def test_parameters_quick_draw():
config.set_file(f"{DATA_DIR}/parameters_only.yml")
date_range = pd.date_range("2020-01-30", "2020-02-01")
dt = 0.25
nt_inter = int((len(date_range) - 1) * (1 / dt)) + 1
nnodes = 200
npi = pd.DataFrame(0.0, index=date_range, columns=range(nnodes))
alpha, beta, sigma, gamma = setup.parameters_quick_draw(
config, nt_inter, nnodes)
assert alpha.shape == (nt_inter, nnodes)
assert (alpha == 0.5).all()
assert beta.shape == (nt_inter, nnodes)
assert (len(np.unique(beta)) == 1)
assert (((1 / 6. * 2) <= beta) & (beta <= (1. / 2.6 * 3))).all()
assert sigma.shape == (nt_inter, nnodes)
assert (sigma == config["sigma"].as_evaled_expression()).all()
assert gamma.shape == (nt_inter, nnodes)
assert (len(np.unique(gamma)) == 1)
assert (((setup.n_Icomp * (1. / 6)) <= gamma) &
(gamma <= (setup.n_Icomp * (1 / 2.6)))).all()
def test_constant_population():
config.set_file(f"{DATA_DIR}/config.yml")
ss = setup.SpatialSetup(setup_name="test_seir",
geodata_file=f"{DATA_DIR}/geodata.csv",
mobility_file=f"{DATA_DIR}/mobility.txt",
popnodes_key="population",
nodenames_key="geoid")
s = setup.Setup(setup_name="test_seir",
spatial_setup=ss,
nsim=1,
npi_scenario="None",
npi_config=config["interventions"]["settings"]["None"],
ti=config["start_date"].as_date(),
tf=config["end_date"].as_date(),
interactive=True,
write_csv=False,
dt=0.25)
seeding = np.zeros((len(s.t_inter), s.nnodes))
mobility_geoid_indices = s.mobility.indices
mobility_data_indices = s.mobility.indptr
mobility_data = s.mobility.data
npi = NPI.NPIBase.execute(npi_config=s.npi_config,
global_config=config,
geoids=s.spatset.nodenames)
parameters = setup.parameters_quick_draw(config["seir"]["parameters"],
len(s.t_inter), s.nnodes)
parameters = setup.parameters_reduce(parameters, npi, s.dt)
states = seir.steps_SEIR_nb(*parameters, seeding, s.dt, s.t_inter,
s.nnodes, s.popnodes, mobility_geoid_indices,
mobility_data_indices, mobility_data,
s.dynfilter)
completepop = s.popnodes.sum()
origpop = s.popnodes
for it in range(len(s.t_inter)):
totalpop = 0
for i in range(s.nnodes):
totalpop += states[:5, i, it].sum()
#Sum of S, E, I#, R for the geoid that is 'i'
assert (origpop[i] == states[:5, i, it].sum())
assert (completepop == totalpop)
def test_steps_SEIR_no_spread():
config.set_file(f"{DATA_DIR}/config.yml")
ss = setup.SpatialSetup(setup_name="test_seir",
geodata_file=f"{DATA_DIR}/geodata.csv",
mobility_file=f"{DATA_DIR}/mobility.txt",
popnodes_key="population",
nodenames_key="geoid")
s = setup.Setup(setup_name="test_seir",
spatial_setup=ss,
nsim=1,
npi_scenario="None",
npi_config=config["interventions"]["settings"]["None"],
ti=config["start_date"].as_date(),
tf=config["end_date"].as_date(),
interactive=True,
write_csv=False,
dt=0.25)
seeding = np.zeros((len(s.t_inter), s.nnodes))
seeding[:, 0] = 100
mobility_geoid_indices = s.mobility.indices
mobility_data_indices = s.mobility.indptr
mobility_data = s.mobility.data * 0
npi = NPI.NPIBase.execute(npi_config=s.npi_config,
global_config=config,
geoids=s.spatset.nodenames)
parameters = setup.parameters_quick_draw(config["seir"]["parameters"],
len(s.t_inter), s.nnodes)
parameters = setup.parameters_reduce(parameters, npi, s.dt)
for i in range(100):
states = seir.steps_SEIR_nb(*parameters, seeding, s.dt, s.t_inter,
s.nnodes, s.popnodes,
mobility_geoid_indices,
mobility_data_indices, mobility_data,
s.dynfilter)
assert states[seir.cumI][1].max() == 0
