    // @(#)root/hist:$Name:  $:$Id: TFractionFitter.cxx,v 1.10 2004/08/18 12:29:44 brun Exp $
    // Author: Frank Filthaut filthaut@hef.kun.nl  20/05/2002
    // with additions by Bran Wijngaarden <dwijngaa@hef.kun.nl>

    ///////////////////////////////////////////////////////////////////////////////
    //
    // Fits MC fractions to data histogram (a la HMCMLL, see R. Barlow and C. Beeston,
    // Comp. Phys. Comm. 77 (1993) 219-228, and http://www.hep.man.ac.uk/~roger/hfrac.f).
    //
    // The virtue of this fit is that it takes into account both data and Monte Carlo
    // statistical uncertainties. The way in which this is done is through a standard
    // likelihood fit using Poisson statistics; however, the template (MC) predictions
    // are also varied within statistics, leading to additional contributions to the
    // overall likelihood. This leads to many more fit parameters (one per bin per
    // template), but the minimisation with respect to these additional parameters is
    // done analytically rather than introducing them as formal fit parameters. Some
    // special care needs to be taken in the case of bins with zero content. For more
    // details please see the original publication cited above.
    //
    // An example application of this fit is given below. For a TH1* histogram
    // ("data") fitted as the sum of three Monte Carlo sources ("mc"):
    //
    // {
    //   TH1F *data;                              //data histogram
    //   TH1F *mc0;                               // first MC histogram
    //   TH1F *mc1;                               // second MC histogram
    //   TH1F *mc2;                               // third MC histogram
    //   ....                                     // retrieve histograms
    //   TObjArray *mc = new TObjArray(3);        // MC histograms are put in this array
    //   mc->Add(mc0);
    //   mc->Add(mc1);
    //   mc->Add(mc2);
    //   TFractionFitter* fit = new TFractionFitter(data, mc); // initialise
    //   fit->Constrain(1,0.0,1.0);               // constrain fraction 1 to be between 0 and 1
    //   fit->SetRangeX(1,15);                    // use only the first 15 bins in the fit
    //   Int_t status = fit->Fit();               // perform the fit
    //   cout << "fit status: " << status << endl;
    //   if (status == 0) {                       // check on fit status
    //     TH1F* prediction = (TH1F*) fit->GetPlot();
    //     data->Draw("Ep");
    //     result->Draw("same");
    //   }
    // }
    //
    //
    // Instantiation
    // =============
    // A fit object is instantiated through
    //     TFractionFitter* fit = new TFractionFitter(data, mc);
    // A number of basic checks (intended to ensure that the template histograms
    // represent the same "kind" of distribution as the data one) are carried out.
    // The TVirtualFitter object is then addressed and all fit parameters (the
    // template fractions) declared (initially unbounded).
    //
    // Applying constraints
    // ====================
    // Fit parameters can be constrained through
    //     fit->Constrain(parameter #, lower bound, upper bound);
    // Setting lower bound = upper bound = 0 removes the constraint (a la Minuit);
    // however, a function
    //     fit->Unconstrain(parameter #)
    // is also provided to simplify this.
    //
    // Setting parameter values
    // ========================
    // The function
    //     TVirtualFitter* vFit = fit->GetFitter();
    // is provided for direct access to the TVirtualFitter object. This allows to
    // set and fix parameter values, and set step sizes directly.
    //
    // Restricting the fit range
    // =========================
    // The fit range can be restricted through
    //     fit->SetRangeX(first bin #, last bin #);
    // and freed using
    //     fit->ReleaseRangeX();
    // For 2D histograms the Y range can be similarly restricted using
    //     fit->SetRangeY(first bin #, last bin #);
    //     fit->ReleaseRangeY();
    // and for 3D histograms also
    //     fit->SetRangeZ(first bin #, last bin #);
    //     fit->ReleaseRangeZ();
    //
    // Weights histograms
    // ==================
    // Weights histograms (for a motivation see the above publication) can be specified
    // for the individual MC sources through
    //     fit->SetWeight(parameter #, pointer to weights histogram);
    // and unset by specifying a null pointer.
    //
    // Obtaining fit results
    // =====================
    // The fit is carried out through
    //     Int_t status = fit->Fit();
    // where  status  is the code returned from the "MINIMIZE" command. For fits
    // that converged, parameter values and errors can be obtained through
    //     fit->GetResult(parameter #, value, error);
    // and the histogram corresponding to the total Monte Carlo prediction (which
    // is not the same as a simple weighted sum of the input Monte Carlo distributions)
    // can be obtained by
    //     TH1* result = fit->GetPlot();
    //
    // Using different histograms
    // ==========================
    // It is possible to change the histogram being fitted through
    //     fit->SetData(TH1* data);
    // and to change the template histogram for a given parameter number through
    //     fit->SetMC(parameter #, TH1* MC);
    // This can speed up code in case of multiple data or template histograms;
    // however, it should be done with care as any settings are taken over from
    // the previous fit. In addition, neither the dimensionality nor the numbers of
    // bins of the histograms should change (in that case it is better to instantiate
    // a new TFractionFitter object).
    //
    // Errors
    // ======
    // Any serious inconsistency results in an error.
    //
    ///////////////////////////////////////////////////////////////////////////////

