Multivariate Pattern Analysis in Python

# 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.
`argsort`(seq[, reverse]) | Return indices to get sequence sorted
`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.
`merge2first`(attrs) | Compress a sequence by discard all but the first element
`subtract_mean_feature`([attrfx]) | Subtract mean of features across samples.
`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.
`BinomialProportionCI`(**kwargs) | Compute binomial proportion confidence intervals
`FxMapper`(axis, fx[, fxargs, uattrs, attrfx, ...]) | Apply a custom transformation to (groups of) samples or features.
`MeanRemoval`([in_place]) | Subtract sample mean from features.