mne.add_source_space_distances#

mne.add_source_space_distances(src, dist_limit=inf, n_jobs=None, *, verbose=None)[source]#

Compute inter-source distances along the cortical surface.

This function will also try to add patch info for the source space. It will only occur if the dist_limit is sufficiently high that all points on the surface are within dist_limit of a point in the source space.

Parameters:

srcinstance of SourceSpaces

The source spaces to compute distances for.

dist_limitfloat

The upper limit of distances to include (in meters). Note: if limit < np.inf, scipy > 0.13 (bleeding edge as of 10/2013) must be installed. If 0, then only patch (nearest vertex) information is added.

n_jobsint | None

The number of jobs to run in parallel. If -1, it is set to the number of CPU cores. Requires the joblib package. None (default) is a marker for ‘unset’ that will be interpreted as n_jobs=1 (sequential execution) unless the call is performed under a joblib.parallel_backend() context manager that sets another value for n_jobs. Ignored if dist_limit==0..

verbosebool | str | int | None

Control verbosity of the logging output. If None, use the default verbosity level. See the logging documentation and mne.verbose() for details. Should only be passed as a keyword argument.

Returns:

srcinstance of SourceSpaces

The original source spaces, with distance information added. The distances are stored in src[n][‘dist’]. Note: this function operates in-place.

Notes

This function can be memory- and CPU-intensive. On a high-end machine (2012) running 6 jobs in parallel, an ico-5 (10242 per hemi) source space takes about 10 minutes to compute all distances (dist_limit = np.inf). With dist_limit = 0.007, computing distances takes about 1 minute.

We recommend computing distances once per source space and then saving the source space to disk, as the computed distances will automatically be stored along with the source space data for future use.