Tags: PSLLab/PyMVPA
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* 2.6.0 (Sat, 26 Aug 2016)
Primarily a bugfix release with some added new functionality. People contributed
code to the release: Matteo Visconti dOC, Yaroslav Halchenko, Nikolaas N. Oosterhof,
Feilong Ma, Christopher J. Markiewicz, Swaroop Guntupalli.
* **IMPORTANT** possibly backward-incompatible fixes
- Dataset's :func:`~mvpa2.base.dataset.vstack` and :func:`~mvpa2.base.dataset.hstack`
now by default would drop those feature or sample (`fa`, `sa`) attributes
which do not have the same value across all datasets ("drop_nonunique").
Previous behaviour was to update aggregated collections, so the attribute
value of the last dataset would have been stored in the stacked dataset.
Such behaviour could be brought back by `fa="update"` for `vstack` or
similar value for `sa="update"` for `hstack` calls.
If you find that some `sa`/`fa` you have relied on using in your code is no
longer available after stacking, verify that you did intend to maintain the
"last known" value, and adjust argument in stacking function to "update".
* Fixes
- Fixed minor bug in computing ico linear divisions while working with surfaces
- Handling of `ref_ds` in :class:`~mvpa2.algorithms.searchlight_hyperalignment.SearchlightHyperalignment`
- Compatibility fixes for :mod:`scipy` 0.18.0 and :mod:`nibabel` 2.1.0.
* New functionality
- `pymvpa2 scatter` command line and :mod:`~mvpa2.misc.plot.scatter` module to scatter plot
datasets and nifti volumes, with coloring based on spatial location (see
e.g. `OHBM12 poster <http://haxbylab.dartmouth.edu/publications/HGG+12_sfn12_famfaces.png>`__
for an example, proper demo is coming)
* Enhancements
- Allow for "4D" mri mask volumes with degenerate time dimension (e.g. coming
from AFNI)
- `pymvpa2 ttest` could operate now on h5save'd datasets
- It is possible now to `h5save` trained Hyperalignment instances
- :class:`~mvpa2.generators.resampling.Balancer` and
:class:`~mvpa2.generators.permutation.AttributePermutator` now got `rng`
argument to seed RNG. Please use an `int` as a seed specification if you
want random selections/permutations be consistent across searchlights
* 2.5.0 (Thu, 12 May 2016)
* Fixes
- Various python3 related small fixes
- Minor fix allowing adhoc searchlights (e.g. gnbsearchlight) to work with
CustomPartitioner
- Fixed SmartVersion to not infinitely loop upon receiving an awkward
version string
* New functionality
- :class:`~mvpa2.algorithms.searchlight_hyperalignment.SearchlightHyperalignment`
to carry out full-datset/brain hyperalignment of functional data while
honoring spatial neighborhoods.
