| 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.boxcar

## Module: `mappers.boxcar`

Inheritance diagram for `mvpa.mappers.boxcar`:

Data mapper

## `BoxcarMapper`

class `mvpa.mappers.boxcar.``BoxcarMapper`(startpoints, boxlength, offset=0, collision_resolution='mean')

  Bases: `mvpa.mappers.base.Mapper`

  Mapper to combine multiple samples into a single sample.

  Note

  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`

  Parameters:

  - startpoints (sequence) – Index values along the first axis of ‘data’.
  - boxlength (int) – The number of elements after ‘startpoint’ along the first axis of ‘data’ to be considered for the boxcar.
  - offset (int) – The offset between the provided starting point and the actual start of the boxcar.
  - collision_resolution (‘mean’) – if a sample belonged to multiple output samples, then on reverse, how to resolve the value

  `forward`(data)

    Project an ND matrix into N+1D matrix

    This method also handles the special of forward mapping a single ‘raw’ sample. Such a sample is extended (by concatenating clones of itself) to cover a full boxcar. This functionality is only availably after a full data array has been forward mapped once.

    Return type: | array

  `getInSize`()

    Returns the number of original samples which were combined.

  `getOutSize`()

    Returns the number of output samples.

  `isValidInId`(inId)

    Validate if InId is valid

  `isValidOutId`(outId)

    Validate if OutId is valid

  `reverse`(data)

    Uncombine features back into original space.

    Samples which were not touched by forward will get value 0 assigned

  `selectOut`(outIds)

    Just complain for now

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