]>
Commit | Line | Data |
---|---|---|
21f3a443 | 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 | ||
16 | ||
17 | /////////////////////////////////////////////////////////////////////////// | |
18 | // Class TStatToolkit | |
19 | // | |
20 | // Subset of matheamtical functions not included in the TMath | |
21 | // | |
22 | ||
23 | /////////////////////////////////////////////////////////////////////////// | |
24 | #include "TMath.h" | |
25 | #include "Riostream.h" | |
26 | #include "TH1F.h" | |
27 | #include "TH3.h" | |
28 | #include "TF1.h" | |
29 | #include "TTree.h" | |
30 | #include "TChain.h" | |
31 | #include "TObjString.h" | |
32 | #include "TLinearFitter.h" | |
33 | ||
34 | // | |
35 | // includes neccessary for test functions | |
36 | // | |
37 | #include "TSystem.h" | |
38 | #include "TRandom.h" | |
39 | #include "TStopwatch.h" | |
40 | #include "TTreeStream.h" | |
41 | ||
42 | #include "TStatToolkit.h" | |
43 | ||
44 | ||
45 | ClassImp(TStatToolkit) // Class implementation to enable ROOT I/O | |
46 | ||
47 | TStatToolkit::TStatToolkit() : TObject() | |
48 | { | |
49 | // | |
50 | // Default constructor | |
51 | // | |
52 | } | |
53 | /////////////////////////////////////////////////////////////////////////// | |
54 | TStatToolkit::~TStatToolkit() | |
55 | { | |
56 | // | |
57 | // Destructor | |
58 | // | |
59 | } | |
60 | ||
61 | ||
62 | //_____________________________________________________________________________ | |
63 | void TStatToolkit::EvaluateUni(Int_t nvectors, Double_t *data, Double_t &mean | |
64 | , Double_t &sigma, Int_t hh) | |
65 | { | |
66 | // | |
67 | // Robust estimator in 1D case MI version - (faster than ROOT version) | |
68 | // | |
69 | // For the univariate case | |
70 | // estimates of location and scatter are returned in mean and sigma parameters | |
71 | // the algorithm works on the same principle as in multivariate case - | |
72 | // it finds a subset of size hh with smallest sigma, and then returns mean and | |
73 | // sigma of this subset | |
74 | // | |
75 | ||
76 | if (hh==0) | |
77 | hh=(nvectors+2)/2; | |
78 | Double_t faclts[]={2.6477,2.5092,2.3826,2.2662,2.1587,2.0589,1.9660,1.879,1.7973,1.7203,1.6473}; | |
79 | Int_t *index=new Int_t[nvectors]; | |
80 | TMath::Sort(nvectors, data, index, kFALSE); | |
81 | ||
82 | Int_t nquant = TMath::Min(Int_t(Double_t(((hh*1./nvectors)-0.5)*40))+1, 11); | |
83 | Double_t factor = faclts[TMath::Max(0,nquant-1)]; | |
84 | ||
85 | Double_t sumx =0; | |
86 | Double_t sumx2 =0; | |
87 | Int_t bestindex = -1; | |
88 | Double_t bestmean = 0; | |
89 | Double_t bestsigma = (data[index[nvectors-1]]-data[index[0]]+1.); // maximal possible sigma | |
90 | bestsigma *=bestsigma; | |
91 | ||
92 | for (Int_t i=0; i<hh; i++){ | |
93 | sumx += data[index[i]]; | |
94 | sumx2 += data[index[i]]*data[index[i]]; | |
95 | } | |
96 | ||
97 | Double_t norm = 1./Double_t(hh); | |
98 | Double_t norm2 = 1./Double_t(hh-1); | |
99 | for (Int_t i=hh; i<nvectors; i++){ | |
100 | Double_t cmean = sumx*norm; | |
101 | Double_t csigma = (sumx2 - hh*cmean*cmean)*norm2; | |
102 | if (csigma<bestsigma){ | |
103 | bestmean = cmean; | |
104 | bestsigma = csigma; | |
105 | bestindex = i-hh; | |
106 | } | |
107 | ||
108 | sumx += data[index[i]]-data[index[i-hh]]; | |
109 | sumx2 += data[index[i]]*data[index[i]]-data[index[i-hh]]*data[index[i-hh]]; | |
110 | } | |
111 | ||
112 | Double_t bstd=factor*TMath::Sqrt(TMath::Abs(bestsigma)); | |
113 | mean = bestmean; | |
114 | sigma = bstd; | |
115 | delete [] index; | |
116 | ||
117 | } | |
118 | ||
119 | ||
120 | ||
121 | void TStatToolkit::EvaluateUniExternal(Int_t nvectors, Double_t *data, Double_t &mean, Double_t &sigma, Int_t hh, Float_t externalfactor) | |
122 | { | |
123 | // Modified version of ROOT robust EvaluateUni | |
124 | // robust estimator in 1D case MI version | |
125 | // added external factor to include precision of external measurement | |
126 | // | |
127 | ||
128 | if (hh==0) | |
129 | hh=(nvectors+2)/2; | |
130 | Double_t faclts[]={2.6477,2.5092,2.3826,2.2662,2.1587,2.0589,1.9660,1.879,1.7973,1.7203,1.6473}; | |
131 | Int_t *index=new Int_t[nvectors]; | |
132 | TMath::Sort(nvectors, data, index, kFALSE); | |
133 | // | |
134 | Int_t nquant = TMath::Min(Int_t(Double_t(((hh*1./nvectors)-0.5)*40))+1, 11); | |
135 | Double_t factor = faclts[0]; | |
136 | if (nquant>0){ | |
137 | // fix proper normalization - Anja | |
138 | factor = faclts[nquant-1]; | |
139 | } | |
140 | ||
141 | // | |
142 | // | |
143 | Double_t sumx =0; | |
144 | Double_t sumx2 =0; | |
145 | Int_t bestindex = -1; | |
146 | Double_t bestmean = 0; | |
147 | Double_t bestsigma = -1; | |
148 | for (Int_t i=0; i<hh; i++){ | |
149 | sumx += data[index[i]]; | |
150 | sumx2 += data[index[i]]*data[index[i]]; | |
151 | } | |
152 | // | |
153 | Double_t kfactor = 2.*externalfactor - externalfactor*externalfactor; | |
154 | Double_t norm = 1./Double_t(hh); | |
155 | for (Int_t i=hh; i<nvectors; i++){ | |
156 | Double_t cmean = sumx*norm; | |
157 | Double_t csigma = (sumx2*norm - cmean*cmean*kfactor); | |
158 | if (csigma<bestsigma || bestsigma<0){ | |
159 | bestmean = cmean; | |
160 | bestsigma = csigma; | |
161 | bestindex = i-hh; | |
162 | } | |
163 | // | |
164 | // | |
165 | sumx += data[index[i]]-data[index[i-hh]]; | |
166 | sumx2 += data[index[i]]*data[index[i]]-data[index[i-hh]]*data[index[i-hh]]; | |
167 | } | |
168 | ||
169 | Double_t bstd=factor*TMath::Sqrt(TMath::Abs(bestsigma)); | |
