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_groceries_from_hdf5(file_path: str):
with h5py.File(file_path, "r", libver="latest", swmr=True) as f:
groceries = f["groceries"]
groceries_list = []
n_groceries = groceries.attrs["n_groceries"]
ids = groceries["id"]
coordinates = groceries["coordinates"]
for k in range(n_groceries):
grocery = Grocery()
grocery.id = ids[k]
grocery.coordinates = coordinates[k]
groceries_list.append(grocery)
return Groceries(groceries_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 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.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
# # Seed the disease
# There are two options implemented in the seed at the moment, either you specify the number of cases and these are then homogeneously distributed by population to the different areas, or you use UK data on cases per region. For now use the first case.
seed = InfectionSeed(
# default config path
config_path = "../configs/config_example.yaml"
# define geography, let's run the first 20 super areas of london
geography = Geography.from_file({"super_area": london_areas[40:60]})
# add buildings
geography.hospitals = Hospitals.for_geography(geography)
geography.companies = Companies.for_geography(geography)
geography.schools = Schools.for_geography(geography)
geography.universities = Universities.for_super_areas(geography.super_areas)
geography.care_homes = CareHomes.for_geography(geography)
# generate world
world = generate_world_from_geography(geography,
include_households=True,
include_commute=True)
# some leisure activities
world.pubs = Pubs.for_geography(geography)
world.cinemas = Cinemas.for_geography(geography)
world.groceries = Groceries.for_geography(geography)
leisure = generate_leisure_for_config(world, config_filename=config_path)
leisure.distribute_social_venues_to_households(
world.households) # this assigns possible social venues to people.
t2 = time.time()
print(f"Took {t2 -t1} seconds to run.")
# save the world to hdf5 to load it later
print("Saving hdf5...")
world.to_hdf5("tests.hdf5")
print("Done :)")