mpiFFT4py
Description
mpiFFT4py performs FFTs in parallel in Python. It is developed to be able to do FFTs in parallel on a three-dimensional computational box (a structured grid), but there are also routines for doing the FFTs on a 2D mesh. It implements both the slab and the pencil decompositions.
Installation
mpiFFT4py requires numpy for basic array oparations, [pyfftw](https://github.com/pyfftw/pyFFTW) for efficient FFTs and [mpi4py](https://bitbucket.org/mpi4py/mpi4py) for MPI communications. However, if pyfftw is not found, then the slower numpy.fft is used instead. [cython](http://cython.org) is used to optimize a few routines. Install using regular python distutils:
python setup.py install --prefix="Path on the PYTHONPATH"
To install in place do:
python setup.py build_ext --inplace
To install using Anaconda, you may either compile it yourselves using (from the main directory):
conda config --add channels conda-forge conda build conf/conda conda install mpiFFT4py --use-local
or use precompiled binaries in the[conda-forge](https://anaconda.org/conda-forge/mpifft4py) or the [spectralDNS](https://anaconda.org/spectralDNS/mpifft4py) channel on Anaconda cloud:
conda install -c conda-forge mpifft4py
or:
conda config --add channels conda-forge conda install -c spectralDNS mpifft4py
There are binaries compiled for both OSX and linux, and several versions of Python. Note that the spectralDNS channel contains bleeding-edge versions of the Software, whereas conda-forge is more stable.
Licence
mpiFFT4py is licensed under the GNU GPL, version 3 or (at your option) any later version. mpiFFT4py is Copyright (2014-2016) by the authors.
Contact
The latest version of this software can be obtained from
Please report bugs and other issues through the issue tracker at:
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
File details
Details for the file mpiFFT4py-1.1.2.tar.gz.
File metadata
- Download URL: mpiFFT4py-1.1.2.tar.gz
- Upload date:
- Size: 25.2 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via:
twine/1.11.0 pkginfo/1.4.2 requests/2.19.1 setuptools/40.4.3 requests-toolbelt/0.8.0 tqdm/4.28.1 CPython/3.6.6
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
d7a40ff1d0f2912307ce24e1be0f82103125f0b075e81e10bb23b4670a5fba87
|
|
| MD5 |
182abb66ee65342e61b420fbd6a35437
|
|
| BLAKE2b-256 |
dc91966aba0378c7a8abcad8156b5f4e80bee5dfb03b5a0827f1b71927208b94
|