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#ifndef PYTHONIC_INCLUDE_NUMPY_FFT_RFFT_HPP
#define PYTHONIC_INCLUDE_NUMPY_FFT_RFFT_HPP
#include "pythonic/include/utils/functor.hpp"
#include "pythonic/include/types/ndarray.hpp"
/**
* **Noteable difference to numpy.fft.rfft:**
* In contrast to numpy.fft.rfft this implementation preserves precision
* of floating point and complex inputs, i.e. complex<float> input yields
* complex<float> output. numpy.fft.fft always returns complex<double>, even for
* long double input. This follows the same reasoning as given by numpy compiled
* with intel_mkl (see here: https://github.com/IntelPython/mkl_fft/issues/10).
* Conversion to double precision causes code to be slower and hurts use cases
* where single precision preservation is desired, e.g. when interacting with
*GPUs
* or instruments. Moreover for the case of long double inputs, this avoids
* loss of precision.
**/
PYTHONIC_NS_BEGIN
namespace numpy
{
namespace fft
{
template <class T, class pS>
types::ndarray<typename std::enable_if<std::is_floating_point<T>::value,
std::complex<T>>::type,
types::array<long, std::tuple_size<pS>::value>>
rfft(types::ndarray<T, pS> const &a, long n = -1, long axis = -1,
types::str const &norm = {});
template <class T, class pS>
types::ndarray<typename std::enable_if<std::is_floating_point<T>::value,
std::complex<T>>::type,
types::array<long, std::tuple_size<pS>::value>>
rfft(types::ndarray<T, pS> const &a, types::none_type n, long axis,
types::str const &norm);
template <class T, class pS>
types::ndarray<typename std::enable_if<std::is_floating_point<T>::value,
std::complex<T>>::type,
types::array<long, std::tuple_size<pS>::value>>
rfft(types::ndarray<T, pS> const &a, long n, long axis,
types::none_type norm);
template <class T, class pS>
types::ndarray<typename std::enable_if<std::is_floating_point<T>::value,
std::complex<T>>::type,
types::array<long, std::tuple_size<pS>::value>>
rfft(types::ndarray<T, pS> const &a, types::none_type n, long axis = -1,
types::none_type norm = types::none_type{});
template <class T, class pS>
types::ndarray<typename std::enable_if<std::is_integral<T>::value,
std::complex<double>>::type,
types::array<long, std::tuple_size<pS>::value>>
rfft(types::ndarray<T, pS> const &a, long n = -1, long axis = -1,
types::str const &norm = {});
template <class T, class pS>
types::ndarray<typename std::enable_if<std::is_integral<T>::value,
std::complex<double>>::type,
types::array<long, std::tuple_size<pS>::value>>
rfft(types::ndarray<T, pS> const &a, types::none_type n, long axis,
types::str const &norm);
template <class T, class pS>
types::ndarray<typename std::enable_if<std::is_integral<T>::value,
std::complex<double>>::type,
types::array<long, std::tuple_size<pS>::value>>
rfft(types::ndarray<T, pS> const &a, long n, long axis,
types::none_type norm);
template <class T, class pS>
types::ndarray<typename std::enable_if<std::is_integral<T>::value,
std::complex<double>>::type,
types::array<long, std::tuple_size<pS>::value>>
rfft(types::ndarray<T, pS> const &a, types::none_type n, long axis = -1,
types::none_type norm = types::none_type{});
NUMPY_EXPR_TO_NDARRAY0_DECL(rfft);
DEFINE_FUNCTOR(pythonic::numpy::fft, rfft);
}
}
PYTHONIC_NS_END
#endif
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