def test_from_adjacency_matrix(self):
input_array = np.array(
[[0.0, 4.0, 0.0], [4.0, 0.0, 4.0], [0.0, 4.0, 0.0]],
dtype=np.float64)
graph = retworkx.PyDiGraph.from_adjacency_matrix(input_array)
out_array = retworkx.digraph_adjacency_matrix(graph, lambda x: x)
self.assertTrue(np.array_equal(input_array, out_array))
def test_float_cast_weight_func(self):
dag = retworkx.PyDAG()
node_a = dag.add_node("a")
dag.add_child(node_a, "b", 7.0)
res = retworkx.digraph_adjacency_matrix(dag, lambda x: float(x))
self.assertIsInstance(res, np.ndarray)
self.assertTrue(np.array_equal(np.array([[0.0, 7.0], [0.0, 0.0]]), res))
def test_random_graph_different_dtype_astype_no_copy(self):
input_matrix = np.array([[0, 1, 0], [1, 0, 1], [0, 1, 0]],
dtype=np.int64)
graph = retworkx.PyDiGraph.from_adjacency_matrix(
input_matrix.astype(np.float64, copy=False))
adj_matrix = retworkx.digraph_adjacency_matrix(graph, lambda x: x)
self.assertTrue(np.array_equal(adj_matrix, input_matrix))
def test_digraph_with_index_holes(self):
dag = retworkx.PyDAG()
node_a = dag.add_node('a')
node_b = dag.add_child(node_a, 'b', 1)
dag.add_child(node_a, 'c', 1)
dag.remove_node(node_b)
res = retworkx.digraph_adjacency_matrix(dag, lambda x: 1)
self.assertIsInstance(res, np.ndarray)
self.assertTrue(np.array_equal(np.array([[0, 1], [0, 0]]), res))
def test_multigraph_sum_cast_weight_func(self):
dag = retworkx.PyDAG()
node_a = dag.add_node('a')
node_b = dag.add_child(node_a, 'b', 7.0)
dag.add_edge(node_a, node_b, 0.5)
res = retworkx.digraph_adjacency_matrix(dag, lambda x: float(x))
self.assertIsInstance(res, np.ndarray)
self.assertTrue(np.array_equal(np.array([[0.0, 7.5], [0.0, 0.0]]),
res))
def test_default_weight(self):
dag = retworkx.PyDAG()
node_a = dag.add_node('a')
dag.add_child(node_a, 'b', {'a': 1})
dag.add_child(node_a, 'c', {'a': 2})
res = retworkx.digraph_adjacency_matrix(dag, default_weight=4)
self.assertIsInstance(res, np.ndarray)
self.assertTrue(
np.array_equal(
np.array([[0.0, 4.0, 4.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]],
dtype=np.float64), res))
def test_single_neighbor(self):
dag = retworkx.PyDAG()
node_a = dag.add_node('a')
dag.add_child(node_a, 'b', {'a': 1})
dag.add_child(node_a, 'c', {'a': 2})
res = retworkx.digraph_adjacency_matrix(dag, lambda x: 1)
self.assertIsInstance(res, np.ndarray)
self.assertTrue(
np.array_equal(
np.array([[0.0, 1.0, 1.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]],
dtype=np.float64), res))
def test_negative_weight(self):
input_matrix = np.array(
[[0, 1, 0], [-1, 0, -1], [0, 1, 0]], dtype=float
)
graph = retworkx.PyDiGraph.from_adjacency_matrix(input_matrix)
adj_matrix = retworkx.digraph_adjacency_matrix(graph, lambda x: x)
self.assertTrue(np.array_equal(adj_matrix, input_matrix))
self.assertEqual(
[(0, 1, 1), (1, 0, -1), (1, 2, -1), (2, 1, 1)],
graph.weighted_edge_list(),
)
def test_no_weight_fn(self):
dag = retworkx.PyDAG()
node_a = dag.add_node("a")
dag.add_child(node_a, "b", {"a": 1})
dag.add_child(node_a, "c", {"a": 2})
res = retworkx.digraph_adjacency_matrix(dag)
self.assertIsInstance(res, np.ndarray)
self.assertTrue(
np.array_equal(
np.array(
[[0.0, 1.0, 1.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]],
dtype=np.float64,
),
res,
)
)
def test_no_edge_digraph_adjacency_matrix(self):
dag = retworkx.PyDAG()
for i in range(50):
dag.add_node(i)
res = retworkx.digraph_adjacency_matrix(dag, lambda x: 1)
self.assertTrue(np.array_equal(np.zeros([50, 50]), res))
import tempfile
from typing import Sized
from PIL import Image
import pydot
import retworkx as rx
import igraph as ig
import numpy as np
G: rx.PyDiGraph = rx.directed_gnm_random_graph(1000, 2000, None)
components: list = rx.strongly_connected_components(G)
largest_component: Sized = max(components, key=len)
H: rx.PyGraph = G.subgraph(largest_component)
# have to fix pydot error
"""
dot = pydot.graph_from_dot_data(G.to_dot())[0]
with tempfile.TemporaryDirectory() as tmpdirname:
tmp_path = os.path.join(tmpdirname, 'dag.png')
dot.write_png(tmp_path)
image = Image.open(tmp_path)
os.remove(tmp_path)
image
"""
n1: np.ndarray = rx.digraph_adjacency_matrix(G, None)
g: ig.Graph = ig.Graph.Weighted_Adjacency(n1.tolist())
ig.plot(g)
def test_random_graph_float_dtype(self):
input_matrix = np.array([[0, 1, 0], [1, 0, 1], [0, 1, 0]], dtype=float)
graph = retworkx.PyDiGraph.from_adjacency_matrix(input_matrix)
adj_matrix = retworkx.digraph_adjacency_matrix(graph, lambda x: x)
self.assertTrue(np.array_equal(adj_matrix, input_matrix))
def test_random_graph_full_path(self):
graph = retworkx.directed_gnp_random_graph(100, 0.95, seed=42)
adjacency_matrix = retworkx.digraph_adjacency_matrix(graph)
new_graph = retworkx.PyDiGraph.from_adjacency_matrix(adjacency_matrix)
new_adjacency_matrix = retworkx.digraph_adjacency_matrix(new_graph)
self.assertTrue(np.array_equal(adjacency_matrix, new_adjacency_matrix))
