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Reference Guide
HFitImpl.cxx
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1// new HFit function
2//______________________________________________________________________________
3
4
5#include "TH1.h"
6#include "TH2.h"
7#include "TF1.h"
8#include "TF2.h"
9#include "TF3.h"
10#include "TError.h"
11#include "TGraph.h"
12#include "TMultiGraph.h"
13#include "TGraph2D.h"
14#include "THnBase.h"
15
16#include "Fit/Fitter.h"
17#include "Fit/FitConfig.h"
18#include "Fit/BinData.h"
19#include "Fit/UnBinData.h"
20#include "Fit/Chi2FCN.h"
22#include "HFitInterface.h"
24#include "Math/Minimizer.h"
25
26#include "Math/WrappedTF1.h"
28
29#include "TList.h"
30#include "TMath.h"
31#include "TROOT.h"
32
33#include "TVirtualPad.h" // for gPad
34
35#include "TBackCompFitter.h"
36#include "TFitResultPtr.h"
37#include "TFitResult.h"
38
39#include <stdlib.h>
40#include <cmath>
41#include <memory>
42#include <limits>
43
44//#define DEBUG
45
46// utility functions used in TH1::Fit
47
48namespace HFit {
49
50
51 int GetDimension(const TH1 * h1) { return h1->GetDimension(); }
52 int GetDimension(const TGraph * ) { return 1; }
53 int GetDimension(const TMultiGraph * ) { return 1; }
54 int GetDimension(const TGraph2D * ) { return 2; }
55 int GetDimension(const THnBase * s1) { return s1->GetNdimensions(); }
56
57 int CheckFitFunction(const TF1 * f1, int hdim);
58
59
60 void GetFunctionRange(const TF1 & f1, ROOT::Fit::DataRange & range);
61
62 void FitOptionsMake(const char *option, Foption_t &fitOption);
63
64 void CheckGraphFitOptions(Foption_t &fitOption);
65
66
72
73
74 template <class FitObject>
75 TFitResultPtr Fit(FitObject * h1, TF1 *f1 , Foption_t & option , const ROOT::Math::MinimizerOptions & moption, const char *goption, ROOT::Fit::DataRange & range);
76
77 template <class FitObject>
78 void StoreAndDrawFitFunction(FitObject * h1, TF1 * f1, const ROOT::Fit::DataRange & range, bool, bool, const char *goption);
79
80 template <class FitObject>
81 double ComputeChi2(const FitObject & h1, TF1 &f1, bool useRange, bool usePL );
82
83
84
85}
86
87int HFit::CheckFitFunction(const TF1 * f1, int dim) {
88 // Check validity of fitted function
89 if (!f1) {
90 Error("Fit", "function may not be null pointer");
91 return -1;
92 }
93 if (f1->IsZombie()) {
94 Error("Fit", "function is zombie");
95 return -2;
96 }
97
98 int npar = f1->GetNpar();
99 if (npar <= 0) {
100 Error("Fit", "function %s has illegal number of parameters = %d", f1->GetName(), npar);
101 return -3;
102 }
103
104 // Check that function has same dimension as histogram
105 if (f1->GetNdim() > dim) {
106 Error("Fit","function %s dimension, %d, is greater than fit object dimension, %d",
107 f1->GetName(), f1->GetNdim(), dim);
108 return -4;
109 }
110 if (f1->GetNdim() < dim-1) {
111 Error("Fit","function %s dimension, %d, is smaller than fit object dimension -1, %d",
112 f1->GetName(), f1->GetNdim(), dim);
113 return -5;
114 }
115
116 return 0;
117
118}
119
120
122 // get the range form the function and fill and return the DataRange object
123 Double_t fxmin, fymin, fzmin, fxmax, fymax, fzmax;
124 f1.GetRange(fxmin, fymin, fzmin, fxmax, fymax, fzmax);
125 // support only one range - so add only if was not set before
126 if (range.Size(0) == 0) range.AddRange(0,fxmin,fxmax);
127 if (range.Size(1) == 0) range.AddRange(1,fymin,fymax);
128 if (range.Size(2) == 0) range.AddRange(2,fzmin,fzmax);
129 return;
130}
131
132
133template<class FitObject>
134TFitResultPtr HFit::Fit(FitObject * h1, TF1 *f1 , Foption_t & fitOption , const ROOT::Math::MinimizerOptions & minOption, const char *goption, ROOT::Fit::DataRange & range)
135{
136 // perform fit of histograms, or graphs using new fitting classes
137 // use same routines for fitting both graphs and histograms
138
139#ifdef DEBUG
140 printf("fit function %s\n",f1->GetName() );
141#endif
142
143 // replacement function using new fitter
144 int hdim = HFit::GetDimension(h1);
145 int iret = HFit::CheckFitFunction(f1, hdim);
146 if (iret != 0) return iret;
147
148
149
150 // integral option is not supported in this case
151 if (f1->GetNdim() < hdim ) {
152 if (fitOption.Integral) Info("Fit","Ignore Integral option. Model function dimension is less than the data object dimension");
153 if (fitOption.Like) Info("Fit","Ignore Likelihood option. Model function dimension is less than the data object dimension");
154 fitOption.Integral = 0;
155 fitOption.Like = 0;
156 }
157
158 Int_t special = f1->GetNumber();
159 Bool_t linear = f1->IsLinear();
160 Int_t npar = f1->GetNpar();
161 if (special==299+npar) linear = kTRUE; // for polynomial functions
162 // do not use linear fitter in these case
163 if (fitOption.Bound || fitOption.Like || fitOption.Errors || fitOption.Gradient || fitOption.More || fitOption.User|| fitOption.Integral || fitOption.Minuit)
164 linear = kFALSE;
165
166 // create an empty TFitResult
167 std::shared_ptr<TFitResult> tfr(new TFitResult() );
168 // create the fitter from an empty fit result
169 std::shared_ptr<ROOT::Fit::Fitter> fitter(new ROOT::Fit::Fitter(std::static_pointer_cast<ROOT::Fit::FitResult>(tfr) ) );
170 ROOT::Fit::FitConfig & fitConfig = fitter->Config();
171
172 // create options
174 opt.fIntegral = fitOption.Integral;
175 opt.fUseRange = fitOption.Range;
176 opt.fExpErrors = fitOption.PChi2; // pearson chi2 with expected errors
177 if (fitOption.Like || fitOption.PChi2) opt.fUseEmpty = true; // use empty bins in log-likelihood fits
178 if (special==300) opt.fCoordErrors = false; // no need to use coordinate errors in a pol0 fit
179 if (fitOption.NoErrX) opt.fCoordErrors = false; // do not use coordinate errors when requested
180 if (fitOption.W1 ) opt.fErrors1 = true;
181 if (fitOption.W1 > 1) opt.fUseEmpty = true; // use empty bins with weight=1
182
183 if (fitOption.BinVolume) {
184 opt.fBinVolume = true; // scale by bin volume
185 if (fitOption.BinVolume == 2) opt.fNormBinVolume = true; // scale by normalized bin volume
186 }
187
188 if (opt.fUseRange) {
189#ifdef DEBUG
190 printf("use range \n" );
191#endif
193 }
194#ifdef DEBUG
195 printf("range size %d\n", range.Size(0) );
196 if (range.Size(0)) {
197 double x1; double x2; range.GetRange(0,x1,x2);
