{"id":17511723,"url":"https://github.com/matthieumeo/pycsou-gsp","last_synced_at":"2025-04-11T04:52:42.780Z","repository":{"id":62579224,"uuid":"344090799","full_name":"matthieumeo/pycsou-gsp","owner":"matthieumeo","description":"Pycsou extension module for linear inverse problems involving signals defined on non Euclidean domains represented as graphs.","archived":false,"fork":false,"pushed_at":"2023-03-16T11:30:11.000Z","size":768,"stargazers_count":1,"open_issues_count":0,"forks_count":2,"subscribers_count":2,"default_branch":"main","last_synced_at":"2025-04-11T04:52:36.729Z","etag":null,"topics":["cvxhull-graphs","graph-convolution","graph-differential-operators","graph-laplacian","graph-signal-processing","healpix","pycsou","python3","spherical-geometry","tessellation-graphs"],"latest_commit_sha":null,"homepage":"https://matthieumeo.github.io/pycsou-gsp/html/index","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/matthieumeo.png","metadata":{"files":{"readme":"README.rst","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2021-03-03T10:40:30.000Z","updated_at":"2024-02-25T14:40:23.000Z","dependencies_parsed_at":"2022-11-03T21:00:26.109Z","dependency_job_id":null,"html_url":"https://github.com/matthieumeo/pycsou-gsp","commit_stats":null,"previous_names":[],"tags_count":5,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/matthieumeo%2Fpycsou-gsp","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/matthieumeo%2Fpycsou-gsp/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/matthieumeo%2Fpycsou-gsp/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/matthieumeo%2Fpycsou-gsp/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/matthieumeo","download_url":"https://codeload.github.com/matthieumeo/pycsou-gsp/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248345291,"owners_count":21088243,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["cvxhull-graphs","graph-convolution","graph-differential-operators","graph-laplacian","graph-signal-processing","healpix","pycsou","python3","spherical-geometry","tessellation-graphs"],"created_at":"2024-10-20T05:09:08.313Z","updated_at":"2025-04-11T04:52:42.758Z","avatar_url":"https://github.com/matthieumeo.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":".. image:: https://matthieumeo.github.io/pycsou/html/_images/pycsou.png\n  :width: 50 %\n  :align: center\n  :target: https://github.com/matthieumeo/pycsou-gsp\n\n.. image:: https://zenodo.org/badge/277582581.svg\n   :target: https://zenodo.org/badge/latestdoi/277582581\n\n\n*Pycsou-gsp* is the graph signal processing extension of the Python 3 package `Pycsou \u003chttps://github.com/matthieumeo/pycsou\u003e`_ 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.\n\nGraphs in *Pycsou-gsp* are instances from the class ``pygsp.graphs.Graph`` from the `pygsp \u003chttps://github.com/epfl-lts2/pygsp\u003e`_ library for graph signal processing with Python. \n\nContent\n=======\n\nThe package, named `pycgsp \u003chttps://pypi.org/project/pycgsp\u003e`_, is organised as follows:\n\n1. The subpackage ``pycgsp.linop`` implements the following common graph linear operators:\n  \n   * Graph convolution operators: ``GraphConvolution``\n   * Graph differential operators: ``GraphLaplacian``, ``GraphGradient``, ``GeneralisedGraphLaplacian``.\n\n2. The subpackage ``pycgsp.graph`` provides routines for generating graphs from discrete tessellations of continuous manifolds such as the sphere. \n   \nInstallation\n============\n\nPycsou-gsp requires Python 3.6 or greater. It is developed and tested on x86_64 systems running MacOS and Linux.\n\n\nDependencies\n------------\n\nBefore installing Pycsou-gsp, make sure that the base package `Pycsou \u003chttps://github.com/matthieumeo/pycsou\u003e`_ is correctly installed on your machine.\nInstallation instructions for Pycsou are available at `that link \u003chttps://matthieumeo.github.io/pycsou/html/general/install.html\u003e`_.\n\nThe package extra dependencies are listed in the files ``requirements.txt`` and ``requirements-conda.txt``.\nIt is recommended to install those extra dependencies using `Miniconda \u003chttps://conda.io/miniconda.html\u003e`_ or\n`Anaconda \u003chttps://www.anaconda.com/download/#linux\u003e`_. This\nis not just a pure stylistic choice but comes with some *hidden* advantages, such as the linking to\n``Intel MKL`` library (a highly optimized BLAS library created by Intel).\n\n.. code-block:: bash\n\n   \u003e\u003e conda install --channel=conda-forge --file=requirements-conda.txt\n\n\nQuick Install\n-------------\n\nPycsou-gsp is also available on `Pypi \u003chttps://pypi.org/project/pycsou-gsp/\u003e`_. You can hence install it very simply via the command:\n\n.. code-block:: bash\n\n   \u003e\u003e pip install pycsou-gsp\n\nIf you have previously activated your conda environment ``pip`` will install Pycsou in said environment.\nOtherwise it will install it in your ``base`` environment together with the various dependencies obtained from the file ``requirements.txt``.\n\n\nDeveloper Install\n------------------\n\nIt is also possible to install Pycsou-gsp from the source for developers:\n\n\n.. code-block:: bash\n\n   \u003e\u003e git clone https://github.com/matthieumeo/pycsou-gsp\n   \u003e\u003e cd \u003crepository_dir\u003e/\n   \u003e\u003e pip install -e .\n\nThe package documentation can be generated with:\n\n.. code-block:: bash\n\n   \u003e\u003e conda install sphinx=='2.1.*'            \\\n                    sphinx_rtd_theme=='0.4.*'\n   \u003e\u003e python3 setup.py build_sphinx\n\nYou can verify that the installation was successful by running the package doctests:\n\n.. code-block:: bash\n\n   \u003e\u003e python3 test.py\n\n\nCite\n====\n\nFor citing this package, please see: http://doi.org/10.5281/zenodo.4486431\n\n\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmatthieumeo%2Fpycsou-gsp","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmatthieumeo%2Fpycsou-gsp","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmatthieumeo%2Fpycsou-gsp/lists"}