把文中代码详细改写成python代码

将MATLAB代码转换为Python代码涉及多个步骤，包括导入必要的库、调整语法和逻辑结构。以下是详细的Python版本代码：

import numpy as np
import random
from scipy.spatial.distance import pdist, squareform

def myfun(x):
    # 定义目标函数
    pass

def DIST(x1, y1, x2, y2):
    return ((x1 - x2)**2 + (y1 - y2)**2)**0.5

def CreateEmptyParticle(nPop):
    particles = [{'Velocity': None, 'Position': None, 'Cost': None, 
                  'Best': {'Position': None, 'Cost': None}} for _ in range(nPop)]
    return particles

def DetermineDomination(particles):
    for p in particles:
        p['dominated'] = False
        for q in particles:
            if all(p['Cost'] <= q['Cost']) and any(p['Cost'] < q['Cost']):
                p['dominated'] = True
                break
    return particles

def GetNonDominatedParticles(particles):
    non_dominated = [p for p in particles if not p.get('dominated', False)]
    return non_dominated

def GetCosts(particles):
    costs = np.array([p['Cost'] for p in particles]).T
    return costs

def CreateHypercubes(costs, nGrid, alpha):
    min_cost = np.min(costs, axis=1)
    max_cost = np.max(costs, axis=1)
    step = (max_cost - min_cost) / nGrid
    hypercubes = []
    for i in range(nGrid):
        for j in range(nGrid):
            hypercubes.append({
                'index': (i, j),
                'sub_index': (i * nGrid + j),
                'particles': []
            })
    return hypercubes, min_cost, max_cost, step

def GetGridIndex(particle, G, min_cost, max_cost, step):
    cost = particle['Cost']
    grid_index = tuple(((cost - min_cost) // step).astype(int))
    sub_index = grid_index[0] * len(G) + grid_index[1]
    return grid_index, sub_index

def SelectLeader(rep, beta):
    leader = random.choices(rep, weights=[r['Cost'][0]**beta for r in rep], k=1)[0]
    return leader

def DeleteFromRep(rep, EXTRA, gamma):
    to_delete = random.sample(range(len(rep)), EXTRA)
    rep = [rep[i] for i in range(len(rep)) if i not in to_delete]
    return rep

def Mutate(position, pm, VarMin, VarMax):
    mutated_position = position.copy()
    for i in range(len(mutated_position)):
        if random.random() < pm:
            mutated_position[i] = random.uniform(VarMin, VarMax)
    return mutated_position

def operator_3(position, start, stop, flag):
    new_position = position.copy()
    if flag == 1:
        new_position[start:stop] = np.flip(new_position[start:stop])
    elif flag == 2:
        new_position[start:stop] = np.roll(new_position[start:stop], 1)
    elif flag == 3:
        new_position[start:stop] = np.roll(new_position[start:stop], -1)
    return new_position

def Dominates(p, q):
    return all(p['Cost'] <= q['Cost']) and any(p['Cost'] < q['Cost'])

def main():
    clc(); clear(); close_all()

    CostFunction = lambda x: myfun(x)
    WPc = 16  # 建筑废料生成地点数量
    RCc = 6   # 回收中心数量
    MMc = 4   # 再生厂商数量
    nVar = RCc + RCc * WPc + MMc + MMc * RCc  # 决策变量个数
    VarSize = [1, nVar]  # 决策变量矩阵大小
    VarMin = 0  # 变量下界
    VarMax = 1  # 变量上界
    VelMax = (VarMax - VarMin) / 5  # 粒子速度步长极限

    # MOPSO 设置
    MaxIt = 200  # 最大迭代次数
    nPop = 100  # 种群大小
    nRep = 100  # 存储规模
    w = 0.5  # 惯性权重
    wdamp = 0.99  # 惯性权重衰减率
    c1 = 1  # 个人学习系数
    c2 = 2  # 全局学习系数
    nGrid = 7  # 每个维度的栅格数
    alpha = 0.1  # 膨胀系数
    beta = 2  # 领导者选择压力
    gamma = 2  # 删除选择压力
    mu = 0.1  # 变异率

    # 初始化
    wmax = 0.9
    wmin = 0.4

    # 混沌部分
    u0 = 0.5
    y0 = 0.5
    U_s = [u0]
    for i in range(2 * MaxIt - 1):
        y = np.cos(np.pi * 2 * u0) + y0 * np.exp(-3)
        u = (u0 + 400 + 12 * y) - np.floor(u0 + 400 + 12 * y)
        u0 = u
        y0 = y
        U_s.append(u0)

    particles = CreateEmptyParticle(nPop)

    for i in range(nPop):
        particles[i]['Velocity'] = np.random.uniform(VarMin, VarMax, VarSize)
        particles[i]['Position'] = np.random.uniform(VarMin, VarMax, VarSize)
        fit, pp = CostFunction(particles[i]['Position'])
        particles[i]['Position'] = pp
        particles[i]['Cost'] = fit.T
        particles[i]['Best']['Position'] = particles[i]['Position'].copy()
        particles[i]['Best']['Cost'] = particles[i]['Cost'].copy()

    particles = DetermineDomination(particles)
    rep = GetNonDominatedParticles(particles)
    rep_costs = GetCosts(rep)
    G, min_cost, max_cost, step = CreateHypercubes(rep_costs, nGrid, alpha)

    for i in range(len(rep)):
        rep[i]['GridIndex'], rep[i]['GridSubIndex'] = GetGridIndex(rep[i], G, min_cost, max_cost, step)

