def test_create_using(self):
cs='ddiiddid'
G=nxt.threshold_graph(cs)
assert_raises(nx.exception.NetworkXError,
nxt.threshold_graph, cs, create_using=nx.DiGraph())
MG=nxt.threshold_graph(cs,create_using=nx.MultiGraph())
assert_equal(sorted(MG.edges()), sorted(G.edges()))
def test_create_using(self):
cs = 'ddiiddid'
G = nxt.threshold_graph(cs)
assert_raises(nx.exception.NetworkXError,
nxt.threshold_graph, cs, create_using=nx.DiGraph())
MG = nxt.threshold_graph(cs, create_using=nx.MultiGraph())
assert_equal(sorted(MG.edges()), sorted(G.edges()))
def test_create_using(self):
cs = "ddiiddid"
G = nxt.threshold_graph(cs)
assert pytest.raises(
nx.exception.NetworkXError,
nxt.threshold_graph,
cs,
create_using=nx.DiGraph(),
)
MG = nxt.threshold_graph(cs, create_using=nx.MultiGraph())
assert sorted(MG.edges()) == sorted(G.edges())
def test_fast_versions_properties_threshold_graphs(self):
cs='ddiiddid'
G=nxt.threshold_graph(cs)
assert_equal(nxt.density('ddiiddid'), nx.density(G))
assert_equal(sorted(nxt.degree_sequence(cs)),
sorted(d for n, d in G.degree()))
ts=nxt.triangle_sequence(cs)
assert_equal(ts, list(nx.triangles(G).values()))
assert_equal(sum(ts) // 3, nxt.triangles(cs))
c1=nxt.cluster_sequence(cs)
c2=list(nx.clustering(G).values())
assert_almost_equal(sum([abs(c-d) for c,d in zip(c1,c2)]), 0)
b1=nx.betweenness_centrality(G).values()
b2=nxt.betweenness_sequence(cs)
assert_true(sum([abs(c-d) for c,d in zip(b1,b2)]) < 1e-14)
assert_equal(nxt.eigenvalues(cs), [0, 1, 3, 3, 5, 7, 7, 8])
# Degree Correlation
assert_true(abs(nxt.degree_correlation(cs)+0.593038821954) < 1e-12)
assert_equal(nxt.degree_correlation('diiiddi'), -0.8)
assert_equal(nxt.degree_correlation('did'), -1.0)
assert_equal(nxt.degree_correlation('ddd'), 1.0)
assert_equal(nxt.eigenvalues('dddiii'), [0, 0, 0, 0, 3, 3])
assert_equal(nxt.eigenvalues('dddiiid'), [0, 1, 1, 1, 4, 4, 7])
def test_fast_versions_properties_threshold_graphs(self):
cs = "ddiiddid"
G = nxt.threshold_graph(cs)
assert nxt.density("ddiiddid") == nx.density(G)
assert sorted(nxt.degree_sequence(cs)) == sorted(
d for n, d in G.degree())
ts = nxt.triangle_sequence(cs)
assert ts == list(nx.triangles(G).values())
assert sum(ts) // 3 == nxt.triangles(cs)
c1 = nxt.cluster_sequence(cs)
c2 = list(nx.clustering(G).values())
assert almost_equal(sum([abs(c - d) for c, d in zip(c1, c2)]), 0)
b1 = nx.betweenness_centrality(G).values()
b2 = nxt.betweenness_sequence(cs)
assert sum([abs(c - d) for c, d in zip(b1, b2)]) < 1e-14
assert nxt.eigenvalues(cs) == [0, 1, 3, 3, 5, 7, 7, 8]
# Degree Correlation
assert abs(nxt.degree_correlation(cs) + 0.593038821954) < 1e-12
assert nxt.degree_correlation("diiiddi") == -0.8
assert nxt.degree_correlation("did") == -1.0
assert nxt.degree_correlation("ddd") == 1.0
assert nxt.eigenvalues("dddiii") == [0, 0, 0, 0, 3, 3]
assert nxt.eigenvalues("dddiiid") == [0, 1, 1, 1, 4, 4, 7]
def test_fast_versions_properties_threshold_graphs(self):
cs = 'ddiiddid'
G = nxt.threshold_graph(cs)
assert_equal(nxt.density('ddiiddid'), nx.density(G))
assert_equal(sorted(nxt.degree_sequence(cs)),
sorted(d for n, d in G.degree()))
ts = nxt.triangle_sequence(cs)
assert_equal(ts, list(nx.triangles(G).values()))
assert_equal(sum(ts) // 3, nxt.triangles(cs))
c1 = nxt.cluster_sequence(cs)
c2 = list(nx.clustering(G).values())
assert_almost_equal(sum([abs(c - d) for c, d in zip(c1, c2)]), 0)
b1 = nx.betweenness_centrality(G).values()
b2 = nxt.betweenness_sequence(cs)
assert_true(sum([abs(c - d) for c, d in zip(b1, b2)]) < 1e-14)
assert_equal(nxt.eigenvalues(cs), [0, 1, 3, 3, 5, 7, 7, 8])