    #include "Riostream.h"
    #include "TH1.h"
    #include "TMath.h"
    #include "TClass.h"

    #include "TFractionFitter.h"

    TVirtualFitter *fractionFitter=0;

    ClassImp(TFractionFitter)

    //______________________________________________________________________________
     TFractionFitter::TFractionFitter() :
      fFitDone(kFALSE),fData(0), fPlot(0)  {
      // TFractionFitter default constructor.

      fractionFitter = 0;
      fIntegralMCs   = 0;
      fFractions     = 0;

      fNpfits        = 0;
      fNDF           = 0;
      fChisquare     = 0;
    }

    //______________________________________________________________________________
     TFractionFitter::TFractionFitter(TH1* data, TObjArray  *MCs) :
      fFitDone(kFALSE), fChisquare(0), fPlot(0)  {
      // TFractionFitter constructor. Does a complete initialisation (including
      // consistency checks, default fit range as the whole histogram but without
      // under- and overflows, and declaration of the fit parameters). Note that
      // the histograms are not copied, only references are used.
      // Arguments:
      //     data: histogram to be fitted
      //     MCs:  array of TH1* corresponding template distributions

      fData = data;
      // Default: include all of the histogram (but without under- and overflows)
      fLowLimitX = 1;
      fHighLimitX = fData->GetNbinsX();
      if (fData->GetDimension() > 1) {
         fLowLimitY = 1;
         fHighLimitY = fData->GetNbinsY();
         if (fData->GetDimension() > 2) {
            fLowLimitZ = 1;
            fHighLimitZ = fData->GetNbinsZ();
         }
      }
      fNpar = MCs->GetEntries();
      Int_t par;
      for (par = 0; par < fNpar; ++par) {
         fMCs.Add(MCs->At(par));
         // Histogram containing template prediction
         TString s = Form("Prediction for MC sample %i",par);
         TH1* pred = (TH1*) ((TH1*)MCs->At(par))->Clone(s);
         pred->SetTitle(s);
         fAji.Add(pred);
      }
      fIntegralMCs = new Double_t[fNpar];
      fFractions = new Double_t[fNpar];

      CheckConsistency();
      fWeights.Expand(fNpar);

      fractionFitter = TVirtualFitter::Fitter(this, fNpar);
      fractionFitter->Clear();
      fractionFitter->SetObjectFit(this);
      fractionFitter->SetFCN(TFractionFitFCN);

      Double_t defaultFraction = 1.0/((Double_t)fNpar);
      Double_t defaultStep = 0.01;
      for (par = 0; par < fNpar; ++par) {
         TString name("frac"); name += par;
         fractionFitter->SetParameter(par, name.Data(), defaultFraction, defaultStep, 0, 0);
      }
    }

    //______________________________________________________________________________
     TFractionFitter::~TFractionFitter() {
      // TFractionFitter default destructor

      delete fractionFitter;
      delete[] fIntegralMCs;
      delete[] fFractions;
    }

