mkl-fft 1.3.6

MKL-based FFT transforms for NumPy arrays

mkl_fft -- a NumPy-based Python interface to Intel (R) MKL FFT functionality

mkl_fft started as a part of Intel (R) Distribution for Python* optimizations to NumPy, and is now being released as a stand-alone package. It can be installed into conda environment using

   conda install -c intel mkl_fft

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To install mkl_fft Pypi package please use following command:

   python -m pip install --index-url https://pypi.anaconda.org/intel/simple  mkl_fft

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Since MKL FFT supports performing discrete Fourier transforms over non-contiguously laid out arrays, MKL can be directly used on any well-behaved floating point array with no internal overlaps for both in-place and not in-place transforms of arrays in single and double floating point precision.

This eliminates the need to copy input array contiguously into an intermediate buffer.

mkl_fft directly supports N-dimensional Fourier transforms.

More details can be found in SciPy 2017 conference proceedings: https://github.com/scipy-conference/scipy_proceedings/tree/2017/papers/oleksandr_pavlyk

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It implements the following functions:

Complex transforms, similar to those in scipy.fftpack:

fft(x, n=None, axis=-1, overwrite_x=False)

ifft(x, n=None, axis=-1, overwrite_x=False)

fft2(x, shape=None, axes=(-2,-1), overwrite_x=False)

ifft2(x, shape=None, axes=(-2,-1), overwrite_x=False)

fftn(x, n=None, axes=None, overwrite_x=False)

ifftn(x, n=None, axes=None, overwrite_x=False)

Real transforms

rfft(x, n=None, axis=-1, overwrite_x=False) - real 1D Fourier transform, like scipy.fftpack.rfft

rfft_numpy(x, n=None, axis=-1) - real 1D Fourier transform, like numpy.fft.rfft

rfft2_numpy(x, s=None, axes=(-2,-1)) - real 2D Fourier transform, like numpy.fft.rfft2

rfftn_numpy(x, s=None, axes=None) - real 2D Fourier transform, like numpy.fft.rfftn

... and similar irfft* functions.

The package also provides mkl_fft._numpy_fft and mkl_fft._scipy_fft interfaces which provide drop-in replacements for equivalent functions in NumPy and SciPy respectively.

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To build mkl_fft from sources on Linux:

• install a recent version of MKL, if necessary;
• execute source /path/to/mklroot/bin/mklvars.sh intel64 ;
• execute pip install .