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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"
3240a856 27#include "TH2F.h"
21f3a443 28#include "TH3.h"
29#include "TF1.h"
30#include "TTree.h"
31#include "TChain.h"
32#include "TObjString.h"
33#include "TLinearFitter.h"
3d7cc0b4 34#include "TGraph2D.h"
35#include "TGraph.h"
b3453fe7 36#include "TGraphErrors.h"
377a7d60 37#include "TMultiGraph.h"
38#include "TCanvas.h"
39#include "TLatex.h"
40#include "TCut.h"
21f3a443 41
42//
43// includes neccessary for test functions
44//
45#include "TSystem.h"
46#include "TRandom.h"
47#include "TStopwatch.h"
48#include "TTreeStream.h"
49
50#include "TStatToolkit.h"
51
52
53ClassImp(TStatToolkit) // Class implementation to enable ROOT I/O
54
55TStatToolkit::TStatToolkit() : TObject()
56{
57 //
58 // Default constructor
59 //
60}
61///////////////////////////////////////////////////////////////////////////
62TStatToolkit::~TStatToolkit()
63{
64 //
65 // Destructor
66 //
67}
68
69
70//_____________________________________________________________________________
71void TStatToolkit::EvaluateUni(Int_t nvectors, Double_t *data, Double_t &mean
72 , Double_t &sigma, Int_t hh)
73{
74 //
75 // Robust estimator in 1D case MI version - (faster than ROOT version)
76 //
77 // For the univariate case
78 // estimates of location and scatter are returned in mean and sigma parameters
79 // the algorithm works on the same principle as in multivariate case -
80 // it finds a subset of size hh with smallest sigma, and then returns mean and
81 // sigma of this subset
82 //
83
84 if (hh==0)
85 hh=(nvectors+2)/2;
86 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};
87 Int_t *index=new Int_t[nvectors];
88 TMath::Sort(nvectors, data, index, kFALSE);
89
90 Int_t nquant = TMath::Min(Int_t(Double_t(((hh*1./nvectors)-0.5)*40))+1, 11);
91 Double_t factor = faclts[TMath::Max(0,nquant-1)];
92
93 Double_t sumx =0;
94 Double_t sumx2 =0;
95 Int_t bestindex = -1;
96 Double_t bestmean = 0;
97 Double_t bestsigma = (data[index[nvectors-1]]-data[index[0]]+1.); // maximal possible sigma
98 bestsigma *=bestsigma;
99
100 for (Int_t i=0; i<hh; i++){
101 sumx += data[index[i]];
102 sumx2 += data[index[i]]*data[index[i]];
103 }
104
105 Double_t norm = 1./Double_t(hh);
bd7b4d18 106 Double_t norm2 = (hh-1)>0 ? 1./Double_t(hh-1):1;
21f3a443 107 for (Int_t i=hh; i<nvectors; i++){
108 Double_t cmean = sumx*norm;
109 Double_t csigma = (sumx2 - hh*cmean*cmean)*norm2;
110 if (csigma<bestsigma){
111 bestmean = cmean;
112 bestsigma = csigma;
113 bestindex = i-hh;
114 }
115
116 sumx += data[index[i]]-data[index[i-hh]];
117 sumx2 += data[index[i]]*data[index[i]]-data[index[i-hh]]*data[index[i-hh]];
118 }
119
120 Double_t bstd=factor*TMath::Sqrt(TMath::Abs(bestsigma));
121 mean = bestmean;
122 sigma = bstd;
123 delete [] index;
124
125}
126
127
128
129void TStatToolkit::EvaluateUniExternal(Int_t nvectors, Double_t *data, Double_t &mean, Double_t &sigma, Int_t hh, Float_t externalfactor)
130{
131 // Modified version of ROOT robust EvaluateUni
132 // robust estimator in 1D case MI version
133 // added external factor to include precision of external measurement
134 //
135
136 if (hh==0)
137 hh=(nvectors+2)/2;
138 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};
139 Int_t *index=new Int_t[nvectors];
140 TMath::Sort(nvectors, data, index, kFALSE);
141 //
142 Int_t nquant = TMath::Min(Int_t(Double_t(((hh*1./nvectors)-0.5)*40))+1, 11);
143 Double_t factor = faclts[0];
144 if (nquant>0){
145 // fix proper normalization - Anja
146 factor = faclts[nquant-1];
147 }
148
149 //
150 //
151 Double_t sumx =0;
152 Double_t sumx2 =0;
153 Int_t bestindex = -1;
154 Double_t bestmean = 0;
155 Double_t bestsigma = -1;
156 for (Int_t i=0; i<hh; i++){
157 sumx += data[index[i]];
158 sumx2 += data[index[i]]*data[index[i]];
159 }
160 //
161 Double_t kfactor = 2.*externalfactor - externalfactor*externalfactor;
162 Double_t norm = 1./Double_t(hh);
163 for (Int_t i=hh; i<nvectors; i++){
164 Double_t cmean = sumx*norm;
165 Double_t csigma = (sumx2*norm - cmean*cmean*kfactor);
166 if (csigma<bestsigma || bestsigma<0){
167 bestmean = cmean;
168 bestsigma = csigma;
169 bestindex = i-hh;
170 }
171 //
172 //
173 sumx += data[index[i]]-data[index[i-hh]];
174 sumx2 += data[index[i]]*data[index[i]]-data[index[i-hh]]*data[index[i-hh]];
175 }
176
177 Double_t bstd=factor*TMath::Sqrt(TMath::Abs(bestsigma));
178 mean = bestmean;
179 sigma = bstd;
180 delete [] index;
181}
182
183
184//_____________________________________________________________________________
185Int_t TStatToolkit::Freq(Int_t n, const Int_t *inlist
186 , Int_t *outlist, Bool_t down)
187{
188 //
189 // Sort eleements according occurancy
190 // The size of output array has is 2*n
191 //
192
193 Int_t * sindexS = new Int_t[n]; // temp array for sorting
194 Int_t * sindexF = new Int_t[2*n];
b8072cce 195 for (Int_t i=0;i<n;i++) sindexS[i]=0;
196 for (Int_t i=0;i<2*n;i++) sindexF[i]=0;
21f3a443 197 //
198 TMath::Sort(n,inlist, sindexS, down);
199 Int_t last = inlist[sindexS[0]];
200 Int_t val = last;
201 sindexF[0] = 1;
202 sindexF[0+n] = last;
203 Int_t countPos = 0;
204 //
205 // find frequency
206 for(Int_t i=1;i<n; i++){
207 val = inlist[sindexS[i]];
208 if (last == val) sindexF[countPos]++;
209 else{
210 countPos++;
211 sindexF[countPos+n] = val;
212 sindexF[countPos]++;
213 last =val;
214 }
215 }
216 if (last==val) countPos++;
217 // sort according frequency
218 TMath::Sort(countPos, sindexF, sindexS, kTRUE);
219 for (Int_t i=0;i<countPos;i++){
220 outlist[2*i ] = sindexF[sindexS[i]+n];
221 outlist[2*i+1] = sindexF[sindexS[i]];
222 }
223 delete [] sindexS;
224 delete [] sindexF;
225
226 return countPos;
227
228}
229
230//___TStatToolkit__________________________________________________________________________
3d7cc0b4 231void TStatToolkit::TruncatedMean(const TH1 * his, TVectorD *param, Float_t down, Float_t up, Bool_t verbose){
21f3a443 232 //
233 //
234 //
235 Int_t nbins = his->GetNbinsX();
236 Float_t nentries = his->GetEntries();
237 Float_t sum =0;
238 Float_t mean = 0;
239 Float_t sigma2 = 0;
240 Float_t ncumul=0;
241 for (Int_t ibin=1;ibin<nbins; ibin++){
242 ncumul+= his->GetBinContent(ibin);
243 Float_t fraction = Float_t(ncumul)/Float_t(nentries);
244 if (fraction>down && fraction<up){
245 sum+=his->GetBinContent(ibin);
246 mean+=his->GetBinCenter(ibin)*his->GetBinContent(ibin);
247 sigma2+=his->GetBinCenter(ibin)*his->GetBinCenter(ibin)*his->GetBinContent(ibin);
248 }
249 }
250 mean/=sum;
251 sigma2= TMath::Sqrt(TMath::Abs(sigma2/sum-mean*mean));
252 if (param){
253 (*param)[0] = his->GetMaximum();
254 (*param)[1] = mean;
255 (*param)[2] = sigma2;
256
257 }
258 if (verbose) printf("Mean\t%f\t Sigma2\t%f\n", mean,sigma2);
259}
260
32d8f622 261void TStatToolkit::LTM(TH1 * his, TVectorD *param , Float_t fraction, Bool_t verbose){
21f3a443 262 //
32d8f622 263 // LTM : Trimmed mean on histogram - Modified version for binned data
21f3a443 264 //
32d8f622 265 // Robust statistic to estimate properties of the distribution
266 // See http://en.wikipedia.org/w/index.php?title=Trimmed_estimator&oldid=582847999
267 //
268 // New faster version is under preparation
269 //
270 if (!param) return;
271 (*param)[0]=0;
272 (*param)[1]=0;
273 (*param)[2]=0;
21f3a443 274 Int_t nbins = his->GetNbinsX();
275 Int_t nentries = (Int_t)his->GetEntries();
