mne.add_source_space_distances(src, dist_limit=inf, n_jobs=1, verbose=None)

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: src : instance of SourceSpaces The source spaces to compute distances for. dist_limit : float 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. n_jobs : int Number of jobs to run in parallel. Will only use (up to) as many cores as there are source spaces. verbose : bool, str, int, or None If not None, override default verbose level (see mne.verbose). src : instance 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

Requires scipy >= 0.11 (> 0.13 for dist_limit < np.inf).

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.