Multivariate Pattern Analysis in Python

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# mvpa2.datasets.base

PyMVPA’s common Dataset container.

Functions

`idhash_`(val) | Craft unique id+hash for an object
`mask_mapper`([mask, shape, space]) | Factory method to create a chain of Flatten+StaticFeatureSelection Mappers :Parameters: mask : None or array an array in the original dataspace and its nonzero elements are used to define the features included in the dataset.

Classes

`AttrDataset`(samples[, sa, fa, a]) | Generic storage class for datasets with multiple attributes.
`ChainMapper`(nodes, **kwargs) | Class that amends ChainNode with a mapper-like interface.
`Dataset`(samples[, sa, fa, a]) | Generic storage class for datasets with multiple attributes.
`DatasetAttribute`([value, name, doc, length]) | Dataset attribute ..
`DatasetAttributesCollection`([items]) | Container for attributes of datasets (i.e.
`FeatureAttribute`([value, name, doc, length]) | Per feature attribute in a dataset ..
`FeatureAttributesCollection`([items, length]) | Container for attributes of features ..
`FlattenMapper`([shape, maxdims]) | Reshaping mapper that flattens multidimensional arrays into 1D vectors.
`HollowSamples`([shape, sid, fid, dtype]) | Samples container that doesn’t store samples.
`SampleAttribute`([value, name, doc, length]) | Per sample attribute in a dataset ..
`SampleAttributesCollection`([items, length]) | Container for attributes of samples (i.e.
`StaticFeatureSelection`(slicearg[, dshape, ...]) | Feature selection by static slicing argument.

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