TBackCompFitter.cxx

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// @(#)root/hist:$Id$
// Author: Lorenzo Moneta
 
/*************************************************************************
 * Copyright (C) 1995-2012, Rene Brun and Fons Rademakers.               *
 * All rights reserved.                                                  *
 *                                                                       *
 * For the licensing terms see $ROOTSYS/LICENSE.                         *
 * For the list of contributors see $ROOTSYS/README/CREDITS.             *
 *************************************************************************/
 
////////////////////////////////////////////////////////////////////////////////
/** \class TBackCompFitter
    \ingroup Hist
    \brief Backward compatible implementation of TVirtualFitter
 
Backward compatible implementation of TVirtualFitter using the
class ROOT::Fit::Fitter. This class is created after fitting an
histogram (TH1), TGraph or TTree and provides in addition to the
methods of the TVirtualFitter hooks to access the fit result class
(ROOT::Fit::FitResult), the fit configuration
(ROOT::Fit::FitConfig) or the fit data (ROOT::Fit::FitData) using
 
~~~~~~~~{.cpp}
    TBackCompFitter * fitter = (TBackCompFitter *) TVirtualFitter::GetFitter();
    ROOT::Fit::FitResult & result = fitter->GetFitResult();
    result.Print(std::cout);
~~~~~~~~
 
Methods for getting the confidence level or contours are also
provided. Note that after a new calls to TH1::Fit (or similar) the
class will be deleted and all reference to the FitResult, FitConfig
or minimizer will be invalid. One could eventually copying the
class before issuing a new fit to avoid deleting this information.
*///////////////////////////////////////////////////////////////////////////////
 
#include "TROOT.h"
#include "TBackCompFitter.h"
 
 
#include "TMethodCall.h"
 
#include "Math/Util.h"
 
#include <iostream>
#include <cassert>
 
//needed by GetCondifenceLevel
#include "Math/IParamFunction.h"
#include "TH1.h"
#include "TH2.h"
#include "TH3.h"
#include "TMath.h"
#include "TGraph.h"
#include "TGraphErrors.h"
#include "TGraph2DErrors.h"
#include "TMultiGraph.h"
#include "HFitInterface.h"
#include "Math/Minimizer.h"
#include "Fit/BinData.h"
#include "Fit/FitConfig.h"
#include "Fit/UnBinData.h"
#include "Fit/PoissonLikelihoodFCN.h"
#include "Fit/LogLikelihoodFCN.h"
#include "Fit/Chi2FCN.h"
#include "Fit/FcnAdapter.h"
#include "TFitResult.h"
 
//#define DEBUG 1
 
 
ClassImp(TBackCompFitter);
 
 
 
////////////////////////////////////////////////////////////////////////////////
/// Constructor needed by TVirtualFitter interface. Same behavior as default constructor.
/// initialize setting name and the global pointer
 
TBackCompFitter::TBackCompFitter( ) :
   fMinimizer(0),
   fObjFunc(0),
   fModelFunc(0)
{
   SetName("BCFitter");
}
 
////////////////////////////////////////////////////////////////////////////////
/// Constructor used after having fit using directly ROOT::Fit::Fitter
/// will create a dummy fitter copying configuration and parameter settings
 
TBackCompFitter::TBackCompFitter(const std::shared_ptr<ROOT::Fit::Fitter> & fitter, const std::shared_ptr<ROOT::Fit::FitData> & data) :
   fFitData(data),
   fFitter(fitter),
   fMinimizer(0),
   fObjFunc(0),
   fModelFunc(0)
{
   SetName("LastFitter");
}
 
////////////////////////////////////////////////////////////////////////////////
/// Destructor - delete the managed objects
 
TBackCompFitter::~TBackCompFitter() {
   if (fMinimizer) delete fMinimizer;
   if (fObjFunc) delete fObjFunc;
   if (fModelFunc) delete fModelFunc;
}
 
////////////////////////////////////////////////////////////////////////////////
/// Do chisquare calculations in case of likelihood fits
/// Do evaluation a the minimum only
 
Double_t TBackCompFitter::Chisquare(Int_t npar, Double_t *params) const {
   const std::vector<double> & minpar = fFitter->Result().Parameters();
   assert (npar == (int) minpar.size() );
   double diff = 0;
   double s = 0;
   for (int i =0; i < npar; ++i) {
      diff += std::abs( params[i] - minpar[i] );
      s += minpar[i];
   }
 
   if (diff > s * 1.E-12 ) Warning("Chisquare","given parameter values are not at minimum - chi2 at minimum is returned");
   return fFitter->Result().Chi2();
}
 
