Python进阶教程：装饰器与生成器深入

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def greet(name):
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    return f"你好，{name}"
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# 函数可以赋值给变量
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say = greet
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print(say("张三"))  # 你好，张三
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# 函数可以作为参数传递
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def call_twice(func, arg):
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    return func(func(arg))
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print(call_twice(greet, "张三"))  # 你好，你好，张三

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import time
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def timer(func):
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    """统计函数执行时间的装饰器"""
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    def wrapper(*args, **kwargs):
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        start = time.perf_counter()
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        result = func(*args, **kwargs)
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        elapsed = time.perf_counter() - start
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        print(f"{func.__name__} 耗时 {elapsed:.4f}s")
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        return result
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    return wrapper
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@timer
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def slow_add(a, b):
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    time.sleep(0.1)
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    return a + b
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print(slow_add(1, 2))  # slow_add 耗时 0.1xxx s
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 3

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import functools
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def repeat(times):
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    """让函数重复执行的装饰器（带参数）"""
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    def decorator(func):
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        @functools.wraps(func)  # 保留原函数信息
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        def wrapper(*args, **kwargs):
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            for _ in range(times):
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                result = func(*args, **kwargs)
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            return result
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        return wrapper
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    return decorator
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@repeat(times=3)
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def say_hello():
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    print("Hello!")
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say_hello()  # 打印 3 次 Hello!

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class MyClass:
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    class_var = 0
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    def __init__(self, value):
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        self._value = value
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    @property
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    def value(self):
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        """只读属性"""
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        return self._value
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    @value.setter
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    def value(self, new_val):
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        """属性赋值检查"""
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        if new_val < 0:
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            raise ValueError("不能为负")
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        self._value = new_val
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    @classmethod
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    def create(cls):
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        """类方法"""
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        return cls(0)
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    @staticmethod
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    def helper():
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        """静态方法"""
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        return "工具函数"
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obj = MyClass(10)
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print(obj.value)       # 10
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obj.value = 20         # 调用 setter
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print(obj.value)       # 20
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print(MyClass.create().value)  # 0
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print(MyClass.helper())        # 工具函数

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def count_up_to(n):
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    i = 1
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    while i <= n:
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        yield i  # 暂停并返回
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        i += 1
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gen = count_up_to(5)
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print(next(gen))  # 1
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print(next(gen))  # 2
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print(list(gen))  # [3, 4, 5] 继续消费剩余

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# 列表推导式 vs 生成器表达式
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squares_list = [x*x for x in range(1000000)]   # 立即占用内存
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squares_gen = (x*x for x in range(1000000))    # 惰性求值
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# 生成器可以传给 sum 等函数
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print(sum(x*x for x in range(10)))  # 285
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# 内存对比
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import sys
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print(sys.getsizeof([x for x in range(100000)]))  # 约 800KB
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print(sys.getsizeof((x for x in range(100000))))  # 约 112B

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def fibonacci():
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    """无限斐波那契序列生成器"""
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    a, b = 0, 1
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    while True:
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        yield a
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        a, b = b, a + b
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fib = fibonacci()
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# 取前 10 个
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print([next(fib) for _ in range(10)])
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# [0, 1, 1, 2, 3, 5, 8, 13, 21, 34]

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import functools
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import time
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def memoize(func):
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    """手写缓存装饰器"""
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    cache = {}
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    @functools.wraps(func)
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    def wrapper(*args):
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        if args not in cache:
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            cache[args] = func(*args)
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        return cache[args]
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    return wrapper
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@memoize
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def expensive(n):
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    print(f"计算 {n}...")
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    time.sleep(1)
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    return n * n
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start = time.perf_counter()
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print(expensive(5))  # 计算 5... 25
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print(expensive(5))  # 直接命中缓存 25（不打印计算）
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print(f"耗时：{time.perf_counter()-start:.1f}s")  # 约 1s