def test_table_walk_through_decreasing_isotropic(self):
table, actual_lambda, quiescent_state = cpl.random_rule_table(
k=3, r=1, lambda_val=1.0, isotropic=True)
table, new_lambda = cpl.table_walk_through(
table,
lambda_val=0.0,
k=3,
r=1,
quiescent_state=quiescent_state,
isotropic=True)
self.assertEqual(new_lambda, 0.0)
def test_table_walk_through_decreasing_strong_quiescence(self):
table, actual_lambda, quiescent_state = cpl.random_rule_table(
k=3, r=1, lambda_val=1.0, strong_quiescence=True)
table, new_lambda = cpl.table_walk_through(
table,
lambda_val=0.0,
k=3,
r=1,
quiescent_state=quiescent_state,
strong_quiescence=True)
np.testing.assert_almost_equal(new_lambda, 0.07, decimal=2)
def test_table_walk_through_increasing(self):
table, actual_lambda, quiescent_state = cpl.random_rule_table(
k=3, r=1, lambda_val=0.0)
table, new_lambda = cpl.table_walk_through(
table, lambda_val=1.0, k=3, r=1, quiescent_state=quiescent_state)
self.assertEqual(new_lambda, 1.0)
import cellpylib as cpl
rule_table, actual_lambda, quiescent_state = cpl.random_rule_table(
lambda_val=0.0, k=4, r=2, strong_quiescence=True, isotropic=True)
lambda_vals = [0.15, 0.37, 0.75]
ca_list = []
titles = []
for i in range(0, 3):
# cellular_automaton = cpl.init_simple(128, val=1)
cellular_automaton = cpl.init_random(128, k=4)
rule_table, actual_lambda = cpl.table_walk_through(
rule_table,
lambda_vals[i],
k=4,
r=2,
quiescent_state=quiescent_state,
strong_quiescence=True)
print(actual_lambda)
# evolve the cellular automaton for 200 time steps
cellular_automaton = cpl.evolve(
cellular_automaton,
timesteps=200,
apply_rule=lambda n, c, t: cpl.table_rule(n, rule_table),
r=2)
ca_list.append(cellular_automaton)
avg_cell_entropy = cpl.average_cell_entropy(cellular_automaton)
avg_mutual_information = cpl.average_mutual_information(cellular_automaton)