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
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
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(