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 Package Organization ==================== The mvpa package contains the following subpackages and modules: :group Algorithms: algorithms :group Anatomical Atlases: atlases :group Basic Data Structures: datasets :group Classifiers (supervised learners): clfs :group Feature Selections: featsel :group Mappers (usually unsupervised learners): mappers :group Measures: measures :group Miscellaneous: base misc support :group Unittests: tests :author: Michael Hanke, Yaroslav Halchenko, `Per B. |
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 |
Generators: Repetitive Data Processing
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 Basic implementation of 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 These kernels do not currently have the ability to run the calculations, so they are not translateable to other kernel types. |
kernels.np |
Kernels for Gaussian Process Regression and Classification. |
kernels.sg |
PyMVPA shogun-based kernels Provides interface to kernels defined in shogun toolbox. |
Measures: Searchlights and Sensitivties
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 |
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misc.plot.base |
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misc.plot.erp |
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misc.plot.lightbox |
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misc.plot.topo |
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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.hamster |
Helper for simple storage facility via cPickle and optionally zlib |
misc.io.meg |
IO helper for MEG datasets. |