    //______________________________________________________________________________
     void TFractionFitter::SetData(TH1* data) {
      // Change the histogram to be fitted to. Notes:
      // - Parameter constraints and settings are retained from a possible previous fit.
      // - Modifying the dimension or number of bins results in an error (in this case
      //   rather instantiate a new TFractionFitter object)

      fData = data;
      fFitDone = kFALSE;
      CheckConsistency();
    }

    //______________________________________________________________________________
     void TFractionFitter::SetMC(Int_t parm, TH1* MC) {
      // Change the histogram for template number <parm>. Notes:
      // - Parameter constraints and settings are retained from a possible previous fit.
      // - Modifying the dimension or number of bins results in an error (in this case
      //   rather instantiate a new TFractionFitter object)

      CheckParNo(parm);
      fMCs.RemoveAt(parm);
      fMCs.AddAt(MC,parm);
      fFitDone = kFALSE;
      CheckConsistency();
    }

    //______________________________________________________________________________
     void TFractionFitter::SetWeight(Int_t parm, TH1* weight) {
      // Set bin by bin weights for template number <parm> (the parameter numbering
      // follows that of the input template vector).
      // Weights can be "unset" by passing a null pointer.
      // Consistency of the weights histogram with the data histogram is checked at
      // this point, and an error in case of problems.

      CheckParNo(parm);
      if (fWeights[parm]) {
         fWeights.RemoveAt(parm);
      }
      if (weight) {
         if (weight->GetNbinsX() != fData->GetNbinsX() ||
    	 (fData->GetDimension() > 1 && weight->GetNbinsY() != fData->GetNbinsY()) ||
    	 (fData->GetDimension() > 2 && weight->GetNbinsZ() != fData->GetNbinsZ())) {
            Error("SetWeight","Inconsistent weights histogram for source %d", parm);
            return;
         }
         TString ts = "weight hist: "; ts += weight->GetName();
         fWeights.AddAt(weight,parm);
      }
    }

    //______________________________________________________________________________
     TVirtualFitter* TFractionFitter::GetFitter() const {
      // Give direct access to the underlying minimisation class. This can be
      // used e.g. to modify parameter values or step sizes.

      return fractionFitter;
    }

    //______________________________________________________________________________
     void TFractionFitter::CheckParNo(Int_t parm) const {
      // Function for internal use, checking parameter validity
      // An invalid parameter results in an error.

      if (parm < 0 || parm > fNpar) {
         Error("CheckParNo","Invalid parameter number %d",parm);
      }
    }

    //______________________________________________________________________________
     void TFractionFitter::SetRangeX(Int_t low, Int_t high) {
      // Set the X range of the histogram to be used in the fit.
      // Use ReleaseRangeX() to go back to fitting the full histogram.
      // The consistency check ensures that no empty fit range occurs (and also
      // recomputes the bin content integrals).
      // Arguments:
      //     low:  lower X bin number
      //     high: upper X bin number

      if (low <=0 || high <= 0) {
         Error("SetRangeX","Invalid fit range");
         return;
      }
      fLowLimitX = (low > 0) ? low : 1;
      fHighLimitX = (high <= fData->GetNbinsX()) ? high : fData->GetNbinsX();
      CheckConsistency();
    }

    //______________________________________________________________________________
     void TFractionFitter::ReleaseRangeX() {
      // Release restrictions on the X range of the histogram to be used in the fit.

      fLowLimitX = 1;
      fHighLimitX = fData->GetNbinsX();
      CheckConsistency();
    }

    //______________________________________________________________________________
     void TFractionFitter::SetRangeY(Int_t low, Int_t high) {
      // Set the Y range of the histogram to be used in the fit (2D or 3D histograms only).
      // Use ReleaseRangeY() to go back to fitting the full histogram.
      // The consistency check ensures that no empty fit range occurs (and also
      // recomputes the bin content integrals).
      // Arguments:
      //     low:  lower Y bin number
      //     high: upper Y bin number

      if (fData->GetDimension() < 2) {
         Error("SetRangeY","Y range cannot be set for 1D histogram");
         return;
      }
      if (low <=0 || high <= 0) {
         Error("SetRangeY","Invalid fit range");
         return;
      }

      fLowLimitY = (low > 0) ? low : 1;
      fHighLimitY = (high <= fData->GetNbinsY()) ? high : fData->GetNbinsY();
      CheckConsistency();
    }

