]>
Commit | Line | Data |
---|---|---|
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. // | |
31 | // For each iteration, the unfolded spectrum is calculated using // | |
32 | // the inverse response : the goal is to get an unfolded spectrum // | |
33 | // similar (according to some criterion) to the a priori one. // | |
34 | // If the difference is too big, another iteration is performed : // | |
35 | // the a priori spectrum is updated to the unfolded one from the // | |
36 | // previous iteration, and so on so forth, until the maximum number // | |
37 | // of iterations or the similarity criterion is reached. // | |
38 | // // | |
39 | // Currently the similarity criterion is the Chi2 between the a priori // | |
40 | // and the unfolded spectrum. // | |
41 | // // | |
42 | // Currently the user has to define the max. number of iterations // | |
43 | // (::SetMaxNumberOfIterations) // | |
44 | // and the chi2 below which the procedure will stop // | |
45 | // (::SetMaxChi2 or ::SetMaxChi2PerDOF) // | |
46 | // // | |
47 | // An optional possibility is to smooth the unfolded spectrum at the // | |
48 | // end of each iteration, either using a fit function // | |
49 | // (only if #dimensions <=3) // | |
50 | // or a simple averaging using the neighbouring bins values. // | |
51 | // This is possible calling the function ::UseSmoothing // | |
52 | // If no argument is passed to this function, then the second option // | |
53 | // is used. // | |
54 | // // | |
55 | //---------------------------------------------------------------------// | |
56 | // Author : renaud.vernet@cern.ch // | |
57 | //---------------------------------------------------------------------// | |
c0b10ad4 | 58 | |
59 | ||
60 | #include "AliCFUnfolding.h" | |
61 | #include "TMath.h" | |
62 | #include "TAxis.h" | |
63 | #include "AliLog.h" | |
85b6bda9 | 64 | #include "TF1.h" |
65 | #include "TH1D.h" | |
66 | #include "TH2D.h" | |
67 | #include "TH3D.h" | |
c0b10ad4 | 68 | |
69 | ClassImp(AliCFUnfolding) | |
70 | ||
71 | //______________________________________________________________ | |
72 | ||
73 | AliCFUnfolding::AliCFUnfolding() : | |
74 | TNamed(), | |
75 | fResponse(0x0), | |
76 | fPrior(0x0), | |
c0b10ad4 | 77 | fEfficiency(0x0), |
78 | fMeasured(0x0), | |
79 | fMaxNumIterations(0), | |
80 | fNVariables(0), | |
81 | fMaxChi2(0), | |
82 | fUseSmoothing(kFALSE), | |
85b6bda9 | 83 | fSmoothFunction(0x0), |
84 | fSmoothOption(""), | |
85 | fOriginalPrior(0x0), | |
c0b10ad4 | 86 | fInverseResponse(0x0), |
87 | fMeasuredEstimate(0x0), | |
88 | fConditional(0x0), | |
89 | fProjResponseInT(0x0), | |
90 | fUnfolded(0x0), | |
91 | fCoordinates2N(0x0), | |
92 | fCoordinatesN_M(0x0), | |
93 | fCoordinatesN_T(0x0) | |
94 | { | |
95 | // | |
96 | // default constructor | |
97 | // | |
98 | } | |
99 | ||
100 | //______________________________________________________________ | |
101 | ||
102 | AliCFUnfolding::AliCFUnfolding(const Char_t* name, const Char_t* title, const Int_t nVar, | |
103 | const THnSparse* response, const THnSparse* efficiency, const THnSparse* measured, const THnSparse* prior) : | |
104 | TNamed(name,title), | |
105 | fResponse((THnSparse*)response->Clone()), | |
106 | fPrior(0x0), | |
c0b10ad4 | 107 | fEfficiency((THnSparse*)efficiency->Clone()), |
108 | fMeasured((THnSparse*)measured->Clone()), | |
109 | fMaxNumIterations(0), | |
110 | fNVariables(nVar), | |
111 | fMaxChi2(0), | |
112 | fUseSmoothing(kFALSE), | |
85b6bda9 | 113 | fSmoothFunction(0x0), |
114 | fSmoothOption(""), | |
115 | fOriginalPrior(0x0), | |
c0b10ad4 | 116 | fInverseResponse(0x0), |
117 | fMeasuredEstimate(0x0), | |
118 | fConditional(0x0), | |
119 | fProjResponseInT(0x0), | |
120 | fUnfolded(0x0), | |
