SimulatorConstants

def __init__(self, p_h: float = 0.58, c_1: float = 0.045, c_2: float = 0.131, a: float = 0.078, theta_w: int = 0,
             v: float = 10, p_firebreak: float = 0.9, p_continue_burn: float = 0.5,
             device: torch.device = torch.device('cpu'), dtype: torch.dtype = torch.float32, ) -> None:
    """
    Initialize the simulator constants with the default values.

    The user may tweak the coefficient of ground elevation (a), as it seems low for general terrain to be effective

    For custom theta_w, just set the value after initialization.

    Default values are copied from the paper https://doi.org/10.1016/j.amc.2008.06.046,
    with meteorological conditions:

    - Wind direction North
    - Wind speed 8-10 m/s (~5 Beaufort)
    - Average temperature 30 °C
    - Humidity 36%

    Parameters:
        p_h:
            The probability that a burnable cell adjacent to a burning cell will catch fire
            at the next time step under no wind and flat terrain
        c_1:
            The coefficient of wind velocity
        c_2:
            The coefficient of wind direction
        a:
            The coefficient of ground elevation
        theta_w:
            The direction of the wind in degrees, measured clockwise from north
        v:
            The wind velocity, unit in m/s
        p_firebreak:
            The probability that a burnable cell will not catch fire even if it is adjacent to a burning cell
        p_continue_burn:
            The probability that a burning cell will continue to burn at the next time step
        device:
            The device to use
        dtype:
            The data type to use
    """
    self.device = device
    self.dtype = dtype
    self.p_h = torch.tensor(p_h, dtype=dtype, device=device)
    self.c_1 = torch.tensor(c_1, dtype=dtype, device=device)
    self.c_2 = torch.tensor(c_2, dtype=dtype, device=device)
    self.a = torch.tensor(a, dtype=dtype, device=device)
    self.theta = None
    self.theta_w = torch.tensor(theta_w, dtype=dtype, device=device)
    self.V = torch.tensor(v, dtype=dtype, device=device)
    self.p_firebreak = torch.tensor(p_firebreak, dtype=dtype, device=device)
    self.p_continue_burn = torch.tensor(p_continue_burn, dtype=dtype, device=device)