def test_google_output():
# check the characteristics of the sciensano data loda function
df = get_google_mobility_data(update=False)
# set of variable names as output
assert set(df.columns) == set([
'retail_recreation', 'grocery', 'parks', 'transport', 'work',
'residential'
])
# index is a datetime index with daily frequency
assert isinstance(df.index, pd.DatetimeIndex)
assert df.index.freq == 'D'
job = 'FULL'
# ---------
# Load data
# ---------
# Contact matrices
initN, Nc_all = model_parameters.get_integrated_willem2012_interaction_matrices(
)
levels = initN.size
# Sciensano public data
df_sciensano = sciensano.get_sciensano_COVID19_data(update=False)
# Sciensano mortality data
df_sciensano_mortality = sciensano.get_mortality_data()
# Google Mobility data
df_google = mobility.get_google_mobility_data(update=False)
# Serological data
df_sero_herzog, df_sero_sciensano = sciensano.get_serological_data()
# ---------------------------------
# Time-dependant parameter function
# ---------------------------------
def compliance_func(t, states, param, l, effectivity):
# Convert tau and l to dates
l_days = pd.Timedelta(l, unit='D')
# Measures
start_measures = pd.to_datetime('2020-03-15')
if t < start_measures:
return param