def __init_

def init()

in src/energy_storage_system/envs.py [0:0]
44 lines of code
8 McCabe index (conditional complexity)

    def __init__(self, env_config: Dict):
        # Capacity: Min energy storage level (MWh)
        self.ENERGY_MIN = 0.0
        # Capacity: Max energy storage level (MWh), battery capacity
        self.ENERGY_MAX = 80.0
        # Starting capacity
        self.STARTING_ENERGY = 40.0
        # Power rating: Max charge rate (MW)
        self.MAX_CHARGE_PWR = 4.0
        # Power rating: Max discharge rate (MW)
        self.MAX_DISCHARGE_PWR = 2.0
        # wear and tear ($/MW)
        self.BETA = 1.0
        # every step is 1 hour
        self.DURATION = 1
        # efficiency constant
        self.EFF = 1.0
        # Historical price horizon for states
        self.HIST_PRICE_HORIZON = 5
        # Each trajectories is one week (168h)
        self.MAX_STEPS_PER_EPISODE = 168

        self.LOCAL = None
        self.FILEPATH = None

        # Default environment configuration. which will be added to env_config
        config_defaults = {
            "ENERGY_MIN": 0.0,
            "ENERGY_MAX": 80.0,
            "STARTING_ENERGY": 40.0,
            "MAX_CHARGE_PWR": 4.0,
            "MAX_DISCHARGE_PWR": 2.0,
            "BETA": 1.0,
            "DURATION": 1,
            "EFF": 1.0,
            "HIST_PRICE_HORIZON": 5,
            "MAX_STEPS_PER_EPISODE": 168,
            "FILEPATH": DATA,
            "LOCAL": True,
        }

        # Add new environment config passed in as params
        for key, default_val in config_defaults.items():
            # Get value for key, if none then return 'val'. env_config take priority
            new_val = env_config.get(
                key, default_val
            )  # Override defaults with constructor parameters
            self.__dict__[key] = new_val
            if key not in env_config:
                env_config[key] = new_val

        # Load energy price ($/MWh)
        if self.LOCAL:
            self.df_price = self._get_data(self.FILEPATH)
        else:
            self.df_price = self._get_data_s3()
        self.price_length = self.df_price.shape[0]

        # TODO Create features
        # self.df_price["time"] = self.df_price["time"]
        # self.df_price["hour"] = self.df_price.time.dt.hour
        # self.df_price["week"] = self.df_price.time.dt.week
        # self.df_price["sin_time"] = np.sin(2 * PI * self.df_price.hour / 24)
        # self.df_price["cos_time"] = np.cos(2 * PI * self.df_price.hour / 24)
        # self.df_price["sin_week"] = np.sin(2 * PI * self.df_price.week / 52)
        # self.df_price["cos_week"] = np.cos(2 * PI * self.df_price.week / 52)

        # ACTION/OBSERVATION space, this will change according hist horizon
        self.action_space = Discrete(3)
        self.observation_space = Box(
            -np.inf, np.inf, shape=(3 + self.HIST_PRICE_HORIZON,), dtype=np.float64
        )
        self.initialized = False
def __init__()

def init()
