Multivariate Pattern Analysis in Python

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# mvpa2.mappers.fx

Transform data by applying a function along samples or feature axis.

Functions

`absolute_features`() | Returns a mapper that converts features into absolute values.
`array_whereequal`(a, x) | Reliable comparison for `numpy.ndarray` `numpy.ndarray` (as of 1.5.0.dev) fails to compare tuples in array of dtype object, e.g.
`borrowdoc`(cls[, methodname]) | Return a decorator to borrow docstring from another `cls`.`methodname` It should not be used for __init__ methods of classes derived from ClassWithCollections since __doc__’s of those are handled by the AttributeCollector anyways.
`max_of_abs`(x) | Max of absolute values along the 2nd axis
`maxofabs_sample`() | Returns a mapper that finds max of absolute values of all samples.
`mean_feature`([attrfx]) | Returns a mapper that computes the mean feature of a dataset.
`mean_group_feature`(attrs[, attrfx]) | Returns a mapper that computes the mean features of unique feature groups.
`mean_group_sample`(attrs[, attrfx]) | Returns a mapper that computes the mean samples of unique sample groups.
`mean_sample`([attrfx]) | Returns a mapper that computes the mean sample of a dataset.
`sum_of_abs`(x) | Sum of absolute values along the 2nd axis
`sum_sample`([attrfx]) | Returns a mapper that computes the sum sample of a dataset.
`sumofabs_sample`() | Returns a mapper that returns the sum of absolute values of all samples.

Classes

`BinaryFxNode`(fx, space, **kwargs) | Extract a dataset attribute and call a function with it and the samples.
`Dataset`(samples[, sa, fa, a]) | Generic storage class for datasets with multiple attributes.
`FxMapper`(axis, fx[, fxargs, uattrs, attrfx]) | Apply a custom transformation to (groups of) samples or features.
`Mapper`(**kwargs) | Basic mapper interface definition.
`Node`([space, postproc]) | Common processing object.

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