def eval_params(params: np.ndarray, max_episode_length: int) -> np.ndarray:
"""Perform a single run of the simulator
:param params: spread rate and social distancing rate
:type params: np.ndarray
:param max_episode_length: length of simulation run in days
:type max_episode_length: int
:returns: an array of cumulative deaths per day in the simulator run
:rtype: np.ndarray
"""
spread_rate = params[:, 0][0]
social_distancing = params[:, 1][0]
num_seeds = 30
deaths = []
numpy_rng = np.random.RandomState(seed=num_seeds)
sim_non_cli_opts = PandemicSimNonCLIOpts(
population_params.above_medium_town_population_params)
sim_opts = PandemicSimOpts(infection_spread_rate_mean=spread_rate)
sim = make_sim(sim_opts, sim_non_cli_opts, numpy_rng=numpy_rng)
covid_regulations = PandemicRegulation(social_distancing=social_distancing,
stage=0)
print(
f'Running with spread rate: {spread_rate} and social distancing: {social_distancing}'
)
for i in trange(max_episode_length, desc='Simulating day'):
sim.execute_regulation(regulation=covid_regulations)
for j in trange(sim_opts.sim_steps_per_regulation):
sim.step()
state = sim.state
num_deaths = state.global_infection_summary[InfectionSummary.DEAD]
deaths.append(num_deaths)
return np.asarray(deaths)
def run(days: int, stage: int, days_per_interval: int):
# setup rng
numpy_rng = np.random.RandomState(seed=100)
# setup simulator options sets
sim_opts = PandemicSimOpts(use_contact_tracer=True)
sim_non_cli_opts = PandemicSimNonCLIOpts(small_town_population_params)
# make sim
sim = make_sim(sim_opts, sim_non_cli_opts, numpy_rng=numpy_rng)
# setup viz
viz = GraphViz(sim,
num_stages=len(austin_regulations),
days_per_interval=days_per_interval)
# run regulation stpes in the simulator
stage_to_regulation = {reg.stage: reg for reg in austin_regulations}
print(f'Stage {stage}:')
for _ in range(100):
print(f'{sim.state.sim_time.day + 1} ', end='')
stdout.flush()
# get the regulation
regulation = stage_to_regulation[stage] # stage 0
# execute the given regulation
sim.execute_regulation(regulation=regulation)
for i in range(sim_opts.sim_steps_per_regulation):
# step sim
sim.step()
# get state
state = sim.state
# visualize
viz.record(state)
print()
# generate plots
viz.plot()
return viz._num_components_per_interval
from tqdm import trange
from pandemic_simulator.environment import austin_regulations, Hospital, PandemicSimOpts, PandemicSimNonCLIOpts
from pandemic_simulator.script_helpers import small_town_population_params, make_sim
from pandemic_simulator.viz import MatplotLibViz
if __name__ == '__main__':
# setup rng
numpy_rng = np.random.RandomState(seed=100)
# setup simulator options sets
sim_opts = PandemicSimOpts()
sim_non_cli_opts = PandemicSimNonCLIOpts(small_town_population_params)
# make sim
sim = make_sim(sim_opts, sim_non_cli_opts, numpy_rng=numpy_rng)
# setup viz
viz = MatplotLibViz(num_persons=sim_non_cli_opts.population_params.num_persons,
hospital_params=sim_non_cli_opts.population_params.location_type_to_params[Hospital],
num_stages=len(austin_regulations),
show_stages=False)
# run regulation stpes in the simulator
stage_to_regulation = {reg.stage: reg for reg in austin_regulations}
for i in trange(100, desc='Simulating day'):
# get the regulation
regulation = stage_to_regulation[0] # stage 0
# execute the given regulation
sim.execute_regulation(regulation=regulation)