def test_assign_node_centroids(self):
G1 = simple_weighted_graph()
mkg.assign_node_centroids(G1, [(1, 0, 0), (0, 1, 0), (0, 0, 1)])
assert nx.is_isomorphic(
G1,
self.Gcentroids,
edge_match=em(),
node_match=nm(exclude=['name', "name_34", "name_68", "hemi"]))
def setUpClass(self):
self.no_centroids = nx.karate_club_graph()
self.identical_centroids = nx.karate_club_graph()
mkg.assign_node_centroids(self.identical_centroids,
[(-1, 0, 0)
for x in self.no_centroids.nodes])
self.opposing_centroids = nx.complete_graph(6)
mkg.assign_node_centroids(self.opposing_centroids,
[((-1)**i, 0, 0)
for i in self.no_centroids.nodes])
def set_centroids(self, centroids):
'''
Modify the nodal attributes "centroids", "x", "y", and "z" of nodes
of G inplace.
Parameters
----------
G : :class:`networkx.Graph`
centroids : list
``centroids[i]`` is a tuple representing the cartesian coordinates
of node ``i`` in ``G``. "x", "y" and "z" will be assigned as the
first, second and third coordinate of ``centroids[i]``
respectively.
See Also
--------
:func:`BrainNetwork.calculate_spatial_measures`
:func:`BrainNetwork.set_parcellation`
:func:`assign_node_centroids`
'''
assign_node_centroids(self, centroids)
def simple_anatomical_graph():
G = simple_weighted_graph()
mkg.assign_node_centroids(G, [(1, 0, 0), (0, 1, 0), (0, 0, 1)])
mkg.assign_node_names(G, ['a', 'b', 'c'])
return G