32d8f622 276 if (nentries<=0) return;
21f3a443 277 Double_t *data = new Double_t[nentries];
278 Int_t npoints=0;
279 for (Int_t ibin=1;ibin<nbins; ibin++){
280 Float_t entriesI = his->GetBinContent(ibin);
281 Float_t xcenter= his->GetBinCenter(ibin);
282 for (Int_t ic=0; ic<entriesI; ic++){
283 if (npoints<nentries){
284 data[npoints]= xcenter;
285 npoints++;
286 }
287 }
288 }
32d8f622 289 Double_t mean, sigma;
21f3a443 290 Int_t npoints2=TMath::Min(Int_t(fraction*Float_t(npoints)),npoints-1);
291 npoints2=TMath::Max(Int_t(0.5*Float_t(npoints)),npoints2);
292 TStatToolkit::EvaluateUni(npoints, data, mean,sigma,npoints2);
293 delete [] data;
294 if (verbose) printf("Mean\t%f\t Sigma2\t%f\n", mean,sigma);if (param){
295 (*param)[0] = his->GetMaximum();
296 (*param)[1] = mean;
297 (*param)[2] = sigma;
298 }
299}
300
94a43b22 301Double_t TStatToolkit::FitGaus(TH1* his, TVectorD *param, TMatrixD */*matrix*/, Float_t xmin, Float_t xmax, Bool_t verbose){
21f3a443 302 //
303 // Fit histogram with gaussian function
304 //
305 // Prameters:
306 // return value- chi2 - if negative ( not enough points)
307 // his - input histogram
308 // param - vector with parameters
309 // xmin, xmax - range to fit - if xmin=xmax=0 - the full histogram range used
310 // Fitting:
311 // 1. Step - make logarithm
312 // 2. Linear fit (parabola) - more robust - always converge
313 // 3. In case of small statistic bins are averaged
314 //
315 static TLinearFitter fitter(3,"pol2");
316 TVectorD par(3);
317 TVectorD sigma(3);
318 TMatrixD mat(3,3);
319 if (his->GetMaximum()<4) return -1;
320 if (his->GetEntries()<12) return -1;
321 if (his->GetRMS()<mat.GetTol()) return -1;
322 Float_t maxEstimate = his->GetEntries()*his->GetBinWidth(1)/TMath::Sqrt((TMath::TwoPi()*his->GetRMS()));
323 Int_t dsmooth = TMath::Nint(6./TMath::Sqrt(maxEstimate));
324
325 if (maxEstimate<1) return -1;
326 Int_t nbins = his->GetNbinsX();
327 Int_t npoints=0;
328 //
329
330
331 if (xmin>=xmax){
332 xmin = his->GetXaxis()->GetXmin();
333 xmax = his->GetXaxis()->GetXmax();
334 }
335 for (Int_t iter=0; iter<2; iter++){
336 fitter.ClearPoints();
337 npoints=0;
338 for (Int_t ibin=1;ibin<nbins+1; ibin++){
339 Int_t countB=1;
340 Float_t entriesI = his->GetBinContent(ibin);
341 for (Int_t delta = -dsmooth; delta<=dsmooth; delta++){
342 if (ibin+delta>1 &&ibin+delta<nbins-1){
343 entriesI += his->GetBinContent(ibin+delta);
344 countB++;
345 }
346 }
347 entriesI/=countB;
348 Double_t xcenter= his->GetBinCenter(ibin);
349 if (xcenter<xmin || xcenter>xmax) continue;
350 Double_t error=1./TMath::Sqrt(countB);
351 Float_t cont=2;
352 if (iter>0){
353 if (par[0]+par[1]*xcenter+par[2]*xcenter*xcenter>20) return 0;
354 cont = TMath::Exp(par[0]+par[1]*xcenter+par[2]*xcenter*xcenter);
355 if (cont>1.) error = 1./TMath::Sqrt(cont*Float_t(countB));
356 }
357 if (entriesI>1&&cont>1){
358 fitter.AddPoint(&xcenter,TMath::Log(Float_t(entriesI)),error);
359 npoints++;
360 }
361 }
362 if (npoints>3){
363 fitter.Eval();
364 fitter.GetParameters(par);
365 }else{
366 break;
367 }
368 }
369 if (npoints<=3){
370 return -1;
371 }
372 fitter.GetParameters(par);
373 fitter.GetCovarianceMatrix(mat);
374 if (TMath::Abs(par[1])<mat.GetTol()) return -1;
375 if (TMath::Abs(par[2])<mat.GetTol()) return -1;
376 Double_t chi2 = fitter.GetChisquare()/Float_t(npoints);
377 //fitter.GetParameters();
378 if (!param) param = new TVectorD(3);
cb1d20de 379 // if (!matrix) matrix = new TMatrixD(3,3); // Covariance matrix to be implemented
21f3a443 380 (*param)[1] = par[1]/(-2.*par[2]);
381 (*param)[2] = 1./TMath::Sqrt(TMath::Abs(-2.*par[2]));
382 (*param)[0] = TMath::Exp(par[0]+ par[1]* (*param)[1] + par[2]*(*param)[1]*(*param)[1]);
383 if (verbose){
384 par.Print();
385 mat.Print();
386 param->Print();
387 printf("Chi2=%f\n",chi2);
388 TF1 * f1= new TF1("f1","[0]*exp(-(x-[1])^2/(2*[2]*[2]))",his->GetXaxis()->GetXmin(),his->GetXaxis()->GetXmax());
389 f1->SetParameter(0, (*param)[0]);
390 f1->SetParameter(1, (*param)[1]);
391 f1->SetParameter(2, (*param)[2]);
392 f1->Draw("same");
393 }
394 return chi2;
395}
396
cb1d20de 397Double_t TStatToolkit::FitGaus(Float_t *arr, Int_t nBins, Float_t xMin, Float_t xMax, TVectorD *param, TMatrixD */*matrix*/, Bool_t verbose){
21f3a443 398 //
399 // Fit histogram with gaussian function
400 //
401 // Prameters:
402 // nbins: size of the array and number of histogram bins
403 // xMin, xMax: histogram range
404 // param: paramters of the fit (0-Constant, 1-Mean, 2-Sigma)
405 // matrix: covariance matrix -- not implemented yet, pass dummy matrix!!!
406 //
407 // Return values:
408 // >0: the chi2 returned by TLinearFitter
409 // -3: only three points have been used for the calculation - no fitter was used
410 // -2: only two points have been used for the calculation - center of gravity was uesed for calculation
411 // -1: only one point has been used for the calculation - center of gravity was uesed for calculation
412 // -4: invalid result!!
413 //
414 // Fitting:
415 // 1. Step - make logarithm
416 // 2. Linear fit (parabola) - more robust - always converge
417 //
418 static TLinearFitter fitter(3,"pol2");
419 static TMatrixD mat(3,3);
420 static Double_t kTol = mat.GetTol();
421 fitter.StoreData(kFALSE);
422 fitter.ClearPoints();
423 TVectorD par(3);
424 TVectorD sigma(3);
3d7cc0b4 425 TMatrixD matA(3,3);
21f3a443 426 TMatrixD b(3,1);
427 Float_t rms = TMath::RMS(nBins,arr);
428 Float_t max = TMath::MaxElement(nBins,arr);
429 Float_t binWidth = (xMax-xMin)/(Float_t)nBins;
430
431 Float_t meanCOG = 0;
432 Float_t rms2COG = 0;
433 Float_t sumCOG = 0;
434
435 Float_t entries = 0;
436 Int_t nfilled=0;
437
438 for (Int_t i=0; i<nBins; i++){
439 entries+=arr[i];
440 if (arr[i]>0) nfilled++;
441 }
442
443 if (max<4) return -4;
444 if (entries<12) return -4;
445 if (rms<kTol) return -4;
446
447 Int_t npoints=0;
448 //
449
450 //
451 for (Int_t ibin=0;ibin<nBins; ibin++){
452 Float_t entriesI = arr[ibin];
453 if (entriesI>1){
454 Double_t xcenter = xMin+(ibin+0.5)*binWidth;
455
456 Float_t error = 1./TMath::Sqrt(entriesI);
457 Float_t val = TMath::Log(Float_t(entriesI));
458 fitter.AddPoint(&xcenter,val,error);
459 if (npoints<3){
3d7cc0b4 460 matA(npoints,0)=1;
461 matA(npoints,1)=xcenter;
462 matA(npoints,2)=xcenter*xcenter;
21f3a443 463 b(npoints,0)=val;
464 meanCOG+=xcenter*entriesI;
465 rms2COG +=xcenter*entriesI*xcenter;
466 sumCOG +=entriesI;
467 }
468 npoints++;
469 }
470 }
471
472
473 Double_t chi2 = 0;
474 if (npoints>=3){
475 if ( npoints == 3 ){
476 //analytic calculation of the parameters for three points
3d7cc0b4 477 matA.Invert();
21f3a443 478 TMatrixD res(1,3);
3d7cc0b4 479 res.Mult(matA,b);
21f3a443 480 par[0]=res(0,0);
481 par[1]=res(0,1);
482 par[2]=res(0,2);
483 chi2 = -3.;
484 } else {
485 // use fitter for more than three points
486 fitter.Eval();
487 fitter.GetParameters(par);
488 fitter.GetCovarianceMatrix(mat);
489 chi2 = fitter.GetChisquare()/Float_t(npoints);
490 }
491 if (TMath::Abs(par[1])<kTol) return -4;
492 if (TMath::Abs(par[2])<kTol) return -4;
493
494 if (!param) param = new TVectorD(3);
cb1d20de 495 //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 496
497 (*param)[1] = par[1]/(-2.*par[2]);
498 (*param)[2] = 1./TMath::Sqrt(TMath::Abs(-2.*par[2]));
499 Double_t lnparam0 = par[0]+ par[1]* (*param)[1] + par[2]*(*param)[1]*(*param)[1];