198 printf(" range in x = [%f,%f] \n",x1,x2);
199 }
200#endif
201
202 // fill data
203 std::shared_ptr<ROOT::Fit::BinData> fitdata(new ROOT::Fit::BinData(opt,range) );
204 ROOT::Fit::FillData(*fitdata, h1, f1);
205 if (fitdata->Size() == 0 ) {
206 Warning("Fit","Fit data is empty ");
207 return -1;
208 }
209
210#ifdef DEBUG
211 printf("HFit:: data size is %d \n",fitdata->Size());
212 for (unsigned int i = 0; i < fitdata->Size(); ++i) {
213 if (fitdata->NDim() == 1) printf(" x[%d] = %f - value = %f \n", i,*(fitdata->Coords(i)),fitdata->Value(i) );
214 }
215#endif
216
217 // switch off linear fitting in case data has coordinate errors and the option is set
218 if (fitdata->GetErrorType() == ROOT::Fit::BinData::kCoordError && fitdata->Opt().fCoordErrors ) linear = false;
219 // linear fit cannot be done also in case of asymmetric errors
220 if (fitdata->GetErrorType() == ROOT::Fit::BinData::kAsymError && fitdata->Opt().fAsymErrors ) linear = false;
221
222 // this functions use the TVirtualFitter
223 if (special != 0 && !fitOption.Bound && !linear) {
224 if (special == 100) ROOT::Fit::InitGaus (*fitdata,f1); // gaussian
225 else if (special == 110 || special == 112) ROOT::Fit::Init2DGaus(*fitdata,f1); // 2D gaussians ( xygaus or bigaus)
226 else if (special == 400) ROOT::Fit::InitGaus (*fitdata,f1); // landau (use the same)
227 else if (special == 410) ROOT::Fit::Init2DGaus(*fitdata,f1); // 2D landau (use the same)
228
229 else if (special == 200) ROOT::Fit::InitExpo (*fitdata, f1); // exponential
230
231 }
232
233
234 // set the fit function
235 // if option grad is specified use gradient
236 if ( (linear || fitOption.Gradient) )
237 fitter->SetFunction(ROOT::Math::WrappedMultiTF1(*f1));
238#ifdef R__HAS_VECCORE
239 else if(f1->IsVectorized())
241#endif
242 else
243 fitter->SetFunction(static_cast<const ROOT::Math::IParamMultiFunction &>(ROOT::Math::WrappedMultiTF1(*f1) ) );
244
245 // error normalization in case of zero error in the data
246 if (fitdata->GetErrorType() == ROOT::Fit::BinData::kNoError) fitConfig.SetNormErrors(true);
247 // error normalization also in case of W or WW options (weights = 1)
248 if (fitdata->Opt().fErrors1) fitConfig.SetNormErrors(true);
249 // normalize errors also in case you are fitting a Ndim histo with a N-1 function
250 if (int(fitdata->NDim()) == hdim -1 ) fitConfig.SetNormErrors(true);
251
252
253 // here need to get some static extra information (like max iterations, error def, etc...)
254
255
256 // parameter settings and transfer the parameters values, names and limits from the functions
257 // is done automatically in the Fitter.cxx
258 for (int i = 0; i < npar; ++i) {
259 ROOT::Fit::ParameterSettings & parSettings = fitConfig.ParSettings(i);
260
261 // check limits
262 double plow,pup;
263 f1->GetParLimits(i,plow,pup);
264 if (plow*pup != 0 && plow >= pup) { // this is a limitation - cannot fix a parameter to zero value
265 parSettings.Fix();
266 }
267 else if (plow < pup ) {
268 if (!TMath::Finite(pup) && TMath::Finite(plow) )
269 parSettings.SetLowerLimit(plow);
270 else if (!TMath::Finite(plow) && TMath::Finite(pup) )
271 parSettings.SetUpperLimit(pup);
272 else
273 parSettings.SetLimits(plow,pup);
274 }
275
276 // set the parameter step size (by default are set to 0.3 of value)
277 // if function provides meaningful error values
278 double err = f1->GetParError(i);
279 if ( err > 0)
280 parSettings.SetStepSize(err);
281 else if (plow < pup && TMath::Finite(plow) && TMath::Finite(pup) ) { // in case of limits improve step sizes
282 double step = 0.1 * (pup - plow);
283 // check if value is not too close to limit otherwise trim value
284 if ( parSettings.Value() < pup && pup - parSettings.Value() < 2 * step )
285 step = (pup - parSettings.Value() ) / 2;
286 else if ( parSettings.Value() > plow && parSettings.Value() - plow < 2 * step )
287 step = (parSettings.Value() - plow ) / 2;
288
289 parSettings.SetStepSize(step);
290 }
291
292
293 }
294
295 // needed for setting precision ?
296 // - Compute sum of squares of errors in the bin range
297 // should maybe use stat[1] ??
298 // Double_t ey, sumw2=0;
299// for (i=hxfirst;i<=hxlast;i++) {
300// ey = GetBinError(i);
301// sumw2 += ey*ey;
302// }
303
304
305 // set all default minimizer options (tolerance, max iterations, etc..)
306 fitConfig.SetMinimizerOptions(minOption);
307
308 // specific print level options
309 if (fitOption.Verbose) fitConfig.MinimizerOptions().SetPrintLevel(3);
310 if (fitOption.Quiet) fitConfig.MinimizerOptions().SetPrintLevel(0);
311
312 // specific minimizer options depending on minimizer
313 if (linear) {
314 if (fitOption.Robust ) {
315 // robust fitting
316 std::string type = "Robust";
317 // if an h is specified print out the value adding to the type
318 if (fitOption.hRobust > 0 && fitOption.hRobust < 1.)
319 type += " (h=" + ROOT::Math::Util::ToString(fitOption.hRobust) + ")";
320 fitConfig.SetMinimizer("Linear",type.c_str());
321 fitConfig.MinimizerOptions().SetTolerance(fitOption.hRobust); // use tolerance for passing robust parameter
322 }
323 else
324 fitConfig.SetMinimizer("Linear","");
325 }
326 else {
327 if (fitOption.More) fitConfig.SetMinimizer("Minuit","MigradImproved");
328 }
329
330
331 // check if Error option (run Hesse and Minos) then
332 if (fitOption.Errors) {
333 // run Hesse and Minos
334 fitConfig.SetParabErrors(true);
335 fitConfig.SetMinosErrors(true);
336 }
337
338
339 // do fitting
340
341#ifdef DEBUG
342 if (fitOption.Like) printf("do likelihood fit...\n");
343 if (linear) printf("do linear fit...\n");
344 printf("do now fit...\n");
345#endif
346
347 bool fitok = false;
348
349
350 // check if can use option user
351 //typedef void (* MinuitFCN_t )(int &npar, double *gin, double &f, double *u, int flag);
352 TVirtualFitter::FCNFunc_t userFcn = 0;
353 if (fitOption.User && TVirtualFitter::GetFitter() ) {
354 userFcn = (TVirtualFitter::GetFitter())->GetFCN();
355 (TVirtualFitter::GetFitter())->SetUserFunc(f1);
356 }
357
358
359 if (fitOption.User && userFcn) // user provided fit objective function
360 fitok = fitter->FitFCN( userFcn );
361 else if (fitOption.Like) {// likelihood fit
362 // perform a weighted likelihood fit by applying weight correction to errors
