tfkbnufft | deploy non-uniform Fast Fourier Transform | Machine Learning library

by zaccharieramzi Python Version: 0.2.5 License: MIT

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kandi X-RAY | tfkbnufft Summary

tfkbnufft is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow applications. tfkbnufft has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can install using 'pip install tfkbnufft' or download it from GitHub, PyPI.

This package is a verly early-stage and modest adaptation to TensorFlow of the torchkbnufft package written by Matthew Muckley for PyTorch. Please cite his work appropriately if you use this package.

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tfkbnufft has a low active ecosystem.

It has 12 star(s) with 4 fork(s). There are 3 watchers for this library.

It had no major release in the last 12 months.

There are 5 open issues and 15 have been closed. On average issues are closed in 17 days. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of tfkbnufft is 0.2.5

Quality

tfkbnufft has no bugs reported.

Security

tfkbnufft has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

tfkbnufft is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

tfkbnufft releases are not available. You will need to build from source code and install.

Deployable package is available in PyPI.

Build file is available. You can build the component from source.

Installation instructions are not available. Examples and code snippets are available.

Top functions reviewed by kandi - BETA

kandi has reviewed tfkbnufft and discovered the below as its top functions. This is intended to give you an instant insight into tfkbnufft implemented functionality, and help decide if they suit your requirements.

Compute the scaling coefficients of the image
Calculates the kaiser coefficient of a kaiser function
Generate k - space profiles
Perform inverse fft and scale on gridded data
Linear interpolation
Wrapper for adjoints
Compute the KbNufft function
Run interpolation
Calculate the coefficients and indices for a given table
Calculate the adjoint NUFFT
Scale an image volume
Perform interpolation
Profile a TFKNNT
Extracts a nufft tensorflow tensor
Cartesian product of multiple arrays
Load requirements txt file
Compute the convolution of an image
Compute tensorflow Fourier transform
Computes the tensorflow tensorflow tensorflow tensor

Get all kandi verified functions for this library.

tfkbnufft Key Features

No Key Features are available at this moment for tfkbnufft.

tfkbnufft Examples and Code Snippets

No Code Snippets are available at this moment for tfkbnufft.

Community Discussions

Trending Discussions on tfkbnufft

Is it possible to have a test on tensor size inside a tensorflow tf.function?

QUESTION

Is it possible to have a test on tensor size inside a tensorflow tf.function?

Asked 2020-Mar-02 at 18:15

I don't understand why the following doesn't work:

...

ANSWER

Answered 2020-Mar-02 at 18:15

So I read more on the limitations of autograph. In particular in this section:

Because tf.shape always evaluates to a Tensor, the if statement above is converted by AutoGraph into a tf.cond, which performs static shape verification of both branches.

I guess this is also true for tf.size. The fix they provide is the following:

Source https://stackoverflow.com/questions/60493396

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install tfkbnufft

You can install using 'pip install tfkbnufft' or download it from GitHub, PyPI.
You can use tfkbnufft like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

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For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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