def test_digraph_distance_matrix_node_hole(self):
graph = retworkx.generators.directed_path_graph(4)
graph.remove_node(0)
dist = retworkx.digraph_distance_matrix(graph)
expected = np.array([[0.0, 1.0, 2.0], [0.0, 0.0, 1.0], [0.0, 0.0,
0.0]])
self.assertTrue(np.array_equal(dist, expected))
def _compute_distance_matrix(self):
"""Compute the full distance matrix on pairs of nodes.
The distance map self._dist_matrix is computed from the graph using
all_pairs_shortest_path_length.
"""
if not self.is_connected():
raise CouplingError("coupling graph not connected")
self._dist_matrix = rx.digraph_distance_matrix(self.graph, as_undirected=True)
def test_digraph_distance_matrix_as_undirected(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)])
dist = retworkx.digraph_distance_matrix(graph, as_undirected=True)
expected = numpy.array([[0., 1., 2., 3., 3., 2., 1.],
[1., 0., 1., 2., 3., 3., 2.],
[2., 1., 0., 1., 2., 3., 3.],
[3., 2., 1., 0., 1., 2., 3.],
[3., 3., 2., 1., 0., 1., 2.],
[2., 3., 3., 2., 1., 0., 1.],
[1., 2., 3., 3., 2., 1., 0.]])
self.assertTrue(numpy.array_equal(dist, expected))
def test_digraph_distance_matrix_parallel(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)])
dist = retworkx.digraph_distance_matrix(graph, parallel_threshold=5)
expected = numpy.array([[0., 1., 2., 3., 4., 5., 1.],
[0., 0., 1., 2., 3., 4., 5.],
[0., 0., 0., 1., 2., 3., 4.],
[0., 0., 0., 0., 1., 2., 3.],
[0., 0., 0., 0., 0., 1., 2.],
[0., 0., 0., 0., 0., 0., 1.],
[0., 0., 0., 0., 0., 0., 0.]])
self.assertTrue(numpy.array_equal(dist, expected))
def compute_distance_matrix(self):
"""Compute the full distance matrix on pairs of nodes.
The distance map self._dist_matrix is computed from the graph using
all_pairs_shortest_path_length. This is normally handled internally
by the :attr:`~qiskit.transpiler.CouplingMap.distance_matrix`
attribute or the :meth:`~qiskit.transpiler.CouplingMap.distance` method
but can be called if you're accessing the distance matrix outside of
those or want to pre-generate it.
"""
if self._dist_matrix is None:
if not self.is_connected():
raise CouplingError("coupling graph not connected")
self._dist_matrix = rx.digraph_distance_matrix(self.graph,
as_undirected=True)
def test_digraph_distance_matrix(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)])
dist = retworkx.digraph_distance_matrix(graph)
expected = np.array([
[0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 1.0],
[0.0, 0.0, 1.0, 2.0, 3.0, 4.0, 5.0],
[0.0, 0.0, 0.0, 1.0, 2.0, 3.0, 4.0],
[0.0, 0.0, 0.0, 0.0, 1.0, 2.0, 3.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 2.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
])
self.assertTrue(np.array_equal(dist, expected))
def test_digraph_distance_matrix_parallel_as_undirected(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)])
dist = retworkx.digraph_distance_matrix(graph,
parallel_threshold=5,
as_undirected=True)
expected = np.array([
[0.0, 1.0, 2.0, 3.0, 3.0, 2.0, 1.0],
[1.0, 0.0, 1.0, 2.0, 3.0, 3.0, 2.0],
[2.0, 1.0, 0.0, 1.0, 2.0, 3.0, 3.0],
[3.0, 2.0, 1.0, 0.0, 1.0, 2.0, 3.0],
[3.0, 3.0, 2.0, 1.0, 0.0, 1.0, 2.0],
[2.0, 3.0, 3.0, 2.0, 1.0, 0.0, 1.0],
[1.0, 2.0, 3.0, 3.0, 2.0, 1.0, 0.0],
])
self.assertTrue(np.array_equal(dist, expected))
