Python高级编程技术：深度解析与实战指南

import logging

# 带参数装饰器：控制日志级别
def log_with_level(level):
    def decorator(func):
        def wrapper(*args, **kwargs):
            logging.basicConfig(level=level)
            logging.info(f"{func.__name__}开始执行")
            result = func(*args, **kwargs)
            logging.info(f"{func.__name__}执行完成")
            return result
        return wrapper
    return decorator

# 类装饰器：实现函数执行计数
class CountCalls:
    def __init__(self, func):
        self.func = func
        self.count = 0

    def __call__(self, *args, **kwargs):
        self.count += 1
        print(f"{self.func.__name__}已执行{self.count}次")
        return self.func(*args, **kwargs)

@log_with_level(logging.INFO)
@CountCalls
def calculate(a, b):
    return a + b

calculate(3, 5)  # 输出日志与执行次数

class ModelMeta(type):
    def __new__(cls, name, bases, attrs):
        # 排除基类本身
        if name != "BaseModel":
            if "table_name" not in attrs:
                raise ValueError(f"https://m.jixing.net/jiemeng/")
            # 自动添加主键属性
            attrs["id"] = "primary_key"
        return super().__new__(cls, name, bases, attrs)

class BaseModel(metaclass=ModelMeta):
    pass

# 合法模型
class User(BaseModel):
    table_name = "user"

# 非法模型（会报错）
# class Product(BaseModel):
#     pass

import asyncio
import aiohttp

# 异步请求API
async def fetch_data(url):
    async with aiohttp.ClientSession() as session:
        async with session.get(url) as response:
            return await response.json()

# 并发执行多个请求
async def main():
    urls = [
        "https://api.example.com/data/1",
        "https://api.example.com/data/2",
        "https://api.example.com/data/3"
    ]
    tasks = [fetch_data(url) for url in urls]
    results = await asyncio.gather(*tasks)
    print("批量请求结果：", results)

asyncio.run(main())

from functools import lru_cache

# 缓存装饰器：缓存已计算的结果
@lru_cache(maxsize=None)
def fib(n):
    if n <= 1:
        return n
    return fib(n-1) + fib(n-2)

# 计算第100个斐波那契数，缓存生效后速度极快
print(fib(100))