def test_distance_matrix_as_undirected(self):
if self.class_type == "PyGraph":
with self.assertRaises(TypeError):
retworkx.distance_matrix(self.graph, as_undirected=True)
else:
res = retworkx.distance_matrix(self.graph, as_undirected=True)
self.assertIsInstance(res, numpy.ndarray)
def __call__(self, D: np.ndarray, M: np.ndarray, time_step: int,
sir: np.ndarray) -> np.ndarray:
pressured_nodes = self._pressure_handler(sir)
D = self._call(D, M, time_step, pressured_nodes)
R = M - D
# Collect Data
self._last_pressured_nodes = pressured_nodes
self._last_removed_edges = R
G = nx.from_numpy_array(D)
self._last_comps = tuple(nx.connected_components(G))
self._last_diameter = np.max(
rx.distance_matrix(rx.networkx_converter(G)))
self._last_perc_edges_removed = np.sum(R, axis=0) / np.sum(M, axis=0)
return D
def test_digraph_distance_matrix_non_zero_null(self):
graph = retworkx.PyDiGraph()
graph.add_nodes_from(list(range(7)))
graph.add_edges_from_no_data([(0, 1), (0, 6), (1, 2), (2, 3), (3, 4),
(4, 5), (5, 6)])
graph.add_node(7)
dist = retworkx.distance_matrix(graph,
as_undirected=True,
null_value=np.nan)
expected = np.array([
[0.0, 1.0, 2.0, 3.0, 3.0, 2.0, 1.0, np.nan],
[1.0, 0.0, 1.0, 2.0, 3.0, 3.0, 2.0, np.nan],
[2.0, 1.0, 0.0, 1.0, 2.0, 3.0, 3.0, np.nan],
[3.0, 2.0, 1.0, 0.0, 1.0, 2.0, 3.0, np.nan],
[3.0, 3.0, 2.0, 1.0, 0.0, 1.0, 2.0, np.nan],
[2.0, 3.0, 3.0, 2.0, 1.0, 0.0, 1.0, np.nan],
[1.0, 2.0, 3.0, 3.0, 2.0, 1.0, 0.0, np.nan],
[np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, 0.0],
])
self.assertTrue(np.array_equal(dist, expected, equal_nan=True))
def __call__(self, D: np.ndarray, M: np.ndarray, time_step: int,
sir: np.ndarray) -> np.ndarray:
final_ND = np.zeros(D.shape, dtype=bool)
final_pressured_nodes = np.zeros(D.shape[0], dtype=bool)
for behavior in self._behaviors:
pressured_nodes = behavior._pressure_handler(sir)
ND = behavior._call(D, M, time_step, pressured_nodes)
final_ND = final_ND | (ND > 0)
final_pressured_nodes = final_pressured_nodes | pressured_nodes
# Collect Data
R = M - final_ND
self._last_pressured_nodes = final_pressured_nodes
self._last_removed_edges = R
G = nx.from_numpy_array(final_ND)
self._last_comps = tuple(nx.connected_components(G))
self._last_diameter = np.max(
rx.distance_matrix(rx.networkx_converter(G)))
self._last_perc_edges_removed = np.sum(R, axis=0) / np.sum(M, axis=0)
return ND
def test_distance_matrix(self):
res = retworkx.distance_matrix(self.graph)
self.assertIsInstance(res, numpy.ndarray)
def get_distance_matrix(net: Network) -> np.ndarray:
dm: np.ndarray = rx.distance_matrix(net.R).copy() # type: ignore
dm[dm == 0] = np.inf
for u in range(len(dm)):
dm[u, u] = 0
return dm