675 lines
25 KiB
Python
675 lines
25 KiB
Python
import pytest
|
|
|
|
np = pytest.importorskip("numpy")
|
|
npt = pytest.importorskip("numpy.testing")
|
|
|
|
import networkx as nx
|
|
from networkx.generators.classic import barbell_graph, cycle_graph, path_graph
|
|
from networkx.utils import graphs_equal
|
|
|
|
|
|
def test_to_numpy_matrix_deprecation():
|
|
pytest.deprecated_call(nx.to_numpy_matrix, nx.Graph())
|
|
|
|
|
|
def test_from_numpy_matrix_deprecation():
|
|
pytest.deprecated_call(nx.from_numpy_matrix, np.eye(2))
|
|
|
|
|
|
def test_to_numpy_recarray_deprecation():
|
|
pytest.deprecated_call(nx.to_numpy_recarray, nx.Graph())
|
|
|
|
|
|
class TestConvertNumpyMatrix:
|
|
# TODO: This entire class can be removed when to/from_numpy_matrix
|
|
# deprecation expires
|
|
def setup_method(self):
|
|
self.G1 = barbell_graph(10, 3)
|
|
self.G2 = cycle_graph(10, create_using=nx.DiGraph)
|
|
|
|
self.G3 = self.create_weighted(nx.Graph())
|
|
self.G4 = self.create_weighted(nx.DiGraph())
|
|
|
|
def test_exceptions(self):
|
|
G = np.array("a")
|
|
pytest.raises(nx.NetworkXError, nx.to_networkx_graph, G)
|
|
|
|
def create_weighted(self, G):
|
|
g = cycle_graph(4)
|
|
G.add_nodes_from(g)
|
|
G.add_weighted_edges_from((u, v, 10 + u) for u, v in g.edges())
|
|
return G
|
|
|
|
def assert_equal(self, G1, G2):
|
|
assert sorted(G1.nodes()) == sorted(G2.nodes())
|
|
assert sorted(G1.edges()) == sorted(G2.edges())
|
|
|
|
def identity_conversion(self, G, A, create_using):
|
|
assert A.sum() > 0
|
|
GG = nx.from_numpy_matrix(A, create_using=create_using)
|
|
self.assert_equal(G, GG)
|
|
GW = nx.to_networkx_graph(A, create_using=create_using)
|
|
self.assert_equal(G, GW)
|
|
GI = nx.empty_graph(0, create_using).__class__(A)
|
|
self.assert_equal(G, GI)
|
|
|
|
def test_shape(self):
|
|
"Conversion from non-square array."
|
|
A = np.array([[1, 2, 3], [4, 5, 6]])
|
|
pytest.raises(nx.NetworkXError, nx.from_numpy_matrix, A)
|
|
|
|
def test_identity_graph_matrix(self):
|
|
"Conversion from graph to matrix to graph."
|
|
A = nx.to_numpy_matrix(self.G1)
|
|
self.identity_conversion(self.G1, A, nx.Graph())
|
|
|
|
def test_identity_graph_array(self):
|
|
"Conversion from graph to array to graph."
|
|
A = nx.to_numpy_matrix(self.G1)
|
|
A = np.asarray(A)
|
|
self.identity_conversion(self.G1, A, nx.Graph())
|
|
|
|
def test_identity_digraph_matrix(self):
|
|
"""Conversion from digraph to matrix to digraph."""
|
|
A = nx.to_numpy_matrix(self.G2)
|
|
self.identity_conversion(self.G2, A, nx.DiGraph())
|
|
|
|
def test_identity_digraph_array(self):
|
|
"""Conversion from digraph to array to digraph."""
|
|
A = nx.to_numpy_matrix(self.G2)
|
|
A = np.asarray(A)
|
|
self.identity_conversion(self.G2, A, nx.DiGraph())
|
|
|
|
def test_identity_weighted_graph_matrix(self):
|
|
"""Conversion from weighted graph to matrix to weighted graph."""
|
|
A = nx.to_numpy_matrix(self.G3)
|
|
self.identity_conversion(self.G3, A, nx.Graph())
|
|
|
|
def test_identity_weighted_graph_array(self):
|
|
"""Conversion from weighted graph to array to weighted graph."""