363 bool weight = ((fitOption.Like & 2) == 2);
364 fitConfig.SetWeightCorrection(weight);
365 bool extended = ((fitOption.Like & 4 ) != 4 );
366 //if (!extended) Info("HFitImpl","Do a not -extended binned fit");
367
368 // pass fitdata as a shared pointer so ownership is shared with Fitter and FitResult class
369 fitok = fitter->LikelihoodFit(fitdata, extended, fitOption.ExecPolicy);
370 }
371 else{ // standard least square fit
372 fitok = fitter->Fit(fitdata, fitOption.ExecPolicy);
373 }
374 if ( !fitok && !fitOption.Quiet )
375 Warning("Fit","Abnormal termination of minimization.");
376 iret |= !fitok;
377
378
379 const ROOT::Fit::FitResult & fitResult = fitter->Result();
380 // one could set directly the fit result in TF1
381 iret = fitResult.Status();
382 if (!fitResult.IsEmpty() ) {
383 // set in f1 the result of the fit
384 f1->SetChisquare(fitResult.Chi2() );
385 f1->SetNDF(fitResult.Ndf() );
386 f1->SetNumberFitPoints(fitdata->Size() );
387
388 assert((Int_t)fitResult.Parameters().size() >= f1->GetNpar() );
389 f1->SetParameters( const_cast<double*>(&(fitResult.Parameters().front())));
390 if ( int( fitResult.Errors().size()) >= f1->GetNpar() )
391 f1->SetParErrors( &(fitResult.Errors().front()) );
392
393
394 }
395
396// - Store fitted function in histogram functions list and draw
397 if (!fitOption.Nostore) {
399 HFit::StoreAndDrawFitFunction(h1, f1, range, !fitOption.Plus, !fitOption.Nograph, goption);
400 }
401
402 // print the result
403 // if using Fitter class must be done here
404 // use old style Minuit for TMinuit and if no corrections have been applied
405 if (!fitOption.Quiet) {
406 if (fitter->GetMinimizer() && fitConfig.MinimizerType() == "Minuit" &&
407 !fitConfig.NormalizeErrors() && fitOption.Like <= 1) {
408 fitter->GetMinimizer()->PrintResults(); // use old style Minuit
409 }
410 else {
411 // print using FitResult class
412 if (fitOption.Verbose) fitResult.PrintCovMatrix(std::cout);
413 fitResult.Print(std::cout);
414 }
415 }
416
417
418 // store result in the backward compatible VirtualFitter
419 // in case multi-thread is not enabled
420 if (!gGlobalMutex) {
422 TBackCompFitter * bcfitter = new TBackCompFitter(fitter, fitdata);
423 bcfitter->SetFitOption(fitOption);
424 bcfitter->SetObjectFit(h1);
425 bcfitter->SetUserFunc(f1);
427 if (userFcn) {
428 bcfitter->SetFCN(userFcn);
429 // for interpreted FCN functions
430 if (lastFitter->GetMethodCall() ) bcfitter->SetMethodCall(lastFitter->GetMethodCall() );
431 }
432
433 // delete last fitter if it has been created here before
434 if (lastFitter) {
435 TBackCompFitter * lastBCFitter = dynamic_cast<TBackCompFitter *> (lastFitter);
436 if (lastBCFitter && lastBCFitter->TestBit(TBackCompFitter::kCanDeleteLast) )
437 delete lastBCFitter;
438 }
439 //N.B= this might create a memory leak if user does not delete the fitter they create
440 TVirtualFitter::SetFitter( bcfitter );
441 }
442
443 // use old-style for printing the results
444 // if (fitOption.Verbose) bcfitter->PrintResults(2,0.);
445 // else if (!fitOption.Quiet) bcfitter->PrintResults(1,0.);
446
447 if (fitOption.StoreResult)
448 {
449 TString name = "TFitResult-";
450 name = name + h1->GetName() + "-" + f1->GetName();
451 TString title = "TFitResult-";
452 title += h1->GetTitle();
453 tfr->SetName(name);
454 tfr->SetTitle(title);
455 return TFitResultPtr(tfr);
456 }
457 else
458 return TFitResultPtr(iret);
459}
460
461
463 // get range from histogram and update the DataRange class
464 // if a ranges already exist in that dimension use that one
465
466 Int_t ndim = GetDimension(h1);
467
468 double xmin = 0, xmax = 0, ymin = 0, ymax = 0, zmin = 0, zmax = 0;
469 if (range.Size(0) == 0) {
470 TAxis & xaxis = *(h1->GetXaxis());
471 Int_t hxfirst = xaxis.GetFirst();
472 Int_t hxlast = xaxis.GetLast();
473 Double_t binwidx = xaxis.GetBinWidth(hxlast);
474 xmin = xaxis.GetBinLowEdge(hxfirst);
475 xmax = xaxis.GetBinLowEdge(hxlast) +binwidx;
476 range.AddRange(xmin,xmax);
477 }
478
479 if (ndim > 1) {
480 if (range.Size(1) == 0) {
481 TAxis & yaxis = *(h1->GetYaxis());
482 Int_t hyfirst = yaxis.GetFirst();
483 Int_t hylast = yaxis.GetLast();
484 Double_t binwidy = yaxis.GetBinWidth(hylast);
485 ymin = yaxis.GetBinLowEdge(hyfirst);
486 ymax = yaxis.GetBinLowEdge(hylast) +binwidy;
487 range.AddRange(1,ymin,ymax);
488 }
489 }
490 if (ndim > 2) {
491 if (range.Size(2) == 0) {
492 TAxis & zaxis = *(h1->GetZaxis());
493 Int_t hzfirst = zaxis.GetFirst();
494 Int_t hzlast = zaxis.GetLast();
495 Double_t binwidz = zaxis.GetBinWidth(hzlast);
496 zmin = zaxis.GetBinLowEdge(hzfirst);
497 zmax = zaxis.GetBinLowEdge(hzlast) +binwidz;
498 range.AddRange(2,zmin,zmax);
499 }
500 }
501#ifdef DEBUG
502 std::cout << "xmin,xmax" << xmin << " " << xmax << std::endl;
503#endif
504
505}
506
508 // get range for graph (used sub-set histogram)
509 // N.B. : this is different than in previous implementation of TGraph::Fit where range used was from xmin to xmax.
510 TH1 * h1 = gr->GetHistogram();
511 // an histogram is normally always returned for a TGraph
512 if (h1) HFit::GetDrawingRange(h1, range);
513}
515 // get range for multi-graph (used sub-set histogram)
516 // N.B. : this is different than in previous implementation of TMultiGraph::Fit where range used was from data xmin to xmax.
517 TH1 * h1 = mg->GetHistogram();
518 if (h1) {
520 }
521 else if (range.Size(0) == 0) {
522 // compute range from all the TGraph's belonging to the MultiGraph
523 double xmin = std::numeric_limits<double>::infinity();
524 double xmax = -std::numeric_limits<double>::infinity();
525 TIter next(mg->GetListOfGraphs() );
526 TGraph * g = 0;
527 while ( (g = (TGraph*) next() ) ) {
528 double x1 = 0, x2 = 0, y1 = 0, y2 = 0;
529 g->ComputeRange(x1,y1,x2,y2);
530 if (x1 < xmin) xmin = x1;
531 if (x2 > xmax) xmax = x2;
532 }
533 range.AddRange(xmin,xmax);
534 }
535}
537 // get range for graph2D (used sub-set histogram)
538 // N.B. : this is different than in previous implementation of TGraph2D::Fit. There range used was always(0,0)
539 // cannot use TGraph2D::GetHistogram which makes an interpolation
540 //TH1 * h1 = gr->GetHistogram();
541 //if (h1) HFit::GetDrawingRange(h1, range);
542 // not very efficient (t.b.i.)
