Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/intelpython/mkl_fft
NumPy-based Python interface to Intel (R) MKL FFT functionality
https://github.com/intelpython/mkl_fft
fft mkl numpy
Last synced: about 4 hours ago
JSON representation
NumPy-based Python interface to Intel (R) MKL FFT functionality
- Host: GitHub
- URL: https://github.com/intelpython/mkl_fft
- Owner: IntelPython
- License: bsd-3-clause
- Created: 2017-10-05T22:13:50.000Z (about 7 years ago)
- Default Branch: master
- Last Pushed: 2024-10-16T11:39:40.000Z (about 1 month ago)
- Last Synced: 2024-11-15T03:04:47.477Z (about 9 hours ago)
- Topics: fft, mkl, numpy
- Language: Python
- Homepage:
- Size: 285 KB
- Stars: 65
- Watchers: 10
- Forks: 16
- Open Issues: 30
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGES.rst
- License: LICENSE.txt
Awesome Lists containing this project
README
## ``mkl_fft`` -- a NumPy-based Python interface to Intel (R) MKL FFT functionality
[](https://github.com/IntelPython/mkl_fft/actions/workflows/conda-package.yml)
[](https://github.com/IntelPython/mkl_fft/actions/workflows/build_pip.yaml)
[](https://github.com/IntelPython/mkl_fft/actions/workflows/conda-package-cf.yml)
`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==
```
Where `` 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 .``