sinergym.utils.wrappers.WeatherForecastingWrapper

class sinergym.utils.wrappers.WeatherForecastingWrapper(env: Env, n: int = 5, delta: int = 1, columns: List[str] = ['Dry Bulb Temperature', 'Relative Humidity', 'Wind Direction', 'Wind Speed', 'Direct Normal Radiation', 'Diffuse Horizontal Radiation'], weather_variability: Dict[str, Tuple[float, float, float]] | None = None)

__init__(env: Env, n: int = 5, delta: int = 1, columns: List[str] = ['Dry Bulb Temperature', 'Relative Humidity', 'Wind Direction', 'Wind Speed', 'Direct Normal Radiation', 'Diffuse Horizontal Radiation'], weather_variability: Dict[str, Tuple[float, float, float]] | None = None)

Adds weather forecast information to the current observation.

Parameters:

env (Env) – Original Gym environment.
n (int, optional) – Number of observations to be added. Default to 5.
delta (int, optional) – Time interval between observations. Defaults to 1.
columns (List[str], optional) – List of the names of the meteorological variables that will make up the weather forecast observation.
weather_variability (Dict[str, Tuple[float, float, float]], optional) – Dictionary with the variation for each column in the weather data. Defaults to None.
sigma (The key is the column name and the value is a tuple with the)
provided (mean and tau for OU process. If not)
variability. (it assumes no)

Raises:

ValueError – If any key in weather_variability is not present in the columns list.

Methods

`__init__`(env[, n, delta, columns, ...])	Adds weather forecast information to the current observation.
`apply_ou_variability`()	Modify weather data using Ornstein-Uhlenbeck process according to the variation specified in the weather_variability dictionary.
`class_name`()	Returns the class name of the wrapper.
`close`()	Closes the wrapper and `env`.
`get_wrapper_attr`(name)	Gets an attribute from the wrapper and lower environments if name doesn't exist in this object.
`observation`(obs, info)	Build the state observation by adding weather forecast information.
`render`()	Uses the `render()` of the `env` that can be overwritten to change the returned data.
`reset`([seed, options])	Resets the environment.
`set_weather_data`()	Set the weather data used to build de state observation.
`step`(action)	Performs the action in the new environment.
`wrapper_spec`(**kwargs)	Generates a WrapperSpec for the wrappers.

Attributes

`action_space`	Return the `Env` `action_space` unless overwritten then the wrapper `action_space` is used.
`logger`
`metadata`	Returns the `Env` `metadata`.
`np_random`	Returns the `Env` `np_random` attribute.
`observation_space`	Return the `Env` `observation_space` unless overwritten then the wrapper `observation_space` is used.
`render_mode`	Returns the `Env` `render_mode`.
`reward_range`	Return the `Env` `reward_range` unless overwritten then the wrapper `reward_range` is used.
`spec`	Returns the `Env` `spec` attribute with the WrapperSpec if the wrapper inherits from EzPickle.
`unwrapped`	Returns the base environment of the wrapper.

apply_ou_variability(): Modify weather data using Ornstein-Uhlenbeck process according to the variation specified in the weather_variability dictionary.

logger = <Logger WRAPPER WeatherForecastingWrapper (INFO)>

observation(obs: ndarray, info: Dict[str, Any]) → ndarray

Build the state observation by adding weather forecast information.

Parameters:

obs (np.ndarray) – Original observation.
info (Dict[str, Any]) – Information about the enviroment.

Returns:

Transformed observation.

Return type:

np.ndarray

reset(seed: int | None = None, options: Dict[str, Any] | None = None) → Tuple[ndarray, Dict[str, Any]]

Resets the environment.

Returns:: Tuple with next observation, and dict with information about the enviroment.
Return type:: Tuple[np.ndarray,Dict[str,Any]]

set_weather_data(): Set the weather data used to build de state observation.

step(action: int | ndarray) → Tuple[ndarray, float, bool, bool, Dict[str, Any]]

Performs the action in the new environment.

Parameters:: action (Union[int, np.ndarray]) – Action to be executed in environment.
Returns:: Tuple with next observation, reward, bool for terminated episode and dict with Information about the enviroment.
Return type:: Tuple[np.ndarray, float, bool, Dict[str, Any]]