Skip to main content

A fast, native non-uniform FFT op for TensorFlow.

Project description

TensorFlow NUFFT

PyPI Build Docs DOI

TensorFlow NUFFT is a fast, native non-uniform fast Fourier transform op for TensorFlow. It provides:

  • Fast CPU/GPU kernels. The TensorFlow framework automatically handles device placement as usual.
  • A simple, well-documented Python interface.
  • Gradient definitions for automatic differentiation.
  • Shape functions to support static shape inference.

The underlying algorithm is based on the NUFFT implementation by the Flatiron Institute. Please refer to FINUFFT and cuFINUFFT for more details.


You can install TensorFlow NUFFT with pip:

pip install tensorflow-nufft

Note that only Linux wheels are currently being provided.

TensorFlow compatibility

Each TensorFlow NUFFT release is compiled against a specific version of TensorFlow. To ensure compatibility, it is recommended to install matching versions of TensorFlow and TensorFlow NUFFT according to the table below.

TensorFlow NUFFT Version TensorFlow Compatibility Release Date
v0.12.0 v2.11.x Nov 27, 2022
v0.11.0 v2.10.x Oct 12, 2022
v0.10.1 v2.10.x Sep 26, 2022
v0.10.0 v2.10.x Sep 7, 2022
v0.9.0 v2.9.x Sep 5, 2022
v0.8.1 v2.9.x Jun 23, 2022
v0.8.0 v2.9.x May 20, 2022
v0.7.3 v2.8.x May 4, 2022
v0.7.2 v2.8.x Apr 29, 2022
v0.7.1 v2.8.x Apr 6, 2022
v0.7.0 v2.8.x Feb 8, 2022
v0.6.0 v2.7.x Jan 27, 2022
v0.5.0 v2.7.x Dec 12, 2021
v0.4.0 v2.7.x Nov 8, 2021
v0.3.2 v2.6.x Aug 18, 2021
v0.3.1 v2.6.x Aug 18, 2021
v0.3.0 v2.6.x Aug 13, 2021


Once installed, you can perform NUFFTs in your TensorFlow code simply as:

import tensorflow_nufft as tfft

outputs = tfft.nufft(inputs, points)

See the documentation for the tfft.nufft function to learn more about the different parameters.


Visit the docs for the API reference and examples of usage.


If you use this package and something does not work as you expected, please file an issue describing your problem. We're here to help!


If you find this software useful in your research, please cite us.


See CONTRIBUTORS for a list of people who have contributed to this project. Thank you!

All contributions are welcome. If there is an issue you would like to address or a feature you would like to add, you might want to begin by commenting on the corresponding issue (or if it doesn't exist yet, by creating a new issue).

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distributions

tensorflow_nufft-0.12.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (17.8 MB view hashes)

Uploaded CPython 3.10 manylinux: glibc 2.17+ x86-64

tensorflow_nufft-0.12.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (17.8 MB view hashes)

Uploaded CPython 3.9 manylinux: glibc 2.17+ x86-64

tensorflow_nufft-0.12.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (17.8 MB view hashes)

Uploaded CPython 3.8 manylinux: glibc 2.17+ x86-64

tensorflow_nufft-0.12.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (17.8 MB view hashes)

Uploaded CPython 3.7m manylinux: glibc 2.17+ x86-64

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page