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from itertools import combinations
from math import sqrt
import random
import networkx as nx
from networkx.generators.geometric import euclidean
def l1dist(x, y):
return sum(abs(a - b) for a, b in zip(x, y))
class TestRandomGeometricGraph:
"""Unit tests for the :func:`~networkx.random_geometric_graph`
function.
"""
def test_number_of_nodes(self):
G = nx.random_geometric_graph(50, 0.25, seed=42)
assert len(G) == 50
G = nx.random_geometric_graph(range(50), 0.25, seed=42)
assert len(G) == 50
def test_distances(self):
"""Tests that pairs of vertices adjacent if and only if they are
within the prescribed radius.
"""
# Use the Euclidean metric, the default according to the
# documentation.
dist = euclidean
G = nx.random_geometric_graph(50, 0.25)
for u, v in combinations(G, 2):
# Adjacent vertices must be within the given distance.
if v in G[u]:
assert dist(G.nodes[u]["pos"], G.nodes[v]["pos"]) <= 0.25
# Nonadjacent vertices must be at greater distance.
else:
assert not dist(G.nodes[u]["pos"], G.nodes[v]["pos"]) <= 0.25
def test_p(self):
"""Tests for providing an alternate distance metric to the
generator.
"""
# Use the L1 metric.
dist = l1dist
G = nx.random_geometric_graph(50, 0.25, p=1)
for u, v in combinations(G, 2):
# Adjacent vertices must be within the given distance.
if v in G[u]:
assert dist(G.nodes[u]["pos"], G.nodes[v]["pos"]) <= 0.25
# Nonadjacent vertices must be at greater distance.
else:
assert not dist(G.nodes[u]["pos"], G.nodes[v]["pos"]) <= 0.25
def test_node_names(self):
"""Tests using values other than sequential numbers as node IDs."""
import string
nodes = list(string.ascii_lowercase)
G = nx.random_geometric_graph(nodes, 0.25)
assert len(G) == len(nodes)
dist = euclidean
for u, v in combinations(G, 2):
# Adjacent vertices must be within the given distance.
if v in G[u]:
assert dist(G.nodes[u]["pos"], G.nodes[v]["pos"]) <= 0.25
# Nonadjacent vertices must be at greater distance.
else:
assert not dist(G.nodes[u]["pos"], G.nodes[v]["pos"]) <= 0.25
class TestSoftRandomGeometricGraph:
"""Unit tests for the :func:`~networkx.soft_random_geometric_graph`
function.
"""
def test_number_of_nodes(self):
G = nx.soft_random_geometric_graph(50, 0.25, seed=42)
assert len(G) == 50
G = nx.soft_random_geometric_graph(range(50), 0.25, seed=42)
assert len(G) == 50
def test_distances(self):
"""Tests that pairs of vertices adjacent if and only if they are
within the prescribed radius.
"""
# Use the Euclidean metric, the default according to the
# documentation.
def dist(x, y):
return sqrt(sum((a - b) ** 2 for a, b in zip(x, y)))
G = nx.soft_random_geometric_graph(50, 0.25)
for u, v in combinations(G, 2):
# Adjacent vertices must be within the given distance.
if v in G[u]:
assert dist(G.nodes[u]["pos"], G.nodes[v]["pos"]) <= 0.25
def test_p(self):
"""Tests for providing an alternate distance metric to the
generator.
"""
# Use the L1 metric.
def dist(x, y):
return sum(abs(a - b) for a, b in zip(x, y))
G = nx.soft_random_geometric_graph(50, 0.25, p=1)
for u, v in combinations(G, 2):
# Adjacent vertices must be within the given distance.
if v in G[u]:
assert dist(G.nodes[u]["pos"], G.nodes[v]["pos"]) <= 0.25
def test_node_names(self):
"""Tests using values other than sequential numbers as node IDs."""
import string
nodes = list(string.ascii_lowercase)
G = nx.soft_random_geometric_graph(nodes, 0.25)
assert len(G) == len(nodes)
def dist(x, y):
return sqrt(sum((a - b) ** 2 for a, b in zip(x, y)))
for u, v in combinations(G, 2):
# Adjacent vertices must be within the given distance.
if v in G[u]:
assert dist(G.nodes[u]["pos"], G.nodes[v]["pos"]) <= 0.25
def test_p_dist_default(self):
"""Tests default p_dict = 0.5 returns graph with edge count <= RGG with
same n, radius, dim and positions
"""
nodes = 50
dim = 2
pos = {v: [random.random() for i in range(dim)] for v in range(nodes)}
RGG = nx.random_geometric_graph(50, 0.25, pos=pos)
SRGG = nx.soft_random_geometric_graph(50, 0.25, pos=pos)
assert len(SRGG.edges()) <= len(RGG.edges())
def test_p_dist_zero(self):
"""Tests if p_dict = 0 returns disconencted graph with 0 edges"""
def p_dist(dist):
return 0
G = nx.soft_random_geometric_graph(50, 0.25, p_dist=p_dist)
assert len(G.edges) == 0
def join(G, u, v, theta, alpha, metric):
"""Returns ``True`` if and only if the nodes whose attributes are
``du`` and ``dv`` should be joined, according to the threshold
condition for geographical threshold graphs.
``G`` is an undirected NetworkX graph, and ``u`` and ``v`` are nodes
in that graph. The nodes must have node attributes ``'pos'`` and
``'weight'``.
``metric`` is a distance metric.
"""
du, dv = G.nodes[u], G.nodes[v]
u_pos, v_pos = du["pos"], dv["pos"]
u_weight, v_weight = du["weight"], dv["weight"]
return (u_weight + v_weight) * metric(u_pos, v_pos) ** alpha >= theta
class TestGeographicalThresholdGraph:
"""Unit tests for the :func:`~networkx.geographical_threshold_graph`
function.
