pygsp

Graph Signal Processing in Python

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

Science Score: 54.0%

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CITATION.cff file
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.zenodo.json file
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DOI references
Found 6 DOI reference(s) in README
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Links to: arxiv.org, zenodo.org
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13 of 25 committers (52.0%) from academic institutions
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Organization epfl-lts2 has institutional domain (lts2.epfl.ch)
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Scientific vocabulary similarity
Low similarity (11.8%) to scientific vocabulary

Keywords

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

Last synced: 6 months ago · JSON representation

Repository

Graph Signal Processing in Python

Basic Info

Host: GitHub
Owner: epfl-lts2
License: bsd-3-clause
Language: Python
Default Branch: master
Homepage: https://pygsp.rtfd.io
Size: 4.29 MB

Statistics

Stars: 510
Watchers: 34
Forks: 94
Open Issues: 31
Releases: 1

Topics

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

Created about 12 years ago · Last pushed about 1 year ago

Metadata Files

Readme Changelog Contributing License

README.rst

========================================
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.

+-----------------------------------+
| |doc|  |pypi|  |conda|  |binder|  |
+-----------------------------------+
| |zenodo|  |license|  |pyversions| |
+-----------------------------------+
| |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/},
    }

Owner

Name: EPFL LTS2
Login: epfl-lts2
Kind: organization
Location: Lausanne, Switzerland

Website: https://lts2.epfl.ch
Repositories: 34
Profile: https://github.com/epfl-lts2

GitHub Events

Total

Issues event: 3
Watch event: 28
Issue comment event: 3
Push event: 1
Fork event: 2

Last Year

Issues event: 3
Watch event: 28
Issue comment event: 3
Push event: 1
Fork event: 2

Committers

Last synced: 10 months ago

All Time

Total Commits: 1,456
Total Committers: 25
Avg Commits per committer: 58.24
Development Distribution Score (DDS): 0.565

Past Year

Commits: 1
Committers: 1
Avg Commits per committer: 1.0
Development Distribution Score (DDS): 0.0

Top Committers

Name	Email	Commits
Michaël Defferrard	m**d@e**h	634
Alexandre Lafaye	l**e@g**m	250
Basile Châtillon	b**n@e**h	172
Lionel Martin	l**n@e**h	124
Charles Gallay	c**y@e**h	85
Nicolas Rod	n**d@e**h	69
Nathanael Perraudin	n****d	29
Maximilien Cuony	m**y@e**h	19
rodrigo-pena	r**a@e**h	18
Alexandre Lafaye	a**x@l**n	16
Basile Chatillon	c**e@g**m	13
Jean-Baptiste Cordonnier	j**r@g**m	6
Kikohs	k**i@e**h	4
Nathanael Perraudin	n**n@e**h	3
Benjamin	b**d@g**m	2
Scott Gigante	s****e	2
Johan Paratte	p**e@t**h	2
filippo	f**i@u**o	1
Charlotte Juillard	c**d@e**h	1
Johan Paratte	p**e@M**l	1
Johann Paratte	j**e@e**h	1
Mark Hale	m**e@p**g	1
Vassilis Kalofolias	v**s@l**h	1
Junki Ishikawa	6**5@g**m	1
EiffL	f**l@g**m	1

Committer Domains (Top 20 + Academic)

epfl.ch: 11 lts2mac10.epfl.ch: 1 physics.org: 1 uit.no: 1 tsf-452-wpa-4-172.epfl.ch: 1 lts2nuc0.lan: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time

Total issues: 63
Total pull requests: 45
Average time to close issues: about 2 months
Average time to close pull requests: about 2 months
Total issue authors: 43
Total pull request authors: 14
Average comments per issue: 2.73
Average comments per pull request: 3.84
Merged pull requests: 32
Bot issues: 0
Bot pull requests: 0

Past Year

Issues: 2
Pull requests: 1
Average time to close issues: N/A
Average time to close pull requests: 5 months
Issue authors: 2
Pull request authors: 1
Average comments per issue: 0.0
Average comments per pull request: 1.0
Merged pull requests: 1
Bot issues: 0
Bot pull requests: 0

View more stats

Top Authors

Issue Authors

StefanBloemheuvel (4)
mdeff (4)
dsacc (4)
pulquero (3)
scottgigante (3)
lionel-martin (2)
aweinstein (2)
codenameAggie (2)
shreyashi2104 (2)
stanleyjs (2)
skmda37 (2)
Gibbsdavidl (2)
WCommm (1)
iglesias (1)
cuminx (1)

Pull Request Authors

mdeff (23)
nperraud (6)
cgallay (3)
FilippoMB (2)
jbcdnr (2)
scottgigante (2)
gcuendet (1)
bricaud (1)
EiffL (1)
Droxef (1)
cafeal (1)
rodrigo-pena (1)
naspert (1)
pulquero (1)

Top Labels

Issue Labels

question (4) enhancement (1)

Pull Request Labels

Packages

Total packages: 2
Total downloads:
- pypi 12,371 last-month

Total dependent packages: 11
(may contain duplicates)
Total dependent repositories: 32
(may contain duplicates)
Total versions: 8
Total maintainers: 2

pypi.org: pygsp

Graph Signal Processing in Python

Homepage: https://github.com/epfl-lts2/pygsp
Documentation: https://pygsp.readthedocs.io/
License: BSD
Latest release: 0.5.1
published about 8 years ago

Versions: 7
Dependent Packages: 9
Dependent Repositories: 29
Downloads: 12,371 Last month

Rankings

Dependent packages count: 1.2%

Dependent repos count: 2.7%

Stargazers count: 2.9%

Average: 2.9%

Downloads: 3.1%

Forks count: 4.8%

Maintainers (2)

LTS2 mdeff

Last synced: 6 months ago

conda-forge.org: pygsp

Homepage: https://github.com/epfl-lts2/pygsp
License: BSD-3-Clause
Latest release: 0.5.1
published over 3 years ago

Versions: 1
Dependent Packages: 2
Dependent Repositories: 3

Rankings

Dependent repos count: 18.0%

Average: 19.6%

Dependent packages count: 19.6%

Forks count: 20.1%

Stargazers count: 20.4%

Last synced: 6 months ago

Dependencies

setup.py pypi

numpy *
scipy *