatal(Form("The prior matrix should have %d dimensions, and it has actually %d",fNVariables,fPrior->GetNdimensions())); | |
c0b10ad4 | 191 | } |
85b6bda9 | 192 | |
193 | if (fEfficiency->GetNdimensions() != fNVariables) | |
194 | AliFatal(Form("The efficiency matrix should have %d dimensions, and it has actually %d",fNVariables,fEfficiency->GetNdimensions())); | |
195 | if (fMeasured->GetNdimensions() != fNVariables) | |
196 | AliFatal(Form("The measured matrix should have %d dimensions, and it has actually %d",fNVariables,fMeasured->GetNdimensions())); | |
197 | if (fResponse->GetNdimensions() != 2*fNVariables) | |
198 | AliFatal(Form("The response matrix should have %d dimensions, and it has actually %d",2*fNVariables,fResponse->GetNdimensions())); | |
c0b10ad4 | 199 | |
85b6bda9 | 200 | |
c0b10ad4 | 201 | for (Int_t iVar=0; iVar<fNVariables; iVar++) { |
202 | AliInfo(Form("prior matrix has %d bins in dimension %d",fPrior ->GetAxis(iVar)->GetNbins(),iVar)); | |
203 | AliInfo(Form("efficiency matrix has %d bins in dimension %d",fEfficiency->GetAxis(iVar)->GetNbins(),iVar)); | |
204 | AliInfo(Form("measured matrix has %d bins in dimension %d",fMeasured ->GetAxis(iVar)->GetNbins(),iVar)); | |
205 | } | |
769f5114 | 206 | |
5a575436 | 207 | fRandomResponse ->SetTitle("Randomized response matrix"); |
208 | fRandomEfficiency->SetTitle("Randomized efficiency"); | |
209 | fRandomMeasured ->SetTitle("Randomized measured"); | |
a9500e70 | 210 | SetMaxConvergencePerDOF(maxConvergencePerDOF) ; |
c0b10ad4 | 211 | Init(); |
212 | } | |
213 | ||
c0b10ad4 | 214 | //______________________________________________________________ |
215 | ||
216 | AliCFUnfolding::~AliCFUnfolding() { | |
217 | // | |
218 | // destructor | |
219 | // | |
220 | if (fResponse) delete fResponse; | |
221 | if (fPrior) delete fPrior; | |
c0b10ad4 | 222 | if (fEfficiency) delete fEfficiency; |
5a575436 | 223 | if (fEfficiencyOrig) delete fEfficiencyOrig; |
c0b10ad4 | 224 | if (fMeasured) delete fMeasured; |
769f5114 | 225 | if (fMeasuredOrig) delete fMeasuredOrig; |
85b6bda9 | 226 | if (fSmoothFunction) delete fSmoothFunction; |
5a575436 | 227 | if (fPriorOrig) delete fPriorOrig; |
c0b10ad4 | 228 | if (fInverseResponse) delete fInverseResponse; |
229 | if (fMeasuredEstimate) delete fMeasuredEstimate; | |
230 | if (fConditional) delete fConditional; | |
a9500e70 | 231 | if (fUnfolded) delete fUnfolded; |
232 | if (fUnfoldedFinal) delete fUnfoldedFinal; | |
c0b10ad4 | 233 | if (fCoordinates2N) delete [] fCoordinates2N; |
234 | if (fCoordinatesN_M) delete [] fCoordinatesN_M; | |
235 | if (fCoordinatesN_T) delete [] fCoordinatesN_T; | |
5a575436 | 236 | if (fRandomResponse) delete fRandomResponse; |
237 | if (fRandomEfficiency) delete fRandomEfficiency; | |
238 | if (fRandomMeasured) delete fRandomMeasured; | |
769f5114 | 239 | if (fRandom3) delete fRandom3; |
769f5114 | 240 | if (fDeltaUnfoldedP) delete fDeltaUnfoldedP; |
a9500e70 | 241 | if (fDeltaUnfoldedN) delete fDeltaUnfoldedN; |
d966b1e6 | 242 | |
c0b10ad4 | 243 | } |
244 | ||
245 | //______________________________________________________________ | |
246 | ||
247 | void AliCFUnfolding::Init() { | |
248 | // | |
249 | // initialisation function : creates internal settings | |
250 | // | |
251 | ||
769f5114 | 252 | fRandom3 = new TRandom3(fRandomSeed); |
253 | ||
c0b10ad4 | 254 | fCoordinates2N = new Int_t[2*fNVariables]; |
255 | fCoordinatesN_M = new Int_t[fNVariables]; | |
256 | fCoordinatesN_T = new Int_t[fNVariables]; | |
257 | ||
258 | // create the matrix of conditional probabilities P(M|T) | |
a9500e70 | 259 | CreateConditional(); //done only once at initialization |
c0b10ad4 | 260 | |
261 | // create the frame of the inverse response matrix | |
262 | fInverseResponse = (THnSparse*) fResponse->Clone(); | |
263 | // create the frame of the unfolded spectrum | |
264 | fUnfolded = (THnSparse*) fPrior->Clone(); | |
a9500e70 | 265 | fUnfolded->SetTitle("Unfolded"); |
c0b10ad4 | 266 | // create the frame of the measurement estimate spectrum |
267 | fMeasuredEstimate = (THnSparse*) fMeasured->Clone(); | |
a9500e70 | 268 | |
