Wavelets with JAX
Project description
Introduction
CR-Wavelets is a Python library that enables efficiently solving a wide variety of sparse representation based signal processing problems. It is a cohesive collection of sub-libraries working together. Individual sub-libraries provide functionalities for: wavelets, linear operators, greedy and convex optimization based sparse recovery algorithms, subspace clustering, standard signal processing transforms, and linear algebra subroutines for solving sparse linear systems. It has been built using Google JAX, which enables the same high level Python code to get efficiently compiled on CPU, GPU and TPU architectures using XLA.
For detailed documentation and usage, please visit online docs.
For theoretical background, please check online notes at Topics in Signal Processing and references therein (still under development).
Supported Platforms
CR-Wavelets can run on any platform supported by JAX. We have tested CR-Wavelets on Mac and Linux platforms and Google Colaboratory.
The latest code in the library has been tested against JAX 0.3.14.
JAX is not officially supported on Windows platforms at the moment. Although, it is possible to build it from source using Windows Subsystems for Linux. Alternatively, you can check out the community supported Windows build for JAX available from https://github.com/cloudhan/jax-windows-builder. This seems to work well and all the unit tests in the library have passed on Windows also.
Installation
Installation from PyPI:
python -m pip install cr-wavelets
Directly from our GITHUB repository:
python -m pip install git+https://github.com/carnotresearch/cr-wavelets.git
Examples/Usage
See the examples gallery in the documentation.
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