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.

All examples listed here utilize the Python API of PyMVPA. Additional examples that demonstrate the command line interface are also available.

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.

Preprocessing

• Visualization of Data Projection Methods
• Simple Data-Exploration

Analysis strategies and Background

• A simple start
• Separating hyperplane tutorial
• Minimal Searchlight Example
• Searchlight on fMRI data
• Surface-based searchlight on fMRI data
• Representational similarity analysis (RSA) on fMRI data
• 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
• 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

Integrate with 3rd-party software

• Using scikit-learn transformers with PyMVPA
• Using scikit-learn classifiers with PyMVPA
• Using scikit-learn regressions with PyMVPA
• Classifying the MNIST handwritten digits with MDP
  • MDP-style classification
  • Doing it the PyMVPA way
  • Visualizing data and results

Special interest and Miscellaneous

• Kernel-Demo
• Efficient cross-validation using a cached kernel
• Curve-Fitting
  • BOLD-Response parameters
  • Searchlight accuracy distributions
• Classifier Sweep
• Analysis of the margin width in a soft-margin SVM
• Compare SMLR to Linear SVM Classifier
• The effect of different hyperparameters in GPR
• Simple model selection: grid search for GPR