5a575436 | 269 | // create the frame of the delta profiles |
a9500e70 | 270 | fDeltaUnfoldedP = (THnSparse*)fPrior->Clone(); |
271 | fDeltaUnfoldedP->SetTitle("#Delta unfolded"); | |
272 | fDeltaUnfoldedP->Reset(); | |
273 | fDeltaUnfoldedN = (THnSparse*)fPrior->Clone(); | |
5a575436 | 274 | fDeltaUnfoldedN->SetTitle(""); |
a9500e70 | 275 | fDeltaUnfoldedN->Reset(); |
d966b1e6 | 276 | |
277 | ||
c0b10ad4 | 278 | } |
279 | ||
a9500e70 | 280 | |
c0b10ad4 | 281 | //______________________________________________________________ |
282 | ||
283 | void AliCFUnfolding::CreateEstMeasured() { | |
284 | // | |
285 | // This function creates a estimate (M) of the reconstructed spectrum | |
7bc6a013 | 286 | // given the a priori distribution (T), the efficiency (E) and the conditional matrix (COND) |
c0b10ad4 | 287 | // |
288 | // --> P(M) = SUM { P(M|T) * P(T) } | |
7bc6a013 | 289 | // --> M(i) = SUM_k { COND(i,k) * T(k) * E (k)} |
c0b10ad4 | 290 | // |
291 | // This is needed to calculate the inverse response matrix | |
292 | // | |
293 | ||
294 | ||
295 | // clean the measured estimate spectrum | |
85f9f9e1 | 296 | fMeasuredEstimate->Reset(); |
297 | ||
85b6bda9 | 298 | THnSparse* priorTimesEff = (THnSparse*) fPrior->Clone(); |
299 | priorTimesEff->Multiply(fEfficiency); | |
300 | ||
c0b10ad4 | 301 | // fill it |
7bc6a013 | 302 | for (Long_t iBin=0; iBin<fConditional->GetNbins(); iBin++) { |
c0b10ad4 | 303 | Double_t conditionalValue = fConditional->GetBinContent(iBin,fCoordinates2N); |
304 | GetCoordinates(); | |
85b6bda9 | 305 | Double_t priorTimesEffValue = priorTimesEff->GetBinContent(fCoordinatesN_T); |
85b6bda9 | 306 | Double_t fill = conditionalValue * priorTimesEffValue ; |
307 | ||
308 | if (fill>0.) { | |
12e419d5 | 309 | fMeasuredEstimate->AddBinContent(fCoordinatesN_M,fill); |
769f5114 | 310 | fMeasuredEstimate->SetBinError(fCoordinatesN_M,0.); |
85b6bda9 | 311 | } |
c0b10ad4 | 312 | } |
85b6bda9 | 313 | delete priorTimesEff ; |
c0b10ad4 | 314 | } |
315 | ||
316 | //______________________________________________________________ | |
317 | ||
318 | void AliCFUnfolding::CreateInvResponse() { | |
319 | // | |
320 | // Creates the inverse response matrix (INV) with Bayesian method | |
321 | // : uses the conditional matrix (COND), the prior probabilities (T) and the efficiency map (E) | |
322 | // | |
323 | // --> P(T|M) = P(M|T) * P(T) * eff(T) / SUM { P(M|T) * P(T) } | |
324 | // --> INV(i,j) = COND(i,j) * T(j) * E(j) / SUM_k { COND(i,k) * T(k) } | |
325 | // | |
326 | ||
85b6bda9 | 327 | THnSparse* priorTimesEff = (THnSparse*) fPrior->Clone(); |
328 | priorTimesEff->Multiply(fEfficiency); | |
329 | ||
7bc6a013 | 330 | for (Long_t iBin=0; iBin<fConditional->GetNbins(); iBin++) { |
c0b10ad4 | 331 | Double_t conditionalValue = fConditional->GetBinContent(iBin,fCoordinates2N); |
332 | GetCoordinates(); | |
85b6bda9 | 333 | Double_t estMeasuredValue = fMeasuredEstimate->GetBinContent(fCoordinatesN_M); |
85b6bda9 | 334 | Double_t priorTimesEffValue = priorTimesEff ->GetBinContent(fCoordinatesN_T); |
85b6bda9 | 335 | Double_t fill = (estMeasuredValue>0. ? conditionalValue * priorTimesEffValue / estMeasuredValue : 0. ) ; |
85b6bda9 | 336 | if (fill>0. || fInverseResponse->GetBinContent(fCoordinates2N)>0.) { |
337 | fInverseResponse->SetBinContent(fCoordinates2N,fill); | |
769f5114 | 338 | fInverseResponse->SetBinError (fCoordinates2N,0.); |
85b6bda9 | 339 | } |
340 | } | |
341 | delete priorTimesEff ; | |
c0b10ad4 | 342 | } |
343 | ||
344 | //______________________________________________________________ | |
345 | ||
346 | void AliCFUnfolding::Unfold() { | |
347 | // | |
348 | // Main routine called by the user : | |
769f5114 | 349 | // it calculates the unfolded spectrum from the response matrix, measured spectrum and efficiency |
350 | // several iterations are performed until a reasonable chi2 or convergence criterion is reached | |
c0b10ad4 | 351 | // |
352 | ||
769f5114 | 353 | Int_t iIterBayes = 0 ; |
354 | Double_t convergence = 0.; | |
c0b10ad4 | 355 | |
356 | for (iIterBayes=0; iIterBayes<fMaxNumIterations; iIterBayes++) { // bayes iterations | |
