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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
45ClassImp(TStatToolkit) // Class implementation to enable ROOT I/O
46
47TStatToolkit::TStatToolkit() : TObject()
48{
49 //
50 // Default constructor
51 //
52}
53///////////////////////////////////////////////////////////////////////////
54TStatToolkit::~TStatToolkit()
55{
56 //
57 // Destructor
58 //
59}
60
61
62//_____________________________________________________________________________
63void 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
121void 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//_____________________________________________________________________________
177Int_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];
187 for (Int_t i=0;i<n;i++) sindexF[i]=0;
188 //
189 TMath::Sort(n,inlist, sindexS, down);
190 Int_t last = inlist[sindexS[0]];
191 Int_t val = last;
192 sindexF[0] = 1;
193 sindexF[0+n] = last;
194 Int_t countPos = 0;
195 //
196 // find frequency
197 for(Int_t i=1;i<n; i++){
198 val = inlist[sindexS[i]];
199 if (last == val) sindexF[countPos]++;
200 else{
201 countPos++;
202 sindexF[countPos+n] = val;
203 sindexF[countPos]++;
204 last =val;
205 }
206 }
207 if (last==val) countPos++;
208 // sort according frequency
209 TMath::Sort(countPos, sindexF, sindexS, kTRUE);
210 for (Int_t i=0;i<countPos;i++){
211 outlist[2*i ] = sindexF[sindexS[i]+n];
212 outlist[2*i+1] = sindexF[sindexS[i]];
213 }
214 delete [] sindexS;
215 delete [] sindexF;
216
217 return countPos;
218
219}
220
221//___TStatToolkit__________________________________________________________________________
222void TStatToolkit::TruncatedMean(TH1F * his, TVectorD *param, Float_t down, Float_t up, Bool_t verbose){
223 //
224 //
225 //
226 Int_t nbins = his->GetNbinsX();
227 Float_t nentries = his->GetEntries();
228 Float_t sum =0;
229 Float_t mean = 0;
230 Float_t sigma2 = 0;
231 Float_t ncumul=0;
232 for (Int_t ibin=1;ibin<nbins; ibin++){
233 ncumul+= his->GetBinContent(ibin);
234 Float_t fraction = Float_t(ncumul)/Float_t(nentries);
235 if (fraction>down && fraction<up){
236 sum+=his->GetBinContent(ibin);
237 mean+=his->GetBinCenter(ibin)*his->GetBinContent(ibin);
238 sigma2+=his->GetBinCenter(ibin)*his->GetBinCenter(ibin)*his->GetBinContent(ibin);
239 }
240 }
241 mean/=sum;
242 sigma2= TMath::Sqrt(TMath::Abs(sigma2/sum-mean*mean));
243 if (param){
244 (*param)[0] = his->GetMaximum();
245 (*param)[1] = mean;
246 (*param)[2] = sigma2;
247
248 }
249 if (verbose) printf("Mean\t%f\t Sigma2\t%f\n", mean,sigma2);
250}
251
252void TStatToolkit::LTM(TH1F * his, TVectorD *param , Float_t fraction, Bool_t verbose){
253 //
254 // LTM
255 //
256 Int_t nbins = his->GetNbinsX();
257 Int_t nentries = (Int_t)his->GetEntries();
258 Double_t *data = new Double_t[nentries];
259 Int_t npoints=0;
260 for (Int_t ibin=1;ibin<nbins; ibin++){
261 Float_t entriesI = his->GetBinContent(ibin);
262 Float_t xcenter= his->GetBinCenter(ibin);
263 for (Int_t ic=0; ic<entriesI; ic++){
264 if (npoints<nentries){
265 data[npoints]= xcenter;
266 npoints++;
267 }
268 }
269 }
270 Double_t mean, sigma;
271 Int_t npoints2=TMath::Min(Int_t(fraction*Float_t(npoints)),npoints-1);
272 npoints2=TMath::Max(Int_t(0.5*Float_t(npoints)),npoints2);
