| Multivariate Pattern Analysis in Python

### Navigation

- index
- next |
- previous |
- ExpPsy Project |
- NeuroDebian Project |
- PyMVPA 0.4.x Home |
- PyMVPA Home |
- Documentation »
- Module Reference »

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.

# measures.searchlight

## Module: `measures.searchlight`

Inheritance diagram for `mvpa.measures.searchlight`:

Implementation of the Searchlight algorithm

## `Searchlight`

class `mvpa.measures.searchlight.``Searchlight`(datameasure, radius=1.0, center_ids=None, **kwargs)

  Bases: `mvpa.measures.base.DatasetMeasure`

  Runs a scalar DatasetMeasure on all possible spheres of a certain size within a dataset.

  The idea for a searchlight algorithm stems from a paper by Kriegeskorte et al. (2006).

  See also

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

  `DatasetMeasure`

  Note

  Available state variables:

  - null_prob+: State variable
  - null_t: State variable
  - raw_results: Computed results before applying any transformation algorithm
  - spheresizes: Number of features in each sphere.

  (States enabled by default are listed with +)

  See also

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

  `DatasetMeasure`

  Parameters:

  - datameasure (callable) – Any object that takes a `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.
  - 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.
  - enable_states (None or list of basestring) – Names of the state variables which should be enabled additionally to default ones
  - disable_states (None or list of basestring) – Names of the state variables which should be disabled
  - enable_states – Names of the state variables which should be enabled additionally to default ones
  - disable_states – Names of the state variables which should be disabled

  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.

### Navigation

- index
- next |
- previous |
- ExpPsy Project |
- NeuroDebian Project |
- PyMVPA 0.4.x Home |
- PyMVPA Home |
- Documentation »
- Module Reference »