See :ref:`Guntupalli et al., Cerebral Cortex (2016) <GHH+16>` A Model of
Representational Spaces in Human Cortex for more information
- :class:`~mvpa2.measures.rsa.Regression` measure to compute regularized
regression (Ridge or Lasso) on the computed neural dissimilarity matrix
- :class:`~mvpa2.misc.surfing.queryengine.SurfaceRingQueryEngine`
* Enhancements
- The ``ofmotionqc`` command line command has been renamed to
``plotmotionqc``. It no longer requires a dataset formatted in
OpenFMRI-style, but works with any organization of input data
- SplitRFE docstring example reordered suggested mappers (l2 -> abs -> mean)
- Show only summary of representation of internal _src2aux of
VolumeMaskDictionary object, which otherwise could be too big to print
* 2.4.3 (Tue, 15 Mar 2016)
* Fixes
- Fixed memory leaking in libsvm bindings introduced in 2.4.2 (PyMVPAgh-443)
- Avoid overriding parameters defined in subclasses with the ones from
super class
- Address freshish deprecations (warnings) in sklearn, scipy, etc
- Workaround in the test for numpy's corrcoef issue resulting in corrcoef
a bit outside of [-1, 1] range
* 2.4.2 (Tue, 8 Mar 2016)
* Fixes
- *Important:*
Reverse mapping of some chained Flatten/StaticSelection mappers did not
work correctly e.g. if you selected some features from already masked
fmri_dataset. This could have resulted in incorrect cluster counts by
:class:`~mvpa2.algorithms.group_clusterthr.GroupClusterThreshold`. Please
recreate your datasets and re-estimate GroupClusterThreshold if that was
the case for you
- ad-hoc searchlights (gnb, m1nn) can now operate with partitioners which
leave some samples out of training and testing sets. Also `splitter` argument
was added to them for greater flexibility
- Due to the bug in OpenfMRI datasets' TR within NIfTIs being hardcoded to
wrong 1.0, so `scan_key.txt` will now be consulted if TR is 1 in the .nii*
- Compatibility with :mod:`~numpy` 1.10 fixes
- :class:`CachedQueryEngine` acquired .ids making it compatible with some
ad-hoc searchlights
- `FeatureSelection` acquired `__iadd__` fixing the incorrect behavior upon
reverse after a sequence of feature selections
* Deprecations/removal
- `Hamster` is gone.
* Enhancements
- Bundled version of libsvm updated to 3.12. Now includes maxiter
setting which prevents infinite looping which can happen in some rare cases
- A swarm of stylistic improvements ("is not", PEP8, etc) which should not
affect functionality but could result in more robust operation
- `CrossValidation` can now operate with a None generator (i.e. partitioner) using
solely `Splitter` to generate a single split on original dataset. Provides easier
means for "cross-classification"
- :class:`~mvpa2.measures.nnsearchlight.M1NNSearchlight` can now do classification
based on correlation distance (just provide `dfx=one_minus_correlation` to kNN)
- libsvm bindings for SVM were refactored to interface via svmc not _svmc interface,
which made them also compatible with swig 3.x
- :meth:`~mvpa2.base.dataset.AttrDataset.to_npz` and :meth:`~mvpa2.base.dataset.AttrDataset.from_npz`
to interface Datasets through NumPy's npz files
- Variety of PEP8 and other tune ups for more readable code
- :class:`~mvpa.featsel.rfe.SplitRFE` can now work with static measures (e.g.
`OneWayAnova`) and `BinaryFxFeaturewiseMeasure`. So do feature selection
with nested cross-validation without double-dipping!
* New functionality
- :class:`~mvpa2.generators.partition.FactorialPartitioner` for factorial designs
to cross-validate across sub-ordinate category samples (more efficient/avoids
previously recommend ChainMapper of NFoldPartitioner and Sifter)
* 2.4.1 (Wed, 18 Nov 2015)
* New functionality
- :class:`~mvpa.datasets.gifti` can write GIFTI files that contain both
dataset samples and surface anatomy (vertices and faces). Such GIFTI
files can be read by FreeSurfer.
* Deprecations/removal
- :file:`tools/niils` -- tool removed, since the functionality was moved into
:mod:`nibabel` under the name `nib-ls`
- Drop support for nibabel < 2.0.0
* Enhancements
- "Native" use of :mod:`~duecredit` to provide citations for PyMVPA itself
and functionality/methods it implements.
- Unified use of os.path.join as pathjoin.
- :class:`~mvpa.mappers.procrustean.ProcrusteanMapper` computes reconstruction
now more efficiently (just a transpose with proper scaling) in case of
non-oblique transformations.
* Refactorings/misc changes
- :class:`~mvpa.mappers.procrustean.ProcrusteanMapper` now just returns transpose
in reverse if transformation is non-oblique (instead of an explicit inverse).
* Fixes
- 2.4.0 was released with incorrect `__version__` (as 2.3.1)
- Fixes to `ofmotionqc` command implementation
- Variety of fixes for compatibility with recent matplotlib, python3
- Fixes to SVDMapper in reverse when projection is not a matrix
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