////////////////////////////////////////////////////////////////////////////////
/// Clear resources for consecutive fits
 
void TBackCompFitter::Clear(Option_t*) {
   // need to do something here ??? to be seen
}
 
////////////////////////////////////////////////////////////////////////////////
/// Execute the command (Fortran Minuit compatible interface)
 
Int_t TBackCompFitter::ExecuteCommand(const char *command, Double_t *args, Int_t nargs) {
#ifdef DEBUG
   std::cout<<"Execute command= "<<command<<std::endl;
#endif
 
   // set also number of parameters in obj function
   DoSetDimension();
 
   TString scommand(command);
   scommand.ToUpper();
 
   // MIGRAD
   if (scommand.Contains("MIG")) {
      if (!fObjFunc) {
         Error("ExecuteCommand","FCN must set before executing this command");
         return -1;
      }
 
      fFitter->Config().SetMinimizer(GetDefaultFitter(), "Migrad");
      bool ret = fFitter->FitFCN(*fObjFunc);
      return  (ret) ? 0 : -1;
   }
 
   //Minimize
   if (scommand.Contains("MINI")) {
 
      fFitter->Config().SetMinimizer(GetDefaultFitter(), "Minimize");
      if (!fObjFunc) {
         Error("ExecuteCommand","FCN must set before executing this command");
         return -1;
      }
      bool ret = fFitter->FitFCN(*fObjFunc);
      return  (ret) ? 0 : -1;
   }
   //Simplex
   if (scommand.Contains("SIM")) {
 
      if (!fObjFunc) {
         Error("ExecuteCommand","FCN must set before executing this command");
         return -1;
      }
 
      fFitter->Config().SetMinimizer(GetDefaultFitter(), "Simplex");
      bool ret = fFitter->FitFCN(*fObjFunc);
      return  (ret) ? 0 : -1;
   }
   //SCan
   if (scommand.Contains("SCA")) {
 
      if (!fObjFunc) {
         Error("ExecuteCommand","FCN must set before executing this command");
         return -1;
      }
 
      fFitter->Config().SetMinimizer(GetDefaultFitter(), "Scan");
      bool ret = fFitter->FitFCN(*fObjFunc);
      return  (ret) ? 0 : -1;
   }
   // MINOS
   else if (scommand.Contains("MINO"))   {
 
      if (fFitter->Config().MinosErrors() ) return 0;
 
      if (!fObjFunc) {
         Error("ExecuteCommand","FCN must set before executing this command");
         return -1;
      }
      // do only MINOS. need access to minimizer. For the moment re-run fitting with minos options
      fFitter->Config().SetMinosErrors(true);
      // set new parameter values
 
      fFitter->Config().SetMinimizer(GetDefaultFitter(), "Migrad"); // redo -minimization with Minos
      bool ret = fFitter->FitFCN(*fObjFunc);
      return  (ret) ? 0 : -1;
 
   }
   //HESSE
   else if (scommand.Contains("HES"))   {
 
      if (fFitter->Config().ParabErrors() ) return 0;
 
      if (!fObjFunc) {
         Error("ExecuteCommand","FCN must set before executing this command");
         return -1;
      }
 
      // do only HESSE. need access to minimizer. For the moment re-run fitting with hesse options
      fFitter->Config().SetParabErrors(true);
      fFitter->Config().SetMinimizer(GetDefaultFitter(), "Migrad"); // redo -minimization with Minos
      bool ret = fFitter->FitFCN(*fObjFunc);
      return  (ret) ? 0 : -1;
   }
 