500 if ( lnparam0>307 ) return -4;
501 (*param)[0] = TMath::Exp(lnparam0);
502 if (verbose){
503 par.Print();
504 mat.Print();
505 param->Print();
506 printf("Chi2=%f\n",chi2);
507 TF1 * f1= new TF1("f1","[0]*exp(-(x-[1])^2/(2*[2]*[2]))",xMin,xMax);
508 f1->SetParameter(0, (*param)[0]);
509 f1->SetParameter(1, (*param)[1]);
510 f1->SetParameter(2, (*param)[2]);
511 f1->Draw("same");
512 }
513 return chi2;
514 }
515
516 if (npoints == 2){
517 //use center of gravity for 2 points
518 meanCOG/=sumCOG;
519 rms2COG /=sumCOG;
520 (*param)[0] = max;
521 (*param)[1] = meanCOG;
522 (*param)[2] = TMath::Sqrt(TMath::Abs(meanCOG*meanCOG-rms2COG));
523 chi2=-2.;
524 }
525 if ( npoints == 1 ){
526 meanCOG/=sumCOG;
527 (*param)[0] = max;
528 (*param)[1] = meanCOG;
529 (*param)[2] = binWidth/TMath::Sqrt(12);
530 chi2=-1.;
531 }
532 return chi2;
533
534}
535
536
3d7cc0b4 537Float_t TStatToolkit::GetCOG(const Short_t *arr, Int_t nBins, Float_t xMin, Float_t xMax, Float_t *rms, Float_t *sum)
21f3a443 538{
539 //
540 // calculate center of gravity rms and sum for array 'arr' with nBins an a x range xMin to xMax
541 // return COG; in case of failure return xMin
542 //
543 Float_t meanCOG = 0;
544 Float_t rms2COG = 0;
545 Float_t sumCOG = 0;
546 Int_t npoints = 0;
547
548 Float_t binWidth = (xMax-xMin)/(Float_t)nBins;
549
550 for (Int_t ibin=0; ibin<nBins; ibin++){
551 Float_t entriesI = (Float_t)arr[ibin];
552 Double_t xcenter = xMin+(ibin+0.5)*binWidth;
553 if ( entriesI>0 ){
554 meanCOG += xcenter*entriesI;
555 rms2COG += xcenter*entriesI*xcenter;
556 sumCOG += entriesI;
557 npoints++;
558 }
559 }
560 if ( sumCOG == 0 ) return xMin;
561 meanCOG/=sumCOG;
562
563 if ( rms ){
564 rms2COG /=sumCOG;
565 (*rms) = TMath::Sqrt(TMath::Abs(meanCOG*meanCOG-rms2COG));
566 if ( npoints == 1 ) (*rms) = binWidth/TMath::Sqrt(12);
567 }
568
569 if ( sum )
570 (*sum) = sumCOG;
571
572 return meanCOG;
573}
574
575
576
577///////////////////////////////////////////////////////////////
578////////////// TEST functions /////////////////////////
579///////////////////////////////////////////////////////////////
580
581
582
583
584
585void TStatToolkit::TestGausFit(Int_t nhistos){
586 //
587 // Test performance of the parabolic - gaussian fit - compare it with
588 // ROOT gauss fit
589 // nhistos - number of histograms to be used for test
590 //
32d8f622 591 TTreeSRedirector *pcstream = new TTreeSRedirector("fitdebug.root","recreate");
21f3a443 592
593 Float_t *xTrue = new Float_t[nhistos];
594 Float_t *sTrue = new Float_t[nhistos];
595 TVectorD **par1 = new TVectorD*[nhistos];
596 TVectorD **par2 = new TVectorD*[nhistos];
597 TMatrixD dummy(3,3);
598
599
600 TH1F **h1f = new TH1F*[nhistos];
601 TF1 *myg = new TF1("myg","gaus");
602 TF1 *fit = new TF1("fit","gaus");
603 gRandom->SetSeed(0);
604
605 //init
606 for (Int_t i=0;i<nhistos; i++){
607 par1[i] = new TVectorD(3);
608 par2[i] = new TVectorD(3);
609 h1f[i] = new TH1F(Form("h1f%d",i),Form("h1f%d",i),20,-10,10);
610 xTrue[i]= gRandom->Rndm();
611 gSystem->Sleep(2);
612 sTrue[i]= .75+gRandom->Rndm()*.5;
613 myg->SetParameters(1,xTrue[i],sTrue[i]);
614 h1f[i]->FillRandom("myg");
615 }
616
32d8f622 617 TStopwatch s;
21f3a443 618 s.Start();
619 //standard gaus fit
620 for (Int_t i=0; i<nhistos; i++){
621 h1f[i]->Fit(fit,"0q");
622 (*par1[i])(0) = fit->GetParameter(0);
623 (*par1[i])(1) = fit->GetParameter(1);
624 (*par1[i])(2) = fit->GetParameter(2);
625 }
626 s.Stop();
627 printf("Gaussian fit\t");
628 s.Print();
629
630 s.Start();
631 //TStatToolkit gaus fit
632 for (Int_t i=0; i<nhistos; i++){
633 TStatToolkit::FitGaus(h1f[i]->GetArray()+1,h1f[i]->GetNbinsX(),h1f[i]->GetXaxis()->GetXmin(),h1f[i]->GetXaxis()->GetXmax(),par2[i],&dummy);
634 }
635
636 s.Stop();
637 printf("Parabolic fit\t");
638 s.Print();
639 //write stream
640 for (Int_t i=0;i<nhistos; i++){
641 Float_t xt = xTrue[i];
642 Float_t st = sTrue[i];
643 (*pcstream)<<"data"
644 <<"xTrue="<<xt
645 <<"sTrue="<<st
646 <<"pg.="<<(par1[i])
647 <<"pa.="<<(par2[i])
648 <<"\n";
649 }
650 //delete pointers
651 for (Int_t i=0;i<nhistos; i++){
652 delete par1[i];
653 delete par2[i];
654 delete h1f[i];
655 }
656 delete pcstream;
657 delete []h1f;
658 delete []xTrue;
659 delete []sTrue;
660 //
661 delete []par1;
662 delete []par2;
663
664}
665
666
667
668TGraph2D * TStatToolkit::MakeStat2D(TH3 * his, Int_t delta0, Int_t delta1, Int_t type){
669 //
670 //
671 //
672 // delta - number of bins to integrate
673 // type - 0 - mean value
674
675 TAxis * xaxis = his->GetXaxis();
676 TAxis * yaxis = his->GetYaxis();
677 // TAxis * zaxis = his->GetZaxis();
678 Int_t nbinx = xaxis->GetNbins();
679 Int_t nbiny = yaxis->GetNbins();
680 char name[1000];
681 Int_t icount=0;
682 TGraph2D *graph = new TGraph2D(nbinx*nbiny);
683 TF1 f1("f1","gaus");
684 for (Int_t ix=0; ix<nbinx;ix++)
685 for (Int_t iy=0; iy<nbiny;iy++){
686 Float_t xcenter = xaxis->GetBinCenter(ix);
687 Float_t ycenter = yaxis->GetBinCenter(iy);
cb1d20de 688 snprintf(name,1000,"%s_%d_%d",his->GetName(), ix,iy);
21f3a443 689 TH1 *projection = his->ProjectionZ(name,ix-delta0,ix+delta0,iy-delta1,iy+delta1);
690 Float_t stat= 0;
691 if (type==0) stat = projection->GetMean();
692 if (type==1) stat = projection->GetRMS();
693 if (type==2 || type==3){
694 TVectorD vec(3);
695 TStatToolkit::LTM((TH1F*)projection,&vec,0.7);
696 if (type==2) stat= vec[1];
697 if (type==3) stat= vec[0];
698 }
699 if (type==4|| type==5){
700 projection->Fit(&f1);
701 if (type==4) stat= f1.GetParameter(1);
702 if (type==5) stat= f1.GetParameter(2);
703 }
704 //printf("%d\t%f\t%f\t%f\n", icount,xcenter, ycenter, stat);
705 graph->SetPoint(icount,xcenter, ycenter, stat);
706 icount++;
707 }
708 return graph;
709}
710
32d8f622 711TGraphErrors * TStatToolkit::MakeStat1D(TH2 * his, Int_t deltaBin, Double_t fraction, Int_t returnType, Int_t markerStyle, Int_t markerColor){
712 //
713 // function to retrieve the "mean and RMS estimate" of 2D histograms
714 //
715 // Robust statistic to estimate properties of the distribution
716 // See http://en.wikipedia.org/wiki/Trimmed_estimator
717 //
718 // deltaBin - number of bins to integrate (bin+-deltaBin)
719 // fraction - fraction of values for the LTM and for the gauss fit
720 // returnType -
721 // 0 - mean value
722 // 1 - RMS
723 // 2 - LTM mean
724 // 3 - LTM sigma
725 // 4 - Gaus fit mean - on LTM range
726 // 5 - Gaus fit sigma - on LTM range
727 //
21f3a443 728 TAxis * xaxis = his->GetXaxis();
21f3a443 729 Int_t nbinx = xaxis->GetNbins();
21f3a443 730 char name[1000];
731 Int_t icount=0;
32d8f622 732 //
733 TVectorD vecX(nbinx);
734 TVectorD vecXErr(nbinx);
735 TVectorD vecY(nbinx);
736 TVectorD vecYErr(nbinx);
737 //
21f3a443 738 TF1 f1("f1","gaus");
32d8f622 739 TVectorD vecLTM(3);
740
21f3a443 741 for (Int_t ix=0; ix<nbinx;ix++){
742 Float_t xcenter = xaxis->GetBinCenter(ix);
cb1d20de 743 snprintf(name,1000,"%s_%d",his->GetName(), ix);
32d8f622 744 TH1 *projection = his->ProjectionY(name,TMath::Max(ix-deltaBin,1),TMath::Min(ix+deltaBin,nbinx));
745 Double_t stat= 0;
746 Double_t err =0;
747 TStatToolkit::LTM((TH1F*)projection,&vecLTM,fraction);
748 //
749 if (returnType==0) {
750 stat = projection->GetMean();
751 err = projection->GetMeanError();
21f3a443 752 }
32d8f622 753 if (returnType==1) {
754 stat = projection->GetRMS();
755 err = projection->GetRMSError();
21f3a443 756 }
