pyFFS is a collection of efficient algorithms to compute Fourier Series and related transforms.
$ pip install pyFFS
Recommended setup using Anaconda, for optimized numerical libraries:
# Create Anaconda environment $ conda create --name pyffs python=3 $ conda activate pyffs # Clone repository $ git clone https://github.com/imagingofthings/pyFFS.git $ cd pyFFS $ # git checkout <commit> # Install requirements with conda $ conda install --file requirements.txt # Optionally install CuPy for GPU support $ conda install -c conda-forge cupy # Install pyFFS $ pip install -e .[dev] $ pytest # Run test suite $ python setup.py build_sphinx # Generate documentation
More information about CuPy setup can be found here.
From master branch of original repo:
# Create tag and upload $ git tag -a vX.X.X -m "Description." $ git push origin vX.X.X # Create package and upload to Pypi $ python setup.py sdist $ python -m twine upload dist/pyFFS-X.X.X.tar.gz
You will need a username and password for uploading to PyPi.
Finally, on GitHub set the new tag as the latest release by pressing on it, at top right selecting "Edit tag", and at the bottom pressing "Publish release".
pyFFS is developed and tested on x86_64 systems running Linux and macOS Catalina.
If you use this package in your own research, please cite our paper.
@article{10.1137/21M1448641,
author = {Bezzam, Eric and Kashani, Sepand and Hurley, Paul and Vetterli, Martin and Simeoni, Matthieu},
title = {PyFFS: A Python Library for Fast Fourier Series Computation and Interpolation with GPU Acceleration},
year = {2022},
issue_date = {Aug 2022},
publisher = {Society for Industrial and Applied Mathematics},
address = {USA},
volume = {44},
number = {4},
issn = {1064-8275},
url = {https://doi.org/10.1137/21M1448641},
doi = {10.1137/21M1448641},
journal = {SIAM J. Sci. Comput.},
month = {jan},
pages = {C346–C366},
numpages = {21},
keywords = {GPU, chirp Z-transform, numerical library, 97N80, fast Fourier series, bandlimited interpolation, 65T40, 42B05, 97N50, Python}
}