121 | fCoordinates2N(0x0), | |
122 | fCoordinatesN_M(0x0), | |
123 | fCoordinatesN_T(0x0) | |
124 | { | |
125 | // | |
126 | // named constructor | |
127 | // | |
128 | ||
129 | AliInfo(Form("\n\n--------------------------\nCreating an unfolder :\n--------------------------\nresponse matrix has %d dimension(s)",fResponse->GetNdimensions())); | |
130 | ||
131 | if (!prior) CreateFlatPrior(); // if no prior distribution declared, simply use a flat distribution | |
132 | else { | |
133 | fPrior = (THnSparse*) prior->Clone(); | |
134 | fOriginalPrior = (THnSparse*)fPrior->Clone(); | |
85b6bda9 | 135 | if (fPrior->GetNdimensions() != fNVariables) |
136 | AliFatal(Form("The prior matrix should have %d dimensions, and it has actually %d",fNVariables,fPrior->GetNdimensions())); | |
c0b10ad4 | 137 | } |
85b6bda9 | 138 | |
139 | if (fEfficiency->GetNdimensions() != fNVariables) | |
140 | AliFatal(Form("The efficiency matrix should have %d dimensions, and it has actually %d",fNVariables,fEfficiency->GetNdimensions())); | |
141 | if (fMeasured->GetNdimensions() != fNVariables) | |
142 | AliFatal(Form("The measured matrix should have %d dimensions, and it has actually %d",fNVariables,fMeasured->GetNdimensions())); | |
143 | if (fResponse->GetNdimensions() != 2*fNVariables) | |
144 | AliFatal(Form("The response matrix should have %d dimensions, and it has actually %d",2*fNVariables,fResponse->GetNdimensions())); | |
c0b10ad4 | 145 | |
85b6bda9 | 146 | |
c0b10ad4 | 147 | for (Int_t iVar=0; iVar<fNVariables; iVar++) { |
148 | AliInfo(Form("prior matrix has %d bins in dimension %d",fPrior ->GetAxis(iVar)->GetNbins(),iVar)); | |
149 | AliInfo(Form("efficiency matrix has %d bins in dimension %d",fEfficiency->GetAxis(iVar)->GetNbins(),iVar)); | |
150 | AliInfo(Form("measured matrix has %d bins in dimension %d",fMeasured ->GetAxis(iVar)->GetNbins(),iVar)); | |
151 | } | |
152 | Init(); | |
153 | } | |
154 | ||
155 | ||
156 | //______________________________________________________________ | |
157 | ||
158 | AliCFUnfolding::AliCFUnfolding(const AliCFUnfolding& c) : | |
159 | TNamed(c), | |
160 | fResponse((THnSparse*)c.fResponse->Clone()), | |
161 | fPrior((THnSparse*)c.fPrior->Clone()), | |
c0b10ad4 | 162 | fEfficiency((THnSparse*)c.fEfficiency->Clone()), |
163 | fMeasured((THnSparse*)c.fMeasured->Clone()), | |
164 | fMaxNumIterations(c.fMaxNumIterations), | |
165 | fNVariables(c.fNVariables), | |
166 | fMaxChi2(c.fMaxChi2), | |
167 | fUseSmoothing(c.fUseSmoothing), | |
85b6bda9 | 168 | fSmoothFunction((TF1*)c.fSmoothFunction->Clone()), |
169 | fSmoothOption(fSmoothOption), | |
170 | fOriginalPrior((THnSparse*)c.fOriginalPrior->Clone()), | |
c0b10ad4 | 171 | fInverseResponse((THnSparse*)c.fInverseResponse->Clone()), |
172 | fMeasuredEstimate((THnSparse*)fMeasuredEstimate->Clone()), | |
173 | fConditional((THnSparse*)c.fConditional->Clone()), | |
174 | fProjResponseInT((THnSparse*)c.fProjResponseInT->Clone()), | |
175 | fUnfolded((THnSparse*)c.fUnfolded->Clone()), | |
176 | fCoordinates2N(new Int_t(*c.fCoordinates2N)), | |
177 | fCoordinatesN_M(new Int_t(*c.fCoordinatesN_M)), | |
178 | fCoordinatesN_T(new Int_t(*c.fCoordinatesN_T)) | |
179 | { | |
180 | // | |
181 | // copy constructor | |
182 | // | |
183 | } | |
184 | ||
185 | //______________________________________________________________ | |
186 | ||
187 | AliCFUnfolding& AliCFUnfolding::operator=(const AliCFUnfolding& c) { | |
188 | // | |
189 | // assignment operator | |
190 | // | |
191 | ||
192 | if (this!=&c) { | |
193 | TNamed::operator=(c); | |
194 | fResponse = (THnSparse*)c.fResponse->Clone() ; | |
195 | fPrior = (THnSparse*)c.fPrior->Clone() ; | |
c0b10ad4 | 196 | fEfficiency = (THnSparse*)c.fEfficiency->Clone() ; |
