def load_universities_from_hdf5(file_path: str, chunk_size: int = 50000):
"""
Loads universities 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:
universities = f["universities"]
universities_list = []
n_universities = universities.attrs["n_universities"]
ids = universities["id"]
n_students_max = universities["n_students_max"]
n_years = universities["n_years"]
coordinates = universities["coordinates"]
ukprns = universities["ukprns"]
for k in range(n_universities):
university = University(
coordinates=coordinates[k],
n_students_max=n_students_max[k],
n_years=n_years[k],
ukprn=ukprns[k]
)
university.id = ids[k]
universities_list.append(university)
return Universities(universities_list)
def load_universities_from_hdf5(file_path: str,
chunk_size: int = 50000,
domain_areas=None):
"""
Loads universities 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:
universities = f["universities"]
universities_list = []
n_universities = universities.attrs["n_universities"]
ids = read_dataset(universities["id"])
n_students_max = read_dataset(universities["n_students_max"])
n_years = read_dataset(universities["n_years"])
ukprns = read_dataset(universities["ukprns"])
areas = read_dataset(universities["area"])
coordinates = read_dataset(universities["coordinates"])
for k in range(n_universities):
if domain_areas is not None:
area = areas[k]
if area == nan_integer:
raise ValueError(
"if ``domain_areas`` is True, I expect not Nones areas."
)
if area not in domain_areas:
continue
university = University(n_students_max=n_students_max[k],
n_years=n_years[k],
ukprn=ukprns[k],
coordinates=coordinates[k])
university.id = ids[k]
universities_list.append(university)
return Universities(universities_list)
def test__university_for_super_areas():
super_area = SuperArea(name="durham",
areas=None,
coordinates=[54.768, -1.571868])
super_areas = SuperAreas([super_area])
unis = Universities.for_super_areas(super_areas)
durham_uni = unis[0]
assert durham_uni.n_students_max == 19025
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 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
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)
world.groceries = Groceries.for_super_areas(world.super_areas,
venues_per_capita=1 / 500)
))
t1 = time.time()
# default config path
config_path = "./config_simulation.yaml"
# define geography, let's run the first 20 super areas of london
geography = Geography.from_file({"super_area": london_areas})
#geography = Geography.from_file({"region": ["North East"]})
# add buildings
geography.hospitals = Hospitals.for_geography(geography)
geography.companies = Companies.for_geography(geography)
geography.schools = Schools.for_geography(geography)
geography.universities = Universities.for_geography(geography)
geography.care_homes = CareHomes.for_geography(geography)
## generate world
world = generate_world_from_geography(geography, include_households=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_areas(
areas=world.areas, super_areas=world.super_areas
) # this assigns possible social venues to people.
travel = Travel()
travel.initialise_commute(world)
t2 = time.time()
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