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Make TGraphErrors if error expression specified
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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"
3d7cc0b4 33#include "TGraph2D.h"
34#include "TGraph.h"
b3453fe7 35#include "TGraphErrors.h"
21f3a443 36
37//
38// includes neccessary for test functions
39//
40#include "TSystem.h"
41#include "TRandom.h"
42#include "TStopwatch.h"
43#include "TTreeStream.h"
44
45#include "TStatToolkit.h"
46
47
48ClassImp(TStatToolkit) // Class implementation to enable ROOT I/O
49
50TStatToolkit::TStatToolkit() : TObject()
51{
52 //
53 // Default constructor
54 //
55}
56///////////////////////////////////////////////////////////////////////////
57TStatToolkit::~TStatToolkit()
58{
59 //
60 // Destructor
61 //
62}
63
64
65//_____________________________________________________________________________
66void TStatToolkit::EvaluateUni(Int_t nvectors, Double_t *data, Double_t &mean
67 , Double_t &sigma, Int_t hh)
68{
69 //
70 // Robust estimator in 1D case MI version - (faster than ROOT version)
71 //
72 // For the univariate case
73 // estimates of location and scatter are returned in mean and sigma parameters
74 // the algorithm works on the same principle as in multivariate case -
75 // it finds a subset of size hh with smallest sigma, and then returns mean and
76 // sigma of this subset
77 //
78
79 if (hh==0)
80 hh=(nvectors+2)/2;
81 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};
82 Int_t *index=new Int_t[nvectors];
83 TMath::Sort(nvectors, data, index, kFALSE);
84
85 Int_t nquant = TMath::Min(Int_t(Double_t(((hh*1./nvectors)-0.5)*40))+1, 11);
86 Double_t factor = faclts[TMath::Max(0,nquant-1)];
87
88 Double_t sumx =0;
89 Double_t sumx2 =0;
90 Int_t bestindex = -1;
91 Double_t bestmean = 0;
92 Double_t bestsigma = (data[index[nvectors-1]]-data[index[0]]+1.); // maximal possible sigma
93 bestsigma *=bestsigma;
94
95 for (Int_t i=0; i<hh; i++){
96 sumx += data[index[i]];
97 sumx2 += data[index[i]]*data[index[i]];
98 }
99
100 Double_t norm = 1./Double_t(hh);
bd7b4d18 101 Double_t norm2 = (hh-1)>0 ? 1./Double_t(hh-1):1;
21f3a443 102 for (Int_t i=hh; i<nvectors; i++){
103 Double_t cmean = sumx*norm;
104 Double_t csigma = (sumx2 - hh*cmean*cmean)*norm2;
105 if (csigma<bestsigma){
106 bestmean = cmean;
107 bestsigma = csigma;
108 bestindex = i-hh;
109 }
110
111 sumx += data[index[i]]-data[index[i-hh]];
112 sumx2 += data[index[i]]*data[index[i]]-data[index[i-hh]]*data[index[i-hh]];
113 }
114
115 Double_t bstd=factor*TMath::Sqrt(TMath::Abs(bestsigma));
116 mean = bestmean;
117 sigma = bstd;
118 delete [] index;
119
120}
121
122
123
124void TStatToolkit::EvaluateUniExternal(Int_t nvectors, Double_t *data, Double_t &mean, Double_t &sigma, Int_t hh, Float_t externalfactor)
125{
126 // Modified version of ROOT robust EvaluateUni
127 // robust estimator in 1D case MI version
128 // added external factor to include precision of external measurement
129 //
130
131 if (hh==0)
132 hh=(nvectors+2)/2;
133 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};
134 Int_t *index=new Int_t[nvectors];
135 TMath::Sort(nvectors, data, index, kFALSE);
136 //
137 Int_t nquant = TMath::Min(Int_t(Double_t(((hh*1./nvectors)-0.5)*40))+1, 11);
138 Double_t factor = faclts[0];
139 if (nquant>0){
140 // fix proper normalization - Anja
141 factor = faclts[nquant-1];
142 }
143
144 //
145 //
146 Double_t sumx =0;
147 Double_t sumx2 =0;
148 Int_t bestindex = -1;
149 Double_t bestmean = 0;
150 Double_t bestsigma = -1;
151 for (Int_t i=0; i<hh; i++){
152 sumx += data[index[i]];
153 sumx2 += data[index[i]]*data[index[i]];
154 }
155 //
156 Double_t kfactor = 2.*externalfactor - externalfactor*externalfactor;
157 Double_t norm = 1./Double_t(hh);
158 for (Int_t i=hh; i<nvectors; i++){
159 Double_t cmean = sumx*norm;
160 Double_t csigma = (sumx2*norm - cmean*cmean*kfactor);
161 if (csigma<bestsigma || bestsigma<0){
162 bestmean = cmean;
163 bestsigma = csigma;
164 bestindex = i-hh;
165 }
166 //
167 //
168 sumx += data[index[i]]-data[index[i-hh]];
169 sumx2 += data[index[i]]*data[index[i]]-data[index[i-hh]]*data[index[i-hh]];
170 }
171
172 Double_t bstd=factor*TMath::Sqrt(TMath::Abs(bestsigma));
173 mean = bestmean;
174 sigma = bstd;
175 delete [] index;
176}
177
178
179//_____________________________________________________________________________
180Int_t TStatToolkit::Freq(Int_t n, const Int_t *inlist
181 , Int_t *outlist, Bool_t down)
182{
183 //
184 // Sort eleements according occurancy
185 // The size of output array has is 2*n
186 //
187
188 Int_t * sindexS = new Int_t[n]; // temp array for sorting
189 Int_t * sindexF = new Int_t[2*n];
b8072cce 190 for (Int_t i=0;i<n;i++) sindexS[i]=0;
191 for (Int_t i=0;i<2*n;i++) sindexF[i]=0;
21f3a443 192 //
193 TMath::Sort(n,inlist, sindexS, down);
194 Int_t last = inlist[sindexS[0]];
195 Int_t val = last;
196 sindexF[0] = 1;
197 sindexF[0+n] = last;
198 Int_t countPos = 0;
199 //
200 // find frequency
201 for(Int_t i=1;i<n; i++){
202 val = inlist[sindexS[i]];
203 if (last == val) sindexF[countPos]++;
204 else{
205 countPos++;
206 sindexF[countPos+n] = val;
207 sindexF[countPos]++;
208 last =val;
209 }
210 }
211 if (last==val) countPos++;
212 // sort according frequency
213 TMath::Sort(countPos, sindexF, sindexS, kTRUE);
214 for (Int_t i=0;i<countPos;i++){
215 outlist[2*i ] = sindexF[sindexS[i]+n];
216 outlist[2*i+1] = sindexF[sindexS[i]];
217 }
218 delete [] sindexS;
219 delete [] sindexF;
220
221 return countPos;
222
223}
224
225//___TStatToolkit__________________________________________________________________________
