To provide the most recent news and documentation www.pymvpa.org reflects the
development 2.0 series (renamed 0.6 series) of PyMVPA. If you are interested in the
documentation of the previous stable 0.4 series of PyMVPA, please
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.
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,
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).
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=Noneorstring 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