   // FIX
   else if (scommand.Contains("FIX"))   {
      for(int i = 0; i < nargs; i++) {
         FixParameter(int(args[i])-1);
      }
      return 0;
   }
   // SET LIMIT (upper and lower)
   else if (scommand.Contains("SET LIM"))   {
      if (nargs < 3) {
         Error("ExecuteCommand","Invalid parameters given in SET LIMIT");
         return -1;
      }
      int ipar = int(args[0]);
      if (!ValidParameterIndex(ipar) )  return -1;
      double low = args[1];
      double up = args[2];
      fFitter->Config().ParSettings(ipar).SetLimits(low,up);
      return 0;
   }
   // SET PRINT
   else if (scommand.Contains("SET PRIN"))   {
      if (nargs < 1) return -1;
      fFitter->Config().MinimizerOptions().SetPrintLevel(int(args[0]) );
      return 0;
   }
   // SET ERR
   else if (scommand.Contains("SET ERR"))   {
      if (nargs < 1) return -1;
      fFitter->Config().MinimizerOptions().SetPrintLevel(int( args[0]) );
      return 0;
   }
   // SET STRATEGY
   else if (scommand.Contains("SET STR"))   {
      if (nargs < 1) return -1;
      fFitter->Config().MinimizerOptions().SetStrategy(int(args[0]) );
      return 0;
   }
   //SET GRAD (not impl.)
   else if (scommand.Contains("SET GRA"))   {
      //     not yet available
      //     fGradient = true;
      return -1;
   }
   //SET NOW (not impl.)
   else if (scommand.Contains("SET NOW"))   {
      //     no warning (works only for TMinuit)
      //     fGradient = true;
      return -1;
   }
   // CALL FCN
   else if (scommand.Contains("CALL FCN"))   {
      //     call fcn function (global pointer to free function)
 
      if (nargs < 1 || fFCN == 0 ) return -1;
      int npar = fObjFunc->NDim();
      // use values in fit result if existing  otherwise in ParameterSettings
      std::vector<double> params(npar);
      for (int i = 0; i < npar; ++i)
         params[i] = GetParameter(i);
 
      double fval = 0;
      (*fFCN)(npar, 0, fval, &params[0],int(args[0]) ) ;
      return 0;
   }
   else {
      // other commands passed
      Error("ExecuteCommand","Invalid or not supported command given %s",command);
      return -1;
   }
}
 
////////////////////////////////////////////////////////////////////////////////
/// Check if ipar is a valid parameter index
 
bool TBackCompFitter::ValidParameterIndex(int ipar) const  {
   int nps  = fFitter->Config().ParamsSettings().size();
   if (ipar  < 0 || ipar >= nps ) {
      std::string msg = ROOT::Math::Util::ToString(ipar) + " is an invalid Parameter index";
      Error("ValidParameterIndex","%s",msg.c_str());
      return false;
   }
   return true;
}
 
////////////////////////////////////////////////////////////////////////////////
/// Fix the parameter
 
void TBackCompFitter::FixParameter(Int_t ipar) {
   if (ValidParameterIndex(ipar) )
      fFitter->Config().ParSettings(ipar).Fix();
}
 
////////////////////////////////////////////////////////////////////////////////
/// Computes point-by-point confidence intervals for the fitted function.
/// \param n    number of points
/// \param ndim dimensions of points
/// \param x    points, at which to compute the intervals, for ndim > 1
///             should be in order: (x0,y0, x1, y1, ... xn, yn)
/// \param ci   computed intervals are returned in this array
/// \param cl   confidence level, default=0.95
///
/// NOTE, that the intervals are approximate for nonlinear(in parameters) models
 
void TBackCompFitter::GetConfidenceIntervals(Int_t n, Int_t ndim, const Double_t *x, Double_t *ci, Double_t cl)
{
   if (!fFitter->Result().IsValid()) {
      Error("GetConfidenceIntervals","Cannot compute confidence intervals with an invalide fit result");
      return;
   }
 
   fFitter->Result().GetConfidenceIntervals(n,ndim,1,x,ci,cl,false);
}
 
////////////////////////////////////////////////////////////////////////////////
/// Computes confidence intervals at level cl. Default is 0.95
/// The TObject parameter can be a TGraphErrors, a TGraph2DErrors or a TH1,2,3.
/// For Graphs, confidence intervals are computed for each point,
/// the value of the graph at that point is set to the function value at that
/// point, and the graph y-errors (or z-errors) are set to the value of
/// the confidence interval at that point.
/// For Histograms, confidence intervals are computed for each bin center
/// The bin content of this bin is then set to the function value at the bin
/// center, and the bin error is set to the confidence interval value.
//
/// NOTE: confidence intervals are approximate for nonlinear models!
///
/// Allowed combinations:
///
/// Fitted object             | Passed object
/// --------------------------|------------------
/// TGraph                    | TGraphErrors, TH1
/// TGraphErrors, AsymmErrors | TGraphErrors, TH1
/// TH1                       | TGraphErrors, TH1
/// TGraph2D                  | TGraph2DErrors, TH2
/// TGraph2DErrors            | TGraph2DErrors, TH2
/// TH2                       | TGraph2DErrors, TH2
/// TH3                       | TH3
 
void TBackCompFitter::GetConfidenceIntervals(TObject *obj, Double_t cl)
{
   if (!fFitter->Result().IsValid() ) {
      Error("GetConfidenceIntervals","Cannot compute confidence intervals with an invalide fit result");
      return;
   }
 