a9500e70 | 357 | |
358 | CreateEstMeasured(); // create measured estimate from prior | |
359 | CreateInvResponse(); // create inverse response from prior | |
360 | CreateUnfolded(); // create unfoled spectrum from measured and inverse response | |
361 | ||
362 | convergence = GetConvergence(); | |
363 | AliDebug(0,Form("convergence at iteration %d is %e",iIterBayes,convergence)); | |
364 | ||
365 | if (fMaxConvergence>0. && convergence<fMaxConvergence && fNCalcCorrErrors == 0) { | |
366 | fNRandomIterations = iIterBayes; | |
367 | AliDebug(0,Form("convergence is met at iteration %d",iIterBayes)); | |
c0b10ad4 | 368 | break; |
369 | } | |
769f5114 | 370 | |
85b6bda9 | 371 | if (fUseSmoothing) { |
372 | if (Smooth()) { | |
373 | AliError("Couldn't smooth the unfolded spectrum!!"); | |
a9500e70 | 374 | if (fNCalcCorrErrors>0) { |
375 | AliInfo(Form("=======================\nUnfold of randomized distribution finished at iteration %d with convergence %e \n",iIterBayes,convergence)); | |
376 | } | |
377 | else { | |
378 | 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 | 379 | } |
85b6bda9 | 380 | return; |
381 | } | |
382 | } | |
769f5114 | 383 | |
a9500e70 | 384 | // update the prior distribution |
385 | if (fPrior) delete fPrior ; | |
386 | fPrior = (THnSparse*)fUnfolded->Clone() ; | |
387 | fPrior->SetTitle("Prior"); | |
388 | ||
389 | } // end bayes iteration | |
390 | ||
391 | if (fNCalcCorrErrors==0) fUnfoldedFinal = (THnSparse*) fUnfolded->Clone() ; | |
392 | ||
393 | // | |
394 | //for (Long_t iBin=0; iBin<fUnfoldedFinal->GetNbins(); iBin++) AliDebug(2,Form("%e\n",fUnfoldedFinal->GetBinError(iBin))); | |
395 | // | |
396 | ||
397 | if (fNCalcCorrErrors == 0) { | |
398 | AliInfo("\n================================================\nFinished bayes iteration, now calculating errors...\n================================================\n"); | |
399 | fNCalcCorrErrors = 1; | |
769f5114 | 400 | CalculateCorrelatedErrors(); |
401 | } | |
402 | ||
a9500e70 | 403 | if (fNCalcCorrErrors >1 ) { |
404 | AliInfo(Form("\n\n=======================\nFinished at iteration %d : convergence is %e and you required it to be < %e\n=======================\n\n",iIterBayes,convergence,fMaxConvergence)); | |
405 | } | |
406 | else if(fNCalcCorrErrors>0) { | |
407 | AliInfo(Form("=======================\nUnfolding of randomized distribution finished at iteration %d with convergence %e \n",iIterBayes,convergence)); | |
c0b10ad4 | 408 | } |
c0b10ad4 | 409 | } |
410 | ||
411 | //______________________________________________________________ | |
412 | ||
413 | void AliCFUnfolding::CreateUnfolded() { | |
414 | // | |
415 | // Creates the unfolded (T) spectrum from the measured spectrum (M) and the inverse response matrix (INV) | |
416 | // We have P(T) = SUM { P(T|M) * P(M) } | |
417 | // --> T(i) = SUM_k { INV(i,k) * M(k) } | |
418 | // | |
419 | ||
420 | ||
421 | // clear the unfolded spectrum | |
a9500e70 | 422 | // if in the process of error calculation, the random unfolded spectrum is created |
423 | // otherwise the normal unfolded spectrum is created | |
424 | ||
425 | fUnfolded->Reset(); | |
c0b10ad4 | 426 | |
7bc6a013 | 427 | for (Long_t iBin=0; iBin<fInverseResponse->GetNbins(); iBin++) { |
c0b10ad4 | 428 | Double_t invResponseValue = fInverseResponse->GetBinContent(iBin,fCoordinates2N); |
429 | GetCoordinates(); | |
85b6bda9 | 430 | Double_t effValue = fEfficiency->GetBinContent(fCoordinatesN_T); |
85b6bda9 | 431 | Double_t measuredValue = fMeasured ->GetBinContent(fCoordinatesN_M); |
85b6bda9 | 432 | Double_t fill = (effValue>0. ? invResponseValue * measuredValue / effValue : 0.) ; |
769f5114 | 433 | |
85b6bda9 | 434 | if (fill>0.) { |
a9500e70 | 435 | // set errors to zero |
436 | // true errors will be filled afterwards | |
769f5114 | 437 | Double_t err = 0.; |
a9500e70 | 438 | fUnfolded->SetBinError (fCoordinatesN_T,err); |
439 | fUnfolded->AddBinContent(fCoordinatesN_T,fill); | |
85b6bda9 | 440 | } |
c0b10ad4 | 441 | } |
442 | } | |
769f5114 | 443 | |
444 | //______________________________________________________________ | |
