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"info": {
"author": "Pierre Augier",
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"Intended Audience :: Education",
"Intended Audience :: Science/Research",
"License :: OSI Approved :: GNU General Public License v2 (GPLv2)",
"Programming Language :: C",
"Programming Language :: Cython",
"Programming Language :: Python",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.6",
"Programming Language :: Python :: 3.7",
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"description": ".. |travis| image:: https://travis-ci.org/fluiddyn/fluidfft.svg?branch=master\n :target: https://travis-ci.org/fluiddyn/fluidfft\n\n.. |appveyor| image:: https://ci.appveyor.com/api/projects/status/s75as4ikeetrk6f3/branch/default?svg=true\n :target: https://ci.appveyor.com/project/fluiddyn/fluiddyn\n :alt: AppVeyor status\n\n.. |binder| image:: https://mybinder.org/badge_logo.svg\n :target: https://mybinder.org/v2/gh/fluiddyn/fluidfft/master?urlpath=lab/tree/doc/ipynb\n :alt: Binder notebook\n\nFluidfft provides C++ classes and their Python wrapper classes written in\nCython useful to perform Fast Fourier Transform (FFT) with different libraries,\nin particular\n\n- `fftw3 `_ and `fftw3-mpi\n `_\n\n- `pfft `_\n\n- `p3dfft `_\n\n- `mpi4py-fft `_\n\n- `cufft `_ (fft library by CUDA\n running on GPU)\n\n`pfft `_, `p3dfft\n`_ and `mpi4py-fft\n`_ are specialized in computing FFT\nefficiently on several cores of big clusters. The data can be split in pencils\nand can be distributed on several processes.\n\n**Documentation**: https://fluidfft.readthedocs.io\n\nGetting started\n---------------\nTo try fluidfft without installation: |binder|\n\nFor a **basic installation** which relies only on a ``pyFFTW`` interface; or\nprovided you have the optional FFT libaries, that you need, installed and\ndiscoverable in your path (see environment variables ``LIBRARY_PATH``,\n``LD_LIBRARY_PATH``, ``CPATH``) it should be sufficient to run::\n\n pip install fluidfft [--user]\n\nAdd ``--user`` flag if you are installing without setting up a virtual\nenvironment.\n\nInstallation\n------------\n\nTo take full advantage of fluidfft, consider installing the following\n(optional) dependencies and configurations before installing fluidfft. Click on\nthe links to know more:\n\n1. OpenMPI or equivalent\n2. FFT libraries such as MPI-enabled FFTW (for 2D and 3D solvers) and P3DFFT,\n PFFT (for 3D solvers) either using a package manager or `from source\n `__\n3. Python packages ``fluiddyn mako cython pyfftw pythran mpi4py``\n4. `A C++11 compiler and BLAS\n libraries `__ and\n `configure\n `__\n ``~/.pythranrc`` to customize compilation of Pythran extensions\n5. `Configure\n `__\n ``~/.fluidfft-site.cfg`` to detect the FFT libraries and install\n ``fluidfft``\n\n**Note**: Detailed instructions to install the above dependencies using Anaconda\n/ Miniconda or in a specific operating system such as Ubuntu, macOS etc. can be\nfound `here\n`__.\n\nC++ API\n*******\n\nSee a `working minimal example with Makefile\n`__ which\nillustrates how to use the C++ API.\n\nTests\n-----\n\nFrom the root directory::\n\n make tests\n make tests_mpi\n\nOr, from the root directory or any of the \"test\" directories::\n\n pytest -s\n mpirun -np 2 pytest -s\n\n\nHow does it work?\n-----------------\n\nFluidfft provides classes to use in a transparent way all these libraries with\nan unified API. These classes are not limited to just performing Fourier\ntransforms. They are also an elegant solution to efficiently perform operations\non data in real and spectral spaces (gradient, divergence, rotational, sum over\nwavenumbers, computation of spectra, etc.) and easily deal with the data\ndistribution (gather the data on one process, scatter the data to many\nprocesses) without having to know the internal organization of every FFT\nlibrary.\n\nFluidfft hides the internal complication of (distributed) FFT libraries and\nallows the user to find (by benchmarking) and to choose the most efficient\nsolution for a particular case. Fluidfft is therefore a very useful tool to\nwrite HPC applications using FFT, as for example pseudo-spectral simulation\ncodes. In particular, fluidfft is used in the Computational Fluid Dynamics\n(CFD) framework `fluidsim `_.\n\nLicense\n-------\n\nFluidfft is distributed under the CeCILL_ License, a GPL compatible\nfrench license.\n\n.. _CeCILL: http://www.cecill.info/index.en.html\n\nMetapapers and citations\n------------------------\n\nIf you use FluidFFT to produce scientific articles, please cite our metapapers\npresenting the `FluidDyn project\n`__\nand `Fluidfft\n`__:\n\n.. code ::\n\n @article{fluiddyn,\n doi = {10.5334/jors.237},\n year = {2019},\n publisher = {Ubiquity Press, Ltd.},\n volume = {7},\n author = {Pierre Augier and Ashwin Vishnu Mohanan and Cyrille Bonamy},\n title = {{FluidDyn}: A Python Open-Source Framework for Research and Teaching in Fluid Dynamics\n by Simulations, Experiments and Data Processing},\n journal = {Journal of Open Research Software}\n }\n\n @article{fluidfft,\n doi = {10.5334/jors.238},\n year = {2019},\n publisher = {Ubiquity Press, Ltd.},\n volume = {7},\n author = {Ashwin Vishnu Mohanan and Cyrille Bonamy and Pierre Augier},\n title = {{FluidFFT}: Common {API} (C$\\mathplus\\mathplus$ and Python)\n for Fast Fourier Transform {HPC} Libraries},\n journal = {Journal of Open Research Software}\n }",
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