3d7cc0b4 226void TStatToolkit::TruncatedMean(const TH1 * his, TVectorD *param, Float_t down, Float_t up, Bool_t verbose){
21f3a443 227 //
228 //
229 //
230 Int_t nbins = his->GetNbinsX();
231 Float_t nentries = his->GetEntries();
232 Float_t sum =0;
233 Float_t mean = 0;
234 Float_t sigma2 = 0;
235 Float_t ncumul=0;
236 for (Int_t ibin=1;ibin<nbins; ibin++){
237 ncumul+= his->GetBinContent(ibin);
238 Float_t fraction = Float_t(ncumul)/Float_t(nentries);
239 if (fraction>down && fraction<up){
240 sum+=his->GetBinContent(ibin);
241 mean+=his->GetBinCenter(ibin)*his->GetBinContent(ibin);
242 sigma2+=his->GetBinCenter(ibin)*his->GetBinCenter(ibin)*his->GetBinContent(ibin);
243 }
244 }
245 mean/=sum;
246 sigma2= TMath::Sqrt(TMath::Abs(sigma2/sum-mean*mean));
247 if (param){
248 (*param)[0] = his->GetMaximum();
249 (*param)[1] = mean;
250 (*param)[2] = sigma2;
251
252 }
253 if (verbose) printf("Mean\t%f\t Sigma2\t%f\n", mean,sigma2);
254}
255
256void TStatToolkit::LTM(TH1F * his, TVectorD *param , Float_t fraction, Bool_t verbose){
257 //
258 // LTM
259 //
260 Int_t nbins = his->GetNbinsX();
261 Int_t nentries = (Int_t)his->GetEntries();
262 Double_t *data = new Double_t[nentries];
263 Int_t npoints=0;
264 for (Int_t ibin=1;ibin<nbins; ibin++){
265 Float_t entriesI = his->GetBinContent(ibin);
266 Float_t xcenter= his->GetBinCenter(ibin);
267 for (Int_t ic=0; ic<entriesI; ic++){
268 if (npoints<nentries){
269 data[npoints]= xcenter;
270 npoints++;
271 }
272 }
273 }
274 Double_t mean, sigma;
275 Int_t npoints2=TMath::Min(Int_t(fraction*Float_t(npoints)),npoints-1);
276 npoints2=TMath::Max(Int_t(0.5*Float_t(npoints)),npoints2);
277 TStatToolkit::EvaluateUni(npoints, data, mean,sigma,npoints2);
278 delete [] data;
279 if (verbose) printf("Mean\t%f\t Sigma2\t%f\n", mean,sigma);if (param){
280 (*param)[0] = his->GetMaximum();
281 (*param)[1] = mean;
282 (*param)[2] = sigma;
283 }
284}
285
94a43b22 286Double_t TStatToolkit::FitGaus(TH1* his, TVectorD *param, TMatrixD */*matrix*/, Float_t xmin, Float_t xmax, Bool_t verbose){
21f3a443 287 //
288 // Fit histogram with gaussian function
289 //
290 // Prameters:
291 // return value- chi2 - if negative ( not enough points)
292 // his - input histogram
293 // param - vector with parameters
294 // xmin, xmax - range to fit - if xmin=xmax=0 - the full histogram range used
295 // Fitting:
296 // 1. Step - make logarithm
297 // 2. Linear fit (parabola) - more robust - always converge
298 // 3. In case of small statistic bins are averaged
299 //
300 static TLinearFitter fitter(3,"pol2");
301 TVectorD par(3);
302 TVectorD sigma(3);
303 TMatrixD mat(3,3);
304 if (his->GetMaximum()<4) return -1;
305 if (his->GetEntries()<12) return -1;
306 if (his->GetRMS()<mat.GetTol()) return -1;
307 Float_t maxEstimate = his->GetEntries()*his->GetBinWidth(1)/TMath::Sqrt((TMath::TwoPi()*his->GetRMS()));
308 Int_t dsmooth = TMath::Nint(6./TMath::Sqrt(maxEstimate));
309
310 if (maxEstimate<1) return -1;
311 Int_t nbins = his->GetNbinsX();
312 Int_t npoints=0;
313 //
314
315
316 if (xmin>=xmax){
317 xmin = his->GetXaxis()->GetXmin();
318 xmax = his->GetXaxis()->GetXmax();
319 }
320 for (Int_t iter=0; iter<2; iter++){
321 fitter.ClearPoints();
322 npoints=0;
323 for (Int_t ibin=1;ibin<nbins+1; ibin++){
324 Int_t countB=1;
325 Float_t entriesI = his->GetBinContent(ibin);
326 for (Int_t delta = -dsmooth; delta<=dsmooth; delta++){
327 if (ibin+delta>1 &&ibin+delta<nbins-1){
328 entriesI += his->GetBinContent(ibin+delta);
329 countB++;
330 }
331 }
332 entriesI/=countB;
333 Double_t xcenter= his->GetBinCenter(ibin);
334 if (xcenter<xmin || xcenter>xmax) continue;
335 Double_t error=1./TMath::Sqrt(countB);
336 Float_t cont=2;
337 if (iter>0){
338 if (par[0]+par[1]*xcenter+par[2]*xcenter*xcenter>20) return 0;
339 cont = TMath::Exp(par[0]+par[1]*xcenter+par[2]*xcenter*xcenter);
340 if (cont>1.) error = 1./TMath::Sqrt(cont*Float_t(countB));
341 }
342 if (entriesI>1&&cont>1){
343 fitter.AddPoint(&xcenter,TMath::Log(Float_t(entriesI)),error);
344 npoints++;
345 }
346 }
347 if (npoints>3){
348 fitter.Eval();
349 fitter.GetParameters(par);
350 }else{
351 break;
352 }
353 }
354 if (npoints<=3){
355 return -1;
356 }
357 fitter.GetParameters(par);
358 fitter.GetCovarianceMatrix(mat);
359 if (TMath::Abs(par[1])<mat.GetTol()) return -1;
360 if (TMath::Abs(par[2])<mat.GetTol()) return -1;
361 Double_t chi2 = fitter.GetChisquare()/Float_t(npoints);
362 //fitter.GetParameters();
363 if (!param) param = new TVectorD(3);
cb1d20de 364 // if (!matrix) matrix = new TMatrixD(3,3); // Covariance matrix to be implemented
21f3a443 365 (*param)[1] = par[1]/(-2.*par[2]);
366 (*param)[2] = 1./TMath::Sqrt(TMath::Abs(-2.*par[2]));
367 (*param)[0] = TMath::Exp(par[0]+ par[1]* (*param)[1] + par[2]*(*param)[1]*(*param)[1]);
368 if (verbose){
369 par.Print();
370 mat.Print();
371 param->Print();
372 printf("Chi2=%f\n",chi2);
373 TF1 * f1= new TF1("f1","[0]*exp(-(x-[1])^2/(2*[2]*[2]))",his->GetXaxis()->GetXmin(),his->GetXaxis()->GetXmax());
374 f1->SetParameter(0, (*param)[0]);
375 f1->SetParameter(1, (*param)[1]);
376 f1->SetParameter(2, (*param)[2]);
377 f1->Draw("same");
378 }
379 return chi2;
380}
381
cb1d20de 382Double_t TStatToolkit::FitGaus(Float_t *arr, Int_t nBins, Float_t xMin, Float_t xMax, TVectorD *param, TMatrixD */*matrix*/, Bool_t verbose){
21f3a443 383 //
384 // Fit histogram with gaussian function
385 //
386 // Prameters:
387 // nbins: size of the array and number of histogram bins
388 // xMin, xMax: histogram range
389 // param: paramters of the fit (0-Constant, 1-Mean, 2-Sigma)
390 // matrix: covariance matrix -- not implemented yet, pass dummy matrix!!!
