def test__generate_leisure_from_world():
geography = Geography.from_file({"super_area": ["E02002135"]})
world = generate_world_from_geography(geography,
include_households=True,
include_commute=False)
world.pubs = Pubs.for_geography(geography)
world.cinemas = Cinemas.for_geography(geography)
world.groceries = Groceries.for_geography(geography)
person = Person.from_attributes(sex="m", age=27)
household = Household()
household.area = world.areas[0]
household.add(person)
person.area = geography.areas[0]
leisure = generate_leisure_for_world(
list_of_leisure_groups=["pubs", "cinemas", "groceries"], world=world)
leisure.distribute_social_venues_to_households([household])
leisure.generate_leisure_probabilities_for_timestep(0.1, False)
n_pubs = 0
n_cinemas = 0
n_groceries = 0
for _ in range(0, 1000):
subgroup = leisure.get_subgroup_for_person_and_housemates(person)
if subgroup is not None:
if subgroup.group.spec == "pub":
n_pubs += 1
elif subgroup.group.spec == "cinema":
n_cinemas += 1
elif subgroup.group.spec == "grocery":
n_groceries += 1
assert 0 < n_pubs < 100
assert 0 < n_cinemas < 100
assert 0 < n_groceries < 107
def load_cinemas_from_hdf5(file_path: str):
with h5py.File(file_path, "r", libver="latest", swmr=True) as f:
cinemas = f["cinemas"]
cinemas_list = []
n_cinemas = cinemas.attrs["n_cinemas"]
ids = cinemas["id"]
coordinates = cinemas["coordinates"]
for k in range(n_cinemas):
cinema = Cinema()
cinema.id = ids[k]
cinema.coordinates = coordinates[k]
cinemas_list.append(cinema)
return Cinemas(cinemas_list)
def create_world(geography_h5):
with h5py.File("test.hdf5", "w"):
pass # reset file
geography = geography_h5
geography.hospitals = Hospitals.for_geography(geography)
geography.schools = Schools.for_geography(geography)
geography.companies = Companies.for_geography(geography)
geography.care_homes = CareHomes.for_geography(geography)
geography.universities = Universities.for_super_areas(geography.super_areas)
world = generate_world_from_geography(
geography=geography, include_households=True, include_commute=True
)
world.pubs = Pubs.for_geography(geography)
world.cinemas = Cinemas.for_geography(geography)
world.groceries = Groceries.for_geography(geography)
leisure = generate_leisure_for_world(
["pubs", "cinemas", "groceries", "household_visits", "care_home_vists"], world
)
leisure.distribute_social_venues_to_households(world.households)
return world
def make_leisure():
pubs = Pubs([Pub()], make_tree=False)
pub_distributor = PubDistributor(
pubs,
male_age_probabilities={"18-50": 0.5},
female_age_probabilities={"10-40": 0.2},
drags_household_probability=0.0,
)
pubs[0].coordinates = [1, 2]
cinemas = Cinemas([Cinema()], make_tree=False)
cinemas[0].coordinates = [1, 2]
cinema_distributor = CinemaDistributor(
cinemas,
male_age_probabilities={"10-40": 0.2},
female_age_probabilities={"10-40": 0.2},
drags_household_probability=1.0,
)
leisure = Leisure(leisure_distributors={
"pubs": pub_distributor,
"cinemas": cinema_distributor
})
leisure.generate_leisure_probabilities_for_timestep(0.01, False)
return leisure
def simulation(args):
gf.print_flush(args)
print("Physical cores:", psutil.cpu_count(logical=False))
print("Total cores:", psutil.cpu_count(logical=True))
print("=" * 20, "Memory Information", "=" * 20)
# get the memory details
svmem = psutil.virtual_memory()
print(f"Total: {get_size(svmem.total)}")
print(f"Available: {get_size(svmem.available)}")
print(f"Used: {get_size(svmem.used)}")
print(f"Percentage: {svmem.percent}%")
pid = os.getpid()
py = psutil.Process(pid)
memoryUse = py.memory_info()[0]
# initialise world from file
gf.print_flush("Initialising world...")
world_file = "{}.hdf5".format(args.world)
world = generate_world_from_hdf5(world_file, chunk_size=1_000_000)
gf.print_flush("World loaded successfully...")
geography = load_geography_from_hdf5(world_file)
# leisure
gf.print_flush("Initialising leisure...")
world.pubs = Pubs.for_geography(geography)
world.cinemas = Cinemas.for_geography(geography)
world.groceries = Groceries.for_super_areas(geography.super_areas)
# cemeteries
gf.print_flush("Initialising cemeteries...")