    //______________________________________________________________________________
     void TFractionFitter::ReleaseRangeY() {
      // Release restrictions on the Y range of the histogram to be used in the fit.

      fLowLimitY = 1;
      fHighLimitY = fData->GetNbinsY();
      CheckConsistency();
    }

    //______________________________________________________________________________
     void TFractionFitter::SetRangeZ(Int_t low, Int_t high) {
      // Set the Z range of the histogram to be used in the fit (3D histograms only).
      // Use ReleaseRangeY() to go back to fitting the full histogram.
      // The consistency check ensures that no empty fit range occurs (and also
      // recomputes the bin content integrals).
      // Arguments:
      //     low:  lower Y bin number
      //     high: upper Y bin number

      if (fData->GetDimension() < 3) {
         Error("SetRangeZ","Z range cannot be set for 1D or 2D histogram");
         return;
      }

      if (low <=0 || high <= 0) {
         Error("SetRangeZ","Invalid fit range");
         return;
      }

      fLowLimitZ = (low > 0) ? low : 1;
      fHighLimitZ = (high <= fData->GetNbinsZ()) ? high : fData->GetNbinsZ();
      CheckConsistency();
    }

    //______________________________________________________________________________
     void TFractionFitter::ReleaseRangeZ() {
      // Release restrictions on the Z range of the histogram to be used in the fit.

      fLowLimitZ = 1;
      fHighLimitZ = fData->GetNbinsZ();
      CheckConsistency();
    }

    //______________________________________________________________________________
     void TFractionFitter::Constrain(Int_t parm, Double_t low, Double_t high) {
      // Constrain the values of parameter number <parm> (the parameter numbering
      // follows that of the input template vector).
      // Use UnConstrain() to remove this constraint.

      CheckParNo(parm);
      Double_t plist[3];
      plist[0] = (Double_t) parm;
      plist[1] = low;
      plist[2] = high;
      fractionFitter->ExecuteCommand("SET LIMIT", plist, 3);
    }

    //______________________________________________________________________________
     void TFractionFitter::UnConstrain(Int_t parm) {
      // Remove the constraints on the possible values of parameter <parm>.

      CheckParNo(parm);
      Double_t plist[3];
      plist[0] = (Double_t) parm;
      plist[1] = 0;
      plist[2] = 0;
      fractionFitter->ExecuteCommand("SET LIMIT", plist, 3);
    }

    //______________________________________________________________________________
     void TFractionFitter::CheckConsistency() {
      // Function used internally to check the consistency between the
      // various histograms. Checks are performed on nonexistent or empty
      // histograms, the precise histogram class, and the number of bins.
      // In addition, integrals over the "allowed" bin ranges are computed.
      // Any inconsistency results in a error.

      if (! fData) {
         Error("CheckConsistency","Nonexistent data histogram");
         return;
      }
      Int_t minX, maxX, minY, maxY, minZ, maxZ;
      Int_t x,y,z,par;
      GetRanges(minX, maxX, minY, maxY, minZ, maxZ);
      fIntegralData = 0;
      fNpfits = 0;
      for (z = minZ; z <= maxZ; ++z) {
         for (y = minY; y <= maxY; ++y) {
            for (x = minX; x <= maxX; ++x) {
    	   fNpfits++;
    	   fIntegralData += fData->GetBinContent(x, y, z);
            }
         }
      }
      if (fIntegralData <= 0) {
         Error("CheckConsistency","Empty data histogram");
         return;
      }
      TClass* cl = fData->Class();

      fNDF = fNpfits - fNpar;

      if (fNpar < 2) {
         Error("CheckConsistency","Need at least two MC histograms");
         return;
      }