|
|
A = nx.to_numpy_matrix(self.G3)
|
|
A = np.asarray(A)
|
|
self.identity_conversion(self.G3, A, nx.Graph())
|
|
|
|
def test_identity_weighted_digraph_matrix(self):
|
|
"""Conversion from weighted digraph to matrix to weighted digraph."""
|
|
A = nx.to_numpy_matrix(self.G4)
|
|
self.identity_conversion(self.G4, A, nx.DiGraph())
|
|
|
|
def test_identity_weighted_digraph_array(self):
|
|
"""Conversion from weighted digraph to array to weighted digraph."""
|
|
A = nx.to_numpy_matrix(self.G4)
|
|
A = np.asarray(A)
|
|
self.identity_conversion(self.G4, A, nx.DiGraph())
|
|
|
|
def test_nodelist(self):
|
|
"""Conversion from graph to matrix to graph with nodelist."""
|
|
P4 = path_graph(4)
|
|
P3 = path_graph(3)
|
|
nodelist = list(P3)
|
|
A = nx.to_numpy_matrix(P4, nodelist=nodelist)
|
|
GA = nx.Graph(A)
|
|
self.assert_equal(GA, P3)
|
|
|
|
assert nx.to_numpy_matrix(P3, nodelist=[]).shape == (0, 0)
|
|
# Test nodelist duplicates.
|
|
long_nodelist = nodelist + [0]
|
|
pytest.raises(nx.NetworkXError, nx.to_numpy_matrix, P3, nodelist=long_nodelist)
|
|
|
|
# Test nodelist contains non-nodes
|
|
nonnodelist = [-1, 0, 1, 2]
|
|
pytest.raises(nx.NetworkXError, nx.to_numpy_matrix, P3, nodelist=nonnodelist)
|
|
|
|
def test_weight_keyword(self):
|
|
WP4 = nx.Graph()
|
|
WP4.add_edges_from((n, n + 1, dict(weight=0.5, other=0.3)) for n in range(3))
|
|
P4 = path_graph(4)
|
|
A = nx.to_numpy_matrix(P4)
|
|
np.testing.assert_equal(A, nx.to_numpy_matrix(WP4, weight=None))
|
|
np.testing.assert_equal(0.5 * A, nx.to_numpy_matrix(WP4))
|
|
np.testing.assert_equal(0.3 * A, nx.to_numpy_matrix(WP4, weight="other"))
|
|
|
|
def test_from_numpy_matrix_type(self):
|
|
pytest.importorskip("scipy")
|
|
|
|
A = np.matrix([[1]])
|
|
G = nx.from_numpy_matrix(A)
|
|
assert type(G[0][0]["weight"]) == int
|
|
|
|
A = np.matrix([[1]]).astype(float)
|
|
G = nx.from_numpy_matrix(A)
|
|
assert type(G[0][0]["weight"]) == float
|
|
|
|
A = np.matrix([[1]]).astype(str)
|
|
G = nx.from_numpy_matrix(A)
|
|
assert type(G[0][0]["weight"]) == str
|
|
|
|
A = np.matrix([[1]]).astype(bool)
|
|
G = nx.from_numpy_matrix(A)
|
|
assert type(G[0][0]["weight"]) == bool
|
|
|
|
A = np.matrix([[1]]).astype(complex)
|
|
G = nx.from_numpy_matrix(A)
|
|
assert type(G[0][0]["weight"]) == complex
|
|
|
|
A = np.matrix([[1]]).astype(object)
|
|
pytest.raises(TypeError, nx.from_numpy_matrix, A)
|
|
|
|
G = nx.cycle_graph(3)
|
|
A = nx.adjacency_matrix(G).todense()
|
|
H = nx.from_numpy_matrix(A)
|
|
assert all(type(m) == int and type(n) == int for m, n in H.edges())
|
|
H = nx.from_numpy_array(A)
|
|
assert all(type(m) == int and type(n) == int for m, n in H.edges())
|
|
|
|
def test_from_numpy_matrix_dtype(self):
|
|
dt = [("weight", float), ("cost", int)]
|
|
A = np.matrix([[(1.0, 2)]], dtype=dt)
|
|
G = nx.from_numpy_matrix(A)
|
|
assert type(G[0][0]["weight"]) == float
|
|
assert type(G[0][0]["cost"]) == int
|
|
assert G[0][0]["cost"] == 2
|
|
assert G[0][0]["weight"] == 1.0
|
|
|
|
def test_to_numpy_recarray(self):
|
|
G = nx.Graph()
|
|
