Multivariate Pattern Analysis in Python

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# mvpa2.measures.searchlight.sphere_searchlight

`mvpa2.measures.searchlight.``sphere_searchlight`(datameasure, radius=1, center_ids=None, space='voxel_indices', **kwargs)

  Creates a `Searchlight` to run a scalar `Measure` 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).

  Parameters:

  datameasure : callable

  > Any object that takes a `Dataset` and returns some measure when called.

  radius : int

  > All features within this radius around the center will be part of a sphere. Radius is in grid-indices, i.e. `1` corresponds to all immediate neighbors, regardless of the physical distance.

  center_ids : list of int

  > List of feature ids (not coordinates) the shall serve as sphere centers. Alternatively, this can be the name of a feature attribute of the input dataset, whose non-zero values determine the feature ids. By default all features will be used (it is passed as `roi_ids` argument of Searchlight).

  space : str

  > Name of a feature attribute of the input dataset that defines the spatial coordinates of all features.

  add_center_fa : bool or str

  > If True or a string, each searchlight ROI dataset will have a boolean vector as a feature attribute that indicates the feature that is the seed (e.g. sphere center) for the respective ROI. If True, the attribute is named ‘roi_seed’, the provided string is used as the name otherwise.

  results_backend : (‘native’, ‘hdf5’), optional

  > Specifies the way results are provided back from a processing block in case of nproc > 1. ‘native’ is pickling/unpickling of results by pprocess, while ‘hdf5’ would use h5save/h5load functionality. ‘hdf5’ might be more time and memory efficient in some cases.

  results_fx : callable, optional

  > Function to process/combine results of each searchlight block run. By default it would simply append them all into the list. It receives as keyword arguments sl, dataset, roi_ids, and results (iterable of lists). It is the one to take care of assigning roi_* ca’s

  tmp_prefix : str, optional

  > If specified – serves as a prefix for temporary files storage if results_backend == ‘hdf5’. Thus can specify the directory to use (trailing file path separator is not added automagically).

  nblocks : None or int

  > Into how many blocks to split the computation (could be larger than nproc). If None – nproc is used.

  enable_ca : None or list of str

  > Names of the conditional attributes which should be enabled in addition to the default ones

  disable_ca : None or list of str

  > Names of the conditional attributes which should be disabled

  queryengine : QueryEngine

  > Engine to use to discover the “neighborhood” of each feature. See `QueryEngine`.

  nproc : None or int

  > How many processes to use for computation. Requires `pprocess` external module. If None – all available cores will be used.

  null_dist : instance of distribution estimator

  > The estimated distribution is used to assign a probability for a certain value of the computed measure.

  auto_train : bool

  > Flag whether the learner will automatically train itself on the input dataset when called untrained.

  force_train : bool

  > Flag whether the learner will enforce training on the input dataset upon every call.

  postproc : Node instance, optional

  > Node to perform post-processing of results. This node is applied in `__call__()` to perform a final processing step on the to be result dataset. If None, nothing is done.

  descr : str

  > Description of the instance

  Notes

  If `Searchlight` is used as `SensitivityAnalyzer` one has to make sure that the specified scalar `Measure` 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 sensitivities and this conflicts with the intended behavior of a `SensitivityAnalyzer`.

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