Vehicle-Anti-Theft-Face-Rec.../venv/Lib/site-packages/networkx/linalg/tests/test_attrmatrix.py

108 lines
2.7 KiB
Python

import pytest
np = pytest.importorskip("numpy")
import numpy.testing as npt
import networkx as nx
def test_attr_matrix():
G = nx.Graph()
G.add_edge(0, 1, thickness=1, weight=3)
G.add_edge(0, 1, thickness=1, weight=3)
G.add_edge(0, 2, thickness=2)
G.add_edge(1, 2, thickness=3)
def node_attr(u):
return G.nodes[u].get("size", 0.5) * 3
def edge_attr(u, v):
return G[u][v].get("thickness", 0.5)
M = nx.attr_matrix(G, edge_attr=edge_attr, node_attr=node_attr)
npt.assert_equal(M[0], np.array([[6.0]]))
assert M[1] == [1.5]
def test_attr_matrix_directed():
G = nx.DiGraph()
G.add_edge(0, 1, thickness=1, weight=3)
G.add_edge(0, 1, thickness=1, weight=3)
G.add_edge(0, 2, thickness=2)
G.add_edge(1, 2, thickness=3)
M = nx.attr_matrix(G, rc_order=[0, 1, 2])
# fmt: off
data = np.array(
[[0., 1., 1.],
[0., 0., 1.],
[0., 0., 0.]]
)
# fmt: on
npt.assert_equal(M, np.array(data))
def test_attr_matrix_multigraph():
G = nx.MultiGraph()
G.add_edge(0, 1, thickness=1, weight=3)
G.add_edge(0, 1, thickness=1, weight=3)
G.add_edge(0, 1, thickness=1, weight=3)
G.add_edge(0, 2, thickness=2)
G.add_edge(1, 2, thickness=3)
M = nx.attr_matrix(G, rc_order=[0, 1, 2])
# fmt: off
data = np.array(
[[0., 3., 1.],
[3., 0., 1.],
[1., 1., 0.]]
)
# fmt: on
npt.assert_equal(M, np.array(data))
M = nx.attr_matrix(G, edge_attr="weight", rc_order=[0, 1, 2])
# fmt: off
data = np.array(
[[0., 9., 1.],
[9., 0., 1.],
[1., 1., 0.]]
)
# fmt: on
npt.assert_equal(M, np.array(data))
M = nx.attr_matrix(G, edge_attr="thickness", rc_order=[0, 1, 2])
# fmt: off
data = np.array(
[[0., 3., 2.],
[3., 0., 3.],
[2., 3., 0.]]
)
# fmt: on
npt.assert_equal(M, np.array(data))
def test_attr_sparse_matrix():
pytest.importorskip("scipy")
G = nx.Graph()
G.add_edge(0, 1, thickness=1, weight=3)
G.add_edge(0, 2, thickness=2)
G.add_edge(1, 2, thickness=3)
M = nx.attr_sparse_matrix(G)
mtx = M[0]
data = np.ones((3, 3), float)
np.fill_diagonal(data, 0)
npt.assert_equal(mtx.todense(), np.array(data))
assert M[1] == [0, 1, 2]
def test_attr_sparse_matrix_directed():
G = nx.DiGraph()
G.add_edge(0, 1, thickness=1, weight=3)
G.add_edge(0, 1, thickness=1, weight=3)
G.add_edge(0, 2, thickness=2)
G.add_edge(1, 2, thickness=3)
M = nx.attr_sparse_matrix(G, rc_order=[0, 1, 2])
# fmt: off
data = np.array(
[[0., 1., 1.],
[0., 0., 1.],
[0., 0., 0.]]
)
# fmt: on
npt.assert_equal(M.todense(), np.array(data))