def load_companies_from_hdf5(file_path: str, chunk_size=50000):
"""
Loads companies from an hdf5 file located at ``file_path``.
Note that this object will not be ready to use, as the links to
object instances of other classes need to be restored first.
This function should be rarely be called oustide world.py
"""
with h5py.File(file_path, "r", libver="latest", swmr=True) as f:
print("loading companies from hdf5 ", end="")
companies = f["companies"]
companies_list = []
n_companies = companies.attrs["n_companies"]
n_chunks = int(np.ceil(n_companies / chunk_size))
for chunk in range(n_chunks):
print(".", end="")
idx1 = chunk * chunk_size
idx2 = min((chunk + 1) * chunk_size, n_companies)
ids = companies["id"][idx1:idx2]
super_areas = companies["super_area"][idx1:idx2]
sectors = companies["sector"][idx1:idx2]
n_workers_maxs = companies["n_workers_max"][idx1:idx2]
for k in range(idx2 - idx1):
super_area = super_areas[k]
if super_area == nan_integer:
super_area = None
company = Company(super_area, n_workers_maxs[k],
sectors[k].decode())
company.id = ids[k]
companies_list.append(company)
print("\n", end="")
return Companies(companies_list)
def make_world(geography):
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,
include_households=False,
include_commute=False)
return world
def load_companies_from_hdf5(file_path: str,
chunk_size=50000,
domain_super_areas=None):
"""
Loads companies from an hdf5 file located at ``file_path``.
Note that this object will not be ready to use, as the links to
object instances of other classes need to be restored first.
This function should be rarely be called oustide world.py
"""
logger.info("loading companies...")
with h5py.File(file_path, "r", libver="latest", swmr=True) as f:
companies = f["companies"]
companies_list = []
n_companies = companies.attrs["n_companies"]
n_chunks = int(np.ceil(n_companies / chunk_size))
for chunk in range(n_chunks):
logger.info(f"Companies chunk {chunk} of {n_chunks}")
idx1 = chunk * chunk_size
idx2 = min((chunk + 1) * chunk_size, n_companies)
length = idx2 - idx1
ids = read_dataset(companies["id"], idx1, idx2)
sectors = read_dataset(companies["sector"], idx1, idx2)
n_workers_maxs = read_dataset(companies["n_workers_max"], idx1,
idx2)
super_areas = read_dataset(companies["super_area"], idx1, idx2)
for k in range(length):
if domain_super_areas is not None:
super_area = super_areas[k]
if super_area == nan_integer:
raise ValueError(
"if ``domain_super_areas`` is True, I expect not Nones super areas."
)
if super_area not in domain_super_areas:
continue
company = Company(
super_area=None,
n_workers_max=n_workers_maxs[k],
sector=sectors[k].decode(),
)
company.id = ids[k]
companies_list.append(company)
return Companies(companies_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)
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
from june.infection.infection import InfectionSelector
from june.world import generate_world_from_hdf5, generate_world_from_geography
geography = Geography.from_file({
"super_area": [
"E02003282",
"E02001720",
"E00088544",
"E02002560",
"E02002559",
"E02004314",
]
})
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,
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)
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