197 | fMeasured = (THnSparse*)c.fMeasured->Clone() ; | |
198 | fMaxNumIterations = c.fMaxNumIterations ; | |
199 | fNVariables = c.fNVariables ; | |
200 | fMaxChi2 = c.fMaxChi2 ; | |
201 | fUseSmoothing = c.fUseSmoothing ; | |
85b6bda9 | 202 | fSmoothFunction = (TF1*)c.fSmoothFunction->Clone(); |
203 | fSmoothOption = c.fSmoothOption ; | |
204 | fOriginalPrior = (THnSparse*)c.fOriginalPrior->Clone() ; | |
c0b10ad4 | 205 | fInverseResponse = (THnSparse*)c.fInverseResponse->Clone() ; |
206 | fMeasuredEstimate = (THnSparse*)fMeasuredEstimate->Clone() ; | |
207 | fConditional = (THnSparse*)c.fConditional->Clone() ; | |
208 | fProjResponseInT = (THnSparse*)c.fProjResponseInT->Clone() ; | |
209 | fUnfolded = (THnSparse*)c.fUnfolded->Clone() ; | |
210 | fCoordinates2N = new Int_t(*c.fCoordinates2N) ; | |
211 | fCoordinatesN_M = new Int_t(*c.fCoordinatesN_M) ; | |
212 | fCoordinatesN_T = new Int_t(*c.fCoordinatesN_T) ; | |
213 | } | |
214 | return *this; | |
215 | } | |
216 | ||
217 | //______________________________________________________________ | |
218 | ||
219 | AliCFUnfolding::~AliCFUnfolding() { | |
220 | // | |
221 | // destructor | |
222 | // | |
223 | if (fResponse) delete fResponse; | |
224 | if (fPrior) delete fPrior; | |
c0b10ad4 | 225 | if (fEfficiency) delete fEfficiency; |
226 | if (fMeasured) delete fMeasured; | |
85b6bda9 | 227 | if (fSmoothFunction) delete fSmoothFunction; |
228 | if (fOriginalPrior) delete fOriginalPrior; | |
c0b10ad4 | 229 | if (fInverseResponse) delete fInverseResponse; |
230 | if (fMeasuredEstimate) delete fMeasuredEstimate; | |
231 | if (fConditional) delete fConditional; | |
232 | if (fProjResponseInT) delete fProjResponseInT; | |
233 | if (fCoordinates2N) delete [] fCoordinates2N; | |
234 | if (fCoordinatesN_M) delete [] fCoordinatesN_M; | |
235 | if (fCoordinatesN_T) delete [] fCoordinatesN_T; | |
236 | } | |
237 | ||
238 | //______________________________________________________________ | |
239 | ||
240 | void AliCFUnfolding::Init() { | |
241 | // | |
242 | // initialisation function : creates internal settings | |
243 | // | |
244 | ||
245 | fCoordinates2N = new Int_t[2*fNVariables]; | |
246 | fCoordinatesN_M = new Int_t[fNVariables]; | |
247 | fCoordinatesN_T = new Int_t[fNVariables]; | |
248 | ||
249 | // create the matrix of conditional probabilities P(M|T) | |
250 | CreateConditional(); | |
251 | ||
252 | // create the frame of the inverse response matrix | |
253 | fInverseResponse = (THnSparse*) fResponse->Clone(); | |
254 | // create the frame of the unfolded spectrum | |
255 | fUnfolded = (THnSparse*) fPrior->Clone(); | |
256 | // create the frame of the measurement estimate spectrum | |
257 | fMeasuredEstimate = (THnSparse*) fMeasured->Clone(); | |
258 | } | |
259 | ||
260 | //______________________________________________________________ | |
261 | ||
262 | void AliCFUnfolding::CreateEstMeasured() { | |
263 | // | |
264 | // This function creates a estimate (M) of the reconstructed spectrum | |
7bc6a013 | 265 | // given the a priori distribution (T), the efficiency (E) and the conditional matrix (COND) |
c0b10ad4 | 266 | // |
267 | // --> P(M) = SUM { P(M|T) * P(T) } | |
7bc6a013 | 268 | // --> M(i) = SUM_k { COND(i,k) * T(k) * E (k)} |
c0b10ad4 | 269 | // |
270 | // This is needed to calculate the inverse response matrix | |
271 | // | |
272 | ||
273 | ||
274 | // clean the measured estimate spectrum | |
7bc6a013 | 275 | for (Long_t i=0; i<fMeasuredEstimate->GetNbins(); i++) { |
c0b10ad4 | 276 | fMeasuredEstimate->GetBinContent(i,fCoordinatesN_M); |
277 | fMeasuredEstimate->SetBinContent(fCoordinatesN_M,0.); | |
85b6bda9 | 278 | fMeasuredEstimate->SetBinError (fCoordinatesN_M,0.); |
c0b10ad4 | 279 | } |
280 | ||
