cr-sparse 0.2.2

Accelerated sparse representations and compressive sensing

Introduction

CR-Sparse 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.

Supported Platforms

CR-Sparse can run on any platform supported by JAX. We have tested CR-Sparse on Mac and Linux platforms and Google Colaboratory.

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.

Installation

Installation from PyPI:

python -m pip install cr-sparse

Directly from our GITHUB repository:

python -m pip install git+https://github.com/carnotresearch/cr-sparse.git

Examples/Usage

See the examples gallery in the documentation. Here is a small selection of examples:

• Sparse recovery using Truncated Newton Interior Points Method

• Sparse recovery with ADMM

• Compressive sensing operators

• Image deblurring with LSQR and FISTA algorithms

• Deconvolution of the effects of a Ricker wavelet

• Wavelet transform operators

• CoSaMP step by step

A more extensive collection of example notebooks is available in the companion repository. Some micro-benchmarks are reported here.

Contribution Guidelines/Code of Conduct

• Contribution Guidelines

• Code of Conduct

Citing CR-Sparse

To cite this repository:

@software{crsparse2021github,
author = {Shailesh Kumar},
title = {{CR-Sparse}: Functional Models and Algorithms for Sparse Signal Processing},
url = {https://cr-sparse.readthedocs.io/en/latest/},
version = {0.2.1},
year = {2021},
doi={10.5281/zenodo.5322044},
}

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