def generate_camp():
world = generate_empty_world({"region": ["CXB-219"]})
populate_world(world)
# distribute people to households
distribute_people_to_households(world)
# medical facilities
hospitals = Hospitals.from_file(filename=camp_data_path /
"input/hospitals/hospitals.csv")
world.hospitals = hospitals
for hospital in world.hospitals:
hospital.area = world.areas.members[0]
hospital_distributor = HospitalDistributor(hospitals,
medic_min_age=20,
patients_per_medic=10)
world.isolation_units = IsolationUnits(
[IsolationUnit(area=world.areas[0])])
hospital_distributor.distribute_medics_from_world(world.people)
world.learning_centers = LearningCenters.for_areas(world.areas, n_shifts=4)
world.pump_latrines = PumpLatrines.for_areas(world.areas)
world.play_groups = PlayGroups.for_areas(world.areas)
world.distribution_centers = DistributionCenters.for_areas(world.areas)
world.communals = Communals.for_areas(world.areas)
world.female_communals = FemaleCommunals.for_areas(world.areas)
world.religiouss = Religiouss.for_areas(world.areas)
world.e_vouchers = EVouchers.for_areas(world.areas)
world.n_f_distribution_centers = NFDistributionCenters.for_areas(
world.areas)
world.shelters = Shelters.for_areas(world.areas)
world.cemeteries = Cemeteries()
shelter_distributor = ShelterDistributor(
sharing_shelter_ratio=0.75
) # proportion of families that share a shelter
for area in world.areas:
shelter_distributor.distribute_people_in_shelters(
area.shelters, area.households)
return world
def test__social_distancing(self, setup_policy_world):
world, pupil, student, worker, sim = setup_policy_world
world.cemeteries = Cemeteries()
start_date = datetime(2020, 3, 10)
end_date = datetime(2020, 3, 12)
beta_factors = {
"box": 0.5,
"pub": 0.5,
"grocery": 0.5,
"cinema": 0.5,
"commute_unit": 0.5,
"commute_city_unit": 0.5,
"hospital": 0.5,
"care_home": 0.5,
"company": 0.5,
"school": 0.5,
"household": 1.0,
"university": 0.5,
}
social_distance = SocialDistancing(start_time="2020-03-10",
end_time="2020-03-12",
beta_factors=beta_factors)
beta_factors2 = {"cinema": 4}
start_date2 = datetime(2020, 3, 12)
end_date2 = datetime(2020, 3, 15)
social_distance2 = SocialDistancing(start_time="2020-03-12",
end_time="2020-03-15",
beta_factors=beta_factors2)
policies = Policies([social_distance, social_distance2])
leisure_instance = leisure.generate_leisure_for_config(
world=world, config_filename=test_config)
leisure_instance.distribute_social_venues_to_households(
world.households)
sim.activity_manager.policies = policies
sim.activity_manager.leisure = leisure_instance
sim.timer.reset()
initial_betas = copy.deepcopy(sim.interaction.beta)
sim.clear_world()
while sim.timer.date <= sim.timer.final_date:
sim.do_timestep()
if sim.timer.date >= start_date and sim.timer.date < end_date:
for group in sim.interaction.beta:
if group != "household":
assert sim.interaction.beta[
group] == initial_betas[group] * 0.5
else:
assert sim.interaction.beta[group] == initial_betas[
group]
next(sim.timer)
continue
if sim.timer.date >= start_date2 and sim.timer.date < end_date2:
for group in sim.interaction.beta:
if group != "cinema":
assert sim.interaction.beta == 4.0
else:
assert sim.interaction.beta[group] == initial_betas[
group]
next(sim.timer)
continue
assert sim.interaction.beta == initial_betas
next(sim.timer)
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
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(
world.super_areas,
def generate_domain_from_hdf5(domain_id,
super_areas_to_domain_dict: dict,
file_path: str,
chunk_size=500000) -> "Domain":
"""
Loads the world from an hdf5 file. All id references are substituted
by actual references to the relevant instances.
Parameters
----------
file_path
path of the hdf5 file
chunk_size
how many units of supergroups to process at a time.