      for (par = 0; par < fNpar; ++par) {
         TH1 *h = (TH1*)fMCs.At(par);
         if (! h) {
            Error("CheckConsistency","Nonexistent MC histogram for source #%d",par);
            return;
         }
         if ((! h->Class()->InheritsFrom(cl)) || h->GetNbinsX() != fData->GetNbinsX() ||
    	 (fData->GetDimension() > 1 && h->GetNbinsY() != fData->GetNbinsY()) ||
    	 (fData->GetDimension() > 2 && h->GetNbinsZ() != fData->GetNbinsZ())) {
            Error("CheckConsistency","Histogram inconsistency for source #%d",par);
            return;
         }
         fIntegralMCs[par] = 0;
         for (z = minZ; z <= maxZ; ++z) {
            for (y = minY; y <= maxY; ++y) {
    	   for (x = minX; x <= maxX; ++x) {
    	      fIntegralMCs[par] += h->GetBinContent(x, y, z);
    	   }
            }
         }
         if (fIntegralMCs[par] <= 0) {
            Error("CheckConsistency","Empty MC histogram #%d",par);
         }
      }
    }

    //______________________________________________________________________________
     Int_t TFractionFitter::Fit() {
      // Perform the fit with the default UP value.
      // The value returned is the minimisation status.

      Double_t plist[1];
      plist[0] = 0.5;
      // set the UP value to 0.5
      fractionFitter->ExecuteCommand("SET ERRDEF",plist,1);
      // remove any existing output histogram
      if (fPlot) {
         delete fPlot; fPlot = 0;
      }

      // Make sure the correct likelihood computation is used
      fractionFitter->SetObjectFit(this);

      // fit
      Int_t status = fractionFitter->ExecuteCommand("MINIMIZE",0,0);
      if (status == 0) fFitDone = kTRUE;

      // determine goodness of fit
      ComputeChisquareLambda();

      return status;
    }

    //______________________________________________________________________________
     void TFractionFitter::ErrorAnalysis(Double_t UP) {
      // Set UP to the given value (see class TMinuit), and perform a MINOS minimisation.

      if (! fFitDone) {
         Error("ErrorAnalysis","Fit not yet performed");
         return;
      }

      // Make sure the correct likelihood computation is used
      fractionFitter->SetObjectFit(this);

      Double_t plist[1];
      plist[0] = UP > 0 ? UP : 0.5;
      fractionFitter->ExecuteCommand("SET ERRDEF",plist,1);
      Int_t status = fractionFitter->ExecuteCommand("MINOS",0,0);
      if (status != 0) {
          Error("ErrorAnalysis","Error return from MINOS: %d",status);
      }
    }

    //______________________________________________________________________________
     void TFractionFitter::GetResult(Int_t parm, Double_t& value, Double_t& error) const {
      // Obtain the fit result for parameter <parm> (the parameter numbering
      // follows that of the input template vector).

      CheckParNo(parm);
      if (! fFitDone) {
          Error("GetResult","Fit not yet performed");
          return;
       }
      char parname[100];
      Double_t vlow, vhigh;

      fractionFitter->GetParameter(parm, parname, value, error, vlow, vhigh);
    }

    //______________________________________________________________________________
     TH1* TFractionFitter::GetPlot() {
      // Return the "template prediction" corresponding to the fit result (this is not
      // the same as the weighted sum of template distributions, as template statistical
      // uncertainties are taken into account).
      // Note that the name of this histogram will simply be the same as that of the
      // "data" histogram, prefixed with the string "Fraction fit to hist: ".

      if (! fFitDone) {
          Error("GetPlot","Fit not yet performed");
          return 0;
      }
      if (! fPlot) {
         Double_t plist[1];
         plist[0] = 3;
         fractionFitter->ExecuteCommand("CALL FCN", plist, 1);
      }
      return fPlot;
    }

    //______________________________________________________________________________
     void TFractionFitter::GetRanges(Int_t& minX, Int_t& maxX, Int_t& minY, Int_t& maxY,
    				Int_t& minZ, Int_t& maxZ) const {
      // Used internally to obtain the bin ranges according to the dimensionality of
      // the histogram and the limits set by hand.