273 TStatToolkit::EvaluateUni(npoints, data, mean,sigma,npoints2);
274 delete [] data;
275 if (verbose) printf("Mean\t%f\t Sigma2\t%f\n", mean,sigma);if (param){
276 (*param)[0] = his->GetMaximum();
277 (*param)[1] = mean;
278 (*param)[2] = sigma;
279 }
280}
281
282Double_t TStatToolkit::FitGaus(TH1F* his, TVectorD *param, TMatrixD *matrix, Float_t xmin, Float_t xmax, Bool_t verbose){
283 //
284 // Fit histogram with gaussian function
285 //
286 // Prameters:
287 // return value- chi2 - if negative ( not enough points)
288 // his - input histogram
289 // param - vector with parameters
290 // xmin, xmax - range to fit - if xmin=xmax=0 - the full histogram range used
291 // Fitting:
292 // 1. Step - make logarithm
293 // 2. Linear fit (parabola) - more robust - always converge
294 // 3. In case of small statistic bins are averaged
295 //
296 static TLinearFitter fitter(3,"pol2");
297 TVectorD par(3);
298 TVectorD sigma(3);
299 TMatrixD mat(3,3);
300 if (his->GetMaximum()<4) return -1;
301 if (his->GetEntries()<12) return -1;
302 if (his->GetRMS()<mat.GetTol()) return -1;
303 Float_t maxEstimate = his->GetEntries()*his->GetBinWidth(1)/TMath::Sqrt((TMath::TwoPi()*his->GetRMS()));
304 Int_t dsmooth = TMath::Nint(6./TMath::Sqrt(maxEstimate));
305
306 if (maxEstimate<1) return -1;
307 Int_t nbins = his->GetNbinsX();
308 Int_t npoints=0;
309 //
310
311
312 if (xmin>=xmax){
313 xmin = his->GetXaxis()->GetXmin();
314 xmax = his->GetXaxis()->GetXmax();
315 }
316 for (Int_t iter=0; iter<2; iter++){
317 fitter.ClearPoints();
318 npoints=0;
319 for (Int_t ibin=1;ibin<nbins+1; ibin++){
320 Int_t countB=1;
321 Float_t entriesI = his->GetBinContent(ibin);
322 for (Int_t delta = -dsmooth; delta<=dsmooth; delta++){
323 if (ibin+delta>1 &&ibin+delta<nbins-1){
324 entriesI += his->GetBinContent(ibin+delta);
325 countB++;
326 }
327 }
328 entriesI/=countB;
329 Double_t xcenter= his->GetBinCenter(ibin);
330 if (xcenter<xmin || xcenter>xmax) continue;
331 Double_t error=1./TMath::Sqrt(countB);
332 Float_t cont=2;
333 if (iter>0){
334 if (par[0]+par[1]*xcenter+par[2]*xcenter*xcenter>20) return 0;
335 cont = TMath::Exp(par[0]+par[1]*xcenter+par[2]*xcenter*xcenter);
336 if (cont>1.) error = 1./TMath::Sqrt(cont*Float_t(countB));
337 }
338 if (entriesI>1&&cont>1){
339 fitter.AddPoint(&xcenter,TMath::Log(Float_t(entriesI)),error);
340 npoints++;
341 }
342 }
343 if (npoints>3){
344 fitter.Eval();
345 fitter.GetParameters(par);
346 }else{
347 break;
348 }
349 }
350 if (npoints<=3){
351 return -1;
352 }
353 fitter.GetParameters(par);
354 fitter.GetCovarianceMatrix(mat);
355 if (TMath::Abs(par[1])<mat.GetTol()) return -1;
356 if (TMath::Abs(par[2])<mat.GetTol()) return -1;
357 Double_t chi2 = fitter.GetChisquare()/Float_t(npoints);
358 //fitter.GetParameters();
359 if (!param) param = new TVectorD(3);
360 if (!matrix) matrix = new TMatrixD(3,3);
361 (*param)[1] = par[1]/(-2.*par[2]);
362 (*param)[2] = 1./TMath::Sqrt(TMath::Abs(-2.*par[2]));
363 (*param)[0] = TMath::Exp(par[0]+ par[1]* (*param)[1] + par[2]*(*param)[1]*(*param)[1]);
364 if (verbose){
365 par.Print();
366 mat.Print();
367 param->Print();
368 printf("Chi2=%f\n",chi2);
