Package Organization

The mvpa package contains the following subpackages and modules:

Author: Michael Hanke, Yaroslav Halchenko, Per B. Sederberg

Requires: Python 2.4+

Version: 0.2.2

See Also:: The PyMVPA webpage, GIT Repository Browser

License: The MIT License

Contributor: Per B. Sederberg

Submodules

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mvpa.base: Base functionality of PyMVPA
- mvpa.base.dochelpers
- mvpa.base.externals: Helper to verify presence of external libraries and modules
mvpa.clfs: Import helper for PyMVPA classifiers
- mvpa.clfs._svmbase: Common to all SVM implementations functionality.
- mvpa.clfs.base: Base classes for all classifiers.
- mvpa.clfs.gpr: Gaussian Process Regression (GPR).
- mvpa.clfs.kernel: Kernels for Gaussian Process Regression and Classification.
- mvpa.clfs.knn: k-Nearest-Neighbour classifier.
- mvpa.clfs.lars: Least angle regression (LARS) classifier.
- mvpa.clfs.libsmlr: Wraper for the stepwise_regression function for SMLR.
  - mvpa.clfs.libsmlr.ctypes_helper: Helpers for wrapping C libraries with ctypes.
  - mvpa.clfs.libsmlr.smlrc
- mvpa.clfs.libsvm: Classifiers provied by LibSVM library
  - mvpa.clfs.libsvm._svm: Python interface to the SWIG-wrapped libsvm
  - mvpa.clfs.libsvm.sens: Provide sensitivity measures for libsvm's SVM.
  - mvpa.clfs.libsvm.svm: Wrap the libsvm package into a very simple class interface.
- mvpa.clfs.model_selector: Model selction.
- mvpa.clfs.plr: Penalized logistic regression classifier.
- mvpa.clfs.ridge: Ridge regression classifier.
- mvpa.clfs.sg: Classifiers provied by shogun (sg) library
  - mvpa.clfs.sg.sens: Provide sensitivity measures for sg's SVM.
  - mvpa.clfs.sg.svm: Wrap the libsvm package into a very simple class interface.
- mvpa.clfs.smlr: Sparse Multinomial Logistic Regression classifier.
- mvpa.clfs.stats: Estimator for classifier error distributions.
- mvpa.clfs.svm: Wrap the libsvm package into a very simple class interface.
- mvpa.clfs.transerror: Utility class to compute the transfer error of classifiers.
- mvpa.clfs.warehouse: Collection of classifiers to ease the exploration.
mvpa.featsel
- mvpa.featsel.base: Feature selection base class and related stuff base classes and helpers.
- mvpa.featsel.helpers
- mvpa.featsel.ifs: Incremental feature search (IFS).
- mvpa.featsel.rfe: Recursive feature elimination.
mvpa.mappers: PyMVPA mappers.
- mvpa.mappers.base: Data mapper
- mvpa.mappers.mask: Data mapper
- mvpa.mappers.metric: Data mapper
- mvpa.mappers.pca: Data mapper
- mvpa.mappers.svd: Data mapper
mvpa.measures
- mvpa.measures.anova: FeaturewiseDatasetMeasure performing a univariate ANOVA.
- mvpa.measures.base: Base class for data measures: algorithms that quantify properties of datasets.
- mvpa.measures.noiseperturbation: This is a FeaturewiseDatasetMeasure that uses a scalar DatasetMeasure and selective noise perturbation to compute a sensitivity map.
- mvpa.measures.searchlight: Implementation of the Searchlight algorithm
- mvpa.measures.splitmeasure: This is a FeaturewiseDatasetMeasure that uses another FeaturewiseDatasetMeasure and runs it multiple times on differents splits of a Dataset.
mvpa.suite: MultiVariate Pattern Analysis -- load helper

Basic Data Structures

mvpa.datasets: PyMVPA datasets and helper classes such as mappers, splitters
- mvpa.datasets.base: Dataset container
- mvpa.datasets.mappeddataset: Mapped dataset
- mvpa.datasets.maskeddataset: Dataset with applied mask
- mvpa.datasets.metric: Classes and functions to provide sense of distances between sample points
- mvpa.datasets.misc: Misc function performing operations on datasets.
- mvpa.datasets.niftidataset: Dataset that gets its samples from a NIfTI file
- mvpa.datasets.splitter: Collection of dataset splitters.

Algorithms

mvpa.algorithms: Import helper for PyMVPA algorithms.
- mvpa.algorithms.cvtranserror: Cross-validate a classifier on a dataset

Miscellaneous

mvpa.misc: Import helper for PyMVPA misc modules
- mvpa.misc.cmdline: Common functions and options definitions for command line
- mvpa.misc.copy: Generic (shallow and deep) copying operations.
- mvpa.misc.data_generators: Miscelaneous data generators for unittests and demos
- mvpa.misc.errorfx: Error functions
- mvpa.misc.exceptions: Exception classes which might get thrown
- mvpa.misc.fsl: Import helper for FSL
  - mvpa.misc.fsl.flobs: Wrapper around FSLs halfcosbasis to generate HRF kernels
  - mvpa.misc.fsl.melodic: Wrapper around the output of MELODIC (part of FSL)
- mvpa.misc.iohelpers: Some little helper for reading (and writing) common formats from and to disk.
- mvpa.misc.param: Parameter representation
- mvpa.misc.signal: Simple preprocessing utilities
- mvpa.misc.state: Classes to control and store state information.
- mvpa.misc.stats: Little statistics helper
- mvpa.misc.support: Support function -- little helpers in everyday life
- mvpa.misc.transformers: Simply functors that transform something.
- mvpa.misc.verbosity: Verbose output and debugging facility
- mvpa.misc.vproperty: C++-like virtual properties

Variables

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__random_seed = 756154437

Imports: psyco, verbose, debug, N, environ, misc