445 | ||
446 | void AliCFUnfolding::CalculateCorrelatedErrors() { | |
447 | ||
5a575436 | 448 | // Step 1: Create randomized distribution (fRandomXXXX) of each bin of |
a9500e70 | 449 | // the measured spectrum to calculate correlated errors. |
450 | // Poisson statistics: mean = measured value of bin | |
769f5114 | 451 | // Step 2: Unfold randomized distribution |
a9500e70 | 452 | // Step 3: Store difference of unfolded spectrum from measured distribution and |
453 | // unfolded distribution from randomized distribution | |
454 | // -> fDeltaUnfoldedP (TProfile with option "S") | |
769f5114 | 455 | // Step 4: Repeat Step 1-3 several times (fNRandomIterations) |
456 | // Step 5: The spread of fDeltaUnfoldedP for each bin is the error on the unfolded spectrum of that specific bin | |
457 | ||
769f5114 | 458 | |
a9500e70 | 459 | //Do fNRandomIterations = bayes iterations performed |
460 | for (int i=0; i<fNRandomIterations; i++) { | |
461 | ||
462 | // reset prior to original one | |
463 | if (fPrior) delete fPrior ; | |
5a575436 | 464 | fPrior = (THnSparse*) fPriorOrig->Clone(); |
a9500e70 | 465 | |
466 | // create randomized distribution and stick measured spectrum to it | |
467 | CreateRandomizedDist(); | |
5a575436 | 468 | |
469 | if (fResponse) delete fResponse ; | |
470 | fResponse = (THnSparse*) fRandomResponse->Clone(); | |
471 | fResponse->SetTitle("Response"); | |
472 | ||
473 | if (fEfficiency) delete fEfficiency ; | |
474 | fEfficiency = (THnSparse*) fRandomEfficiency->Clone(); | |
475 | fEfficiency->SetTitle("Efficiency"); | |
476 | ||
477 | if (fMeasured) delete fMeasured ; | |
478 | fMeasured = (THnSparse*) fRandomMeasured->Clone(); | |
a9500e70 | 479 | fMeasured->SetTitle("Measured"); |
480 | ||
5a575436 | 481 | //unfold with randomized distributions |
a9500e70 | 482 | Unfold(); |
483 | FillDeltaUnfoldedProfile(); | |
484 | } | |
769f5114 | 485 | |
a9500e70 | 486 | // Get statistical errors for final unfolded spectrum |
487 | // ie. spread of each pt bin in fDeltaUnfoldedP | |
d966b1e6 | 488 | Double_t meanx2 = 0.; |
489 | Double_t mean = 0.; | |
490 | Double_t checksigma = 0.; | |
491 | Double_t entriesInBin = 0.; | |
a9500e70 | 492 | for (Long_t iBin=0; iBin<fUnfoldedFinal->GetNbins(); iBin++) { |
f21c7024 | 493 | fUnfoldedFinal->GetBinContent(iBin,fCoordinatesN_M); |
d966b1e6 | 494 | mean = fDeltaUnfoldedP->GetBinContent(fCoordinatesN_M); |
495 | meanx2 = fDeltaUnfoldedP->GetBinError(fCoordinatesN_M); | |
496 | entriesInBin = fDeltaUnfoldedN->GetBinContent(fCoordinatesN_M); | |
497 | if(entriesInBin > 1.) checksigma = TMath::Sqrt((entriesInBin/(entriesInBin-1.))*TMath::Abs(meanx2-mean*mean)); | |
498 | //printf("mean %f, meanx2 %f, sigmacheck %f, nentries %f\n",mean, meanx2, checksigma,entriesInBin); | |
a9500e70 | 499 | //AliDebug(2,Form("filling error %e\n",sigma)); |
d966b1e6 | 500 | fUnfoldedFinal->SetBinError(fCoordinatesN_M,checksigma); |
769f5114 | 501 | } |
a9500e70 | 502 | |
503 | // now errors are calculated | |
504 | fNCalcCorrErrors = 2; | |
769f5114 | 505 | } |
a9500e70 | 506 | |
769f5114 | 507 | //______________________________________________________________ |
508 | void AliCFUnfolding::CreateRandomizedDist() { | |
509 | // | |
a9500e70 | 510 | // Create randomized dist from original measured distribution |
511 | // This distribution is created several times, each time with a different random number | |
769f5114 | 512 | // |
513 | ||
5a575436 | 514 | for (Long_t iBin=0; iBin<fResponseOrig->GetNbins(); iBin++) { |
515 | Double_t val = fResponseOrig->GetBinContent(iBin,fCoordinatesN_M); //used as mean | |
516 | Double_t err = fResponseOrig->GetBinError(fCoordinatesN_M); //used as sigma | |
517 | Double_t ran = fRandom3->Gaus(val,err); | |
518 | // random = fRandom3->PoissonD(measuredValue); //doesn't work for normalized spectra, use Gaus (assuming raw counts in bin is large >10) | |
519 | fRandomResponse->SetBinContent(iBin,ran); | |
520 | } | |
521 | for (Long_t iBin=0; iBin<fEfficiencyOrig->GetNbins(); iBin++) { | |
522 | Double_t val = fEfficiencyOrig->GetBinContent(iBin,fCoordinatesN_M); //used as mean | |