G.add_edge(1, 2, weight=7.0, cost=5)
|
|
A = nx.to_numpy_recarray(G, dtype=[("weight", float), ("cost", int)])
|
|
assert sorted(A.dtype.names) == ["cost", "weight"]
|
|
assert A.weight[0, 1] == 7.0
|
|
assert A.weight[0, 0] == 0.0
|
|
assert A.cost[0, 1] == 5
|
|
assert A.cost[0, 0] == 0
|
|
|
|
def test_numpy_multigraph(self):
|
|
G = nx.MultiGraph()
|
|
G.add_edge(1, 2, weight=7)
|
|
G.add_edge(1, 2, weight=70)
|
|
A = nx.to_numpy_matrix(G)
|
|
assert A[1, 0] == 77
|
|
A = nx.to_numpy_matrix(G, multigraph_weight=min)
|
|
assert A[1, 0] == 7
|
|
A = nx.to_numpy_matrix(G, multigraph_weight=max)
|
|
assert A[1, 0] == 70
|
|
|
|
def test_from_numpy_matrix_parallel_edges(self):
|
|
"""Tests that the :func:`networkx.from_numpy_matrix` function
|
|
interprets integer weights as the number of parallel edges when
|
|
creating a multigraph.
|
|
|
|
"""
|
|
A = np.matrix([[1, 1], [1, 2]])
|
|
# First, with a simple graph, each integer entry in the adjacency
|
|
# matrix is interpreted as the weight of a single edge in the graph.
|
|
expected = nx.DiGraph()
|
|
edges = [(0, 0), (0, 1), (1, 0)]
|
|
expected.add_weighted_edges_from([(u, v, 1) for (u, v) in edges])
|
|
expected.add_edge(1, 1, weight=2)
|
|
actual = nx.from_numpy_matrix(A, parallel_edges=True, create_using=nx.DiGraph)
|
|
assert graphs_equal(actual, expected)
|
|
actual = nx.from_numpy_matrix(A, parallel_edges=False, create_using=nx.DiGraph)
|
|
assert graphs_equal(actual, expected)
|
|
# Now each integer entry in the adjacency matrix is interpreted as the
|
|
# number of parallel edges in the graph if the appropriate keyword
|
|
# argument is specified.
|
|
edges = [(0, 0), (0, 1), (1, 0), (1, 1), (1, 1)]
|
|
expected = nx.MultiDiGraph()
|
|
expected.add_weighted_edges_from([(u, v, 1) for (u, v) in edges])
|
|
actual = nx.from_numpy_matrix(
|
|
A, parallel_edges=True, create_using=nx.MultiDiGraph
|
|
)
|
|
assert graphs_equal(actual, expected)
|
|
expected = nx.MultiDiGraph()
|
|
expected.add_edges_from(set(edges), weight=1)
|
|
# The sole self-loop (edge 0) on vertex 1 should have weight 2.
|
|
expected[1][1][0]["weight"] = 2
|
|
actual = nx.from_numpy_matrix(
|
|
A, parallel_edges=False, create_using=nx.MultiDiGraph
|
|
)
|
|
assert graphs_equal(actual, expected)
|
|
|
|
def test_symmetric(self):
|
|
"""Tests that a symmetric matrix has edges added only once to an
|
|
undirected multigraph when using :func:`networkx.from_numpy_matrix`.
|
|
|
|
"""
|
|
A = np.matrix([[0, 1], [1, 0]])
|
|
G = nx.from_numpy_matrix(A, create_using=nx.MultiGraph)
|
|
expected = nx.MultiGraph()
|
|
expected.add_edge(0, 1, weight=1)
|
|
assert graphs_equal(G, expected)
|
|
|
|
def test_dtype_int_graph(self):
|
|
"""Test that setting dtype int actually gives an integer matrix.
|
|
|
|
For more information, see GitHub pull request #1363.
|
|
|
|
"""
|
|
G = nx.complete_graph(3)
|
|
A = nx.to_numpy_matrix(G, dtype=int)
|
|
assert A.dtype == int
|
|
|
|
def test_dtype_int_multigraph(self):
|
|
"""Test that setting dtype int actually gives an integer matrix.
