def test_build_path_graph(n=10):
with pytest.raises(ValueError):
G = graphs.build_path_graph(1)
for i in range(2, n):
G = graphs.build_path_graph(i)
assert G.number_of_nodes() == i
assert G.number_of_edges() == i - 1
def test_color_matrix_path_4():
G = graphs.build_path_graph(4)
paths = nx.shortest_path(G)
c = greedy.color_matrix(G, paths)
lb = 1
assert c[0][lb - 1] == 0
assert c[1][lb - 1] == 1
assert c[2][lb - 1] == 2
assert c[3][lb - 1] == 3
lb = 2
assert c[0][lb - 1] == 0
assert c[1][lb - 1] == 0
assert c[2][lb - 1] == 1
assert c[3][lb - 1] == 1
lb = 3
assert c[0][lb - 1] == 0
assert c[1][lb - 1] == 0
assert c[2][lb - 1] == 0
assert c[3][lb - 1] == 1
def test_color_matrix_path_2():
G = graphs.build_path_graph(2)
paths = nx.shortest_path(G)
c = greedy.color_matrix(G, paths)
assert c[0][0] == 0
assert c[1][0] == 1
def test_number_of_boxes_path():
for n in range(10, 30):
G = graphs.build_path_graph(n)
paths = nx.shortest_path(G)
# Test lb = 1
assert greedy.num_boxes_from_graph(G, 1) == n
# Test lb = 2
if n % 2 == 0:
assert greedy.num_boxes_from_graph(G, 2) == n / 2
else:
assert greedy.num_boxes_from_graph(G, 2) == (n - 1) / 2 + 1
# Test lb = n - 1 (n - 1 is the diameter of the network)
assert greedy.num_boxes_from_graph(G, n - 1) == 2
def test_graph_diameter():
for i in range(2,10):
G = graphs.build_path_graph(i)
paths = nx.shortest_path(G)
assert greedy.graph_diameter(paths) == i - 1
for i in range(2, 10):
G = graphs.build_lattice_graph(i)
paths = nx.shortest_path(G)
assert greedy.graph_diameter(paths) == 2 * i - 2
G = graphs.build_song2007_graph()
paths = nx.shortest_path(G)
assert greedy.graph_diameter(paths) == 4
1), l_boxes, np.array(n_boxes)
if __name__ == "__main__":
from tfdppin import graphs
import matplotlib.pyplot as plt
import os
pbc = False
fuzzy = False
f = plt.figure(figsize=(12, 5))
N = 200
G = graphs.build_path_graph(N, pbc=pbc)
if fuzzy:
p, lb, Nb = tfd_fuzzy(G)
else:
#p, lb, Nb = tfd_greedy_slow(G)
p, lb, Nb = tfd_greedy(G)
print("TDF Path:", p[0])
f.add_subplot(1, 2, 1)
plt.title("Path (N = {})".format(N))
plt.xlabel("log($l_B$)")
plt.ylabel("log($N_B$)")
plt.loglog(lb, Nb, 'o')
x = np.linspace(min(np.log(lb)), max(np.log(lb)), 100)