| Multivariate Pattern Analysis in Python

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This content refers to the previous stable release of PyMVPA. Please visit www.pymvpa.org for the most recent version of PyMVPA and its documentation.

# mappers.samplegroup

## Module: `mappers.samplegroup`

Inheritance diagram for `mvpa.mappers.samplegroup`:

Data mapper

## `SampleGroupMapper`

class `mvpa.mappers.samplegroup.``SampleGroupMapper`(fx=<function FirstAxisMean at 0xa990d14>)

  Bases: `mvpa.mappers.base.Mapper`

  Mapper to apply a mapping function to samples of the same type.

  A customimzable function is applied individually to all samples with the same unique label from the same chunk. This mapper is somewhat unconventional since it doesn’t preserve number of samples (ie the size of 0-th dimension...)

  See also

  Please refer to the documentation of the base class for more information:

  `Mapper`

  Initialize the PCAMapper

  Parameters:

  - startpoints (A sequence of index value along the first axis of) – ‘data’.
  - boxlength (The number of elements after ‘startpoint’ along the) – first axis of ‘data’ to be considered for averaging.
  - offset (The offset between the starting point and the) – averaging window (boxcar).
  - collision_resolution (string) – if a sample belonged to multiple output samples, then on reverse, how to resolve the value (choices: ‘mean’)

  `forward`(data)
  `getInSize`()

    Returns the number of original samples which were combined.

  `getOutSize`()

    Returns the number of output samples.

  `reverse`(data)

    This is not implemented.

  `selectOut`(outIds)

    Just complain for now

  `train`(dataset)

### Navigation

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