543 if (range.Size(0) == 0) {
544 double xmin = gr->GetXmin();
545 double xmax = gr->GetXmax();
546 range.AddRange(0,xmin,xmax);
547 }
548 if (range.Size(1) == 0) {
549 double ymin = gr->GetYmin();
550 double ymax = gr->GetYmax();
551 range.AddRange(1,ymin,ymax);
552 }
553}
554
556 // get range from histogram and update the DataRange class
557 // if a ranges already exist in that dimension use that one
558
559 Int_t ndim = GetDimension(s1);
560
561 for ( int i = 0; i < ndim; ++i ) {
562 if ( range.Size(i) == 0 ) {
563 TAxis *axis = s1->GetAxis(i);
564 range.AddRange(i, axis->GetXmin(), axis->GetXmax());
565 }
566 }
567}
568
569template<class FitObject>
570void HFit::StoreAndDrawFitFunction(FitObject * h1, TF1 * f1, const ROOT::Fit::DataRange & range, bool delOldFunction, bool drawFunction, const char *goption) {
571// - Store fitted function in histogram functions list and draw
572// should have separate functions for 1,2,3d ? t.b.d in case
573
574#ifdef DEBUG
575 std::cout <<"draw and store fit function " << f1->GetName() << std::endl;
576#endif
577
578
579 Int_t ndim = GetDimension(h1);
580 double xmin = 0, xmax = 0, ymin = 0, ymax = 0, zmin = 0, zmax = 0;
581 if (range.Size(0) ) range.GetRange(0,xmin,xmax);
582 if (range.Size(1) ) range.GetRange(1,ymin,ymax);
583 if (range.Size(2) ) range.GetRange(2,zmin,zmax);
584
585
586#ifdef DEBUG
587 std::cout <<"draw and store fit function " << f1->GetName()
588 << " Range in x = [ " << xmin << " , " << xmax << " ]" << std::endl;
589#endif
590
591 TList * funcList = h1->GetListOfFunctions();
592 if (funcList == 0){
593 Error("StoreAndDrawFitFunction","Function list has not been created - cannot store the fitted function");
594 return;
595 }
596
597 // delete the function in the list only if
598 // the function we are fitting is not in that list
599 // If this is the case we re-use that function object and
600 // we do not create a new one (if delOldFunction is true)
601 bool reuseOldFunction = false;
602 if (delOldFunction) {
603 TIter next(funcList, kIterBackward);
604 TObject *obj;
605 while ((obj = next())) {
606 if (obj->InheritsFrom(TF1::Class())) {
607 if (obj != f1) {
608 funcList->Remove(obj);
609 delete obj;
610 }
611 else {
612 reuseOldFunction = true;
613 }
614 }
615 }
616 }
617
618 TF1 *fnew1 = 0;
619 TF2 *fnew2 = 0;
620 TF3 *fnew3 = 0;
621
622 // copy TF1 using TClass to avoid slicing in case of derived classes
623 if (ndim < 2) {
624 if (!reuseOldFunction) {
625 fnew1 = (TF1*)f1->IsA()->New();
626 R__ASSERT(fnew1);
627 f1->Copy(*fnew1);
628 funcList->Add(fnew1);
629 }
630 else {
631 fnew1 = f1;
632 }
633 fnew1->SetParent( h1 );
634 fnew1->SetRange(xmin,xmax);
635 fnew1->Save(xmin,xmax,0,0,0,0);
636 if (!drawFunction) fnew1->SetBit(TF1::kNotDraw);
637 fnew1->AddToGlobalList(false);
638 } else if (ndim < 3) {
639 if (!reuseOldFunction) {
640 fnew2 = (TF2*)f1->IsA()->New();
641 R__ASSERT(fnew2);
642 f1->Copy(*fnew2);
643 funcList->Add(fnew2);
644 }
645 else {
646 fnew2 = dynamic_cast<TF2*>(f1);
647 R__ASSERT(fnew2);
648 }
649 fnew2->SetRange(xmin,ymin,xmax,ymax);
650 fnew2->SetParent( h1 );
651 fnew2->Save(xmin,xmax,ymin,ymax,0,0);
652 if (!drawFunction) fnew2->SetBit(TF1::kNotDraw);
653 fnew2->AddToGlobalList(false);
654 } else {
655 if (!reuseOldFunction) {
656 fnew3 = (TF3*)f1->IsA()->New();
657 R__ASSERT(fnew3);
658 f1->Copy(*fnew3);
659 funcList->Add(fnew3);
660 }
661 else {
662 fnew2 = dynamic_cast<TF3*>(f1);
663 R__ASSERT(fnew3);
664 }
665 fnew3->SetRange(xmin,ymin,zmin,xmax,ymax,zmax);
666 fnew3->SetParent( h1 );
667 fnew3->Save(xmin,xmax,ymin,ymax,zmin,zmax);
668 if (!drawFunction) fnew3->SetBit(TF1::kNotDraw);
669 fnew3->AddToGlobalList(false);
670 }
671 if (h1->TestBit(kCanDelete)) return;
672 // draw only in case of histograms
673 if (drawFunction && ndim < 3 && h1->InheritsFrom(TH1::Class() ) ) {
674 // no need to re-draw the histogram if the histogram is already in the pad
675 // in that case the function will be just drawn (if option N is not set)
676 if (!gPad || (gPad && gPad->GetListOfPrimitives()->FindObject(h1) == NULL ) )
677 h1->Draw(goption);
678 }
679 if (gPad) gPad->Modified(); // this is not in TH1 code (needed ??)
680
681 return;
682}
683
684
685void ROOT::Fit::FitOptionsMake(EFitObjectType type, const char *option, Foption_t &fitOption) {
686 // - Decode list of options into fitOption (used by both TGraph and TH1)
687 // works for both histograms and graph depending on the enum FitObjectType defined in HFit
690 }
691
692 if (option == 0) return;
693 if (!option[0]) return;
694
695 TString opt = option;
696 opt.ToUpper();
697
698 // parse firt the specific options
699 if (type == kHistogram) {
700
701 if (opt.Contains("WIDTH")) {
702 fitOption.BinVolume = 1; // scale content by the bin width
703 if (opt.Contains("NORMWIDTH")) {
704 // for variable bins: scale content by the bin width normalized by a reference value (typically the minimum bin)
705 // this option is for variable bin widths
706 fitOption.BinVolume = 2;
707 opt.ReplaceAll("NORMWIDTH","");
708 }
709 else
710 opt.ReplaceAll("WIDTH","");
711 }
712
713 // if (opt.Contains("MULTIPROC")) {
714 // fitOption.ExecPolicy = ROOT::Fit::kMultiprocess;
715 // opt.ReplaceAll("MULTIPROC","");
716 // }
717
718 if (opt.Contains("SERIAL")) {
720 opt.ReplaceAll("SERIAL","");
721 }
722
723 if (opt.Contains("MULTITHREAD")) {
725 opt.ReplaceAll("MULTITHREAD","");
726 }
727
728 if (opt.Contains("I")) fitOption.Integral= 1; // integral of function in the bin (no sense for graph)
729 if (opt.Contains("WW")) fitOption.W1 = 2; //all bins have weight=1, even empty bins
730 }
731
732 // specific Graph options (need to be parsed before)
733 else if (type == kGraph) {
734 opt.ReplaceAll("ROB", "H");
735 opt.ReplaceAll("EX0", "T");
736
737 //for robust fitting, see if # of good points is defined
738 // decode parameters for robust fitting
739 Double_t h=0;
740 if (opt.Contains("H=0.")) {
741 int start = opt.Index("H=0.");
742 int numpos = start + strlen("H=0.");
743 int numlen = 0;
744 int len = opt.Length();
745 while( (numpos+numlen<len) && isdigit(opt[numpos+numlen]) ) numlen++;
746 TString num = opt(numpos,numlen);
747 opt.Remove(start+strlen("H"),strlen("=0.")+numlen);
748 h = atof(num.Data());
749 h*=TMath::Power(10, -numlen);
750 }
751
752 if (opt.Contains("H")) { fitOption.Robust = 1; fitOption.hRobust = h; }
753 if (opt.Contains("T")) fitOption.NoErrX = 1; // no error in X
754
755 }
756
757 if (opt.Contains("U")) fitOption.User = 1;
758 if (opt.Contains("Q")) fitOption.Quiet = 1;
759 if (opt.Contains("V")) {fitOption.Verbose = 1; fitOption.Quiet = 0;}
