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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(), | |
102 | fResponse(0x0), | |
103 | fPrior(0x0), | |
c0b10ad4 | 104 | fEfficiency(0x0), |
105 | fMeasured(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), |
85b6bda9 | 114 | fOriginalPrior(0x0), |
c0b10ad4 | 115 | fInverseResponse(0x0), |
116 | fMeasuredEstimate(0x0), | |
117 | fConditional(0x0), | |
c0b10ad4 | 118 | fUnfolded(0x0), |
a9500e70 | 119 | fUnfoldedFinal(0x0), |
c0b10ad4 | 120 | fCoordinates2N(0x0), |
121 | fCoordinatesN_M(0x0), | |
769f5114 | 122 | fCoordinatesN_T(0x0), |
123 | fRandomizedDist(0x0), | |
124 | fRandom3(0x0), | |
769f5114 | 125 | fDeltaUnfoldedP(0x0), |
a9500e70 | 126 | fDeltaUnfoldedN(0x0), |
769f5114 | 127 | fNCalcCorrErrors(0), |
128 | fRandomSeed(0) | |
c0b10ad4 | 129 | { |
130 | // | |
131 | // default constructor | |
132 | // | |
133 | } | |
134 | ||
135 | //______________________________________________________________ | |
136 | ||
137 | AliCFUnfolding::AliCFUnfolding(const Char_t* name, const Char_t* title, const Int_t nVar, | |
a9500e70 | 138 | const THnSparse* response, const THnSparse* efficiency, const THnSparse* measured, const THnSparse* prior , |
139 | Double_t maxConvergencePerDOF, UInt_t randomSeed, Int_t maxNumIterations | |
140 | ) : | |
c0b10ad4 | 141 | TNamed(name,title), |
142 | fResponse((THnSparse*)response->Clone()), | |
143 | fPrior(0x0), | |
c0b10ad4 | 144 | fEfficiency((THnSparse*)efficiency->Clone()), |
145 | fMeasured((THnSparse*)measured->Clone()), | |
769f5114 | 146 | fMeasuredOrig((THnSparse*)measured->Clone()), |
a9500e70 | 147 | fMaxNumIterations(maxNumIterations), |
c0b10ad4 | 148 | fNVariables(nVar), |
c0b10ad4 | 149 | fUseSmoothing(kFALSE), |
85b6bda9 | 150 | fSmoothFunction(0x0), |
a9500e70 | 151 | fSmoothOption("iremn"), |
769f5114 | 152 | fMaxConvergence(0), |
a9500e70 | 153 | fNRandomIterations(maxNumIterations), |
85b6bda9 | 154 | fOriginalPrior(0x0), |
c0b10ad4 | 155 | fInverseResponse(0x0), |
156 | fMeasuredEstimate(0x0), | |
157 | fConditional(0x0), | |
c0b10ad4 | 158 | fUnfolded(0x0), |
a9500e70 | 159 | fUnfoldedFinal(0x0), |
c0b10ad4 | 160 | fCoordinates2N(0x0), |
161 | fCoordinatesN_M(0x0), | |
769f5114 | 162 | fCoordinatesN_T(0x0), |
163 | fRandomizedDist(0x0), | |
164 | fRandom3(0x0), | |
769f5114 | 165 | fDeltaUnfoldedP(0x0), |
a9500e70 | 166 | fDeltaUnfoldedN(0x0), |
769f5114 | 167 | fNCalcCorrErrors(0), |
a9500e70 | 168 | fRandomSeed(randomSeed) |
c0b10ad4 | 169 | { |
170 | // | |
171 | // named constructor | |
172 | // | |
173 | ||
174 | AliInfo(Form("\n\n--------------------------\nCreating an unfolder :\n--------------------------\nresponse matrix has %d dimension(s)",fResponse->GetNdimensions())); | |
a9500e70 | 175 | |
c0b10ad4 | 176 | if (!prior) CreateFlatPrior(); // if no prior distribution declared, simply use a flat distribution |
177 | else { | |
178 | fPrior = (THnSparse*) prior->Clone(); | |
179 | fOriginalPrior = (THnSparse*)fPrior->Clone(); | |
85b6bda9 | 180 | if (fPrior->GetNdimensions() != fNVariables) |
181 | AliFatal(Form("The prior matrix should have %d dimensions, and it has actually %d",fNVariables,fPrior->GetNdimensions())); | |
c0b10ad4 | 182 | } |
85b6bda9 | 183 | |
184 | if (fEfficiency->GetNdimensions() != fNVariables) | |
185 | AliFatal(Form("The efficiency matrix should have %d dimensions, and it has actually %d",fNVariables,fEfficiency->GetNdimensions())); | |
186 | if (fMeasured->GetNdimensions() != fNVariables) | |
187 | AliFatal(Form("The measured matrix should have %d dimensions, and it has actually %d",fNVariables,fMeasured->GetNdimensions())); | |
188 | if (fResponse->GetNdimensions() != 2*fNVariables) | |
189 | AliFatal(Form("The response matrix should have %d dimensions, and it has actually %d",2*fNVariables,fResponse->GetNdimensions())); | |
c0b10ad4 | 190 | |
85b6bda9 | 191 | |
c0b10ad4 | 192 | for (Int_t iVar=0; iVar<fNVariables; iVar++) { |
193 | AliInfo(Form("prior matrix has %d bins in dimension %d",fPrior ->GetAxis(iVar)->GetNbins(),iVar)); | |
194 | AliInfo(Form("efficiency matrix has %d bins in dimension %d",fEfficiency->GetAxis(iVar)->GetNbins(),iVar)); | |
195 | AliInfo(Form("measured matrix has %d bins in dimension %d",fMeasured ->GetAxis(iVar)->GetNbins(),iVar)); | |
196 | } | |
769f5114 | 197 | |
a9500e70 | 198 | fRandomizedDist = (THnSparse*) fMeasuredOrig->Clone(); |
199 | fRandomizedDist->SetTitle("Randomized"); | |
200 | SetMaxConvergencePerDOF(maxConvergencePerDOF) ; | |
c0b10ad4 | 201 | Init(); |
202 | } | |
203 | ||
204 | ||
205 | //______________________________________________________________ | |
206 | ||
207 | AliCFUnfolding::AliCFUnfolding(const AliCFUnfolding& c) : | |