369 TF1 * f1= new TF1("f1","[0]*exp(-(x-[1])^2/(2*[2]*[2]))",his->GetXaxis()->GetXmin(),his->GetXaxis()->GetXmax());
370 f1->SetParameter(0, (*param)[0]);
371 f1->SetParameter(1, (*param)[1]);
372 f1->SetParameter(2, (*param)[2]);
373 f1->Draw("same");
374 }
375 return chi2;
376}
377
378Double_t TStatToolkit::FitGaus(Float_t *arr, Int_t nBins, Float_t xMin, Float_t xMax, TVectorD *param, TMatrixD *matrix, Bool_t verbose){
379 //
380 // Fit histogram with gaussian function
381 //
382 // Prameters:
383 // nbins: size of the array and number of histogram bins
384 // xMin, xMax: histogram range
385 // param: paramters of the fit (0-Constant, 1-Mean, 2-Sigma)
386 // matrix: covariance matrix -- not implemented yet, pass dummy matrix!!!
387 //
388 // Return values:
389 // >0: the chi2 returned by TLinearFitter
390 // -3: only three points have been used for the calculation - no fitter was used
391 // -2: only two points have been used for the calculation - center of gravity was uesed for calculation
392 // -1: only one point has been used for the calculation - center of gravity was uesed for calculation
393 // -4: invalid result!!
394 //
395 // Fitting:
396 // 1. Step - make logarithm
397 // 2. Linear fit (parabola) - more robust - always converge
398 //
399 static TLinearFitter fitter(3,"pol2");
400 static TMatrixD mat(3,3);
401 static Double_t kTol = mat.GetTol();
402 fitter.StoreData(kFALSE);
403 fitter.ClearPoints();
404 TVectorD par(3);
405 TVectorD sigma(3);
406 TMatrixD A(3,3);
407 TMatrixD b(3,1);
408 Float_t rms = TMath::RMS(nBins,arr);
409 Float_t max = TMath::MaxElement(nBins,arr);
410 Float_t binWidth = (xMax-xMin)/(Float_t)nBins;
411
412 Float_t meanCOG = 0;
413 Float_t rms2COG = 0;
414 Float_t sumCOG = 0;
415
416 Float_t entries = 0;
417 Int_t nfilled=0;
418
419 for (Int_t i=0; i<nBins; i++){
420 entries+=arr[i];
421 if (arr[i]>0) nfilled++;
422 }
423
424 if (max<4) return -4;
425 if (entries<12) return -4;
426 if (rms<kTol) return -4;
427
428 Int_t npoints=0;
429 //
430
431 //
432 for (Int_t ibin=0;ibin<nBins; ibin++){
433 Float_t entriesI = arr[ibin];
434 if (entriesI>1){
435 Double_t xcenter = xMin+(ibin+0.5)*binWidth;
436
437 Float_t error = 1./TMath::Sqrt(entriesI);
438 Float_t val = TMath::Log(Float_t(entriesI));
439 fitter.AddPoint(&xcenter,val,error);
440 if (npoints<3){
441 A(npoints,0)=1;
442 A(npoints,1)=xcenter;
443 A(npoints,2)=xcenter*xcenter;
444 b(npoints,0)=val;
445 meanCOG+=xcenter*entriesI;
446 rms2COG +=xcenter*entriesI*xcenter;
447 sumCOG +=entriesI;
448 }
449 npoints++;
450 }
451 }
452
453
454 Double_t chi2 = 0;
455 if (npoints>=3){
456 if ( npoints == 3 ){
457 //analytic calculation of the parameters for three points
458 A.Invert();
459 TMatrixD res(1,3);
460 res.Mult(A,b);
461 par[0]=res(0,0);
462 par[1]=res(0,1);
463 par[2]=res(0,2);
464 chi2 = -3.;
465 } else {
466 // use fitter for more than three points
467 fitter.Eval();
468 fitter.GetParameters(par);
469 fitter.GetCovarianceMatrix(mat);
470 chi2 = fitter.GetChisquare()/Float_t(npoints);
471 }
472 if (TMath::Abs(par[1])<kTol) return -4;
473 if (TMath::Abs(par[2])<kTol) return -4;
474
475 if (!param) param = new TVectorD(3);
476 if (!matrix) matrix = new TMatrixD(3,3); // !!!!might be a memory leek. use dummy matrix pointer to call this function!