391 //
392 // Return values:
393 // >0: the chi2 returned by TLinearFitter
394 // -3: only three points have been used for the calculation - no fitter was used
395 // -2: only two points have been used for the calculation - center of gravity was uesed for calculation
396 // -1: only one point has been used for the calculation - center of gravity was uesed for calculation
397 // -4: invalid result!!
398 //
399 // Fitting:
400 // 1. Step - make logarithm
401 // 2. Linear fit (parabola) - more robust - always converge
402 //
403 static TLinearFitter fitter(3,"pol2");
404 static TMatrixD mat(3,3);
405 static Double_t kTol = mat.GetTol();
406 fitter.StoreData(kFALSE);
407 fitter.ClearPoints();
408 TVectorD par(3);
409 TVectorD sigma(3);
3d7cc0b4 410 TMatrixD matA(3,3);
21f3a443 411 TMatrixD b(3,1);
412 Float_t rms = TMath::RMS(nBins,arr);
413 Float_t max = TMath::MaxElement(nBins,arr);
414 Float_t binWidth = (xMax-xMin)/(Float_t)nBins;
415
416 Float_t meanCOG = 0;
417 Float_t rms2COG = 0;
418 Float_t sumCOG = 0;
419
420 Float_t entries = 0;
421 Int_t nfilled=0;
422
423 for (Int_t i=0; i<nBins; i++){
424 entries+=arr[i];
425 if (arr[i]>0) nfilled++;
426 }
427
428 if (max<4) return -4;
429 if (entries<12) return -4;
430 if (rms<kTol) return -4;
431
432 Int_t npoints=0;
433 //
434
435 //
436 for (Int_t ibin=0;ibin<nBins; ibin++){
437 Float_t entriesI = arr[ibin];
438 if (entriesI>1){
439 Double_t xcenter = xMin+(ibin+0.5)*binWidth;
440
441 Float_t error = 1./TMath::Sqrt(entriesI);
442 Float_t val = TMath::Log(Float_t(entriesI));
443 fitter.AddPoint(&xcenter,val,error);
444 if (npoints<3){
3d7cc0b4 445 matA(npoints,0)=1;
446 matA(npoints,1)=xcenter;
447 matA(npoints,2)=xcenter*xcenter;
21f3a443 448 b(npoints,0)=val;
449 meanCOG+=xcenter*entriesI;
450 rms2COG +=xcenter*entriesI*xcenter;
451 sumCOG +=entriesI;
452 }
453 npoints++;
454 }
455 }
456
457
458 Double_t chi2 = 0;
459 if (npoints>=3){
460 if ( npoints == 3 ){
461 //analytic calculation of the parameters for three points
3d7cc0b4 462 matA.Invert();
21f3a443 463 TMatrixD res(1,3);
3d7cc0b4 464 res.Mult(matA,b);
21f3a443 465 par[0]=res(0,0);
466 par[1]=res(0,1);
467 par[2]=res(0,2);
468 chi2 = -3.;
469 } else {
470 // use fitter for more than three points
471 fitter.Eval();
472 fitter.GetParameters(par);
473 fitter.GetCovarianceMatrix(mat);
474 chi2 = fitter.GetChisquare()/Float_t(npoints);
475 }
476 if (TMath::Abs(par[1])<kTol) return -4;
477 if (TMath::Abs(par[2])<kTol) return -4;
478
479 if (!param) param = new TVectorD(3);
cb1d20de 480 //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 481
482 (*param)[1] = par[1]/(-2.*par[2]);
483 (*param)[2] = 1./TMath::Sqrt(TMath::Abs(-2.*par[2]));
484 Double_t lnparam0 = par[0]+ par[1]* (*param)[1] + par[2]*(*param)[1]*(*param)[1];
485 if ( lnparam0>307 ) return -4;
486 (*param)[0] = TMath::Exp(lnparam0);
487 if (verbose){
488 par.Print();
489 mat.Print();
490 param->Print();
491 printf("Chi2=%f\n",chi2);
492 TF1 * f1= new TF1("f1","[0]*exp(-(x-[1])^2/(2*[2]*[2]))",xMin,xMax);
493 f1->SetParameter(0, (*param)[0]);
494 f1->SetParameter(1, (*param)[1]);
495 f1->SetParameter(2, (*param)[2]);
496 f1->Draw("same");
497 }
498 return chi2;
499 }
500
501 if (npoints == 2){
502 //use center of gravity for 2 points
503 meanCOG/=sumCOG;
504 rms2COG /=sumCOG;
505 (*param)[0] = max;
506 (*param)[1] = meanCOG;
507 (*param)[2] = TMath::Sqrt(TMath::Abs(meanCOG*meanCOG-rms2COG));
508 chi2=-2.;
509 }
510 if ( npoints == 1 ){
511 meanCOG/=sumCOG;
512 (*param)[0] = max;
513 (*param)[1] = meanCOG;
514 (*param)[2] = binWidth/TMath::Sqrt(12);
515 chi2=-1.;
516 }
517 return chi2;
518
519}
520
521
3d7cc0b4 522Float_t TStatToolkit::GetCOG(const Short_t *arr, Int_t nBins, Float_t xMin, Float_t xMax, Float_t *rms, Float_t *sum)
21f3a443 523{
524 //
525 // calculate center of gravity rms and sum for array 'arr' with nBins an a x range xMin to xMax
526 // return COG; in case of failure return xMin
527 //
528 Float_t meanCOG = 0;
529 Float_t rms2COG = 0;
530 Float_t sumCOG = 0;
531 Int_t npoints = 0;
532
533 Float_t binWidth = (xMax-xMin)/(Float_t)nBins;
534
535 for (Int_t ibin=0; ibin<nBins; ibin++){
536 Float_t entriesI = (Float_t)arr[ibin];
537 Double_t xcenter = xMin+(ibin+0.5)*binWidth;
538 if ( entriesI>0 ){
539 meanCOG += xcenter*entriesI;
540 rms2COG += xcenter*entriesI*xcenter;
541 sumCOG += entriesI;
542 npoints++;
543 }
544 }
545 if ( sumCOG == 0 ) return xMin;
546 meanCOG/=sumCOG;
547
548 if ( rms ){
549 rms2COG /=sumCOG;
550 (*rms) = TMath::Sqrt(TMath::Abs(meanCOG*meanCOG-rms2COG));
551 if ( npoints == 1 ) (*rms) = binWidth/TMath::Sqrt(12);
552 }
553