523 | Double_t err = fEfficiencyOrig->GetBinError(fCoordinatesN_M); //used as sigma | |
524 | Double_t ran = fRandom3->Gaus(val,err); | |
525 | // random = fRandom3->PoissonD(measuredValue); //doesn't work for normalized spectra, use Gaus (assuming raw counts in bin is large >10) | |
526 | fRandomEfficiency->SetBinContent(iBin,ran); | |
527 | } | |
528 | for (Long_t iBin=0; iBin<fMeasuredOrig->GetNbins(); iBin++) { | |
529 | Double_t val = fMeasuredOrig->GetBinContent(iBin,fCoordinatesN_M); //used as mean | |
530 | Double_t err = fMeasuredOrig->GetBinError(fCoordinatesN_M); //used as sigma | |
531 | Double_t ran = fRandom3->Gaus(val,err); | |
769f5114 | 532 | // random = fRandom3->PoissonD(measuredValue); //doesn't work for normalized spectra, use Gaus (assuming raw counts in bin is large >10) |
5a575436 | 533 | fRandomMeasured->SetBinContent(iBin,ran); |
769f5114 | 534 | } |
535 | } | |
536 | ||
537 | //______________________________________________________________ | |
538 | void AliCFUnfolding::FillDeltaUnfoldedProfile() { | |
539 | // | |
a9500e70 | 540 | // Store difference of unfolded spectrum from measured distribution and unfolded spectrum from randomized distribution |
541 | // The delta profile has been set to a THnSparse to handle N dimension | |
542 | // The THnSparse contains in each bin the mean value and spread of the difference | |
543 | // This function updates the profile wrt to its previous mean and error | |
544 | // The relation between iterations (n+1) and n is as follows : | |
545 | // mean_{n+1} = (n*mean_n + value_{n+1}) / (n+1) | |
546 | // sigma_{n+1} = sqrt { 1/(n+1) * [ n*sigma_n^2 + (n^2+n)*(mean_{n+1}-mean_n)^2 ] } (can this be optimized?) | |
547 | ||
548 | for (Long_t iBin=0; iBin<fUnfolded->GetNbins(); iBin++) { | |
549 | Double_t deltaInBin = fUnfoldedFinal->GetBinContent(iBin,fCoordinatesN_M) - fUnfolded->GetBinContent(iBin); | |
550 | Double_t entriesInBin = fDeltaUnfoldedN->GetBinContent(fCoordinatesN_M); | |
551 | //AliDebug(2,Form("%e %e ==> delta = %e\n",fUnfoldedFinal->GetBinContent(iBin,fCoordinatesN_M),fUnfolded->GetBinContent(iBin),deltaInBin)); | |
552 | ||
d966b1e6 | 553 | //printf("deltaInBin %f\n",deltaInBin); |
554 | //printf("pt %f\n",ptaxis->GetBinCenter(iBin+1)); | |
555 | ||
a9500e70 | 556 | Double_t mean_n = fDeltaUnfoldedP->GetBinContent(fCoordinatesN_M) ; |
557 | Double_t mean_nplus1 = mean_n ; | |
558 | mean_nplus1 *= entriesInBin ; | |
559 | mean_nplus1 += deltaInBin ; | |
560 | mean_nplus1 /= (entriesInBin+1) ; | |
561 | ||
d966b1e6 | 562 | Double_t meanx2_n = fDeltaUnfoldedP->GetBinError(fCoordinatesN_M) ; |
563 | Double_t meanx2_nplus1 = meanx2_n ; | |
564 | meanx2_nplus1 *= entriesInBin ; | |
565 | meanx2_nplus1 += (deltaInBin*deltaInBin) ; | |
566 | meanx2_nplus1 /= (entriesInBin+1) ; | |
a9500e70 | 567 | |
568 | //AliDebug(2,Form("sigma = %e\n",sigma)); | |
569 | ||
d966b1e6 | 570 | fDeltaUnfoldedP->SetBinError(fCoordinatesN_M,meanx2_nplus1) ; |
a9500e70 | 571 | fDeltaUnfoldedP->SetBinContent(fCoordinatesN_M,mean_nplus1) ; |
a9500e70 | 572 | fDeltaUnfoldedN->SetBinContent(fCoordinatesN_M,entriesInBin+1); |
769f5114 | 573 | } |
574 | } | |
575 | ||
85b6bda9 | 576 | //______________________________________________________________ |
577 | ||
c0b10ad4 | 578 | void AliCFUnfolding::GetCoordinates() { |
579 | // | |
580 | // assign coordinates in Measured and True spaces (dim=N) from coordinates in global space (dim=2N) | |
581 | // | |
582 | for (Int_t i = 0; i<fNVariables ; i++) { | |
583 | fCoordinatesN_M[i] = fCoordinates2N[i]; | |
584 | fCoordinatesN_T[i] = fCoordinates2N[i+fNVariables]; | |
585 | } | |
586 | } | |
587 | ||
588 | //______________________________________________________________ | |
589 | ||
590 | void AliCFUnfolding::CreateConditional() { | |
591 | // | |
592 | // creates the conditional probability matrix (R*) holding the P(M|T), given the reponse matrix R | |
593 | // | |
594 | // --> R*(i,j) = R(i,j) / SUM_k{ R(k,j) } | |
595 | // | |
596 | ||
5a575436 | 597 | fConditional = (THnSparse*) fResponse->Clone(); // output of this function |