170 | mean = bestmean; | |
171 | sigma = bstd; | |
172 | delete [] index; | |
173 | } | |
174 | ||
175 | ||
176 | //_____________________________________________________________________________ | |
177 | Int_t TStatToolkit::Freq(Int_t n, const Int_t *inlist | |
178 | , Int_t *outlist, Bool_t down) | |
179 | { | |
180 | // | |
181 | // Sort eleements according occurancy | |
182 | // The size of output array has is 2*n | |
183 | // | |
184 | ||
185 | Int_t * sindexS = new Int_t[n]; // temp array for sorting | |
186 | Int_t * sindexF = new Int_t[2*n]; | |
b8072cce | 187 | for (Int_t i=0;i<n;i++) sindexS[i]=0; |
188 | for (Int_t i=0;i<2*n;i++) sindexF[i]=0; | |
21f3a443 | 189 | // |
190 | TMath::Sort(n,inlist, sindexS, down); | |
191 | Int_t last = inlist[sindexS[0]]; | |
192 | Int_t val = last; | |
193 | sindexF[0] = 1; | |
194 | sindexF[0+n] = last; | |
195 | Int_t countPos = 0; | |
196 | // | |
197 | // find frequency | |
198 | for(Int_t i=1;i<n; i++){ | |
199 | val = inlist[sindexS[i]]; | |
200 | if (last == val) sindexF[countPos]++; | |
201 | else{ | |
202 | countPos++; | |
203 | sindexF[countPos+n] = val; | |
204 | sindexF[countPos]++; | |
205 | last =val; | |
206 | } | |
207 | } | |
208 | if (last==val) countPos++; | |
209 | // sort according frequency | |
210 | TMath::Sort(countPos, sindexF, sindexS, kTRUE); | |
211 | for (Int_t i=0;i<countPos;i++){ | |
212 | outlist[2*i ] = sindexF[sindexS[i]+n]; | |
213 | outlist[2*i+1] = sindexF[sindexS[i]]; | |
214 | } | |
215 | delete [] sindexS; | |
216 | delete [] sindexF; | |
217 | ||
218 | return countPos; | |
219 | ||
220 | } | |
221 | ||
222 | //___TStatToolkit__________________________________________________________________________ | |
223 | void TStatToolkit::TruncatedMean(TH1F * his, TVectorD *param, Float_t down, Float_t up, Bool_t verbose){ | |
224 | // | |
225 | // | |
226 | // | |
227 | Int_t nbins = his->GetNbinsX(); | |
228 | Float_t nentries = his->GetEntries(); | |
229 | Float_t sum =0; | |
230 | Float_t mean = 0; | |
231 | Float_t sigma2 = 0; | |
232 | Float_t ncumul=0; | |
233 | for (Int_t ibin=1;ibin<nbins; ibin++){ | |
234 | ncumul+= his->GetBinContent(ibin); | |
235 | Float_t fraction = Float_t(ncumul)/Float_t(nentries); | |
236 | if (fraction>down && fraction<up){ | |
237 | sum+=his->GetBinContent(ibin); | |
238 | mean+=his->GetBinCenter(ibin)*his->GetBinContent(ibin); | |
239 | sigma2+=his->GetBinCenter(ibin)*his->GetBinCenter(ibin)*his->GetBinContent(ibin); | |
240 | } | |
241 | } | |
242 | mean/=sum; | |
243 | sigma2= TMath::Sqrt(TMath::Abs(sigma2/sum-mean*mean)); | |
244 | if (param){ | |
245 | (*param)[0] = his->GetMaximum(); | |
246 | (*param)[1] = mean; | |
247 | (*param)[2] = sigma2; | |
248 | ||
249 | } | |
250 | if (verbose) printf("Mean\t%f\t Sigma2\t%f\n", mean,sigma2); | |
251 | } | |
252 | ||
253 | void TStatToolkit::LTM(TH1F * his, TVectorD *param , Float_t fraction, Bool_t verbose){ | |
254 | // | |
255 | // LTM | |
256 | // | |
257 | Int_t nbins = his->GetNbinsX(); | |
258 | Int_t nentries = (Int_t)his->GetEntries(); | |
259 | Double_t *data = new Double_t[nentries]; | |
260 | Int_t npoints=0; | |
261 | for (Int_t ibin=1;ibin<nbins; ibin++){ | |
262 | Float_t entriesI = his->GetBinContent(ibin); | |
263 | Float_t xcenter= his->GetBinCenter(ibin); | |
264 | for (Int_t ic=0; ic<entriesI; ic++){ | |
265 | if (npoints<nentries){ | |
266 | data[npoints]= xcenter; | |
267 | npoints++; | |
268 | } | |
269 | } | |
270 | } | |
271 | Double_t mean, sigma; | |
272 | Int_t npoints2=TMath::Min(Int_t(fraction*Float_t(npoints)),npoints-1); | |
273 | npoints2=TMath::Max(Int_t(0.5*Float_t(npoints)),npoints2); | |
274 | TStatToolkit::EvaluateUni(npoints, data, mean,sigma,npoints2); | |
275 | delete [] data; | |
276 | if (verbose) printf("Mean\t%f\t Sigma2\t%f\n", mean,sigma);if (param){ | |
277 | (*param)[0] = his->GetMaximum(); | |
278 | (*param)[1] = mean; | |
279 | (*param)[2] = sigma; | |
280 | } | |
281 | } | |
282 | ||
cb1d20de | 283 | Double_t TStatToolkit::FitGaus(TH1F* his, TVectorD *param, TMatrixD */*matrix*/, Float_t xmin, Float_t xmax, Bool_t verbose){ |
21f3a443 | 284 | // |
285 | // Fit histogram with gaussian function | |
286 | // | |
287 | // Prameters: | |
288 | // return value- chi2 - if negative ( not enough points) | |
289 | // his - input histogram | |
290 | // param - vector with parameters | |
291 | // xmin, xmax - range to fit - if xmin=xmax=0 - the full histogram range used | |
292 | // Fitting: | |
293 | // 1. Step - make logarithm | |
294 | // 2. Linear fit (parabola) - more robust - always converge | |
295 | // 3. In case of small statistic bins are averaged | |
296 | // | |
297 | static TLinearFitter fitter(3,"pol2"); | |
298 | TVectorD par(3); | |
299 | TVectorD sigma(3); | |
300 | TMatrixD mat(3,3); | |
301 | if (his->GetMaximum()<4) return -1; | |
302 | if (his->GetEntries()<12) return -1; | |
303 | if (his->GetRMS()<mat.GetTol()) return -1; | |
304 | Float_t maxEstimate = his->GetEntries()*his->GetBinWidth(1)/TMath::Sqrt((TMath::TwoPi()*his->GetRMS())); | |
305 | Int_t dsmooth = TMath::Nint(6./TMath::Sqrt(maxEstimate)); | |
306 | ||
307 | if (maxEstimate<1) return -1; | |
308 | Int_t nbins = his->GetNbinsX(); | |
309 | Int_t npoints=0; | |
310 | // | |
311 | ||
312 | ||
313 | if (xmin>=xmax){ | |
314 | xmin = his->GetXaxis()->GetXmin(); | |
315 | xmax = his->GetXaxis()->GetXmax(); | |
316 | } | |
317 | for (Int_t iter=0; iter<2; iter++){ | |
