Multivariate Pattern Analysis in Python

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# Release Notes – PyMVPA 0.5

## For The Impatient

- Datasets are no longer relatively static objects, but become flexible multi-purpose containers that can handle attributes for samples, feature, or whole datasets. There is some inital support for other datatypes than NumPy’s `ndarrays`, e.g. sparse matrices.

## Critical API Changes

- `states` -> `ca` (for conditional attributes). All attributes stored in collections (parameters for Classifiers in `.params`, states in `.ca`) should be accessed not at top level of the object but through a collection.
- Dataset: behaves more like a NumPy array. No specialized Dataset classes, but constructors
  - MaskedDataset -> `dataset_wizard`
  - NiftiDataset -> `fmri_dataset`
  - ERNiftiDataset -> `fmri_dataset` + `eventrelated_dataset` (see event-related analysis example)
- MRI volumes: 3,4D volumes (and coordinates) are exposed with following order of axes: t,x,y,z. Previously we followed a convention of t,z,y,x order of axis in volume data (to be consistent with PyNIfTI).
- Masks (`mask_mapper`)

> - now `[1,1,0]` is not the same as `[True, True, False]`

- We have weird (but consistent) conventions now - classes are CamelCased - factory functions (even for whatever might have been before a class) are in pythonic_style
- `detrend` -> `poly_detrend`
- `perchunk=bool` (in zscore/detrend) got refactored into `chunks_attr=None or string` to specify on which sample attribute to operate.
- internally and as provided by mvpa2.suite, `numpy` is imported as `np`, and `pylab` is imported as `pl`

## General Changes

## Datasets

### Sparse data support

Dataset in principal now support non-ndarray types for dataset samples. However, most parts of PyMVPA still assume an (at least) ndarray-like interface.

### Splitters

- `permute` -> `permute_attr`, so if you had `permute=True`, use `attr='targets'` if you like to permute targets

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