a9500e70 | 598 | |
85b6bda9 | 599 | Int_t* dim = new Int_t [fNVariables]; |
600 | 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 | 601 | |
602 | THnSparse* responseInT = fConditional->Projection(fNVariables,dim,"E"); // output denominator : | |
603 | // projection of the response matrix on the TRUE axis | |
85b6bda9 | 604 | delete [] dim; |
769f5114 | 605 | |
c0b10ad4 | 606 | // fill the conditional probability matrix |
7bc6a013 | 607 | for (Long_t iBin=0; iBin<fResponse->GetNbins(); iBin++) { |
c0b10ad4 | 608 | Double_t responseValue = fResponse->GetBinContent(iBin,fCoordinates2N); |
609 | GetCoordinates(); | |
a9500e70 | 610 | Double_t projValue = responseInT->GetBinContent(fCoordinatesN_T); |
769f5114 | 611 | |
85b6bda9 | 612 | Double_t fill = responseValue / projValue ; |
613 | if (fill>0. || fConditional->GetBinContent(fCoordinates2N)>0.) { | |
614 | fConditional->SetBinContent(fCoordinates2N,fill); | |
769f5114 | 615 | Double_t err = 0.; |
85b6bda9 | 616 | fConditional->SetBinError (fCoordinates2N,err); |
617 | } | |
c0b10ad4 | 618 | } |
a9500e70 | 619 | delete responseInT ; |
c0b10ad4 | 620 | } |
769f5114 | 621 | //______________________________________________________________ |
622 | ||
623 | Int_t AliCFUnfolding::GetDOF() { | |
624 | // | |
625 | // number of dof = number of bins | |
626 | // | |
627 | ||
628 | Int_t nDOF = 1 ; | |
629 | for (Int_t iDim=0; iDim<fNVariables; iDim++) { | |
630 | nDOF *= fPrior->GetAxis(iDim)->GetNbins(); | |
631 | } | |
632 | AliDebug(0,Form("Number of degrees of freedom = %d",nDOF)); | |
633 | return nDOF; | |
634 | } | |
c0b10ad4 | 635 | |
636 | //______________________________________________________________ | |
637 | ||
638 | Double_t AliCFUnfolding::GetChi2() { | |
639 | // | |
640 | // Returns the chi2 between unfolded and a priori spectrum | |
769f5114 | 641 | // This function became absolute with the correlated error calculation. |
642 | // Errors on the unfolded distribution are not known until the end | |
643 | // Use the convergence criterion instead | |
c0b10ad4 | 644 | // |
645 | ||
769f5114 | 646 | Double_t chi2 = 0. ; |
647 | Double_t error_unf = 0.; | |
7bc6a013 | 648 | for (Long_t iBin=0; iBin<fPrior->GetNbins(); iBin++) { |
85f9f9e1 | 649 | Double_t priorValue = fPrior->GetBinContent(iBin,fCoordinatesN_T); |
769f5114 | 650 | error_unf = fUnfolded->GetBinError(fCoordinatesN_T); |
85f9f9e1 | 651 | chi2 += (error_unf > 0. ? TMath::Power((fUnfolded->GetBinContent(fCoordinatesN_T) - priorValue)/error_unf,2) / priorValue : 0.) ; |
c0b10ad4 | 652 | } |
653 | return chi2; | |
654 | } | |
655 | ||
656 | //______________________________________________________________ | |
657 | ||
769f5114 | 658 | Double_t AliCFUnfolding::GetConvergence() { |
659 | // | |
660 | // Returns convergence criterion = \sum_t ((U_t^{n-1}-U_t^n)/U_t^{n-1})^2 | |
661 | // U is unfolded spectrum, t is the bin, n = current, n-1 = previous | |
662 | // | |
663 | Double_t convergence = 0.; | |
664 | Double_t priorValue = 0.; | |
665 | Double_t currentValue = 0.; | |
666 | for (Long_t iBin=0; iBin < fPrior->GetNbins(); iBin++) { | |
667 | priorValue = fPrior->GetBinContent(iBin,fCoordinatesN_T); | |
a9500e70 | 668 | currentValue = fUnfolded->GetBinContent(fCoordinatesN_T); |
769f5114 | 669 | |
670 | if (priorValue > 0.) | |
671 | convergence += ((priorValue-currentValue)/priorValue)*((priorValue-currentValue)/priorValue); | |
a9500e70 | 672 | else |
769f5114 | 673 | AliWarning(Form("priorValue = %f. Adding 0 to convergence criterion.",priorValue)); |
769f5114 | 674 | } |
675 | return convergence; | |
676 | } | |
677 | ||
678 | //______________________________________________________________ | |
679 | ||
680 | void AliCFUnfolding::SetMaxConvergencePerDOF(Double_t val) { | |
c0b10ad4 | 681 | // |
769f5114 | 682 | // Max. convergence criterion per degree of freedom : user setting |
683 | // convergence criterion = DOF*val; DOF = number of bins | |
684 | // In Jan-Fiete's multiplicity note: Convergence criterion = DOF*0.001^2 | |
c0b10ad4 | 685 | // |
686 | ||
769f5114 | 687 | Int_t nDOF = GetDOF() ; |