760 if (opt.Contains("L")) fitOption.Like = 1;
761 if (opt.Contains("X")) fitOption.Chi2 = 1;
762 if (opt.Contains("P")) fitOption.PChi2 = 1;
763
764
765 // likelihood fit options
766 if (fitOption.Like == 1) {
767 //if (opt.Contains("LL")) fitOption.Like = 2;
768 if (opt.Contains("W")){ fitOption.Like = 2; fitOption.W1=0;}// (weighted likelihood)
769 if (opt.Contains("MULTI")) {
770 if (fitOption.Like == 2) fitOption.Like = 6; // weighted multinomial
771 else fitOption.Like = 4; // multinomial likelihood fit instead of Poisson
772 opt.ReplaceAll("MULTI","");
773 }
774 // in case of histogram give precedence for likelihood options
775 if (type == kHistogram) {
776 if (fitOption.Chi2 == 1 || fitOption.PChi2 == 1)
777 Warning("Fit","Cannot use P or X option in combination of L. Ignore the chi2 option and perform a likelihood fit");
778 }
779
780 } else {
781 if (opt.Contains("W")) fitOption.W1 = 1; // all non-empty bins have weight =1 (for chi2 fit)
782 }
783
784 if (fitOption.PChi2 && fitOption.W1) {
785 Warning("FitOptionsMake", "Ignore option W or WW when used together with option P (Pearson chi2)");
786 fitOption.W1 = 0; // with Pearson chi2 W option is ignored
787 }
788
789 if (opt.Contains("E")) fitOption.Errors = 1;
790 if (opt.Contains("R")) fitOption.Range = 1;
791 if (opt.Contains("G")) fitOption.Gradient= 1;
792 if (opt.Contains("M")) fitOption.More = 1;
793 if (opt.Contains("N")) fitOption.Nostore = 1;
794 if (opt.Contains("0")) fitOption.Nograph = 1;
795 if (opt.Contains("+")) fitOption.Plus = 1;
796 if (opt.Contains("B")) fitOption.Bound = 1;
797 if (opt.Contains("C")) fitOption.Nochisq = 1;
798 if (opt.Contains("F")) fitOption.Minuit = 1;
799 if (opt.Contains("S")) fitOption.StoreResult = 1;
800
801}
802
804 if (foption.Like) {
805 Info("CheckGraphFitOptions","L (Log Likelihood fit) is an invalid option when fitting a graph. It is ignored");
806 foption.Like = 0;
807 }
808 if (foption.Integral) {
809 Info("CheckGraphFitOptions","I (use function integral) is an invalid option when fitting a graph. It is ignored");
810 foption.Integral = 0;
811 }
812 return;
813}
814
815// implementation of unbin fit function (defined in HFitInterface)
816
818 // do unbin fit, ownership of fitdata is passed later to the TBackFitter class
819
820 // create a shared pointer to the fit data to managed it
821 std::shared_ptr<ROOT::Fit::UnBinData> fitdata(data);
822
823#ifdef DEBUG
824 printf("tree data size is %d \n",fitdata->Size());
825 for (unsigned int i = 0; i < fitdata->Size(); ++i) {
826 if (fitdata->NDim() == 1) printf(" x[%d] = %f \n", i,*(fitdata->Coords(i) ) );
827 }
828#endif
829 if (fitdata->Size() == 0 ) {
830 Warning("Fit","Fit data is empty ");
831 return -1;
832 }
833
834 // create an empty TFitResult
835 std::shared_ptr<TFitResult> tfr(new TFitResult() );
836 // create the fitter
837 std::shared_ptr<ROOT::Fit::Fitter> fitter(new ROOT::Fit::Fitter(tfr) );
838 ROOT::Fit::FitConfig & fitConfig = fitter->Config();
839
840 // dimension is given by data because TF1 pointer can have wrong one
841 unsigned int dim = fitdata->NDim();
842
843 // set the fit function
844 // if option grad is specified use gradient
845 // need to create a wrapper for an automatic normalized TF1 ???
846 if ( fitOption.Gradient ) {
847 assert ( (int) dim == fitfunc->GetNdim() );
848 fitter->SetFunction(ROOT::Math::WrappedMultiTF1(*fitfunc) );
849 }
850 else
851 fitter->SetFunction(static_cast<const ROOT::Math::IParamMultiFunction &>(ROOT::Math::WrappedMultiTF1(*fitfunc, dim) ) );
852
853 // parameter setting is done automaticaly in the Fitter class
854 // need only to set limits
855 int npar = fitfunc->GetNpar();
856 for (int i = 0; i < npar; ++i) {
857 ROOT::Fit::ParameterSettings & parSettings = fitConfig.ParSettings(i);
858 double plow,pup;
859 fitfunc->GetParLimits(i,plow,pup);
860 // this is a limitation of TF1 interface - cannot fix a parameter to zero value
861 if (plow*pup != 0 && plow >= pup) {
862 parSettings.Fix();
863 }
864 else if (plow < pup ) {
865 if (!TMath::Finite(pup) && TMath::Finite(plow) )
866 parSettings.SetLowerLimit(plow);
867 else if (!TMath::Finite(plow) && TMath::Finite(pup) )
868 parSettings.SetUpperLimit(pup);
869 else
870 parSettings.SetLimits(plow,pup);
871 }
872
873 // set the parameter step size (by default are set to 0.3 of value)
874 // if function provides meaningful error values
875 double err = fitfunc->GetParError(i);
876 if ( err > 0)
877 parSettings.SetStepSize(err);
878 else if (plow < pup && TMath::Finite(plow) && TMath::Finite(pup) ) { // in case of limits improve step sizes
879 double step = 0.1 * (pup - plow);
880 // check if value is not too close to limit otherwise trim value
881 if ( parSettings.Value() < pup && pup - parSettings.Value() < 2 * step )
882 step = (pup - parSettings.Value() ) / 2;
883 else if ( parSettings.Value() > plow && parSettings.Value() - plow < 2 * step )
884 step = (parSettings.Value() - plow ) / 2;
885
886 parSettings.SetStepSize(step);
887 }
888
889 }
890
891 fitConfig.SetMinimizerOptions(minOption);
892
893 if (fitOption.Verbose) fitConfig.MinimizerOptions().SetPrintLevel(3);
894 if (fitOption.Quiet) fitConfig.MinimizerOptions().SetPrintLevel(0);
895
896 // more
897 if (fitOption.More) fitConfig.SetMinimizer("Minuit","MigradImproved");
898
899 // chech if Minos or more options
900 if (fitOption.Errors) {
901 // run Hesse and Minos
902 fitConfig.SetParabErrors(true);
903 fitConfig.SetMinosErrors(true);
904 }
905 // use weight correction
906 if ( (fitOption.Like & 2) == 2)
907 fitConfig.SetWeightCorrection(true);
908
909 bool extended = (fitOption.Like & 1) == 1;
910
911 bool fitok = false;
912 fitok = fitter->LikelihoodFit(fitdata, extended, fitOption.ExecPolicy);
913 if ( !fitok && !fitOption.Quiet )
914 Warning("UnBinFit","Abnormal termination of minimization.");
915
916 const ROOT::Fit::FitResult & fitResult = fitter->Result();
917 // one could set directly the fit result in TF1
918 int iret = fitResult.Status();
919 if (!fitResult.IsEmpty() ) {
920 // set in fitfunc the result of the fit
921 fitfunc->SetNDF(fitResult.Ndf() );
922 fitfunc->SetNumberFitPoints(fitdata->Size() );
923
924 assert( (Int_t)fitResult.Parameters().size() >= fitfunc->GetNpar() );
925 fitfunc->SetParameters( const_cast<double*>(&(fitResult.Parameters().front())));
926 if ( int( fitResult.Errors().size()) >= fitfunc->GetNpar() )
927 fitfunc->SetParErrors( &(fitResult.Errors().front()) );
928
929 }
930
931 // store result in the backward compatible VirtualFitter
932 // in case not running in a multi-thread enabled mode
933 if (gGlobalMutex) {