85b6bda9 | 281 | THnSparse* priorTimesEff = (THnSparse*) fPrior->Clone(); |
282 | priorTimesEff->Multiply(fEfficiency); | |
283 | ||
c0b10ad4 | 284 | // fill it |
7bc6a013 | 285 | for (Long_t iBin=0; iBin<fConditional->GetNbins(); iBin++) { |
c0b10ad4 | 286 | Double_t conditionalValue = fConditional->GetBinContent(iBin,fCoordinates2N); |
85b6bda9 | 287 | Double_t conditionalError = fConditional->GetBinError (iBin); |
c0b10ad4 | 288 | GetCoordinates(); |
85b6bda9 | 289 | Double_t priorTimesEffValue = priorTimesEff->GetBinContent(fCoordinatesN_T); |
290 | Double_t priorTimesEffError = priorTimesEff->GetBinError (fCoordinatesN_T); | |
291 | Double_t fill = conditionalValue * priorTimesEffValue ; | |
292 | ||
293 | if (fill>0.) { | |
294 | fMeasuredEstimate->AddBinContent(fCoordinatesN_M,fill); | |
295 | ||
296 | // error calculation : gaussian error propagation (may be overestimated...) | |
297 | Double_t err2 = TMath::Power(fMeasuredEstimate->GetBinError(fCoordinatesN_M),2) ; | |
298 | err2 += TMath::Power(conditionalValue*priorTimesEffError,2) + TMath::Power(conditionalError*priorTimesEffValue,2) ; | |
299 | Double_t err = TMath::Sqrt(err2); | |
300 | fMeasuredEstimate->SetBinError(fCoordinatesN_M,err); | |
301 | } | |
c0b10ad4 | 302 | } |
85b6bda9 | 303 | delete priorTimesEff ; |
c0b10ad4 | 304 | } |
305 | ||
306 | //______________________________________________________________ | |
307 | ||
308 | void AliCFUnfolding::CreateInvResponse() { | |
309 | // | |
310 | // Creates the inverse response matrix (INV) with Bayesian method | |
311 | // : uses the conditional matrix (COND), the prior probabilities (T) and the efficiency map (E) | |
312 | // | |
313 | // --> P(T|M) = P(M|T) * P(T) * eff(T) / SUM { P(M|T) * P(T) } | |
314 | // --> INV(i,j) = COND(i,j) * T(j) * E(j) / SUM_k { COND(i,k) * T(k) } | |
315 | // | |
316 | ||
85b6bda9 | 317 | THnSparse* priorTimesEff = (THnSparse*) fPrior->Clone(); |
318 | priorTimesEff->Multiply(fEfficiency); | |
319 | ||
7bc6a013 | 320 | for (Long_t iBin=0; iBin<fConditional->GetNbins(); iBin++) { |
c0b10ad4 | 321 | Double_t conditionalValue = fConditional->GetBinContent(iBin,fCoordinates2N); |
85b6bda9 | 322 | Double_t conditionalError = fConditional->GetBinError (iBin); |
c0b10ad4 | 323 | GetCoordinates(); |
85b6bda9 | 324 | Double_t estMeasuredValue = fMeasuredEstimate->GetBinContent(fCoordinatesN_M); |
325 | Double_t estMeasuredError = fMeasuredEstimate->GetBinError (fCoordinatesN_M); | |
326 | Double_t priorTimesEffValue = priorTimesEff ->GetBinContent(fCoordinatesN_T); | |
327 | Double_t priorTimesEffError = priorTimesEff ->GetBinError (fCoordinatesN_T); | |
328 | Double_t fill = (estMeasuredValue>0. ? conditionalValue * priorTimesEffValue / estMeasuredValue : 0. ) ; | |
329 | // error calculation : gaussian error propagation (may be overestimated...) | |
330 | Double_t err = 0. ; | |
331 | if (estMeasuredValue>0.) { | |
332 | err = TMath::Sqrt( TMath::Power(conditionalError * priorTimesEffValue * estMeasuredValue ,2) + | |
333 | TMath::Power(conditionalValue * priorTimesEffError * estMeasuredValue ,2) + | |
334 | TMath::Power(conditionalValue * priorTimesEffValue * estMeasuredError ,2) ) | |
335 | / TMath::Power(estMeasuredValue,2) ; | |
336 | } | |
337 | if (fill>0. || fInverseResponse->GetBinContent(fCoordinates2N)>0.) { | |
338 | fInverseResponse->SetBinContent(fCoordinates2N,fill); | |
339 | fInverseResponse->SetBinError (fCoordinates2N,err ); | |
340 | } | |
341 | } | |
342 | delete priorTimesEff ; | |
c0b10ad4 | 343 | } |
344 | ||
345 | //______________________________________________________________ | |
346 | ||
347 | void AliCFUnfolding::Unfold() { | |
348 | // | |
349 | // Main routine called by the user : | |
