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https://github.com/epfl-lts2/pygsp

Graph Signal Processing in Python
https://github.com/epfl-lts2/pygsp

graph-representation graph-signal-processing graphs signal-processing

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Graph Signal Processing in Python

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README 

        
========================================

PyGSP: Graph Signal Processing in Python

========================================



The PyGSP is a Python package to ease

`Signal Processing on Graphs `_.

The documentation is available on

`Read the Docs `_

and development takes place on

`GitHub `_.

A (mostly unmaintained) `Matlab version `_ exists.



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| |doc|  |pypi|  |conda|  |binder|  |

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| |zenodo|  |license|  |pyversions| |

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| |travis|  |coveralls|  |github|   |

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.. |doc| image:: https://readthedocs.org/projects/pygsp/badge/?version=latest

   :target: https://pygsp.readthedocs.io

.. |pypi| image:: https://img.shields.io/pypi/v/pygsp.svg

   :target: https://pypi.org/project/PyGSP

.. |zenodo| image:: https://zenodo.org/badge/DOI/10.5281/zenodo.1003157.svg

   :target: https://doi.org/10.5281/zenodo.1003157

.. |license| image:: https://img.shields.io/pypi/l/pygsp.svg

   :target: https://github.com/epfl-lts2/pygsp/blob/master/LICENSE.txt

.. |pyversions| image:: https://img.shields.io/pypi/pyversions/pygsp.svg

   :target: https://pypi.org/project/PyGSP

.. |travis| image:: https://img.shields.io/travis/com/epfl-lts2/pygsp.svg

   :target: https://app.travis-ci.com/github/epfl-lts2/pygsp

.. |coveralls| image:: https://img.shields.io/coveralls/github/epfl-lts2/pygsp.svg

   :target: https://coveralls.io/github/epfl-lts2/pygsp

.. |github| image:: https://img.shields.io/github/stars/epfl-lts2/pygsp.svg?style=social

   :target: https://github.com/epfl-lts2/pygsp

.. |binder| image:: https://static.mybinder.org/badge_logo.svg

   :target: https://mybinder.org/v2/gh/epfl-lts2/pygsp/master?urlpath=lab/tree/examples/playground.ipynb

.. |conda| image:: https://img.shields.io/conda/vn/conda-forge/pygsp.svg

   :target: https://anaconda.org/conda-forge/pygsp



The PyGSP facilitates a wide variety of operations on graphs, like computing

their Fourier basis, filtering or interpolating signals, plotting graphs,

signals, and filters. Its core is spectral graph theory, and many of the

provided operations scale to very large graphs. The package includes a wide

range of graphs, from point clouds like the Stanford bunny and the Swiss roll;

to networks like the Minnesota road network; to models for generating random

graphs like stochastic block models, sensor networks, Erdős–Rényi model,

Barabási-Albert model; to simple graphs like the path, the ring, and the grid.

Many filter banks are also provided, e.g. various wavelets like the Mexican

hat, Meyer, Half Cosine; some low-pass filters like the heat kernel and the

exponential window; and Gabor filters. Despite all the pre-defined models, you

can easily use a custom graph by defining its adjacency matrix, and a custom

filter bank by defining a set of functions in the spectral domain.



While NetworkX_ and graph-tool_ are tools to analyze the topology of graphs,

the aim of the PyGSP is to analyze graph signals, also known as features or

properties (i.e., not the graph itself).

Those three tools are complementary and work well together with the provided

import / export facility.



.. _NetworkX: https://networkx.org

.. _graph-tool: https://graph-tool.skewed.de



The following demonstrates how to instantiate a graph and a filter, the two

main objects of the package.



>>> from pygsp import graphs, filters

>>> G = graphs.Logo()

>>> G.compute_fourier_basis()  # Fourier to plot the eigenvalues.

>>> # G.estimate_lmax() is otherwise sufficient.

>>> g = filters.Heat(G, scale=50)

>>> fig, ax = g.plot()



.. image:: ../pygsp/data/readme_example_filter.png

    :alt:

.. image:: pygsp/data/readme_example_filter.png

    :alt:



Let's now create a graph signal: a set of three Kronecker deltas for that

example. We can now look at one step of heat diffusion by filtering the deltas

with the above defined filter. Note how the diffusion follows the local

structure!



>>> import numpy as np

>>> DELTAS = [20, 30, 1090]

>>> s = np.zeros(G.N)

>>> s[DELTAS] = 1

>>> s = g.filter(s)

>>> fig, ax = G.plot(s, highlight=DELTAS)



.. image:: ../pygsp/data/readme_example_graph.png

    :alt:

.. image:: pygsp/data/readme_example_graph.png

    :alt:



You can

`try it online `_,

look at the

`tutorials `_

to learn how to use it, or look at the

`reference guide `_

for an exhaustive documentation of the API. Enjoy!



Installation

------------



The PyGSP is available on PyPI::



    $ pip install pygsp



The PyGSP is available on `conda-forge `_::



    $ conda install -c conda-forge pygsp



The PyGSP is available in the `Arch User Repository `_::



   $ git clone https://aur.archlinux.org/python-pygsp.git

   $ cd python-pygsp

   $ makepkg -csi



Contributing

------------



See the guidelines for contributing in ``CONTRIBUTING.rst``.



Acknowledgments

---------------



The PyGSP was started in 2014 as an academic open-source project for

research purpose at the `EPFL LTS2 laboratory `_.

This project has been partly funded by the Swiss National Science Foundation

under grant 200021_154350 "Towards Signal Processing on Graphs".



It is released under the terms of the BSD 3-Clause license.



If you are using the library for your research, for the sake of

reproducibility, please cite the version you used as indexed by

`Zenodo `_.

Or cite the generic concept as::



    @misc{pygsp,

      title = {PyGSP: Graph Signal Processing in Python},

      author = {Defferrard, Micha\"el and Martin, Lionel and Pena, Rodrigo and Perraudin, Nathana\"el},

      doi = {10.5281/zenodo.1003157},

      url = {https://github.com/epfl-lts2/pygsp/},

    }