688 | fMaxConvergence = val * nDOF ; | |
689 | AliInfo(Form("MaxConvergence = %e. Number of degrees of freedom = %d",fMaxConvergence,nDOF)); | |
c0b10ad4 | 690 | } |
691 | ||
692 | //______________________________________________________________ | |
693 | ||
85b6bda9 | 694 | Short_t AliCFUnfolding::Smooth() { |
c0b10ad4 | 695 | // |
696 | // Smoothes the unfolded spectrum | |
85b6bda9 | 697 | // |
698 | // By default each cell content is replaced by the average with the neighbouring bins (but not diagonally-neighbouring bins) | |
699 | // However, if a specific function fcn has been defined in UseSmoothing(fcn), the unfolded will be fit and updated using fcn | |
c0b10ad4 | 700 | // |
701 | ||
85b6bda9 | 702 | if (fSmoothFunction) { |
7bc6a013 | 703 | AliDebug(2,Form("Smoothing spectrum with fit function %p",fSmoothFunction)); |
85b6bda9 | 704 | return SmoothUsingFunction(); |
705 | } | |
7036630f | 706 | else return SmoothUsingNeighbours(fUnfolded); |
85b6bda9 | 707 | } |
708 | ||
709 | //______________________________________________________________ | |
710 | ||
7036630f | 711 | Short_t AliCFUnfolding::SmoothUsingNeighbours(THnSparse* hist) { |
85b6bda9 | 712 | // |
713 | // Smoothes the unfolded spectrum using neighouring bins | |
714 | // | |
715 | ||
7036630f | 716 | Int_t const nDimensions = hist->GetNdimensions() ; |
717 | Int_t* coordinates = new Int_t[nDimensions]; | |
718 | ||
719 | Int_t* numBins = new Int_t[nDimensions]; | |
720 | for (Int_t iVar=0; iVar<nDimensions; iVar++) numBins[iVar] = hist->GetAxis(iVar)->GetNbins(); | |
c0b10ad4 | 721 | |
7036630f | 722 | //need a copy because hist will be updated during the loop, and this creates problems |
723 | THnSparse* copy = (THnSparse*)hist->Clone(); | |
c0b10ad4 | 724 | |
7bc6a013 | 725 | for (Long_t iBin=0; iBin<copy->GetNbins(); iBin++) { //loop on non-empty bins |
7036630f | 726 | Double_t content = copy->GetBinContent(iBin,coordinates); |
7bc6a013 | 727 | Double_t error2 = TMath::Power(copy->GetBinError(iBin),2); |
c0b10ad4 | 728 | |
729 | // skip the under/overflow bins... | |
730 | Bool_t isOutside = kFALSE ; | |
7036630f | 731 | for (Int_t iVar=0; iVar<nDimensions; iVar++) { |
732 | if (coordinates[iVar]<1 || coordinates[iVar]>numBins[iVar]) { | |
c0b10ad4 | 733 | isOutside=kTRUE; |
734 | break; | |
735 | } | |
736 | } | |
737 | if (isOutside) continue; | |
738 | ||
739 | Int_t neighbours = 0; // number of neighbours to average with | |
740 | ||
7036630f | 741 | for (Int_t iVar=0; iVar<nDimensions; iVar++) { |
742 | if (coordinates[iVar] > 1) { // must not be on low edge border | |
743 | coordinates[iVar]-- ; //get lower neighbouring bin | |
744 | content += copy->GetBinContent(coordinates); | |
745 | error2 += TMath::Power(copy->GetBinError(coordinates),2); | |
c0b10ad4 | 746 | neighbours++; |
7036630f | 747 | coordinates[iVar]++ ; //back to initial coordinate |
c0b10ad4 | 748 | } |
7036630f | 749 | if (coordinates[iVar] < numBins[iVar]) { // must not be on up edge border |
750 | coordinates[iVar]++ ; //get upper neighbouring bin | |
751 | content += copy->GetBinContent(coordinates); | |
752 | error2 += TMath::Power(copy->GetBinError(coordinates),2); | |
c0b10ad4 | 753 | neighbours++; |
7036630f | 754 | coordinates[iVar]-- ; //back to initial coordinate |
c0b10ad4 | 755 | } |
756 | } | |
85b6bda9 | 757 | // make an average |
7036630f | 758 | hist->SetBinContent(coordinates,content/(1.+neighbours)); |
759 | hist->SetBinError (coordinates,TMath::Sqrt(error2)/(1.+neighbours)); | |
c0b10ad4 | 760 | } |
761 | delete [] numBins; | |
7036630f | 762 | delete [] coordinates ; |
c0b10ad4 | 763 | delete copy; |
85b6bda9 | 764 | return 0; |
c0b10ad4 | 765 | } |
766 | ||
85b6bda9 | 767 | //______________________________________________________________ |
768 | ||
769 | Short_t AliCFUnfolding::SmoothUsingFunction() { | |
770 | // | |
771 | // Fits the unfolded spectrum using the function fSmoothFunction | |
772 | // | |
773 | ||
7bc6a013 | 774 | AliDebug(0,Form("Smooth function is a %s with option \"%s\" and has %d parameters : ",fSmoothFunction->ClassName(),fSmoothOption,fSmoothFunction->GetNpar())); |
