RooBreitWigner.cxx

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/*****************************************************************************
 * Project: RooFit                                                           *
 * Package: RooFitModels                                                     *
 * @(#)root/roofit:$Id$
 * Authors:                                                                  *
 *   AS, Abi Soffer, Colorado State University, abi@slac.stanford.edu        *
 *   TS, Thomas Schietinger, SLAC, schieti@slac.stanford.edu                 *
 *                                                                           *
 * Copyright (c) 2000-2005, Regents of the University of California          *
 *                          Colorado State University                        *
 *                          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 RooBreitWigner
    \ingroup Roofit

Class RooBreitWigner is a RooAbsPdf implementation
that models a non-relativistic Breit-Wigner shape
**/

#include "RooFit.h"

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

#include "RooBreitWigner.h"
#include "RooAbsReal.h"
#include "RooRealVar.h"
#include "BatchHelpers.h"
// #include "RooFitTools/RooRandom.h"

using namespace std;

ClassImp(RooBreitWigner);

////////////////////////////////////////////////////////////////////////////////

RooBreitWigner::RooBreitWigner(const char *name, const char *title,
          RooAbsReal& _x, RooAbsReal& _mean,
          RooAbsReal& _width) :
  RooAbsPdf(name,title),
  x("x","Dependent",this,_x),
  mean("mean","Mean",this,_mean),
  width("width","Width",this,_width)
{
}

////////////////////////////////////////////////////////////////////////////////

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

////////////////////////////////////////////////////////////////////////////////

Double_t RooBreitWigner::evaluate() const
{
  Double_t arg= x - mean;
  return 1. / (arg*arg + 0.25*width*width);
}

////////////////////////////////////////////////////////////////////////////////

namespace {
//Author: Emmanouil Michalainas, CERN 21 August 2019

template<class Tx, class Tmean, class Twidth>
void compute(  size_t batchSize,
              double * __restrict output,
              Tx X, Tmean M, Twidth W)
{
  for (size_t i=0; i<batchSize; i++) {
    const double arg = X[i]-M[i];
    output[i] = 1 / (arg*arg + 0.25*W[i]*W[i]);
  }
}
};

RooSpan<double> RooBreitWigner::evaluateBatch(std::size_t begin, std::size_t batchSize) const {
  using namespace BatchHelpers;
  auto xData = x.getValBatch(begin, batchSize);
  auto meanData = mean.getValBatch(begin, batchSize);
  auto widthData = width.getValBatch(begin, batchSize);
  const bool batchX = !xData.empty();
  const bool batchMean = !meanData.empty();
  const bool batchWidth = !widthData.empty();

  if (!batchX && !batchMean && !batchWidth) {
    return {};
  }
  batchSize = findSize({ xData, meanData, widthData });
  auto output = _batchData.makeWritableBatchUnInit(begin, batchSize);

  if (batchX && !batchMean && !batchWidth ) {
    compute(batchSize, output.data(), xData, BracketAdapter<double>(mean), BracketAdapter<double>(width));
  }
  else if (!batchX && batchMean && !batchWidth ) {
    compute(batchSize, output.data(), BracketAdapter<double>(x), meanData, BracketAdapter<double>(width));
  }
  else if (batchX && batchMean && !batchWidth ) {
    compute(batchSize, output.data(), xData, meanData, BracketAdapter<double>(width));
  }
  else if (!batchX && !batchMean && batchWidth ) {
    compute(batchSize, output.data(), BracketAdapter<double>(x), BracketAdapter<double>(mean), widthData);
  }
  else if (batchX && !batchMean && batchWidth ) {
    compute(batchSize, output.data(), xData, BracketAdapter<double>(mean), widthData);
  }
  else if (!batchX && batchMean && batchWidth ) {
    compute(batchSize, output.data(), BracketAdapter<double>(x), meanData, widthData);
  }
  else if (batchX && batchMean && batchWidth ) {
    compute(batchSize, output.data(), xData, meanData, widthData);
  }
  return output;
}

////////////////////////////////////////////////////////////////////////////////

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

////////////////////////////////////////////////////////////////////////////////

Double_t RooBreitWigner::analyticalIntegral(Int_t code, const char* rangeName) const
{
  switch(code) {
  case 1:
    {
      Double_t c = 2./width;
      return c*(atan(c*(x.max(rangeName)-mean)) - atan(c*(x.min(rangeName)-mean)));
    }
  }

  assert(0) ;
  return 0 ;
}