citylearn.internal.loading module

class citylearn.internal.loading.CityLearnLoadingService(env: CityLearnEnv)

Bases: object

Internal loader service that builds env components from schema.

load(schema: Mapping[str, Any], **kwargs) → Tuple[PathLike | str, List[Building], List[ElectricVehicle], int | List[Tuple[int, int]], bool, bool, float, RewardFunction, bool, List[str], EpisodeTracker]

Return env objects as defined by schema.

load_building(index: int, building_name: str, schema: dict, episode_tracker: EpisodeTracker, pv_sizing_data: DataFrame, battery_sizing_data: DataFrame, **kwargs) → Building

Initialize and return a building model.

load_deferrable_appliance(appliance_name: str, building_name: str, schema: dict, appliance_schema: dict, episode_tracker: EpisodeTracker) → DeferrableAppliance

Load sparse profile/schedule data and initialize a deferrable appliance.

load_electric_vehicle(electric_vehicle_name: str, schema: dict, electric_vehicle_schema: dict, episode_tracker: EpisodeTracker, time_step_ratio) → ElectricVehicle

Initialize and return an electric vehicle model.

load_escalator(escalator_name: str, building_name: str, schema: dict, escalator_schema: dict, episode_tracker: EpisodeTracker, *, expected_rows: int, window: Tuple[int, int] | None, time_step_ratio: float) → Escalator

Load one aggregate escalator demand series and operating specification.

load_washing_machine(*args, **kwargs)

process_metadata(schema, building_schema, chargers_list, deferrable_appliances_list, escalators_list, index, energy_simulation: EnergySimulation, **kwargs)

Build observation and action metadata for one building.

class citylearn.internal.loading.LoadContext(schema: Mapping[str, Any], kwargs: Mapping[str, Any])

Bases: object

Lightweight holder for loading inputs.

kwargs: Mapping[str, Any]

schema: Mapping[str, Any]