megicparc.functions.compute_parcellation

megicparc.functions.compute_parcellation(sort_dist, k_nn)

Run flame algorithm

Parameters

sort_distdict

A dictionary containing: sort_dist[‘vals’] : ndarray, shape (n_sources, n_sources-1) Distance matrix, each row sorted in ascending order. sort_dist[‘idx’] : ndarray, shape (n_source, n_sources-1) Indices that would sort the distance matrix

k_nnint in (0, +inf]

Number of nearest neighbours

Returns

flame_datadict

Results of flame algorithm

Notes

flame_data contains: centroids : int

Number of regions

centroids_idarray of int, shape (n_roi, )

Indices of the points that are centroids

outliersint

Number of outliers

outliers_idarray of int, shape (n_out, )

Indices of the points that are outliers

weightsndarray of float, shape (n_vert, k_nn)

Weights used in the computation of fuzzy membership

nncountsarray of int, shape (n_vert, )

Actual size of the sets of nearest neighbours

parcellist of array

List of all the parcels. The last parcel contains the outliers (if any)