   // get data dimension from fit object
   int datadim = 1;
   TObject * fitobj = GetObjectFit();
   if (!fitobj) {
      Error("GetConfidenceIntervals","Cannot compute confidence intervals without a fitting object");
      return;
   }
 
   if (fitobj->InheritsFrom(TGraph2D::Class())) datadim = 2;
   if (fitobj->InheritsFrom(TH1::Class())) {
      TH1 * h1 = dynamic_cast<TH1*>(fitobj);
      assert(h1 != 0);
      datadim = h1->GetDimension();
   }
 
   if (datadim == 1) {
      if (!obj->InheritsFrom(TGraphErrors::Class()) && !obj->InheritsFrom(TH1::Class() ) )  {
         Error("GetConfidenceIntervals", "Invalid object passed for storing confidence level data, must be a TGraphErrors or a TH1");
         return;
      }
   }
   if (datadim == 2) {
      if (!obj->InheritsFrom(TGraph2DErrors::Class()) && !obj->InheritsFrom(TH2::Class() ) )  {
         Error("GetConfidenceIntervals", "Invalid object passed for storing confidence level data, must be a TGraph2DErrors or a TH2");
         return;
      }
   }
   if (datadim == 3) {
      if (!obj->InheritsFrom(TH3::Class() ) )  {
         Error("GetConfidenceIntervals", "Invalid object passed for storing confidence level data, must be a TH3");
         return;
      }
   }
 
   // fill bin data (for the moment use all ranges) according to object passed
   ROOT::Fit::BinData data;
   data.Opt().fUseEmpty = true; // need to use all bins of given histograms
   // call appropriate function according to type of object
   if (obj->InheritsFrom(TGraph::Class()) )
      ROOT::Fit::FillData(data, dynamic_cast<TGraph *>(obj) );
   else if (obj->InheritsFrom(TGraph2D::Class()) )
      ROOT::Fit::FillData(data, dynamic_cast<TGraph2D *>(obj) );
//    else if (obj->InheritsFrom(TMultiGraph::Class()) )
//       ROOT::Fit::FillData(data, dynamic_cast<TMultiGraph *>(obj) );
   else if (obj->InheritsFrom(TH1::Class()) )
      ROOT::Fit::FillData(data, dynamic_cast<TH1 *>(obj) );
 
 
   unsigned int n = data.Size();
 
   std::vector<double> ci( n );
 
   fFitter->Result().GetConfidenceIntervals(data,&ci[0],cl,false);
 
   const ROOT::Math::IParamMultiFunction * func =  fFitter->Result().FittedFunction();
   assert(func != 0);
 
   // fill now the object with cl data
   for (unsigned int i = 0; i < n; ++i) {
      const double * x = data.Coords(i);
      double y = (*func)( x ); // function is evaluated using its  parameters
 
      if (obj->InheritsFrom(TGraphErrors::Class()) ) {
         TGraphErrors * gr = dynamic_cast<TGraphErrors *> (obj);
         assert(gr != 0);
         gr->SetPoint(i, *x, y);
         gr->SetPointError(i, 0, ci[i]);
      }
      if (obj->InheritsFrom(TGraph2DErrors::Class()) ) {
         TGraph2DErrors * gr = dynamic_cast<TGraph2DErrors *> (obj);
         assert(gr != 0);
         gr->SetPoint(i, x[0], x[1], y);
         gr->SetPointError(i, 0, 0, ci[i]);
      }
      if (obj->InheritsFrom(TH1::Class()) ) {
         TH1 * h1 = dynamic_cast<TH1 *> (obj);
         assert(h1 != 0);
         int ibin = 0;
         if (datadim == 1) ibin = h1->FindBin(*x);
         if (datadim == 2) ibin = h1->FindBin(x[0],x[1]);
         if (datadim == 3) ibin = h1->FindBin(x[0],x[1],x[2]);
         h1->SetBinContent(ibin, y);
         h1->SetBinError(ibin, ci[i]);
      }
   }
 