208 | TNamed(c), | |
209 | fResponse((THnSparse*)c.fResponse->Clone()), | |
210 | fPrior((THnSparse*)c.fPrior->Clone()), | |
c0b10ad4 | 211 | fEfficiency((THnSparse*)c.fEfficiency->Clone()), |
212 | fMeasured((THnSparse*)c.fMeasured->Clone()), | |
769f5114 | 213 | fMeasuredOrig((THnSparse*)c.fMeasuredOrig->Clone()), |
c0b10ad4 | 214 | fMaxNumIterations(c.fMaxNumIterations), |
215 | fNVariables(c.fNVariables), | |
c0b10ad4 | 216 | fUseSmoothing(c.fUseSmoothing), |
85b6bda9 | 217 | fSmoothFunction((TF1*)c.fSmoothFunction->Clone()), |
7c5606f9 | 218 | fSmoothOption(c.fSmoothOption), |
769f5114 | 219 | fMaxConvergence(c.fMaxConvergence), |
769f5114 | 220 | fNRandomIterations(c.fNRandomIterations), |
85b6bda9 | 221 | fOriginalPrior((THnSparse*)c.fOriginalPrior->Clone()), |
c0b10ad4 | 222 | fInverseResponse((THnSparse*)c.fInverseResponse->Clone()), |
223 | fMeasuredEstimate((THnSparse*)fMeasuredEstimate->Clone()), | |
224 | fConditional((THnSparse*)c.fConditional->Clone()), | |
c0b10ad4 | 225 | fUnfolded((THnSparse*)c.fUnfolded->Clone()), |
a9500e70 | 226 | fUnfoldedFinal((THnSparse*)c.fUnfoldedFinal->Clone()), |
c0b10ad4 | 227 | fCoordinates2N(new Int_t(*c.fCoordinates2N)), |
228 | fCoordinatesN_M(new Int_t(*c.fCoordinatesN_M)), | |
769f5114 | 229 | fCoordinatesN_T(new Int_t(*c.fCoordinatesN_T)), |
230 | fRandomizedDist((THnSparse*)c.fRandomizedDist->Clone()), | |
231 | fRandom3((TRandom3*)c.fRandom3->Clone()), | |
a9500e70 | 232 | fDeltaUnfoldedP((THnSparse*)c.fDeltaUnfoldedP), |
233 | fDeltaUnfoldedN((THnSparse*)c.fDeltaUnfoldedN), | |
769f5114 | 234 | fNCalcCorrErrors(c.fNCalcCorrErrors), |
235 | fRandomSeed(c.fRandomSeed) | |
c0b10ad4 | 236 | { |
237 | // | |
238 | // copy constructor | |
239 | // | |
240 | } | |
241 | ||
242 | //______________________________________________________________ | |
243 | ||
244 | AliCFUnfolding& AliCFUnfolding::operator=(const AliCFUnfolding& c) { | |
245 | // | |
246 | // assignment operator | |
247 | // | |
248 | ||
249 | if (this!=&c) { | |
250 | TNamed::operator=(c); | |
251 | fResponse = (THnSparse*)c.fResponse->Clone() ; | |
252 | fPrior = (THnSparse*)c.fPrior->Clone() ; | |
c0b10ad4 | 253 | fEfficiency = (THnSparse*)c.fEfficiency->Clone() ; |
254 | fMeasured = (THnSparse*)c.fMeasured->Clone() ; | |
769f5114 | 255 | fMeasuredOrig = ((THnSparse*)c.fMeasuredOrig->Clone()), |
c0b10ad4 | 256 | fMaxNumIterations = c.fMaxNumIterations ; |
257 | fNVariables = c.fNVariables ; | |
769f5114 | 258 | fMaxConvergence = c.fMaxConvergence ; |
c0b10ad4 | 259 | fUseSmoothing = c.fUseSmoothing ; |
85b6bda9 | 260 | fSmoothFunction = (TF1*)c.fSmoothFunction->Clone(); |
261 | fSmoothOption = c.fSmoothOption ; | |
769f5114 | 262 | fNRandomIterations = c.fNRandomIterations; |
85b6bda9 | 263 | fOriginalPrior = (THnSparse*)c.fOriginalPrior->Clone() ; |
c0b10ad4 | 264 | fInverseResponse = (THnSparse*)c.fInverseResponse->Clone() ; |
265 | fMeasuredEstimate = (THnSparse*)fMeasuredEstimate->Clone() ; | |
266 | fConditional = (THnSparse*)c.fConditional->Clone() ; | |
c0b10ad4 | 267 | fUnfolded = (THnSparse*)c.fUnfolded->Clone() ; |
a9500e70 | 268 | fUnfoldedFinal = (THnSparse*)c.fUnfoldedFinal->Clone() ; |
c0b10ad4 | 269 | fCoordinates2N = new Int_t(*c.fCoordinates2N) ; |
270 | fCoordinatesN_M = new Int_t(*c.fCoordinatesN_M) ; | |
271 | fCoordinatesN_T = new Int_t(*c.fCoordinatesN_T) ; | |
769f5114 | 272 | fRandomizedDist = (THnSparse*)c.fRandomizedDist->Clone(); |
273 | fRandom3 = (TRandom3*)c.fRandom3->Clone(); | |
a9500e70 | 274 | fDeltaUnfoldedP = (THnSparse*)c.fDeltaUnfoldedP; |
275 | fDeltaUnfoldedN = (THnSparse*)c.fDeltaUnfoldedN; | |
276 | fNCalcCorrErrors = c.fNCalcCorrErrors ; | |
769f5114 | 277 | fRandomSeed = c.fRandomSeed ; |
c0b10ad4 | 278 | } |
279 | return *this; | |
280 | } | |
281 | ||
282 | //______________________________________________________________ | |
283 | ||
284 | AliCFUnfolding::~AliCFUnfolding() { | |
285 | // | |
286 | // destructor | |
287 | // | |
288 | if (fResponse) delete fResponse; | |
289 | if (fPrior) delete fPrior; | |
c0b10ad4 | 290 | if (fEfficiency) delete fEfficiency; |
291 | if (fMeasured) delete fMeasured; | |
769f5114 | 292 | if (fMeasuredOrig) delete fMeasuredOrig; |
85b6bda9 | 293 | if (fSmoothFunction) delete fSmoothFunction; |
294 | if (fOriginalPrior) delete fOriginalPrior; | |
c0b10ad4 | 295 | if (fInverseResponse) delete fInverseResponse; |
296 | if (fMeasuredEstimate) delete fMeasuredEstimate; | |
297 | if (fConditional) delete fConditional; | |
a9500e70 | 298 | if (fUnfolded) delete fUnfolded; |
299 | if (fUnfoldedFinal) delete fUnfoldedFinal; | |
c0b10ad4 | 300 | if (fCoordinates2N) delete [] fCoordinates2N; |
301 | if (fCoordinatesN_M) delete [] fCoordinatesN_M; | |