32d8f622 757 if (returnType==2 || returnType==3){
758 if (returnType==2) stat= vecLTM[1];
759 if (returnType==3) stat= vecLTM[2];
760 }
761 if (returnType==4|| returnType==5){
762 projection->Fit(&f1,"QNR","QNR");
763 if (returnType==4) {
764 stat= f1.GetParameter(1);
765 err=f1.GetParError(1);
766 }
767 if (returnType==5) {
768 stat= f1.GetParameter(2);
769 err=f1.GetParError(2);
770 }
771 }
772 vecX[icount]=xcenter;
773 vecY[icount]=stat;
774 vecYErr[icount]=err;
21f3a443 775 icount++;
32d8f622 776 delete projection;
21f3a443 777 }
32d8f622 778 TGraphErrors *graph = new TGraphErrors(icount,vecX.GetMatrixArray(), vecY.GetMatrixArray(),0, vecYErr.GetMatrixArray());
779 graph->SetMarkerStyle(markerStyle);
780 graph->SetMarkerColor(markerColor);
21f3a443 781 return graph;
782}
783
784
785
786
787
88b1c775 788TString* 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 789 //
790 // fit an arbitrary function, specified by formula into the data, specified by drawCommand and cuts
791 // returns chi2, fitParam and covMatrix
792 // returns TString with fitted formula
793 //
dd46129c 794
21f3a443 795 TString formulaStr(formula);
796 TString drawStr(drawCommand);
797 TString cutStr(cuts);
dd46129c 798 TString ferr("1");
799
800 TString strVal(drawCommand);
801 if (strVal.Contains(":")){
802 TObjArray* valTokens = strVal.Tokenize(":");
803 drawStr = valTokens->At(0)->GetName();
804 ferr = valTokens->At(1)->GetName();
09d5920f 805 delete valTokens;
dd46129c 806 }
807
21f3a443 808
809 formulaStr.ReplaceAll("++", "~");
810 TObjArray* formulaTokens = formulaStr.Tokenize("~");
811 Int_t dim = formulaTokens->GetEntriesFast();
812
813 fitParam.ResizeTo(dim);
814 covMatrix.ResizeTo(dim,dim);
815
816 TLinearFitter* fitter = new TLinearFitter(dim+1, Form("hyp%d",dim));
817 fitter->StoreData(kTRUE);
818 fitter->ClearPoints();
819
820 Int_t entries = tree->Draw(drawStr.Data(), cutStr.Data(), "goff", stop-start, start);
09d5920f 821 if (entries == -1) {
822 delete formulaTokens;
823 return new TString("An ERROR has occured during fitting!");
824 }
bd7b4d18 825 Double_t **values = new Double_t*[dim+1] ;
826 for (Int_t i=0; i<dim+1; i++) values[i]=NULL;
dd46129c 827 //
828 entries = tree->Draw(ferr.Data(), cutStr.Data(), "goff", stop-start, start);
b8072cce 829 if (entries == -1) {
09d5920f 830 delete formulaTokens;
b8072cce 831 delete []values;
832 return new TString("An ERROR has occured during fitting!");
833 }
dd46129c 834 Double_t *errors = new Double_t[entries];
835 memcpy(errors, tree->GetV1(), entries*sizeof(Double_t));
21f3a443 836
837 for (Int_t i = 0; i < dim + 1; i++){
838 Int_t centries = 0;
839 if (i < dim) centries = tree->Draw(((TObjString*)formulaTokens->At(i))->GetName(), cutStr.Data(), "goff", stop-start,start);
840 else centries = tree->Draw(drawStr.Data(), cutStr.Data(), "goff", stop-start,start);
841
b8072cce 842 if (entries != centries) {
843 delete []errors;
844 delete []values;
845 return new TString("An ERROR has occured during fitting!");
846 }
21f3a443 847 values[i] = new Double_t[entries];
848 memcpy(values[i], tree->GetV1(), entries*sizeof(Double_t));
849 }
850
851 // add points to the fitter
852 for (Int_t i = 0; i < entries; i++){
853 Double_t x[1000];
854 for (Int_t j=0; j<dim;j++) x[j]=values[j][i];
dd46129c 855 fitter->AddPoint(x, values[dim][i], errors[i]);
21f3a443 856 }
857
858 fitter->Eval();
2c629c56 859 if (frac>0.5 && frac<1){
860 fitter->EvalRobust(frac);
88b1c775 861 }else{
862 if (fix0) {
863 fitter->FixParameter(0,0);
864 fitter->Eval();
865 }
2c629c56 866 }
21f3a443 867 fitter->GetParameters(fitParam);
868 fitter->GetCovarianceMatrix(covMatrix);
869 chi2 = fitter->GetChisquare();
b8072cce 870 npoints = entries;
21f3a443 871 TString *preturnFormula = new TString(Form("( %f+",fitParam[0])), &returnFormula = *preturnFormula;
872
873 for (Int_t iparam = 0; iparam < dim; iparam++) {
874 returnFormula.Append(Form("%s*(%f)",((TObjString*)formulaTokens->At(iparam))->GetName(),fitParam[iparam+1]));
875 if (iparam < dim-1) returnFormula.Append("+");
876 }
877 returnFormula.Append(" )");
4d61c301 878
879
b8072cce 880 for (Int_t j=0; j<dim+1;j++) delete [] values[j];
4d61c301 881
882
cb1d20de 883 delete formulaTokens;
884 delete fitter;
885 delete[] values;
b8072cce 886 delete[] errors;
cb1d20de 887 return preturnFormula;
888}
889
890TString* 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){
891 //
892 // fit an arbitrary function, specified by formula into the data, specified by drawCommand and cuts
893 // returns chi2, fitParam and covMatrix
894 // returns TString with fitted formula
895 //
896
897 TString formulaStr(formula);
898 TString drawStr(drawCommand);
899 TString cutStr(cuts);
900 TString ferr("1");
901
902 TString strVal(drawCommand);
903 if (strVal.Contains(":")){
904 TObjArray* valTokens = strVal.Tokenize(":");
905 drawStr = valTokens->At(0)->GetName();
906 ferr = valTokens->At(1)->GetName();
09d5920f 907 delete valTokens;
cb1d20de 908 }
909
910
911 formulaStr.ReplaceAll("++", "~");
912 TObjArray* formulaTokens = formulaStr.Tokenize("~");
913 Int_t dim = formulaTokens->GetEntriesFast();
914
915 fitParam.ResizeTo(dim);
916 covMatrix.ResizeTo(dim,dim);
917
918 TLinearFitter* fitter = new TLinearFitter(dim+1, Form("hyp%d",dim));
919 fitter->StoreData(kTRUE);
920 fitter->ClearPoints();
921
922 Int_t entries = tree->Draw(drawStr.Data(), cutStr.Data(), "goff", stop-start, start);
09d5920f 923 if (entries == -1) {
924 delete formulaTokens;
925 return new TString("An ERROR has occured during fitting!");
926 }
cb1d20de 927 Double_t **values = new Double_t*[dim+1] ;
bd7b4d18 928 for (Int_t i=0; i<dim+1; i++) values[i]=NULL;
cb1d20de 929 //
930 entries = tree->Draw(ferr.Data(), cutStr.Data(), "goff", stop-start, start);
b8072cce 931 if (entries == -1) {
09d5920f 932 delete formulaTokens;
b8072cce 933 delete [] values;
934 return new TString("An ERROR has occured during fitting!");
935 }
cb1d20de 936 Double_t *errors = new Double_t[entries];
937 memcpy(errors, tree->GetV1(), entries*sizeof(Double_t));
938
939 for (Int_t i = 0; i < dim + 1; i++){
940 Int_t centries = 0;
941 if (i < dim) centries = tree->Draw(((TObjString*)formulaTokens->At(i))->GetName(), cutStr.Data(), "goff", stop-start,start);
942 else centries = tree->Draw(drawStr.Data(), cutStr.Data(), "goff", stop-start,start);
943
b8072cce 944 if (entries != centries) {
945 delete []errors;
946 delete []values;
09d5920f 947 delete formulaTokens;
b8072cce 948 return new TString("An ERROR has occured during fitting!");
949 }
cb1d20de 950 values[i] = new Double_t[entries];
951 memcpy(values[i], tree->GetV1(), entries*sizeof(Double_t));
952 }
953
954 // add points to the fitter
955 for (Int_t i = 0; i < entries; i++){
956 Double_t x[1000];
957 for (Int_t j=0; j<dim;j++) x[j]=values[j][i];
958 fitter->AddPoint(x, values[dim][i], errors[i]);
959 }
960 if (constrain>0){
961 for (Int_t i = 0; i < dim; i++){
962 Double_t x[1000];
963 for (Int_t j=0; j<dim;j++) if (i!=j) x[j]=0;
964 x[i]=1.;
965 fitter->AddPoint(x, 0, constrain);
966 }
967 }
968
969
970 fitter->Eval();
971 if (frac>0.5 && frac<1){
972 fitter->EvalRobust(frac);
973 }
974 fitter->GetParameters(fitParam);
975 fitter->GetCovarianceMatrix(covMatrix);
976 chi2 = fitter->GetChisquare();
977 npoints = entries;
cb1d20de 978
979 TString *preturnFormula = new TString(Form("( %f+",fitParam[0])), &returnFormula = *preturnFormula;
