元胞自动机python一维

元胞自动机的一维版本是一种简化的模型，其中单元格排列在一条线上。每个单元格具有两个状态（通常是0和1），并且根据一组规则进行状态转换。

在Python中，可以使用numpy库和matplotlib库来实现一维元胞自动机的模拟和可视化。下面是一个简单的例子：

import numpy as np
import matplotlib.pyplot as plt

# 定义元胞自动机的规则
def rule(curr_state, left_state, right_state):
    # 根据当前状态和邻居状态计算下一个状态
    if left_state == 1 and curr_state == 1 and right_state == 1:
        return 0
    elif left_state == 1 and curr_state == 1 and right_state == 0:
        return 1
    elif left_state == 1 and curr_state == 0 and right_state == 1:
        return 0
    elif left_state == 1 and curr_state == 0 and right_state == 0:
        return 1
    elif left_state == 0 and curr_state == 1 and right_state == 1:
        return 0
    elif left_state == 0 and curr_state == 1 and right_state == 0:
        return 1
    elif left_state == 0 and curr_state == 0 and right_state == 1:
        return 1
    elif left_state == 0 and curr_state == 0 and right_state == 0:
        return 0

# 初始化元胞自动机的初始状态
size = 100  # 元胞自动机的大小
steps = 100  # 模拟的步数
state = np.random.randint(0, 2, size)  # 初始状态随机生成

# 模拟元胞自动机的演化
for _ in range(steps):
    new_state = np.zeros(size, dtype=int)
    for i in range(size):
        left_state = state[i-1] if i > 0 else state[size-1]
        right_state = state[i+1] if i < size-1 else state[0]
        new_state[i] = rule(state[i], left_state, right_state)
    state = new_state

# 可视化元胞自动机的演化过程
plt.imshow([state], cmap='binary', aspect='auto')
plt.show()

这是一个简单的一维元胞自动机模拟，使用了一个规则来定义状态转换。在模拟过程中，每个单元格的状态会根据其自身和相邻单元格的状态进行更新。初始状态可以随机生成，模拟后的结果通过imshow函数进行可视化。

希望对你有帮助！如果你有更多问题，请继续提问。