world.cemeteries = Cemeteries()
# commute
gf.print_flush("Initialising commute...")
world.initialise_commuting()
# infection selector
gf.print_flush("Selecting infection...")
selector = InfectionSelector.from_file()
interaction = ContactAveraging.from_file(selector=selector)
lhs_array = np.load("lhs_array.npy")
parameters = generate_parameters_from_lhs(lhs_array, args.idx)
interaction = set_interaction_parameters(parameters, interaction)
gf.print_flush("Betas = {}, alpha = {}".format(interaction.beta,
interaction.alpha_physical))
if not os.path.exists(SAVE_PATH):
os.makedirs(SAVE_PATH)
# save out parameters for later
with open(SAVE_PATH + '/parameters.json', 'w') as f:
json.dump(parameters, f)
# seed infections
seed = Seed.from_file(
super_areas=world.super_areas,
selector=selector,
)
print(f"Memory used by JUNE's world: {get_size(memoryUse)}")
simulator = Simulator.from_file(world,
interaction,
selector,
seed=seed,
config_filename=CONFIG_PATH,
save_path=SAVE_PATH)
simulator.run()
# read = ReadLogger(SAVE_PATH)
# world_df = read.world_summary()
# ages_df = read.age_summary([0,10,20,30,40,
# 50,60,70,80,90,100])
# loc_df = read.get_locations_infections()
# r_df = read.get_r()
# world_df.to_csv(SAVE_PATH + '/world_df.csv')
# ages_df.to_csv(SAVE_PATH + '/ages_df.csv')
# loc_df.to_csv(SAVE_PATH + '/loc_df.csv')
# r_df.to_csv(SAVE_PATH + '/r_df.csv')
gf.print_flush("Simulation finished!!!!")
return None
def simulation(args):
gf.print_flush(args)
msoaslist = [
"E02005702", "E02005704", "E02005736", "E02005734", "E02001697",
"E02001701", "E02001704", "E02001702", "E02001812", "E02001803",
"E02001806", "E02001796", "E02001801", "E02001802", "E02001795",
"E02001818", "E02001821", "E02001814", "E02001808", "E02001817",
"E02001816", "E02001819", "E02001813", "E02001804", "E02001811",
"E02001805", "E02001791", "E02001794", "E02001792", "E02004320",
"E02004321", "E02004322", "E02004325", "E02004327", "E02004329",
"E02004330", "E02004328", "E02001798", "E02001793", "E02005706",
"E02002496", "E02002498", "E02002500", "E02002503", "E02002504",
"E02002515", "E02002516", "E02006910", "E02002518", "E02002519",
"E02002513", "E02002550", "E02002555", "E02002549", "E02002542",
"E02002547", "E02002545", "E02002543", "E02002537", "E02002544",
"E02002541", "E02002523", "E02002540", "E02002536", "E02002538",
"E02002535", "E02006909", "E02002489", "E02002484", "E02002487",
"E02002485", "E02002483", "E02002493", "E02002490", "E02002492",
"E02002494", "E02002488", "E02002491", "E02004332", "E02002505",
"E02002497", "E02002502", "E02006812", "E02002499", "E02002506",
"E02006811", "E02002509", "E02002501", "E02002508", "E02002507",
"E02002529", "E02002514", "E02002512"
]
gf.print_flush("Generating world from msoalist...")
geography = Geography.from_file({"msoa": msoaslist})
print('memory % used:', psutil.virtual_memory()[2])
geography.hospitals = Hospitals.for_geography(geography)
geography.schools = Schools.for_geography(geography)
geography.companies = Companies.for_geography(geography)
geography.care_homes = CareHomes.for_geography(geography)
demography = Demography.for_geography(geography)
gf.print_flush("Geography and demography generated...")
world = World(geography,
demography,
include_households=True,
include_commute=False)
gf.print_flush("World generated...")
# leisure
world.cinemas = Cinemas.for_geography(geography)
world.pubs = Pubs.for_geography(geography)
world.groceries = Groceries.for_super_areas(world.super_areas,
venues_per_capita=1 / 500)
gf.print_flush("Initialised leisure...")
# commute
world.initialise_commuting()
gf.print_flush("Initialised commute...")
# cemeteries
world.cemeteries = Cemeteries()
gf.print_flush("Initialised cemeteries...")
# infection selector
selector = InfectionSelector.from_file()
interaction = ContactAveraging.from_file(selector=selector)
gf.print_flush("Infection selected...")
