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 Framework for multivariate pattern analysis (MVPA)

Basic Facilities¶

`base`	Plumbing layer for 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.constraints`	Helper for parameter validation, documentation and conversion
`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.progress`	Helper to print pretty progress indicator.
`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.channel`	Dataset handling data structured in channels.
`datasets.eventrelated`	Functions for event segmentation or modeling of dataset.
`datasets.eep`	Support for the binary EEP file format for EEG data
`datasets.formats`	Support for commonly used data source formats.
`datasets.gifti`	Support for surface-based GIFTI data IO.
`datasets.mri`	Support for magnetic resonance imaging (MRI) data IO.
`datasets.niml`	Support for storage using the NeuroImaging Markup Language (NIML).
`datasets.cosmo`	Dataset from CoSMoMVPA
`datasets.eeglab`	Support for EEGLAB’s electrode-time series text file format.
`datasets.miscfx`	Miscellaneous functions to perform operations on datasets.
`datasets.sources`	Datasets originating from code outside of PyMVPA
`datasets.sources.native`	Loaders for PyMVPA’s own demo datasets
`datasets.sources.bids`	I/O support for neuroimaging dataset in BIDS_ format
`datasets.sources.openfmri`	Helpers to build PyMVPA dataset instances from openfmri.org dataset
`datasets.sources.skl_data`	Wrapper for sklearn datasets/data generators.

Mappers: Data Transformations¶

`mappers`	Algorithms for (reversible) data transformation.
`mappers.base`	Basic, general purpose and meta mappers.
`mappers.boxcar`	Transform consecutive samples into individual multi-dimensional samples
`mappers.detrend`	Polynomial de-trending and regression.
`mappers.filters`	Spectral filtering and FFT-based resampling.
`mappers.flatten`	Flatten multi-dimensional samples
`mappers.fx`	Transform data by applying a function along samples or feature axis.
`mappers.fxy`	Evaluate functions on pairs of datasets
`mappers.glm`	Transform datasets into parameter estimates of a general linear model fit.
`mappers.lle`	Local Linear Embedding.
`mappers.mdp_adaptor`	Wrapper to use MDP nodes and flows as PyMVPA mappers.
`mappers.procrustean`	Procrustean rotation mapper
`mappers.projection`	Base class for mappers doing linear transformations
`mappers.prototype`	Project data onto a space defined by prototypes via a similarity function
`mappers.shape`	Basic dataset shape modifications.
`mappers.skl_adaptor`	Use scikit-learn transformer as mappers.
`mappers.slicing`	Collection of dataset slicing procedures.
`mappers.som`	Self-organizing map (SOM).
`mappers.staticprojection`	Transform data via static projection matrices
`mappers.svd`	Singular-value decomposition
`mappers.wavelet`	Wavelet transformation
`mappers.zscore`	Data normalization by Z-Scoring.

Generators: Repetitive Data Processing¶

`generators`	Generate multiple output datasets form a single input
`generators.base`	Repeat and filter as sequence of dataset
`generators.partition`	Dataset partitioning strategies
`generators.permutation`	Generator nodes to permute datasets.
`generators.resampling`	Dataset content resampling (e.g.
`generators.splitters`	Split a single input dataset into multiple parts

Classifiers and Errors¶

`clfs.base`	Plumbing for all learners (classifiers and regressions)
`clfs.meta`	Meta classifiers – classifiers which use other classifiers or preprocessing
`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.mass`	Generic wrappers for learners (classifiers) provided by R’s MASS
`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 provided 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`	Plumbing for measures: algorithms that quantify properties of datasets.
`measures.anova`	Univariate ANOVA
`measures.corrstability`	Stability of labels across chunks based on correlation.
`measures.corrcoef`	FeaturewiseMeasure of correlation with the labels.
`measures.irelief`	Multivariate Iterative RELIEF
`measures.noiseperturbation`	Derive sensitivity maps for a metric by selective noise perturbation
`measures.gnbsearchlight`	An efficient implementation of searchlight for GNB.
`measures.nnsearchlight`	An efficient implementation of searchlight for M1NN.
`measures.rsa`	Representational (dis)similarity analysis
`measures.searchlight`	Searchlight implementation for arbitrary measures and spaces
`measures.statsmodels_adaptor`	Wrap models of the StatsModels package into a FeaturewiseMeasure.
`measures.winner`	Data aggregation procedures

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`	Helpers for feature selection (scoring, selection strategies)

Additional Algorithms¶

`algorithms`	Import helper for PyMVPA algorithms.
`algorithms.hyperalignment`	Transformation of individual feature spaces into a common space
`algorithms.searchlight_hyperalignment`	Searchlight-based hyperalignment
`algorithms.group_clusterthr`	Cluster thresholding algorithm for a group-level searchlight analysis

Algorithm benchmarks¶

`algorithms.benchmarks`	Benchmarks for various analyses
`algorithms.benchmarks.hyperalignment`	Benchmarks for hyperalignment algorithms

Miscellaneous¶

`atlases`	Import helper for PyMVPA anatomical atlases
`atlases.base`	Base classes for Anatomy atlases support
`atlases.fsl`	FSL atlases interfaces
`atlases.warehouse`	Collection of the known atlases
`misc.args`	Helpers for arguments handling.
`misc.attrmap`	Helper to map literal attribute to numerical ones (and back)
`misc.data_generators`	Miscellaneous data generators for unittests and demos
`misc.dcov`	Compute dcov/dcorr measures for independence testing
`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.support.afni`
`misc.surfing`	Import helper for surfing (surface-based information mapping)
`misc.surfing.queryengine`	QueryEngine for querying feature ids based on the surface nodes
`misc.surfing.surf_voxel_selection`	Functionality for surface-based voxel selection
`misc.surfing.volgeom`	Volume geometry to map between world and voxel coordinates.
`misc.surfing.volsurf`	Associate volume geometry with two surface meshes (typically pial and white matter boundaries of the grey matter).
`misc.surfing.volume_mask_dict`	Dictionary (mapping) for storing several volume masks.
`misc.transformers`	Simply functors that transform something.
`misc.vproperty`	C++-like virtual properties
`support.bayes`	Bayes (BayesConfusionHypothesis) support code

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¶

`viz`	Visualization of datasets
`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.flat_surf`	Plot flat maps of cortical surfaces.
`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).
`misc.plot.scatter`	Routines to scatterplot data

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 disk.
`misc.io.meg`	IO helper for MEG datasets.
`support.nibabel`	AFNI/SUMA file format I/O functions
`support.nibabel.afni_niml_annot`	Experimental support for AFNI NIML annotation files
`support.nibabel.afni_niml_dset`	AFNI NIML dataset I/O support.
`support.nibabel.afni_niml_roi`	AFNI NIML ROI (region of interest) read support
`support.nibabel.afni_niml`	General AFNI NIML I/O support
`support.nibabel.afni_suma_1d`	Very simple AFNI 1D support
`support.nibabel.afni_suma_spec`	Support for ANFI SUMA surface specification (.spec) files
`support.nibabel.surf_fs_asc`	Simple FreeSurfer ASCII surface file I/O functions
`support.nibabel.surf_caret`	Caret binary file support
`support.nibabel.surf_gifti`	GIFTI surface functions (wrapper) using nibabel.gifti
`support.nibabel.surf`	General support for cortical surface meshes