302 | if (fCoordinatesN_T) delete [] fCoordinatesN_T; | |
769f5114 | 303 | if (fRandomizedDist) delete fRandomizedDist; |
304 | if (fRandom3) delete fRandom3; | |
769f5114 | 305 | if (fDeltaUnfoldedP) delete fDeltaUnfoldedP; |
a9500e70 | 306 | if (fDeltaUnfoldedN) delete fDeltaUnfoldedN; |
c0b10ad4 | 307 | } |
308 | ||
309 | //______________________________________________________________ | |
310 | ||
311 | void AliCFUnfolding::Init() { | |
312 | // | |
313 | // initialisation function : creates internal settings | |
314 | // | |
315 | ||
769f5114 | 316 | fRandom3 = new TRandom3(fRandomSeed); |
317 | ||
c0b10ad4 | 318 | fCoordinates2N = new Int_t[2*fNVariables]; |
319 | fCoordinatesN_M = new Int_t[fNVariables]; | |
320 | fCoordinatesN_T = new Int_t[fNVariables]; | |
321 | ||
322 | // create the matrix of conditional probabilities P(M|T) | |
a9500e70 | 323 | CreateConditional(); //done only once at initialization |
c0b10ad4 | 324 | |
325 | // create the frame of the inverse response matrix | |
326 | fInverseResponse = (THnSparse*) fResponse->Clone(); | |
327 | // create the frame of the unfolded spectrum | |
328 | fUnfolded = (THnSparse*) fPrior->Clone(); | |
a9500e70 | 329 | fUnfolded->SetTitle("Unfolded"); |
c0b10ad4 | 330 | // create the frame of the measurement estimate spectrum |
331 | fMeasuredEstimate = (THnSparse*) fMeasured->Clone(); | |
769f5114 | 332 | // create the frame of the original measurement spectrum |
333 | fMeasuredOrig = (THnSparse*) fMeasured->Clone(); | |
a9500e70 | 334 | |
335 | fDeltaUnfoldedP = (THnSparse*)fPrior->Clone(); | |
336 | fDeltaUnfoldedP->SetTitle("#Delta unfolded"); | |
337 | fDeltaUnfoldedP->Reset(); | |
338 | fDeltaUnfoldedN = (THnSparse*)fPrior->Clone(); | |
339 | fDeltaUnfoldedP->SetTitle(""); | |
340 | fDeltaUnfoldedN->Reset(); | |
c0b10ad4 | 341 | } |
342 | ||
a9500e70 | 343 | |
c0b10ad4 | 344 | //______________________________________________________________ |
345 | ||
346 | void AliCFUnfolding::CreateEstMeasured() { | |
347 | // | |
348 | // This function creates a estimate (M) of the reconstructed spectrum | |
7bc6a013 | 349 | // given the a priori distribution (T), the efficiency (E) and the conditional matrix (COND) |
c0b10ad4 | 350 | // |
351 | // --> P(M) = SUM { P(M|T) * P(T) } | |
7bc6a013 | 352 | // --> M(i) = SUM_k { COND(i,k) * T(k) * E (k)} |
c0b10ad4 | 353 | // |
354 | // This is needed to calculate the inverse response matrix | |
355 | // | |
356 | ||
357 | ||
358 | // clean the measured estimate spectrum | |
85f9f9e1 | 359 | fMeasuredEstimate->Reset(); |
360 | ||
85b6bda9 | 361 | THnSparse* priorTimesEff = (THnSparse*) fPrior->Clone(); |
362 | priorTimesEff->Multiply(fEfficiency); | |
363 | ||
c0b10ad4 | 364 | // fill it |
7bc6a013 | 365 | for (Long_t iBin=0; iBin<fConditional->GetNbins(); iBin++) { |
c0b10ad4 | 366 | Double_t conditionalValue = fConditional->GetBinContent(iBin,fCoordinates2N); |
367 | GetCoordinates(); | |
85b6bda9 | 368 | Double_t priorTimesEffValue = priorTimesEff->GetBinContent(fCoordinatesN_T); |
85b6bda9 | 369 | Double_t fill = conditionalValue * priorTimesEffValue ; |
370 | ||
371 | if (fill>0.) { | |
12e419d5 | 372 | fMeasuredEstimate->AddBinContent(fCoordinatesN_M,fill); |
769f5114 | 373 | fMeasuredEstimate->SetBinError(fCoordinatesN_M,0.); |
85b6bda9 | 374 | } |
c0b10ad4 | 375 | } |
85b6bda9 | 376 | delete priorTimesEff ; |
c0b10ad4 | 377 | } |
378 | ||
379 | //______________________________________________________________ | |
380 | ||
381 | void AliCFUnfolding::CreateInvResponse() { | |
382 | // | |
383 | // Creates the inverse response matrix (INV) with Bayesian method | |
384 | // : uses the conditional matrix (COND), the prior probabilities (T) and the efficiency map (E) | |
385 | // | |
386 | // --> P(T|M) = P(M|T) * P(T) * eff(T) / SUM { P(M|T) * P(T) } | |
387 | // --> INV(i,j) = COND(i,j) * T(j) * E(j) / SUM_k { COND(i,k) * T(k) } | |
388 | // | |
389 | ||
85b6bda9 | 390 | THnSparse* priorTimesEff = (THnSparse*) fPrior->Clone(); |
391 | priorTimesEff->Multiply(fEfficiency); | |
392 | ||
7bc6a013 | 393 | for (Long_t iBin=0; iBin<fConditional->GetNbins(); iBin++) { |
c0b10ad4 | 394 | Double_t conditionalValue = fConditional->GetBinContent(iBin,fCoordinates2N); |
395 | GetCoordinates(); | |
85b6bda9 | 396 | Double_t estMeasuredValue = fMeasuredEstimate->GetBinContent(fCoordinatesN_M); |
85b6bda9 | 397 | Double_t priorTimesEffValue = priorTimesEff ->GetBinContent(fCoordinatesN_T); |
85b6bda9 | 398 | Double_t fill = (estMeasuredValue>0. ? conditionalValue * priorTimesEffValue / estMeasuredValue : 0. ) ; |
85b6bda9 | 399 | if (fill>0. || fInverseResponse->GetBinContent(fCoordinates2N)>0.) { |
