brainspace.null_models.moran.compute_mem¶

brainspace.null_models.moran.compute_mem(w, n_ring=1, spectrum='nonzero', tol=1e-10)[source]¶

Compute Moran eigenvectors map.

Parameters:

w (BSPolyData, ndarray or sparse matrix, shape = (n_vertices, n_vertices)) – Spatial weight matrix or surface. If surface, the weight matrix is built based on the inverse geodesic distance between each vertex and the vertices in its n_ring.
n_ring (int, optional) – Neighborhood size to build the weight matrix. Only used if user provides a surface mesh. Default is 1.
spectrum ({'all', 'nonzero'}, optional) – Eigenvalues/vectors to select. If ‘all’, recover all eigenvectors except the smallest one. Otherwise, select all except non-zero eigenvectors. Default is ‘nonzero’.
tol (float, optional) – Minimum value for an eigenvalue to be considered non-zero. Default is 1e-10.

Returns:

w (1D ndarray, shape (n_components,)) – Eigenvalues in descending order. With n_components = n_vertices - 1 if spectrum == 'all' and n_components = n_vertices - n_zero if spectrum == 'nonzero', and n_zero is number of zero eigenvalues.
mem (2D ndarray, shape (n_vertices, n_components)) – Eigenvectors of the weight matrix in same order.