6、函数工具模块

# 以下，其实就是partial的内部实现逻辑
def partial(func, *args, **keywords):
    def newfunc(*fargs, **fkeywords):
        newkeywords = keywords.copy()
        newkeywords.update(fkeywords)  # 关键部分
        return func(*(args + fargs), **newkeywords)
    newfunc.func = func
    newfunc.args = args
    newfunc.keywords = keywords
    return newfunc

from functools import partial

def add(x, y):
    print("x的值为:%s，y的值为:%s" % (x, y))
    return x + y

f1 = partial(add, 1)  # 这个1就赋予给了x，实际上就将add函数包装成了def add(x=1,y)
print(f1(5))
f2 = partial(add, y=1)  # 这个1就赋予给了y，实际上就将add函数包装成了def add(x,y=1)
print(f2(2))
# x的值为:1，y的值为:5
# 6
# x的值为:2，y的值为:1
# 3
f3 = partial(add, 1)
print(f3(2, 3))  # 此时就会报错，因为在上面的partial(add, 1)以及给x赋予了一个值为1，下面又给定了一个1，此时就会多处一个参数
# 当然，此时，如果我们使用关键字传参则能解决这个问题，因为它是直接update，可以覆盖原有值的
print(f3(x=2, y=3)) # ，此时x从1就变成了2

# 源码
from functools import partial
WRAPPER_ASSIGNMENTS = ('__module__', '__name__', '__qualname__', '__doc__',
                       '__annotations__')
WRAPPER_UPDATES = ('__dict__',)
def update_wrapper(wrapper,
                   wrapped,
                   assigned = WRAPPER_ASSIGNMENTS,
                   updated = WRAPPER_UPDATES):
    for attr in assigned:
        try:
            value = getattr(wrapped, attr)
        except AttributeError:
            pass
        else:
            setattr(wrapper, attr, value)
    for attr in updated:
        getattr(wrapper, attr).update(getattr(wrapped, attr, {}))
    wrapper.__wrapped__ = wrapped
    return wrapper

def wraps(wrapped,
          assigned = WRAPPER_ASSIGNMENTS,
          updated = WRAPPER_UPDATES):
    return partial(update_wrapper, wrapped=wrapped,
                   assigned=assigned, updated=updated)
---
# 实现
from functools import wraps
def timer(func):
    @wraps(func) # 等价于 wrapper=update_wrapper(func)(wrapper)
    def wrapper(*args, **kwargs):
        start_time = time.time()
        result = func(*args, **kwargs)
        stop_time = time.time()
        return result, stop_time - start_time
    return wrapper

# 案例一
import time
from functools import lru_cache
@lru_cache()
def cce(x):
    time.sleep(3)
    return x
print(cce(1)) # lru_cache会记录函数的调用和输出信息
print(cce(1)) # 第二次调用将不回调用add，而是直接从缓存中获取，所以速度很快

# 案例二
import time
from functools import lru_cache
@lru_cache()
def cce(x, y):
    time.sleep(3)
    return x + y
print(cce(1, 2))
print(cce(x=1, y=2))
print(cce(y=2, x=1))

# 增加缓存的斐波那契数列
from functools import lru_cache

@lru_cache()
def fib(n):
    return 1 if n < 3 else fib(n - 1) + fib(n - 2)
print(fib(100))