      if (fData->GetDimension() < 2) {
        minY = maxY = minZ = maxZ = 0;
        minX = fLowLimitX;
        maxX = fHighLimitX;
      } else if (fData->GetDimension() < 3) {
        minZ = maxZ = 0;
        minX = fLowLimitX;
        maxX = fHighLimitX;
        minY = fLowLimitY;
        maxY = fHighLimitY;
      } else {
        minX = fLowLimitX;
        maxX = fHighLimitX;
        minY = fLowLimitY;
        maxY = fHighLimitY;
        minZ = fLowLimitZ;
        maxZ = fHighLimitZ;
      }
    }

    //______________________________________________________________________________
     void TFractionFitter::ComputeFCN(Int_t& /*npar*/, Double_t* /*gin*/,
                                     Double_t& f, Double_t* xx, Int_t flag)
    {
      // Used internally to compute the likelihood value.

      // normalise the fit parameters
      Int_t bin, mc;
      Int_t minX, maxX, minY, maxY, minZ, maxZ;
      Int_t x,y,z;
      GetRanges(minX, maxX, minY, maxY, minZ, maxZ);
      for (mc = 0; mc < fNpar; ++mc) {
         Double_t tot;
         TH1 *h  = (TH1*)fMCs[mc];
         TH1 *hw = (TH1*)fWeights[mc];
         if (hw) {
            tot = 0;
    	for (z = minZ; z <= maxZ; ++z) {
    	   for (y = minY; y <= maxY; ++y) {
    	      for (x = minX; x <= maxX; ++x) {
    	         Double_t weight = hw->GetBinContent(x, y, z);
    	         if (weight <= 0) {
    		    Error("ComputeFCN","Invalid weight encountered for MC source %d",mc);
    		    return;
    	         }
    	         tot += weight * h->GetBinContent(x, y, z);
    	      }
    	   }
    	}
        } else tot = fIntegralMCs[mc];
        fFractions[mc] = xx[mc] * fIntegralData / tot;
      }

      if (flag == 3) {
         TString ts = "Fraction fit to hist: "; ts += fData->GetName();
         fPlot = (TH1*) fData->Clone(ts.Data());
         fPlot->Reset();
      }
      // likelihood computation
      Double_t result = 0;
      for (z = minZ; z <= maxZ; ++z) {
        for (y = minY; y <= maxY; ++y) {
          for (x = minX; x <= maxX; ++x) {
    	bin = fData->GetBin(x, y, z);

    	// Solve for the "predictions"
    	int k0;
    	Double_t Ti; Double_t Aki;
    	FindPrediction(bin, fFractions, Ti, k0, Aki);

    	Double_t prediction = 0;
    	for (mc = 0; mc < fNpar; ++mc) {
    	  TH1 *h  = (TH1*)fMCs[mc];
    	  TH1 *hw = (TH1*)fWeights[mc];
    	  Double_t binPrediction;
    	  Double_t binContent = h->GetBinContent(bin);
    	  Double_t weight = hw ? hw->GetBinContent(bin) : 1;
    	  if (k0 >= 0 && fFractions[mc] == fFractions[k0]) {
    	     binPrediction = Aki;
    	  } else {
    	     binPrediction = binContent > 0 ? binContent / (1+weight*fFractions[mc]*Ti) : 0;
    	  }

    	  prediction += fFractions[mc]*weight*binPrediction;
    	  result -= binPrediction;
    	  if (binContent > 0 && binPrediction > 0)
    	    result += binContent*TMath::Log(binPrediction);

    	  if (flag == 3) {
    	     ((TH1*)fAji.At(mc))->SetBinContent(bin, binPrediction);
    	  }
    	}

    	if (flag == 3) {
    	   fPlot->SetBinContent(bin, prediction);
    	}

    	result -= prediction;
    	Double_t found = fData->GetBinContent(bin);
    	if (found > 0)
    	   result += found*TMath::Log(prediction);
          }
        }
      }

      f = -result;
    }

    //______________________________________________________________________________
     void TFractionFitter::FindPrediction(int bin, Double_t *fractions, Double_t &Ti, int& k0, Double_t &Aki) const {
      // Function used internally to obtain the template prediction in the individual bins