|
|
|
|
For more information, see GitHub pull request #1363.
|
|
|
|
"""
|
|
G = nx.MultiGraph(nx.complete_graph(3))
|
|
A = nx.to_numpy_matrix(G, dtype=int)
|
|
assert A.dtype == int
|
|
|
|
|
|
class TestConvertNumpyArray:
|
|
def setup_method(self):
|
|
self.G1 = barbell_graph(10, 3)
|
|
self.G2 = cycle_graph(10, create_using=nx.DiGraph)
|
|
self.G3 = self.create_weighted(nx.Graph())
|
|
self.G4 = self.create_weighted(nx.DiGraph())
|
|
|
|
def create_weighted(self, G):
|
|
g = cycle_graph(4)
|
|
G.add_nodes_from(g)
|
|
G.add_weighted_edges_from((u, v, 10 + u) for u, v in g.edges())
|
|
return G
|
|
|
|
def assert_equal(self, G1, G2):
|
|
assert sorted(G1.nodes()) == sorted(G2.nodes())
|
|
assert sorted(G1.edges()) == sorted(G2.edges())
|
|
|
|
def identity_conversion(self, G, A, create_using):
|
|
assert A.sum() > 0
|
|
GG = nx.from_numpy_array(A, create_using=create_using)
|
|
self.assert_equal(G, GG)
|
|
GW = nx.to_networkx_graph(A, create_using=create_using)
|
|
self.assert_equal(G, GW)
|
|
GI = nx.empty_graph(0, create_using).__class__(A)
|
|
self.assert_equal(G, GI)
|
|
|
|
def test_shape(self):
|
|
"Conversion from non-square array."
|
|
A = np.array([[1, 2, 3], [4, 5, 6]])
|
|
pytest.raises(nx.NetworkXError, nx.from_numpy_array, A)
|
|
|
|
def test_identity_graph_array(self):
|
|
"Conversion from graph to array to graph."
|
|
A = nx.to_numpy_array(self.G1)
|
|
self.identity_conversion(self.G1, A, nx.Graph())
|
|
|
|
def test_identity_digraph_array(self):
|
|
"""Conversion from digraph to array to digraph."""
|
|
A = nx.to_numpy_array(self.G2)
|
|
self.identity_conversion(self.G2, A, nx.DiGraph())
|
|
|
|
def test_identity_weighted_graph_array(self):
|
|
"""Conversion from weighted graph to array to weighted graph."""
|
|
A = nx.to_numpy_array(self.G3)
|
|
self.identity_conversion(self.G3, A, nx.Graph())
|
|
|
|
def test_identity_weighted_digraph_array(self):
|
|
"""Conversion from weighted digraph to array to weighted digraph."""
|
|
A = nx.to_numpy_array(self.G4)
|
|
self.identity_conversion(self.G4, A, nx.DiGraph())
|
|
|
|
def test_nodelist(self):
|
|
"""Conversion from graph to array to graph with nodelist."""
|
|
P4 = path_graph(4)
|
|
P3 = path_graph(3)
|
|
nodelist = list(P3)
|
|
A = nx.to_numpy_array(P4, nodelist=nodelist)
|
|
GA = nx.Graph(A)
|
|
self.assert_equal(GA, P3)
