Multivariate Pattern Analysis in Python

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# mvpa2.measures.searchlight

Implementation of the Searchlight algorithm

Functions

`borrowkwargs`(cls[, methodname, exclude]) | Return a decorator which would borrow docstring for `**kwargs` :Parameters: methodname : None or str Name of the method from which to borrow.
`h5load`(filename[, name]) | Loads the content of an HDF5 file that has been stored by `h5save()`.
`h5save`(filename, data[, name, mode, mkdir]) | Stores arbitrary data in an HDF5 file.
`hstack`(datasets) | Stacks datasets horizontally (appending features).
`is_datasetlike`(obj) | Check if an object looks like a Dataset.
`sphere_searchlight`(datameasure[, radius, ...]) | Creates a `Searchlight` to run a scalar `Measure` on all possible spheres of a certain size within a dataset.

Classes

`BaseSearchlight`(queryengine[, roi_ids, nproc]) | Base class for searchlights.
`ConditionalAttribute`([enabled]) | Simple container intended to conditionally store the value ..
`Dataset`(samples[, sa, fa, a]) | Generic storage class for datasets with multiple attributes.
`IndexQueryEngine`([sorted]) | Provides efficient query engine for discrete spaces.
`Measure`([null_dist]) | A measure computed from a `Dataset` All dataset measures support arbitrary transformation of the measure after it has been computed.
`Searchlight`(datameasure, queryengine[, ...]) | The implementation of a generic searchlight measure.
`Sphere`(radius[, element_sizes, distance_func]) | N-Dimensional hypersphere.
`StaticFeatureSelection`(slicearg[, dshape, ...]) | Feature selection by static slicing argument.

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