To provide the most recent news and documentation www.pymvpa.org reflects the development 2.0 series (renamed 0.6 series) of PyMVPA. If you are interested in the documentation of the previous stable 0.4 series of PyMVPA, please visit v04.pymvpa.org.

Example Analyses and Scripts

Each of the examples in this section is a stand-alone script containing all necessary code to run some analysis. All examples are shipped with PyMVPA and can be found in the doc/examples/ directory in the source package. This directory might include some more special-interest examples which are not listed here.

Some examples need to access a sample dataset available in the data/ directory within the root of the PyMVPA hierarchy, and thus have to be invoked directly from PyMVPA root (e.g. doc/examples/searchlight.py). Alternatively, one can download a full example dataset, which is explained in the next section.

Preprocessing

• Visualization of Data Projection Methods
• Simple Data-Exploration

Analysis

• Separating hyperplane tutorial
• Tiny Example of a Full Cross-Validation
• Compare SMLR to Linear SVM Classifier
• Classifier Sweep
• The effect of different hyperparameters in GPR
• Simple model selection: grid search for GPR
• Efficient cross-validation using a cached kernel
• Minimal Searchlight Example
• Searchlight on fMRI data
• A searchlight computing a dissimilarity matrix measure
• Sensitivity Measure
• Classification of SVD-mapped Datasets
• Monte-Carlo testing of Classifier-based Analyses
• Nested Cross-Validation
• Determine the Distribution of some Variable
• Spatio-temporal Analysis of event-related fMRI data
• Hyperalignment for between-subject analysis
  • Analysis setup
  • Within-subject classification
  • Between-subject classification using anatomically aligned data
  • Between-subject classification with Hyperalignment(TM)
  • Comparing the results
    • Performance
    • Similarity structures
  • Regularized Hyperalignment
• Classifying the MNIST handwritten digits with MDP
  • MDP-style classification
  • Doing it the PyMVPA way
  • Visualizing data and results
• Analysis of eye movement patterns
  • Plotting the results

Visualization

• ERP/ERF-Plots
• kNN – Model Flexibility in Pictures
• Simple Plotting of Classifier Behavior
• Generating Topography plots
• Self-organizing Maps
• Basic (f)MRI plotting

Miscellaneous

• Kernel-Demo
• Curve-Fitting
  • BOLD-Response parameters
  • Searchlight accuracy distributions