|
|
|
|
# Make nodelist ambiguous by containing duplicates.
|
|
nodelist += [nodelist[0]]
|
|
pytest.raises(nx.NetworkXError, nx.to_numpy_array, P3, nodelist=nodelist)
|
|
|
|
def test_weight_keyword(self):
|
|
WP4 = nx.Graph()
|
|
WP4.add_edges_from((n, n + 1, dict(weight=0.5, other=0.3)) for n in range(3))
|
|
P4 = path_graph(4)
|
|
A = nx.to_numpy_array(P4)
|
|
np.testing.assert_equal(A, nx.to_numpy_array(WP4, weight=None))
|
|
np.testing.assert_equal(0.5 * A, nx.to_numpy_array(WP4))
|
|
np.testing.assert_equal(0.3 * A, nx.to_numpy_array(WP4, weight="other"))
|
|
|
|
def test_from_numpy_array_type(self):
|
|
A = np.array([[1]])
|
|
G = nx.from_numpy_array(A)
|
|
assert type(G[0][0]["weight"]) == int
|
|
|
|
A = np.array([[1]]).astype(float)
|
|
G = nx.from_numpy_array(A)
|
|
assert type(G[0][0]["weight"]) == float
|
|
|
|
A = np.array([[1]]).astype(str)
|
|
G = nx.from_numpy_array(A)
|
|
assert type(G[0][0]["weight"]) == str
|
|
|
|
A = np.array([[1]]).astype(bool)
|
|
G = nx.from_numpy_array(A)
|
|
assert type(G[0][0]["weight"]) == bool
|
|
|
|
A = np.array([[1]]).astype(complex)
|
|
G = nx.from_numpy_array(A)
|
|
assert type(G[0][0]["weight"]) == complex
|
|
|
|
A = np.array([[1]]).astype(object)
|
|
pytest.raises(TypeError, nx.from_numpy_array, A)
|
|
|
|
def test_from_numpy_array_dtype(self):
|
|
dt = [("weight", float), ("cost", int)]
|
|
A = np.array([[(1.0, 2)]], dtype=dt)
|
|
G = nx.from_numpy_array(A)
|
|
assert type(G[0][0]["weight"]) == float
|
|
assert type(G[0][0]["cost"]) == int
|
|
assert G[0][0]["cost"] == 2
|
|
assert G[0][0]["weight"] == 1.0
|
|
|
|
def test_from_numpy_array_parallel_edges(self):
|
|
"""Tests that the :func:`networkx.from_numpy_array` function
|
|
interprets integer weights as the number of parallel edges when
|
|
creating a multigraph.
|
|
|
|
"""
|
|
A = np.array([[1, 1], [1, 2]])
|
|
# First, with a simple graph, each integer entry in the adjacency
|
|
# matrix is interpreted as the weight of a single edge in the graph.
|
|
expected = nx.DiGraph()
|
|
edges = [(0, 0), (0, 1), (1, 0)]
|
|
expected.add_weighted_edges_from([(u, v, 1) for (u, v) in edges])
|
|
expected.add_edge(1, 1, weight=2)
|
|
actual = nx.from_numpy_array(A, parallel_edges=True, create_using=nx.DiGraph)
|
|
assert graphs_equal(actual, expected)
|
|
actual = nx.from_numpy_array(A, parallel_edges=False, create_using=nx.DiGraph)
|
|
assert graphs_equal(actual, expected)
|
|
# Now each integer entry in the adjacency matrix is interpreted as the
|
|
# number of parallel edges in the graph if the appropriate keyword
|
|
# argument is specified.
|
|
edges = [(0, 0), (0, 1), (1, 0), (1, 1), (1, 1)]
|
|
expected = nx.MultiDiGraph()
|
|
expected.add_weighted_edges_from([(u, v, 1) for (u, v) in edges])
|
|
actual = nx.from_numpy_array(
|
|
A, parallel_edges=True, create_using=nx.MultiDiGraph
|
|
)
|
|
assert graphs_equal(actual, expected)
|
|
expected = nx.MultiDiGraph()
|
|
expected.add_edges_from(set(edges), weight=1)
|
|
# The sole self-loop (edge 0) on vertex 1 should have weight 2.
|
|
expected[1][1][0]["weight"] = 2
|
|
actual = nx.from_numpy_array(
|
|
A, parallel_edges=False, create_using=nx.MultiDiGraph
|
|
)
|
|
assert graphs_equal(actual, expected)
|
|
|
|
def test_symmetric(self):
|
|
"""Tests that a symmetric array has edges added only once to an
|
|
undirected multigraph when using :func:`networkx.from_numpy_array`.
|
|
|
|
"""
|
|
A = np.array([[0, 1], [1, 0]])
|
|
G = nx.from_numpy_array(A, create_using=nx.MultiGraph)
|
|
expected = nx.MultiGraph()
|
|
expected.add_edge(0, 1, weight=1)
|
|
assert graphs_equal(G, expected)
|
|
|
|
def test_dtype_int_graph(self):
|
|
"""Test that setting dtype int actually gives an integer array.
|
|
|
|
For more information, see GitHub pull request #1363.