400 | fInverseResponse->SetBinContent(fCoordinates2N,fill); | |
769f5114 | 401 | fInverseResponse->SetBinError (fCoordinates2N,0.); |
85b6bda9 | 402 | } |
403 | } | |
404 | delete priorTimesEff ; | |
c0b10ad4 | 405 | } |
406 | ||
407 | //______________________________________________________________ | |
408 | ||
409 | void AliCFUnfolding::Unfold() { | |
410 | // | |
411 | // Main routine called by the user : | |
769f5114 | 412 | // it calculates the unfolded spectrum from the response matrix, measured spectrum and efficiency |
413 | // several iterations are performed until a reasonable chi2 or convergence criterion is reached | |
c0b10ad4 | 414 | // |
415 | ||
769f5114 | 416 | Int_t iIterBayes = 0 ; |
417 | Double_t convergence = 0.; | |
c0b10ad4 | 418 | |
419 | for (iIterBayes=0; iIterBayes<fMaxNumIterations; iIterBayes++) { // bayes iterations | |
a9500e70 | 420 | |
421 | CreateEstMeasured(); // create measured estimate from prior | |
422 | CreateInvResponse(); // create inverse response from prior | |
423 | CreateUnfolded(); // create unfoled spectrum from measured and inverse response | |
424 | ||
425 | convergence = GetConvergence(); | |
426 | AliDebug(0,Form("convergence at iteration %d is %e",iIterBayes,convergence)); | |
427 | ||
428 | if (fMaxConvergence>0. && convergence<fMaxConvergence && fNCalcCorrErrors == 0) { | |
429 | fNRandomIterations = iIterBayes; | |
430 | AliDebug(0,Form("convergence is met at iteration %d",iIterBayes)); | |
c0b10ad4 | 431 | break; |
432 | } | |
769f5114 | 433 | |
85b6bda9 | 434 | if (fUseSmoothing) { |
435 | if (Smooth()) { | |
436 | AliError("Couldn't smooth the unfolded spectrum!!"); | |
a9500e70 | 437 | if (fNCalcCorrErrors>0) { |
438 | AliInfo(Form("=======================\nUnfold of randomized distribution finished at iteration %d with convergence %e \n",iIterBayes,convergence)); | |
439 | } | |
440 | else { | |
441 | 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 | 442 | } |
85b6bda9 | 443 | return; |
444 | } | |
445 | } | |
769f5114 | 446 | |
a9500e70 | 447 | // update the prior distribution |
448 | if (fPrior) delete fPrior ; | |
449 | fPrior = (THnSparse*)fUnfolded->Clone() ; | |
450 | fPrior->SetTitle("Prior"); | |
451 | ||
452 | } // end bayes iteration | |
453 | ||
454 | if (fNCalcCorrErrors==0) fUnfoldedFinal = (THnSparse*) fUnfolded->Clone() ; | |
455 | ||
456 | // | |
457 | //for (Long_t iBin=0; iBin<fUnfoldedFinal->GetNbins(); iBin++) AliDebug(2,Form("%e\n",fUnfoldedFinal->GetBinError(iBin))); | |
458 | // | |
459 | ||
460 | if (fNCalcCorrErrors == 0) { | |
461 | AliInfo("\n================================================\nFinished bayes iteration, now calculating errors...\n================================================\n"); | |
462 | fNCalcCorrErrors = 1; | |
769f5114 | 463 | CalculateCorrelatedErrors(); |
464 | } | |
465 | ||
a9500e70 | 466 | if (fNCalcCorrErrors >1 ) { |
467 | AliInfo(Form("\n\n=======================\nFinished at iteration %d : convergence is %e and you required it to be < %e\n=======================\n\n",iIterBayes,convergence,fMaxConvergence)); | |
468 | } | |
469 | else if(fNCalcCorrErrors>0) { | |
470 | AliInfo(Form("=======================\nUnfolding of randomized distribution finished at iteration %d with convergence %e \n",iIterBayes,convergence)); | |
c0b10ad4 | 471 | } |
c0b10ad4 | 472 | } |
473 | ||
474 | //______________________________________________________________ | |
475 | ||
476 | void AliCFUnfolding::CreateUnfolded() { | |
477 | // | |
478 | // Creates the unfolded (T) spectrum from the measured spectrum (M) and the inverse response matrix (INV) | |
479 | // We have P(T) = SUM { P(T|M) * P(M) } | |
480 | // --> T(i) = SUM_k { INV(i,k) * M(k) } | |
481 | // | |
482 | ||
483 | ||
484 | // clear the unfolded spectrum | |
a9500e70 | 485 | // if in the process of error calculation, the random unfolded spectrum is created |
486 | // otherwise the normal unfolded spectrum is created | |
487 | ||
488 | fUnfolded->Reset(); | |
c0b10ad4 | 489 | |
7bc6a013 | 490 | for (Long_t iBin=0; iBin<fInverseResponse->GetNbins(); iBin++) { |
c0b10ad4 | 491 | Double_t invResponseValue = fInverseResponse->GetBinContent(iBin,fCoordinates2N); |
492 | GetCoordinates(); | |
85b6bda9 | 493 | Double_t effValue = fEfficiency->GetBinContent(fCoordinatesN_T); |
85b6bda9 | 494 | Double_t measuredValue = fMeasured ->GetBinContent(fCoordinatesN_M); |
85b6bda9 | 495 | Double_t fill = (effValue>0. ? invResponseValue * measuredValue / effValue : 0.) ; |
769f5114 | 496 | |
85b6bda9 | 497 | if (fill>0.) { |
a9500e70 | 498 | // set errors to zero |
499 | // true errors will be filled afterwards | |
769f5114 | 500 | Double_t err = 0.; |
a9500e70 | 501 | fUnfolded->SetBinError (fCoordinatesN_T,err); |
