22. 种族隔离#

import matplotlib.pyplot as plt
from random import uniform, seed
from math import sqrt
import numpy as np
import matplotlib as mpl
FONTPATH = "fonts/SourceHanSerifSC-SemiBold.otf"
mpl.font_manager.fontManager.addfont(FONTPATH)
plt.rcParams['font.family'] = ['Source Han Serif SC']

class Agent:

    def __init__(self, type):
        self.type = type
        self.draw_location()

    def draw_location(self):
        self.location = uniform(0, 1), uniform(0, 1)

    def get_distance(self, other):
        "计算自己与另一个体之间的欧几里得距离。"
        a = (self.location[0] - other.location[0])**2
        b = (self.location[1] - other.location[1])**2
        return sqrt(a + b)

    def happy(self,
                agents,                # 其他个体的列表
                num_neighbors=10,      # 视为邻居的个体数量
                require_same_type=5):  # 必须是同一类型的邻居数量
        """
            如果有足够多的最近的邻居是同一类型，则返回True。
        """

        distances = []

        # distances是一个包含(d, agent)的列表，其中d是agent到self的距离
        for agent in agents:
            if self != agent:
                distance = self.get_distance(agent)
                distances.append((distance, agent))

        # 根据距离从小到大排序
        distances.sort()

        # 提取相邻的个体
        neighbors = [agent for d, agent in distances[:num_neighbors]]

        # 计算有多少邻居与自己类型相同
        num_same_type = sum(self.type == agent.type for agent in neighbors)
        return num_same_type >= require_same_type

    def update(self, agents):
        "如果不满意（不快乐），随机选择新位置直到满意（快乐）。"
        while not self.happy(agents):
            self.draw_location()

def plot_distribution(agents, cycle_num):
    "绘制经过cycle_num轮循环后的个体分布图。"
    x_values_0, y_values_0 = [], []
    x_values_1, y_values_1 = [], []
    # == 获取每种类型的位置 == #
    for agent in agents:
        x, y = agent.location
        if agent.type == 0:
            x_values_0.append(x)
            y_values_0.append(y)
        else:
            x_values_1.append(x)
            y_values_1.append(y)
    fig, ax = plt.subplots()
    plot_args = {'markersize': 8, 'alpha': 0.8}
    ax.set_facecolor('azure')
    ax.plot(x_values_0, y_values_0,
        'o', markerfacecolor='orange', **plot_args)
    ax.plot(x_values_1, y_values_1,
        'o', markerfacecolor='green', **plot_args)
    ax.set_title(f'周期 {cycle_num-1}')
    plt.show()

def run_simulation(num_of_type_0=600,
                   num_of_type_1=600,
                   max_iter=100_000,       # 最大迭代次数
                   set_seed=1234):

    # 设置种子以确保可重复性
    seed(set_seed)

    # 创建类型0的个体列表
    agents = [Agent(0) for i in range(num_of_type_0)]
    # 添加类型1的个体列表
    agents.extend(Agent(1) for i in range(num_of_type_1))

    # 初始化计数器
    count = 1

    # 绘制初始分布
    plot_distribution(agents, count)

    # 循环直到没有个体愿意移动
    while count < max_iter:
        print('进入循环 ', count)
        count += 1
        no_one_moved = True
        for agent in agents:
            old_location = agent.location
            agent.update(agents)
            if agent.location != old_location:
                no_one_moved = False
        if no_one_moved:
            break

    # 绘制最终分布
    plot_distribution(agents, count)

    if count < max_iter:
        print(f'在 {count} 次迭代后收敛。')
    else:
        print('达到迭代上限并终止。')

run_simulation()

sim_random_select(max_iter=50_000, flip_prob=0.01, test_freq=10_000)