|
|
|
|
"""
|
|
G = nx.complete_graph(3)
|
|
A = nx.to_numpy_array(G, dtype=int)
|
|
assert A.dtype == int
|
|
|
|
def test_dtype_int_multigraph(self):
|
|
"""Test that setting dtype int actually gives an integer array.
|
|
|
|
For more information, see GitHub pull request #1363.
|
|
|
|
"""
|
|
G = nx.MultiGraph(nx.complete_graph(3))
|
|
A = nx.to_numpy_array(G, dtype=int)
|
|
assert A.dtype == int
|
|
|
|
|
|
@pytest.fixture
|
|
def recarray_test_graph():
|
|
G = nx.Graph()
|
|
G.add_edge(1, 2, weight=7.0, cost=5)
|
|
return G
|
|
|
|
|
|
def test_to_numpy_recarray(recarray_test_graph):
|
|
A = nx.to_numpy_recarray(
|
|
recarray_test_graph, dtype=[("weight", float), ("cost", int)]
|
|
)
|
|
assert sorted(A.dtype.names) == ["cost", "weight"]
|
|
assert A.weight[0, 1] == 7.0
|
|
assert A.weight[0, 0] == 0.0
|
|
assert A.cost[0, 1] == 5
|
|
assert A.cost[0, 0] == 0
|
|
with pytest.raises(AttributeError, match="has no attribute"):
|
|
A.color[0, 1]
|
|
|
|
|
|
def test_to_numpy_recarray_default_dtype(recarray_test_graph):
|
|
A = nx.to_numpy_recarray(recarray_test_graph)
|
|
assert A.dtype.names == ("weight",)
|
|
assert A.weight[0, 0] == 0
|
|
assert A.weight[0, 1] == 7
|
|
with pytest.raises(AttributeError, match="has no attribute"):
|
|
A.cost[0, 1]
|
|
|
|
|
|
def test_to_numpy_recarray_directed(recarray_test_graph):
|
|
G = recarray_test_graph.to_directed()
|
|
G.remove_edge(2, 1)
|
|
A = nx.to_numpy_recarray(G, dtype=[("weight", float), ("cost", int)])
|
|
np.testing.assert_array_equal(A.weight, np.array([[0, 7.0], [0, 0]]))
|
|
np.testing.assert_array_equal(A.cost, np.array([[0, 5], [0, 0]]))
|
|
|
|
|
|
def test_to_numpy_recarray_default_dtype_no_weight():
|
|
G = nx.Graph()
|
|
G.add_edge(0, 1, color="red")
|
|
with pytest.raises(KeyError):
|
|
A = nx.to_numpy_recarray(G)
|
|
A = nx.to_numpy_recarray(G, dtype=[("color", "U8")])
|
|
assert A.color[0, 1] == "red"
|
|
|
|
|
|
@pytest.fixture
|
|
def recarray_nodelist_test_graph():
|
|
G = nx.Graph()
|
|
G.add_edges_from(
|
|
[(0, 1, {"weight": 1.0}), (0, 2, {"weight": 2.0}), (1, 2, {"weight": 0.5})]
|
|
)
|
|
return G
|
|
|
|
|
|
def test_to_numpy_recarray_nodelist(recarray_nodelist_test_graph):
|
|
A = nx.to_numpy_recarray(recarray_nodelist_test_graph, nodelist=[0, 1])
|
|
np.testing.assert_array_equal(A.weight, np.array([[0, 1], [1, 0]]))
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
("nodelist", "errmsg"),
|
|
(([2, 3], "in nodelist is not in G"), ([1, 1], "nodelist contains duplicates")),
|
|
)
|
|
def test_to_numpy_recarray_bad_nodelist(recarray_nodelist_test_graph, nodelist, errmsg):
|
|
with pytest.raises(nx.NetworkXError, match=errmsg):
|
|
A = nx.to_numpy_recarray(recarray_nodelist_test_graph, nodelist=nodelist)
