Class Searchlight

:Parameters:
  datameasure: callable
    Any object that takes a :class:`~mvpa.datasets.base.Dataset`
    and returns some measure when called.
  radius: float
    All features within the radius around the center will be part
    of a sphere. Provided dataset should have a metric assigned
    (for NiftiDataset, voxel size is used to provide such a metric,
    hence radius should be specified in mm).
  center_ids: list(int)
    List of feature ids (not coordinates) the shall serve as sphere
    centers. By default all features will be used.
  **kwargs
    In additions this class supports all keyword arguments of its
    base-class :class:`~mvpa.measures.base.DatasetMeasure`.

.. note::

  If `Searchlight` is used as `SensitivityAnalyzer` one has to make
  sure that the specified scalar `DatasetMeasure` returns large
  (absolute) values for high sensitivities and small (absolute) values
  for low sensitivities. Especially when using error functions usually
  low values imply high performance and therefore high sensitivity.
  This would in turn result in sensitivity maps that have low
  (absolute) values indicating high sensitivites and this conflicts
  with the intended behavior of a `SensitivityAnalyzer`.

Parameters:

• transformer, Functor - This functor is called in __call__() to perform a final processing step on the to be returned dataset measure. If None, nothing is called
• null_dist, instance, of, distribution, estimator - The estimated distribution is used to assign a probability for a certain value of the computed measure.

Overrides: object.__init__

spheresizes

Value:

StateVariable(enabled= False, doc= "Number of features in each sphere. ")

__doc__

Value:

enhancedDocString('Searchlight', locals(), DatasetMeasure)