318 | fitter.ClearPoints(); | |
319 | npoints=0; | |
320 | for (Int_t ibin=1;ibin<nbins+1; ibin++){ | |
321 | Int_t countB=1; | |
322 | Float_t entriesI = his->GetBinContent(ibin); | |
323 | for (Int_t delta = -dsmooth; delta<=dsmooth; delta++){ | |
324 | if (ibin+delta>1 &&ibin+delta<nbins-1){ | |
325 | entriesI += his->GetBinContent(ibin+delta); | |
326 | countB++; | |
327 | } | |
328 | } | |
329 | entriesI/=countB; | |
330 | Double_t xcenter= his->GetBinCenter(ibin); | |
331 | if (xcenter<xmin || xcenter>xmax) continue; | |
332 | Double_t error=1./TMath::Sqrt(countB); | |
333 | Float_t cont=2; | |
334 | if (iter>0){ | |
335 | if (par[0]+par[1]*xcenter+par[2]*xcenter*xcenter>20) return 0; | |
336 | cont = TMath::Exp(par[0]+par[1]*xcenter+par[2]*xcenter*xcenter); | |
337 | if (cont>1.) error = 1./TMath::Sqrt(cont*Float_t(countB)); | |
338 | } | |
339 | if (entriesI>1&&cont>1){ | |
340 | fitter.AddPoint(&xcenter,TMath::Log(Float_t(entriesI)),error); | |
341 | npoints++; | |
342 | } | |
343 | } | |
344 | if (npoints>3){ | |
345 | fitter.Eval(); | |
346 | fitter.GetParameters(par); | |
347 | }else{ | |
348 | break; | |
349 | } | |
350 | } | |
351 | if (npoints<=3){ | |
352 | return -1; | |
353 | } | |
354 | fitter.GetParameters(par); | |
355 | fitter.GetCovarianceMatrix(mat); | |
356 | if (TMath::Abs(par[1])<mat.GetTol()) return -1; | |
357 | if (TMath::Abs(par[2])<mat.GetTol()) return -1; | |
358 | Double_t chi2 = fitter.GetChisquare()/Float_t(npoints); | |
359 | //fitter.GetParameters(); | |
360 | if (!param) param = new TVectorD(3); | |
cb1d20de | 361 | // if (!matrix) matrix = new TMatrixD(3,3); // Covariance matrix to be implemented |
21f3a443 | 362 | (*param)[1] = par[1]/(-2.*par[2]); |
363 | (*param)[2] = 1./TMath::Sqrt(TMath::Abs(-2.*par[2])); | |
364 | (*param)[0] = TMath::Exp(par[0]+ par[1]* (*param)[1] + par[2]*(*param)[1]*(*param)[1]); | |
365 | if (verbose){ | |
366 | par.Print(); | |
367 | mat.Print(); | |
368 | param->Print(); | |
369 | printf("Chi2=%f\n",chi2); | |
370 | TF1 * f1= new TF1("f1","[0]*exp(-(x-[1])^2/(2*[2]*[2]))",his->GetXaxis()->GetXmin(),his->GetXaxis()->GetXmax()); | |
371 | f1->SetParameter(0, (*param)[0]); | |
372 | f1->SetParameter(1, (*param)[1]); | |
373 | f1->SetParameter(2, (*param)[2]); | |
374 | f1->Draw("same"); | |
375 | } | |
376 | return chi2; | |
377 | } | |
378 | ||
cb1d20de | 379 | Double_t TStatToolkit::FitGaus(Float_t *arr, Int_t nBins, Float_t xMin, Float_t xMax, TVectorD *param, TMatrixD */*matrix*/, Bool_t verbose){ |
21f3a443 | 380 | // |
381 | // Fit histogram with gaussian function | |
382 | // | |
383 | // Prameters: | |
384 | // nbins: size of the array and number of histogram bins | |
385 | // xMin, xMax: histogram range | |
386 | // param: paramters of the fit (0-Constant, 1-Mean, 2-Sigma) | |
387 | // matrix: covariance matrix -- not implemented yet, pass dummy matrix!!! | |
388 | // | |
389 | // Return values: | |
390 | // >0: the chi2 returned by TLinearFitter | |
391 | // -3: only three points have been used for the calculation - no fitter was used | |
392 | // -2: only two points have been used for the calculation - center of gravity was uesed for calculation | |
393 | // -1: only one point has been used for the calculation - center of gravity was uesed for calculation | |
394 | // -4: invalid result!! | |
395 | // | |
396 | // Fitting: | |
397 | // 1. Step - make logarithm | |
398 | // 2. Linear fit (parabola) - more robust - always converge | |
399 | // | |
400 | static TLinearFitter fitter(3,"pol2"); | |
401 | static TMatrixD mat(3,3); | |
402 | static Double_t kTol = mat.GetTol(); | |
403 | fitter.StoreData(kFALSE); | |
404 | fitter.ClearPoints(); | |
405 | TVectorD par(3); | |
406 | TVectorD sigma(3); | |
407 | TMatrixD A(3,3); | |
408 | TMatrixD b(3,1); | |
409 | Float_t rms = TMath::RMS(nBins,arr); | |
410 | Float_t max = TMath::MaxElement(nBins,arr); | |
411 | Float_t binWidth = (xMax-xMin)/(Float_t)nBins; | |
412 | ||
413 | Float_t meanCOG = 0; | |
414 | Float_t rms2COG = 0; | |
415 | Float_t sumCOG = 0; | |
416 | ||
417 | Float_t entries = 0; | |
418 | Int_t nfilled=0; | |
419 | ||
420 | for (Int_t i=0; i<nBins; i++){ | |
421 | entries+=arr[i]; | |
422 | if (arr[i]>0) nfilled++; | |
423 | } | |
424 | ||
425 | if (max<4) return -4; | |
426 | if (entries<12) return -4; | |
427 | if (rms<kTol) return -4; | |
428 | ||
429 | Int_t npoints=0; | |
430 | // | |
431 | ||
432 | // | |
433 | for (Int_t ibin=0;ibin<nBins; ibin++){ | |
434 | Float_t entriesI = arr[ibin]; | |
435 | if (entriesI>1){ | |
436 | Double_t xcenter = xMin+(ibin+0.5)*binWidth; | |
437 | ||
438 | Float_t error = 1./TMath::Sqrt(entriesI); | |
439 | Float_t val = TMath::Log(Float_t(entriesI)); | |
440 | fitter.AddPoint(&xcenter,val,error); | |
441 | if (npoints<3){ | |
442 | A(npoints,0)=1; | |
443 | A(npoints,1)=xcenter; | |
444 | A(npoints,2)=xcenter*xcenter; | |
445 | b(npoints,0)=val; | |
446 | meanCOG+=xcenter*entriesI; | |
447 | rms2COG +=xcenter*entriesI*xcenter; | |
448 | sumCOG +=entriesI; | |
449 | } | |
450 | npoints++; | |
451 | } | |
452 | } | |
453 | ||
454 | ||
455 | Double_t chi2 = 0; | |
456 | if (npoints>=3){ | |
457 | if ( npoints == 3 ){ | |
458 | //analytic calculation of the parameters for three points | |
459 | A.Invert(); | |
460 | TMatrixD res(1,3); | |
461 | res.Mult(A,b); | |
462 | par[0]=res(0,0); | |
463 | par[1]=res(0,1); | |
464 | par[2]=res(0,2); | |
465 | chi2 = -3.; | |
466 | } else { | |