85b6bda9 | 775 | |
7bc6a013 | 776 | for (Int_t iPar=0; iPar<fSmoothFunction->GetNpar(); iPar++) AliDebug(0,Form("par[%d]=%e",iPar,fSmoothFunction->GetParameter(iPar))); |
777 | ||
778 | Int_t fitResult = 0; | |
85b6bda9 | 779 | |
780 | switch (fNVariables) { | |
7bc6a013 | 781 | case 1 : fitResult = fUnfolded->Projection(0) ->Fit(fSmoothFunction,fSmoothOption); break; |
782 | case 2 : fitResult = fUnfolded->Projection(1,0) ->Fit(fSmoothFunction,fSmoothOption); break; // (1,0) instead of (0,1) -> TAxis issue | |
783 | case 3 : fitResult = fUnfolded->Projection(0,1,2)->Fit(fSmoothFunction,fSmoothOption); break; | |
784 | default: AliFatal(Form("Cannot handle such fit in %d dimensions",fNVariables)) ; return 1; | |
785 | } | |
786 | ||
787 | if (fitResult != 0) { | |
788 | AliWarning(Form("Fit failed with status %d, stopping the loop",fitResult)); | |
789 | return 1; | |
85b6bda9 | 790 | } |
791 | ||
792 | Int_t nDim = fNVariables; | |
793 | Int_t* bins = new Int_t[nDim]; // number of bins for each variable | |
794 | Long_t nBins = 1; // used to calculate the total number of bins in the THnSparse | |
795 | ||
796 | for (Int_t iVar=0; iVar<nDim; iVar++) { | |
797 | bins[iVar] = fUnfolded->GetAxis(iVar)->GetNbins(); | |
798 | nBins *= bins[iVar]; | |
799 | } | |
800 | ||
801 | Int_t *bin = new Int_t[nDim]; // bin to fill the THnSparse (holding the bin coordinates) | |
802 | Double_t x[3] = {0,0,0} ; // value in bin center (max dimension is 3 (TF3)) | |
803 | ||
804 | // loop on the bins and update of fUnfolded | |
805 | // THnSparse::Multiply(TF1*) doesn't exist, so let's do it bin by bin | |
806 | for (Long_t iBin=0; iBin<nBins; iBin++) { | |
807 | Long_t bin_tmp = iBin ; | |
808 | for (Int_t iVar=0; iVar<nDim; iVar++) { | |
809 | bin[iVar] = 1 + bin_tmp % bins[iVar] ; | |
810 | bin_tmp /= bins[iVar] ; | |
811 | x[iVar] = fUnfolded->GetAxis(iVar)->GetBinCenter(bin[iVar]); | |
812 | } | |
813 | Double_t functionValue = fSmoothFunction->Eval(x[0],x[1],x[2]) ; | |
12e419d5 | 814 | fUnfolded->SetBinError (bin,fUnfolded->GetBinError(bin)*functionValue/fUnfolded->GetBinContent(bin)); |
85b6bda9 | 815 | fUnfolded->SetBinContent(bin,functionValue); |
85b6bda9 | 816 | } |
700a1189 | 817 | delete [] bins; |
818 | delete [] bin ; | |
85b6bda9 | 819 | return 0; |
820 | } | |
c0b10ad4 | 821 | |
822 | //______________________________________________________________ | |
823 | ||
824 | void AliCFUnfolding::CreateFlatPrior() { | |
825 | // | |
826 | // Creates a flat prior distribution | |
827 | // | |
828 | ||
829 | AliInfo("Creating a flat a priori distribution"); | |
830 | ||
831 | // create the frame of the THnSparse given (for example) the one from the efficiency map | |
832 | fPrior = (THnSparse*) fEfficiency->Clone(); | |
a9500e70 | 833 | fPrior->SetTitle("Prior"); |
c0b10ad4 | 834 | |
85b6bda9 | 835 | if (fNVariables != fPrior->GetNdimensions()) |
836 | AliFatal(Form("The prior matrix should have %d dimensions, and it has actually %d",fNVariables,fPrior->GetNdimensions())); | |
837 | ||
c0b10ad4 | 838 | Int_t nDim = fNVariables; |
839 | Int_t* bins = new Int_t[nDim]; // number of bins for each variable | |
840 | Long_t nBins = 1; // used to calculate the total number of bins in the THnSparse | |
841 | ||
842 | for (Int_t iVar=0; iVar<nDim; iVar++) { | |
843 | bins[iVar] = fPrior->GetAxis(iVar)->GetNbins(); | |
844 | nBins *= bins[iVar]; | |
845 | } | |
846 | ||
847 | Int_t *bin = new Int_t[nDim]; // bin to fill the THnSparse (holding the bin coordinates) | |
848 | ||
849 | // loop that sets 1 in each bin | |
850 | for (Long_t iBin=0; iBin<nBins; iBin++) { | |
851 | Long_t bin_tmp = iBin ; | |
852 | for (Int_t iVar=0; iVar<nDim; iVar++) { | |
853 | bin[iVar] = 1 + bin_tmp % bins[iVar] ; | |
854 | bin_tmp /= bins[iVar] ; | |
855 | } | |
856 | fPrior->SetBinContent(bin,1.); // put 1 everywhere | |
85b6bda9 | 857 | fPrior->SetBinError (bin,0.); // put 0 everywhere |
c0b10ad4 | 858 | } |
859 | ||
5a575436 | 860 | fPriorOrig = (THnSparse*)fPrior->Clone(); |
c0b10ad4 | 861 | |
862 | delete [] bin; | |
863 | delete [] bins; | |
864 | } |