[pymvpa] PyMVPA 0.3.1

[Michael Hanke]

Hiho,

I'm glad to be able to announce another update of PyMVPA. The changelog
of 0.3.1 is:

  * New manual section about feature selection with a focus on RFE.
    Contributed by James M. Hughes.
  * New dataset type `ChannelDataset` for data structured in channels. Might
    be useful for data modalities like EEG and MEG. This dataset includes
    support for common preprocessing steps like resampling and baseline
    signal substraction.
  * Plotting of topographies on heads. Thanks to Ingo Fründ for contributing
    this code. Additionally, a new example shows how to do such plots.
  * New general purpose function for generating barplots and candlestick plots
    with error bars (`plotBars()`).
  * Dataset supports mapping of string labels onto numerical labels, removing
    the need to perform this mapping manually in user code. 'clfs_examples.py'
    is adjusted accordingly to demonstrate the new feature.
  * New Classifier.summary() method to dump classifier settings.
  * Improved and more flexible plotERPs().
  * New I-RELIEF sensitivity analyzer.
  * Added visualization of confusion matrices via `ConfusionMatrix.plot()`
    inspired by Ingo Fründ.
  * The PyMVPA version is now globally available in `mvpa.pymvpa_version`.
  * BugFix: TuebingenMEG reader failed in some cases.
  * Several improvements (docs and implementation) for building PyMVPA on
    MacOS X.
  * New convenience accessor methods (`select()`, `where()` and
    `__getitem__()`) for the `Dataset` base class.
  * New `seed()` function to configure the random number generators from user
    code.
  * Added reader for a MEG sensor locations format
    (`TuebingenMEGSensorLocations`).
  * Initial model selection support for GRP (using openopt).
  * And tons of minor bugfixes, additional tests and improved documentation.


Grab the source from the project website. Binary packages and installer
updates will follow during the next week.


Enjoy,

Michael



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