467 | // use fitter for more than three points | |
468 | fitter.Eval(); | |
469 | fitter.GetParameters(par); | |
470 | fitter.GetCovarianceMatrix(mat); | |
471 | chi2 = fitter.GetChisquare()/Float_t(npoints); | |
472 | } | |
473 | if (TMath::Abs(par[1])<kTol) return -4; | |
474 | if (TMath::Abs(par[2])<kTol) return -4; | |
475 | ||
476 | if (!param) param = new TVectorD(3); | |
cb1d20de | 477 | //if (!matrix) matrix = new TMatrixD(3,3); // !!!!might be a memory leek. use dummy matrix pointer to call this function! // Covariance matrix to be implemented |
21f3a443 | 478 | |
479 | (*param)[1] = par[1]/(-2.*par[2]); | |
480 | (*param)[2] = 1./TMath::Sqrt(TMath::Abs(-2.*par[2])); | |
481 | Double_t lnparam0 = par[0]+ par[1]* (*param)[1] + par[2]*(*param)[1]*(*param)[1]; | |
482 | if ( lnparam0>307 ) return -4; | |
483 | (*param)[0] = TMath::Exp(lnparam0); | |
484 | if (verbose){ | |
485 | par.Print(); | |
486 | mat.Print(); | |
487 | param->Print(); | |
488 | printf("Chi2=%f\n",chi2); | |
489 | TF1 * f1= new TF1("f1","[0]*exp(-(x-[1])^2/(2*[2]*[2]))",xMin,xMax); | |
490 | f1->SetParameter(0, (*param)[0]); | |
491 | f1->SetParameter(1, (*param)[1]); | |
492 | f1->SetParameter(2, (*param)[2]); | |
493 | f1->Draw("same"); | |
494 | } | |
495 | return chi2; | |
496 | } | |
497 | ||
498 | if (npoints == 2){ | |
499 | //use center of gravity for 2 points | |
500 | meanCOG/=sumCOG; | |
501 | rms2COG /=sumCOG; | |
502 | (*param)[0] = max; | |
503 | (*param)[1] = meanCOG; | |
504 | (*param)[2] = TMath::Sqrt(TMath::Abs(meanCOG*meanCOG-rms2COG)); | |
505 | chi2=-2.; | |
506 | } | |
507 | if ( npoints == 1 ){ | |
508 | meanCOG/=sumCOG; | |
509 | (*param)[0] = max; | |
510 | (*param)[1] = meanCOG; | |
511 | (*param)[2] = binWidth/TMath::Sqrt(12); | |
512 | chi2=-1.; | |
513 | } | |
514 | return chi2; | |
515 | ||
516 | } | |
517 | ||
518 | ||
519 | Float_t TStatToolkit::GetCOG(Short_t *arr, Int_t nBins, Float_t xMin, Float_t xMax, Float_t *rms, Float_t *sum) | |
520 | { | |
521 | // | |
522 | // calculate center of gravity rms and sum for array 'arr' with nBins an a x range xMin to xMax | |
523 | // return COG; in case of failure return xMin | |
524 | // | |
525 | Float_t meanCOG = 0; | |
526 | Float_t rms2COG = 0; | |
527 | Float_t sumCOG = 0; | |
528 | Int_t npoints = 0; | |
529 | ||
530 | Float_t binWidth = (xMax-xMin)/(Float_t)nBins; | |
531 | ||
532 | for (Int_t ibin=0; ibin<nBins; ibin++){ | |
533 | Float_t entriesI = (Float_t)arr[ibin]; | |
534 | Double_t xcenter = xMin+(ibin+0.5)*binWidth; | |
535 | if ( entriesI>0 ){ | |
536 | meanCOG += xcenter*entriesI; | |
537 | rms2COG += xcenter*entriesI*xcenter; | |
538 | sumCOG += entriesI; | |
539 | npoints++; | |
540 | } | |
541 | } | |
542 | if ( sumCOG == 0 ) return xMin; | |
543 | meanCOG/=sumCOG; | |
544 | ||
545 | if ( rms ){ | |
546 | rms2COG /=sumCOG; | |
547 | (*rms) = TMath::Sqrt(TMath::Abs(meanCOG*meanCOG-rms2COG)); | |
548 | if ( npoints == 1 ) (*rms) = binWidth/TMath::Sqrt(12); | |
549 | } | |
550 | ||
551 | if ( sum ) | |
552 | (*sum) = sumCOG; | |
553 | ||
554 | return meanCOG; | |
555 | } | |
556 | ||
557 | ||
558 | ||
559 | /////////////////////////////////////////////////////////////// | |
560 | ////////////// TEST functions ///////////////////////// | |
561 | /////////////////////////////////////////////////////////////// | |
562 | ||
563 | ||
564 | ||
565 | ||
566 | ||
567 | void TStatToolkit::TestGausFit(Int_t nhistos){ | |
568 | // | |
569 | // Test performance of the parabolic - gaussian fit - compare it with | |
570 | // ROOT gauss fit | |
571 | // nhistos - number of histograms to be used for test | |
572 | // | |
573 | TTreeSRedirector *pcstream = new TTreeSRedirector("fitdebug.root"); | |
574 | ||
575 | Float_t *xTrue = new Float_t[nhistos]; | |
576 | Float_t *sTrue = new Float_t[nhistos]; | |
577 | TVectorD **par1 = new TVectorD*[nhistos]; | |
578 | TVectorD **par2 = new TVectorD*[nhistos]; | |
579 | TMatrixD dummy(3,3); | |
580 | ||
581 | ||
582 | TH1F **h1f = new TH1F*[nhistos]; | |
583 | TF1 *myg = new TF1("myg","gaus"); | |
584 | TF1 *fit = new TF1("fit","gaus"); | |
585 | gRandom->SetSeed(0); | |
586 | ||
587 | //init | |
588 | for (Int_t i=0;i<nhistos; i++){ | |
589 | par1[i] = new TVectorD(3); | |
590 | par2[i] = new TVectorD(3); | |
591 | h1f[i] = new TH1F(Form("h1f%d",i),Form("h1f%d",i),20,-10,10); | |
592 | xTrue[i]= gRandom->Rndm(); | |
593 | gSystem->Sleep(2); | |
594 | sTrue[i]= .75+gRandom->Rndm()*.5; | |
595 | myg->SetParameters(1,xTrue[i],sTrue[i]); | |
596 | h1f[i]->FillRandom("myg"); | |
597 | } | |
598 | ||
599 | TStopwatch s; | |
600 | s.Start(); | |
601 | //standard gaus fit | |
602 | for (Int_t i=0; i<nhistos; i++){ | |
603 | h1f[i]->Fit(fit,"0q"); | |
604 | (*par1[i])(0) = fit->GetParameter(0); | |
605 | (*par1[i])(1) = fit->GetParameter(1); | |
606 | (*par1[i])(2) = fit->GetParameter(2); | |
607 | } | |
608 | s.Stop(); | |
609 | printf("Gaussian fit\t"); | |
610 | s.Print(); | |
611 | ||
612 | s.Start(); | |
613 | //TStatToolkit gaus fit | |
614 | for (Int_t i=0; i<nhistos; i++){ | |
615 | TStatToolkit::FitGaus(h1f[i]->GetArray()+1,h1f[i]->GetNbinsX(),h1f[i]->GetXaxis()->GetXmin(),h1f[i]->GetXaxis()->GetXmax(),par2[i],&dummy); | |
616 | } | |
617 | ||
618 | s.Stop(); | |
619 | printf("Parabolic fit\t"); | |
620 | s.Print(); | |
621 | //write stream | |
622 | for (Int_t i=0;i<nhistos; i++){ | |
623 | Float_t xt = xTrue[i]; | |
624 | Float_t st = sTrue[i]; | |
625 | (*pcstream)<<"data" | |