    # MOPSO 主循环
    for it in range(1, MaxIt + 1):
        w = wmax - ((wmax - wmin) / MaxIt) * it
        for i in range(nPop):
            rep_h = SelectLeader(rep, beta)
            index = random.sample(range(2 * MaxIt), 2)
            particles[i]['Velocity'] = (w * particles[i]['Velocity'] +
                                        c1 * U_s[index[0]] * (particles[i]['Best']['Position'] - particles[i]['Position']) +
                                        c2 * U_s[index[1]] * (rep_h['Position'] - particles[i]['Position']))
            particles[i]['Velocity'] = np.clip(particles[i]['Velocity'], -VelMax, VelMax)
            particles[i]['Position'] += particles[i]['Velocity']

            flag = (particles[i]['Position'] < VarMin) | (particles[i]['Position'] > VarMax)
            particles[i]['Velocity'][flag] = -particles[i]['Velocity'][flag]
            particles[i]['Position'] = np.clip(particles[i]['Position'], VarMin, VarMax)

            fit, pp = CostFunction(particles[i]['Position'])
            particles[i]['Position'] = pp
            particles[i]['Cost'] = fit.T

            if random.random() < mu:
                flag = random.randint(1, 3)
                NewSol = {'Position': operator_3(particles[i]['Position'], 1, nVar, flag)}
                fit, pp = CostFunction(NewSol['Position'])
                NewSol['Position'] = pp
                NewSol['Cost'] = fit.T

                if Dominates(NewSol, particles[i]):
                    particles[i]['Position'] = NewSol['Position']
                    particles[i]['Cost'] = NewSol['Cost']
                elif Dominates(particles[i], NewSol):
                    pass
                else:
                    if random.random() < 0.5:
                        particles[i]['Position'] = NewSol['Position']
                        particles[i]['Cost'] = NewSol['Cost']

            if Dominates(particles[i], particles[i]['Best']):
                particles[i]['Best']['Position'] = particles[i]['Position'].copy()
                particles[i]['Best']['Cost'] = particles[i]['Cost'].copy()
            elif not Dominates(particles[i]['Best'], particles[i]):
                if random.random() < 0.5:
                    particles[i]['Best']['Position'] = particles[i]['Position'].copy()
                    particles[i]['Best']['Cost'] = particles[i]['Cost'].copy()

        particles = DetermineDomination(particles)
        nd_particles = GetNonDominatedParticles(particles)
        rep.extend(nd_particles)
        rep = DetermineDomination(rep)
        rep = GetNonDominatedParticles(rep)

        for i in range(len(rep)):
            rep[i]['GridIndex'], rep[i]['GridSubIndex'] = GetGridIndex(rep[i], G, min_cost, max_cost, step)

        if len(rep) > nRep:
            EXTRA = len(rep) - nRep
            rep = DeleteFromRep(rep, EXTRA, gamma)
            rep_costs = GetCosts(rep)
            G, min_cost, max_cost, step = CreateHypercubes(rep_costs, nGrid, alpha)

        print(f'IMOPSO Iteration {it}: Number of Repository Particles = {len(rep)}')

    # 结果
    costs = GetCosts(particles)
    rep_costs = GetCosts(rep)
    import matplotlib.pyplot as plt
    plt.plot(rep_costs[1, :], rep_costs[0, :], 'ro')
    plt.grid(True)
    plt.show()

if __name__ == "__main__":
    main()

### 说明
1. **导入必要的库**：使用 `numpy` 和 `random` 处理数值和随机操作，使用 `scipy.spatial.distance` 计算距离。
2. **定义辅助函数**：如 `DIST`, `CreateEmptyParticle`, `DetermineDomination`, `GetNonDominatedParticles`, `GetCosts`, `CreateHypercubes`, `GetGridIndex`, `SelectLeader`, `DeleteFromRep`, `Mutate`, `operator_3`, `Dominates`。
3. **主函数 `main`**：包含主要的算法逻辑，初始化参数，生成初始种群，执行 MOPSO 主循环，绘制结果。
4. **目标函数 `myfun`**：需要根据具体问题定义。

请根据实际需求进一步完善和调试代码。