def test_flagella_metabolism(seed=1):
random.seed(seed)
np.random.seed(seed)
# make the compartment and state
compartment = FlagellaExpressionMetabolism({'divide': False})
initial_state = get_flagella_metabolism_initial_state()
name = compartment.name
# run simulation
settings = {
'total_time': 60,
'emit_step': COMPARTMENT_TIMESTEP,
'initial_state': initial_state,
}
timeseries = simulate_compartment_in_experiment(compartment, settings)
# remove non-numerical data for timeseries comparison, convert to path_timeseries
del timeseries['chromosome']
del timeseries['ribosomes']
del timeseries['dimensions']
del timeseries['boundary']['divide']
del timeseries['fields']
del timeseries['null']
path_timeseries = path_timeseries_from_embedded_timeseries(timeseries)
# # save reference timeseries
# out_dir = os.path.join(COMPARTMENT_OUT_DIR, name)
# if not os.path.exists(out_dir):
# os.makedirs(out_dir)
# save_flat_timeseries(path_timeseries, out_dir)
reference = load_timeseries(
os.path.join(REFERENCE_DATA_DIR, name + '.csv'))
assert_timeseries_close(path_timeseries, reference)
def test_experiment(seed=1):
random.seed(seed)
np.random.seed(seed)
if not os.path.exists(OUT_DIR):
os.makedirs(OUT_DIR)
data, _ = run_experiment(
start_locations=[[0.3, 0.3], [0.5, 0.5]],
growth_rate=0.001,
)
path_ts = path_timeseries_from_data(data)
filtered = {
path: timeseries
for path, timeseries in path_ts.items()
# Angles are computed randomly by multibody physics
if path[-1] != 'angle'
}
processed_for_csv = process_path_timeseries_for_csv(filtered)
save_flat_timeseries(processed_for_csv, OUT_DIR, 'test_output.csv')
test_output = load_timeseries(os.path.join(OUT_DIR, 'test_output.csv'))
expected = load_timeseries(os.path.join(REFERENCE_DATA_DIR, NAME + '.csv'))
assert_timeseries_close(
test_output,
expected,
default_tolerance=(1 - 1e-5),
)
def test_transport_metabolism_experiment(seed=1):
random.seed(seed)
np.random.seed(seed)
experiment_name = 'transport_metabolism_experiment'
agent_config = agents_library['transport_metabolism']
agents_config = [agent_config]
environment_config = environments_library['shallow_iAF1260b']
experiment_settings = get_experiment_settings(total_time=60)
# simulate
data = run_lattice_experiment(
agents_config=agents_config,
environment_config=environment_config,
# initial_state=initial_state,
# initial_agent_state=initial_agent_state,
experiment_settings=experiment_settings,
)
path_ts = path_timeseries_from_data(data)
del path_ts[('agents', 'transport_metabolism', 'boundary', 'divide')] # these contain 'False'
# save timeseries as csv
processed_for_csv = process_path_timeseries_for_csv(path_ts)
make_dir(OUT_DIR)
save_flat_timeseries(
processed_for_csv,
OUT_DIR,
experiment_name + '.csv')
# compare timeseries to reference
test_output = load_timeseries(os.path.join(OUT_DIR, experiment_name + '.csv'))
expected = load_timeseries(os.path.join(REFERENCE_DATA_DIR, experiment_name + '.csv'))
assert_timeseries_close(
test_output, expected,
default_tolerance=(1 - 1e-5),
)
def metabolism_similar_to_reference():
config = get_iAF1260b_config()
metabolism = Metabolism(config)
timeseries = run_metabolism(metabolism, reference_sim_settings)
reference = load_timeseries(os.path.join(REFERENCE_DATA_DIR,
NAME + '.csv'))
assert_timeseries_close(timeseries, reference)
def test_convenience_kinetics_correlated_to_reference():
timeseries = test_convenience_kinetics()
flattened = flatten_timeseries(timeseries)
reference_timeseries = load_timeseries(
os.path.join(REFERENCE_DATA_DIR, NAME + '.csv'))
assert_timeseries_close(flattened,
reference_timeseries,
tolerances={
'internal_pyr_c': 9,
'internal_pep_c': 9,
'internal_g6p_c': 9,
})
def test_antibiotics_composite_similar_to_reference():
timeseries = run_antibiotics_composite()
flattened = flatten_timeseries(timeseries)
reference = load_timeseries(
os.path.join(REFERENCE_DATA_DIR, Antibiotics.name + '.csv'))
assert_timeseries_close(flattened,
reference,
tolerances={
'cell_counts_AcrAB-TolC': 99999,
'cell_counts_antibiotic': 999,
'cell_counts_AcrAB-TolC_RNA': 9,
'cell_counts_porin': 9999,
})
def test_antibiotic_transport():
timeseries = run_antibiotic_transport()
flattened = flatten_timeseries(timeseries)
reference = load_timeseries(os.path.join(
REFERENCE_DATA_DIR, AntibioticTransport.name + '.csv'))
assert_timeseries_close(flattened, reference)
def test_convenience_kinetics_correlated_to_reference():
timeseries = test_convenience_kinetics()
flattened = flatten_timeseries(timeseries)
reference_timeseries = load_timeseries(
os.path.join(REFERENCE_DATA_DIR, NAME + '.csv'))
assert_timeseries_close(flattened, reference_timeseries)