626 | <<"xTrue="<<xt | |
627 | <<"sTrue="<<st | |
628 | <<"pg.="<<(par1[i]) | |
629 | <<"pa.="<<(par2[i]) | |
630 | <<"\n"; | |
631 | } | |
632 | //delete pointers | |
633 | for (Int_t i=0;i<nhistos; i++){ | |
634 | delete par1[i]; | |
635 | delete par2[i]; | |
636 | delete h1f[i]; | |
637 | } | |
638 | delete pcstream; | |
639 | delete []h1f; | |
640 | delete []xTrue; | |
641 | delete []sTrue; | |
642 | // | |
643 | delete []par1; | |
644 | delete []par2; | |
645 | ||
646 | } | |
647 | ||
648 | ||
649 | ||
650 | TGraph2D * TStatToolkit::MakeStat2D(TH3 * his, Int_t delta0, Int_t delta1, Int_t type){ | |
651 | // | |
652 | // | |
653 | // | |
654 | // delta - number of bins to integrate | |
655 | // type - 0 - mean value | |
656 | ||
657 | TAxis * xaxis = his->GetXaxis(); | |
658 | TAxis * yaxis = his->GetYaxis(); | |
659 | // TAxis * zaxis = his->GetZaxis(); | |
660 | Int_t nbinx = xaxis->GetNbins(); | |
661 | Int_t nbiny = yaxis->GetNbins(); | |
662 | char name[1000]; | |
663 | Int_t icount=0; | |
664 | TGraph2D *graph = new TGraph2D(nbinx*nbiny); | |
665 | TF1 f1("f1","gaus"); | |
666 | for (Int_t ix=0; ix<nbinx;ix++) | |
667 | for (Int_t iy=0; iy<nbiny;iy++){ | |
668 | Float_t xcenter = xaxis->GetBinCenter(ix); | |
669 | Float_t ycenter = yaxis->GetBinCenter(iy); | |
cb1d20de | 670 | snprintf(name,1000,"%s_%d_%d",his->GetName(), ix,iy); |
21f3a443 | 671 | TH1 *projection = his->ProjectionZ(name,ix-delta0,ix+delta0,iy-delta1,iy+delta1); |
672 | Float_t stat= 0; | |
673 | if (type==0) stat = projection->GetMean(); | |
674 | if (type==1) stat = projection->GetRMS(); | |
675 | if (type==2 || type==3){ | |
676 | TVectorD vec(3); | |
677 | TStatToolkit::LTM((TH1F*)projection,&vec,0.7); | |
678 | if (type==2) stat= vec[1]; | |
679 | if (type==3) stat= vec[0]; | |
680 | } | |
681 | if (type==4|| type==5){ | |
682 | projection->Fit(&f1); | |
683 | if (type==4) stat= f1.GetParameter(1); | |
684 | if (type==5) stat= f1.GetParameter(2); | |
685 | } | |
686 | //printf("%d\t%f\t%f\t%f\n", icount,xcenter, ycenter, stat); | |
687 | graph->SetPoint(icount,xcenter, ycenter, stat); | |
688 | icount++; | |
689 | } | |
690 | return graph; | |
691 | } | |
692 | ||
693 | TGraph * TStatToolkit::MakeStat1D(TH3 * his, Int_t delta1, Int_t type){ | |
694 | // | |
695 | // | |
696 | // | |
697 | // delta - number of bins to integrate | |
698 | // type - 0 - mean value | |
699 | ||
700 | TAxis * xaxis = his->GetXaxis(); | |
701 | TAxis * yaxis = his->GetYaxis(); | |
702 | // TAxis * zaxis = his->GetZaxis(); | |
703 | Int_t nbinx = xaxis->GetNbins(); | |
704 | Int_t nbiny = yaxis->GetNbins(); | |
705 | char name[1000]; | |
706 | Int_t icount=0; | |
707 | TGraph *graph = new TGraph(nbinx); | |
708 | TF1 f1("f1","gaus"); | |
709 | for (Int_t ix=0; ix<nbinx;ix++){ | |
710 | Float_t xcenter = xaxis->GetBinCenter(ix); | |
711 | // Float_t ycenter = yaxis->GetBinCenter(iy); | |
cb1d20de | 712 | snprintf(name,1000,"%s_%d",his->GetName(), ix); |
21f3a443 | 713 | TH1 *projection = his->ProjectionZ(name,ix-delta1,ix+delta1,0,nbiny); |
714 | Float_t stat= 0; | |
715 | if (type==0) stat = projection->GetMean(); | |
716 | if (type==1) stat = projection->GetRMS(); | |
717 | if (type==2 || type==3){ | |
718 | TVectorD vec(3); | |
719 | TStatToolkit::LTM((TH1F*)projection,&vec,0.7); | |
720 | if (type==2) stat= vec[1]; | |
721 | if (type==3) stat= vec[0]; | |
722 | } | |
723 | if (type==4|| type==5){ | |
724 | projection->Fit(&f1); | |
725 | if (type==4) stat= f1.GetParameter(1); | |
726 | if (type==5) stat= f1.GetParameter(2); | |
727 | } | |
728 | //printf("%d\t%f\t%f\t%f\n", icount,xcenter, ycenter, stat); | |
729 | graph->SetPoint(icount,xcenter, stat); | |
730 | icount++; | |
731 | } | |
732 | return graph; | |
733 | } | |
734 | ||
735 | ||
736 | ||
737 | ||
738 | ||
88b1c775 | 739 | TString* TStatToolkit::FitPlane(TTree *tree, const char* drawCommand, const char* formula, const char* cuts, Double_t & chi2, Int_t &npoints, TVectorD &fitParam, TMatrixD &covMatrix, Float_t frac, Int_t start, Int_t stop,Bool_t fix0){ |
21f3a443 | 740 | // |
741 | // fit an arbitrary function, specified by formula into the data, specified by drawCommand and cuts | |
742 | // returns chi2, fitParam and covMatrix | |
743 | // returns TString with fitted formula | |
744 | // | |
dd46129c | 745 | |
21f3a443 | 746 | TString formulaStr(formula); |
747 | TString drawStr(drawCommand); | |
748 | TString cutStr(cuts); | |
dd46129c | 749 | TString ferr("1"); |
750 | ||
751 | TString strVal(drawCommand); | |
752 | if (strVal.Contains(":")){ | |
753 | TObjArray* valTokens = strVal.Tokenize(":"); | |
754 | drawStr = valTokens->At(0)->GetName(); | |
755 | ferr = valTokens->At(1)->GetName(); | |
756 | } | |
757 | ||
21f3a443 | 758 | |
759 | formulaStr.ReplaceAll("++", "~"); | |
760 | TObjArray* formulaTokens = formulaStr.Tokenize("~"); | |
761 | Int_t dim = formulaTokens->GetEntriesFast(); | |
762 | ||
763 | fitParam.ResizeTo(dim); | |
764 | covMatrix.ResizeTo(dim,dim); | |
765 | ||
766 | TLinearFitter* fitter = new TLinearFitter(dim+1, Form("hyp%d",dim)); | |
767 | fitter->StoreData(kTRUE); | |
768 | fitter->ClearPoints(); | |
769 | ||
770 | Int_t entries = tree->Draw(drawStr.Data(), cutStr.Data(), "goff", stop-start, start); | |
771 | if (entries == -1) return new TString("An ERROR has occured during fitting!"); | |
772 | Double_t **values = new Double_t*[dim+1] ; | |
dd46129c | 773 | // |
