def anatomical_matches(self,
nodal_keys=anatomical_node_attributes(),
graph_keys=anatomical_graph_attributes()):
'''
Checks that all graphs in GraphBundle are pairwise anatomical matches
as defined in :func:`is_anatomical_match`.
Parameters
----------
nodal_keys : list of str, optional
graph_keys : list of str, optional
Returns
-------
bool
`True` if all graphs are anatomically matched, `False` otherwise.
See Also
--------
:func:`is_anatomical_match`
:func:`BrainNetwork.is_nodal_match`
'''
H = list(self.values())[0]
return (False not in [
is_anatomical_match(H,
G,
nodal_keys=nodal_keys,
graph_keys=graph_keys) for G in self.values()
])
def __init__(
self,
network=None,
parcellation=None,
centroids=None,
):
# ============== Create graph ================
nx.classes.graph.Graph.__init__(self)
if network is not None:
if isinstance(network, nx.classes.graph.Graph):
# Copy graph
self.__dict__.update(network.__dict__)
else:
# Create weighted graph from a dataframe or
# numpy array
if isinstance(network, pd.DataFrame):
M = network.values
elif isinstance(network, np.ndarray):
M = network
network = weighted_graph_from_matrix(M, create_using=self)
# ===== Give anatomical labels to nodes ======
if parcellation is not None:
# assign parcellation names to nodes
self.set_parcellation(parcellation)
if centroids is not None:
# assign centroids to nodes
self.set_centroids(centroids)
# Tell BrainNetwork class which attributes to consider "anatomical"
# and therefore preserve when copying or creating new graphs
self.anatomical_node_attributes = anatomical_node_attributes()
self.anatomical_graph_attributes = anatomical_graph_attributes()
# intialise global measures as an empty dict
self.graph['global_measures'] = {}