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.

Module Reference

This module reference extends the manual with a comprehensive overview of the currently available functionality, that is built into PyMVPA. However, instead of a full list including every single line of the PyMVPA code base, this reference limits itself to the relevant pieces of the application programming interface (API) that are of particular interest to users of this framework.

Each module in the package is documented by a general summary of its purpose and the list of classes and functions it provides.

Entry Point

mvpa2

MultiVariate Pattern Analysis

Basic Facilities

base	Base functionality of PyMVPA
base.attributes	Module with some special objects to be used as magic attributes with dedicated containers aka.
base.collections	Module with some special objects to be used as magic attributes with dedicated containers aka.
base.config	Registry-like monster
base.dochelpers	Various helpers to improve docstrings and textual output
base.externals	Helper to verify presence of external libraries and modules
base.hdf5	HDF5-based file IO for PyMVPA objects.
base.info	Provide system and PyMVPA information useful while reporting bugs
base.learner	Implementation of a common trainable processing object (Learner).
base.node	Implementation of a common processing object (node).
base.param	Parameter representation
base.report	Creating simple PDF reports using reportlab
base.state	Classes to control and store state information.
base.types	Things concerned with types and type-checking in PyMVPA
base.verbosity	Verbose output and debugging facility

Datasets: Input, Output, Storage and Preprocessing

base.dataset	Multi-purpose dataset container with support for attributes.
datasets.base	PyMVPA’s common Dataset container.
datasets.eventrelated	Dataset for event-related samples.
datasets.eep	Dataset that gets its samples from an EEP binary file
datasets.formats	I/O helpers for some commonly used formats for datasets.
datasets.mri	Dataset for magnetic resonance imaging (MRI) data.
datasets.miscfx	Misc function performing operations on datasets.

Mappers: Data Transformations

mappers	Algorithms for (reversible) data transformation.
mappers.base	Basic, general purpose and meta mappers.
mappers.boxcar	Data mapper
mappers.detrend	Mapper for data detrending.
mappers.filters	Filtering mappers.
mappers.flatten	Data mapper
mappers.fx	Transform data by applying a function along samples or feature axis.
mappers.lle	Local Linear Embedding Data mapper.
mappers.mdp_adaptor	MDP interface module.
mappers.procrustean	Procrustean rotation mapper
mappers.projection
mappers.prototype	Prototype-based Mapper.
mappers.slicing	Mappers for Dataset slicing.
mappers.som	Self-organizing map (SOM) mapper.
mappers.svd	Singular-value decomposition mapper
mappers.wavelet	Wavelet mappers
mappers.zscore	Mapper for data normalization by Z-Scoring.

Generators: Repetitive Data Processing

generators	Nodes that generate multiple datasets.
generators.partition
generators.permutation	Generator nodes to permute datasets.
generators.resampling	Generators for dataset resampling.
generators.splitters	Generator nodes to split dataset into multiple parts.

Classifiers and Errors

clfs.base	Base class for all XXX learners: classifiers and regressions.
clfs.meta	Classes for meta classifiers – classifiers which use other classifiers
clfs.blr	Bayesian Linear Regression (BLR).
clfs.enet	Elastic-Net (ENET) regression classifier.
clfs.gda	Gaussian Discriminant Analyses: LDA and QDA
clfs.glmnet	GLM-Net (GLMNET) regression and classifier.
clfs.gnb	Gaussian Naive Bayes Classifier
clfs.gpr	Gaussian Process Regression (GPR).
clfs.knn	k-Nearest-Neighbour classifier.
clfs.lars	Least angle regression (LARS).
clfs.model_selector	Model selction.
clfs.plr	Penalized logistic regression classifier.
clfs.ridge	Ridge regression classifier.
clfs.similarity	Similarity functions for prototype-based projection.
clfs.skl	Classifiers provided by scikit-learn (skl) library
clfs.smlr	Sparse Multinomial Logistic Regression classifier.
clfs.svm	Importer for the available SVM and SVR machines.
clfs.sg	Classifiers provided by shogun (sg) library
clfs.libsvmc	Classifiers provied by LibSVM library
clfs.distance	Distance functions to be used in kernels and elsewhere
clfs.similarity	Similarity functions for prototype-based projection.
clfs.stats	Estimator for classifier error distributions.
clfs.transerror	Utility class to compute the transfer error of classifiers.
clfs.warehouse	Collection of classifiers to ease the exploration.