502 | fUnfolded->AddBinContent(fCoordinatesN_T,fill); | |
85b6bda9 | 503 | } |
c0b10ad4 | 504 | } |
505 | } | |
769f5114 | 506 | |
507 | //______________________________________________________________ | |
508 | ||
509 | void AliCFUnfolding::CalculateCorrelatedErrors() { | |
510 | ||
a9500e70 | 511 | // Step 1: Create randomized distribution (fRandomizedDist) of each bin of |
512 | // the measured spectrum to calculate correlated errors. | |
513 | // Poisson statistics: mean = measured value of bin | |
769f5114 | 514 | // Step 2: Unfold randomized distribution |
a9500e70 | 515 | // Step 3: Store difference of unfolded spectrum from measured distribution and |
516 | // unfolded distribution from randomized distribution | |
517 | // -> fDeltaUnfoldedP (TProfile with option "S") | |
769f5114 | 518 | // Step 4: Repeat Step 1-3 several times (fNRandomIterations) |
519 | // Step 5: The spread of fDeltaUnfoldedP for each bin is the error on the unfolded spectrum of that specific bin | |
520 | ||
769f5114 | 521 | |
a9500e70 | 522 | //Do fNRandomIterations = bayes iterations performed |
523 | for (int i=0; i<fNRandomIterations; i++) { | |
524 | ||
525 | // reset prior to original one | |
526 | if (fPrior) delete fPrior ; | |
527 | fPrior = (THnSparse*) fOriginalPrior->Clone(); | |
528 | ||
529 | // create randomized distribution and stick measured spectrum to it | |
530 | CreateRandomizedDist(); | |
531 | if (fMeasured) delete fMeasured ; | |
532 | fMeasured = (THnSparse*) fRandomizedDist->Clone(); | |
533 | fMeasured->SetTitle("Measured"); | |
534 | ||
535 | //unfold fRandomizedDist | |
536 | Unfold(); | |
537 | FillDeltaUnfoldedProfile(); | |
538 | } | |
769f5114 | 539 | |
a9500e70 | 540 | // Get statistical errors for final unfolded spectrum |
541 | // ie. spread of each pt bin in fDeltaUnfoldedP | |
542 | Double_t sigma = 0.; | |
543 | Double_t dummy = 0.; | |
544 | for (Long_t iBin=0; iBin<fUnfoldedFinal->GetNbins(); iBin++) { | |
545 | dummy = fUnfoldedFinal->GetBinContent(iBin,fCoordinatesN_M); | |
546 | sigma = fDeltaUnfoldedP->GetBinError(fCoordinatesN_M); | |
547 | //AliDebug(2,Form("filling error %e\n",sigma)); | |
548 | fUnfoldedFinal->SetBinError(fCoordinatesN_M,sigma); | |
769f5114 | 549 | } |
a9500e70 | 550 | |
551 | // now errors are calculated | |
552 | fNCalcCorrErrors = 2; | |
769f5114 | 553 | } |
a9500e70 | 554 | |
769f5114 | 555 | //______________________________________________________________ |
556 | void AliCFUnfolding::CreateRandomizedDist() { | |
557 | // | |
a9500e70 | 558 | // Create randomized dist from original measured distribution |
559 | // This distribution is created several times, each time with a different random number | |
769f5114 | 560 | // |
561 | ||
562 | Double_t random = 0.; | |
563 | Double_t measuredValue = 0.; | |
564 | Double_t measuredError = 0.; | |
a9500e70 | 565 | |
769f5114 | 566 | for (Long_t iBin=0; iBin<fRandomizedDist->GetNbins(); iBin++) { |
567 | measuredValue = fMeasuredOrig->GetBinContent(iBin,fCoordinatesN_M); //used as mean | |
a9500e70 | 568 | measuredError = fMeasuredOrig->GetBinError(fCoordinatesN_M); //used as sigma |
769f5114 | 569 | // random = fRandom3->PoissonD(measuredValue); //doesn't work for normalized spectra, use Gaus (assuming raw counts in bin is large >10) |
570 | random = fRandom3->Gaus(measuredValue,measuredError); | |
571 | fRandomizedDist->SetBinContent(iBin,random); | |
572 | } | |
573 | } | |
574 | ||
575 | //______________________________________________________________ | |
576 | void AliCFUnfolding::FillDeltaUnfoldedProfile() { | |
577 | // | |
a9500e70 | 578 | // Store difference of unfolded spectrum from measured distribution and unfolded spectrum from randomized distribution |
579 | // The delta profile has been set to a THnSparse to handle N dimension | |
580 | // The THnSparse contains in each bin the mean value and spread of the difference | |
581 | // This function updates the profile wrt to its previous mean and error | |
582 | // The relation between iterations (n+1) and n is as follows : | |
583 | // mean_{n+1} = (n*mean_n + value_{n+1}) / (n+1) | |
584 | // sigma_{n+1} = sqrt { 1/(n+1) * [ n*sigma_n^2 + (n^2+n)*(mean_{n+1}-mean_n)^2 ] } (can this be optimized?) | |
585 | ||
586 | for (Long_t iBin=0; iBin<fUnfolded->GetNbins(); iBin++) { | |
587 | Double_t deltaInBin = fUnfoldedFinal->GetBinContent(iBin,fCoordinatesN_M) - fUnfolded->GetBinContent(iBin); | |
588 | Double_t entriesInBin = fDeltaUnfoldedN->GetBinContent(fCoordinatesN_M); | |
589 | //AliDebug(2,Form("%e %e ==> delta = %e\n",fUnfoldedFinal->GetBinContent(iBin,fCoordinatesN_M),fUnfolded->GetBinContent(iBin),deltaInBin)); | |
590 | ||
