def test_simulate_until_target_substate_or_max_t_C():
"""
`simulate_until_target_substate_or_max_t`
Feature C: not finding target substate if time cap is violated.
"""
predecessor_node_lists, truth_tables = \
build_predecessor_nodes_lists_and_truth_tables(UPDATE_RULES_B)
initial_state = [False, False, False, False, False, False]
substate_node_set = {0, 1, 2, 3, 4, 5}
_encode_state, _ = configure_encode_and_simulate(
substate_node_set=substate_node_set)
_, target_substate_code = _encode_state(
[True, True, False, False, False, True])
max_t = 1
_, _simulate_until_attractor_or_target_substate_or_max_t = \
configure_encode_and_simulate(max_t=max_t, substate_node_set=substate_node_set,
target_substate_code=target_substate_code)
# Test for {no perturbations, perturbations}.
perturbed_nodes_by_t_1 = dict()
perturbed_nodes_by_t_2 = {1: {3: False}}
for perturbed_nodes_by_t in [
perturbed_nodes_by_t_1, perturbed_nodes_by_t_2
]:
simulation_states = simulate_until_target_substate_or_max_t(
_simulate_until_attractor_or_target_substate_or_max_t,
initial_state, perturbed_nodes_by_t, predecessor_node_lists,
truth_tables)
test_description = generate_test_description(locals(), 'max_t',
'perturbed_nodes_by_t')
assert simulation_states is None
def test_simulate_until_attractor_or_max_t_storing_all_states_B():
"""
`simulate_until_attractor_or_max_t_storing_all_states`
Feature B: not finding attractor if time cap is violated.
"""
initial_state = [False, False, True, False, False, False]
max_t = 3
# Test for {no length constraint, length constraint}.
max_attractor_l_1 = inf
max_attractor_l_2 = 100
# Test for {no perturbations, perturbations}.
perturbed_nodes_by_t_1 = dict()
perturbed_nodes_by_t_2 = {1: {3: False}}
for max_attractor_l, perturbed_nodes_by_t in product(
[max_attractor_l_1, max_attractor_l_2],
[perturbed_nodes_by_t_1, perturbed_nodes_by_t_2]):
predecessor_node_lists, truth_tables = \
build_predecessor_nodes_lists_and_truth_tables(UPDATE_RULES_B)
_, _simulate_until_attractor_or_target_substate_or_max_t = \
configure_encode_and_simulate(max_t=max_t)
attractor = simulate_until_attractor_or_max_t_storing_all_states(
max_attractor_l,
_simulate_until_attractor_or_target_substate_or_max_t,
initial_state, perturbed_nodes_by_t, predecessor_node_lists,
truth_tables)
test_description = generate_test_description(locals(),
'max_attractor_l',
'perturbed_nodes_by_t')
assert attractor is None, test_description
def test_simulate_A():
"""
`simulate_until_max_t`
Feature A: simulating until the time cap.
"""
predecessor_node_lists, truth_tables = build_predecessor_nodes_lists_and_truth_tables(
UPDATE_RULES_A)
initial_state = [False, True, True, False, False]
perturbed_nodes_by_t = dict()
# Test for {not reaching attractor, reaching attractor}.
max_t_1 = 3
max_t_2 = 20
expected_simulation_states_1 = \
[initial_state, [False, False, True, False, True], [True, False, False, False, True],
[True, True, False, True, True]]
expected_simulation_states_2 = \
4 * [initial_state, [False, False, True, False, True], [True, False, False, False, True],
[True, True, False, True, True], [True, True, True, True, False]] + \
[initial_state]
for max_t, expected_simulation_states in zip(
[max_t_1, max_t_2],
[expected_simulation_states_1, expected_simulation_states_2]):
_, _simulate_until_attractor_or_target_substate_or_max_t = \
configure_encode_and_simulate(max_t=max_t)
simulation_states = simulate_until_max_t(
max_t, _simulate_until_attractor_or_target_substate_or_max_t,
initial_state, perturbed_nodes_by_t, predecessor_node_lists,
truth_tables)
test_description = generate_test_description(locals(), 'max_t')
assert expected_simulation_states == simulation_states, test_description
def test_simulate_until_target_substate_or_max_t_B():
"""
`simulate_until_target_substate_or_max_t`
Feature B: finding target substate that is part of attractor.
"""
predecessor_node_lists, truth_tables = \
build_predecessor_nodes_lists_and_truth_tables(UPDATE_RULES_B)
initial_state = [False, False, False, False, False, False]
substate_node_set = {0, 1, 2, 3, 4, 5}
_encode_state, _ = configure_encode_and_simulate(
substate_node_set=substate_node_set)
_, target_substate_code = _encode_state(
[True, True, False, False, False, False])
# Test for {no time cap, time cap}.
max_t_1 = inf
max_t_2 = 10
# Test for {no perturbations, perturbations}.
perturbed_nodes_by_t_1 = dict()
perturbed_nodes_by_t_2 = {1: {3: False}}
expected_simulation_states = [initial_state] + [[
True, False, False, False, True, False
], [True, True, False, False, False, True
], [False, True, False, False, False, False
], [True, False, True, False, False, False
], [True, True, False, True, False, False
], [True, True, False, False, False, False]]
for max_t, perturbed_nodes_by_t in product(
[max_t_1, max_t_2], [perturbed_nodes_by_t_1, perturbed_nodes_by_t_2]):
_, _simulate_until_attractor_or_target_substate_or_max_t = \
configure_encode_and_simulate(max_t=max_t, substate_node_set=substate_node_set,
target_substate_code=target_substate_code)
simulation_states = simulate_until_target_substate_or_max_t(
_simulate_until_attractor_or_target_substate_or_max_t,
initial_state, perturbed_nodes_by_t, predecessor_node_lists,
truth_tables)
test_description = generate_test_description(locals(), 'max_t',
'perturbed_nodes_by_t')
assert expected_simulation_states == simulation_states
def test_target_master_B():
"""
`target_master`
Feature B: finding no more than requested number of simulations
reaching target state.