|
|
|
|
|
|
@pytest.fixture
|
|
def multigraph_test_graph():
|
|
G = nx.MultiGraph()
|
|
G.add_edge(1, 2, weight=7)
|
|
G.add_edge(1, 2, weight=70)
|
|
return G
|
|
|
|
|
|
@pytest.mark.parametrize(("operator", "expected"), ((sum, 77), (min, 7), (max, 70)))
|
|
def test_numpy_multigraph(multigraph_test_graph, operator, expected):
|
|
A = nx.to_numpy_array(multigraph_test_graph, multigraph_weight=operator)
|
|
assert A[1, 0] == expected
|
|
|
|
|
|
def test_to_numpy_array_multigraph_nodelist(multigraph_test_graph):
|
|
G = multigraph_test_graph
|
|
G.add_edge(0, 1, weight=3)
|
|
A = nx.to_numpy_array(G, nodelist=[1, 2])
|
|
assert A.shape == (2, 2)
|
|
assert A[1, 0] == 77
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"G, expected",
|
|
[
|
|
(nx.Graph(), np.array([[0, 1 + 2j], [1 + 2j, 0]], dtype=complex)),
|
|
(nx.DiGraph(), np.array([[0, 1 + 2j], [0, 0]], dtype=complex)),
|
|
],
|
|
)
|
|
def test_to_numpy_array_complex_weights(G, expected):
|
|
G.add_edge(0, 1, weight=1 + 2j)
|
|
A = nx.to_numpy_array(G, dtype=complex)
|
|
npt.assert_array_equal(A, expected)
|
|
|
|
|
|
def test_to_numpy_array_arbitrary_weights():
|
|
G = nx.DiGraph()
|
|
w = 922337203685477580102 # Out of range for int64
|
|
G.add_edge(0, 1, weight=922337203685477580102) # val not representable by int64
|
|
A = nx.to_numpy_array(G, dtype=object)
|
|
expected = np.array([[0, w], [0, 0]], dtype=object)
|
|
npt.assert_array_equal(A, expected)
|
|
|
|
# Undirected
|
|
A = nx.to_numpy_array(G.to_undirected(), dtype=object)
|
|
expected = np.array([[0, w], [w, 0]], dtype=object)
|
|
npt.assert_array_equal(A, expected)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"func, expected",
|
|
((min, -1), (max, 10), (sum, 11), (np.mean, 11 / 3), (np.median, 2)),
|
|
)
|
|
def test_to_numpy_array_multiweight_reduction(func, expected):
|
|
"""Test various functions for reducing multiedge weights."""
|
|
G = nx.MultiDiGraph()
|
|
weights = [-1, 2, 10.0]
|
|
for w in weights:
|
|
G.add_edge(0, 1, weight=w)
|
|
A = nx.to_numpy_array(G, multigraph_weight=func, dtype=float)
|
|
assert np.allclose(A, [[0, expected], [0, 0]])
|
|
|
|
# Undirected case
|
|
A = nx.to_numpy_array(G.to_undirected(), multigraph_weight=func, dtype=float)
|
|
assert np.allclose(A, [[0, expected], [expected, 0]])
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
("G, expected"),
|
|
[
|
|
(nx.Graph(), [[(0, 0), (10, 5)], [(10, 5), (0, 0)]]),
|
|
(nx.DiGraph(), [[(0, 0), (10, 5)], [(0, 0), (0, 0)]]),
|
|
],
|
|
)
|
|
def test_to_numpy_array_structured_dtype_attrs_from_fields(G, expected):
|
|
"""When `dtype` is structured (i.e. has names) and `weight` is None, use
|
|
the named fields of the dtype to look up edge attributes."""
