Metadata-Version: 2.1
Name: mkl-fft
Version: 1.3.8
Summary: MKL-based FFT transforms for NumPy arrays
Home-page: http://github.com/IntelPython/mkl_fft
Download-URL: http://github.com/IntelPython/mkl_fft
Author: Intel Corporation
Maintainer: Intel Corp.
Maintainer-email: scripting@intel.com
License: BSD
Keywords: DFTI,FFT,Fourier,MKL
Platform: Windows
Platform: Linux
Platform: Mac OS-X
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved
Classifier: Programming Language :: C
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Topic :: Software Development
Classifier: Topic :: Scientific/Engineering
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS
Requires-Python: >=3.7
Description-Content-Type: text/markdown
License-File: LICENSE.txt
Requires-Dist: numpy >=1.16
Requires-Dist: mkl-service

## ``mkl_fft`` --  a NumPy-based Python interface to Intel (R) MKL FFT functionality
[![Build Status](https://travis-ci.com/IntelPython/mkl_fft.svg?branch=master)](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 intel mkl_fft
```

---

To install mkl_fft Pypi package please use following command:

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

```
   python -m pip install --index-url https://pypi.anaconda.org/intel/simple --extra-index-url https://pypi.org/simple mkl_fft numpy==<numpy_version>
```

Where `<numpy_version>` should be the latest version from https://anaconda.org/intel/numpy

---

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 .``