554 if ( sum )
555 (*sum) = sumCOG;
556
557 return meanCOG;
558}
559
560
561
562///////////////////////////////////////////////////////////////
563////////////// TEST functions /////////////////////////
564///////////////////////////////////////////////////////////////
565
566
567
568
569
570void TStatToolkit::TestGausFit(Int_t nhistos){
571 //
572 // Test performance of the parabolic - gaussian fit - compare it with
573 // ROOT gauss fit
574 // nhistos - number of histograms to be used for test
575 //
576 TTreeSRedirector *pcstream = new TTreeSRedirector("fitdebug.root");
577
578 Float_t *xTrue = new Float_t[nhistos];
579 Float_t *sTrue = new Float_t[nhistos];
580 TVectorD **par1 = new TVectorD*[nhistos];
581 TVectorD **par2 = new TVectorD*[nhistos];
582 TMatrixD dummy(3,3);
583
584
585 TH1F **h1f = new TH1F*[nhistos];
586 TF1 *myg = new TF1("myg","gaus");
587 TF1 *fit = new TF1("fit","gaus");
588 gRandom->SetSeed(0);
589
590 //init
591 for (Int_t i=0;i<nhistos; i++){
592 par1[i] = new TVectorD(3);
593 par2[i] = new TVectorD(3);
594 h1f[i] = new TH1F(Form("h1f%d",i),Form("h1f%d",i),20,-10,10);
595 xTrue[i]= gRandom->Rndm();
596 gSystem->Sleep(2);
597 sTrue[i]= .75+gRandom->Rndm()*.5;
598 myg->SetParameters(1,xTrue[i],sTrue[i]);
599 h1f[i]->FillRandom("myg");
600 }
601
602 TStopwatch s;
603 s.Start();
604 //standard gaus fit
605 for (Int_t i=0; i<nhistos; i++){
606 h1f[i]->Fit(fit,"0q");
607 (*par1[i])(0) = fit->GetParameter(0);
608 (*par1[i])(1) = fit->GetParameter(1);
609 (*par1[i])(2) = fit->GetParameter(2);
610 }
611 s.Stop();
612 printf("Gaussian fit\t");
613 s.Print();
614
615 s.Start();
616 //TStatToolkit gaus fit
617 for (Int_t i=0; i<nhistos; i++){
618 TStatToolkit::FitGaus(h1f[i]->GetArray()+1,h1f[i]->GetNbinsX(),h1f[i]->GetXaxis()->GetXmin(),h1f[i]->GetXaxis()->GetXmax(),par2[i],&dummy);
619 }
620
621 s.Stop();
622 printf("Parabolic fit\t");
623 s.Print();
624 //write stream
625 for (Int_t i=0;i<nhistos; i++){
626 Float_t xt = xTrue[i];
627 Float_t st = sTrue[i];
628 (*pcstream)<<"data"
629 <<"xTrue="<<xt
630 <<"sTrue="<<st
631 <<"pg.="<<(par1[i])
632 <<"pa.="<<(par2[i])
633 <<"\n";
634 }
635 //delete pointers
636 for (Int_t i=0;i<nhistos; i++){
637 delete par1[i];
638 delete par2[i];
639 delete h1f[i];
640 }
641 delete pcstream;
642 delete []h1f;
643 delete []xTrue;
644 delete []sTrue;
645 //
646 delete []par1;
647 delete []par2;
648
649}
650
651
652
653TGraph2D * TStatToolkit::MakeStat2D(TH3 * his, Int_t delta0, Int_t delta1, Int_t type){
654 //
655 //
656 //
657 // delta - number of bins to integrate
658 // type - 0 - mean value
659
660 TAxis * xaxis = his->GetXaxis();
661 TAxis * yaxis = his->GetYaxis();
662 // TAxis * zaxis = his->GetZaxis();
663 Int_t nbinx = xaxis->GetNbins();
664 Int_t nbiny = yaxis->GetNbins();
665 char name[1000];
666 Int_t icount=0;
667 TGraph2D *graph = new TGraph2D(nbinx*nbiny);
668 TF1 f1("f1","gaus");
669 for (Int_t ix=0; ix<nbinx;ix++)
670 for (Int_t iy=0; iy<nbiny;iy++){
671 Float_t xcenter = xaxis->GetBinCenter(ix);
672 Float_t ycenter = yaxis->GetBinCenter(iy);
cb1d20de 673 snprintf(name,1000,"%s_%d_%d",his->GetName(), ix,iy);
21f3a443 674 TH1 *projection = his->ProjectionZ(name,ix-delta0,ix+delta0,iy-delta1,iy+delta1);
675 Float_t stat= 0;
676 if (type==0) stat = projection->GetMean();
677 if (type==1) stat = projection->GetRMS();
678 if (type==2 || type==3){
679 TVectorD vec(3);
680 TStatToolkit::LTM((TH1F*)projection,&vec,0.7);
681 if (type==2) stat= vec[1];
682 if (type==3) stat= vec[0];
683 }
684 if (type==4|| type==5){
685 projection->Fit(&f1);
686 if (type==4) stat= f1.GetParameter(1);
687 if (type==5) stat= f1.GetParameter(2);
688 }
689 //printf("%d\t%f\t%f\t%f\n", icount,xcenter, ycenter, stat);
690 graph->SetPoint(icount,xcenter, ycenter, stat);
691 icount++;
692 }
693 return graph;
694}
695
696TGraph * TStatToolkit::MakeStat1D(TH3 * his, Int_t delta1, Int_t type){
697 //
698 //
699 //
700 // delta - number of bins to integrate
701 // type - 0 - mean value
702
703 TAxis * xaxis = his->GetXaxis();
704 TAxis * yaxis = his->GetYaxis();
705 // TAxis * zaxis = his->GetZaxis();
706 Int_t nbinx = xaxis->GetNbins();
707 Int_t nbiny = yaxis->GetNbins();
708 char name[1000];
709 Int_t icount=0;
710 TGraph *graph = new TGraph(nbinx);
711 TF1 f1("f1","gaus");
712 for (Int_t ix=0; ix<nbinx;ix++){
713 Float_t xcenter = xaxis->GetBinCenter(ix);
714 // Float_t ycenter = yaxis->GetBinCenter(iy);
cb1d20de 715 snprintf(name,1000,"%s_%d",his->GetName(), ix);
21f3a443 716 TH1 *projection = his->ProjectionZ(name,ix-delta1,ix+delta1,0,nbiny);
717 Float_t stat= 0;
718 if (type==0) stat = projection->GetMean();
