Graph signal processing tutorial, presented at the GraphSiP summer school.
Presented at the GraphSiP summer school by Michaël Defferrard and Nicolas Tremblay. GraphSiP is about Graph Signal Processing with Applications to 3D Clouds of Points and Neuroscience.
We suggest you follow the installation guide to setup your own computer. If you don't succeed, you can work in the cloud using binder.
The material covers the following topics:
The content is inspired by the following resources:
For a local installation, you will need git, Python >= 3.6, Jupyter, and packages from the Python scientific stack. If you don't know how to install those on your platform, we recommend to install Miniconda, a distribution of the conda package and environment manager. Please follow the below instructions to install it and create an environment for the course.
.exe
file.bash Miniconda3-latest-MacOSX-x86_64.sh
in your terminal.bash Miniconda3-latest-Linux-x86_64.sh
in your terminal.conda install git
.git clone https://github.com/mdeff/pygsp_tutorial_graphsip
or by pressing the green "Clone or download" button on the top of this page.conda create --name pygsp_tutorial_graphsip
.
(you can also do this by launching Anaconda Navigator --> Environments --> Create)conda activate pygsp_tutorial_graphsip
(or activate pygsp_tutorial_graphsip
, or source activate pygsp_tutorial_graphsip
).conda install jupyter numpy scipy matplotlib networkx scikit-learn
and pip install pygsp
.Every time you want to work, do the following:
conda activate pygsp_tutorial_graphsip
(or activate pygsp_tutorial_graphsip
, or source activate pygsp_tutorial_graphsip
).jupyter notebook
or jupyter lab
. The command should
open a new tab in your web browser.You can try to run the Jupyter notebook mini_test.ipynb
to make sure that the main toolboxes are at least callable.
The content is released under the terms of the MIT License.