"""
predecessor_node_lists, truth_tables = build_predecessor_nodes_lists_and_truth_tables(
UPDATE_RULES_B)
initial_state = [False, False, False, False, False, False]
substate_node_set = {0, 1, 2, 3, 4, 5}
_encode_state, _ = configure_encode_and_simulate(
substate_node_set=substate_node_set)
_, target_substate_code = _encode_state(
[True, True, False, False, False, True])
max_t = inf
n_simulations_to_reach_target_substate = 1
initial_state_variations = [0, 1, 2, 3, 4, 5]
fixed_nodes_variations = []
perturbed_nodes_by_t_variations = []
fixed_nodes = {0: True, 1: True, 2: False, 3: False, 4: False, 5: True}
perturbed_nodes_by_t = dict()
n_simulation_problems = count_simulation_problems(
initial_state_variations, fixed_nodes_variations,
perturbed_nodes_by_t_variations)
# Test for {single batch per process, multiple batches per process}.
n_simulation_problem_batches_per_process_1 = 1
n_simulation_problem_batches_per_process_2 = 5
expected_simulations = [
Simulation([initial_state] + [[True, True, False, False, False, True]],
fixed_nodes, perturbed_nodes_by_t)
]
for n_simulation_problem_batches_per_process in [
n_simulation_problem_batches_per_process_1,
n_simulation_problem_batches_per_process_2
]:
db_conn = ZODB.connection(None)
init_simulation_db_structure(db_conn)
target_master(MPICommWrapper(),
n_simulation_problem_batches_per_process,
(initial_state, fixed_nodes, perturbed_nodes_by_t),
(initial_state_variations, fixed_nodes_variations,
perturbed_nodes_by_t_variations), target_substate_code,
substate_node_set, predecessor_node_lists, truth_tables,
n_simulations_to_reach_target_substate, max_t,
n_simulation_problems, db_conn, None)
test_description = generate_test_description(
locals(), 'n_simulation_problem_batches_per_process')
assert list(db_conn.root.simulations.values()
) == expected_simulations, test_description
assert db_conn.root.n_simulations() == len(
expected_simulations), test_description
def test_simulate_until_attractor_or_max_t_storing_all_states_A():
"""
`simulate_until_attractor_or_max_t_storing_all_states`
Feature A: finding attractor.
"""
initial_state = [False, False, True, False, False, False]
# Test for {no time cap, time cap}.
max_t_1 = inf
max_t_2 = 10
# Test for {no length constraint, length constraint}.
max_attractor_l_1 = inf
max_attractor_l_2 = 10
# Test for {no perturbations, perturbations}.
perturbed_nodes_by_t_1 = dict()
perturbed_nodes_by_t_2 = {1: {3: False}}
attractor_states_1 = [[True, False, False, True, True, False],
[True, True, False, False, True, True],
[False, True, False, False, False, True],
[False, False, True, False, False, False]]
attractor_states_2 = [[True, True, False, False, False, False]]
expected_attractor_states_list = [attractor_states_1, attractor_states_2
] * 4
expected_trajectory_l_list = [0, 6] * 4
for (max_t, max_attractor_l, perturbed_nodes_by_t), \
(expected_attractor_states, expected_trajectory_l) in zip(
product([max_t_1, max_t_2],
[max_attractor_l_1, max_attractor_l_2],
[perturbed_nodes_by_t_1, perturbed_nodes_by_t_2]),
zip(expected_attractor_states_list, expected_trajectory_l_list)):
predecessor_node_lists, truth_tables = \
build_predecessor_nodes_lists_and_truth_tables(UPDATE_RULES_B)
_, _simulate_until_attractor_or_target_substate_or_max_t = \
configure_encode_and_simulate(max_t=max_t)
expected_attractor_state_codes = \
[encode_state(set(), state)[0] for state in expected_attractor_states]
expected_attractor_key = min(expected_attractor_state_codes)
attractor_key, attractor_state_codes, attractor_states, trajectory_l = \
simulate_until_attractor_or_max_t_storing_all_states(
max_attractor_l, _simulate_until_attractor_or_target_substate_or_max_t,
initial_state, perturbed_nodes_by_t, predecessor_node_lists, truth_tables)
test_description = generate_test_description(locals(), 'max_t',
'max_attractor_l',
'perturbed_nodes_by_t')
assert expected_attractor_key == attractor_key, test_description
assert expected_attractor_state_codes == attractor_state_codes, test_description
assert expected_attractor_states == attractor_states, test_description
assert expected_trajectory_l == trajectory_l, test_description