477
478 (*param)[1] = par[1]/(-2.*par[2]);
479 (*param)[2] = 1./TMath::Sqrt(TMath::Abs(-2.*par[2]));
480 Double_t lnparam0 = par[0]+ par[1]* (*param)[1] + par[2]*(*param)[1]*(*param)[1];
481 if ( lnparam0>307 ) return -4;
482 (*param)[0] = TMath::Exp(lnparam0);
483 if (verbose){
484 par.Print();
485 mat.Print();
486 param->Print();
487 printf("Chi2=%f\n",chi2);
488 TF1 * f1= new TF1("f1","[0]*exp(-(x-[1])^2/(2*[2]*[2]))",xMin,xMax);
489 f1->SetParameter(0, (*param)[0]);
490 f1->SetParameter(1, (*param)[1]);
491 f1->SetParameter(2, (*param)[2]);
492 f1->Draw("same");
493 }
494 return chi2;
495 }
496
497 if (npoints == 2){
498 //use center of gravity for 2 points
499 meanCOG/=sumCOG;
500 rms2COG /=sumCOG;
501 (*param)[0] = max;
502 (*param)[1] = meanCOG;
503 (*param)[2] = TMath::Sqrt(TMath::Abs(meanCOG*meanCOG-rms2COG));
504 chi2=-2.;
505 }
506 if ( npoints == 1 ){
507 meanCOG/=sumCOG;
508 (*param)[0] = max;
509 (*param)[1] = meanCOG;
510 (*param)[2] = binWidth/TMath::Sqrt(12);
511 chi2=-1.;
512 }
513 return chi2;
514
515}
516
517
518Float_t TStatToolkit::GetCOG(Short_t *arr, Int_t nBins, Float_t xMin, Float_t xMax, Float_t *rms, Float_t *sum)
519{
520 //
521 // calculate center of gravity rms and sum for array 'arr' with nBins an a x range xMin to xMax
522 // return COG; in case of failure return xMin
523 //
524 Float_t meanCOG = 0;
525 Float_t rms2COG = 0;
526 Float_t sumCOG = 0;
527 Int_t npoints = 0;
528
529 Float_t binWidth = (xMax-xMin)/(Float_t)nBins;
530
531 for (Int_t ibin=0; ibin<nBins; ibin++){
532 Float_t entriesI = (Float_t)arr[ibin];
533 Double_t xcenter = xMin+(ibin+0.5)*binWidth;
534 if ( entriesI>0 ){
535 meanCOG += xcenter*entriesI;
536 rms2COG += xcenter*entriesI*xcenter;
537 sumCOG += entriesI;
538 npoints++;
539 }
540 }
541 if ( sumCOG == 0 ) return xMin;
542 meanCOG/=sumCOG;
543
544 if ( rms ){
545 rms2COG /=sumCOG;
546 (*rms) = TMath::Sqrt(TMath::Abs(meanCOG*meanCOG-rms2COG));
547 if ( npoints == 1 ) (*rms) = binWidth/TMath::Sqrt(12);
548 }
549
550 if ( sum )
551 (*sum) = sumCOG;
552
553 return meanCOG;
554}
555
556
557
558///////////////////////////////////////////////////////////////
559////////////// TEST functions /////////////////////////
560///////////////////////////////////////////////////////////////
561
562
563
564
565
566void TStatToolkit::TestGausFit(Int_t nhistos){
567 //
568 // Test performance of the parabolic - gaussian fit - compare it with
569 // ROOT gauss fit
570 // nhistos - number of histograms to be used for test
571 //
572 TTreeSRedirector *pcstream = new TTreeSRedirector("fitdebug.root");
573
574 Float_t *xTrue = new Float_t[nhistos];
