def run_simu(n_people=None,
init_percent_sick=0,
start_time=datetime.datetime(2020, 2, 28, 0, 0),
simulation_days=10,
outfile=None,
out_chunk_size=None,
print_progress=False,
seed=0,
port=6688,
n_jobs=1,
other_monitors=[]):
rng = np.random.RandomState(seed)
env = Env(start_time)
city_x_range = (0, 1000)
city_y_range = (0, 1000)
city = City(env, n_people, rng, city_x_range, city_y_range, start_time,
init_percent_sick, Human)
monitors = [
EventMonitor(f=120, dest=outfile, chunk_size=out_chunk_size),
SEIRMonitor(f=1440)
]
# run the simulation
if print_progress:
monitors.append(TimeMonitor(1440)) # print every day
if other_monitors:
monitors += other_monitors
for human in city.humans:
env.process(human.run(city=city))
for m in monitors:
env.process(m.run(env, city=city))
all_possible_symptoms = [
'moderate', 'mild', 'severe', 'extremely-severe', 'fever', 'chills',
'gastro', 'diarrhea', 'nausea_vomiting', 'fatigue', 'unusual',
'hard_time_waking_up', 'headache', 'confused', 'lost_consciousness',
'trouble_breathing', 'sneezing', 'cough', 'runny_nose', 'aches',
'sore_throat', 'severe_chest_pain', 'loss_of_taste',
'mild_trouble_breathing', 'light_trouble_breathing',
'moderate_trouble_breathing', 'heavy_trouble_breathing'
]
monitors[0].dump()
monitors[0].join_iothread()
env.process(
city.run(1440, outfile, start_time, all_possible_symptoms, port,
n_jobs))
env.run(until=simulation_days * 24 * 60 / TICK_MINUTE)
return monitors, city.tracker
def run_simu(n_people=None,
init_percent_sick=0.0,
start_time=datetime.datetime(2020, 2, 28, 0, 0),
simulation_days=10,
outfile=None,
out_chunk_size=None,
print_progress=False,
seed=0,
port=6688,
n_jobs=1,
other_monitors=[]):
rng = np.random.RandomState(seed)
env = Env(start_time)
city_x_range = (0, 1000)
city_y_range = (0, 1000)
city = City(env, n_people, rng, city_x_range, city_y_range, start_time,
init_percent_sick, Human)
monitors = [
EventMonitor(f=1800, dest=outfile, chunk_size=out_chunk_size),
SEIRMonitor(f=1440)
]
# run the simulation
if print_progress:
monitors.append(TimeMonitor(1440)) # print every day
if other_monitors:
monitors += other_monitors
# run city
all_possible_symptoms = [""] * len(SYMPTOMS_META)
for k, v in SYMPTOMS_META.items():
all_possible_symptoms[v] = k
monitors[0].dump()
monitors[0].join_iothread()
env.process(
city.run(1440, outfile, start_time, all_possible_symptoms, port,
n_jobs))
# run humans
for human in city.humans:
env.process(human.run(city=city))
# run monitors
for m in monitors:
env.process(m.run(env, city=city))
env.run(until=simulation_days * 24 * 60 / TICK_MINUTE)
return monitors, city.tracker
def run_simu(n_people=None,
init_percent_sick=0,
start_time=datetime.datetime(2020, 2, 28, 0, 0),
simulation_days=10,
outfile=None,
out_chunk_size=None,
print_progress=False,
seed=0,
other_monitors=[]):
rng = np.random.RandomState(seed)
env = Env(start_time)
city_x_range = (0, 1000)
city_y_range = (0, 1000)
city = City(env, n_people, rng, city_x_range, city_y_range, start_time,
init_percent_sick, Human, simulation_days)
monitors = [
EventMonitor(f=120, dest=outfile, chunk_size=out_chunk_size),
SEIRMonitor(f=1440)
]
# run the simulation
if print_progress:
monitors.append(TimeMonitor(1440)) # print every day
if other_monitors:
monitors += other_monitors
for human in city.humans:
env.process(human.run(city=city))
for m in monitors:
env.process(m.run(env, city=city))
env.run(until=simulation_days * 24 * 60 / TICK_MINUTE)
return monitors, city.tracker
def run_simu(n_stores=None,
n_people=None,
n_parks=None,
n_misc=None,
init_percent_sick=0,
store_capacity=30,
misc_capacity=30,
start_time=datetime.datetime(2020, 2, 28, 0, 0),
simulation_days=10,
outfile=None,
print_progress=False):
env = Env(start_time)
city_limit = ((0, 1000), (0, 1000))
stores = [
Location(
env,
capacity=_draw_random_discreet_gaussian(store_capacity,
int(0.5 * store_capacity)),
cont_prob=0.1,
location_type='store',
name=f'store{i}',
lat=random.randint(*city_limit[0]),
lon=random.randint(*city_limit[1]),
) for i in range(n_stores)
]
parks = [
Location(env,
cont_prob=0.02,
name=f'park{i}',
location_type='park',
lat=random.randint(*city_limit[0]),
lon=random.randint(*city_limit[1])) for i in range(n_parks)
]
households = [
Location(
env,
cont_prob=1,
name=f'household{i}',
location_type='household',
lat=random.randint(*city_limit[0]),
lon=random.randint(*city_limit[1]),
