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"""Unit tests for the :mod:`networkx.generators.expanders` module.
"""
import pytest
import networkx as nx
@pytest.mark.parametrize("n", (2, 3, 5, 6, 10))
def test_margulis_gabber_galil_graph_properties(n):
g = nx.margulis_gabber_galil_graph(n)
assert g.number_of_nodes() == n * n
for node in g:
assert g.degree(node) == 8
assert len(node) == 2
for i in node:
assert int(i) == i
assert 0 <= i < n
@pytest.mark.parametrize("n", (2, 3, 5, 6, 10))
def test_margulis_gabber_galil_graph_eigvals(n):
np = pytest.importorskip("numpy")
sp = pytest.importorskip("scipy")
import scipy.linalg
g = nx.margulis_gabber_galil_graph(n)
# Eigenvalues are already sorted using the scipy eigvalsh,
# but the implementation in numpy does not guarantee order.
w = sorted(sp.linalg.eigvalsh(nx.adjacency_matrix(g).toarray()))
assert w[-2] < 5 * np.sqrt(2)
@pytest.mark.parametrize("p", (3, 5, 7, 11)) # Primes
def test_chordal_cycle_graph(p):
"""Test for the :func:`networkx.chordal_cycle_graph` function."""
G = nx.chordal_cycle_graph(p)
assert len(G) == p
# TODO The second largest eigenvalue should be smaller than a constant,
# independent of the number of nodes in the graph:
#
# eigs = sorted(sp.linalg.eigvalsh(nx.adjacency_matrix(G).toarray()))
# assert_less(eigs[-2], ...)
#
@pytest.mark.parametrize("p", (3, 5, 7, 11, 13)) # Primes
def test_paley_graph(p):
"""Test for the :func:`networkx.paley_graph` function."""
G = nx.paley_graph(p)
# G has p nodes
assert len(G) == p
# G is (p-1)/2-regular
in_degrees = {G.in_degree(node) for node in G.nodes}
out_degrees = {G.out_degree(node) for node in G.nodes}
assert len(in_degrees) == 1 and in_degrees.pop() == (p - 1) // 2
assert len(out_degrees) == 1 and out_degrees.pop() == (p - 1) // 2
# If p = 1 mod 4, -1 is a square mod 4 and therefore the
# edge in the Paley graph are symmetric.
if p % 4 == 1:
for (u, v) in G.edges:
assert (v, u) in G.edges
@pytest.mark.parametrize("graph_type", (nx.Graph, nx.DiGraph))
def test_margulis_gabber_galil_graph_badinput(graph_type):
with pytest.raises(
nx.NetworkXError, match="`create_using` must be an undirected multigraph"
):
nx.margulis_gabber_galil_graph(3, create_using=graph_type)
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