It is advise to keep it around 1e6
"""
logger.info(f"loading domain {domain_id} from HDF5")
# import here to avoid recurisve imports
from june.domain import Domain
# get the super area ids of this domain
super_area_ids = set()
for super_area, did in super_areas_to_domain_dict.items():
if did == domain_id:
super_area_ids.add(super_area)
domain = Domain()
# get keys in hdf5 file
with h5py.File(file_path, "r", libver="latest", swmr=True) as f:
f_keys = list(f.keys()).copy()
geography = load_geography_from_hdf5(file_path=file_path,
chunk_size=chunk_size,
domain_super_areas=super_area_ids)
domain.areas = geography.areas
area_ids = set([area.id for area in domain.areas])
domain.super_areas = geography.super_areas
domain.regions = geography.regions
# load world data
if "hospitals" in f_keys:
logger.info("loading hospitals...")
domain.hospitals = load_hospitals_from_hdf5(
file_path=file_path,
chunk_size=chunk_size,
domain_super_areas=super_area_ids,
super_areas_to_domain_dict=super_areas_to_domain_dict,
)
if "schools" in f_keys:
logger.info("loading schools...")
domain.schools = load_schools_from_hdf5(
file_path=file_path,
chunk_size=chunk_size,
domain_super_areas=super_area_ids,
)
if "companies" in f_keys:
domain.companies = load_companies_from_hdf5(
file_path=file_path,
chunk_size=chunk_size,
domain_super_areas=super_area_ids,
)
if "care_homes" in f_keys:
logger.info("loading care homes...")
domain.care_homes = load_care_homes_from_hdf5(
file_path=file_path,
chunk_size=chunk_size,
domain_super_areas=super_area_ids,
)
if "universities" in f_keys:
logger.info("loading universities...")
domain.universities = load_universities_from_hdf5(
file_path=file_path,
chunk_size=chunk_size,
domain_areas=area_ids,
)
if "cities" in f_keys:
logger.info("loading cities...")
domain.cities = load_cities_from_hdf5(
file_path=file_path,
domain_super_areas=super_area_ids,
super_areas_to_domain_dict=super_areas_to_domain_dict,
)
if "stations" in f_keys:
logger.info("loading stations...")
(
domain.stations,
domain.inter_city_transports,
domain.city_transports,
) = load_stations_from_hdf5(
file_path,
domain_super_areas=super_area_ids,
super_areas_to_domain_dict=super_areas_to_domain_dict,
)
if "households" in f_keys:
domain.households = load_households_from_hdf5(
file_path,
chunk_size=chunk_size,
domain_super_areas=super_area_ids)
if "population" in f_keys:
domain.people = load_population_from_hdf5(
file_path,
chunk_size=chunk_size,
domain_super_areas=super_area_ids)
if "social_venues" in f_keys:
logger.info("loading social venues...")
social_venues_dict = load_social_venues_from_hdf5(
file_path, domain_areas=area_ids)
for social_venues_spec, social_venues in social_venues_dict.items():
setattr(domain, social_venues_spec, social_venues)
# restore world
logger.info("restoring world...")
restore_geography_properties_from_hdf5(
world=domain,
file_path=file_path,
chunk_size=chunk_size,
domain_super_areas=super_area_ids,
super_areas_to_domain_dict=super_areas_to_domain_dict,
)
if "population" in f_keys:
restore_population_properties_from_hdf5(
world=domain,
file_path=file_path,
chunk_size=chunk_size,
domain_super_areas=super_area_ids,
super_areas_to_domain_dict=super_areas_to_domain_dict,
)
if "households" in f_keys:
restore_households_properties_from_hdf5(
world=domain,
file_path=file_path,
chunk_size=chunk_size,
domain_super_areas=super_area_ids,
)
if "care_homes" in f_keys:
logger.info("restoring care homes...")
restore_care_homes_properties_from_hdf5(
world=domain,
file_path=file_path,
chunk_size=chunk_size,
domain_super_areas=super_area_ids,
)
if "hospitals" in f_keys:
logger.info("restoring hospitals...")
restore_hospital_properties_from_hdf5(
world=domain,
file_path=file_path,
chunk_size=chunk_size,
domain_super_areas=super_area_ids,
domain_areas=area_ids,
super_areas_to_domain_dict=super_areas_to_domain_dict,
)
if "companies" in f_keys:
logger.info("restoring companies...")
restore_companies_properties_from_hdf5(
world=domain,
file_path=file_path,
chunk_size=chunk_size,
domain_super_areas=super_area_ids,
)
if "schools" in f_keys:
logger.info("restoring schools...")
restore_school_properties_from_hdf5(
world=domain,
file_path=file_path,
chunk_size=chunk_size,
domain_super_areas=super_area_ids,
)
if "universities" in f_keys:
logger.info("restoring unis...")