350 | // it calculates the unfolded spectrum from the response matrix and the measured spectrum | |
351 | // several iterations are performed until a reasonable chi2 is reached | |
352 | // | |
353 | ||
354 | Int_t iIterBayes=0 ; | |
355 | Double_t chi2=0 ; | |
356 | ||
357 | for (iIterBayes=0; iIterBayes<fMaxNumIterations; iIterBayes++) { // bayes iterations | |
358 | CreateEstMeasured(); | |
359 | CreateInvResponse(); | |
360 | CreateUnfolded(); | |
361 | chi2 = GetChi2(); | |
7bc6a013 | 362 | AliDebug(1,Form("Chi2 at iteration %d is %e",iIterBayes,chi2)); |
c0b10ad4 | 363 | if (fMaxChi2>0. && chi2<fMaxChi2) { |
364 | break; | |
365 | } | |
366 | // update the prior distribution | |
85b6bda9 | 367 | if (fUseSmoothing) { |
368 | if (Smooth()) { | |
369 | AliError("Couldn't smooth the unfolded spectrum!!"); | |
7bc6a013 | 370 | AliInfo(Form("\n\n=======================\nFinished at iteration %d : Chi2 is %e and you required it to be < %e\n=======================\n\n",iIterBayes,chi2,fMaxChi2)); |
85b6bda9 | 371 | return; |
372 | } | |
373 | } | |
c0b10ad4 | 374 | fPrior = (THnSparse*)fUnfolded->Clone() ; // this should be changed (memory) |
375 | } | |
85b6bda9 | 376 | AliInfo(Form("\n\n=======================\nFinished at iteration %d : Chi2 is %e and you required it to be < %e\n=======================\n\n",iIterBayes,chi2,fMaxChi2)); |
c0b10ad4 | 377 | } |
378 | ||
379 | //______________________________________________________________ | |
380 | ||
381 | void AliCFUnfolding::CreateUnfolded() { | |
382 | // | |
383 | // Creates the unfolded (T) spectrum from the measured spectrum (M) and the inverse response matrix (INV) | |
384 | // We have P(T) = SUM { P(T|M) * P(M) } | |
385 | // --> T(i) = SUM_k { INV(i,k) * M(k) } | |
386 | // | |
387 | ||
388 | ||
389 | // clear the unfolded spectrum | |
7bc6a013 | 390 | for (Long_t i=0; i<fUnfolded->GetNbins(); i++) { |
c0b10ad4 | 391 | fUnfolded->GetBinContent(i,fCoordinatesN_T); |
392 | fUnfolded->SetBinContent(fCoordinatesN_T,0.); | |
85b6bda9 | 393 | fUnfolded->SetBinError (fCoordinatesN_T,0.); |
c0b10ad4 | 394 | } |
395 | ||
7bc6a013 | 396 | for (Long_t iBin=0; iBin<fInverseResponse->GetNbins(); iBin++) { |
c0b10ad4 | 397 | Double_t invResponseValue = fInverseResponse->GetBinContent(iBin,fCoordinates2N); |
85b6bda9 | 398 | Double_t invResponseError = fInverseResponse->GetBinError (iBin); |
c0b10ad4 | 399 | GetCoordinates(); |
85b6bda9 | 400 | Double_t effValue = fEfficiency->GetBinContent(fCoordinatesN_T); |
401 | Double_t effError = fEfficiency->GetBinError (fCoordinatesN_T); | |
402 | Double_t measuredValue = fMeasured ->GetBinContent(fCoordinatesN_M); | |
403 | Double_t measuredError = fMeasured ->GetBinError (fCoordinatesN_M); | |
404 | Double_t fill = (effValue>0. ? invResponseValue * measuredValue / effValue : 0.) ; | |
405 | ||
406 | if (fill>0.) { | |
407 | fUnfolded->AddBinContent(fCoordinatesN_T,fill); | |
408 | ||
409 | // error calculation : gaussian error propagation (may be overestimated...) | |
410 | Double_t err2 = TMath::Power(fUnfolded->GetBinError(fCoordinatesN_T),2) ; | |
411 | err2 += TMath::Power(invResponseError * measuredValue * effValue,2) / TMath::Power(effValue,4) ; | |
412 | err2 += TMath::Power(invResponseValue * measuredError * effValue,2) / TMath::Power(effValue,4) ; | |
413 | err2 += TMath::Power(invResponseValue * measuredValue * effError,2) / TMath::Power(effValue,4) ; | |
414 | Double_t err = TMath::Sqrt(err2); | |
415 | fUnfolded->SetBinError(fCoordinatesN_T,err); | |
416 | } | |
c0b10ad4 | 417 | } |
418 | } | |
419 | ||
85b6bda9 | 420 | //______________________________________________________________ |
421 | ||
c0b10ad4 | 422 | void AliCFUnfolding::GetCoordinates() { |
423 | // | |