719 if (type==1) stat = projection->GetRMS();
720 if (type==2 || type==3){
721 TVectorD vec(3);
722 TStatToolkit::LTM((TH1F*)projection,&vec,0.7);
723 if (type==2) stat= vec[1];
724 if (type==3) stat= vec[0];
725 }
726 if (type==4|| type==5){
727 projection->Fit(&f1);
728 if (type==4) stat= f1.GetParameter(1);
729 if (type==5) stat= f1.GetParameter(2);
730 }
731 //printf("%d\t%f\t%f\t%f\n", icount,xcenter, ycenter, stat);
732 graph->SetPoint(icount,xcenter, stat);
733 icount++;
734 }
735 return graph;
736}
737
738
739
740
741
88b1c775 742TString* 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 743 //
744 // fit an arbitrary function, specified by formula into the data, specified by drawCommand and cuts
745 // returns chi2, fitParam and covMatrix
746 // returns TString with fitted formula
747 //
dd46129c 748
21f3a443 749 TString formulaStr(formula);
750 TString drawStr(drawCommand);
751 TString cutStr(cuts);
dd46129c 752 TString ferr("1");
753
754 TString strVal(drawCommand);
755 if (strVal.Contains(":")){
756 TObjArray* valTokens = strVal.Tokenize(":");
757 drawStr = valTokens->At(0)->GetName();
758 ferr = valTokens->At(1)->GetName();
759 }
760
21f3a443 761
762 formulaStr.ReplaceAll("++", "~");
763 TObjArray* formulaTokens = formulaStr.Tokenize("~");
764 Int_t dim = formulaTokens->GetEntriesFast();
765
766 fitParam.ResizeTo(dim);
767 covMatrix.ResizeTo(dim,dim);
768
769 TLinearFitter* fitter = new TLinearFitter(dim+1, Form("hyp%d",dim));
770 fitter->StoreData(kTRUE);
771 fitter->ClearPoints();
772
773 Int_t entries = tree->Draw(drawStr.Data(), cutStr.Data(), "goff", stop-start, start);
774 if (entries == -1) return new TString("An ERROR has occured during fitting!");
bd7b4d18 775 Double_t **values = new Double_t*[dim+1] ;
776 for (Int_t i=0; i<dim+1; i++) values[i]=NULL;
dd46129c 777 //
778 entries = tree->Draw(ferr.Data(), cutStr.Data(), "goff", stop-start, start);
b8072cce 779 if (entries == -1) {
780 delete []values;
781 return new TString("An ERROR has occured during fitting!");
782 }
dd46129c 783 Double_t *errors = new Double_t[entries];
784 memcpy(errors, tree->GetV1(), entries*sizeof(Double_t));
21f3a443 785
786 for (Int_t i = 0; i < dim + 1; i++){
787 Int_t centries = 0;
788 if (i < dim) centries = tree->Draw(((TObjString*)formulaTokens->At(i))->GetName(), cutStr.Data(), "goff", stop-start,start);
789 else centries = tree->Draw(drawStr.Data(), cutStr.Data(), "goff", stop-start,start);
790
b8072cce 791 if (entries != centries) {
792 delete []errors;
793 delete []values;
794 return new TString("An ERROR has occured during fitting!");
795 }
21f3a443 796 values[i] = new Double_t[entries];
797 memcpy(values[i], tree->GetV1(), entries*sizeof(Double_t));
798 }
799
800 // add points to the fitter
801 for (Int_t i = 0; i < entries; i++){
802 Double_t x[1000];
803 for (Int_t j=0; j<dim;j++) x[j]=values[j][i];
dd46129c 804 fitter->AddPoint(x, values[dim][i], errors[i]);
21f3a443 805 }
806
807 fitter->Eval();
2c629c56 808 if (frac>0.5 && frac<1){
809 fitter->EvalRobust(frac);
88b1c775 810 }else{
811 if (fix0) {
812 fitter->FixParameter(0,0);
813 fitter->Eval();
814 }
2c629c56 815 }
21f3a443 816 fitter->GetParameters(fitParam);
817 fitter->GetCovarianceMatrix(covMatrix);
818 chi2 = fitter->GetChisquare();
b8072cce 819 npoints = entries;
21f3a443 820 TString *preturnFormula = new TString(Form("( %f+",fitParam[0])), &returnFormula = *preturnFormula;
821
822 for (Int_t iparam = 0; iparam < dim; iparam++) {
823 returnFormula.Append(Form("%s*(%f)",((TObjString*)formulaTokens->At(iparam))->GetName(),fitParam[iparam+1]));
824 if (iparam < dim-1) returnFormula.Append("+");
825 }
826 returnFormula.Append(" )");
4d61c301 827
828
b8072cce 829 for (Int_t j=0; j<dim+1;j++) delete [] values[j];
4d61c301 830
831
cb1d20de 832 delete formulaTokens;
833 delete fitter;
834 delete[] values;
b8072cce 835 delete[] errors;
cb1d20de 836 return preturnFormula;
837}
838
839TString* 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){
840 //
841 // fit an arbitrary function, specified by formula into the data, specified by drawCommand and cuts
842 // returns chi2, fitParam and covMatrix
843 // returns TString with fitted formula
844 //
845
846 TString formulaStr(formula);
847 TString drawStr(drawCommand);
848 TString cutStr(cuts);
849 TString ferr("1");
850
851 TString strVal(drawCommand);
852 if (strVal.Contains(":")){
853 TObjArray* valTokens = strVal.Tokenize(":");
854 drawStr = valTokens->At(0)->GetName();
855 ferr = valTokens->At(1)->GetName();
856 }
857
858
859 formulaStr.ReplaceAll("++", "~");
860 TObjArray* formulaTokens = formulaStr.Tokenize("~");