774 | entries = tree->Draw(ferr.Data(), cutStr.Data(), "goff", stop-start, start); | |
b8072cce | 775 | if (entries == -1) { |
776 | delete []values; | |
777 | return new TString("An ERROR has occured during fitting!"); | |
778 | } | |
dd46129c | 779 | Double_t *errors = new Double_t[entries]; |
780 | memcpy(errors, tree->GetV1(), entries*sizeof(Double_t)); | |
21f3a443 | 781 | |
782 | for (Int_t i = 0; i < dim + 1; i++){ | |
783 | Int_t centries = 0; | |
784 | if (i < dim) centries = tree->Draw(((TObjString*)formulaTokens->At(i))->GetName(), cutStr.Data(), "goff", stop-start,start); | |
785 | else centries = tree->Draw(drawStr.Data(), cutStr.Data(), "goff", stop-start,start); | |
786 | ||
b8072cce | 787 | if (entries != centries) { |
788 | delete []errors; | |
789 | delete []values; | |
790 | return new TString("An ERROR has occured during fitting!"); | |
791 | } | |
21f3a443 | 792 | values[i] = new Double_t[entries]; |
793 | memcpy(values[i], tree->GetV1(), entries*sizeof(Double_t)); | |
794 | } | |
795 | ||
796 | // add points to the fitter | |
797 | for (Int_t i = 0; i < entries; i++){ | |
798 | Double_t x[1000]; | |
799 | for (Int_t j=0; j<dim;j++) x[j]=values[j][i]; | |
dd46129c | 800 | fitter->AddPoint(x, values[dim][i], errors[i]); |
21f3a443 | 801 | } |
802 | ||
803 | fitter->Eval(); | |
2c629c56 | 804 | if (frac>0.5 && frac<1){ |
805 | fitter->EvalRobust(frac); | |
88b1c775 | 806 | }else{ |
807 | if (fix0) { | |
808 | fitter->FixParameter(0,0); | |
809 | fitter->Eval(); | |
810 | } | |
2c629c56 | 811 | } |
21f3a443 | 812 | fitter->GetParameters(fitParam); |
813 | fitter->GetCovarianceMatrix(covMatrix); | |
814 | chi2 = fitter->GetChisquare(); | |
b8072cce | 815 | npoints = entries; |
21f3a443 | 816 | TString *preturnFormula = new TString(Form("( %f+",fitParam[0])), &returnFormula = *preturnFormula; |
817 | ||
818 | for (Int_t iparam = 0; iparam < dim; iparam++) { | |
819 | returnFormula.Append(Form("%s*(%f)",((TObjString*)formulaTokens->At(iparam))->GetName(),fitParam[iparam+1])); | |
820 | if (iparam < dim-1) returnFormula.Append("+"); | |
821 | } | |
822 | returnFormula.Append(" )"); | |
4d61c301 | 823 | |
824 | ||
b8072cce | 825 | for (Int_t j=0; j<dim+1;j++) delete [] values[j]; |
4d61c301 | 826 | |
827 | ||
cb1d20de | 828 | delete formulaTokens; |
829 | delete fitter; | |
830 | delete[] values; | |
b8072cce | 831 | delete[] errors; |
cb1d20de | 832 | return preturnFormula; |
833 | } | |
834 | ||
835 | TString* TStatToolkit::FitPlaneConstrain(TTree *tree, const char* drawCommand, const char* formula, const char* cuts, Double_t & chi2, Int_t &npoints, TVectorD &fitParam, TMatrixD &covMatrix, Float_t frac, Int_t start, Int_t stop,Double_t constrain){ | |
836 | // | |
837 | // fit an arbitrary function, specified by formula into the data, specified by drawCommand and cuts | |
838 | // returns chi2, fitParam and covMatrix | |
839 | // returns TString with fitted formula | |
840 | // | |
841 | ||
842 | TString formulaStr(formula); | |
843 | TString drawStr(drawCommand); | |
844 | TString cutStr(cuts); | |
845 | TString ferr("1"); | |
846 | ||
847 | TString strVal(drawCommand); | |
848 | if (strVal.Contains(":")){ | |
849 | TObjArray* valTokens = strVal.Tokenize(":"); | |
850 | drawStr = valTokens->At(0)->GetName(); | |
851 | ferr = valTokens->At(1)->GetName(); | |
852 | } | |
853 | ||
854 | ||
855 | formulaStr.ReplaceAll("++", "~"); | |
856 | TObjArray* formulaTokens = formulaStr.Tokenize("~"); | |
857 | Int_t dim = formulaTokens->GetEntriesFast(); | |
858 | ||
859 | fitParam.ResizeTo(dim); | |
860 | covMatrix.ResizeTo(dim,dim); | |
861 | ||
862 | TLinearFitter* fitter = new TLinearFitter(dim+1, Form("hyp%d",dim)); | |
863 | fitter->StoreData(kTRUE); | |
864 | fitter->ClearPoints(); | |
865 | ||
866 | Int_t entries = tree->Draw(drawStr.Data(), cutStr.Data(), "goff", stop-start, start); | |
867 | if (entries == -1) return new TString("An ERROR has occured during fitting!"); | |
868 | Double_t **values = new Double_t*[dim+1] ; | |
869 | // | |
870 | entries = tree->Draw(ferr.Data(), cutStr.Data(), "goff", stop-start, start); | |
b8072cce | 871 | if (entries == -1) { |
872 | delete [] values; | |
873 | return new TString("An ERROR has occured during fitting!"); | |
874 | } | |
cb1d20de | 875 | Double_t *errors = new Double_t[entries]; |
876 | memcpy(errors, tree->GetV1(), entries*sizeof(Double_t)); | |
877 | ||
878 | for (Int_t i = 0; i < dim + 1; i++){ | |
879 | Int_t centries = 0; | |
880 | if (i < dim) centries = tree->Draw(((TObjString*)formulaTokens->At(i))->GetName(), cutStr.Data(), "goff", stop-start,start); | |
881 | else centries = tree->Draw(drawStr.Data(), cutStr.Data(), "goff", stop-start,start); | |
882 | ||
b8072cce | 883 | if (entries != centries) { |
884 | delete []errors; | |
885 | delete []values; | |
886 | return new TString("An ERROR has occured during fitting!"); | |
887 | } | |
cb1d20de | 888 | values[i] = new Double_t[entries]; |
889 | memcpy(values[i], tree->GetV1(), entries*sizeof(Double_t)); | |
890 | } | |
891 | ||
892 | // add points to the fitter | |
893 | for (Int_t i = 0; i < entries; i++){ | |
894 | Double_t x[1000]; | |
895 | for (Int_t j=0; j<dim;j++) x[j]=values[j][i]; | |
896 | fitter->AddPoint(x, values[dim][i], errors[i]); | |
897 | } | |
898 | if (constrain>0){ | |
899 | for (Int_t i = 0; i < dim; i++){ | |
900 | Double_t x[1000]; | |
901 | for (Int_t j=0; j<dim;j++) if (i!=j) x[j]=0; | |
902 | x[i]=1.; | |
903 | fitter->AddPoint(x, 0, constrain); | |
