Metadata-Version: 2.1
Name: cr-sparse
Version: 0.2.2
Summary: Accelerated sparse representations and compressive sensing
Home-page: https://carnotresearch.github.io/cr-sparse
Author: CR-Sparse 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/v0.2.2.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>`_.
        
        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:
        
        .. 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 repository:
        
        .. code:: tex
        
            @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},
            }
        
        
        
        
        `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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            :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