861 Int_t dim = formulaTokens->GetEntriesFast();
862
863 fitParam.ResizeTo(dim);
864 covMatrix.ResizeTo(dim,dim);
865
866 TLinearFitter* fitter = new TLinearFitter(dim+1, Form("hyp%d",dim));
867 fitter->StoreData(kTRUE);
868 fitter->ClearPoints();
869
870 Int_t entries = tree->Draw(drawStr.Data(), cutStr.Data(), "goff", stop-start, start);
871 if (entries == -1) return new TString("An ERROR has occured during fitting!");
872 Double_t **values = new Double_t*[dim+1] ;
bd7b4d18 873 for (Int_t i=0; i<dim+1; i++) values[i]=NULL;
cb1d20de 874 //
875 entries = tree->Draw(ferr.Data(), cutStr.Data(), "goff", stop-start, start);
b8072cce 876 if (entries == -1) {
877 delete [] values;
878 return new TString("An ERROR has occured during fitting!");
879 }
cb1d20de 880 Double_t *errors = new Double_t[entries];
881 memcpy(errors, tree->GetV1(), entries*sizeof(Double_t));
882
883 for (Int_t i = 0; i < dim + 1; i++){
884 Int_t centries = 0;
885 if (i < dim) centries = tree->Draw(((TObjString*)formulaTokens->At(i))->GetName(), cutStr.Data(), "goff", stop-start,start);
886 else centries = tree->Draw(drawStr.Data(), cutStr.Data(), "goff", stop-start,start);
887
b8072cce 888 if (entries != centries) {
889 delete []errors;
890 delete []values;
891 return new TString("An ERROR has occured during fitting!");
892 }
cb1d20de 893 values[i] = new Double_t[entries];
894 memcpy(values[i], tree->GetV1(), entries*sizeof(Double_t));
895 }
896
897 // add points to the fitter
898 for (Int_t i = 0; i < entries; i++){
899 Double_t x[1000];
900 for (Int_t j=0; j<dim;j++) x[j]=values[j][i];
901 fitter->AddPoint(x, values[dim][i], errors[i]);
902 }
903 if (constrain>0){
904 for (Int_t i = 0; i < dim; i++){
905 Double_t x[1000];
906 for (Int_t j=0; j<dim;j++) if (i!=j) x[j]=0;
907 x[i]=1.;
908 fitter->AddPoint(x, 0, constrain);
909 }
910 }
911
912
913 fitter->Eval();
914 if (frac>0.5 && frac<1){
915 fitter->EvalRobust(frac);
916 }
917 fitter->GetParameters(fitParam);
918 fitter->GetCovarianceMatrix(covMatrix);
919 chi2 = fitter->GetChisquare();
920 npoints = entries;
cb1d20de 921
922 TString *preturnFormula = new TString(Form("( %f+",fitParam[0])), &returnFormula = *preturnFormula;
923
924 for (Int_t iparam = 0; iparam < dim; iparam++) {
925 returnFormula.Append(Form("%s*(%f)",((TObjString*)formulaTokens->At(iparam))->GetName(),fitParam[iparam+1]));
926 if (iparam < dim-1) returnFormula.Append("+");
927 }
928 returnFormula.Append(" )");
929
b8072cce 930 for (Int_t j=0; j<dim+1;j++) delete [] values[j];
cb1d20de 931
932
933
934 delete formulaTokens;
935 delete fitter;
936 delete[] values;
b8072cce 937 delete[] errors;
cb1d20de 938 return preturnFormula;
939}
940
941
942
943TString* 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){
944 //
945 // fit an arbitrary function, specified by formula into the data, specified by drawCommand and cuts
946 // returns chi2, fitParam and covMatrix
947 // returns TString with fitted formula
948 //
949
950 TString formulaStr(formula);
951 TString drawStr(drawCommand);
952 TString cutStr(cuts);
953 TString ferr("1");
954
955 TString strVal(drawCommand);
956 if (strVal.Contains(":")){
957 TObjArray* valTokens = strVal.Tokenize(":");
958 drawStr = valTokens->At(0)->GetName();
959 ferr = valTokens->At(1)->GetName();
960 }
961
962
963 formulaStr.ReplaceAll("++", "~");
964 TObjArray* formulaTokens = formulaStr.Tokenize("~");
965 Int_t dim = formulaTokens->GetEntriesFast();
966
967 fitParam.ResizeTo(dim);
968 covMatrix.ResizeTo(dim,dim);
969 TString fitString="x0";
970 for (Int_t i=1; i<dim; i++) fitString+=Form("++x%d",i);
971 TLinearFitter* fitter = new TLinearFitter(dim, fitString.Data());
972 fitter->StoreData(kTRUE);
973 fitter->ClearPoints();
974
975 Int_t entries = tree->Draw(drawStr.Data(), cutStr.Data(), "goff", stop-start, start);
976 if (entries == -1) return new TString("An ERROR has occured during fitting!");
977 Double_t **values = new Double_t*[dim+1] ;
bd7b4d18 978 for (Int_t i=0; i<dim+1; i++) values[i]=NULL;
cb1d20de 979 //
980 entries = tree->Draw(ferr.Data(), cutStr.Data(), "goff", stop-start, start);
b8072cce 981 if (entries == -1) {
982 delete []values;
983 return new TString("An ERROR has occured during fitting!");
984 }
cb1d20de 985 Double_t *errors = new Double_t[entries];
986 memcpy(errors, tree->GetV1(), entries*sizeof(Double_t));
987
988 for (Int_t i = 0; i < dim + 1; i++){
989 Int_t centries = 0;
990 if (i < dim) centries = tree->Draw(((TObjString*)formulaTokens->At(i))->GetName(), cutStr.Data(), "goff", stop-start,start);
991 else centries = tree->Draw(drawStr.Data(), cutStr.Data(), "goff", stop-start,start);