424 | // assign coordinates in Measured and True spaces (dim=N) from coordinates in global space (dim=2N) | |
425 | // | |
426 | for (Int_t i = 0; i<fNVariables ; i++) { | |
427 | fCoordinatesN_M[i] = fCoordinates2N[i]; | |
428 | fCoordinatesN_T[i] = fCoordinates2N[i+fNVariables]; | |
429 | } | |
430 | } | |
431 | ||
432 | //______________________________________________________________ | |
433 | ||
434 | void AliCFUnfolding::CreateConditional() { | |
435 | // | |
436 | // creates the conditional probability matrix (R*) holding the P(M|T), given the reponse matrix R | |
437 | // | |
438 | // --> R*(i,j) = R(i,j) / SUM_k{ R(k,j) } | |
439 | // | |
440 | ||
441 | fConditional = (THnSparse*) fResponse->Clone(); // output of this function | |
442 | fProjResponseInT = (THnSparse*) fPrior->Clone(); // output denominator : | |
443 | // projection of the response matrix on the TRUE axis | |
85b6bda9 | 444 | Int_t* dim = new Int_t [fNVariables]; |
445 | for (Int_t iDim=0; iDim<fNVariables; iDim++) dim[iDim] = fNVariables+iDim ; //dimensions corresponding to TRUE values (i.e. from N to 2N-1) | |
446 | fProjResponseInT = fConditional->Projection(fNVariables,dim,"E"); //project | |
447 | delete [] dim; | |
c0b10ad4 | 448 | |
449 | // fill the conditional probability matrix | |
7bc6a013 | 450 | for (Long_t iBin=0; iBin<fResponse->GetNbins(); iBin++) { |
c0b10ad4 | 451 | Double_t responseValue = fResponse->GetBinContent(iBin,fCoordinates2N); |
85b6bda9 | 452 | Double_t responseError = fResponse->GetBinError (iBin); |
c0b10ad4 | 453 | GetCoordinates(); |
85b6bda9 | 454 | Double_t projValue = fProjResponseInT->GetBinContent(fCoordinatesN_T); |
455 | Double_t projError = fProjResponseInT->GetBinError (fCoordinatesN_T); | |
456 | ||
457 | Double_t fill = responseValue / projValue ; | |
458 | if (fill>0. || fConditional->GetBinContent(fCoordinates2N)>0.) { | |
459 | fConditional->SetBinContent(fCoordinates2N,fill); | |
460 | // gaussian error for the moment | |
461 | Double_t err2 = TMath::Power(responseError*projValue,2) + TMath::Power(responseValue*projError,2) ; | |
462 | Double_t err = TMath::Sqrt(err2); | |
463 | err /= TMath::Power(projValue,2) ; | |
464 | fConditional->SetBinError (fCoordinates2N,err); | |
465 | } | |
c0b10ad4 | 466 | } |
467 | } | |
468 | ||
469 | //______________________________________________________________ | |
470 | ||
471 | Double_t AliCFUnfolding::GetChi2() { | |
472 | // | |
473 | // Returns the chi2 between unfolded and a priori spectrum | |
474 | // | |
475 | ||
476 | Double_t chi2 = 0. ; | |
7bc6a013 | 477 | for (Long_t iBin=0; iBin<fPrior->GetNbins(); iBin++) { |
c0b10ad4 | 478 | Double_t priorValue = fPrior->GetBinContent(iBin); |
7bc6a013 | 479 | // chi2 += (priorValue>0. ? TMath::Power(fUnfolded->GetBinContent(iBin) - priorValue,2) / priorValue : 0.) ; |
480 | chi2 += (fUnfolded->GetBinError(iBin)>0. ? TMath::Power((fUnfolded->GetBinContent(iBin) - priorValue)/fUnfolded->GetBinError(iBin),2) / priorValue : 0.) ; | |
c0b10ad4 | 481 | } |
482 | return chi2; | |
483 | } | |
484 | ||
485 | //______________________________________________________________ | |
486 | ||
487 | void AliCFUnfolding::SetMaxChi2PerDOF(Double_t val) { | |
488 | // | |
489 | // Max. chi2 per degree of freedom : user setting | |
490 | // | |
491 | ||
492 | Int_t nDOF = 1 ; | |
493 | for (Int_t iDim=0; iDim<fNVariables; iDim++) { | |
494 | nDOF *= fPrior->GetAxis(iDim)->GetNbins(); | |
495 | } | |
496 | AliInfo(Form("Number of degrees of freedom = %d",nDOF)); | |
497 | fMaxChi2 = val * nDOF ; | |
498 | } | |
499 | ||
500 | //______________________________________________________________ | |
501 | ||
85b6bda9 | 502 | Short_t AliCFUnfolding::Smooth() { |
c0b10ad4 | 503 | // |