}
 
////////////////////////////////////////////////////////////////////////////////
/// Get the error matrix in a pointer to a NxN array.
/// excluding the fixed parameters
 
Double_t* TBackCompFitter::GetCovarianceMatrix() const {
   unsigned int nfreepar =   GetNumberFreeParameters();
   unsigned int ntotpar =   GetNumberTotalParameters();
 
   if (fCovar.size() !=  nfreepar*nfreepar )
      fCovar.resize(nfreepar*nfreepar);
 
   if (!fFitter->Result().IsValid() ) {
      Warning("GetCovarianceMatrix","Invalid fit result");
      return 0;
   }
 
   unsigned int l = 0;
   for (unsigned int i = 0; i < ntotpar; ++i) {
      if (fFitter->Config().ParSettings(i).IsFixed() ) continue;
      unsigned int m = 0;
      for (unsigned int j = 0; j < ntotpar; ++j) {
         if (fFitter->Config().ParSettings(j).IsFixed() ) continue;
         unsigned int index = nfreepar*l + m;
         assert(index < fCovar.size() );
         fCovar[index] = fFitter->Result().CovMatrix(i,j);
         m++;
      }
      l++;
   }
   return &(fCovar.front());
}
 
////////////////////////////////////////////////////////////////////////////////
/// Get error matrix element (return all zero if matrix is not available)
 
Double_t TBackCompFitter::GetCovarianceMatrixElement(Int_t i, Int_t j) const {
   unsigned int np2 = fCovar.size();
   unsigned int npar = GetNumberFreeParameters();
   if ( np2 == 0 || np2 != npar *npar ) {
      double * c = GetCovarianceMatrix();
      if (c == 0) return 0;
   }
   return fCovar[i*npar + j];
}
 
////////////////////////////////////////////////////////////////////////////////
/// Get fit errors
 
Int_t TBackCompFitter::GetErrors(Int_t ipar,Double_t &eplus, Double_t &eminus, Double_t &eparab, Double_t &globcc) const {
   if (!ValidParameterIndex(ipar) )   return -1;
 
   const ROOT::Fit::FitResult & result = fFitter->Result();
   if (!result.IsValid() ) {
      Warning("GetErrors","Invalid fit result");
      return -1;
   }
 
   eparab = result.Error(ipar);
   eplus = result.UpperError(ipar);
   eminus = result.LowerError(ipar);
   globcc = result.GlobalCC(ipar);
   return 0;
}
 
////////////////////////////////////////////////////////////////////////////////
/// Number of total parameters
 
Int_t TBackCompFitter::GetNumberTotalParameters() const {
   return fFitter->Result().NTotalParameters();
}
Int_t TBackCompFitter::GetNumberFreeParameters() const {
   // number of variable parameters
   return fFitter->Result().NFreeParameters();
}
 
////////////////////////////////////////////////////////////////////////////////
/// Parameter error
 
Double_t TBackCompFitter::GetParError(Int_t ipar) const {
   if (fFitter->Result().IsEmpty() ) {
      if (ValidParameterIndex(ipar) )  return  fFitter->Config().ParSettings(ipar).StepSize();
      else return 0;
   }
   return fFitter->Result().Error(ipar);
}
 
////////////////////////////////////////////////////////////////////////////////
/// Parameter value
 
Double_t TBackCompFitter::GetParameter(Int_t ipar) const {
   if (fFitter->Result().IsEmpty() ) {
      if (ValidParameterIndex(ipar) )  return  fFitter->Config().ParSettings(ipar).Value();
      else return 0;
   }
   return fFitter->Result().Value(ipar);
}
 
////////////////////////////////////////////////////////////////////////////////
/// Get all parameter info (name, value, errors)
 
