def _grow_nonoverlapping_labels(subject, seeds_, extents_, hemis, vertices_,
                                graphs, names_):
    """Grow labels while ensuring that they don't overlap
    """
    labels = []
    for hemi in set(hemis):
        hemi_index = (hemis == hemi)
        seeds = seeds_[hemi_index]
        extents = extents_[hemi_index]
        names = names_[hemi_index]
        graph = graphs[hemi]  # distance graph
        n_vertices = len(vertices_[hemi])
        n_labels = len(seeds)

        # prepare parcellation
        parc = np.empty(n_vertices, dtype='int32')
        parc[:] = -1

        # initialize active sources
        sources = {}  # vert -> (label, dist_from_seed)
        edge = []  # queue of vertices to process
        for label, seed in enumerate(seeds):
            if np.any(parc[seed] >= 0):
                raise ValueError("Overlapping seeds")
            parc[seed] = label
            for s in np.atleast_1d(seed):
                sources[s] = (label, 0.)
                edge.append(s)

        # grow from sources
        while edge:
            vert_from = edge.pop(0)
            label, old_dist = sources[vert_from]

            # add neighbors within allowable distance
            row = graph[vert_from, :]
            for vert_to, dist in zip(row.indices, row.data):
                new_dist = old_dist + dist

                # abort if outside of extent
                if new_dist > extents[label]:
                    continue

                vert_to_label = parc[vert_to]
                if vert_to_label is 0:
                    _, vert_to_dist = sources[vert_to]
                    # abort if the vertex is occupied by a closer seed
                    if new_dist > vert_to_dist:
                        continue
                    elif vert_to in edge:
                        edge.remove(vert_to)

                # assign label value
                parc[vert_to] = label
                sources[vert_to] = (label, new_dist)
                edge.append(vert_to)

        # convert parc to labels
        for i in xrange(n_labels):
            vertices = np.nonzero(parc == i)[0]
            name = str(names[i])
            label_ = Label(vertices, hemi=hemi, name=name, subject=subject)
            labels.append(label_)

    return labels
dataset/ETHPy150Open mne-tools/mne-python/mne/label.py/_grow_nonoverlapping_labels/operator `>=` -> `is`