935 // pass ownership of Fitter and Fitdata to TBackCompFitter (fitter pointer cannot be used afterwards)
936 TBackCompFitter * bcfitter = new TBackCompFitter(fitter, fitdata);
937 // cannot use anymore now fitdata (given away ownership)
938 fitdata = 0;
939 bcfitter->SetFitOption(fitOption);
940 //bcfitter->SetObjectFit(fTree);
941 bcfitter->SetUserFunc(fitfunc);
942
943 if (lastFitter) delete lastFitter;
944 TVirtualFitter::SetFitter( bcfitter );
945
946 // use old-style for printing the results
947 // if (fitOption.Verbose) bcfitter->PrintResults(2,0.);
948 // else if (!fitOption.Quiet) bcfitter->PrintResults(1,0.);
949
950 }
951 // print results
952 if (fitOption.Verbose) fitResult.PrintCovMatrix(std::cout);
953 else if (!fitOption.Quiet) fitResult.Print(std::cout);
954
955 if (fitOption.StoreResult)
956 {
957 TString name = "TFitResult-";
958 name = name + "UnBinData-" + fitfunc->GetName();
959 TString title = "TFitResult-";
960 title += name;
961 tfr->SetName(name);
962 tfr->SetTitle(title);
963 return TFitResultPtr(tfr);
964 }
965 else
966 return TFitResultPtr(iret);
967}
968
969
970// implementations of ROOT::Fit::FitObject functions (defined in HFitInterface) in terms of the template HFit::Fit
971
973moption, const char *goption, ROOT::Fit::DataRange & range) {
974 // check fit options
975 // check if have weights in case of weighted likelihood
976 if ( ((foption.Like & 2) == 2) && h1->GetSumw2N() == 0) {
977 Warning("HFit::FitObject","A weighted likelihood fit is requested but histogram is not weighted - do a standard Likelihood fit");
978 foption.Like = 1;
979 }
980 // histogram fitting
981 return HFit::Fit(h1,f1,foption,moption,goption,range);
982}
983
984TFitResultPtr ROOT::Fit::FitObject(TGraph * gr, TF1 *f1 , Foption_t & foption , const ROOT::Math::MinimizerOptions & moption, const char *goption, ROOT::Fit::DataRange & range) {
985 // exclude options not valid for graphs
987 // TGraph fitting
988 return HFit::Fit(gr,f1,foption,moption,goption,range);
989}
990
991TFitResultPtr ROOT::Fit::FitObject(TMultiGraph * gr, TF1 *f1 , Foption_t & foption , const ROOT::Math::MinimizerOptions & moption, const char *goption, ROOT::Fit::DataRange & range) {
992 // exclude options not valid for graphs
994 // TMultiGraph fitting
995 return HFit::Fit(gr,f1,foption,moption,goption,range);
996}
997
998TFitResultPtr ROOT::Fit::FitObject(TGraph2D * gr, TF1 *f1 , Foption_t & foption , const ROOT::Math::MinimizerOptions & moption, const char *goption, ROOT::Fit::DataRange & range) {
999 // exclude options not valid for graphs
1001 // TGraph2D fitting
1002 return HFit::Fit(gr,f1,foption,moption,goption,range);
1003}
1004
1005TFitResultPtr ROOT::Fit::FitObject(THnBase * s1, TF1 *f1 , Foption_t & foption , const ROOT::Math::MinimizerOptions & moption, const char *goption, ROOT::Fit::DataRange & range) {
1006 // sparse histogram fitting
1007 return HFit::Fit(s1,f1,foption,moption,goption,range);
1008}
1009
1010
1011
1012// Int_t TGraph2D::DoFit(TF2 *f2 ,Option_t *option ,Option_t *goption) {
1013// // internal graph2D fitting methods
1014// Foption_t fitOption;
1015// ROOT::Fit::FitOptionsMake(option,fitOption);
1016
1017// // create range and minimizer options with default values
1018// ROOT::Fit::DataRange range(2);
1019// ROOT::Math::MinimizerOptions minOption;
1020// return ROOT::Fit::FitObject(this, f2 , fitOption , minOption, goption, range);
1021// }
1022
1023
1024// function to compute the simple chi2 for graphs and histograms
1025
1026double ROOT::Fit::Chisquare(const TH1 & h1, TF1 & f1, bool useRange, bool usePL) {
1027 return HFit::ComputeChi2(h1,f1,useRange, usePL);
1028}
1029
1030double ROOT::Fit::Chisquare(const TGraph & g, TF1 & f1, bool useRange) {
1031 return HFit::ComputeChi2(g,f1, useRange, false);
1032}
1033
1034template<class FitObject>
1035double HFit::ComputeChi2(const FitObject & obj, TF1 & f1, bool useRange, bool usePL ) {
1036
1037 // implement using the fitting classes
1039 if (usePL) opt.fUseEmpty=true;
1041 // get range of function
1042 if (useRange) HFit::GetFunctionRange(f1,range);
1043 // fill the data set
1044 ROOT::Fit::BinData data(opt,range);
1045 ROOT::Fit::FillData(data, &obj, &f1);
1046 if (data.Size() == 0 ) {
1047 Warning("Chisquare","data set is empty - return -1");
1048 return -1;
1049 }
1051 if (usePL) {
1052 // use the poisson log-lokelihood (Baker-Cousins chi2)
1053 ROOT::Fit::PoissonLLFunction nll(data, wf1);
1054 return 2.* nll( f1.GetParameters() ) ;
1055 }
1056 ROOT::Fit::Chi2Function chi2(data, wf1);
1057 return chi2(f1.GetParameters() );
1058
1059}
@ kGraph
Definition: Buttons.h:34
int Int_t
Definition: CPyCppyy.h:43
void Class()
Definition: Class.C:29
#define g(i)
Definition: RSha256.hxx:105
#define s1(x)
Definition: RSha256.hxx:91
#define h(i)
Definition: RSha256.hxx:106
static const double x2[5]
static const double x1[5]
const Bool_t kFALSE
Definition: RtypesCore.h:90
bool Bool_t
Definition: RtypesCore.h:61
double Double_t
Definition: RtypesCore.h:57
const Bool_t kTRUE
Definition: RtypesCore.h:89
const Bool_t kIterBackward
Definition: TCollection.h:41
#define R__ASSERT(e)
Definition: TError.h:96
void Info(const char *location, const char *msgfmt,...)
void Error(const char *location, const char *msgfmt,...)
void Warning(const char *location, const char *msgfmt,...)
char name[80]
Definition: TGX11.cxx:109
float xmin
Definition: THbookFile.cxx:93
float ymin
Definition: THbookFile.cxx:93
float xmax
Definition: THbookFile.cxx:93
float ymax
Definition: THbookFile.cxx:93
@ kCanDelete
Definition: TObject.h:354
R__EXTERN TVirtualMutex * gGlobalMutex
Definition: TVirtualMutex.h:29
#define gPad
Definition: TVirtualPad.h:287
Class describing the binned data sets : vectors of x coordinates, y values and optionally error on y ...
Definition: BinData.h:53
Chi2FCN class for binnned fits using the least square methods.
Definition: Chi2FCN.h:49
class describing the range in the coordinates it supports multiple range in a coordinate.
Definition: DataRange.h:34
void AddRange(unsigned int icoord, double xmin, double xmax)
add a range [xmin,xmax] for the new coordinate icoord Adding a range does not delete existing one,...
Definition: DataRange.cxx:94
unsigned int Size(unsigned int icoord=0) const
return range size for coordinate icoord (starts from zero) Size == 0 indicates no range is present [-...
Definition: DataRange.h:70
void GetRange(unsigned int irange, unsigned int icoord, double &xmin, double &xmax) const
get the i-th range for given coordinate.
Definition: DataRange.h:103
Class describing the configuration of the fit, options and parameter settings using the ROOT::Fit::Pa...
