In this tutorial we are going to take a look at all major parts of PyMVPA, introduce the most important concepts, and explore particular functionality in real-life analysis examples. This tutorial also serves as basic course material for workshops on introductions to MVPA. Please contact us, if you are interested in hosting a PyMVPA workshop at your institution.
Please note that this tutorial is only concerned with aspects directly related to PyMVPA. It does not teach basic Python programming. If you are new to Python, it is recommend that you take a look at the Tutorial Prerequisites for information about what you should know and how to obtain that knowledge.
Throughout the tutorial there will be little exercises with tasks that are meant to deepen the understanding of a particular problem or to train important skills. However, even without a dedicated exercise the reader is advised to run the tutorial code interactively and explore code snippets beyond what is touched by the tutorial. Typically, only the most important aspects will be mentioned and each building block in PyMVPA can be used in more flexible ways than what is shown. Enjoy the ride.
Through the course of the tutorial we would analyze real BOLD fMRI data. Therefore, to be able to run the code in this tutorial, you need to download the corresponding data from the PyMVPA website. Once downloaded, extract the tarball, open a terminal, go into the directory with the extracted tarball content and run: ./start_tutorial_session.sh. On a NeuroDebian-enabled system, the tutorial data is also available from the python-mvpa2-tutorialdata package.
If you want to prevent yourself from re-typing all code snippets into the terminal window, you might want to investigate IPython’s %cpaste command.