992
b8072cce 993 if (entries != centries) {
994 delete []errors;
995 delete []values;
996 return new TString("An ERROR has occured during fitting!");
997 }
cb1d20de 998 values[i] = new Double_t[entries];
999 memcpy(values[i], tree->GetV1(), entries*sizeof(Double_t));
1000 }
1001
1002 // add points to the fitter
1003 for (Int_t i = 0; i < entries; i++){
1004 Double_t x[1000];
1005 for (Int_t j=0; j<dim;j++) x[j]=values[j][i];
1006 fitter->AddPoint(x, values[dim][i], errors[i]);
1007 }
1008
1009 fitter->Eval();
1010 if (frac>0.5 && frac<1){
1011 fitter->EvalRobust(frac);
1012 }
1013 fitter->GetParameters(fitParam);
1014 fitter->GetCovarianceMatrix(covMatrix);
1015 chi2 = fitter->GetChisquare();
1016 npoints = entries;
cb1d20de 1017
1018 TString *preturnFormula = new TString("("), &returnFormula = *preturnFormula;
1019
1020 for (Int_t iparam = 0; iparam < dim; iparam++) {
1021 returnFormula.Append(Form("%s*(%f)",((TObjString*)formulaTokens->At(iparam))->GetName(),fitParam[iparam]));
1022 if (iparam < dim-1) returnFormula.Append("+");
1023 }
1024 returnFormula.Append(" )");
1025
1026
b8072cce 1027 for (Int_t j=0; j<dim+1;j++) delete [] values[j];
cb1d20de 1028
21f3a443 1029 delete formulaTokens;
1030 delete fitter;
1031 delete[] values;
b8072cce 1032 delete[] errors;
21f3a443 1033 return preturnFormula;
1034}
7c9cf6e4 1035
1036
1037
1038
1039
3d7cc0b4 1040Int_t TStatToolkit::GetFitIndex(const TString fString, const TString subString){
7c9cf6e4 1041 //
1042 // fitString - ++ separated list of fits
1043 // substring - ++ separated list of the requiered substrings
1044 //
1045 // return the last occurance of substring in fit string
1046 //
1047 TObjArray *arrFit = fString.Tokenize("++");
1048 TObjArray *arrSub = subString.Tokenize("++");
1049 Int_t index=-1;
1050 for (Int_t i=0; i<arrFit->GetEntries(); i++){
1051 Bool_t isOK=kTRUE;
1052 TString str =arrFit->At(i)->GetName();
1053 for (Int_t isub=0; isub<arrSub->GetEntries(); isub++){
1054 if (str.Contains(arrSub->At(isub)->GetName())==0) isOK=kFALSE;
1055 }
1056 if (isOK) index=i;
1057 }
1058 return index;
1059}
1060
1061
3d7cc0b4 1062TString TStatToolkit::FilterFit(const TString &input, const TString filter, TVectorD &param, TMatrixD & covar){
7c9cf6e4 1063 //
1064 // Filter fit expression make sub-fit
1065 //
1066 TObjArray *array0= input.Tokenize("++");
1067 TObjArray *array1= filter.Tokenize("++");
1068 //TString *presult=new TString("(0");
1069 TString result="(0.0";
1070 for (Int_t i=0; i<array0->GetEntries(); i++){
1071 Bool_t isOK=kTRUE;
1072 TString str(array0->At(i)->GetName());
1073 for (Int_t j=0; j<array1->GetEntries(); j++){
1074 if (str.Contains(array1->At(j)->GetName())==0) isOK=kFALSE;
1075 }
1076 if (isOK) {
1077 result+="+"+str;
1078 result+=Form("*(%f)",param[i+1]);
1079 printf("%f\t%f\t%s\n",param[i+1], TMath::Sqrt(covar(i+1,i+1)),str.Data());
1080 }
1081 }
1082 result+="-0.)";
1083 return result;
1084}
1085
1086void TStatToolkit::Update1D(Double_t delta, Double_t sigma, Int_t s1, TMatrixD &vecXk, TMatrixD &covXk){
1087 //
1088 // Update parameters and covariance - with one measurement
1089 // Input:
1090 // vecXk - input vector - Updated in function
1091 // covXk - covariance matrix - Updated in function
1092 // delta, sigma, s1 - new measurement, rms of new measurement and the index of measurement
1093 const Int_t knMeas=1;
1094 Int_t knElem=vecXk.GetNrows();
1095
1096 TMatrixD mat1(knElem,knElem); // update covariance matrix
1097 TMatrixD matHk(1,knElem); // vector to mesurement
1098 TMatrixD vecYk(knMeas,1); // Innovation or measurement residual
1099 TMatrixD matHkT(knElem,knMeas); // helper matrix Hk transpose
1100 TMatrixD matSk(knMeas,knMeas); // Innovation (or residual) covariance
1101 TMatrixD matKk(knElem,knMeas); // Optimal Kalman gain
1102 TMatrixD covXk2(knElem,knElem); // helper matrix
1103 TMatrixD covXk3(knElem,knElem); // helper matrix
1104 TMatrixD vecZk(1,1);
1105 TMatrixD measR(1,1);
1106 vecZk(0,0)=delta;
1107 measR(0,0)=sigma*sigma;
1108 //
1109 // reset matHk
1110 for (Int_t iel=0;iel<knElem;iel++)
1111 for (Int_t ip=0;ip<knMeas;ip++) matHk(ip,iel)=0;
1112 //mat1
1113 for (Int_t iel=0;iel<knElem;iel++) {
1114 for (Int_t jel=0;jel<knElem;jel++) mat1(iel,jel)=0;
1115 mat1(iel,iel)=1;
1116 }
1117 //
1118 matHk(0, s1)=1;
1119 vecYk = vecZk-matHk*vecXk; // Innovation or measurement residual
1120 matHkT=matHk.T(); matHk.T();
1121 matSk = (matHk*(covXk*matHkT))+measR; // Innovation (or residual) covariance
1122 matSk.Invert();
1123 matKk = (covXk*matHkT)*matSk; // Optimal Kalman gain
1124 vecXk += matKk*vecYk; // updated vector
