use_cases

Functions related to the four different use cases

apportion_home(home_df: DataFrame, num_spots: int, config: dict)

distribute_by_poi(region_poi: GeoDataFrame, num_points: int | float)

home(home_data: GeoDataFrame, uc_dict: dict) → GeoDataFrame

Calculate placements and energy distribution for use case hpc.

Parameters:

• home_data – gpd.GeoDataFrame info about house types

• uc_dict – dict contains basic run info like region boundary and save directory

home_charge_spots(house_array: pd.Series | np.array, config: dict)

hpc(hpc_points: GeoDataFrame, uc_dict: dict) → GeoDataFrame

Calculate placements and energy distribution for use case hpc.

Parameters:

• hpc_points – gpd.GeoDataFrame GeoDataFrame of possible hpc locations

• uc_dict – dict contains basic run info like region boundary and save directory

match_existing_points(region_points: GeoDataFrame, region_poi: GeoDataFrame)

public(public_points: GeoDataFrame, public_data: GeoDataFrame, uc_dict: dict) → GeoDataFrame

Calculate placements and energy distribution for use case hpc.

Parameters:

• public_points – gpd.GeoDataFrame existing public charging points

• public_data – gpd.GeoDataFrame clustered POI

• uc_dict – dict contains basic run info like region boundary and save directory

work(landuse: GeoDataFrame, weights_dict: dict, uc_dict: dict) → GeoDataFrame

Calculate placements and energy distribution for use case hpc.

Parameters:

• landuse – gpd.GeoDataFrame work areas by land use

• weights_dict – dict weights for different land use types

• uc_dict – dict contains basic run info like region boundary and save directory