# define groups for betas
groups = {
"leisure": ['pub', 'grocery', 'cinema'],
"commute": ['commute_unit', 'commute_city_unit', 'travel_unit'],
"hospital": ['hospital'],
"care_home": ['care_home'],
"company": ['company'],
"school": ['school'],
"household": ['household']
}
# define problem for latin hypercube sampling
problem = {
'num_vars': len(groups),
'names': list(groups.keys()),
'bounds': [[1, 2] for _ in range(len(groups))]
}
lhs = latin.sample(problem, N=args.num_runs, seed=1)[args.idx]
betas = {}
for i, key in enumerate(groups):
for sub_key in groups[key]:
betas[sub_key] = lhs[i]
# save out betas for later
with open(SAVE_PATH + '/betas.json', 'w') as f:
json.dump(betas, f)
# set betas in simulation to sampled ones
for key in betas:
interaction.beta[key] = betas[key]
gf.print_flush(interaction.beta)
# seed infections
seed = Seed(
world.super_areas,
selector,
)
n_cases = int(len(world.people) / 10)
seed.unleash_virus(n_cases)
simulator = Simulator.from_file(world,
interaction,
selector,
config_filename=CONFIG_PATH,
save_path=SAVE_PATH)
print('memory % used:', psutil.virtual_memory()[2])
simulator.run()
gf.print_flush("Simulation finished!!!!")
return None
geography.schools = Schools.for_geography(geography)
geography.companies = Companies.for_geography(geography)
geography.care_homes = CareHomes.for_geography(geography)
geography.universities = Universities.for_super_areas(geography.super_areas)
world = generate_world_from_geography(geography,
include_households=True,
include_commute=True)
print("World length", len(world.people))
world.to_hdf5("world.hdf5")
world = generate_world_from_hdf5("world.hdf5")
# leisure
geography = load_geography_from_hdf5("world.hdf5")
world.cinemas = Cinemas.for_geography(geography)
world.pubs = Pubs.for_geography(geography)
world.groceries = Groceries.for_super_areas(world.super_areas,
venues_per_capita=1 / 500)
world.cemeteries = Cemeteries()
selector = InfectionSelector.from_file()
interaction = Interaction.from_file()
print(interaction.beta)
# modify interactions (example x 2)
interaction.beta['household'] *= 2
print(interaction.beta)
interaction.alpha_physical
interaction.alpha_physical /= 2
interaction.alpha_physical
def make_dummy_world(geog):
super_area = geog.super_areas.members[0]
company = Company(super_area=super_area, n_workers_max=100, sector="Q")
household1 = Household()
household1.area = super_area.areas[0]
hospital = Hospital(
n_beds=40,
n_icu_beds=5,
super_area=super_area.name,
coordinates=super_area.coordinates,
)
uni = University(
coordinates=super_area.coordinates,
n_students_max=2500,
)
worker1 = Person.from_attributes(age=44,
sex='f',
ethnicity='A1',
socioecon_index=5)
worker1.area = super_area.areas[0]
household1.add(worker1, subgroup_type=household1.SubgroupType.adults)
worker1.sector = "Q"
company.add(worker1)
worker2 = Person.from_attributes(age=42,
sex='m',
ethnicity='B1',
socioecon_index=5)
worker2.area = super_area.areas[0]
household1.add(worker2, subgroup_type=household1.SubgroupType.adults)
worker2.sector = "Q"
company.add(worker2)
student1 = Person.from_attributes(age=20,
sex='f',
ethnicity='A1',
socioecon_index=5)
student1.area = super_area.areas[0]
household1.add(student1, subgroup_type=household1.SubgroupType.adults)
uni.add(student1)
pupil1 = Person.from_attributes(age=8,
sex='m',
ethnicity='C1',
socioecon_index=5)
pupil1.area = super_area.areas[0]
household1.add(pupil1, subgroup_type=household1.SubgroupType.kids)
#school.add(pupil1)
pupil2 = Person.from_attributes(age=5,
sex='f',
ethnicity='A1',
socioecon_index=5)
pupil2.area = super_area.areas[0]
household1.add(pupil2, subgroup_type=household1.SubgroupType.kids)
#school.add(pupil2)
world = World()
world.schools = Schools([])
world.hospitals = Hospitals([hospital])
world.households = Households([household1])
world.universities = Universities([uni])
world.companies = Companies([company])
world.people = Population([worker1, worker2, student1, pupil1, pupil2])
world.super_areas = geog.super_areas
world.areas = geog.areas
world.cemeteries = Cemeteries()
cinema = Cinema()
cinema.coordinates = super_area.coordinates
world.cinemas = Cinemas([cinema])
pub = Pub()
pub.coordinates = super_area.coordinates
world.pubs = Pubs([pub])
world.areas[0].people = world.people
return world