def test_microcanonical_averages_number_of_iterations(grid_3x3_graph):
spanning_cluster = grid_3x3_graph.graph['span']
n = 0
for microcanonical_average in percolate.microcanonical_averages(
grid_3x3_graph, spanning_cluster=spanning_cluster):
assert microcanonical_average['n'] == n
n += 1
assert microcanonical_average['n'] == 12
def test_microcanonical_averages_initial_iteration(grid_3x3_graph):
spanning_cluster = grid_3x3_graph.graph['span']
ret = next(
percolate.microcanonical_averages(grid_3x3_graph,
spanning_cluster=spanning_cluster))
assert ret['n'] == 0
assert ret['max_cluster_size'] == 1.0
np.testing.assert_allclose(ret['max_cluster_size_ci'], np.ones(2))
np.testing.assert_allclose(ret['moments'], np.ones(5) * 8)
np.testing.assert_allclose(ret['moments_ci'], np.ones((5, 2)) * 8)
assert ('spanning_cluster' in ret) == spanning_cluster
assert ('spanning_cluster_ci' in ret) == spanning_cluster
if spanning_cluster:
assert ret['spanning_cluster'] == 1 / 42
np.testing.assert_allclose(
np.array([0, 1]) + np.array([1, -1]) *
scipy.stats.beta.cdf(ret['spanning_cluster_ci'], a=1, b=41),
scipy.stats.norm.cdf(-1) * np.ones(2))
def test_microcanonical_averages_zero_alpha(grid_3x3_graph):
with pytest.raises(ValueError):
next(percolate.microcanonical_averages(grid_3x3_graph, alpha=0.0))
def test_microcanonical_averages_nonfloat_alpha(grid_3x3_graph):
with pytest.raises(ValueError):
next(percolate.microcanonical_averages(grid_3x3_graph, alpha='huge'))
def test_microcanonical_averages_nonpositive_runs(grid_3x3_graph):
with pytest.raises(ValueError):
next(percolate.microcanonical_averages(grid_3x3_graph, runs=0))
def test_microcanonical_averages_noninteger_runs(grid_3x3_graph):
with pytest.raises(ValueError):
next(percolate.microcanonical_averages(grid_3x3_graph, runs='many'))
# number of runs
runs = 1
# system sizes
number_of_nodes = [10**5, 10**5]
constants = [5]
#
even = False
sequences = []
for i in number_of_nodes:
while even == False:
rng = np.random.default_rng()
sequence = rng.poisson(constants[0], i)
if (sum(sequence) % 2) == 0:
even == True
sequences.append(sequence)
graphs = [nx.configuration_model(i) for i in sequences]
# compute the microcanonical averages for all system sizes
microcanonical_averages = [
percolate.microcanonical_averages(graph=graph, runs=runs)
for graph in graphs
]
# combine microcanonical averages into one array
microcanonical_averages_arrays = [
percolate.microcanonical_averages_arrays(avg)
for avg in microcanonical_averages
]
print(microcanonical_averages_arrays)