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functools 源码阅读
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functools主要包括这几个东西: wraps
, partial
, lru_cache
, 还有一些内置
帮助函数例如 c3_mro
。我们主要看上面三个:
wraps
wraps实现很简单,就是把a函数的某些属性拷贝到b函数。
partial
partial的实现,看下面代码就可以了:
class Partial: def __new__(*args, **kwargs): cls, func, *args = args self = super(Partial, cls).__new__(cls) self.func = func self.args = args self.kwargs = kwargs return self def __call__(*args, **kwargs): self, *new_args = args new_kwargs = self.kwargs.copy() new_kwargs.update(kwargs) return self.func(*self.args, *new_args, **new_kwargs) mypartial = Partial # noqa if __name__ == "__main__": def foo(first, second, hello="world"): print("first, second, hello = %s, %s, %s" % (first, second, hello)) bar = mypartial(foo, second=2, hello="hello") bar(1)
主要原理就是先用一个类来保存原函数的一些状态,然后重写 __call__
方法,把
对应的已经partial的参数一起塞进去。
lru_cache
(Least-recently-used cache)
这个比较有意思, def lru_cache(maxsize=128, typed=False):
,如果maxsize为0,
那就直接返回函数结果,如果为None,那就直接用一个字典存储,如果为具体数值,
那么还会有一个环状双向链表来保存顺序。
lru_cache
的实现主要包括三个部分:
- `class _HashedSeq(list)` - `def _make_key(...省略一把参数...)` - `def lru_cache(maxsize=128, typed=False)`
其中第一个类用来保存hash值,第二个用来根据函数的参数生成key,第三个基于前两个
实现了 lru_cache
。
下面是我的一个实现:
class Link: __slots__ = 'prev', 'next', 'key', 'value' def _make_key(args, kwargs, kwargs_mark=(object(), )): key = args if kwargs: sorted_kwargs = sorted(kwargs.items()) key += kwargs_mark for kwarg in sorted_kwargs: key += kwarg return hash(key) def lru_cache(maxsize=128): def decorator(user_func): return _lru_cache_wrapper(user_func, maxsize) return decorator def _lru_cache_wrapper(user_func, maxsize): cache = {} root = Link() root.prev, root.next, root.key, root.value = root, root, None, None def decorator(*args, **kwargs): nonlocal cache, root key = _make_key(args, kwargs) if key in cache: value = cache[key] else: value = user_func(*args, **kwargs) # 更新环状双向链表 last = root.prev link = Link() link.prev = last link.next = root link.key, link.value = key, value root.prev = last.next = link if key not in cache: # 更新缓存信息和环状双向链表,因为缓存有大小限制 cache[key] = value last = root.prev root = root.next root.prev = last last.next = root return value return decorator if __name__ == "__main__": import time # 普通递归版fib函数 start = time.time() def fib(x): if x == 0 or x == 1: return x else: return fib(x - 1) + fib(x - 2) result = fib(35) end = time.time() print("result: %s, use time: %.6f" % (result, end - start)) # 为了方便看,还是不用函数形式的decorator,而是直接把fib函数抄一遍 start = time.time() @lru_cache() def fib_cache(x): if x == 0 or x == 1: return x else: return fib_cache(x - 1) + fib_cache(x - 2) result = fib_cache(35) end = time.time() print("result: %s, use time: %.6f" % (result, end - start))
运行结果:
root@arch tests: python myfunctools.py result: 9227465, use time: 4.576693 result: 9227465, use time: 0.000146
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