Definition: FitConfig.h:46
void SetMinosErrors(bool on=true)
set Minos erros computation to be performed after fitting
Definition: FitConfig.h:230
void SetNormErrors(bool on=true)
set the option to normalize the error on the result according to chi2/ndf
Definition: FitConfig.h:224
ROOT::Math::MinimizerOptions & MinimizerOptions()
access to the minimizer control parameter (non const method)
Definition: FitConfig.h:166
const ParameterSettings & ParSettings(unsigned int i) const
get the parameter settings for the i-th parameter (const method)
Definition: FitConfig.h:75
void SetMinimizer(const char *type, const char *algo=0)
set minimizer type
Definition: FitConfig.h:180
bool NormalizeErrors() const
flag to check if resulting errors are be normalized according to chi2/ndf
Definition: FitConfig.h:203
void SetMinimizerOptions(const ROOT::Math::MinimizerOptions &minopt)
set all the minimizer options using class MinimizerOptions
Definition: FitConfig.cxx:256
void SetWeightCorrection(bool on=true)
apply the weight correction for error matric computation
Definition: FitConfig.h:233
const std::string & MinimizerType() const
return type of minimizer package
Definition: FitConfig.h:188
void SetParabErrors(bool on=true)
set parabolic erros
Definition: FitConfig.h:227
unsigned int Size() const
return number of fit points
Definition: FitData.h:303
class containg the result of the fit and all the related information (fitted parameter values,...
Definition: FitResult.h:47
bool IsEmpty() const
True if a fit result does not exist (even invalid) with parameter values.
Definition: FitResult.h:117
unsigned int Ndf() const
Number of degree of freedom.
Definition: FitResult.h:163
double Chi2() const
Chi2 fit value in case of likelihood must be computed ?
Definition: FitResult.h:160
void Print(std::ostream &os, bool covmat=false) const
print the result and optionaly covariance matrix and correlations
Definition: FitResult.cxx:428
void PrintCovMatrix(std::ostream &os) const
print error matrix and correlations
Definition: FitResult.cxx:486
const std::vector< double > & Errors() const
parameter errors (return st::vector)
Definition: FitResult.h:169
const std::vector< double > & Parameters() const
parameter values (return std::vector)
Definition: FitResult.h:174
int Status() const
minimizer status code
Definition: FitResult.h:137
Fitter class, entry point for performing all type of fits.
Definition: Fitter.h:77
Class, describing value, limits and step size of the parameters Provides functionality also to set/re...
void SetStepSize(double err)
set the step size
void SetLimits(double low, double up)
set a double side limit, if low == up the parameter is fixed if low > up the limits are removed The c...
double Value() const
copy constructor and assignment operators (leave them to the compiler)
void SetUpperLimit(double up)
set a single upper limit
void Fix()
fix the parameter
void SetLowerLimit(double low)
set a single lower limit
class evaluating the log likelihood for binned Poisson likelihood fits it is template to distinguish ...
Class describing the unbinned data sets (just x coordinates values) of any dimensions.
Definition: UnBinData.h:42
IParamFunction interface (abstract class) describing multi-dimensional parameteric functions It is a ...
void SetPrintLevel(int level)
set print level
void SetTolerance(double tol)
set the tolerance
Class to Wrap a ROOT Function class (like TF1) in a IParamMultiFunction interface of multi-dimensions...
Class to manage histogram axis.
Definition: TAxis.h:30
Double_t GetXmax() const
Definition: TAxis.h:134
virtual Double_t GetBinLowEdge(Int_t bin) const
Return low edge of bin.
Definition: TAxis.cxx:515
Int_t GetLast() const
Return last bin on the axis i.e.
Definition: TAxis.cxx:466
Double_t GetXmin() const
Definition: TAxis.h:133
virtual Double_t GetBinWidth(Int_t bin) const
Return bin width.
Definition: TAxis.cxx:537
Int_t GetFirst() const
Return first bin on the axis i.e.
Definition: TAxis.cxx:455
Backward compatible implementation of TVirtualFitter.
virtual void SetMethodCall(TMethodCall *m)
virtual void SetFCN(void(*fcn)(Int_t &, Double_t *, Double_t &f, Double_t *, Int_t))
Override setFCN to use the Adapter to Minuit2 FCN interface To set the address of the minimization fu...
1-Dim function class
Definition: TF1.h:210
virtual Int_t GetNumber() const
Definition: TF1.h:492
virtual void GetParLimits(Int_t ipar, Double_t &parmin, Double_t &parmax) const
Return limits for parameter ipar.
Definition: TF1.cxx:1923
virtual void SetNDF(Int_t ndf)
Set the number of degrees of freedom ndf should be the number of points used in a fit - the number of...
Definition: TF1.cxx:3421
virtual Double_t GetParError(Int_t ipar) const
Return value of parameter number ipar.
Definition: TF1.cxx:1913
virtual void SetChisquare(Double_t chi2)
Definition: TF1.h:606
virtual void Copy(TObject &f1) const
Copy this F1 to a new F1.
Definition: TF1.cxx:992
virtual void SetRange(Double_t xmin, Double_t xmax)
Initialize the upper and lower bounds to draw the function.
Definition: TF1.cxx:3521
virtual void SetParent(TObject *p=0)
Definition: TF1.h:659
virtual Int_t GetNpar() const
Definition: TF1.h:475
virtual void SetParErrors(const Double_t *errors)
Set errors for all active parameters when calling this function, the array errors must have at least ...
Definition: TF1.cxx:3486
virtual void SetNumberFitPoints(Int_t npfits)
Definition: TF1.h:618
@ kNotDraw
Definition: TF1.h:320
virtual void GetRange(Double_t *xmin, Double_t *xmax) const
Return range of a generic N-D function.
Definition: TF1.cxx:2266
virtual Bool_t IsLinear() const
Definition: TF1.h:596
bool IsVectorized()
Definition: TF1.h:434
virtual Double_t * GetParameters() const
Definition: TF1.h:514
virtual void SetParameters(const Double_t *params)
Definition: TF1.h:638
virtual void Save(Double_t xmin, Double_t xmax, Double_t ymin, Double_t ymax, Double_t zmin, Double_t zmax)
Save values of function in array fSave.
Definition: TF1.cxx:3136
virtual Int_t GetNdim() const
Definition: TF1.h:479
virtual Bool_t AddToGlobalList(Bool_t on=kTRUE)
Add to global list of functions (gROOT->GetListOfFunctions() ) return previous status (true if the fu...
Definition: TF1.cxx:833
A 2-Dim function with parameters.
Definition: TF2.h:29
virtual void Save(Double_t xmin, Double_t xmax, Double_t ymin, Double_t ymax, Double_t zmin, Double_t zmax)
Save values of function in array fSave.
Definition: TF2.cxx:776
virtual void SetRange(Double_t xmin, Double_t xmax)
Initialize the upper and lower bounds to draw the function.
Definition: TF2.h:150
A 3-Dim function with parameters.
Definition: TF3.h:28
virtual void SetRange(Double_t xmin, Double_t xmax)
Initialize the upper and lower bounds to draw the function.
Definition: TF3.h:146
virtual void Save(Double_t xmin, Double_t xmax, Double_t ymin, Double_t ymax, Double_t zmin, Double_t zmax)
Save values of function in array fSave.
Definition: TF3.cxx:530
Provides an indirection to the TFitResult class and with a semantics identical to a TFitResult pointe...
Definition: TFitResultPtr.h:31
Extends the ROOT::Fit::Result class with a TNamed inheritance providing easy possibility for I/O.
Definition: TFitResult.h:32
Graphics object made of three arrays X, Y and Z with the same number of points each.
Definition: TGraph2D.h:41
A TGraph is an object made of two arrays X and Y with npoints each.
