def run_model(param_updates, n_steps=200, lockdown_at=None):
params = setup_params(param_updates)
# Create an instance of the Model
model = Model(params)
m_out = []
for step in trange(n_steps):
# Evaluate each step and save the results
model.one_time_step()
# LOGGER.info(
# model.one_time_step_results()
# ) # If we want to see the results as we go uncomment this block
m_out.append(model.one_time_step_results())
if lockdown_at:
if step == lockdown_at:
model.update_running_params("lockdown_on", 1)
LOGGER.info(f"turning on lock down at step {step}")
LOGGER.info(
f'lockdown_house_interaction_multiplier = {params.get_param("lockdown_house_interaction_multiplier")}'
)
LOGGER.info(
f'lockdown_random_network_multiplier = {params.get_param("lockdown_random_network_multiplier")}'
)
for oc_net in OccupationNetworkEnum:
LOGGER.info(
f'lockdown_occupation_multiplier{oc_net.name} = {params.get_param(f"lockdown_occupation_multiplier{oc_net.name}")}'
)
df = pd.DataFrame(m_out)
model.write_output_files()
return df
def test_run_model_read_prama_file_false(self):
all_params = pd.read_csv("tests/data/baseline_parameters.csv")
p = Parameters(
read_param_file=False,
input_households="tests/data/baseline_household_demographics.csv",
)
for key in all_params:
value = all_params[key][0]
try:
p.set_param(key, float(value))
except TypeError:
p.set_param(key, int(value))
m = Model(p)
m.one_time_step()
assert m.one_time_step_results()["time"] == 1