980
981 for (Int_t iparam = 0; iparam < dim; iparam++) {
982 returnFormula.Append(Form("%s*(%f)",((TObjString*)formulaTokens->At(iparam))->GetName(),fitParam[iparam+1]));
983 if (iparam < dim-1) returnFormula.Append("+");
984 }
985 returnFormula.Append(" )");
986
b8072cce 987 for (Int_t j=0; j<dim+1;j++) delete [] values[j];
cb1d20de 988
989
990
991 delete formulaTokens;
992 delete fitter;
993 delete[] values;
b8072cce 994 delete[] errors;
cb1d20de 995 return preturnFormula;
996}
997
998
999
1000TString* 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){
1001 //
1002 // fit an arbitrary function, specified by formula into the data, specified by drawCommand and cuts
1003 // returns chi2, fitParam and covMatrix
1004 // returns TString with fitted formula
1005 //
1006
1007 TString formulaStr(formula);
1008 TString drawStr(drawCommand);
1009 TString cutStr(cuts);
1010 TString ferr("1");
1011
1012 TString strVal(drawCommand);
1013 if (strVal.Contains(":")){
1014 TObjArray* valTokens = strVal.Tokenize(":");
1015 drawStr = valTokens->At(0)->GetName();
09d5920f 1016 ferr = valTokens->At(1)->GetName();
1017 delete valTokens;
cb1d20de 1018 }
1019
1020
1021 formulaStr.ReplaceAll("++", "~");
1022 TObjArray* formulaTokens = formulaStr.Tokenize("~");
1023 Int_t dim = formulaTokens->GetEntriesFast();
1024
1025 fitParam.ResizeTo(dim);
1026 covMatrix.ResizeTo(dim,dim);
1027 TString fitString="x0";
1028 for (Int_t i=1; i<dim; i++) fitString+=Form("++x%d",i);
1029 TLinearFitter* fitter = new TLinearFitter(dim, fitString.Data());
1030 fitter->StoreData(kTRUE);
1031 fitter->ClearPoints();
1032
1033 Int_t entries = tree->Draw(drawStr.Data(), cutStr.Data(), "goff", stop-start, start);
09d5920f 1034 if (entries == -1) {
1035 delete formulaTokens;
1036 return new TString("An ERROR has occured during fitting!");
1037 }
cb1d20de 1038 Double_t **values = new Double_t*[dim+1] ;
bd7b4d18 1039 for (Int_t i=0; i<dim+1; i++) values[i]=NULL;
cb1d20de 1040 //
1041 entries = tree->Draw(ferr.Data(), cutStr.Data(), "goff", stop-start, start);
b8072cce 1042 if (entries == -1) {
1043 delete []values;
09d5920f 1044 delete formulaTokens;
b8072cce 1045 return new TString("An ERROR has occured during fitting!");
1046 }
cb1d20de 1047 Double_t *errors = new Double_t[entries];
1048 memcpy(errors, tree->GetV1(), entries*sizeof(Double_t));
1049
1050 for (Int_t i = 0; i < dim + 1; i++){
1051 Int_t centries = 0;
1052 if (i < dim) centries = tree->Draw(((TObjString*)formulaTokens->At(i))->GetName(), cutStr.Data(), "goff", stop-start,start);
1053 else centries = tree->Draw(drawStr.Data(), cutStr.Data(), "goff", stop-start,start);
1054
b8072cce 1055 if (entries != centries) {
1056 delete []errors;
1057 delete []values;
09d5920f 1058 delete formulaTokens;
b8072cce 1059 return new TString("An ERROR has occured during fitting!");
1060 }
cb1d20de 1061 values[i] = new Double_t[entries];
1062 memcpy(values[i], tree->GetV1(), entries*sizeof(Double_t));
1063 }
1064
1065 // add points to the fitter
1066 for (Int_t i = 0; i < entries; i++){
1067 Double_t x[1000];
1068 for (Int_t j=0; j<dim;j++) x[j]=values[j][i];
1069 fitter->AddPoint(x, values[dim][i], errors[i]);
1070 }
1071
1072 fitter->Eval();
1073 if (frac>0.5 && frac<1){
1074 fitter->EvalRobust(frac);
1075 }
1076 fitter->GetParameters(fitParam);
1077 fitter->GetCovarianceMatrix(covMatrix);
1078 chi2 = fitter->GetChisquare();
1079 npoints = entries;
cb1d20de 1080
1081 TString *preturnFormula = new TString("("), &returnFormula = *preturnFormula;
1082
1083 for (Int_t iparam = 0; iparam < dim; iparam++) {
1084 returnFormula.Append(Form("%s*(%f)",((TObjString*)formulaTokens->At(iparam))->GetName(),fitParam[iparam]));
1085 if (iparam < dim-1) returnFormula.Append("+");
1086 }
1087 returnFormula.Append(" )");
1088
1089
b8072cce 1090 for (Int_t j=0; j<dim+1;j++) delete [] values[j];
cb1d20de 1091
21f3a443 1092 delete formulaTokens;
1093 delete fitter;
1094 delete[] values;
b8072cce 1095 delete[] errors;
21f3a443 1096 return preturnFormula;
1097}
7c9cf6e4 1098
1099
1100
1101
1102
3d7cc0b4 1103Int_t TStatToolkit::GetFitIndex(const TString fString, const TString subString){
7c9cf6e4 1104 //
1105 // fitString - ++ separated list of fits
1106 // substring - ++ separated list of the requiered substrings
1107 //
1108 // return the last occurance of substring in fit string
1109 //
1110 TObjArray *arrFit = fString.Tokenize("++");
1111 TObjArray *arrSub = subString.Tokenize("++");
1112 Int_t index=-1;
1113 for (Int_t i=0; i<arrFit->GetEntries(); i++){
1114 Bool_t isOK=kTRUE;
1115 TString str =arrFit->At(i)->GetName();
1116 for (Int_t isub=0; isub<arrSub->GetEntries(); isub++){
1117 if (str.Contains(arrSub->At(isub)->GetName())==0) isOK=kFALSE;
1118 }
1119 if (isOK) index=i;
1120 }
09d5920f 1121 delete arrFit;
1122 delete arrSub;
7c9cf6e4 1123 return index;
1124}
1125
1126
3d7cc0b4 1127TString TStatToolkit::FilterFit(const TString &input, const TString filter, TVectorD &param, TMatrixD & covar){
7c9cf6e4 1128 //
1129 // Filter fit expression make sub-fit
1130 //
1131 TObjArray *array0= input.Tokenize("++");
1132 TObjArray *array1= filter.Tokenize("++");
1133 //TString *presult=new TString("(0");
1134 TString result="(0.0";
1135 for (Int_t i=0; i<array0->GetEntries(); i++){
1136 Bool_t isOK=kTRUE;
1137 TString str(array0->At(i)->GetName());
1138 for (Int_t j=0; j<array1->GetEntries(); j++){
1139 if (str.Contains(array1->At(j)->GetName())==0) isOK=kFALSE;
1140 }
1141 if (isOK) {
1142 result+="+"+str;
1143 result+=Form("*(%f)",param[i+1]);
1144 printf("%f\t%f\t%s\n",param[i+1], TMath::Sqrt(covar(i+1,i+1)),str.Data());
1145 }
1146 }
1147 result+="-0.)";
09d5920f 1148 delete array0;
1149 delete array1;
7c9cf6e4 1150 return result;
1151}
1152
1153void TStatToolkit::Update1D(Double_t delta, Double_t sigma, Int_t s1, TMatrixD &vecXk, TMatrixD &covXk){
1154 //
1155 // Update parameters and covariance - with one measurement
1156 // Input:
1157 // vecXk - input vector - Updated in function
1158 // covXk - covariance matrix - Updated in function
1159 // delta, sigma, s1 - new measurement, rms of new measurement and the index of measurement
1160 const Int_t knMeas=1;
1161 Int_t knElem=vecXk.GetNrows();
1162
1163 TMatrixD mat1(knElem,knElem); // update covariance matrix
1164 TMatrixD matHk(1,knElem); // vector to mesurement
1165 TMatrixD vecYk(knMeas,1); // Innovation or measurement residual
1166 TMatrixD matHkT(knElem,knMeas); // helper matrix Hk transpose
1167 TMatrixD matSk(knMeas,knMeas); // Innovation (or residual) covariance
1168 TMatrixD matKk(knElem,knMeas); // Optimal Kalman gain
1169 TMatrixD covXk2(knElem,knElem); // helper matrix
1170 TMatrixD covXk3(knElem,knElem); // helper matrix
1171 TMatrixD vecZk(1,1);
1172 TMatrixD measR(1,1);
1173 vecZk(0,0)=delta;
1174 measR(0,0)=sigma*sigma;
1175 //
1176 // reset matHk
1177 for (Int_t iel=0;iel<knElem;iel++)
1178 for (Int_t ip=0;ip<knMeas;ip++) matHk(ip,iel)=0;
1179 //mat1
1180 for (Int_t iel=0;iel<knElem;iel++) {
1181 for (Int_t jel=0;jel<knElem;jel++) mat1(iel,jel)=0;
1182 mat1(iel,iel)=1;
1183 }
1184 //
1185 matHk(0, s1)=1;
1186 vecYk = vecZk-matHk*vecXk; // Innovation or measurement residual
1187 matHkT=matHk.T(); matHk.T();
1188 matSk = (matHk*(covXk*matHkT))+measR; // Innovation (or residual) covariance
1189 matSk.Invert();
1190 matKk = (covXk*matHkT)*matSk; // Optimal Kalman gain