) for i in range(int(n_people / 2))
]
workplaces = [
Location(
env,
cont_prob=1,
name=f'workplace{i}',
location_type='workplace',
lat=random.randint(*city_limit[0]),
lon=random.randint(*city_limit[1]),
) for i in range(int(n_people / 30))
]
miscs = [
Location(env,
cont_prob=1,
capacity=_draw_random_discreet_gaussian(
misc_capacity, int(0.5 * misc_capacity)),
name=f'misc{i}',
location_type='misc',
lat=random.randint(*city_limit[0]),
lon=random.randint(*city_limit[1])) for i in range(n_misc)
]
humans = [
Human(env=env,
name=i,
infection_timestamp=start_time
if i < n_people * init_percent_sick else None,
household=np.random.choice(households),
workplace=np.random.choice(workplaces)) for i in range(n_people)
]
city = City(stores=stores, parks=parks, humans=humans, miscs=miscs)
monitors = [EventMonitor(f=120)]
# run the simulation
if print_progress:
monitors.append(TimeMonitor(60))
for human in humans:
env.process(human.run(city=city))
for m in monitors:
env.process(m.run(env, city=city))
env.run(until=simulation_days * 24 * 60 / TICK_MINUTE)
monitors[0].dump(outfile)
return monitors[0].data
def run_simu(n_stores=None,
n_people=None,
n_parks=None,
n_misc=None,
init_percent_sick=0,
store_capacity=30,
misc_capacity=30,
start_time=datetime.datetime(2020, 2, 28, 0, 0),
simulation_days=10,
outfile=None,
print_progress=False,
seed=0,
Human=None):
if Human is None:
from simulator import Human
rng = np.random.RandomState(seed)
env = Env(start_time)
city_limit = ((0, 1000), (0, 1000))
total_area = (city_limit[0][1] - city_limit[0][0]) * (city_limit[1][1] -
city_limit[1][0])
area_dict = {
'store':
_get_random_area('store', n_stores, total_area, rng),
'park':
_get_random_area('park', n_parks, total_area, rng),
'misc':
_get_random_area('misc', n_misc, total_area, rng),
'household':
_get_random_area('household', int(n_people / 2), total_area, rng),
'workplace':
_get_random_area('workplace', int(n_people / 30), total_area, rng)
}
stores = [
Location(env,
rng,
capacity=_draw_random_discreet_gaussian(
store_capacity, int(0.5 * store_capacity), rng),
cont_prob=0.6,
location_type='store',
name=f'store{i}',
area=area_dict['store'][i],
lat=rng.randint(*city_limit[0]),
lon=rng.randint(*city_limit[1]),
surface_prob=[0.1, 0.1, 0.3, 0.2, 0.3])
for i in range(n_stores)
]
parks = [
Location(env,
rng,
cont_prob=0.05,
name=f'park{i}',
area=area_dict['park'][i],
location_type='park',
lat=rng.randint(*city_limit[0]),
lon=rng.randint(*city_limit[1]),
surface_prob=[0.7, 0.05, 0.05, 0.1, 0.1])
for i in range(n_parks)
]
households = [
Location(env,
rng,
cont_prob=1,
name=f'household{i}',
location_type='household',
area=area_dict['household'][i],
lat=rng.randint(*city_limit[0]),
lon=rng.randint(*city_limit[1]),
surface_prob=[0.05, 0.05, 0.05, 0.05, 0.8])
for i in range(int(n_people / 2))
]
workplaces = [
Location(env,
rng,
cont_prob=0.3,
name=f'workplace{i}',
location_type='workplace',
area=area_dict['workplace'][i],
lat=rng.randint(*city_limit[0]),
lon=rng.randint(*city_limit[1]),
surface_prob=[0.1, 0.1, 0.3, 0.2, 0.3])
for i in range(int(n_people / 30))
]
miscs = [
Location(env,
rng,
cont_prob=1,
capacity=_draw_random_discreet_gaussian(
misc_capacity, int(0.5 * misc_capacity), rng),
name=f'misc{i}',
area=area_dict['misc'][i],
location_type='misc',
lat=rng.randint(*city_limit[0]),
lon=rng.randint(*city_limit[1]),
surface_prob=[0.1, 0.1, 0.3, 0.2, 0.3]) for i in range(n_misc)
]
humans = [
Human(env=env,
name=i,
rng=rng,
age=_get_random_age(rng),
infection_timestamp=start_time
if i < n_people * init_percent_sick else None,
household=rng.choice(households),
workplace=rng.choice(workplaces)) for i in range(n_people)
]
city = City(stores=stores, parks=parks, humans=humans, miscs=miscs)
monitors = [EventMonitor(f=120), SEIRMonitor(f=1440)]
extra_info = {}
extra_info['init_percent_sick'] = init_percent_sick
extra_info['initial_states'] = [
'infected' if i < n_people * init_percent_sick else 'not_infected'
for i in range(n_people)
]
extra_info['recovery_probs'] = [1.0 / h.recovery_days for h in humans]
import json
with open('extra_info.json', 'w') as f:
json.dump(extra_info, f)
# run the simulation
if print_progress:
monitors.append(TimeMonitor(60))
for human in humans:
env.process(human.run(city=city))
for m in monitors:
env.process(m.run(env, city=city))
env.run(until=simulation_days * 24 * 60 / TICK_MINUTE)
return monitors