Kernels

kernels	Import helper for PyMVPA kernels/similarities and alike
kernels.base	Base Kernel classes
kernels.libsvm	PyMVPA LibSVM-based kernels
kernels.np	Kernels for Gaussian Process Regression and Classification.
kernels.sg	PyMVPA shogun-based kernels

Measures: Searchlights and Sensitivties

measures.base	Base classes for measures: algorithms that quantify properties of datasets.
measures.anova	FeaturewiseMeasure performing a univariate ANOVA.
measures.corrcoef	FeaturewiseMeasure of correlation with the labels.
measures.corrstability	FeaturewiseMeasure of stability of labels across chunks based
measures.ds	Dissimilarity measure.
measures.glm	The general linear model (GLM).
measures.gnbsearchlight	An efficient implementation of searchlight for GNB.
measures.irelief	FeaturewiseMeasure performing multivariate Iterative RELIEF
measures.noiseperturbation	This is a FeaturewiseMeasure that uses a scalar Measure and
measures.pls	PLS is not yet implemented
measures.searchlight	Implementation of the Searchlight algorithm
measures.gnbsearchlight	An efficient implementation of searchlight for GNB.

Feature Selection

featsel.base	Feature selection base class and related stuff base classes and helpers.
featsel.ifs	Incremental feature search (IFS).
featsel.rfe	Recursive feature elimination.
featsel.helpers

Additional Algorithms

algorithms.hyperalignment

Hyperalignment of functional data to the common space

Miscellaneous

atlases	Import helper for PyMVPA anatomical atlases
misc.args	Helpers for arguments handling.
misc.attrmap	Helper to map literal attribute to numerical ones (and back)
misc.cmdline	Common functions and options definitions for command line
misc.data_generators	Miscellaneous data generators for unittests and demos
misc.errorfx	Error functions helpers.
misc.exceptions	Exception classes which might get thrown
misc.fx	Misc.
misc.neighborhood	Neighborhood objects
misc.sampleslookup	Helper to map and validate samples’ origids into indices
misc.stats	Little statistics helper
misc.support	Support function – little helpers in everyday life
misc.transformers	Simply functors that transform something.
misc.vproperty	C++-like virtual properties

Testing

testing	Helpers to unify/facilitate unittesting within PyMVPA
testing.clfs	Provides clfs dictionary with instances of all available classifiers.
testing.datasets	Provides convenience datasets for unittesting.
testing.tools	A Collection of tools found useful in unittests.
testing.sweepargs(**kwargs)	Decorator function to sweep over a given set of classifiers
tests	Unit test interface for PyMVPA

Basic Plotting Utilities

misc.plot	Import helper for miscellaneous PyMVPA plotting functions (mvpa2.misc.plot)
misc.plot.base	Misc.
misc.plot.erp	Basic ERP (here ERP = Event Related Plot ;-)) plotting
misc.plot.lightbox	Basic (f)MRI plotting with ability to interactively perform thresholding
misc.plot.topo	Plot parameter distributions on a head surface (topography plots).

3rd-party Interfaces

misc.bv	Import helper for Brain Voyager
misc.bv.base	Tiny snippets to interface with FSL easily.
misc.fsl	Import helper for FSL
misc.fsl.base	Tiny snippets to interface with FSL easily.
misc.fsl.flobs	Wrapper around FSLs halfcosbasis to generate HRF kernels
misc.fsl.melodic	Wrapper around the output of MELODIC (part of FSL)
misc.io	Import helper for IO helpers
misc.io.base	Some little helper for reading (and writing) common formats from and to
misc.io.hamster	Helper for simple storage facility via cPickle and optionally zlib
misc.io.meg	IO helper for MEG datasets.