[pymvpa] PyMVPA 2.0.1 is out

[Yaroslav Halchenko]

Dear PyMVPA-ers,

We are glad to announce the availability of a minor but long awaited
2.0.1 bugfix release of PyMVPA.

Sources:
  Tagged and pushed to http://github.com/PyMVPA/PyMVPA
  Tarballs available from https://github.com/PyMVPA/PyMVPA/tags
Debian/Ubuntu:
  2.0.1 is available from the http://neuro.debian.net
  and was uploaded to Debian unstable (i.e. sid)
Windows
  installer is available for Python 2.6 at
  https://github.com/PyMVPA/PyMVPA/downloads
  [although once again -- we recommend Windows/OSX users to have a 
   look at http://neuro.debian.net/vm.html]
  Hopefully soon http://www.lfd.uci.edu/~gohlke/pythonlibs collection by
  Christoph Gohlke would get 2.0.1 as well
Website: updated

And here is what would you are getting with the upgrade:

* 2.0.1 (Tue, Mar 27 2012)

  Primarily a bugfix release

  * Fixes (21 BF commits)

    - hdf5 storage -- handle loading of objects with bound
      builtin_function_or_method
    - use system-wide autosummary/generate.py for sphinx >= 1.1.2
      (Closes: #658593)
    - ConditionalAttribute should not loose value in deepcopy
      when default off (Fixes #63)
    - correct handling of scalar mean/std values for ZScoreMapper
    - MRI data import via _img2data now works with unicode
      filenames (Fixes #60)
    - should work with IPython >= 0.11 now (Fixes #59)
    - various small fixes to improve tests and functionality
    - fix SMLR segfaults on Windows (Thanks cgohlke for the patch)

  * Enhancements (29 ENH, OPT, and NF commits)

    - FxMapper calls functions natively (instead of a slow
      row/column at a time) if they carry axis as the 2nd argument.
      Provides tremendous speed up for mean_sample etc.
    - xrandom_unique_combinations generator for random unique
      combinations
    - dual_gaussian made more robust by not handling negative
      values for the standard deviations
    - dual_positive_gaussian
    - expose "sensitivities" for PLR
    - NFoldSplitter float option for cvtype and
      intelligent behavior on 'random' limited by 'count' given a
      large number of folds
    - few additional learners from sklearn to the warehouses:
      ExtraTrees, RandomForest, LassoLarsIC
    - __repr__ for Partitioners 
    - new performance metric -- F1 score -- in the confusion matrix
      summary stats
    - CachedQueryEngine does not rely now on id but on the
      exact value of the query parameters (converted to hashable
      types). Before it could behave incorrectly on rare occasions.

  * API changes

    - deprecating .splitattr if favor of .attr in
      Partitioner


Enjoy!
-- 
=------------------------------------------------------------------=
Yaroslav O. Halchenko
Postdoctoral Fellow,   Department of Psychological and Brain Sciences
Dartmouth College, 419 Moore Hall, Hinman Box 6207, Hanover, NH 03755
Phone: +1 (603) 646-9834                       Fax: +1 (603) 646-1419
WWW:   http://www.linkedin.com/in/yarik