restore_universities_properties_from_hdf5(world=domain,
file_path=file_path,
domain_areas=area_ids)
if "cities" and "stations" in f_keys:
logger.info("restoring commute...")
restore_cities_and_stations_properties_from_hdf5(
world=domain,
file_path=file_path,
chunk_size=chunk_size,
domain_super_areas=super_area_ids,
super_areas_to_domain_dict=super_areas_to_domain_dict,
)
if "social_venues" in f_keys:
logger.info("restoring social venues...")
restore_social_venues_properties_from_hdf5(world=domain,
file_path=file_path,
domain_areas=area_ids)
domain.cemeteries = Cemeteries()
return domain
def generate_world_from_hdf5(file_path: str, chunk_size=500000) -> World:
"""
Loads the world from an hdf5 file. All id references are substituted
by actual references to the relevant instances.
Parameters
----------
file_path
path of the hdf5 file
chunk_size
how many units of supergroups to process at a time.
It is advise to keep it around 1e6
"""
logger.info("loading world from HDF5")
world = World()
with h5py.File(file_path, "r", libver="latest", swmr=True) as f:
f_keys = list(f.keys()).copy()
geography = load_geography_from_hdf5(file_path=file_path,
chunk_size=chunk_size)
world.areas = geography.areas
world.super_areas = geography.super_areas
world.regions = geography.regions
if "hospitals" in f_keys:
logger.info("loading hospitals...")
world.hospitals = load_hospitals_from_hdf5(file_path=file_path,
chunk_size=chunk_size)
if "schools" in f_keys:
logger.info("loading schools...")
world.schools = load_schools_from_hdf5(file_path=file_path,
chunk_size=chunk_size)
if "companies" in f_keys:
world.companies = load_companies_from_hdf5(file_path=file_path,
chunk_size=chunk_size)
if "care_homes" in f_keys:
logger.info("loading care homes...")
world.care_homes = load_care_homes_from_hdf5(file_path=file_path,
chunk_size=chunk_size)
if "universities" in f_keys:
logger.info("loading universities...")
world.universities = load_universities_from_hdf5(file_path=file_path,
chunk_size=chunk_size)
if "cities" in f_keys:
logger.info("loading cities...")
world.cities = load_cities_from_hdf5(file_path)
if "stations" in f_keys:
logger.info("loading stations...")
(
world.stations,
world.inter_city_transports,
world.city_transports,
) = load_stations_from_hdf5(file_path)
if "households" in f_keys:
world.households = load_households_from_hdf5(file_path,
chunk_size=chunk_size)
if "population" in f_keys:
world.people = load_population_from_hdf5(file_path,
chunk_size=chunk_size)
if "social_venues" in f_keys:
logger.info("loading social venues...")
social_venues_dict = load_social_venues_from_hdf5(file_path)
for social_venues_spec, social_venues in social_venues_dict.items():
setattr(world, social_venues_spec, social_venues)
# restore world
logger.info("restoring world...")
restore_geography_properties_from_hdf5(world=world,
file_path=file_path,
chunk_size=chunk_size)
if "population" in f_keys:
restore_population_properties_from_hdf5(world=world,
file_path=file_path,
chunk_size=chunk_size)
if "households" in f_keys:
restore_households_properties_from_hdf5(world=world,
file_path=file_path,
chunk_size=chunk_size)
if "care_homes" in f_keys:
logger.info("restoring care homes...")
restore_care_homes_properties_from_hdf5(world=world,
file_path=file_path,
chunk_size=chunk_size)
if "hospitals" in f_keys:
logger.info("restoring hospitals...")
restore_hospital_properties_from_hdf5(world=world,
file_path=file_path,
chunk_size=chunk_size)
if "cities" and "stations" in f_keys:
logger.info("restoring commute...")
restore_cities_and_stations_properties_from_hdf5(world=world,
file_path=file_path,
chunk_size=chunk_size)
if "companies" in f_keys:
logger.info("restoring companies...")
restore_companies_properties_from_hdf5(
world=world,
file_path=file_path,
chunk_size=chunk_size,
)
if "schools" in f_keys:
logger.info("restoring schools...")
restore_school_properties_from_hdf5(
world=world,
file_path=file_path,
chunk_size=chunk_size,
)
if "universities" in f_keys:
logger.info("restoring unis...")
restore_universities_properties_from_hdf5(world=world,
file_path=file_path)
if "social_venues" in f_keys:
logger.info("restoring social venues...")
restore_social_venues_properties_from_hdf5(world=world,
file_path=file_path)
world.cemeteries = Cemeteries()
return world
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