1191 vecXk += matKk*vecYk; // updated vector
1192 covXk2= (mat1-(matKk*matHk));
1193 covXk3 = covXk2*covXk;
1194 covXk = covXk3;
1195 Int_t nrows=covXk3.GetNrows();
1196
1197 for (Int_t irow=0; irow<nrows; irow++)
1198 for (Int_t icol=0; icol<nrows; icol++){
1199 // rounding problems - make matrix again symteric
1200 covXk(irow,icol)=(covXk3(irow,icol)+covXk3(icol,irow))*0.5;
1201 }
1202}
1203
1204
1205
3d7cc0b4 1206void TStatToolkit::Constrain1D(const TString &input, const TString filter, TVectorD &param, TMatrixD & covar, Double_t mean, Double_t sigma){
7c9cf6e4 1207 //
1208 // constrain linear fit
1209 // input - string description of fit function
1210 // filter - string filter to select sub fits
1211 // param,covar - parameters and covariance matrix of the fit
1212 // mean,sigma - new measurement uning which the fit is updated
1213 //
ae45c94d 1214
7c9cf6e4 1215 TObjArray *array0= input.Tokenize("++");
1216 TObjArray *array1= filter.Tokenize("++");
1217 TMatrixD paramM(param.GetNrows(),1);
1218 for (Int_t i=0; i<=array0->GetEntries(); i++){paramM(i,0)=param(i);}
1219
ae45c94d 1220 if (filter.Length()==0){
1221 TStatToolkit::Update1D(mean, sigma, 0, paramM, covar);//
1222 }else{
1223 for (Int_t i=0; i<array0->GetEntries(); i++){
1224 Bool_t isOK=kTRUE;
1225 TString str(array0->At(i)->GetName());
1226 for (Int_t j=0; j<array1->GetEntries(); j++){
1227 if (str.Contains(array1->At(j)->GetName())==0) isOK=kFALSE;
1228 }
1229 if (isOK) {
1230 TStatToolkit::Update1D(mean, sigma, i+1, paramM, covar);//
1231 }
7c9cf6e4 1232 }
1233 }
1234 for (Int_t i=0; i<=array0->GetEntries(); i++){
1235 param(i)=paramM(i,0);
1236 }
09d5920f 1237 delete array0;
1238 delete array1;
7c9cf6e4 1239}
1240
ae45c94d 1241TString TStatToolkit::MakeFitString(const TString &input, const TVectorD &param, const TMatrixD & covar, Bool_t verbose){
7c9cf6e4 1242 //
1243 //
1244 //
1245 TObjArray *array0= input.Tokenize("++");
ae45c94d 1246 TString result=Form("(%f",param[0]);
1247 printf("%f\t%f\t\n", param[0], TMath::Sqrt(covar(0,0)));
7c9cf6e4 1248 for (Int_t i=0; i<array0->GetEntries(); i++){
1249 TString str(array0->At(i)->GetName());
1250 result+="+"+str;
1251 result+=Form("*(%f)",param[i+1]);
ae45c94d 1252 if (verbose) printf("%f\t%f\t%s\n", param[i+1], TMath::Sqrt(covar(i+1,i+1)),str.Data());
7c9cf6e4 1253 }
1254 result+="-0.)";
09d5920f 1255 delete array0;
7c9cf6e4 1256 return result;
1257}
df0a2a0a 1258
1a8f4649 1259TGraphErrors * TStatToolkit::MakeGraphErrors(TTree * tree, const char * expr, const char * cut, Int_t mstyle, Int_t mcolor, Float_t msize, Float_t offset){
1260 //
1261 // Query a graph errors
1262 // return TGraphErrors specified by expr and cut
1263 // Example usage TStatToolkit::MakeGraphError(tree,"Y:X:ErrY","X>0", 25,2,0.4)
1264 // tree - tree with variable
1265 // expr - examp
1266 const Int_t entries = tree->Draw(expr,cut,"goff");
1267 if (entries<=0) {
1268 TStatToolkit t;
1269 t.Error("TStatToolkit::MakeGraphError",Form("Empty or Not valid expression (%s) or cut *%s)", expr,cut));
1270 return 0;
1271 }
1272 if ( tree->GetV2()==0){
1273 TStatToolkit t;
1274 t.Error("TStatToolkit::MakeGraphError",Form("Not valid expression (%s) ", expr));
1275 return 0;
1276 }
1277 TGraphErrors * graph=0;
1278 if ( tree->GetV3()!=0){
1279 graph = new TGraphErrors (entries, tree->GetV2(),tree->GetV1(),0,tree->GetV3());
1280 }else{
1281 graph = new TGraphErrors (entries, tree->GetV2(),tree->GetV1(),0,0);
1282 }
1283 graph->SetMarkerStyle(mstyle);
1284 graph->SetMarkerColor(mcolor);
1285 graph->SetLineColor(mcolor);
1286 if (msize>0) graph->SetMarkerSize(msize);
1287 for(Int_t i=0;i<graph->GetN();i++) graph->GetX()[i]+=offset;
1288 return graph;
1289
1290}
1291
df0a2a0a 1292
377a7d60 1293TGraph * TStatToolkit::MakeGraphSparse(TTree * tree, const char * expr, const char * cut, Int_t mstyle, Int_t mcolor, Float_t msize, Float_t offset){
df0a2a0a 1294 //
1295 // Make a sparse draw of the variables
d02b8756 1296 // Format of expr : Var:Run or Var:Run:ErrorY or Var:Run:ErrorY:ErrorX
1297 // offset : points can slightly be shifted in x for better visibility with more graphs
1298 //
1299 // Written by Weilin.Yu
1300 // updated & merged with QA-code by Patrick Reichelt
1301 //
1302 const Int_t entries = tree->Draw(expr,cut,"goff");
8fb3bea1 1303 if (entries<=0) {
1304 TStatToolkit t;
d02b8756 1305 t.Error("TStatToolkit::MakeGraphSparse",Form("Empty or Not valid expression (%s) or cut (%s)", expr, cut));
8fb3bea1 1306 return 0;
1307 }
df0a2a0a 1308 // TGraph * graph = (TGraph*)gPad->GetPrimitive("Graph"); // 2D
d02b8756 1309
1310 Double_t *graphY, *graphX;
1311 graphY = tree->GetV1();
1312 graphX = tree->GetV2();
1313
1314 // sort according to run number
eda18694 1315 Int_t *index = new Int_t[entries*4];
d02b8756 1316 TMath::Sort(entries,graphX,index,kFALSE);
df0a2a0a 1317
d02b8756 1318 // define arrays for the new graph
1319 Double_t *unsortedX = new Double_t[entries];
1320 Int_t *runNumber = new Int_t[entries];
df0a2a0a 1321 Double_t count = 0.5;
d02b8756 1322
1323 // evaluate arrays for the new graph according to the run-number
baa0041d 1324 Int_t icount=0;
d02b8756 1325 //first entry
1326 unsortedX[index[0]] = count;
1327 runNumber[0] = graphX[index[0]];
1328 // loop the rest of entries
1329 for(Int_t i=1;i<entries;i++)
1330 {
1331 if(graphX[index[i]]==graphX[index[i-1]])
1332 unsortedX[index[i]] = count;
1333 else if(graphX[index[i]]!=graphX[index[i-1]]){
df0a2a0a 1334 count++;
baa0041d 1335 icount++;
d02b8756 1336 unsortedX[index[i]] = count;
1337 runNumber[icount]=graphX[index[i]];
df0a2a0a 1338 }
1339 }
d02b8756 1340
1341 // count the number of xbins (run-wise) for the new graph
df0a2a0a 1342 const Int_t newNbins = int(count+0.5);
1343 Double_t *newBins = new Double_t[newNbins+1];
1344 for(Int_t i=0; i<=count+1;i++){
1345 newBins[i] = i;
1346 }
d02b8756 1347
1348 // define and fill the new graph
b3453fe7 1349 TGraph *graphNew = 0;
d02b8756 1350 if (tree->GetV3()) {
1351 if (tree->GetV4()) {
1352 graphNew = new TGraphErrors(entries,unsortedX,graphY,tree->GetV4(),tree->GetV3());
1353 }
1354 else { graphNew = new TGraphErrors(entries,unsortedX,graphY,0,tree->GetV3()); }
1355 }
1356 else { graphNew = new TGraphErrors(entries,unsortedX,graphY,0,0); }
1357 // with "Set(...)", the x-axis is being sorted
df0a2a0a 1358 graphNew->GetXaxis()->Set(newNbins,newBins);
d02b8756 1359
1360 // set the bins for the x-axis, apply shifting of points
df0a2a0a 1361 Char_t xName[50];
df0a2a0a 1362 for(Int_t i=0;i<count;i++){
d02b8756 1363 snprintf(xName,50,"%d",runNumber[i]);
df0a2a0a 1364 graphNew->GetXaxis()->SetBinLabel(i+1,xName);
d02b8756 1365 graphNew->GetX()[i]+=offset;
df0a2a0a 1366 }
d02b8756 1367
df0a2a0a 1368 graphNew->GetHistogram()->SetTitle("");
d02b8756 1369 graphNew->SetMarkerStyle(mstyle);
b3453fe7 1370 graphNew->SetMarkerColor(mcolor);
70989f8d 1371 if (msize>0) graphNew->SetMarkerSize(msize);
d02b8756 1372 delete [] unsortedX;
1373 delete [] runNumber;
df0a2a0a 1374 delete [] index;
1375 delete [] newBins;
d02b8756 1376 //
df0a2a0a 1377 return graphNew;
1378}
1379
377a7d60 1380
1381
1382//
d02b8756 1383// functions used for the trending
377a7d60 1384//
1385
1386Int_t TStatToolkit::MakeStatAlias(TTree * tree, const char * expr, const char * cut, const char * alias)
1387{
1388 //
1389 // Add alias using statistical values of a given variable.