591 | Double_t mean_n = fDeltaUnfoldedP->GetBinContent(fCoordinatesN_M) ; | |
592 | Double_t mean_nplus1 = mean_n ; | |
593 | mean_nplus1 *= entriesInBin ; | |
594 | mean_nplus1 += deltaInBin ; | |
595 | mean_nplus1 /= (entriesInBin+1) ; | |
596 | ||
597 | Double_t sigma = fDeltaUnfoldedP->GetBinContent(fCoordinatesN_M) ; | |
598 | sigma *= sigma ; | |
599 | sigma *= entriesInBin ; | |
600 | sigma += ( (entriesInBin*entriesInBin+entriesInBin) * TMath::Power(mean_nplus1 - mean_n,2) ) ; | |
601 | sigma /= (entriesInBin+1) ; | |
602 | sigma = TMath::Sqrt(sigma) ; | |
603 | ||
604 | //AliDebug(2,Form("sigma = %e\n",sigma)); | |
605 | ||
606 | fDeltaUnfoldedP->SetBinContent(fCoordinatesN_M,mean_nplus1) ; | |
607 | fDeltaUnfoldedP->SetBinError (fCoordinatesN_M,sigma) ; | |
608 | fDeltaUnfoldedN->SetBinContent(fCoordinatesN_M,entriesInBin+1); | |
769f5114 | 609 | } |
610 | } | |
611 | ||
85b6bda9 | 612 | //______________________________________________________________ |
613 | ||
c0b10ad4 | 614 | void AliCFUnfolding::GetCoordinates() { |
615 | // | |
616 | // assign coordinates in Measured and True spaces (dim=N) from coordinates in global space (dim=2N) | |
617 | // | |
618 | for (Int_t i = 0; i<fNVariables ; i++) { | |
619 | fCoordinatesN_M[i] = fCoordinates2N[i]; | |
620 | fCoordinatesN_T[i] = fCoordinates2N[i+fNVariables]; | |
621 | } | |
622 | } | |
623 | ||
624 | //______________________________________________________________ | |
625 | ||
626 | void AliCFUnfolding::CreateConditional() { | |
627 | // | |
628 | // creates the conditional probability matrix (R*) holding the P(M|T), given the reponse matrix R | |
629 | // | |
630 | // --> R*(i,j) = R(i,j) / SUM_k{ R(k,j) } | |
631 | // | |
632 | ||
a9500e70 | 633 | fConditional = (THnSparse*) fResponse->Clone(); // output of this function |
634 | ||
85b6bda9 | 635 | Int_t* dim = new Int_t [fNVariables]; |
636 | 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 | 637 | |
638 | THnSparse* responseInT = fConditional->Projection(fNVariables,dim,"E"); // output denominator : | |
639 | // projection of the response matrix on the TRUE axis | |
85b6bda9 | 640 | delete [] dim; |
769f5114 | 641 | |
c0b10ad4 | 642 | // fill the conditional probability matrix |
7bc6a013 | 643 | for (Long_t iBin=0; iBin<fResponse->GetNbins(); iBin++) { |
c0b10ad4 | 644 | Double_t responseValue = fResponse->GetBinContent(iBin,fCoordinates2N); |
645 | GetCoordinates(); | |
a9500e70 | 646 | Double_t projValue = responseInT->GetBinContent(fCoordinatesN_T); |
769f5114 | 647 | |
85b6bda9 | 648 | Double_t fill = responseValue / projValue ; |
649 | if (fill>0. || fConditional->GetBinContent(fCoordinates2N)>0.) { | |
650 | fConditional->SetBinContent(fCoordinates2N,fill); | |
769f5114 | 651 | Double_t err = 0.; |
85b6bda9 | 652 | fConditional->SetBinError (fCoordinates2N,err); |
653 | } | |
c0b10ad4 | 654 | } |
a9500e70 | 655 | delete responseInT ; |
c0b10ad4 | 656 | } |
769f5114 | 657 | //______________________________________________________________ |
658 | ||
659 | Int_t AliCFUnfolding::GetDOF() { | |
660 | // | |
661 | // number of dof = number of bins | |
662 | // | |
663 | ||
664 | Int_t nDOF = 1 ; | |
665 | for (Int_t iDim=0; iDim<fNVariables; iDim++) { | |
666 | nDOF *= fPrior->GetAxis(iDim)->GetNbins(); | |
667 | } | |
668 | AliDebug(0,Form("Number of degrees of freedom = %d",nDOF)); | |
669 | return nDOF; | |
670 | } | |
c0b10ad4 | 671 | |
672 | //______________________________________________________________ | |
673 | ||
674 | Double_t AliCFUnfolding::GetChi2() { | |
675 | // | |
676 | // Returns the chi2 between unfolded and a priori spectrum | |
769f5114 | 677 | // This function became absolute with the correlated error calculation. |
678 | // Errors on the unfolded distribution are not known until the end | |
679 | // Use the convergence criterion instead | |
c0b10ad4 | 680 | // |
681 | ||
769f5114 | 682 | Double_t chi2 = 0. ; |
683 | Double_t error_unf = 0.; | |
7bc6a013 | 684 | for (Long_t iBin=0; iBin<fPrior->GetNbins(); iBin++) { |
85f9f9e1 | 685 | Double_t priorValue = fPrior->GetBinContent(iBin,fCoordinatesN_T); |
769f5114 | 686 | error_unf = fUnfolded->GetBinError(fCoordinatesN_T); |
85f9f9e1 | 687 | chi2 += (error_unf > 0. ? TMath::Power((fUnfolded->GetBinContent(fCoordinatesN_T) - priorValue)/error_unf,2) / priorValue : 0.) ; |
c0b10ad4 | 688 | } |
689 | return chi2; | |
690 | } | |
691 | ||
692 | //______________________________________________________________ | |
693 | ||
769f5114 | 694 | Double_t AliCFUnfolding::GetConvergence() { |
695 | // | |
696 | // Returns convergence criterion = \sum_t ((U_t^{n-1}-U_t^n)/U_t^{n-1})^2 | |
697 | // U is unfolded spectrum, t is the bin, n = current, n-1 = previous | |
