Multivariate Pattern Analysis in Python

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# Tutorial Introduction to PyMVPA

This chapter offers a tutorial introduction into PyMVPA. In the 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.

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.

- Tutorial Prerequisites
  - What Do I Need To Get Python Running
  - Recommended Reading and Viewing
- Part 1: A Gentle Start
  - Getting the data
  - Dealing With A Classifier
  - Cross-validation
  - References
- Part 2: Dataset Basics and Concepts
  - Attributes
  - Slicing, resampling, feature selection
  - Loading fMRI data
  - Storage
- Part 3: Mappers – The Swiss Army Knife
  - Doing `get_haxby2001_data()` From Scratch
  - There and back again – a Mapper’s tale
- Part 4: Classifiers – All Alike, Yet Different
  - We Need To Take A Closer Look
  - Meta-Classifiers To Make Complex Stuff Simple
- Part 5: Searchlite
  - Measures
  - Searching, searching, searching, ...
  - For real!
- Part 6: Looking Without Searching – Sensitivity Analysis
  - It’s A Kind Of Magic
  - Thanks For The Fish
  - Dissect The Classifier
  - Closing Words
- Part 7: “When Is The Signal” – Event-related Data Analysis
  - Event-related Pre-processing Is Not Event-related
  - From Timeseries To Events
  - A Plotting Example
  - If That Was Easy...
  - After all is done
- Part 8: The Earth Is Round – Significance Testing
  - Null hypothesis testing
  - The following content is incomplete and experimental
  - Evaluating multi-class classifications
  - Previously in part 8
  - Statistical Tools in Python
  - Dataset Exploration for Confounds
  - Hypothesis Testing
  - Statistical Treatment of Sensitivities
  - References

### Navigation

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- modules |
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- previous |
- PyMVPA Home |
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