Int_t TBackCompFitter::GetParameter(Int_t ipar,char *name,Double_t &value,Double_t &verr,Double_t &vlow, Double_t &vhigh) const {
   if (!ValidParameterIndex(ipar) )    {
      return -1;
   }
   const std::string & pname = fFitter->Config().ParSettings(ipar).Name();
   const char * c = pname.c_str();
   std::copy(c,c + pname.size(),name);
 
   if (fFitter->Result().IsEmpty() ) {
      value = fFitter->Config().ParSettings(ipar).Value();
      verr  = fFitter->Config().ParSettings(ipar).Value();  // error is step size in this case
      vlow  = fFitter->Config().ParSettings(ipar).LowerLimit();  // vlow is lower limit in this case
      vhigh   = fFitter->Config().ParSettings(ipar).UpperLimit();  // vlow is lower limit in this case
      return 1;
   }
   else {
      value =  fFitter->Result().Value(ipar);
      verr = fFitter->Result().Error(ipar);
      vlow = fFitter->Result().LowerError(ipar);
      vhigh = fFitter->Result().UpperError(ipar);
   }
   return 0;
}
 
////////////////////////////////////////////////////////////////////////////////
/// Return name of parameter ipar
 
const char *TBackCompFitter::GetParName(Int_t ipar) const {
   if (!ValidParameterIndex(ipar) )    {
      return 0;
   }
   return fFitter->Config().ParSettings(ipar).Name().c_str();
}
 
////////////////////////////////////////////////////////////////////////////////
/// Get fit statistical information
 
Int_t TBackCompFitter::GetStats(Double_t &amin, Double_t &edm, Double_t &errdef, Int_t &nvpar, Int_t &nparx) const {
   const ROOT::Fit::FitResult & result = fFitter->Result();
   amin = result.MinFcnValue();
   edm = result.Edm();
   errdef = fFitter->Config().MinimizerOptions().ErrorDef();
   nvpar = result.NFreeParameters();
   nparx = result.NTotalParameters();
   return 0;
}
 
////////////////////////////////////////////////////////////////////////////////
/// Sum of log (un-needed)
 
Double_t TBackCompFitter::GetSumLog(Int_t) {
   Warning("GetSumLog","Dummy  method - returned 0");
   return 0.;
}
 
////////////////////////////////////////////////////////////////////////////////
/// Query if parameter ipar is fixed
 
Bool_t TBackCompFitter::IsFixed(Int_t ipar) const {
   if (!ValidParameterIndex(ipar) )    {
      return false;
   }
   return fFitter->Config().ParSettings(ipar).IsFixed();
}
 
////////////////////////////////////////////////////////////////////////////////
/// Print the fit result.
/// Use PrintResults function in case of Minuit for old -style printing
 
void TBackCompFitter::PrintResults(Int_t level, Double_t ) const {
   if (fFitter->GetMinimizer() && fFitter->Config().MinimizerType() == "Minuit")
      fFitter->GetMinimizer()->PrintResults();
   else {
      if (level > 0) fFitter->Result().Print(std::cout);
      if (level > 1)  fFitter->Result().PrintCovMatrix(std::cout);
   }
   // need to print minos errors and globalCC + other info
}
 
////////////////////////////////////////////////////////////////////////////////
/// Release a fit parameter
 
void TBackCompFitter::ReleaseParameter(Int_t ipar) {
   if (ValidParameterIndex(ipar) )
      fFitter->Config().ParSettings(ipar).Release();
}
 
////////////////////////////////////////////////////////////////////////////////
/// Set fit method (chi2 or likelihood).
/// According to the method the appropriate FCN function will be created
 
void TBackCompFitter::SetFitMethod(const char *) {
   Info("SetFitMethod","non supported method");
}
 
////////////////////////////////////////////////////////////////////////////////
/// Set (add) a new fit parameter passing initial value, step size (verr) and parameter limits
/// if vlow > vhigh the parameter is unbounded
/// if the stepsize (verr) == 0 the parameter is treated as fixed
 
Int_t TBackCompFitter::SetParameter(Int_t ipar,const char *parname,Double_t value,Double_t verr,Double_t vlow, Double_t vhigh) {
   std::vector<ROOT::Fit::ParameterSettings> & parlist = fFitter->Config().ParamsSettings();
   if ( ipar >= (int) parlist.size() ) parlist.resize(ipar+1);
   ROOT::Fit::ParameterSettings ps(parname, value, verr);
   if (verr == 0) ps.Fix();
   if (vlow < vhigh) ps.SetLimits(vlow, vhigh);
   parlist[ipar] = ps;
   return 0;
}
 
