    /*****************************************************************************
     * Project: RooFit                                                           *
     * Package: RooFitModels                                                     *
     *    File: $Id: RooGaussian.cc,v 1.26 2005/06/20 15:51:06 wverkerke Exp $
     * Authors:                                                                  *
     *   WV, Wouter Verkerke, UC Santa Barbara, verkerke@slac.stanford.edu       *
     *   DK, David Kirkby,    UC Irvine,         dkirkby@uci.edu                 *
     *                                                                           *
     * Copyright (c) 2000-2005, Regents of the University of California          *
     *                          and Stanford University. All rights reserved.    *
     *                                                                           *
     * Redistribution and use in source and binary forms,                        *
     * with or without modification, are permitted according to the terms        *
     * listed in LICENSE (http://roofit.sourceforge.net/license.txt)             *
     *****************************************************************************/

    // -- CLASS DESCRIPTION [PDF] --
    // Gaussian PDF...

    #include "RooFit.h"

    #include "Riostream.h"
    #include "Riostream.h"
    #include <math.h>

    #include "RooGaussian.h"
    #include "RooAbsReal.h"
    #include "RooRealVar.h"
    #include "RooRandom.h"
    #include "RooMath.h"

    ClassImp(RooGaussian)

     RooGaussian::RooGaussian(const char *name, const char *title,
    			 RooAbsReal& _x, RooAbsReal& _mean,
    			 RooAbsReal& _sigma) :
      RooAbsPdf(name,title),
      x("x","Dependent",this,_x),
      mean("mean","Mean",this,_mean),
      sigma("sigma","Width",this,_sigma)
    {
    }

     RooGaussian::RooGaussian(const RooGaussian& other, const char* name) :
      RooAbsPdf(other,name), x("x",this,other.x), mean("mean",this,other.mean),
      sigma("sigma",this,other.sigma)
    {
    }

     Double_t RooGaussian::evaluate() const
    {
      Double_t arg= x - mean;
      return exp(-0.5*arg*arg/(sigma*sigma)) ;
    }

     Int_t RooGaussian::getAnalyticalIntegral(RooArgSet& allVars, RooArgSet& analVars, const char* /*rangeName*/) const
    {
      if (matchArgs(allVars,analVars,x)) return 1 ;
      return 0 ;
    }

     Double_t RooGaussian::analyticalIntegral(Int_t code, const char* rangeName) const
    {
      assert(code==1) ;

      static const Double_t root2 = sqrt(2.) ;
      static const Double_t rootPiBy2 = sqrt(atan2(0.0,-1.0)/2.0);

      Double_t xscale = root2*sigma;
      return rootPiBy2*sigma*(RooMath::erf((x.max(rangeName)-mean)/xscale)-RooMath::erf((x.min(rangeName)-mean)/xscale));
    }

     Int_t RooGaussian::getGenerator(const RooArgSet& directVars, RooArgSet &generateVars, Bool_t /*staticInitOK*/) const
    {
      if (matchArgs(directVars,generateVars,x)) return 1 ;
      return 0 ;
    }

     void RooGaussian::generateEvent(Int_t code)
    {
      assert(code==1) ;
      Double_t xgen ;
      while(1) {
        xgen = RooRandom::randomGenerator()->Gaus(mean,sigma);
        if (xgen<x.max() && xgen>x.min()) {
          x = xgen ;
          break;
        }
      }
      return;
    }