698 | // | |
699 | Double_t convergence = 0.; | |
700 | Double_t priorValue = 0.; | |
701 | Double_t currentValue = 0.; | |
702 | for (Long_t iBin=0; iBin < fPrior->GetNbins(); iBin++) { | |
703 | priorValue = fPrior->GetBinContent(iBin,fCoordinatesN_T); | |
a9500e70 | 704 | currentValue = fUnfolded->GetBinContent(fCoordinatesN_T); |
769f5114 | 705 | |
706 | if (priorValue > 0.) | |
707 | convergence += ((priorValue-currentValue)/priorValue)*((priorValue-currentValue)/priorValue); | |
a9500e70 | 708 | else |
769f5114 | 709 | AliWarning(Form("priorValue = %f. Adding 0 to convergence criterion.",priorValue)); |
769f5114 | 710 | } |
711 | return convergence; | |
712 | } | |
713 | ||
714 | //______________________________________________________________ | |
715 | ||
716 | void AliCFUnfolding::SetMaxConvergencePerDOF(Double_t val) { | |
c0b10ad4 | 717 | // |
769f5114 | 718 | // Max. convergence criterion per degree of freedom : user setting |
719 | // convergence criterion = DOF*val; DOF = number of bins | |
720 | // In Jan-Fiete's multiplicity note: Convergence criterion = DOF*0.001^2 | |
c0b10ad4 | 721 | // |
722 | ||
769f5114 | 723 | Int_t nDOF = GetDOF() ; |
724 | fMaxConvergence = val * nDOF ; | |
725 | AliInfo(Form("MaxConvergence = %e. Number of degrees of freedom = %d",fMaxConvergence,nDOF)); | |
c0b10ad4 | 726 | } |
727 | ||
728 | //______________________________________________________________ | |
729 | ||
85b6bda9 | 730 | Short_t AliCFUnfolding::Smooth() { |
c0b10ad4 | 731 | // |
732 | // Smoothes the unfolded spectrum | |
85b6bda9 | 733 | // |
734 | // By default each cell content is replaced by the average with the neighbouring bins (but not diagonally-neighbouring bins) | |
735 | // However, if a specific function fcn has been defined in UseSmoothing(fcn), the unfolded will be fit and updated using fcn | |
c0b10ad4 | 736 | // |
737 | ||
85b6bda9 | 738 | if (fSmoothFunction) { |
7bc6a013 | 739 | AliDebug(2,Form("Smoothing spectrum with fit function %p",fSmoothFunction)); |
85b6bda9 | 740 | return SmoothUsingFunction(); |
741 | } | |
7036630f | 742 | else return SmoothUsingNeighbours(fUnfolded); |
85b6bda9 | 743 | } |
744 | ||
745 | //______________________________________________________________ | |
746 | ||
7036630f | 747 | Short_t AliCFUnfolding::SmoothUsingNeighbours(THnSparse* hist) { |
85b6bda9 | 748 | // |
749 | // Smoothes the unfolded spectrum using neighouring bins | |
750 | // | |
751 | ||
7036630f | 752 | Int_t const nDimensions = hist->GetNdimensions() ; |
753 | Int_t* coordinates = new Int_t[nDimensions]; | |
754 | ||
755 | Int_t* numBins = new Int_t[nDimensions]; | |
756 | for (Int_t iVar=0; iVar<nDimensions; iVar++) numBins[iVar] = hist->GetAxis(iVar)->GetNbins(); | |
c0b10ad4 | 757 | |
7036630f | 758 | //need a copy because hist will be updated during the loop, and this creates problems |
759 | THnSparse* copy = (THnSparse*)hist->Clone(); | |
c0b10ad4 | 760 | |
7bc6a013 | 761 | for (Long_t iBin=0; iBin<copy->GetNbins(); iBin++) { //loop on non-empty bins |
7036630f | 762 | Double_t content = copy->GetBinContent(iBin,coordinates); |
7bc6a013 | 763 | Double_t error2 = TMath::Power(copy->GetBinError(iBin),2); |
c0b10ad4 | 764 | |
765 | // skip the under/overflow bins... | |
766 | Bool_t isOutside = kFALSE ; | |
7036630f | 767 | for (Int_t iVar=0; iVar<nDimensions; iVar++) { |
768 | if (coordinates[iVar]<1 || coordinates[iVar]>numBins[iVar]) { | |
c0b10ad4 | 769 | isOutside=kTRUE; |
770 | break; | |
771 | } | |
772 | } | |
773 | if (isOutside) continue; | |
774 | ||
775 | Int_t neighbours = 0; // number of neighbours to average with | |
776 | ||
7036630f | 777 | for (Int_t iVar=0; iVar<nDimensions; iVar++) { |
778 | if (coordinates[iVar] > 1) { // must not be on low edge border | |
779 | coordinates[iVar]-- ; //get lower neighbouring bin | |
780 | content += copy->GetBinContent(coordinates); | |
781 | error2 += TMath::Power(copy->GetBinError(coordinates),2); | |
c0b10ad4 | 782 | neighbours++; |
7036630f | 783 | coordinates[iVar]++ ; //back to initial coordinate |
c0b10ad4 | 784 | } |
7036630f | 785 | if (coordinates[iVar] < numBins[iVar]) { // must not be on up edge border |
786 | coordinates[iVar]++ ; //get upper neighbouring bin | |
787 | content += copy->GetBinContent(coordinates); | |
788 | error2 += TMath::Power(copy->GetBinError(coordinates),2); | |
c0b10ad4 | 789 | neighbours++; |
7036630f | 790 | coordinates[iVar]-- ; //back to initial coordinate |
c0b10ad4 | 791 | } |
792 | } | |
85b6bda9 | 793 | // make an average |
7036630f | 794 | hist->SetBinContent(coordinates,content/(1.+neighbours)); |
795 | hist->SetBinError (coordinates,TMath::Sqrt(error2)/(1.+neighbours)); | |
