PyGSP: Graph Signal Processing in Python¶
The PyGSP is a Python package to ease Signal Processing on Graphs (a Matlab counterpart exists). It is a free software, distributed under the BSD license, and available on PyPI. The documentation is available on Read the Docs and development takes place on GitHub.
This example demonstrates how to create a graph, a filter and analyse a signal on the graph.
>>> import pygsp >>> G = pygsp.graphs.Logo() >>> f = pygsp.filters.Heat(G) >>> Sl = f.analysis(G.L.todense(), method='cheby')
This package facilitates graph constructions and give tools to perform signal processing on them.
A whole list of pre-constructed graphs can be used as well as core functions to create any other graph among which:
- Neighest Neighbor Graphs - Bunny - Cube - Sphere - TwoMoons - ImgPatches - Grid2dImgPatches - Airfoil - BarabasiAlbert - Comet - Community - DavidSensorNet - ErdosRenyi - FullConnected - Grid2d - Logo GSP - LowStretchTree - Minnesota - Path - RandomRegular - RandomRing - Ring - Sensor - StochasticBlockModel - SwissRoll - Torus
On these graphs, filters can be applied to do signal processing. To this end, there is also a list of predefined filters on this toolbox:
- Abspline - Expwin - Gabor - HalfCosine - Heat - Held - Itersine - MexicanHat - Meyer - Papadakis - Regular - Simoncelli - SimpleTf
The PyGSP is available on PyPI:
$ pip install pygsp
See the guidelines for contributing in