//______________________________________________________________________________
// static method evaluating FCN
// void TBackCompFitter::FCN( int &, double * , double & f, double * x , int /* iflag */) {
//    // get static instance of fitter
//    TBackCompFitter * fitter = dynamic_cast<TBackCompFitter *>(TVirtualFitter::GetFitter());
//    assert(fitter);
//    if (fitter->fObjFunc == 0) fitter->RecreateFCN();
//    assert(fitter->fObjFunc);
//    f = (*(fitter.fObjFunc) )(x);
// }
 
////////////////////////////////////////////////////////////////////////////////
/// Recreate a minimizer instance using the function and data
/// set objective function in minimizers function to re-create FCN from stored data object and fit options
 
void TBackCompFitter::ReCreateMinimizer() {
   assert(fFitData.get());
 
   // case of standard fits (not made fia Fitter::FitFCN)
   if (fFitter->Result().FittedFunction() != 0) {
 
      if (fModelFunc) delete fModelFunc;
      fModelFunc =  dynamic_cast<ROOT::Math::IParamMultiFunction *>((fFitter->Result().FittedFunction())->Clone());
      assert(fModelFunc);
 
      // create fcn functions, should consider also gradient case
      const ROOT::Fit::BinData * bindata = dynamic_cast<const ROOT::Fit::BinData *>(fFitData.get());
      if (bindata) {
         if (GetFitOption().Like )
            fObjFunc = new ROOT::Fit::PoissonLikelihoodFCN<ROOT::Math::IMultiGenFunction>(*bindata, *fModelFunc);
         else
            fObjFunc = new ROOT::Fit::Chi2FCN<ROOT::Math::IMultiGenFunction>(*bindata, *fModelFunc);
      }
      else {
         const ROOT::Fit::UnBinData * unbindata = dynamic_cast<const ROOT::Fit::UnBinData *>(fFitData.get());
         assert(unbindata);
         fObjFunc = new ROOT::Fit::LogLikelihoodFCN<ROOT::Math::IMultiGenFunction>(*unbindata, *fModelFunc);
      }
   }
 
   // recreate the minimizer
   fMinimizer = fFitter->Config().CreateMinimizer();
   if (fMinimizer == 0) {
      Error("SetMinimizerFunction","cannot create minimizer %s",fFitter->Config().MinimizerType().c_str() );
   }
   else {
      if (!fObjFunc) {
         Error("SetMinimizerFunction","Object Function pointer is NULL");
      }
      else
         fMinimizer->SetFunction(*fObjFunc);
   }
 
}
 
////////////////////////////////////////////////////////////////////////////////
/// Override setFCN to use the Adapter to Minuit2 FCN interface
/// To set the address of the minimization function
 
void TBackCompFitter::SetFCN(void (*fcn)(Int_t &, Double_t *, Double_t &f, Double_t *, Int_t))
{
   fFCN = fcn;
   if (fObjFunc) delete fObjFunc;
   fObjFunc = new ROOT::Fit::FcnAdapter(fFCN);
   DoSetDimension();
}
 
 
////////////////////////////////////////////////////////////////////////////////
/// Set the objective function for fitting
/// Needed if fitting directly using TBackCompFitter class
/// The class clones a copy of the function and manages it
 
void TBackCompFitter::SetObjFunction(ROOT::Math::IMultiGenFunction   * fcn) {
   if (fObjFunc) delete fObjFunc;
   fObjFunc = fcn->Clone();
}
 
////////////////////////////////////////////////////////////////////////////////
/// Private method to set dimension in objective function
 
void TBackCompFitter::DoSetDimension() {
   if (!fObjFunc) return;
   ROOT::Fit::FcnAdapter * fobj = dynamic_cast<ROOT::Fit::FcnAdapter*>(fObjFunc);
   assert(fobj != 0);
   int ndim = fFitter->Config().ParamsSettings().size();
   if (ndim != 0) fobj->SetDimension(ndim);
}
 
////////////////////////////////////////////////////////////////////////////////
/// Return a pointer to the objective function (FCN)
/// If fitting directly using TBackCompFitter the pointer is managed by the class,
/// which has been set previously when calling SetObjFunction or SetFCN
/// Otherwise if the class is used in the backward compatible mode (e.g. after having fitted a TH1)
/// the return pointer will be valid after fitting and as long a new fit will not be done.
 
