Tutorials#
This section contains a number of tutorials, to get you started with PyRASA. In order to easily run the tutorials you need to add some additional python libraries to your current environment. You can do so using pip or conda-forge.
Using pip#
pip install neurodsp seaborn
Using conda-forge#
conda install -c conda-forge neurodsp seaborn
If you want to use PyRASA together with MNE Python (see Tutorial 4. IRASA MNE). You also need MNE Python installed in your current enviroment (see install for further instructions).
Introductory#
1. Getting Started#
This notebook gets you familiar with the IRASA algorithm and shows you the basic functionality of PyRASA.
2. Improving your IRASA models#
This notebook shows you how to improve your IRASA model fits.
3. Pitfalls when using IRASA#
This notebook outlines common pitfalls when fitting IRASA models.
3. hset Optimization#
IRASA comes only with a single hyperparameter - the set of up-/downsampling factors. Here we introduce a method to optimize this hset to get the most out of your model.
4. IRASA MNE#
Are you analysing M/EEG data using MNE Python? You might be happy to hear that you can directly apply IRASA to your raw or epoched data objects. Open the notebook to see how its done.
4. Time Frequency IRASA#
Did you know that IRASA can be used in the timefrequency domain for a time resolved spectral parametrization? Open this notebook to see how its done.
Advanced#
1. Custom Aperiodic Fit Functions#
PyRASA allows you to define your own functions to model aperiodic activity. This notebook shows you how its done.