504 | // Smoothes the unfolded spectrum | |
85b6bda9 | 505 | // |
506 | // By default each cell content is replaced by the average with the neighbouring bins (but not diagonally-neighbouring bins) | |
507 | // However, if a specific function fcn has been defined in UseSmoothing(fcn), the unfolded will be fit and updated using fcn | |
c0b10ad4 | 508 | // |
509 | ||
85b6bda9 | 510 | if (fSmoothFunction) { |
7bc6a013 | 511 | AliDebug(2,Form("Smoothing spectrum with fit function %p",fSmoothFunction)); |
85b6bda9 | 512 | return SmoothUsingFunction(); |
513 | } | |
514 | else return SmoothUsingNeighbours(); | |
515 | } | |
516 | ||
517 | //______________________________________________________________ | |
518 | ||
519 | Short_t AliCFUnfolding::SmoothUsingNeighbours() { | |
520 | // | |
521 | // Smoothes the unfolded spectrum using neighouring bins | |
522 | // | |
523 | ||
c0b10ad4 | 524 | Int_t* numBins = new Int_t[fNVariables]; |
525 | for (Int_t iVar=0; iVar<fNVariables; iVar++) numBins[iVar]=fUnfolded->GetAxis(iVar)->GetNbins(); | |
526 | ||
527 | //need a copy because fUnfolded will be updated during the loop, and this creates problems | |
528 | THnSparse* copy = (THnSparse*)fUnfolded->Clone(); | |
529 | ||
7bc6a013 | 530 | for (Long_t iBin=0; iBin<copy->GetNbins(); iBin++) { //loop on non-empty bins |
c0b10ad4 | 531 | Double_t content = copy->GetBinContent(iBin,fCoordinatesN_T); |
7bc6a013 | 532 | Double_t error2 = TMath::Power(copy->GetBinError(iBin),2); |
c0b10ad4 | 533 | |
534 | // skip the under/overflow bins... | |
535 | Bool_t isOutside = kFALSE ; | |
536 | for (Int_t iVar=0; iVar<fNVariables; iVar++) { | |
537 | if (fCoordinatesN_T[iVar]<1 || fCoordinatesN_T[iVar]>numBins[iVar]) { | |
538 | isOutside=kTRUE; | |
539 | break; | |
540 | } | |
541 | } | |
542 | if (isOutside) continue; | |
543 | ||
544 | Int_t neighbours = 0; // number of neighbours to average with | |
545 | ||
546 | for (Int_t iVar=0; iVar<fNVariables; iVar++) { | |
547 | if (fCoordinatesN_T[iVar] > 1) { // must not be on low edge border | |
548 | fCoordinatesN_T[iVar]-- ; //get lower neighbouring bin | |
85b6bda9 | 549 | content += copy->GetBinContent(fCoordinatesN_T); |
550 | error2 += TMath::Power(copy->GetBinError(fCoordinatesN_T),2); | |
c0b10ad4 | 551 | neighbours++; |
552 | fCoordinatesN_T[iVar]++ ; //back to initial coordinate | |
553 | } | |
554 | if (fCoordinatesN_T[iVar] < numBins[iVar]) { // must not be on up edge border | |
555 | fCoordinatesN_T[iVar]++ ; //get upper neighbouring bin | |
85b6bda9 | 556 | content += copy->GetBinContent(fCoordinatesN_T); |
557 | error2 += TMath::Power(copy->GetBinError(fCoordinatesN_T),2); | |
c0b10ad4 | 558 | neighbours++; |
559 | fCoordinatesN_T[iVar]-- ; //back to initial coordinate | |
560 | } | |
561 | } | |
85b6bda9 | 562 | // make an average |
563 | fUnfolded->SetBinContent(fCoordinatesN_T,content/(1.+neighbours)); | |
564 | fUnfolded->SetBinError (fCoordinatesN_T,TMath::Sqrt(error2)/(1.+neighbours)); | |
c0b10ad4 | 565 | } |
566 | delete [] numBins; | |
567 | delete copy; | |
85b6bda9 | 568 | return 0; |
c0b10ad4 | 569 | } |
570 | ||
85b6bda9 | 571 | //______________________________________________________________ |
572 | ||
573 | Short_t AliCFUnfolding::SmoothUsingFunction() { | |
574 | // | |
575 | // Fits the unfolded spectrum using the function fSmoothFunction | |
576 | // | |
577 | ||
7bc6a013 | 578 | AliDebug(0,Form("Smooth function is a %s with option \"%s\" and has %d parameters : ",fSmoothFunction->ClassName(),fSmoothOption,fSmoothFunction->GetNpar())); |
85b6bda9 | 579 | |
7bc6a013 | 580 | for (Int_t iPar=0; iPar<fSmoothFunction->GetNpar(); iPar++) AliDebug(0,Form("par[%d]=%e",iPar,fSmoothFunction->GetParameter(iPar))); |
