def test_integrand_grids(alchemy, default_ages, default_times):
g = integrand_grids(alchemy=alchemy, integrands=['iota', 'prevalence'])
np.testing.assert_array_equal(g['prevalence']['age'], default_ages)
np.testing.assert_array_equal(g['prevalence']['time'], default_times)
np.testing.assert_array_equal(g['iota']['age'],
np.array([0., 5., 10., 50., 100.]))
np.testing.assert_array_equal(
g['iota']['time'],
np.array([1990., 1995., 2000., 2005., 2010., 2015., 2016.]))
def test_get_prior_avgint_grid():
settings = load_settings(BASE_CASE)
alchemy = Alchemy(settings)
rates = ['pini', 'iota', 'chi']
grids = integrand_grids(alchemy=alchemy, integrands=rates)
df = get_prior_avgint_grid(
grids=grids,
sexes=[1, 2],
locations=[1]
)
assert type(df) == pd.DataFrame
assert sorted(df['integrand_id'].unique()) == [0, 2, 7]
assert all(df.location_id == 1)
def fill_avgint_with_priors_grid(inputs: MeasurementInputs, alchemy: Alchemy,
settings: SettingsConfig,
source_db_path: Union[str, Path],
child_locations: List[int],
child_sexes: List[int]):
sourceDB = DismodIO(path=source_db_path)
rates = [r.rate for r in settings.rate]
grids = integrand_grids(alchemy=alchemy, integrands=rates)
posterior_grid = get_prior_avgint_grid(grids=grids,
sexes=child_sexes,
locations=child_locations,
midpoint=False)
posterior_grid = inputs.add_covariates_to_data(df=posterior_grid)
posterior_grid = prep_data_avgint(df=posterior_grid,
node_df=sourceDB.node,
covariate_df=sourceDB.covariate)
posterior_grid.rename(columns={'sex_id': 'c_sex_id'}, inplace=True)
sourceDB.avgint = posterior_grid
def fill_avgint_with_priors_grid(inputs: MeasurementInputs, alchemy: Alchemy,
settings: SettingsConfig,
source_db_path: Union[str, Path],
child_locations: List[int],
child_sexes: List[int]):
"""
Fill the average integrand table with the grid that the priors are on.
This is so that we can "predict" the prior for the next level of the cascade.
Parameters
----------
inputs
An inputs object
alchemy
A grid alchemy object
settings
A settings configuration object
source_db_path
The path of the source database that has had a fit on it
child_locations
The child locations to predict for
child_sexes
The child sexes to predict for
"""
sourceDB = DismodIO(path=source_db_path)
rates = [r.rate for r in settings.rate]
grids = integrand_grids(alchemy=alchemy, integrands=rates)
posterior_grid = get_prior_avgint_grid(grids=grids,
sexes=child_sexes,
locations=child_locations,
midpoint=False)
posterior_grid = inputs.add_covariates_to_data(df=posterior_grid)
posterior_grid = prep_data_avgint(df=posterior_grid,
node_df=sourceDB.node,
covariate_df=sourceDB.covariate)
posterior_grid.rename(columns={'sex_id': 'c_sex_id'}, inplace=True)
sourceDB.avgint = posterior_grid