RooNormalizedPdf.cxx

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/*
 * Project: RooFit
 * Authors:
 *   Jonas Rembser, CERN 2022
 *
 * Copyright (c) 2022, CERN
 *
 * 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)
 */
 
#include "RooNormalizedPdf.h"
#include "RooBatchCompute.h"
 
/**
 * \class RooNormalizedPdf
 *
 * A RooNormalizedPdf wraps a pdf divided by its integral for a given
 * normalization set into a new self-normalized pdf.
 */
 
void RooNormalizedPdf::computeBatch(cudaStream_t *stream, double *output, size_t nEvents,
                                    RooFit::Detail::DataMap const &dataMap) const
{
   auto nums = dataMap.at(_pdf);
   auto integralSpan = dataMap.at(_normIntegral);
 
   // We use the extraArgs as output parameter to count evaluation errors.
   RooBatchCompute::ArgVector extraArgs{0.0, 0.0, 0.0};
 
   auto dispatch = stream ? RooBatchCompute::dispatchCUDA : RooBatchCompute::dispatchCPU;
   dispatch->compute(stream, RooBatchCompute::NormalizedPdf, output, nEvents, {nums, integralSpan}, extraArgs);
 
   std::size_t nEvalErrorsType0 = extraArgs[0];
   std::size_t nEvalErrorsType1 = extraArgs[1];
   std::size_t nEvalErrorsType2 = extraArgs[2];
 
   for (std::size_t i = 0; i < nEvalErrorsType0; ++i) {
      logEvalError("p.d.f normalization integral is zero or negative");
   }
   for (std::size_t i = 0; i < nEvalErrorsType1; ++i) {
      logEvalError("p.d.f value is less than zero, trying to recover");
   }
   for (std::size_t i = 0; i < nEvalErrorsType2; ++i) {
      logEvalError("p.d.f value is Not-a-Number");
   }
}