575 Float_t *sTrue = new Float_t[nhistos];
576 TVectorD **par1 = new TVectorD*[nhistos];
577 TVectorD **par2 = new TVectorD*[nhistos];
578 TMatrixD dummy(3,3);
579
580
581 TH1F **h1f = new TH1F*[nhistos];
582 TF1 *myg = new TF1("myg","gaus");
583 TF1 *fit = new TF1("fit","gaus");
584 gRandom->SetSeed(0);
585
586 //init
587 for (Int_t i=0;i<nhistos; i++){
588 par1[i] = new TVectorD(3);
589 par2[i] = new TVectorD(3);
590 h1f[i] = new TH1F(Form("h1f%d",i),Form("h1f%d",i),20,-10,10);
591 xTrue[i]= gRandom->Rndm();
592 gSystem->Sleep(2);
593 sTrue[i]= .75+gRandom->Rndm()*.5;
594 myg->SetParameters(1,xTrue[i],sTrue[i]);
595 h1f[i]->FillRandom("myg");
596 }
597
598 TStopwatch s;
599 s.Start();
600 //standard gaus fit
601 for (Int_t i=0; i<nhistos; i++){
602 h1f[i]->Fit(fit,"0q");
603 (*par1[i])(0) = fit->GetParameter(0);
604 (*par1[i])(1) = fit->GetParameter(1);
605 (*par1[i])(2) = fit->GetParameter(2);
606 }
607 s.Stop();
608 printf("Gaussian fit\t");
609 s.Print();
610
611 s.Start();
612 //TStatToolkit gaus fit
613 for (Int_t i=0; i<nhistos; i++){
614 TStatToolkit::FitGaus(h1f[i]->GetArray()+1,h1f[i]->GetNbinsX(),h1f[i]->GetXaxis()->GetXmin(),h1f[i]->GetXaxis()->GetXmax(),par2[i],&dummy);
615 }
616
617 s.Stop();
618 printf("Parabolic fit\t");
619 s.Print();
620 //write stream
621 for (Int_t i=0;i<nhistos; i++){
622 Float_t xt = xTrue[i];
623 Float_t st = sTrue[i];
624 (*pcstream)<<"data"
625 <<"xTrue="<<xt
626 <<"sTrue="<<st
627 <<"pg.="<<(par1[i])
628 <<"pa.="<<(par2[i])
629 <<"\n";
630 }
631 //delete pointers
632 for (Int_t i=0;i<nhistos; i++){
633 delete par1[i];
634 delete par2[i];
635 delete h1f[i];
636 }
637 delete pcstream;
638 delete []h1f;
639 delete []xTrue;
640 delete []sTrue;
641 //
642 delete []par1;
643 delete []par2;
644
645}
646
647
648
649TGraph2D * TStatToolkit::MakeStat2D(TH3 * his, Int_t delta0, Int_t delta1, Int_t type){
650 //
651 //
652 //
653 // delta - number of bins to integrate
654 // type - 0 - mean value
655
656 TAxis * xaxis = his->GetXaxis();
657 TAxis * yaxis = his->GetYaxis();
658 // TAxis * zaxis = his->GetZaxis();
659 Int_t nbinx = xaxis->GetNbins();
660 Int_t nbiny = yaxis->GetNbins();
661 char name[1000];
662 Int_t icount=0;
663 TGraph2D *graph = new TGraph2D(nbinx*nbiny);
664 TF1 f1("f1","gaus");
665 for (Int_t ix=0; ix<nbinx;ix++)
666 for (Int_t iy=0; iy<nbiny;iy++){
667 Float_t xcenter = xaxis->GetBinCenter(ix);
668 Float_t ycenter = yaxis->GetBinCenter(iy);
669 sprintf(name,"%s_%d_%d",his->GetName(), ix,iy);
670 TH1 *projection = his->ProjectionZ(name,ix-delta0,ix+delta0,iy-delta1,iy+delta1);
671 Float_t stat= 0;