1390 // (by MI, Patrick Reichelt)
1391 //
1392 // tree - input tree
1393 // expr - variable expression
1394 // cut - selection criteria
1395 // Output - return number of entries used to define variable
1396 // In addition mean, rms, median, and robust mean and rms (choosing fraction of data with smallest RMS)
1397 //
d02b8756 1398 /* Example usage:
1399 1.) create the robust estimators for variable expr="QA.TPC.CPass1.meanTPCncl" and create a corresponding
1400 aliases with the prefix alias[0]="ncl", calculated using fraction alias[1]="0.90"
1401
1402 TStatToolkit::MakeStatAlias(tree,"QA.TPC.CPass1.meanTPCncl","QA.TPC.CPass1.status>0","ncl:0.9");
1403 root [4] tree->GetListOfAliases().Print()
1404 OBJ: TNamed ncl_Median (130.964333+0)
1405 OBJ: TNamed ncl_Mean (122.120387+0)
1406 OBJ: TNamed ncl_RMS (33.509623+0)
1407 OBJ: TNamed ncl_Mean90 (131.503862+0)
1408 OBJ: TNamed ncl_RMS90 (3.738260+0)
1409 */
377a7d60 1410 //
d02b8756 1411 Int_t entries = tree->Draw(expr,cut,"goff");
377a7d60 1412 if (entries<=1){
1413 printf("Expression or cut not valid:\t%s\t%s\n", expr, cut);
1414 return 0;
1415 }
1416 //
1417 TObjArray* oaAlias = TString(alias).Tokenize(":");
1418 if (oaAlias->GetEntries()<2) return 0;
1419 Float_t entryFraction = atof( oaAlias->At(1)->GetName() );
1420 //
1421 Double_t median = TMath::Median(entries,tree->GetV1());
d02b8756 1422 Double_t mean = TMath::Mean(entries,tree->GetV1());
377a7d60 1423 Double_t rms = TMath::RMS(entries,tree->GetV1());
1424 Double_t meanEF=0, rmsEF=0;
1425 TStatToolkit::EvaluateUni(entries, tree->GetV1(), meanEF, rmsEF, entries*entryFraction);
1426 //
d02b8756 1427 tree->SetAlias(Form("%s_Median",oaAlias->At(0)->GetName()), Form("(%f+0)",median));
377a7d60 1428 tree->SetAlias(Form("%s_Mean",oaAlias->At(0)->GetName()), Form("(%f+0)",mean));
1429 tree->SetAlias(Form("%s_RMS",oaAlias->At(0)->GetName()), Form("(%f+0)",rms));
377a7d60 1430 tree->SetAlias(Form("%s_Mean%d",oaAlias->At(0)->GetName(),Int_t(entryFraction*100)), Form("(%f+0)",meanEF));
1431 tree->SetAlias(Form("%s_RMS%d",oaAlias->At(0)->GetName(),Int_t(entryFraction*100)), Form("(%f+0)",rmsEF));
1432 delete oaAlias;
1433 return entries;
1434}
1435
1436Int_t TStatToolkit::SetStatusAlias(TTree * tree, const char * expr, const char * cut, const char * alias)
1437{
1438 //
1439 // Add alias to trending tree using statistical values of a given variable.
1440 // (by MI, Patrick Reichelt)
1441 //
1442 // format of expr : varname (e.g. meanTPCncl)
1443 // format of cut : char like in TCut
1444 // format of alias: alias:query:entryFraction(EF) (fraction of entries used for uniformity evaluation)
1445 // e.g.: varname_Out:(abs(varname-meanEF)>6.*rmsEF):0.8
d02b8756 1446 // available internal variables are: 'varname, Median, Mean, MeanEF, RMS, RMSEF'
1447 // in the alias, 'varname' will be replaced by its content, and 'EF' by the percentage (e.g. MeanEF -> Mean80)
377a7d60 1448 //
1449 /* Example usage:
d02b8756 1450 1.) Define robust mean (possible, but easier done with TStatToolkit::MakeStatAlias(...))
1451 TStatToolkit::SetStatusAlias(tree, "meanTPCnclF", "meanTPCnclF>0", "meanTPCnclF_MeanEF:MeanEF:0.80") ;
377a7d60 1452 root [10] tree->GetListOfAliases()->Print()
1453 Collection name='TList', class='TList', size=1
d02b8756 1454 OBJ: TNamed meanTPCnclF_Mean80 0.899308
377a7d60 1455 2.) create alias outlyers - 6 sigma cut
d02b8756 1456 TStatToolkit::SetStatusAlias(tree, "meanTPCnclF", "meanTPCnclF>0", "meanTPCnclF_Out:(abs(meanTPCnclF-MeanEF)>6.*RMSEF):0.8")
1457 meanTPCnclF_Out ==> (abs(meanTPCnclF-0.899308)>6.*0.016590)
377a7d60 1458 3.) the same functionality as in 2.)
d02b8756 1459 TStatToolkit::SetStatusAlias(tree, "meanTPCnclF", "meanTPCnclF>0", "varname_Out2:(abs(varname-MeanEF)>6.*RMSEF):0.8")
1460 meanTPCnclF_Out2 ==> (abs(meanTPCnclF-0.899308)>6.*0.016590)
377a7d60 1461 */
1462 //
d02b8756 1463 Int_t entries = tree->Draw(expr,cut,"goff");
1464 if (entries<=1){
1465 printf("Expression or cut not valid:\t%s\t%s\n", expr, cut);
1466 return 0;
1467 }
1468 //
377a7d60 1469 TObjArray* oaVar = TString(expr).Tokenize(":");
1470 char varname[50];
377a7d60 1471 snprintf(varname,50,"%s", oaVar->At(0)->GetName());
d02b8756 1472 //
377a7d60 1473 TObjArray* oaAlias = TString(alias).Tokenize(":");
d02b8756 1474 if (oaAlias->GetEntries()<3) return 0;
377a7d60 1475 Float_t entryFraction = atof( oaAlias->At(2)->GetName() );
1476 //
377a7d60 1477 Double_t median = TMath::Median(entries,tree->GetV1());
d02b8756 1478 Double_t mean = TMath::Mean(entries,tree->GetV1());
377a7d60 1479 Double_t rms = TMath::RMS(entries,tree->GetV1());
1480 Double_t meanEF=0, rmsEF=0;
1481 TStatToolkit::EvaluateUni(entries, tree->GetV1(), meanEF, rmsEF, entries*entryFraction);
377a7d60 1482 //
1483 TString sAlias( oaAlias->At(0)->GetName() );
1484 sAlias.ReplaceAll("varname",varname);
d02b8756 1485 sAlias.ReplaceAll("MeanEF", Form("Mean%1.0f",entryFraction*100) );
1486 sAlias.ReplaceAll("RMSEF", Form("RMS%1.0f",entryFraction*100) );
377a7d60 1487 TString sQuery( oaAlias->At(1)->GetName() );
1488 sQuery.ReplaceAll("varname",varname);
1489 sQuery.ReplaceAll("MeanEF", Form("%f",meanEF) );
d02b8756 1490 sQuery.ReplaceAll("RMSEF", Form("%f",rmsEF) ); //make sure to replace 'RMSEF' before 'RMS'...
377a7d60 1491 sQuery.ReplaceAll("Median", Form("%f",median) );
d02b8756 1492 sQuery.ReplaceAll("Mean", Form("%f",mean) );
377a7d60 1493 sQuery.ReplaceAll("RMS", Form("%f",rms) );
d02b8756 1494 printf("define alias:\t%s = %s\n", sAlias.Data(), sQuery.Data());
377a7d60 1495 //
1496 char query[200];
1497 char aname[200];
1498 snprintf(query,200,"%s", sQuery.Data());
1499 snprintf(aname,200,"%s", sAlias.Data());
1500 tree->SetAlias(aname, query);
d02b8756 1501 delete oaVar;
1502 delete oaAlias;
377a7d60 1503 return entries;
1504}
1505
1506TMultiGraph* TStatToolkit::MakeStatusMultGr(TTree * tree, const char * expr, const char * cut, const char * alias, Int_t igr)
1507{
1508 //
1509 // Compute a trending multigraph that shows for which runs a variable has outliers.