Definition: TGraph.h:41
TH1F * GetHistogram() const
Returns a pointer to the histogram used to draw the axis Takes into account the two following cases.
Definition: TGraph.cxx:1482
The TH1 histogram class.
Definition: TH1.h:56
TAxis * GetXaxis()
Get the behaviour adopted by the object about the statoverflows. See EStatOverflows for more informat...
Definition: TH1.h:316
virtual Int_t GetDimension() const
Definition: TH1.h:278
TList * GetListOfFunctions() const
Definition: TH1.h:239
TAxis * GetYaxis()
Definition: TH1.h:317
virtual void Draw(Option_t *option="")
Draw this histogram with options.
Definition: TH1.cxx:2998
virtual Int_t GetSumw2N() const
Definition: TH1.h:310
TAxis * GetZaxis()
Definition: TH1.h:318
Multidimensional histogram base.
Definition: THnBase.h:43
A doubly linked list.
Definition: TList.h:44
virtual void Add(TObject *obj)
Definition: TList.h:87
virtual TObject * Remove(TObject *obj)
Remove object from the list.
Definition: TList.cxx:821
A TMultiGraph is a collection of TGraph (or derived) objects.
Definition: TMultiGraph.h:36
virtual const char * GetName() const
Returns name of object.
Definition: TNamed.h:47
virtual const char * GetTitle() const
Returns title of object.
Definition: TNamed.h:48
Mother of all ROOT objects.
Definition: TObject.h:37
R__ALWAYS_INLINE Bool_t TestBit(UInt_t f) const
Definition: TObject.h:187
R__ALWAYS_INLINE Bool_t IsZombie() const
Definition: TObject.h:149
void SetBit(UInt_t f, Bool_t set)
Set or unset the user status bits as specified in f.
Definition: TObject.cxx:694
virtual Bool_t InheritsFrom(const char *classname) const
Returns kTRUE if object inherits from class "classname".
Definition: TObject.cxx:443
Basic string class.
Definition: TString.h:131
Ssiz_t Length() const
Definition: TString.h:405
TString & ReplaceAll(const TString &s1, const TString &s2)
Definition: TString.h:687
void ToUpper()
Change string to upper case.
Definition: TString.cxx:1138
const char * Data() const
Definition: TString.h:364
TString & Remove(Ssiz_t pos)
Definition: TString.h:668
Bool_t Contains(const char *pat, ECaseCompare cmp=kExact) const
Definition: TString.h:619
Ssiz_t Index(const char *pat, Ssiz_t i=0, ECaseCompare cmp=kExact) const
Definition: TString.h:634
Abstract Base Class for Fitting.
virtual void SetFitOption(Foption_t option)
virtual void SetObjectFit(TObject *obj)
TMethodCall * GetMethodCall() const
void(* FCNFunc_t)(Int_t &npar, Double_t *gin, Double_t &f, Double_t *u, Int_t flag)
virtual void SetUserFunc(TObject *userfunc)
static TVirtualFitter * GetFitter()
static: return the current Fitter
static void SetFitter(TVirtualFitter *fitter, Int_t maxpar=25)
Static function to set an alternative fitter.
TGraphErrors * gr
Definition: legend1.C:25
TH1F * h1
Definition: legend1.C:5
TF1 * f1
Definition: legend1.C:11
void GetDrawingRange(TH1 *h1, ROOT::Fit::DataRange &range)
Definition: HFitImpl.cxx:462
void GetFunctionRange(const TF1 &f1, ROOT::Fit::DataRange &range)
Definition: HFitImpl.cxx:121
int CheckFitFunction(const TF1 *f1, int hdim)
Definition: HFitImpl.cxx:87
TFitResultPtr Fit(FitObject *h1, TF1 *f1, Foption_t &option, const ROOT::Math::MinimizerOptions &moption, const char *goption, ROOT::Fit::DataRange &range)
Definition: HFitImpl.cxx:134
void FitOptionsMake(const char *option, Foption_t &fitOption)
void CheckGraphFitOptions(Foption_t &fitOption)
Definition: HFitImpl.cxx:803
void StoreAndDrawFitFunction(FitObject *h1, TF1 *f1, const ROOT::Fit::DataRange &range, bool, bool, const char *goption)
Definition: HFitImpl.cxx:570
int GetDimension(const THnBase *s1)
Definition: HFitImpl.cxx:55
double ComputeChi2(const FitObject &h1, TF1 &f1, bool useRange, bool usePL)
Definition: HFitImpl.cxx:1035
int GetDimension(const TH1 *h1)
Definition: HFitImpl.cxx:51
TFitResultPtr FitObject(TH1 *h1, TF1 *f1, Foption_t &option, const ROOT::Math::MinimizerOptions &moption, const char *goption, ROOT::Fit::DataRange &range)
fitting function for a TH1 (called from TH1::Fit)
Definition: HFitImpl.cxx:972
void FitOptionsMake(EFitObjectType type, const char *option, Foption_t &fitOption)
Decode list of options into fitOption.
Definition: HFitImpl.cxx:685
void Init2DGaus(const ROOT::Fit::BinData &data, TF1 *f1)
compute initial parameter for 2D gaussian function given the fit data Set the sigma limits for zero t...
TFitResultPtr UnBinFit(ROOT::Fit::UnBinData *data, TF1 *f1, Foption_t &option, const ROOT::Math::MinimizerOptions &moption)
fit an unbin data set (from tree or from histogram buffer) using a TF1 pointer and fit options.
Definition: HFitImpl.cxx:817
void FillData(BinData &dv, const TH1 *hist, TF1 *func=0)
fill the data vector from a TH1.
void InitExpo(const ROOT::Fit::BinData &data, TF1 *f1)
compute initial parameter for an exponential function given the fit data Set the constant and slope a...
void InitGaus(const ROOT::Fit::BinData &data, TF1 *f1)
compute initial parameter for gaussian function given the fit data Set the sigma limits for zero top ...
double Chisquare(const TH1 &h1, TF1 &f1, bool useRange, bool usePL=false)
compute the chi2 value for an histogram given a function (see TH1::Chisquare for the documentation)
Definition: HFitImpl.cxx:1026
std::string ToString(const T &val)
Utility function for conversion to strings.
Definition: Util.h:50
Bool_t IsImplicitMTEnabled()
Returns true if the implicit multi-threading in ROOT is enabled.
Definition: TROOT.cxx:557
static constexpr double s
static constexpr double mg
Int_t Finite(Double_t x)
Check if it is finite with a mask in order to be consistent in presence of fast math.
Definition: TMath.h:761
LongDouble_t Power(LongDouble_t x, LongDouble_t y)
Definition: TMath.h:725
int Range
Definition: Foption.h:39
int Nograph
Definition: Foption.h:42
ROOT::Fit::ExecutionPolicy ExecPolicy
Definition: Foption.h:52
int Quiet
Definition: Foption.h:29
int Like
Definition: Foption.h:34
int W1
Definition: Foption.h:36
int Gradient
Definition: Foption.h:40
int StoreResult
Definition: Foption.h:49
int Nochisq
Definition: Foption.h:45
int Robust
Definition: Foption.h:48
double hRobust
Definition: Foption.h:51
int Plus
Definition: Foption.h:43
int Integral
Definition: Foption.h:44
int Bound
Definition: Foption.h:31
int Nostore
Definition: Foption.h:41
int More
Definition: Foption.h:38
int PChi2
Definition: Foption.h:33
int Chi2
Definition: Foption.h:32
int Minuit
Definition: Foption.h:46
int Errors
Definition: Foption.h:37
int NoErrX
Definition: Foption.h:47
int Verbose
Definition: Foption.h:30
int User
Definition: Foption.h:35
int BinVolume
Definition: Foption.h:50
DataOptions : simple structure holding the options on how the data are filled.
Definition: DataOptions.h:28