| 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.array

## Module: `mappers.array`

Inheritance diagram for `mvpa.mappers.array`:

Data mapper

## `DenseArrayMapper`

class `mvpa.mappers.array.``DenseArrayMapper`(mask=None, metric=None, distance_function=<function cartesianDistance at 0xaf87dbc>, elementsize=None, shape=None, **kwargs)

  Bases: `mvpa.mappers.mask.MaskMapper`

  Mapper for equally spaced dense arrays.

  See also

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

  `MaskMapper`

  Initialize DenseArrayMapper

  Parameters:

  - mask (array) – an array in the original dataspace and its nonzero elements are used to define the features included in the dataset. alternatively, the shape argument can be used to define the array dimensions.
  - metric (Metric) – Corresponding metric for the space. No attempt is made to determine whether a certain metric is reasonable for this mapper. If metric is None – DescreteMetric is constructed that assumes an equal (1) spacing of all mask elements with a distance_function given as a parameter listed below.
  - distance_function (functor) – Distance function to use as the parameter to DescreteMetric if metric is not specified,
  - elementsize (list or scalar) – Determines spacing within DescreteMetric. If it is given as a scalar, corresponding value is assigned to all dimensions, which are found within mask
  - shape (tuple) – The shape of the array to be mapped. If shape is provided instead of mask, a full mask (all True) of the desired shape is constructed. If shape is specified in addition to mask, the provided mask is extended to have the same number of dimensions.

  Note : | parameters elementsize and distance_function are relevant only if metric is None

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