RooDecay.cxx

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/*****************************************************************************
 * 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 RooDecay
    \ingroup Roofit
 
Single or double sided decay function that can be analytically convolved
with any RooResolutionModel implementation. It declares the basis functions
for the analytical convolution with a RooResolutionModel. See RooAbsAnaConvPdf.
\f[
  \mathrm{basis} = \begin{cases}
    \exp\left(-\frac{t}{\tau}\right) & \mathrm{SingleSided} \\
    \exp\left( \frac{t}{\tau}\right) & \mathrm{Flipped} \\
    \exp\left(-\frac{|t|}{\tau}\right) & \mathrm{DoubleSided}
    \end{cases}
\f]
**/
 
#include "RooDecay.h"
 
#include "RooFit.h"
#include "RooRealVar.h"
#include "RooRandom.h"
 
#include "TError.h"
 
using namespace std;
 
ClassImp(RooDecay);
 
////////////////////////////////////////////////////////////////////////////////
/// Create a new RooDecay.
/// \param[in] name Name of this object.
/// \param[in] title Title (for *e.g.* plotting)
/// \param[in] t Convolution variable (*e.g.* time).
/// \param[in] tau Decay constant.
/// \param[in] model Resolution model for the convolution.
/// \param[in] type One of the decays types `SingleSided, Flipped, DoubleSided`
RooDecay::RooDecay(const char *name, const char *title,
         RooRealVar& t, RooAbsReal& tau,
         const RooResolutionModel& model, DecayType type) :
  RooAbsAnaConvPdf(name,title,model,t),
  _t("t","time",this,t),
  _tau("tau","decay time",this,tau),
  _type(type)
{
  switch(type) {
  case SingleSided:
    _basisExp = declareBasis("exp(-@0/@1)",tau) ;
    break ;
  case Flipped:
    _basisExp = declareBasis("exp(@0/@1)",tau) ;
    break ;
  case DoubleSided:
    _basisExp = declareBasis("exp(-abs(@0)/@1)",tau) ;
    break ;
  }
}
 
////////////////////////////////////////////////////////////////////////////////
/// Copy constructor
 
RooDecay::RooDecay(const RooDecay& other, const char* name) :
  RooAbsAnaConvPdf(other,name),
  _t("t",this,other._t),
  _tau("tau",this,other._tau),
  _type(other._type),
  _basisExp(other._basisExp)
{
}
 
////////////////////////////////////////////////////////////////////////////////
/// Destructor
 
RooDecay::~RooDecay()
{
}
 
////////////////////////////////////////////////////////////////////////////////
 
Double_t RooDecay::coefficient(Int_t /*basisIndex*/) const
{
  return 1 ;
}
 
////////////////////////////////////////////////////////////////////////////////
 
Int_t RooDecay::getGenerator(const RooArgSet& directVars, RooArgSet &generateVars, Bool_t /*staticInitOK*/) const
{
  if (matchArgs(directVars,generateVars,_t)) return 1 ;
  return 0 ;
}
 
////////////////////////////////////////////////////////////////////////////////
 
void RooDecay::generateEvent(Int_t code)
{
  R__ASSERT(code==1) ;
 
  // Generate delta-t dependent
  while(1) {
    Double_t rand = RooRandom::uniform() ;
    Double_t tval(0) ;
 
    switch(_type) {
    case SingleSided:
      tval = -_tau*log(rand);
      break ;
    case Flipped:
      tval= +_tau*log(rand);
      break ;
    case DoubleSided:
      tval = (rand<=0.5) ? -_tau*log(2*rand) : +_tau*log(2*(rand-0.5)) ;
      break ;
    }
 
    if (tval<_t.max() && tval>_t.min()) {
      _t = tval ;
      break ;
    }
  }
}