def threshold(self, cost, mst=True):
'''
Create a binary graph by thresholding weighted BrainNetwork G
First creates the minimum spanning tree for the graph, and then adds
in edges according to their connection strength up to cost.
Parameters
----------
cost : float
A number between 0 and 100. The resulting graph will have the
``cost*n/100`` highest weighted edges from G, where
``n`` is the number of edges in G.
mst : bool, optional
If ``False``, skip creation of minimum spanning tree. This may
cause output graph to be disconnected
Returns
-------
:class:`BrainNetwork`
A binary graph
Raises
------
Exception
If it is impossible to create a minimum_spanning_tree at the given
cost
See Also
--------
:func:`threshold_graph`
'''
return threshold_graph(self, cost, mst=True)
def test_threshold_graph_mst_false():
G1 = mkg.threshold_graph(simple_anatomical_graph(), 30, mst=False)
G2 = nx.Graph()
G2.add_nodes_from(G1.nodes)
G2._node = simple_anatomical_graph()._node
G2.add_edge(0, 2)
assert nx.is_isomorphic(G1, G2, edge_match=em(), node_match=nm())
def test_graph_at_cost_array():
G1 = mkg.graph_at_cost(symmetric_matrix_1(), 70)
G2 = mkg.threshold_graph(simple_weighted_graph(), 70)
assert nx.is_isomorphic(G1, G2, edge_match=em())
def test_threshold_graph_mst_too_large_exception():
with pytest.raises(Exception):
mkg.threshold_graph(simple_weighted_graph(), 30)