RooGaussian.cxx

Go to the documentation of this file.

/*****************************************************************************
 * Project: RooFit                                                           *
 * Package: RooFitModels                                                     *
 * @(#)root/roofit:$Id$
 * 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 RooGaussian
    \ingroup Roofit
 
Plain Gaussian p.d.f
**/
 
#include "RooGaussian.h"
#include "RooBatchCompute.h"
#include "RooHelpers.h"
#include "RooRandom.h"
 
#include <RooFit/Detail/AnalyticalIntegrals.h>
#include <RooFit/Detail/EvaluateFuncs.h>
 
#include <vector>
 
ClassImp(RooGaussian);
 
////////////////////////////////////////////////////////////////////////////////
 
RooGaussian::RooGaussian(const char *name, const char *title,
          RooAbsReal::Ref _x, RooAbsReal::Ref _mean,
          RooAbsReal::Ref _sigma) :
  RooAbsPdf(name,title),
  x("x","Observable",this,_x),
  mean("mean","Mean",this,_mean),
  sigma("sigma","Width",this,_sigma)
{
  RooHelpers::checkRangeOfParameters(this, {&static_cast<RooAbsReal&>(_sigma)}, 0);
}
 
////////////////////////////////////////////////////////////////////////////////
 
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 RooGaussian::evaluate() const
{
   return RooFit::Detail::EvaluateFuncs::gaussianEvaluate(x, mean, sigma);
}
 
 
////////////////////////////////////////////////////////////////////////////////
/// Compute multiple values of Gaussian distribution.
void RooGaussian::computeBatch(double* output, size_t nEvents, RooFit::Detail::DataMap const& dataMap) const
{
  RooBatchCompute::compute(dataMap.config(this), RooBatchCompute::Gaussian, output, nEvents,
          {dataMap.at(x), dataMap.at(mean), dataMap.at(sigma)});
}
 
////////////////////////////////////////////////////////////////////////////////
 
Int_t RooGaussian::getAnalyticalIntegral(RooArgSet& allVars, RooArgSet& analVars, const char* /*rangeName*/) const
{
  if (matchArgs(allVars,analVars,x)) return 1 ;
  if (matchArgs(allVars,analVars,mean)) return 2 ;
  return 0 ;
}
 
////////////////////////////////////////////////////////////////////////////////
 
double RooGaussian::analyticalIntegral(Int_t code, const char* rangeName) const
{
   using namespace RooFit::Detail::AnalyticalIntegrals;
 
   auto& constant  = code == 1 ? mean : x;
   auto& integrand = code == 1 ? x : mean;
 
   return gaussianIntegral(integrand.min(rangeName), integrand.max(rangeName), constant, sigma);
}
 
////////////////////////////////////////////////////////////////////////////////
 
Int_t RooGaussian::getGenerator(const RooArgSet& directVars, RooArgSet &generateVars, bool /*staticInitOK*/) const
{
  if (matchArgs(directVars,generateVars,x)) return 1 ;
  if (matchArgs(directVars,generateVars,mean)) return 2 ;
  return 0 ;
}
 
////////////////////////////////////////////////////////////////////////////////
 
void RooGaussian::generateEvent(Int_t code)
{
  assert(code==1 || code==2) ;
  double xgen ;
  if(code==1){
    while(true) {
      xgen = RooRandom::randomGenerator()->Gaus(mean,sigma);
      if (xgen<x.max() && xgen>x.min()) {
   x = xgen ;
   break;
      }
    }
  } else if(code==2){
    while(true) {
      xgen = RooRandom::randomGenerator()->Gaus(x,sigma);
      if (xgen<mean.max() && xgen>mean.min()) {
   mean = xgen ;
   break;
      }
    }
  } else {
    std::cout << "error in RooGaussian generateEvent"<< std::endl;
  }
 
  return;
}
 
////////////////////////////////////////////////////////////////////////////////
 
void RooGaussian::translate(RooFit::Detail::CodeSquashContext &ctx) const
{
   // Build a call to the stateless gaussian defined later.
   ctx.addResult(this, ctx.buildCall("RooFit::Detail::EvaluateFuncs::gaussianEvaluate", x, mean, sigma));
}
 
////////////////////////////////////////////////////////////////////////////////
 
std::string RooGaussian::buildCallToAnalyticIntegral(Int_t code, const char *rangeName,
                                                     RooFit::Detail::CodeSquashContext &ctx) const
{
   auto& constant  = code == 1 ? mean : x;
   auto& integrand = code == 1 ? x : mean;
 
   return ctx.buildCall("RooFit::Detail::AnalyticalIntegrals::gaussianIntegral",
                        integrand.min(rangeName), integrand.max(rangeName), constant, sigma);
}