def load_data(paths, settings, dtype):
M_tt, age_groups = load_age_mixing(paths['age_mixing_matrix_term'])
M_hh, _ = load_age_mixing(paths['age_mixing_matrix_hol'])
C = collapse_commute_data(paths['mobility_matrix'])
la_names = C.index.to_numpy()
w_period = [
settings['inference_period'][0], settings['prediction_period'][1]
]
W = load_commute_volume(paths['commute_volume'], w_period)['percent']
pop = collapse_pop(paths['population_size'])
M_tt = M_tt.astype(DTYPE)
M_hh = M_hh.astype(DTYPE)
C = C.to_numpy().astype(DTYPE)
np.fill_diagonal(C, 0.)
W = W.astype(DTYPE)
pop['n'] = pop['n'].astype(DTYPE)
return {
'M_tt': M_tt,
'M_hh': M_hh,
'C': C,
'la_names': la_names,
'age_groups': age_groups,
'W': W,
'pop': pop
}
def load_data(paths, settings, dtype=DTYPE):
C = load_mobility_matrix(paths["mobility_matrix"])
la_names = C.index.to_numpy()
w_period = [
settings["inference_period"][0], settings["inference_period"][1]
]
W = load_commute_volume(paths["commute_volume"], w_period)["percent"]
pop = load_population(paths["population_size"])
C = C.to_numpy().astype(DTYPE)
np.fill_diagonal(C, 0.0)
W = W.to_numpy().astype(DTYPE)
pop = pop.to_numpy().astype(DTYPE)
return {
"C": C,
"la_names": la_names,
"W": W,
"pop": pop,
}
help="configuration file")
options, args = parser.parse_args()
with open(options.config, 'r') as ymlfile:
config = yaml.load(ymlfile)
param = sanitise_parameter(config['parameter'])
settings = sanitise_settings(config['settings'])
K_tt, age_groups = load_age_mixing(
config['data']['age_mixing_matrix_term'])
K_hh, _ = load_age_mixing(config['data']['age_mixing_matrix_hol'])
T, la_names = load_mobility_matrix(config['data']['mobility_matrix'])
np.fill_diagonal(T, 0.)
W = load_commute_volume(config['data']['commute_volume'],
settings['inference_period'])['percent']
N, n_names = load_population(config['data']['population_size'])
K_tt = K_tt.astype(DTYPE)
K_hh = K_hh.astype(DTYPE)
W = W.to_numpy().astype(DTYPE)
T = T.astype(DTYPE)
N = N.astype(DTYPE)
case_reports = pd.read_csv(config['data']['reported_cases'])
case_reports['DateVal'] = pd.to_datetime(case_reports['DateVal'])
case_reports = case_reports[case_reports['DateVal'] >= '2020-02-19']
date_range = [case_reports['DateVal'].min(), case_reports['DateVal'].max()]
y = case_reports['CumCases'].to_numpy()
y_incr = np.round((y[1:] - y[:-1]) * 0.8)