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(MG.edges(), 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(MG.edges(), 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(G.degree().values()))
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_equal(nxt.density('ddiiddid'), nx.density(G))
assert_equal(sorted(nxt.degree_sequence(cs)),
sorted(G.degree().values()))
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):
try:
import numpy as N
eigenval=N.linalg.eigvals
except ImportError:
raise SkipTest('NumPy 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
except ImportError:
raise SkipTest('NumPy 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(G)
tgev=[ dot(lv,dot(lapl,lv)) for lv in tgevec ]
assert_true(sum([abs(c-d) for c,d in zip(tgev,tgeval)]) < 1e-9)
tgev.sort()
lev=list(eigenval(lapl))
lev.sort()
assert_true(sum([abs(c-d) for c,d in zip(tgev,lev)]) < 1e-9)
def test_eigenvectors(self):
try:
import numpy as N
eigenval=N.linalg.eigvals
except ImportError:
raise SkipTest('NumPy 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(G)
tgev=[ dot(lv,dot(lapl,lv)) for lv in tgevec ]
assert_true(sum([abs(c-d) for c,d in zip(tgev,tgeval)]) < 1e-9)
tgev.sort()
lev=list(eigenval(lapl))
lev.sort()
assert_true(sum([abs(c-d) for c,d in zip(tgev,lev)]) < 1e-9)