Metadata-Version: 2.1
Name: cr-sparse
Version: 0.3.2
Summary: Accelerated sparse representations and compressive sensing
Home-page: https://carnotresearch.github.io/cr-sparse
Author: CR-Suite Development Team
Author-email: contact@carnotresearch.com
License: Apache 2.0: http://www.apache.org/licenses/LICENSE-2.0
Download-URL: https://github.com/carnotresearch/cr-sparse/archive/v0.3.2.tar.gz
Project-URL: Issue Tracker, https://github.com/carnotresearch/cr-sparse/issues
Description: Functional Models and Algorithms for Sparse Signal Processing   
        ==================================================================
        
        
        |pypi| |license| |zenodo| |docs| |unit_tests| |coverage| |joss|
        
        
        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 <https://jax.readthedocs.io/en/latest/>`_, 
        which enables the same high level
        Python code to get efficiently compiled on CPU, GPU and TPU architectures
        using `XLA <https://www.tensorflow.org/xla>`_. 
        
        .. image:: docs/images/srr_cs.png
        
        For detailed documentation and usage, please visit `online docs <https://cr-sparse.readthedocs.io/en/latest>`_.
        
        For theoretical background, please check online notes at `Topics in Signal Processing <https://tisp.indigits.com>`_
        and references therein (still under development).
        
        ``CR-Sparse`` is part of
        `CR-Suite <https://carnotresearch.github.io/cr-suite/>`_.
        
        Related libraries:
        
        * `CR-Nimble <https://cr-nimble.readthedocs.io>`_
        * `CR-Wavelets <https://cr-wavelets.readthedocs.io>`_
        
        
        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.
        
        * The latest code in the library has been tested against JAX 0.3.14.
        * The last released version of CR-Sparse (0.2.2) was tested against JAX 0.1.55 and later JAX 0.1.x versions. 
        
        ``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:
        
        .. code:: shell
        
            python -m pip install cr-sparse
        
        Directly from our GITHUB repository:
        
        .. code:: shell
        
            python -m pip install git+https://github.com/carnotresearch/cr-sparse.git
        
        
        
        Examples/Usage
        ----------------
        
        See the `examples gallery <https://cr-sparse.readthedocs.io/en/latest/gallery/index.html>`_ in the documentation.
        Here is a small selection of examples:
        
        * `Sparse recovery using Truncated Newton Interior Points Method <https://cr-sparse.readthedocs.io/en/latest/gallery/rec_l1/spikes_l1ls.html>`_ 
        * `Sparse recovery with ADMM <https://cr-sparse.readthedocs.io/en/latest/gallery/rec_l1/partial_wh_sensor_cosine_basis.html>`_ 
        * `Compressive sensing operators <https://cr-sparse.readthedocs.io/en/latest/gallery/lop/cs_operators.html>`_ 
        * `Image deblurring with LSQR and FISTA algorithms <https://cr-sparse.readthedocs.io/en/latest/gallery/lop/deblurring.html>`_ 
        * `Deconvolution of the effects of a Ricker wavelet <https://cr-sparse.readthedocs.io/en/latest/gallery/lop/deconvolution.html>`_ 
        * `Wavelet transform operators <https://cr-sparse.readthedocs.io/en/latest/gallery/lop/wt_op.html>`_ 
        * `CoSaMP step by step <https://cr-sparse.readthedocs.io/en/latest/gallery/pursuit/cosamp_step_by_step.html>`_ 
        
        
        A more extensive collection of example notebooks is available in the `companion repository <https://github.com/carnotresearch/cr-sparse-companion>`_.
        Some micro-benchmarks are reported `here <https://github.com/carnotresearch/cr-sparse/blob/master/paper/paper.md#runtime-comparisons>`_.
        
        
        Contribution Guidelines/Code of Conduct
        ----------------------------------------
        
        * `Contribution Guidelines <CONTRIBUTING.md>`_
        * `Code of Conduct <CODE_OF_CONDUCT.md>`_
        
        Citing CR-Sparse
        ------------------------
        
        
        To cite this library:
        
        .. code:: tex
        
            @article{Kumar2021,
              doi = {10.21105/joss.03917},
              url = {https://doi.org/10.21105/joss.03917},
              year = {2021},
              publisher = {The Open Journal},
              volume = {6},
              number = {68},
              pages = {3917},
              author = {Shailesh Kumar},
              title = {CR-Sparse: Hardware accelerated functional algorithms for sparse signal processing in Python using JAX},
              journal = {Journal of Open Source Software}
            }
        
        
        
        
        `Documentation <https://carnotresearch.github.io/cr-sparse>`_ | 
        `Code <https://github.com/carnotresearch/cr-sparse>`_ | 
        `Issues <https://github.com/carnotresearch/cr-sparse/issues>`_ | 
        `Discussions <https://github.com/carnotresearch/cr-sparse/discussions>`_ |
        
        
        .. |docs| image:: https://readthedocs.org/projects/cr-sparse/badge/?version=latest
            :target: https://cr-sparse.readthedocs.io/en/latest/?badge=latest
            :alt: Documentation Status
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        .. |unit_tests| image:: https://github.com/carnotresearch/cr-sparse/actions/workflows/ci.yml/badge.svg
            :alt: Unit Tests
            :scale: 100%
            :target: https://github.com/carnotresearch/cr-sparse/actions/workflows/ci.yml
        
        
        .. |pypi| image:: https://badge.fury.io/py/cr-sparse.svg
            :alt: PyPI cr-sparse
            :scale: 100%
            :target: https://badge.fury.io/py/cr-sparse
        
        .. |coverage| image:: https://codecov.io/gh/carnotresearch/cr-sparse/branch/master/graph/badge.svg?token=JZQW6QU3S4
            :alt: Coverage
            :scale: 100%
            :target: https://codecov.io/gh/carnotresearch/cr-sparse
        
        
        .. |license| image:: https://img.shields.io/badge/License-Apache%202.0-blue.svg
            :alt: License
            :scale: 100%
            :target: https://opensource.org/licenses/Apache-2.0
        
        .. |codacy| image:: https://app.codacy.com/project/badge/Grade/36905009377e4a968124dabb6cd24aae
            :alt: Codacy Badge
            :scale: 100%
            :target: https://www.codacy.com/gh/carnotresearch/cr-sparse/dashboard?utm_source=github.com&amp;utm_medium=referral&amp;utm_content=carnotresearch/cr-sparse&amp;utm_campaign=Badge_Grade
        
        .. |zenodo| image:: https://zenodo.org/badge/323566858.svg
            :alt: DOI
            :scale: 100%
            :target: https://zenodo.org/badge/latestdoi/323566858
        
        .. |joss| image:: https://joss.theoj.org/papers/ebd4e5ca27a5db705b1dc382b64e0bed/status.svg
            :alt: JOSS
            :scale: 100%
            :target: https://joss.theoj.org/papers/ebd4e5ca27a5db705b1dc382b64e0bed
        
Keywords: Computer Vision
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Topic :: Multimedia
Classifier: Topic :: Multimedia :: Video
Classifier: Topic :: Scientific/Engineering :: Image Processing
Classifier: Topic :: Scientific/Engineering :: Image Recognition
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: Unix
Classifier: Operating System :: POSIX
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: Implementation :: CPython
Requires-Python: >=3.6
Description-Content-Type: text/x-rst
Provides-Extra: dev
Provides-Extra: docs
Provides-Extra: test
Provides-Extra: examples