1125 covXk2= (mat1-(matKk*matHk));
1126 covXk3 = covXk2*covXk;
1127 covXk = covXk3;
1128 Int_t nrows=covXk3.GetNrows();
1129
1130 for (Int_t irow=0; irow<nrows; irow++)
1131 for (Int_t icol=0; icol<nrows; icol++){
1132 // rounding problems - make matrix again symteric
1133 covXk(irow,icol)=(covXk3(irow,icol)+covXk3(icol,irow))*0.5;
1134 }
1135}
1136
1137
1138
3d7cc0b4 1139void TStatToolkit::Constrain1D(const TString &input, const TString filter, TVectorD &param, TMatrixD & covar, Double_t mean, Double_t sigma){
7c9cf6e4 1140 //
1141 // constrain linear fit
1142 // input - string description of fit function
1143 // filter - string filter to select sub fits
1144 // param,covar - parameters and covariance matrix of the fit
1145 // mean,sigma - new measurement uning which the fit is updated
1146 //
ae45c94d 1147
7c9cf6e4 1148 TObjArray *array0= input.Tokenize("++");
1149 TObjArray *array1= filter.Tokenize("++");
1150 TMatrixD paramM(param.GetNrows(),1);
1151 for (Int_t i=0; i<=array0->GetEntries(); i++){paramM(i,0)=param(i);}
1152
ae45c94d 1153 if (filter.Length()==0){
1154 TStatToolkit::Update1D(mean, sigma, 0, paramM, covar);//
1155 }else{
1156 for (Int_t i=0; i<array0->GetEntries(); i++){
1157 Bool_t isOK=kTRUE;
1158 TString str(array0->At(i)->GetName());
1159 for (Int_t j=0; j<array1->GetEntries(); j++){
1160 if (str.Contains(array1->At(j)->GetName())==0) isOK=kFALSE;
1161 }
1162 if (isOK) {
1163 TStatToolkit::Update1D(mean, sigma, i+1, paramM, covar);//
1164 }
7c9cf6e4 1165 }
1166 }
1167 for (Int_t i=0; i<=array0->GetEntries(); i++){
1168 param(i)=paramM(i,0);
1169 }
1170}
1171
ae45c94d 1172TString TStatToolkit::MakeFitString(const TString &input, const TVectorD &param, const TMatrixD & covar, Bool_t verbose){
7c9cf6e4 1173 //
1174 //
1175 //
1176 TObjArray *array0= input.Tokenize("++");
ae45c94d 1177 TString result=Form("(%f",param[0]);
1178 printf("%f\t%f\t\n", param[0], TMath::Sqrt(covar(0,0)));
7c9cf6e4 1179 for (Int_t i=0; i<array0->GetEntries(); i++){
1180 TString str(array0->At(i)->GetName());
1181 result+="+"+str;
1182 result+=Form("*(%f)",param[i+1]);
ae45c94d 1183 if (verbose) printf("%f\t%f\t%s\n", param[i+1], TMath::Sqrt(covar(i+1,i+1)),str.Data());
7c9cf6e4 1184 }
1185 result+="-0.)";
1186 return result;
1187}
df0a2a0a 1188
1189
b3453fe7 1190TGraph * TStatToolkit::MakeGraphSparse(TTree * tree, const char * expr, const char * cut, Int_t mstyle, Int_t mcolor){
df0a2a0a 1191 //
1192 // Make a sparse draw of the variables
b3453fe7 1193 // Writen by Weilin.Yu
df0a2a0a 1194 const Int_t entries = tree->Draw(expr,cut,"goff");
1195 // TGraph * graph = (TGraph*)gPad->GetPrimitive("Graph"); // 2D
b3453fe7 1196 TGraph * graph = 0;
1197 if (tree->GetV3()) graph = new TGraphErrors (entries, tree->GetV2(),tree->GetV1(),0,tree->GetV3());
1198 graph = new TGraphErrors (entries, tree->GetV2(),tree->GetV1(),0,0);
1199 graph->SetMarkerStyle(mstyle);
1200 graph->SetMarkerColor(mcolor);
df0a2a0a 1201 //
1202 Int_t *index = new Int_t[entries];
1203 TMath::Sort(entries,graph->GetX(),index,kFALSE);
1204
3d7cc0b4 1205 Double_t *tempArray = new Double_t[entries];
df0a2a0a 1206
1207 Double_t count = 0.5;
baa0041d 1208 Double_t *vrun = new Double_t[entries];
1209 Int_t icount=0;
1210 //
3d7cc0b4 1211 tempArray[index[0]] = count;
baa0041d 1212 vrun[0] = graph->GetX()[index[0]];
df0a2a0a 1213 for(Int_t i=1;i<entries;i++){
1214 if(graph->GetX()[index[i]]==graph->GetX()[index[i-1]])
3d7cc0b4 1215 tempArray[index[i]] = count;
df0a2a0a 1216 else if(graph->GetX()[index[i]]!=graph->GetX()[index[i-1]]){
1217 count++;
baa0041d 1218 icount++;
3d7cc0b4 1219 tempArray[index[i]] = count;
baa0041d 1220 vrun[icount]=graph->GetX()[index[i]];
df0a2a0a 1221 }
1222 }
1223
1224 const Int_t newNbins = int(count+0.5);
1225 Double_t *newBins = new Double_t[newNbins+1];
1226 for(Int_t i=0; i<=count+1;i++){
1227 newBins[i] = i;
1228 }
1229
b3453fe7 1230 TGraph *graphNew = 0;
1231 if (tree->GetV3()) graphNew = new TGraphErrors(entries,tempArray,graph->GetY(),0,tree->GetV3());
1232 else
1233 graphNew = new TGraphErrors(entries,tempArray,graph->GetY(),0,0);
df0a2a0a 1234 graphNew->GetXaxis()->Set(newNbins,newBins);
1235
1236 Char_t xName[50];
df0a2a0a 1237 for(Int_t i=0;i<count;i++){
baa0041d 1238 snprintf(xName,50,"%d",Int_t(vrun[i]));
df0a2a0a 1239 graphNew->GetXaxis()->SetBinLabel(i+1,xName);
1240 }
1241 graphNew->GetHistogram()->SetTitle("");
b3453fe7 1242 graphNew->SetMarkerStyle(mstyle);
1243 graphNew->SetMarkerColor(mcolor);
3d7cc0b4 1244 delete [] tempArray;
df0a2a0a 1245 delete [] index;
1246 delete [] newBins;
baa0041d 1247 delete [] vrun;
df0a2a0a 1248 return graphNew;
1249}
1250