672 if (type==0) stat = projection->GetMean();
673 if (type==1) stat = projection->GetRMS();
674 if (type==2 || type==3){
675 TVectorD vec(3);
676 TStatToolkit::LTM((TH1F*)projection,&vec,0.7);
677 if (type==2) stat= vec[1];
678 if (type==3) stat= vec[0];
679 }
680 if (type==4|| type==5){
681 projection->Fit(&f1);
682 if (type==4) stat= f1.GetParameter(1);
683 if (type==5) stat= f1.GetParameter(2);
684 }
685 //printf("%d\t%f\t%f\t%f\n", icount,xcenter, ycenter, stat);
686 graph->SetPoint(icount,xcenter, ycenter, stat);
687 icount++;
688 }
689 return graph;
690}
691
692TGraph * TStatToolkit::MakeStat1D(TH3 * his, Int_t delta1, Int_t type){
693 //
694 //
695 //
696 // delta - number of bins to integrate
697 // type - 0 - mean value
698
699 TAxis * xaxis = his->GetXaxis();
700 TAxis * yaxis = his->GetYaxis();
701 // TAxis * zaxis = his->GetZaxis();
702 Int_t nbinx = xaxis->GetNbins();
703 Int_t nbiny = yaxis->GetNbins();
704 char name[1000];
705 Int_t icount=0;
706 TGraph *graph = new TGraph(nbinx);
707 TF1 f1("f1","gaus");
708 for (Int_t ix=0; ix<nbinx;ix++){
709 Float_t xcenter = xaxis->GetBinCenter(ix);
710 // Float_t ycenter = yaxis->GetBinCenter(iy);
711 sprintf(name,"%s_%d",his->GetName(), ix);
712 TH1 *projection = his->ProjectionZ(name,ix-delta1,ix+delta1,0,nbiny);
713 Float_t stat= 0;
714 if (type==0) stat = projection->GetMean();
715 if (type==1) stat = projection->GetRMS();
716 if (type==2 || type==3){
717 TVectorD vec(3);
718 TStatToolkit::LTM((TH1F*)projection,&vec,0.7);
719 if (type==2) stat= vec[1];
720 if (type==3) stat= vec[0];
721 }
722 if (type==4|| type==5){
723 projection->Fit(&f1);
724 if (type==4) stat= f1.GetParameter(1);
725 if (type==5) stat= f1.GetParameter(2);
726 }
727 //printf("%d\t%f\t%f\t%f\n", icount,xcenter, ycenter, stat);
728 graph->SetPoint(icount,xcenter, stat);
729 icount++;
730 }
731 return graph;
732}
733
734
735
736
737
738TString* 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){
739 //
740 // fit an arbitrary function, specified by formula into the data, specified by drawCommand and cuts
741 // returns chi2, fitParam and covMatrix
742 // returns TString with fitted formula
743 //
744
745 TString formulaStr(formula);
746 TString drawStr(drawCommand);
747 TString cutStr(cuts);
748 TString ferr("1");
749
750 TString strVal(drawCommand);
751 if (strVal.Contains(":")){
752 TObjArray* valTokens = strVal.Tokenize(":");
753 drawStr = valTokens->At(0)->GetName();
754 ferr = valTokens->At(1)->GetName();
755 }
756
757
758 formulaStr.ReplaceAll("++", "~");
759 TObjArray* formulaTokens = formulaStr.Tokenize("~");
760 Int_t dim = formulaTokens->GetEntriesFast();