|
|
G.add_edge(0, 1, weight=10, cost=5.0)
|
|
dtype = np.dtype([("weight", int), ("cost", int)])
|
|
A = nx.to_numpy_array(G, dtype=dtype, weight=None)
|
|
expected = np.asarray(expected, dtype=dtype)
|
|
npt.assert_array_equal(A, expected)
|
|
|
|
|
|
def test_to_numpy_array_structured_dtype_single_attr_default():
|
|
G = nx.path_graph(3)
|
|
dtype = np.dtype([("weight", float)]) # A single named field
|
|
A = nx.to_numpy_array(G, dtype=dtype, weight=None)
|
|
expected = np.array([[0, 1, 0], [1, 0, 1], [0, 1, 0]], dtype=float)
|
|
npt.assert_array_equal(A["weight"], expected)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
("field_name", "expected_attr_val"),
|
|
[
|
|
("weight", 1),
|
|
("cost", 3),
|
|
],
|
|
)
|
|
def test_to_numpy_array_structured_dtype_single_attr(field_name, expected_attr_val):
|
|
G = nx.Graph()
|
|
G.add_edge(0, 1, cost=3)
|
|
dtype = np.dtype([(field_name, float)])
|
|
A = nx.to_numpy_array(G, dtype=dtype, weight=None)
|
|
expected = np.array([[0, expected_attr_val], [expected_attr_val, 0]], dtype=float)
|
|
npt.assert_array_equal(A[field_name], expected)
|
|
|
|
|
|
@pytest.mark.parametrize("graph_type", (nx.Graph, nx.DiGraph))
|
|
@pytest.mark.parametrize(
|
|
"edge",
|
|
[
|
|
(0, 1), # No edge attributes
|
|
(0, 1, {"weight": 10}), # One edge attr
|
|
(0, 1, {"weight": 5, "flow": -4}), # Multiple but not all edge attrs
|
|
(0, 1, {"weight": 2.0, "cost": 10, "flow": -45}), # All attrs
|
|
],
|
|
)
|
|
def test_to_numpy_array_structured_dtype_multiple_fields(graph_type, edge):
|
|
G = graph_type([edge])
|
|
dtype = np.dtype([("weight", float), ("cost", float), ("flow", float)])
|
|
A = nx.to_numpy_array(G, dtype=dtype, weight=None)
|
|
for attr in dtype.names:
|
|
expected = nx.to_numpy_array(G, dtype=float, weight=attr)
|
|
npt.assert_array_equal(A[attr], expected)
|
|
|
|
|
|
@pytest.mark.parametrize("G", (nx.Graph(), nx.DiGraph()))
|
|
def test_to_numpy_array_structured_dtype_scalar_nonedge(G):
|
|
G.add_edge(0, 1, weight=10)
|
|
dtype = np.dtype([("weight", float), ("cost", float)])
|
|
A = nx.to_numpy_array(G, dtype=dtype, weight=None, nonedge=np.nan)
|
|
for attr in dtype.names:
|
|
expected = nx.to_numpy_array(G, dtype=float, weight=attr, nonedge=np.nan)
|
|
npt.assert_array_equal(A[attr], expected)
|
|
|
|
|
|
@pytest.mark.parametrize("G", (nx.Graph(), nx.DiGraph()))
|
|
def test_to_numpy_array_structured_dtype_nonedge_ary(G):
|
|
"""Similar to the scalar case, except has a different non-edge value for
|
|
each named field."""
|
|
G.add_edge(0, 1, weight=10)
|
|
dtype = np.dtype([("weight", float), ("cost", float)])
|
|
nonedges = np.array([(0, np.inf)], dtype=dtype)
|
|
A = nx.to_numpy_array(G, dtype=dtype, weight=None, nonedge=nonedges)
|
|
for attr in dtype.names:
|
|
nonedge = nonedges[attr]
|
|
expected = nx.to_numpy_array(G, dtype=float, weight=attr, nonedge=nonedge)
|
|
npt.assert_array_equal(A[attr], expected)
|
|
|
|
|
|
def test_to_numpy_array_structured_dtype_with_weight_raises():
|
|
"""Using both a structured dtype (with named fields) and specifying a `weight`
|
|
parameter is ambiguous."""
|
|
G = nx.path_graph(3)
|
|
dtype = np.dtype([("weight", int), ("cost", int)])
|
|
exception_msg = "Specifying `weight` not supported for structured dtypes"
|
|
with pytest.raises(ValueError, match=exception_msg):
|
|
nx.to_numpy_array(G, dtype=dtype) # Default is weight="weight"
|
|
with pytest.raises(ValueError, match=exception_msg):
|
|
nx.to_numpy_array(G, dtype=dtype, weight="cost")
|
|
|
|
|
|
@pytest.mark.parametrize("graph_type", (nx.MultiGraph, nx.MultiDiGraph))
|
|
def test_to_numpy_array_structured_multigraph_raises(graph_type):
|
|
G = nx.path_graph(3, create_using=graph_type)
|
|
dtype = np.dtype([("weight", int), ("cost", int)])
|
|
with pytest.raises(nx.NetworkXError, match="Structured arrays are not supported"):
|
|
nx.to_numpy_array(G, dtype=dtype, weight=None)
|