# Degree Correlation
assert_true(abs(nxt.degree_correlation(cs) + 0.593038821954) < 1e-12)
assert_equal(nxt.degree_correlation('diiiddi'), -0.8)
assert_equal(nxt.degree_correlation('did'), -1.0)
assert_equal(nxt.degree_correlation('ddd'), 1.0)
assert_equal(nxt.eigenvalues('dddiii'), [0, 0, 0, 0, 3, 3])
assert_equal(nxt.eigenvalues('dddiiid'), [0, 1, 1, 1, 4, 4, 7])
def test_creation_sequences(self):
deg=[3,2,2,1]
G=nx.generators.havel_hakimi_graph(deg)
cs0=nxt.creation_sequence(deg)
H0=nxt.threshold_graph(cs0)
assert_equal(''.join(cs0), 'ddid')
cs1=nxt.creation_sequence(deg, with_labels=True)
H1=nxt.threshold_graph(cs1)
assert_equal(cs1, [(1, 'd'), (2, 'd'), (3, 'i'), (0, 'd')])
cs2=nxt.creation_sequence(deg, compact=True)
H2=nxt.threshold_graph(cs2)
assert_equal(cs2, [2, 1, 1])
assert_equal(''.join(nxt.uncompact(cs2)), 'ddid')
assert_true(graph_could_be_isomorphic(H0,G))
assert_true(graph_could_be_isomorphic(H0,H1))
assert_true(graph_could_be_isomorphic(H0,H2))
def test_creation_sequences(self):
deg = [3, 2, 2, 1]
G = nx.generators.havel_hakimi_graph(deg)
cs0 = nxt.creation_sequence(deg)
H0 = nxt.threshold_graph(cs0)
assert_equal(''.join(cs0), 'ddid')
cs1 = nxt.creation_sequence(deg, with_labels=True)
H1 = nxt.threshold_graph(cs1)
assert_equal(cs1, [(1, 'd'), (2, 'd'), (3, 'i'), (0, 'd')])
cs2 = nxt.creation_sequence(deg, compact=True)
H2 = nxt.threshold_graph(cs2)
assert_equal(cs2, [2, 1, 1])
assert_equal(''.join(nxt.uncompact(cs2)), 'ddid')
assert_true(graph_could_be_isomorphic(H0, G))
assert_true(graph_could_be_isomorphic(H0, H1))
assert_true(graph_could_be_isomorphic(H0, H2))
def test_eigenvectors(self):
np = pytest.importorskip("numpy")
eigenval = np.linalg.eigvals
scipy = pytest.importorskip("scipy")
cs = "ddiiddid"
G = nxt.threshold_graph(cs)
(tgeval, tgevec) = nxt.eigenvectors(cs)
dot = np.dot
assert [abs(dot(lv, lv) - 1.0) < 1e-9 for lv in tgevec] == [True] * 8
lapl = nx.laplacian_matrix(G)
def test_eigenvectors(self):
np = pytest.importorskip("numpy")
eigenval = np.linalg.eigvals
pytest.importorskip("scipy")
cs = "ddiiddid"
G = nxt.threshold_graph(cs)
(tgeval, tgevec) = nxt.eigenvectors(cs)
np.testing.assert_allclose([np.dot(lv, lv) for lv in tgevec],
1.0,
rtol=1e-9)
lapl = nx.laplacian_matrix(G)
def test_creation_sequences(self):
deg = [3, 2, 2, 1]
G = nx.generators.havel_hakimi_graph(deg)
with pytest.raises(ValueError):
nxt.creation_sequence(deg, with_labels=True, compact=True)
cs0 = nxt.creation_sequence(deg)
H0 = nxt.threshold_graph(cs0)
assert "".join(cs0) == "ddid"
cs1 = nxt.creation_sequence(deg, with_labels=True)
H1 = nxt.threshold_graph(cs1)
assert cs1 == [(1, "d"), (2, "d"), (3, "i"), (0, "d")]
cs2 = nxt.creation_sequence(deg, compact=True)
H2 = nxt.threshold_graph(cs2)
assert cs2 == [2, 1, 1]
assert "".join(nxt.uncompact(cs2)) == "ddid"
assert graph_could_be_isomorphic(H0, G)
assert graph_could_be_isomorphic(H0, H1)
assert graph_could_be_isomorphic(H0, H2)
def test_eigenvectors(self):
try:
import numpy as N
eigenval=N.linalg.eigvals
import scipy
except ImportError:
raise SkipTest('SciPy not available.')
cs='ddiiddid'
G=nxt.threshold_graph(cs)
(tgeval,tgevec)=nxt.eigenvectors(cs)
dot=N.dot
assert_equal([ abs(dot(lv,lv)-1.0)<1e-9 for lv in tgevec ], [True]*8)
lapl=nx.laplacian_matrix(G)
def test_eigenvectors(self):
try:
import numpy as N
eigenval = N.linalg.eigvals
import scipy
except ImportError:
raise SkipTest('SciPy not available.')
cs = 'ddiiddid'
G = nxt.threshold_graph(cs)
(tgeval, tgevec) = nxt.eigenvectors(cs)
dot = N.dot
assert_equal([abs(dot(lv, lv) - 1.0) < 1e-9 for lv in tgevec], [True] * 8)
lapl = nx.laplacian_matrix(G)