c0b10ad4 | 796 | } |
797 | delete [] numBins; | |
7036630f | 798 | delete [] coordinates ; |
c0b10ad4 | 799 | delete copy; |
85b6bda9 | 800 | return 0; |
c0b10ad4 | 801 | } |
802 | ||
85b6bda9 | 803 | //______________________________________________________________ |
804 | ||
805 | Short_t AliCFUnfolding::SmoothUsingFunction() { | |
806 | // | |
807 | // Fits the unfolded spectrum using the function fSmoothFunction | |
808 | // | |
809 | ||
7bc6a013 | 810 | AliDebug(0,Form("Smooth function is a %s with option \"%s\" and has %d parameters : ",fSmoothFunction->ClassName(),fSmoothOption,fSmoothFunction->GetNpar())); |
85b6bda9 | 811 | |
7bc6a013 | 812 | for (Int_t iPar=0; iPar<fSmoothFunction->GetNpar(); iPar++) AliDebug(0,Form("par[%d]=%e",iPar,fSmoothFunction->GetParameter(iPar))); |
813 | ||
814 | Int_t fitResult = 0; | |
85b6bda9 | 815 | |
816 | switch (fNVariables) { | |
7bc6a013 | 817 | case 1 : fitResult = fUnfolded->Projection(0) ->Fit(fSmoothFunction,fSmoothOption); break; |
818 | case 2 : fitResult = fUnfolded->Projection(1,0) ->Fit(fSmoothFunction,fSmoothOption); break; // (1,0) instead of (0,1) -> TAxis issue | |
819 | case 3 : fitResult = fUnfolded->Projection(0,1,2)->Fit(fSmoothFunction,fSmoothOption); break; | |
820 | default: AliFatal(Form("Cannot handle such fit in %d dimensions",fNVariables)) ; return 1; | |
821 | } | |
822 | ||
823 | if (fitResult != 0) { | |
824 | AliWarning(Form("Fit failed with status %d, stopping the loop",fitResult)); | |
825 | return 1; | |
85b6bda9 | 826 | } |
827 | ||
828 | Int_t nDim = fNVariables; | |
829 | Int_t* bins = new Int_t[nDim]; // number of bins for each variable | |
830 | Long_t nBins = 1; // used to calculate the total number of bins in the THnSparse | |
831 | ||
832 | for (Int_t iVar=0; iVar<nDim; iVar++) { | |
833 | bins[iVar] = fUnfolded->GetAxis(iVar)->GetNbins(); | |
834 | nBins *= bins[iVar]; | |
835 | } | |
836 | ||
837 | Int_t *bin = new Int_t[nDim]; // bin to fill the THnSparse (holding the bin coordinates) | |
838 | Double_t x[3] = {0,0,0} ; // value in bin center (max dimension is 3 (TF3)) | |
839 | ||
840 | // loop on the bins and update of fUnfolded | |
841 | // THnSparse::Multiply(TF1*) doesn't exist, so let's do it bin by bin | |
842 | for (Long_t iBin=0; iBin<nBins; iBin++) { | |
843 | Long_t bin_tmp = iBin ; | |
844 | for (Int_t iVar=0; iVar<nDim; iVar++) { | |
845 | bin[iVar] = 1 + bin_tmp % bins[iVar] ; | |
846 | bin_tmp /= bins[iVar] ; | |
847 | x[iVar] = fUnfolded->GetAxis(iVar)->GetBinCenter(bin[iVar]); | |
848 | } | |
849 | Double_t functionValue = fSmoothFunction->Eval(x[0],x[1],x[2]) ; | |
12e419d5 | 850 | fUnfolded->SetBinError (bin,fUnfolded->GetBinError(bin)*functionValue/fUnfolded->GetBinContent(bin)); |
85b6bda9 | 851 | fUnfolded->SetBinContent(bin,functionValue); |
85b6bda9 | 852 | } |
700a1189 | 853 | delete [] bins; |
854 | delete [] bin ; | |
85b6bda9 | 855 | return 0; |
856 | } | |
c0b10ad4 | 857 | |
858 | //______________________________________________________________ | |
859 | ||
860 | void AliCFUnfolding::CreateFlatPrior() { | |
861 | // | |
862 | // Creates a flat prior distribution | |
863 | // | |
864 | ||
865 | AliInfo("Creating a flat a priori distribution"); | |
866 | ||
867 | // create the frame of the THnSparse given (for example) the one from the efficiency map | |
868 | fPrior = (THnSparse*) fEfficiency->Clone(); | |
a9500e70 | 869 | fPrior->SetTitle("Prior"); |
c0b10ad4 | 870 | |
85b6bda9 | 871 | if (fNVariables != fPrior->GetNdimensions()) |
872 | AliFatal(Form("The prior matrix should have %d dimensions, and it has actually %d",fNVariables,fPrior->GetNdimensions())); | |
873 | ||
c0b10ad4 | 874 | Int_t nDim = fNVariables; |
875 | Int_t* bins = new Int_t[nDim]; // number of bins for each variable | |
876 | Long_t nBins = 1; // used to calculate the total number of bins in the THnSparse | |
877 | ||
878 | for (Int_t iVar=0; iVar<nDim; iVar++) { | |
879 | bins[iVar] = fPrior->GetAxis(iVar)->GetNbins(); | |
880 | nBins *= bins[iVar]; | |
881 | } | |
882 | ||
883 | Int_t *bin = new Int_t[nDim]; // bin to fill the THnSparse (holding the bin coordinates) | |
884 | ||
885 | // loop that sets 1 in each bin | |
886 | for (Long_t iBin=0; iBin<nBins; iBin++) { | |
887 | Long_t bin_tmp = iBin ; | |
888 | for (Int_t iVar=0; iVar<nDim; iVar++) { | |
889 | bin[iVar] = 1 + bin_tmp % bins[iVar] ; | |
890 | bin_tmp /= bins[iVar] ; | |
891 | } | |
892 | fPrior->SetBinContent(bin,1.); // put 1 everywhere | |
85b6bda9 | 893 | fPrior->SetBinError (bin,0.); // put 0 everywhere |
c0b10ad4 | 894 | } |
895 | ||
896 | fOriginalPrior = (THnSparse*)fPrior->Clone(); | |
897 | ||
898 | delete [] bin; | |
899 | delete [] bins; | |
900 | } |