Source code for mcs.ca

from .mcs import *




class CA(MCS):
    """Cellular automata simulation.

    Override this class to customize configuration and state-transition function.

    Attributes:
        max_step: The max step.
        size_x: Number of cells in x dimension.
        size_y: Number of cells in y dimension.
        seed: NumPy random seed.
        s: An `~numpy.ndarray` of shape (max_step, size_x) or (max_step, size_y, size_x) representing the states.
        step: The current step.
    """

    def __init__(self, max_step: int, size_x: int, size_y: int = 1, seed: int = 42):
        super().__init__(max_step)
        self.size_x = size_x
        self.size_y = size_y
        self.seed = seed
        if size_y == 1:
            self.s = np.zeros((max_step, size_x))
        else:
            self.s = np.zeros((max_step, size_y, size_x))



    def initialize(self):
        """Sets up the initial configuration."""
        np.random.seed(self.seed)
        self.s[0] = np.random.randint(2, size=self.s[0].shape)




    def update(self, *, F: Callable = None):
        """Updates the states in the next step.

        Args:
            F: A state transition function which returns an `~numpy.ndarray`.
        """
        if F is None:
            F = self._identity
        config = self.s[self.step]
        self.step += 1
        self.s[self.step] = F(config)


    @staticmethod
    def rule184(config):
        assert config.ndim == 1
        size = len(config)
        config_next = np.zeros(size)
        neighborhoods = set([(1, 1, 1), (1, 0, 1), (1, 0, 0), (0, 1, 1)])
        for x in range(size):
            pattern = (config[(x - 1) % size], config[x], config[(x + 1) % size])
            if pattern in neighborhoods:
                config_next[x] = 1
        return config_next

    @staticmethod
    def game_of_life(config):
        assert config.ndim == 2
        config_next = np.copy(config)
        num_alive = signal.convolve2d(config, np.ones((3, 3)), mode="same", boundary="wrap")
        config_next[(config == 0) & (num_alive == 3)] = 1
        config_next[(config == 1) & ((num_alive < 3) | (num_alive > 4))] = 0
        return config_next



    def visualize(self, *, step: int = -1):
        """Visualizes the states of the system using an image of shape (step, size_x) or (size_y, size_x).

        Args:
            step: The step to plot.
        Returns:
            A `matplotlib.figure.Figure` object.
        """
        fig, ax = plt.subplots()
        if self.s.ndim == 2:
            ax.imshow(self.s[:step], cmap=plt.cm.binary)
        elif self.s.ndim == 3:
            ax.imshow(self.s[step], cmap=plt.cm.binary)
        return fig