Metadata-Version: 2.1
Name: pycgsp
Version: 1.0.2
Summary: Graph signal processing extensions for Pycsou.
Home-page: https://github.com/matthieumeo/pycsou-gsp
Author: Matthieu SIMEONI
Author-email: matthieu.simeoni@gmail.com
License: MIT
Download-URL: https://github.com/matthieumeo/pycsou-gsp
Description: .. image:: https://matthieumeo.github.io/pycsou/html/_images/pycsou.png
          :width: 50 %
          :align: center
          :target: https://github.com/matthieumeo/pycsou-gsp
        
        .. image:: https://zenodo.org/badge/277582581.svg
           :target: https://zenodo.org/badge/latestdoi/277582581
        
        
        *Pycsou-gsp* is the graph signal processing extension of the Python 3 package `Pycsou <https://github.com/matthieumeo/pycsou>`_ for solving linear inverse problems. The extension offers implementations of graph *convolution* and *differential* operators, compatible with Pycsou's interface for linear operators. Such tools can be useful when solving linear inverse problems involving signals defined on non Euclidean discrete manifolds.
        
        Graphs in *Pycsou-gsp* are instances from the class ``pygsp.graphs.Graph`` from the `pygsp <https://github.com/epfl-lts2/pygsp>`_ library for graph signal processing with Python. 
        
        Content
        =======
        
        The package, named `pycgsp <https://pypi.org/project/pycgsp>`_, is organised as follows:
        
        1. The subpackage ``pycgsp.linop`` implements the following common graph linear operators:
          
           * Graph convolution operators: ``GraphConvolution``
           * Graph differential operators: ``GraphLaplacian``, ``GraphGradient``, ``GeneralisedGraphLaplacian``.
        
        2. The subpackage ``pycgsp.tesselation`` provides routines for generating graphs from discrete tessellations of continuous manifolds such as the sphere. 
           
        Installation
        ============
        
        Pycsou-gsp requires Python 3.6 or greater. It is developed and tested on x86_64 systems running MacOS and Linux.
        
        
        Dependencies
        ------------
        
        Before installing Pycsou-gsp, make sure that the base package `Pycsou <https://github.com/matthieumeo/pycsou>`_ is correctly installed on your machine.
        Installation instructions for Pycsou are available at `that link <https://matthieumeo.github.io/pycsou/html/general/install.html>`_.
        
        The package extra dependencies are listed in the files ``requirements.txt`` and ``requirements-conda.txt``.
        It is recommended to install those extra dependencies using `Miniconda <https://conda.io/miniconda.html>`_ or
        `Anaconda <https://www.anaconda.com/download/#linux>`_. This
        is not just a pure stylistic choice but comes with some *hidden* advantages, such as the linking to
        ``Intel MKL`` library (a highly optimized BLAS library created by Intel).
        
        .. code-block:: bash
        
           >> conda install --channel=conda-forge --file=requirements-conda.txt
        
        
        Quick Install
        -------------
        
        Pycsou-gsp is also available on `Pypi <https://pypi.org/project/pycsou-gsp/>`_. You can hence install it very simply via the command:
        
        .. code-block:: bash
        
           >> pip install pycsou-gsp
        
        If you have previously activated your conda environment ``pip`` will install Pycsou in said environment.
        Otherwise it will install it in your ``base`` environment together with the various dependencies obtained from the file ``requirements.txt``.
        
        
        Developer Install
        ------------------
        
        It is also possible to install Pycsou-gsp from the source for developers:
        
        
        .. code-block:: bash
        
           >> git clone https://github.com/matthieumeo/pycsou-gsp
           >> cd <repository_dir>/
           >> pip install -e .
        
        The package documentation can be generated with:
        
        .. code-block:: bash
        
           >> conda install sphinx=='2.1.*'            \
                            sphinx_rtd_theme=='0.4.*'
           >> python3 setup.py build_sphinx
        
        You can verify that the installation was successful by running the package doctests:
        
        .. code-block:: bash
        
           >> python3 test.py
        
        
        Cite
        ====
        
        For citing this package, please see: http://doi.org/10.5281/zenodo.4486431
        
        
        
        
        
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Topic :: Scientific/Engineering
Requires-Python: >=3.6
Description-Content-Type: text/x-rst; charset=UTF-8
