ws网络下的SIR模型代码

以下是一个基于SIR模型的简单传染病传播模拟的示例代码，使用Python和websocket实现：

import asyncio
import json
import random
import websockets

# 模拟人口数量
POPULATION = 1000
# 初始感染人数
INIT_INFECTED = 10
# SIR参数(beta, gamma)
BETA = 0.2
GAMMA = 0.1

# 初始化人口
population = [{'id': i, 'state': 'S'} for i in range(POPULATION)]
# 随机选取INIT_INFECTED个人作为初始感染者
for i in random.sample(range(POPULATION), INIT_INFECTED):
    population[i]['state'] = 'I'

# 根据传染病模型进行模拟
async def simulate():
    # 初始状态
    state = {'S': POPULATION - INIT_INFECTED, 'I': INIT_INFECTED, 'R': 0}
    # 循环模拟
    while True:
        # 计算每个人的下一个状态
        next_population = []
        for person in population:
            if person['state'] == 'S':
                # 计算被感染的概率
                infected_prob = sum([1 for neighbor in population
                                     if neighbor['state'] == 'I'
                                     and neighbor['id'] != person['id']]) / POPULATION * BETA
                # 根据概率随机决定是否被感染
                if random.random() < infected_prob:
                    person['state'] = 'I'
            elif person['state'] == 'I':
                # 根据gamma概率恢复
                if random.random() < GAMMA:
                    person['state'] = 'R'
            next_population.append(person)
        population[:] = next_population
        # 统计状态数量
        state = {'S': 0, 'I': 0, 'R': 0}
        for person in population:
            state[person['state']] += 1
        # 将状态发送给所有连接的客户端
        await asyncio.wait([websocket.send(json.dumps(state)) for websocket in websockets])

# 与客户端建立连接并发送初始状态
async def handler(websocket, path):
    await websocket.send(json.dumps({'S': POPULATION - INIT_INFECTED, 'I': INIT_INFECTED, 'R': 0}))

# 启动websocket服务器
start_server = websockets.serve(handler, 'localhost', 8765)

# 启动模拟
asyncio.get_event_loop().run_until_complete(asyncio.gather(start_server, simulate()))

这个代码使用了Python的asyncio模块来实现协程，使用websockets库来实现websocket服务器。SIR模型中的beta和gamma参数可以根据实际情况进行调整。