1510 // (by MI, Patrick Reichelt)
1511 //
1512 // format of expr : varname:xaxis (e.g. meanTPCncl:run)
1513 // format of cut : char like in TCut
1514 // format of alias: (1):(varname_Out==0):(varname_Out)[:(varname_Warning):...]
1515 // in the alias, 'varname' will be replaced by its content (e.g. varname_Out -> meanTPCncl_Out)
d02b8756 1516 // note: the aliases 'varname_Out' etc have to be defined by function TStatToolkit::SetStatusAlias(...)
377a7d60 1517 // counter igr is used to shift the multigraph in y when filling a TObjArray.
1518 //
1519 TObjArray* oaVar = TString(expr).Tokenize(":");
d02b8756 1520 if (oaVar->GetEntries()<2) return 0;
377a7d60 1521 char varname[50];
1522 char var_x[50];
1523 snprintf(varname,50,"%s", oaVar->At(0)->GetName());
1524 snprintf(var_x ,50,"%s", oaVar->At(1)->GetName());
d02b8756 1525 //
377a7d60 1526 TString sAlias(alias);
1527 sAlias.ReplaceAll("varname",varname);
1528 TObjArray* oaAlias = TString(sAlias.Data()).Tokenize(":");
d02b8756 1529 if (oaAlias->GetEntries()<3) return 0;
377a7d60 1530 //
1531 char query[200];
1532 TMultiGraph* multGr = new TMultiGraph();
1533 Int_t marArr[6] = {24+igr%2, 20+igr%2, 20+igr%2, 20+igr%2, 22, 23};
1534 Int_t colArr[6] = {kBlack, kBlack, kRed, kOrange, kMagenta, kViolet};
1535 Double_t sizArr[6] = {1.2, 1.1, 1.0, 1.0, 1, 1};
1536 const Int_t ngr = oaAlias->GetEntriesFast();
1537 for (Int_t i=0; i<ngr; i++){
1538 if (i==2) continue; // the Fatal(Out) graph will be added in the end to be plotted on top!
1539 snprintf(query,200, "%f*(%s-0.5):%s", 1.+igr, oaAlias->At(i)->GetName(), var_x);
d02b8756 1540 multGr->Add( (TGraphErrors*) TStatToolkit::MakeGraphSparse(tree,query,cut,marArr[i],colArr[i],sizArr[i]) );
377a7d60 1541 }
1542 snprintf(query,200, "%f*(%s-0.5):%s", 1.+igr, oaAlias->At(2)->GetName(), var_x);
d02b8756 1543 multGr->Add( (TGraphErrors*) TStatToolkit::MakeGraphSparse(tree,query,cut,marArr[2],colArr[2],sizArr[2]) );
377a7d60 1544 //
1545 multGr->SetName(varname);
1546 multGr->SetTitle(varname); // used for y-axis labels. // details to be included!
d02b8756 1547 delete oaVar;
1548 delete oaAlias;
377a7d60 1549 return multGr;
1550}
1551
1552
1553void TStatToolkit::AddStatusPad(TCanvas* c1, Float_t padratio, Float_t bottommargin)
1554{
1555 //
1556 // add pad to bottom of canvas for Status graphs (by Patrick Reichelt)
1557 // call function "DrawStatusGraphs(...)" afterwards
1558 //
1559 TCanvas* c1_clone = (TCanvas*) c1->Clone("c1_clone");
1560 c1->Clear();
1561 // produce new pads
1562 c1->cd();
1563 TPad* pad1 = new TPad("pad1", "pad1", 0., padratio, 1., 1.);
1564 pad1->Draw();
1565 pad1->SetNumber(1); // so it can be called via "c1->cd(1);"
1566 c1->cd();
1567 TPad* pad2 = new TPad("pad2", "pad2", 0., 0., 1., padratio);
1568 pad2->Draw();
1569 pad2->SetNumber(2);
1570 // draw original canvas into first pad
1571 c1->cd(1);
1572 c1_clone->DrawClonePad();
1573 pad1->SetBottomMargin(0.001);
1574 pad1->SetRightMargin(0.01);
1575 // set up second pad
1576 c1->cd(2);
1577 pad2->SetGrid(3);
1578 pad2->SetTopMargin(0);
1579 pad2->SetBottomMargin(bottommargin); // for the long x-axis labels (runnumbers)
1580 pad2->SetRightMargin(0.01);
1581}
1582
1583
1584void TStatToolkit::DrawStatusGraphs(TObjArray* oaMultGr)
1585{
1586 //
1587 // draw Status graphs into active pad of canvas (by MI, Patrick Reichelt)
1588 // ...into bottom pad, if called after "AddStatusPad(...)"
1589 //
1590 const Int_t nvars = oaMultGr->GetEntriesFast();
1591 TGraph* grAxis = (TGraph*) ((TMultiGraph*) oaMultGr->At(0))->GetListOfGraphs()->At(0);
1592 grAxis->SetMaximum(0.5*nvars+0.5);
1593 grAxis->SetMinimum(0);
1594 grAxis->GetYaxis()->SetLabelSize(0);
1595 Int_t entries = grAxis->GetN();
1596 printf("entries (via GetN()) = %d\n",entries);
1597 grAxis->GetXaxis()->SetLabelSize(5.7*TMath::Min(TMath::Max(5./entries,0.01),0.03));
1598 grAxis->GetXaxis()->LabelsOption("v");
1599 grAxis->Draw("ap");
1600 //
1601 // draw multigraphs & names of status variables on the y axis
1602 for (Int_t i=0; i<nvars; i++){
1603 ((TMultiGraph*) oaMultGr->At(i))->Draw("p");
1604 TLatex* ylabel = new TLatex(-0.1, 0.5*i+0.5, ((TMultiGraph*) oaMultGr->At(i))->GetTitle());
1605 ylabel->SetTextAlign(32); //hor:right & vert:centered
1606 ylabel->SetTextSize(0.025/gPad->GetHNDC());
1607 ylabel->Draw();
1608 }
1609}
376089b6 1610
1611
1612void TStatToolkit::DrawHistogram(TTree * tree, const char* drawCommand, const char* cuts, const char* histoname, const char* histotitle, Int_t nsigma, Float_t fraction )
1613{
1614 //
1615 // Draw histogram from TTree with robust range
1616 // Only for 1D so far!
1617 //
1618 // Parameters:
1619 // - histoname: name of histogram
1620 // - histotitle: title of histgram
1621 // - fraction: fraction of data to define the robust mean
1622 // - nsigma: nsigma value for range
1623 //
1624
1625 TString drawStr(drawCommand);
1626 TString cutStr(cuts);
1627 Int_t dim = 1;
1628
376089b6 1629 if(!tree) {
1630 cerr<<" Tree pointer is NULL!"<<endl;
1631 return;
1632 }
1633
3240a856 1634 // get entries
376089b6 1635 Int_t entries = tree->Draw(drawStr.Data(), cutStr.Data(), "goff");
1636 if (entries == -1) {
1637 cerr<<"TTree draw returns -1"<<endl;
1638 return;
1639 }
1640
3240a856 1641 // get dimension
1642 if(tree->GetV1()) dim = 1;
1643 if(tree->GetV2()) dim = 2;
1644 if(tree->GetV3()) dim = 3;
1645 if(dim > 2){
1646 cerr<<"TTree has more than 2 dimensions (not yet supported)"<<endl;
1647 return;
1648 }
1649
1650 // draw robust
1651 Double_t meanX, rmsX=0;
1652 Double_t meanY, rmsY=0;
1653 TStatToolkit::EvaluateUni(entries, tree->GetV1(),meanX,rmsX, fraction*entries);
1654 if(dim==2){
1655 TStatToolkit::EvaluateUni(entries, tree->GetV1(),meanY,rmsY, fraction*entries);
1656 TStatToolkit::EvaluateUni(entries, tree->GetV2(),meanX,rmsX, fraction*entries);
1657 }
1658 TH1* hOut;
1659 if(dim==1){
1660 hOut = new TH1F(histoname, histotitle, 200, meanX-nsigma*rmsX, meanX+nsigma*rmsX);
1661 for (Int_t i=0; i<entries; i++) hOut->Fill(tree->GetV1()[i]);
1662 hOut->GetXaxis()->SetTitle(tree->GetHistogram()->GetXaxis()->GetTitle());
1663 hOut->Draw();
1664 }
1665 else if(dim==2){
1666 hOut = new TH2F(histoname, histotitle, 200, meanX-nsigma*rmsX, meanX+nsigma*rmsX,200, meanY-nsigma*rmsY, meanY+nsigma*rmsY);
1667 for (Int_t i=0; i<entries; i++) hOut->Fill(tree->GetV2()[i],tree->GetV1()[i]);
1668 hOut->GetXaxis()->SetTitle(tree->GetHistogram()->GetXaxis()->GetTitle());
1669 hOut->GetYaxis()->SetTitle(tree->GetHistogram()->GetYaxis()->GetTitle());
1670 hOut->Draw("colz");
1671 }
376089b6 1672
1673}