def get_locations_modeled_and_missing(data_interface: ForecastDataInterface):
hierarchy = load_hierarchy(data_interface)
modeled_locations = data_interface.load_location_ids()
most_detailed_locs = hierarchy.loc[hierarchy.most_detailed == 1,
'location_id'].unique().tolist()
missing_locations = list(
set(most_detailed_locs).difference(modeled_locations))
locations_modeled_and_missing = {
'modeled': modeled_locations,
'missing': missing_locations
}
return locations_modeled_and_missing
def postprocess_covariate(data_interface: ForecastDataInterface,
resampling_map: Dict[int, Dict[str, List[int]]],
scenario_spec: ScenarioSpecification,
scenario_name: str, covariate: str) -> None:
covariate_config = COVARIATES[covariate]
logger.info(f'Loading {covariate}.')
covariate_data = covariate_config.loader(covariate, covariate_config.time_varying, scenario_name, data_interface)
logger.info(f'Concatenating and resampling {covariate}.')
covariate_data = pd.concat(covariate_data, axis=1)
covariate_data = pp.resample_draws(covariate_data, resampling_map)
if covariate_config.aggregator is not None:
hierarchy = pp.load_modeled_hierarchy(data_interface)
population = pp.load_populations(data_interface)
covariate_data = covariate_config.aggregator(covariate_data, hierarchy, population)
covariate_version = scenario_spec.covariates[covariate]
location_ids = data_interface.load_location_ids()
n_draws = data_interface.get_n_draws()
logger.info(f'Loading and processing input data for {covariate}.')
input_covariate_data = data_interface.load_covariate(covariate, covariate_version, location_ids, with_observed=True)
covariate_observed = input_covariate_data.reset_index(level='observed')
covariate_data = covariate_data.merge(covariate_observed, left_index=True,
right_index=True, how='outer').reset_index()
draw_cols = [f'draw_{i}' for i in range(n_draws)]
if 'date' in covariate_data.columns:
index_cols = ['location_id', 'date', 'observed']
else:
index_cols = ['location_id', 'observed']
covariate_data = covariate_data.set_index(index_cols)[draw_cols]
covariate_data['modeled'] = covariate_data.notnull().all(axis=1).astype(int)
input_covariate = pd.concat([input_covariate_data.reorder_levels(index_cols)] * n_draws, axis=1)
input_covariate.columns = draw_cols
covariate_data = covariate_data.combine_first(input_covariate).set_index('modeled', append=True)
logger.info(f'Saving data for {covariate}.')
if covariate_config.draw_level:
data_interface.save_output_draws(covariate_data.reset_index(), scenario_name, covariate_config.label)
summarized_data = pp.summarize(covariate_data)
data_interface.save_output_summaries(summarized_data.reset_index(), scenario_name, covariate_config.label)