904 | } | |
905 | } | |
906 | ||
907 | ||
908 | fitter->Eval(); | |
909 | if (frac>0.5 && frac<1){ | |
910 | fitter->EvalRobust(frac); | |
911 | } | |
912 | fitter->GetParameters(fitParam); | |
913 | fitter->GetCovarianceMatrix(covMatrix); | |
914 | chi2 = fitter->GetChisquare(); | |
915 | npoints = entries; | |
cb1d20de | 916 | |
917 | TString *preturnFormula = new TString(Form("( %f+",fitParam[0])), &returnFormula = *preturnFormula; | |
918 | ||
919 | for (Int_t iparam = 0; iparam < dim; iparam++) { | |
920 | returnFormula.Append(Form("%s*(%f)",((TObjString*)formulaTokens->At(iparam))->GetName(),fitParam[iparam+1])); | |
921 | if (iparam < dim-1) returnFormula.Append("+"); | |
922 | } | |
923 | returnFormula.Append(" )"); | |
924 | ||
b8072cce | 925 | for (Int_t j=0; j<dim+1;j++) delete [] values[j]; |
cb1d20de | 926 | |
927 | ||
928 | ||
929 | delete formulaTokens; | |
930 | delete fitter; | |
931 | delete[] values; | |
b8072cce | 932 | delete[] errors; |
cb1d20de | 933 | return preturnFormula; |
934 | } | |
935 | ||
936 | ||
937 | ||
938 | TString* TStatToolkit::FitPlaneFixed(TTree *tree, const char* drawCommand, const char* formula, const char* cuts, Double_t & chi2, Int_t &npoints, TVectorD &fitParam, TMatrixD &covMatrix, Float_t frac, Int_t start, Int_t stop){ | |
939 | // | |
940 | // fit an arbitrary function, specified by formula into the data, specified by drawCommand and cuts | |
941 | // returns chi2, fitParam and covMatrix | |
942 | // returns TString with fitted formula | |
943 | // | |
944 | ||
945 | TString formulaStr(formula); | |
946 | TString drawStr(drawCommand); | |
947 | TString cutStr(cuts); | |
948 | TString ferr("1"); | |
949 | ||
950 | TString strVal(drawCommand); | |
951 | if (strVal.Contains(":")){ | |
952 | TObjArray* valTokens = strVal.Tokenize(":"); | |
953 | drawStr = valTokens->At(0)->GetName(); | |
954 | ferr = valTokens->At(1)->GetName(); | |
955 | } | |
956 | ||
957 | ||
958 | formulaStr.ReplaceAll("++", "~"); | |
959 | TObjArray* formulaTokens = formulaStr.Tokenize("~"); | |
960 | Int_t dim = formulaTokens->GetEntriesFast(); | |
961 | ||
962 | fitParam.ResizeTo(dim); | |
963 | covMatrix.ResizeTo(dim,dim); | |
964 | TString fitString="x0"; | |
965 | for (Int_t i=1; i<dim; i++) fitString+=Form("++x%d",i); | |
966 | TLinearFitter* fitter = new TLinearFitter(dim, fitString.Data()); | |
967 | fitter->StoreData(kTRUE); | |
968 | fitter->ClearPoints(); | |
969 | ||
970 | Int_t entries = tree->Draw(drawStr.Data(), cutStr.Data(), "goff", stop-start, start); | |
971 | if (entries == -1) return new TString("An ERROR has occured during fitting!"); | |
972 | Double_t **values = new Double_t*[dim+1] ; | |
973 | // | |
974 | entries = tree->Draw(ferr.Data(), cutStr.Data(), "goff", stop-start, start); | |
b8072cce | 975 | if (entries == -1) { |
976 | delete []values; | |
977 | return new TString("An ERROR has occured during fitting!"); | |
978 | } | |
cb1d20de | 979 | Double_t *errors = new Double_t[entries]; |
980 | memcpy(errors, tree->GetV1(), entries*sizeof(Double_t)); | |
981 | ||
982 | for (Int_t i = 0; i < dim + 1; i++){ | |
983 | Int_t centries = 0; | |
984 | if (i < dim) centries = tree->Draw(((TObjString*)formulaTokens->At(i))->GetName(), cutStr.Data(), "goff", stop-start,start); | |
985 | else centries = tree->Draw(drawStr.Data(), cutStr.Data(), "goff", stop-start,start); | |
986 | ||
b8072cce | 987 | if (entries != centries) { |
988 | delete []errors; | |
989 | delete []values; | |
990 | return new TString("An ERROR has occured during fitting!"); | |
991 | } | |
cb1d20de | 992 | values[i] = new Double_t[entries]; |
993 | memcpy(values[i], tree->GetV1(), entries*sizeof(Double_t)); | |
994 | } | |
995 | ||
996 | // add points to the fitter | |
997 | for (Int_t i = 0; i < entries; i++){ | |
998 | Double_t x[1000]; | |
999 | for (Int_t j=0; j<dim;j++) x[j]=values[j][i]; | |
1000 | fitter->AddPoint(x, values[dim][i], errors[i]); | |
1001 | } | |
1002 | ||
1003 | fitter->Eval(); | |
1004 | if (frac>0.5 && frac<1){ | |
1005 | fitter->EvalRobust(frac); | |
1006 | } | |
1007 | fitter->GetParameters(fitParam); | |
1008 | fitter->GetCovarianceMatrix(covMatrix); | |
1009 | chi2 = fitter->GetChisquare(); | |
1010 | npoints = entries; | |
cb1d20de | 1011 | |
1012 | TString *preturnFormula = new TString("("), &returnFormula = *preturnFormula; | |
1013 | ||
1014 | for (Int_t iparam = 0; iparam < dim; iparam++) { | |
1015 | returnFormula.Append(Form("%s*(%f)",((TObjString*)formulaTokens->At(iparam))->GetName(),fitParam[iparam])); | |
1016 | if (iparam < dim-1) returnFormula.Append("+"); | |
1017 | } | |
1018 | returnFormula.Append(" )"); | |
1019 | ||
1020 | ||
b8072cce | 1021 | for (Int_t j=0; j<dim+1;j++) delete [] values[j]; |
cb1d20de | 1022 | |
21f3a443 | 1023 | delete formulaTokens; |
1024 | delete fitter; | |
1025 | delete[] values; | |
b8072cce | 1026 | delete[] errors; |
21f3a443 | 1027 | return preturnFormula; |
1028 | } | |
7c9cf6e4 | 1029 | |
1030 | ||
1031 | ||
1032 | ||
1033 | ||
1034 | Int_t TStatToolkit::GetFitIndex(TString fString, TString subString){ | |
1035 | // | |
1036 | // fitString - ++ separated list of fits | |
1037 | // substring - ++ separated list of the requiered substrings | |
1038 | // | |
1039 | // return the last occurance of substring in fit string | |
1040 | // | |
1041 | TObjArray *arrFit = fString.Tokenize("++"); | |
1042 | TObjArray *arrSub = subString.Tokenize("++"); | |
1043 | Int_t index=-1; | |
