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(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};
 
   RooBatchCompute::compute(dataMap.config(this), 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");
   }
}
 
void RooNormalizedPdf::translate(RooFit::Detail::CodeSquashContext &ctx) const
{
   // For now just return function/normalization integral.
   ctx.addResult(this, ctx.getResult(_pdf) + "/" + ctx.getResult(_normIntegral));
}