use_cases

Functions related to the four different use cases

apportion_home(home_df: pandas.core.frame.DataFrame, num_spots: int, config: dict)[source]
distribute_by_poi(region_poi: gpd.GeoDataFrame, num_points: int | float)[source]
home(home_data: geopandas.geodataframe.GeoDataFrame, uc_dict: dict) → geopandas.geodataframe.GeoDataFrame[source]

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)[source]
hpc(hpc_points: geopandas.geodataframe.GeoDataFrame, uc_dict: dict) → geopandas.geodataframe.GeoDataFrame[source]

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: geopandas.geodataframe.GeoDataFrame, region_poi: geopandas.geodataframe.GeoDataFrame)[source]
public(public_points: geopandas.geodataframe.GeoDataFrame, public_data: geopandas.geodataframe.GeoDataFrame, uc_dict: dict) → geopandas.geodataframe.GeoDataFrame[source]

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: geopandas.geodataframe.GeoDataFrame, weights_dict: dict, uc_dict: dict) → geopandas.geodataframe.GeoDataFrame[source]

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