761
762 fitParam.ResizeTo(dim);
763 covMatrix.ResizeTo(dim,dim);
764
765 TLinearFitter* fitter = new TLinearFitter(dim+1, Form("hyp%d",dim));
766 fitter->StoreData(kTRUE);
767 fitter->ClearPoints();
768
769 Int_t entries = tree->Draw(drawStr.Data(), cutStr.Data(), "goff", stop-start, start);
770 if (entries == -1) return new TString("An ERROR has occured during fitting!");
771 Double_t **values = new Double_t*[dim+1] ;
772 //
773 entries = tree->Draw(ferr.Data(), cutStr.Data(), "goff", stop-start, start);
774 if (entries == -1) return new TString("An ERROR has occured during fitting!");
775 Double_t *errors = new Double_t[entries];
776 memcpy(errors, tree->GetV1(), entries*sizeof(Double_t));
777
778 for (Int_t i = 0; i < dim + 1; i++){
779 Int_t centries = 0;
780 if (i < dim) centries = tree->Draw(((TObjString*)formulaTokens->At(i))->GetName(), cutStr.Data(), "goff", stop-start,start);
781 else centries = tree->Draw(drawStr.Data(), cutStr.Data(), "goff", stop-start,start);
782
783 if (entries != centries) return new TString("An ERROR has occured during fitting!");
784 values[i] = new Double_t[entries];
785 memcpy(values[i], tree->GetV1(), entries*sizeof(Double_t));
786 }
787
788 // add points to the fitter
789 for (Int_t i = 0; i < entries; i++){
790 Double_t x[1000];
791 for (Int_t j=0; j<dim;j++) x[j]=values[j][i];
792 fitter->AddPoint(x, values[dim][i], errors[i]);
793 }
794
795 fitter->Eval();
796 if (frac>0.5 && frac<1){
797 fitter->EvalRobust(frac);
798 }else{
799 if (fix0) {
800 fitter->FixParameter(0,0);
801 fitter->Eval();
802 }
803 }
804 fitter->GetParameters(fitParam);
805 fitter->GetCovarianceMatrix(covMatrix);
806 chi2 = fitter->GetChisquare();
807 chi2 = chi2;
808 npoints = entries;
809// TString *preturnFormula = new TString(Form("%f*(",fitParam[0])), &returnFormula = *preturnFormula;
810
811// for (Int_t iparam = 0; iparam < dim; iparam++) {
812// returnFormula.Append(Form("%s*(%f)",((TObjString*)formulaTokens->At(iparam))->GetName(),fitParam[iparam+1]/fitParam[0]));
813// if (iparam < dim-1) returnFormula.Append("+");
814// }
815// returnFormula.Append(" )");
816
817 TString *preturnFormula = new TString(Form("( %f+",fitParam[0])), &returnFormula = *preturnFormula;
818
819 for (Int_t iparam = 0; iparam < dim; iparam++) {
820 returnFormula.Append(Form("%s*(%f)",((TObjString*)formulaTokens->At(iparam))->GetName(),fitParam[iparam+1]));
821 if (iparam < dim-1) returnFormula.Append("+");
822 }
823 returnFormula.Append(" )");
824
825
826
827
828 delete formulaTokens;
829 delete fitter;
830 delete[] values;
831 return preturnFormula;
832}