581 | ||
582 | Int_t fitResult = 0; | |
85b6bda9 | 583 | |
584 | switch (fNVariables) { | |
7bc6a013 | 585 | case 1 : fitResult = fUnfolded->Projection(0) ->Fit(fSmoothFunction,fSmoothOption); break; |
586 | case 2 : fitResult = fUnfolded->Projection(1,0) ->Fit(fSmoothFunction,fSmoothOption); break; // (1,0) instead of (0,1) -> TAxis issue | |
587 | case 3 : fitResult = fUnfolded->Projection(0,1,2)->Fit(fSmoothFunction,fSmoothOption); break; | |
588 | default: AliFatal(Form("Cannot handle such fit in %d dimensions",fNVariables)) ; return 1; | |
589 | } | |
590 | ||
591 | if (fitResult != 0) { | |
592 | AliWarning(Form("Fit failed with status %d, stopping the loop",fitResult)); | |
593 | return 1; | |
85b6bda9 | 594 | } |
595 | ||
596 | Int_t nDim = fNVariables; | |
597 | Int_t* bins = new Int_t[nDim]; // number of bins for each variable | |
598 | Long_t nBins = 1; // used to calculate the total number of bins in the THnSparse | |
599 | ||
600 | for (Int_t iVar=0; iVar<nDim; iVar++) { | |
601 | bins[iVar] = fUnfolded->GetAxis(iVar)->GetNbins(); | |
602 | nBins *= bins[iVar]; | |
603 | } | |
604 | ||
605 | Int_t *bin = new Int_t[nDim]; // bin to fill the THnSparse (holding the bin coordinates) | |
606 | Double_t x[3] = {0,0,0} ; // value in bin center (max dimension is 3 (TF3)) | |
607 | ||
608 | // loop on the bins and update of fUnfolded | |
609 | // THnSparse::Multiply(TF1*) doesn't exist, so let's do it bin by bin | |
610 | for (Long_t iBin=0; iBin<nBins; iBin++) { | |
611 | Long_t bin_tmp = iBin ; | |
612 | for (Int_t iVar=0; iVar<nDim; iVar++) { | |
613 | bin[iVar] = 1 + bin_tmp % bins[iVar] ; | |
614 | bin_tmp /= bins[iVar] ; | |
615 | x[iVar] = fUnfolded->GetAxis(iVar)->GetBinCenter(bin[iVar]); | |
616 | } | |
617 | Double_t functionValue = fSmoothFunction->Eval(x[0],x[1],x[2]) ; | |
618 | fUnfolded->SetBinContent(bin,functionValue); | |
619 | fUnfolded->SetBinError (bin,functionValue*fUnfolded->GetBinError(bin)); | |
620 | } | |
621 | return 0; | |
622 | } | |
c0b10ad4 | 623 | |
624 | //______________________________________________________________ | |
625 | ||
626 | void AliCFUnfolding::CreateFlatPrior() { | |
627 | // | |
628 | // Creates a flat prior distribution | |
629 | // | |
630 | ||
631 | AliInfo("Creating a flat a priori distribution"); | |
632 | ||
633 | // create the frame of the THnSparse given (for example) the one from the efficiency map | |
634 | fPrior = (THnSparse*) fEfficiency->Clone(); | |
635 | ||
85b6bda9 | 636 | if (fNVariables != fPrior->GetNdimensions()) |
637 | AliFatal(Form("The prior matrix should have %d dimensions, and it has actually %d",fNVariables,fPrior->GetNdimensions())); | |
638 | ||
c0b10ad4 | 639 | Int_t nDim = fNVariables; |
640 | Int_t* bins = new Int_t[nDim]; // number of bins for each variable | |
641 | Long_t nBins = 1; // used to calculate the total number of bins in the THnSparse | |
642 | ||
643 | for (Int_t iVar=0; iVar<nDim; iVar++) { | |
644 | bins[iVar] = fPrior->GetAxis(iVar)->GetNbins(); | |
645 | nBins *= bins[iVar]; | |
646 | } | |
647 | ||
648 | Int_t *bin = new Int_t[nDim]; // bin to fill the THnSparse (holding the bin coordinates) | |
649 | ||
650 | // loop that sets 1 in each bin | |
651 | for (Long_t iBin=0; iBin<nBins; iBin++) { | |
652 | Long_t bin_tmp = iBin ; | |
653 | for (Int_t iVar=0; iVar<nDim; iVar++) { | |
654 | bin[iVar] = 1 + bin_tmp % bins[iVar] ; | |
655 | bin_tmp /= bins[iVar] ; | |
656 | } | |
657 | fPrior->SetBinContent(bin,1.); // put 1 everywhere | |
85b6bda9 | 658 | fPrior->SetBinError (bin,0.); // put 0 everywhere |
c0b10ad4 | 659 | } |
660 | ||
661 | fOriginalPrior = (THnSparse*)fPrior->Clone(); | |
662 | ||
663 | delete [] bin; | |
664 | delete [] bins; | |
665 | } |