def gen_no_policy_simulation():
"""
for 500 rounds, run the simulation and update each day ranges according to the global min and max values per state.
"""
consts = Consts.from_file(consts_file)
range_per_day_dict = [
deepcopy(test_states) for _ in range(consts.total_steps)
]
for i in range(500):
print(i)
sm = SimulationManager(supervisable_makers=test_states.keys(),
consts=consts)
sm.run()
for supervisable in sm.supervisor.supervisables:
name = supervisable.name()
val_per_day = supervisable.y
for test_state, val in zip(range_per_day_dict, val_per_day):
test_state[name].update(val)
# translate to dict
for i in range(consts.total_steps):
range_per_day_dict[i] = {
state: val.to_dict()
for state, val in range_per_day_dict[i].items()
}
# dump to json
with open("simulation_test_ranges.json", "w") as f:
json.dump(range_per_day_dict, f)
def test_no_policy_simulation():
"""
run the simulation with no policy and fixed R0
Ranges for each state was generate from real sim runs, might be flaky.
"""
consts = Consts.from_file(consts_file)
keys = range_per_day_dict[0].keys()
sm = SimulationManager(supervisable_makers=keys, consts=consts)
sm.run()
for supervisable in sm.supervisor.supervisables:
name = supervisable.name()
val_per_day = supervisable.y
# check for each day if value is in range
for test_state, val in zip(range_per_day_dict, val_per_day):
state = test_state[name]
assert state["min"] <= val <= state["max"]
def compare_simulations_example():
sm1 = SimulationManager(
(
Supervisable.Sum(
"Symptomatic", "Asymptomatic", "Latent", "Silent", "ICU", "Hospitalized", "Recovered", "Deceased"
),
"Symptomatic",
"Recovered",
),
consts=Consts(r0=1.5),
)
sm1.run()
sm2 = SimulationManager(
(
Supervisable.Sum(
"Symptomatic", "Asymptomatic", "Latent", "Silent", "ICU", "Hospitalized", "Recovered", "Deceased"
),
"Symptomatic",
"Recovered",
),
consts=Consts(r0=1.8),
)
sm2.run()
from pathlib import Path
from time import time
from warnings import warn
import yappi
from manager import SimulationManager
qcachegrind_path = Path(
r"C:\Users\avrah\Downloads\qcachegrind074-32bit-x86\qcachegrind074-x86\qcachegrind.exe"
)
profile_gen = False
if __name__ == "__main__":
if profile_gen:
yappi.start()
sm = SimulationManager()
if not profile_gen:
yappi.start()
sm.run()
stats = yappi.get_func_stats()
dest_path = "..\profilings\callgrind.out." + str(int(time()))
stats.save(dest_path, "CALLGRIND")
if not qcachegrind_path:
pass
if qcachegrind_path.exists():
subprocess.Popen([str(qcachegrind_path.absolute()), dest_path],
close_fds=True)
else:
warn(f"set qqcachegrind_path to a valid path to open results")
import yappi
from manager import SimulationManager
qcachegrind_path = Path(
r"D:\chrome downloads\qcachegrind074-32bit-x86\qcachegrind074-x86\qcachegrind.exe"
)
profile_gen = False
if __name__ == "__main__":
if profile_gen:
yappi.start()
sm = SimulationManager((
"Recovered",
"Deceased",
"Symptomatic",
"Asymptomatic",
"Hospitalized",
"ICU",
"Latent",
"Silent",
))
if not profile_gen:
yappi.start()
sm.run()
stats = yappi.get_func_stats()
dest_path = r"..\..\profilings\callgrind.out." + str(int(time()))
stats.save(dest_path, "CALLGRIND")
if not qcachegrind_path:
pass
if qcachegrind_path.exists():
def run_simulation(args):
matrix_data = MatrixData.import_matrix_data(args.matrix_data)
population_data = PopulationData.import_population_data(args.population_data)
initial_agent_constraints = InitialAgentsConstraints(args.agent_constraints_path)
if args.simulation_parameters_path:
consts = Consts.from_file(args.simulation_parameters_path)
else:
consts = Consts()
set_seeds(args.seed)
sm = SimulationManager(
(
# "Latent",
Supervisable.State.AddedPerDay("Asymptomatic"),
Supervisable.State.Current("Asymptomatic"),
Supervisable.State.TotalSoFar("Asymptomatic"),
# "Silent",
# "Asymptomatic",
# "Symptomatic",
# "Deceased",
# "Hospitalized",
# "ICU",
# "Susceptible",
# "Recovered",
Supervisable.Sum(
"Latent",
"Latent-Asymp",
"Latent-Presymp",
"Asymptomatic",
"Pre-Symptomatic",
"Mild-Condition",
"NeedOfCloseMedicalCare",
"NeedICU",
"ImprovingHealth",
"PreRecovered",
name="currently sick"
),
# LambdaValueSupervisable("ever hospitalized", lambda manager: len(manager.medical_machine["Hospitalized"].ever_visited)),
LambdaValueSupervisable(
"was ever sick",
lambda manager: len(manager.agents) - manager.medical_machine["Susceptible"].agent_count,
),
Supervisable.NewCasesCounter(),
Supervisable.Wrappers.Growth(Supervisable.NewCasesCounter(), 1),
Supervisable.Wrappers.RunningAverage(Supervisable.Wrappers.Growth(Supervisable.NewCasesCounter()), 7),
Supervisable.Wrappers.Growth(Supervisable.NewCasesCounter(), 7),
# Supervisable.GrowthFactor(
# Supervisable.Sum("Symptomatic", "Asymptomatic", "Latent", "Silent", "ICU", "Hospitalized"),
# LambdaValueSupervisable("Detected Daily", lambda manager: manager.new_detected_daily),
# LambdaValueSupervisable("Current Confirmed Cases", lambda manager: sum(manager.tested_positive_vector)),
# Supervisable.R0(),
# Supervisable.Delayed("Symptomatic", 3),
),
population_data,
matrix_data,
initial_agent_constraints,
run_args=args,
consts=consts,
)
print(sm)
sm.run()
df: pd.DataFrame = sm.dump(filename=args.output)
df.plot()
if args.figure_path:
if not os.path.splitext(args.figure_path)[1]:
args.figure_path = args.figure_path+'.png'
plt.savefig(args.figure_path)
else:
plt.show()