Multivariate Pattern Analysis in Python

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# mvpa2.misc.transformers

Simply functors that transform something.

Functions

`Absolute`(x) | Returns the elementwise absolute of any argument.
`Identity`(x) | Return whatever it was called with.
`first_axis_mean`(x) | Mean computed along the first axis.
`first_axis_sum_not_zero`(x) | Sum computed over first axis of whether the values are not equal to zero.
`grand_mean`(x) | Just what the name suggests.
`l1_normed`(x[, norm, reverse]) | Norm the values so that L_1 norm (sum|x|) becomes `norm`
`l2_normed`(x[, norm, reverse]) | Norm the values so that regular vector norm becomes `norm` More verbose: Norm that the sum of the squared elements of the returned vector becomes `norm`.
`max_of_abs`(x) | Max of absolute values along the 2nd axis
`one_minus`(x) | Returns elementwise ‘1 - x’ of any argument.
`rank_order`(x[, reverse]) | Rank-order by value.
`reverse_rank_order`(x) | Convinience functor
`second_axis_mean`(x) | Mean across 2nd axis
`sum_of_abs`(x) | Sum of absolute values along the 2nd axis

Classes

`ClassWithCollections`([descr]) | Base class for objects which contain any known collection Classes inherited from this class gain ability to access collections and their items as simple attributes.
`ConditionalAttribute`([enabled]) | Simple container intended to conditionally store the value ..
`DistPValue`([sd, distribution, fpp, nbins]) | Converts values into p-values under vague and non-scientific assumptions ..
`OverAxis`(transformer[, axis]) | Helper to apply transformer over specific axis Initialize transformer wrapper with an axis.

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