      // Sanity check: none of the fractions should be =0
      Int_t par;
      TH1 *hw;
      for (par = 0; par < fNpar; ++par) {
         hw = (TH1*)fWeights.At(par);
         Double_t weightedFraction = hw ?
            hw->GetBinContent(bin) * fractions[par] : fractions[par];
         if (weightedFraction == 0) {
            Error("FindPrediction","Fraction[%d] = 0!",par);
            return;
         }
      }

      // Case data = 0
      if (TMath::Nint(fData->GetBinContent(bin)) == 0) {
         Ti = 1;
         k0 = -1;
         Aki=0;
         return;
      }

      // Case one or more of the MC bin contents = 0: find largest fraction
      k0 = 0;
      TH1 *hw0 = (TH1*)fWeights.At(0);
      Double_t refWeightedFraction = hw0 ?
         hw0->GetBinContent(bin) * fractions[0] : fractions[0];
      for (par = 1; par < fNpar; ++par) {
         hw = (TH1*)fWeights.At(par);
         Double_t weightedFraction = hw ?
          hw->GetBinContent(bin) * fractions[par] : fractions[par];
         if (weightedFraction > refWeightedFraction) {
            k0 = par;
            refWeightedFraction = weightedFraction;
         }
      }
      int nMax = 1;
      Double_t contentsMax = ((TH1*)fMCs.At(k0))->GetBinContent(bin);
      for (par = 0; par < fNpar; ++par) {
         if (par == k0) continue;
         hw = (TH1*)fWeights.At(par);
         Double_t weightedFraction = hw ?
          hw->GetBinContent(bin) * fractions[par] : fractions[par];
         if (weightedFraction == refWeightedFraction) {
            nMax++;
            contentsMax += ((TH1*)fMCs.At(par))->GetBinContent(bin);
         }
      }
      Double_t Tmin = -1/refWeightedFraction;

      if (contentsMax == 0) {
         Aki = fData->GetBinContent(bin)/(1+refWeightedFraction);
         for (par = 0; par < fNpar; ++par) {
            hw = (TH1*)fWeights.At(par);
            if (par != k0) {
    	   Double_t weightedFraction = hw ?
    	     hw->GetBinContent(bin) * fractions[par] : fractions[par];
    	   if (weightedFraction != refWeightedFraction)
    	     Aki -= ((TH1*)fMCs.At(par))->GetBinContent(bin)*weightedFraction/ (refWeightedFraction - weightedFraction);
            }
         }
         if (Aki > 0) {
            if (nMax) Aki /= nMax;
            Ti = Tmin;
            return;
         }
      }
      k0 = -1;

      // Case of nonzero histogram contents: solve for Ti using Newton's method
      Ti = 0;
      for (Double_t step = 0.2;;) {
         if (Ti >= 1 || Ti < Tmin) {
            step /= 10;
            Ti = 0;
         }
         Double_t Function   = - fData->GetBinContent(bin)/(1-Ti);
         Double_t Derivative = Function / (1-Ti);
         for (par = 0; par < fNpar; ++par) {
            TH1 *h = (TH1*)fMCs.At(par);
            hw = (TH1*)fWeights.At(par);
            Double_t weightedFraction = hw ?
    	  hw->GetBinContent(bin) * fractions[par] : fractions[par];
            Double_t D = 1/(Ti+1/weightedFraction);
            Function   += h->GetBinContent(bin)*D;
            Derivative -= h->GetBinContent(bin)*D*D;
         }
         if (TMath::Abs(Function) < 1e-12) break;
         Double_t Delta = -Function/Derivative;
         if (TMath::Abs(Delta) > step)
            Delta = (Delta > 0) ? step : -step;
         Ti += Delta;
         if (TMath::Abs(Delta) < 1e-13) break;
      }

      return;
    }

    //______________________________________________________________________________
    void TFractionFitFCN(Int_t& npar, Double_t* gin, Double_t& f, Double_t* par, Int_t flag) {
      // Function called by the minimisation package. The actual functionality is passed
      // on to the TFractionFitter::ComputeFCN member function.

