## ``mkl_fft`` -- a NumPy-based Python interface to Intel (R) MKL FFT functionality
[](https://travis-ci.com/IntelPython/mkl_fft)
`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 https://software.repos.intel.com/python/conda mkl_fft
```
or from conda-forge channel:
```
conda install -c conda-forge mkl_fft
```
---
To install mkl_fft Pypi package please use following command:
```
python -m pip install --index-url https://software.repos.intel.com/python/pypi --extra-index-url https://pypi.org/simple mkl_fft
```
If command above installs NumPy package from the Pypi, please use following command to install Intel optimized NumPy wheel package from Intel Pypi Cloud:
```
python -m pip install --index-url https://software.repos.intel.com/python/pypi --extra-index-url https://pypi.org/simple mkl_fft numpy==<numpy_version>
```
Where `<numpy_version>` should be the latest version from https://software.repos.intel.com/python/conda/
---
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
---
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.
---
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 .``
Raw data
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"description": "## ``mkl_fft`` -- a NumPy-based Python interface to Intel (R) MKL FFT functionality\r\n[](https://travis-ci.com/IntelPython/mkl_fft)\r\n\r\n`mkl_fft` started as a part of Intel (R) Distribution for Python* optimizations to NumPy, and is now being released\r\nas a stand-alone package. It can be installed into conda environment using\r\n\r\n```\r\n conda install -c https://software.repos.intel.com/python/conda mkl_fft\r\n```\r\n\r\nor from conda-forge channel:\r\n\r\n```\r\n conda install -c conda-forge mkl_fft\r\n```\r\n\r\n---\r\n\r\nTo install mkl_fft Pypi package please use following command:\r\n\r\n```\r\n python -m pip install --index-url https://software.repos.intel.com/python/pypi --extra-index-url https://pypi.org/simple mkl_fft\r\n```\r\n\r\nIf command above installs NumPy package from the Pypi, please use following command to install Intel optimized NumPy wheel package from Intel Pypi Cloud:\r\n\r\n```\r\n python -m pip install --index-url https://software.repos.intel.com/python/pypi --extra-index-url https://pypi.org/simple mkl_fft numpy==<numpy_version>\r\n```\r\n\r\nWhere `<numpy_version>` should be the latest version from https://software.repos.intel.com/python/conda/\r\n\r\n---\r\n\r\nSince MKL FFT supports performing discrete Fourier transforms over non-contiguously laid out arrays, MKL can be directly\r\nused on any well-behaved floating point array with no internal overlaps for both in-place and not in-place transforms of\r\narrays in single and double floating point precision.\r\n\r\nThis eliminates the need to copy input array contiguously into an intermediate buffer.\r\n\r\n`mkl_fft` directly supports N-dimensional Fourier transforms.\r\n\r\nMore details can be found in SciPy 2017 conference proceedings:\r\n https://github.com/scipy-conference/scipy_proceedings/tree/2017/papers/oleksandr_pavlyk\r\n\r\n---\r\n\r\nIt implements the following functions:\r\n\r\n### Complex transforms, similar to those in `scipy.fftpack`:\r\n\r\n`fft(x, n=None, axis=-1, overwrite_x=False)`\r\n\r\n`ifft(x, n=None, axis=-1, overwrite_x=False)`\r\n\r\n`fft2(x, shape=None, axes=(-2,-1), overwrite_x=False)`\r\n\r\n`ifft2(x, shape=None, axes=(-2,-1), overwrite_x=False)`\r\n\r\n`fftn(x, n=None, axes=None, overwrite_x=False)`\r\n\r\n`ifftn(x, n=None, axes=None, overwrite_x=False)`\r\n\r\n### Real transforms\r\n\r\n`rfft(x, n=None, axis=-1, overwrite_x=False)` - real 1D Fourier transform, like `scipy.fftpack.rfft`\r\n\r\n`rfft_numpy(x, n=None, axis=-1)` - real 1D Fourier transform, like `numpy.fft.rfft`\r\n\r\n`rfft2_numpy(x, s=None, axes=(-2,-1))` - real 2D Fourier transform, like `numpy.fft.rfft2`\r\n\r\n`rfftn_numpy(x, s=None, axes=None)` - real 2D Fourier transform, like `numpy.fft.rfftn`\r\n\r\n... and similar `irfft*` functions.\r\n\r\n\r\nThe 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.\r\n\r\n---\r\n\r\nTo build ``mkl_fft`` from sources on Linux:\r\n - install a recent version of MKL, if necessary;\r\n - execute ``source /path/to/mklroot/bin/mklvars.sh intel64`` ;\r\n - execute ``pip install .``\r\n",
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