"""
def test_number_of_nodes(self):
G = nx.geographical_threshold_graph(50, 100, seed=42)
assert len(G) == 50
G = nx.geographical_threshold_graph(range(50), 100, seed=42)
assert len(G) == 50
def test_distances(self):
"""Tests that pairs of vertices adjacent if and only if their
distances meet the given threshold.
"""
# Use the Euclidean metric and alpha = -2
# the default according to the documentation.
dist = euclidean
G = nx.geographical_threshold_graph(50, 10)
for u, v in combinations(G, 2):
# Adjacent vertices must exceed the threshold.
if v in G[u]:
assert join(G, u, v, 10, -2, dist)
# Nonadjacent vertices must not exceed the threshold.
else:
assert not join(G, u, v, 10, -2, dist)
def test_metric(self):
"""Tests for providing an alternate distance metric to the
generator.
"""
# Use the L1 metric.
dist = l1dist
G = nx.geographical_threshold_graph(50, 10, metric=dist)
for u, v in combinations(G, 2):
# Adjacent vertices must exceed the threshold.
if v in G[u]:
assert join(G, u, v, 10, -2, dist)
# Nonadjacent vertices must not exceed the threshold.
else:
assert not join(G, u, v, 10, -2, dist)
def test_p_dist_zero(self):
"""Tests if p_dict = 0 returns disconencted graph with 0 edges"""
def p_dist(dist):
return 0
G = nx.geographical_threshold_graph(50, 1, p_dist=p_dist)
assert len(G.edges) == 0
class TestWaxmanGraph:
"""Unit tests for the :func:`~networkx.waxman_graph` function."""
def test_number_of_nodes_1(self):
G = nx.waxman_graph(50, 0.5, 0.1, seed=42)
assert len(G) == 50
G = nx.waxman_graph(range(50), 0.5, 0.1, seed=42)
assert len(G) == 50
def test_number_of_nodes_2(self):
G = nx.waxman_graph(50, 0.5, 0.1, L=1)
assert len(G) == 50
G = nx.waxman_graph(range(50), 0.5, 0.1, L=1)
assert len(G) == 50
def test_metric(self):
"""Tests for providing an alternate distance metric to the
generator.
"""
# Use the L1 metric.
dist = l1dist
G = nx.waxman_graph(50, 0.5, 0.1, metric=dist)
assert len(G) == 50
class TestNavigableSmallWorldGraph:
def test_navigable_small_world(self):
G = nx.navigable_small_world_graph(5, p=1, q=0, seed=42)
gg = nx.grid_2d_graph(5, 5).to_directed()
assert nx.is_isomorphic(G, gg)
G = nx.navigable_small_world_graph(5, p=1, q=0, dim=3)
gg = nx.grid_graph([5, 5, 5]).to_directed()
assert nx.is_isomorphic(G, gg)
G = nx.navigable_small_world_graph(5, p=1, q=0, dim=1)
gg = nx.grid_graph([5]).to_directed()
assert nx.is_isomorphic(G, gg)
class TestThresholdedRandomGeometricGraph:
"""Unit tests for the :func:`~networkx.thresholded_random_geometric_graph`
function.
"""
def test_number_of_nodes(self):
G = nx.thresholded_random_geometric_graph(50, 0.2, 0.1, seed=42)
assert len(G) == 50
G = nx.thresholded_random_geometric_graph(range(50), 0.2, 0.1)
assert len(G) == 50
def test_distances(self):
"""Tests that pairs of vertices adjacent if and only if they are
within the prescribed radius.
"""
# Use the Euclidean metric, the default according to the
# documentation.
def dist(x, y):
return sqrt(sum((a - b) ** 2 for a, b in zip(x, y)))
G = nx.thresholded_random_geometric_graph(50, 0.25, 0.1)
for u, v in combinations(G, 2):
# Adjacent vertices must be within the given distance.
if v in G[u]:
assert dist(G.nodes[u]["pos"], G.nodes[v]["pos"]) <= 0.25
def test_p(self):
"""Tests for providing an alternate distance metric to the
generator.
"""
# Use the L1 metric.
def dist(x, y):
return sum(abs(a - b) for a, b in zip(x, y))
G = nx.thresholded_random_geometric_graph(50, 0.25, 0.1, p=1)
for u, v in combinations(G, 2):
# Adjacent vertices must be within the given distance.
if v in G[u]:
assert dist(G.nodes[u]["pos"], G.nodes[v]["pos"]) <= 0.25
def test_node_names(self):
"""Tests using values other than sequential numbers as node IDs."""
import string
nodes = list(string.ascii_lowercase)
G = nx.thresholded_random_geometric_graph(nodes, 0.25, 0.1)
assert len(G) == len(nodes)
def dist(x, y):
return sqrt(sum((a - b) ** 2 for a, b in zip(x, y)))
for u, v in combinations(G, 2):
# Adjacent vertices must be within the given distance.
if v in G[u]:
assert dist(G.nodes[u]["pos"], G.nodes[v]["pos"]) <= 0.25
def test_theta(self):
"""Tests that pairs of vertices adjacent if and only if their sum
weights exceeds the threshold parameter theta.
"""
G = nx.thresholded_random_geometric_graph(50, 0.25, 0.1)
for u, v in combinations(G, 2):
# Adjacent vertices must be within the given distance.
if v in G[u]:
assert (G.nodes[u]["weight"] + G.nodes[v]["weight"]) >= 0.1
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