1044 | for (Int_t i=0; i<arrFit->GetEntries(); i++){ | |
1045 | Bool_t isOK=kTRUE; | |
1046 | TString str =arrFit->At(i)->GetName(); | |
1047 | for (Int_t isub=0; isub<arrSub->GetEntries(); isub++){ | |
1048 | if (str.Contains(arrSub->At(isub)->GetName())==0) isOK=kFALSE; | |
1049 | } | |
1050 | if (isOK) index=i; | |
1051 | } | |
1052 | return index; | |
1053 | } | |
1054 | ||
1055 | ||
1056 | TString TStatToolkit::FilterFit(TString &input, TString filter, TVectorD ¶m, TMatrixD & covar){ | |
1057 | // | |
1058 | // Filter fit expression make sub-fit | |
1059 | // | |
1060 | TObjArray *array0= input.Tokenize("++"); | |
1061 | TObjArray *array1= filter.Tokenize("++"); | |
1062 | //TString *presult=new TString("(0"); | |
1063 | TString result="(0.0"; | |
1064 | for (Int_t i=0; i<array0->GetEntries(); i++){ | |
1065 | Bool_t isOK=kTRUE; | |
1066 | TString str(array0->At(i)->GetName()); | |
1067 | for (Int_t j=0; j<array1->GetEntries(); j++){ | |
1068 | if (str.Contains(array1->At(j)->GetName())==0) isOK=kFALSE; | |
1069 | } | |
1070 | if (isOK) { | |
1071 | result+="+"+str; | |
1072 | result+=Form("*(%f)",param[i+1]); | |
1073 | printf("%f\t%f\t%s\n",param[i+1], TMath::Sqrt(covar(i+1,i+1)),str.Data()); | |
1074 | } | |
1075 | } | |
1076 | result+="-0.)"; | |
1077 | return result; | |
1078 | } | |
1079 | ||
1080 | void TStatToolkit::Update1D(Double_t delta, Double_t sigma, Int_t s1, TMatrixD &vecXk, TMatrixD &covXk){ | |
1081 | // | |
1082 | // Update parameters and covariance - with one measurement | |
1083 | // Input: | |
1084 | // vecXk - input vector - Updated in function | |
1085 | // covXk - covariance matrix - Updated in function | |
1086 | // delta, sigma, s1 - new measurement, rms of new measurement and the index of measurement | |
1087 | const Int_t knMeas=1; | |
1088 | Int_t knElem=vecXk.GetNrows(); | |
1089 | ||
1090 | TMatrixD mat1(knElem,knElem); // update covariance matrix | |
1091 | TMatrixD matHk(1,knElem); // vector to mesurement | |
1092 | TMatrixD vecYk(knMeas,1); // Innovation or measurement residual | |
1093 | TMatrixD matHkT(knElem,knMeas); // helper matrix Hk transpose | |
1094 | TMatrixD matSk(knMeas,knMeas); // Innovation (or residual) covariance | |
1095 | TMatrixD matKk(knElem,knMeas); // Optimal Kalman gain | |
1096 | TMatrixD covXk2(knElem,knElem); // helper matrix | |
1097 | TMatrixD covXk3(knElem,knElem); // helper matrix | |
1098 | TMatrixD vecZk(1,1); | |
1099 | TMatrixD measR(1,1); | |
1100 | vecZk(0,0)=delta; | |
1101 | measR(0,0)=sigma*sigma; | |
1102 | // | |
1103 | // reset matHk | |
1104 | for (Int_t iel=0;iel<knElem;iel++) | |
1105 | for (Int_t ip=0;ip<knMeas;ip++) matHk(ip,iel)=0; | |
1106 | //mat1 | |
1107 | for (Int_t iel=0;iel<knElem;iel++) { | |
1108 | for (Int_t jel=0;jel<knElem;jel++) mat1(iel,jel)=0; | |
1109 | mat1(iel,iel)=1; | |
1110 | } | |
1111 | // | |
1112 | matHk(0, s1)=1; | |
1113 | vecYk = vecZk-matHk*vecXk; // Innovation or measurement residual | |
1114 | matHkT=matHk.T(); matHk.T(); | |
1115 | matSk = (matHk*(covXk*matHkT))+measR; // Innovation (or residual) covariance | |
1116 | matSk.Invert(); | |
1117 | matKk = (covXk*matHkT)*matSk; // Optimal Kalman gain | |
1118 | vecXk += matKk*vecYk; // updated vector | |
1119 | covXk2= (mat1-(matKk*matHk)); | |
1120 | covXk3 = covXk2*covXk; | |
1121 | covXk = covXk3; | |
1122 | Int_t nrows=covXk3.GetNrows(); | |
1123 | ||
1124 | for (Int_t irow=0; irow<nrows; irow++) | |
1125 | for (Int_t icol=0; icol<nrows; icol++){ | |
1126 | // rounding problems - make matrix again symteric | |
1127 | covXk(irow,icol)=(covXk3(irow,icol)+covXk3(icol,irow))*0.5; | |
1128 | } | |
1129 | } | |
1130 | ||
1131 | ||
1132 | ||
1133 | void TStatToolkit::Constrain1D(TString &input, TString filter, TVectorD ¶m, TMatrixD & covar, Double_t mean, Double_t sigma){ | |
1134 | // | |
1135 | // constrain linear fit | |
1136 | // input - string description of fit function | |
1137 | // filter - string filter to select sub fits | |
1138 | // param,covar - parameters and covariance matrix of the fit | |
1139 | // mean,sigma - new measurement uning which the fit is updated | |
1140 | // | |
1141 | TObjArray *array0= input.Tokenize("++"); | |
1142 | TObjArray *array1= filter.Tokenize("++"); | |
1143 | TMatrixD paramM(param.GetNrows(),1); | |
1144 | for (Int_t i=0; i<=array0->GetEntries(); i++){paramM(i,0)=param(i);} | |
1145 | ||
1146 | for (Int_t i=0; i<array0->GetEntries(); i++){ | |
1147 | Bool_t isOK=kTRUE; | |
1148 | TString str(array0->At(i)->GetName()); | |
1149 | for (Int_t j=0; j<array1->GetEntries(); j++){ | |
1150 | if (str.Contains(array1->At(j)->GetName())==0) isOK=kFALSE; | |
1151 | } | |
1152 | if (isOK) { | |
1153 | TStatToolkit::Update1D(mean, sigma, i+1, paramM, covar);// | |
1154 | } | |
1155 | } | |
1156 | for (Int_t i=0; i<=array0->GetEntries(); i++){ | |
1157 | param(i)=paramM(i,0); | |
1158 | } | |
1159 | } | |
1160 | ||
1161 | TString TStatToolkit::MakeFitString(TString &input, TVectorD ¶m, TMatrixD & covar){ | |
1162 | // | |
1163 | // | |
1164 | // | |
1165 | TObjArray *array0= input.Tokenize("++"); | |
1166 | TString result="(0.0"; | |
1167 | for (Int_t i=0; i<array0->GetEntries(); i++){ | |
1168 | TString str(array0->At(i)->GetName()); | |
1169 | result+="+"+str; | |
1170 | result+=Form("*(%f)",param[i+1]); | |
1171 | printf("%f\t%f\t%s\n", param[i+1], TMath::Sqrt(covar(i+1,i+1)),str.Data()); | |
1172 | } | |
1173 | result+="-0.)"; | |
1174 | return result; | |
1175 | } |