ROOT::Math::IMultiGenFunction * TBackCompFitter::GetObjFunction( ) const {
   if (fObjFunc) return fObjFunc;
   return fFitter->GetFCN();
}
 
////////////////////////////////////////////////////////////////////////////////
/// Return a pointer to the minimizer.
/// the return pointer will be valid after fitting and as long a new fit will not be done.
/// For keeping a minimizer pointer the method ReCreateMinimizer() could eventually be used
 
ROOT::Math::Minimizer * TBackCompFitter::GetMinimizer( ) const {
   if (fMinimizer) return fMinimizer;
   return fFitter->GetMinimizer();
}
 
////////////////////////////////////////////////////////////////////////////////
/// Return a new copy of the TFitResult object which needs to be deleted later by the user
 
TFitResult * TBackCompFitter::GetTFitResult( ) const {
   if (!fFitter.get() ) return 0;
   return new TFitResult( fFitter->Result() );
}
 
////////////////////////////////////////////////////////////////////////////////
///  Scan parameter ipar between value of xmin and xmax
///  A graph must be given which will be on return filled with the scan resul
///  If the graph size is zero, a default size n = 40 will be used
 
bool TBackCompFitter::Scan(unsigned int ipar, TGraph * gr, double xmin, double xmax )
{
 
   if (!gr) return false;
   ROOT::Math::Minimizer * minimizer = fFitter->GetMinimizer();
   if (!minimizer) {
      Error("Scan","Minimizer is not available - cannot scan before fitting");
      return false;
   }
 
   unsigned int npoints = gr->GetN();
   if (npoints == 0)  {
      npoints = 40;
      gr->Set(npoints);
   }
   bool ret = minimizer->Scan( ipar, npoints, gr->GetX(), gr->GetY(), xmin, xmax);
   if ((int) npoints < gr->GetN() ) gr->Set(npoints);
   return ret;
}
 
//______________________________________________________________________________
// bool  TBackCompFitter::Scan2D(unsigned int ipar, unsigned int jpar, TGraph2D * gr,
//                       double xmin = 0, double xmax = 0, double ymin = 0, double ymax = 0) {
//    //     scan the parameters ipar between values of [xmin,xmax] and
//    //     jpar between values of [ymin,ymax] and
//    //     a graph2D must be given which will be on return filled with the scan resul
//    //     If the graph size is zero, a default size n = 20x20 will be used
//    //*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*
 
//    if (!gr) return false;
//    if (!fMinimizer) {
//       Error("Scan","Minimizer is not available - cannot scan before fitting");
//       return false;
//    }
//    unsigned int npoints = gr->GetN();
//    if (npoints == 0)  {
//       npoints = 40;
//       gr->Set(npoints);
//    }
//    // to be implemented
//    for (unsigned int ix = 0; ix < npoints; ++ix) {
//       return fMinimizer->Scan( ipar, npoints, gr->GetX(), gr->GetY(), xmin, xmax);
 
// }
 
////////////////////////////////////////////////////////////////////////////////
/// Create a 2D contour around the minimum for the parameter ipar and jpar
/// if a minimum does not exist or is invalid it will return false
/// on exit a TGraph is filled with the contour points
/// the number of contour points is determined by the size of the TGraph.
/// if the size is zero a default number of points = 20 is used
/// pass optionally the confidence level, default is 0.683
/// it is assumed that ErrorDef() defines the right error definition
/// (i.e 1 sigma error for one parameter). If not the confidence level are scaled to new level
 
bool  TBackCompFitter::Contour(unsigned int ipar, unsigned int jpar, TGraph * gr, double confLevel) {
   if (!gr) return false;
   ROOT::Math::Minimizer * minimizer = fFitter->GetMinimizer();
   if (!minimizer) {
      Error("Scan","Minimizer is not available - cannot scan before fitting");
      return false;
   }
 
   // get error level used for fitting
   double upScale = fFitter->Config().MinimizerOptions().ErrorDef();
 
   double upVal = TMath::ChisquareQuantile( confLevel, 2);  // 2 is number of parameter we do the contour
 
   // set required error definition in minimizer
   minimizer->SetErrorDef (upScale * upVal);
 
   unsigned int npoints = gr->GetN();
   if (npoints == 0)  {
      npoints = 40;
      gr->Set(npoints);
   }
   bool ret =  minimizer->Contour( ipar, jpar, npoints, gr->GetX(), gr->GetY());
   if ((int) npoints < gr->GetN() ) gr->Set(npoints);
 
   // restore the error level used for fitting
   minimizer->SetErrorDef ( upScale);
 
   return ret;
}