      TFractionFitter* fitter = dynamic_cast<TFractionFitter*>(fractionFitter->GetObjectFit());
      if (!fitter) {
         fitter->Error("TFractionFitFCN","Invalid fit object encountered!");
         return;
      }
      fitter->ComputeFCN(npar, gin, f, par, flag);
    }

    //______________________________________________________________________________
     Double_t TFractionFitter::GetChisquare() const
    {
    // Return the likelihood ratio Chi-squared (chi2) for the fit.
    // The value is computed when the fit is executed successfully.
    // Chi2 calculation is based on the "likelihood ratio" lambda,
    // lambda = L(y;n) / L(m;n),
    // where L(y;n) is the likelihood of the fit result <y> describing the data <n>
    // and L(m;n) is the likelihood of an unknown "true" underlying distribution
    // <m> describing the data <n>. Since <m> is unknown, the data distribution is
    // used instead,
    // lambda = L(y;n) / L(n;n).
    // Note that this ratio is 1 if the fit is perfect. The chi2 value is then
    // computed according to
    // chi2 = -2*ln(lambda).
    // This parameter can be shown to follow a Chi-square distribution. See for
    // example S. Baker and R. Cousins, "Clarification of the use of chi-square
    // and likelihood functions in fits to histograms", Nucl. Instr. Meth. A221,
    // pp. 437-442 (1984)

       return fChisquare;
    }

    //______________________________________________________________________________
      Int_t TFractionFitter::GetNDF() const
    {
    // return the number of degrees of freedom in the fit
    // the fNDF parameter has been previously computed during a fit.
    // The number of degrees of freedom corresponds to the number of points
    // used in the fit minus the number of templates.

       if (fNDF == 0) return fNpfits-fNpar;
       return fNDF;
    }

    //______________________________________________________________________________
      Double_t TFractionFitter::GetProb() const
    {
    // return the fit probability

       Int_t ndf = fNpfits - fNpar;
       if (ndf <= 0) return 0;
       return TMath::Prob(fChisquare,ndf);
    }

    //______________________________________________________________________________
      void TFractionFitter::ComputeChisquareLambda()
    {
    // Method used internally to compute the likelihood ratio chi2
    // See the function GetChisquare() for details

       if ( !fFitDone ) {
          Error("ComputeChisquareLambda","Fit not yet (successfully) performed");
          fChisquare = 0;
          return;
       }

       // fPlot must be initialized and filled. Leave this to the GetPlot() method.
       if (! fPlot)
         GetPlot();

       Int_t minX, maxX, minY, maxY, minZ, maxZ;
       GetRanges(minX, maxX, minY, maxY, minZ, maxZ);

       Double_t logLyn = 0; // likelihood of prediction
       Double_t logLmn = 0; // likelihood of data ("true" distribution)
       for(Int_t x = minX; x <= maxX; x++) {
          for(Int_t y = minY; y <= maxY; y++) {
    	 for(Int_t z = minZ; z <= maxZ; z++) {
    	    Double_t di = fData->GetBinContent(x, y, z);
    	    Double_t fi = fPlot->GetBinContent(x, y, z);
    	    if(fi != 0) logLyn += di * TMath::Log(fi) - fi;
    	    if(di != 0) logLmn += di * TMath::Log(di) - di;
    	    for(Int_t j = 0; j < fNpar; j++) {
    	       Double_t aji = ((TH1*)fMCs.At(j))->GetBinContent(x, y, z);
    	       Double_t Aji = ((TH1*)fAji.At(j))->GetBinContent(x, y, z);
    	       if(Aji != 0) logLyn += aji * TMath::Log(Aji) - aji;
    	       if(aji != 0) logLmn += aji * TMath::Log(aji) - aji;
    	    }
    	 }
          }
       }

       fChisquare = -2*logLyn + 2*logLmn;

       return;
    }

    //______________________________________________________________________________
     TH1* TFractionFitter::GetMCPrediction(Int_t parm) const
    {
    // Return the adjusted MC template (Aji) for template (parm).
    // Note that the (Aji) times fractions only sum to the total prediction
    // of the fit if all weights are 1.

       CheckParNo(parm);
       if ( !fFitDone ) {
          Error("GetMCPrediction","Fit not yet performed");
          return 0;
       }
       return (TH1*) fAji.At(parm);
    }