Python记忆/延迟查找属性修饰符

boltons .

作为图书馆的一部分 cachedproperty

from boltons.cacheutils import cachedproperty

class Foo(object):
    def __init__(self):
        self.value = 4

    @cachedproperty
    def cached_prop(self):
        self.value += 1
        return self.value


f = Foo()
print(f.value)  # initial value
print(f.cached_prop)  # cached property is calculated
f.value = 1
print(f.cached_prop)  # same value for the cached property - it isn't calculated again
print(f.value)  # the backing value is different (it's essentially unrelated value)

import functools

def lazyprop(fn):
    attr_name = '_lazy_' + fn.__name__

    @property
    @functools.wraps(fn)
    def _lazyprop(self):
        if not hasattr(self, attr_name):
            setattr(self, attr_name, fn(self))
        return getattr(self, attr_name)

    return _lazyprop


class Test(object):

    @lazyprop
    def a(self):
        print 'generating "a"'
        return range(5)

交互式会话:

>>> t = Test()
>>> t.__dict__
{}
>>> t.a
generating "a"
[0, 1, 2, 3, 4]
>>> t.__dict__
{'_lazy_a': [0, 1, 2, 3, 4]}
>>> t.a
[0, 1, 2, 3, 4]

一次性

import functools

class lazy_property(object):
    '''
    meant to be used for lazy evaluation of an object attribute.
    property should represent non-mutable data, as it replaces itself.
    '''

    def __init__(self, fget):
        self.fget = fget

        # copy the getter function's docstring and other attributes
        functools.update_wrapper(self, fget)

    def __get__(self, obj, cls):
        if obj is None:
            return self

        value = self.fget(obj)
        setattr(obj, self.fget.__name__, value)
        return value


class Test(object):

    @lazy_property
    def results(self):
        calcs = 1  # Do a lot of calculation here
        return calcs

注:以下为 lazy_property 班级是a non-data descriptor ,这意味着它是只读的。添加 __set__ 方法将阻止它正常工作。

在 __call__ 你也可以在网上复制 __name__ , __doc__ __module__ 从func的命名空间:

import time

class Lazyproperty(object):

    def __init__(self, timeout=None):
        self.timeout = timeout
        self._cache = {}

    def __call__(self, func):
        self.func = func
        return self

    def __get__(self, obj, objcls):
        if obj not in self._cache or \
          (self.timeout and time.time() - self._cache[key][1] > self.timeout):
            self._cache[obj] = (self.func(obj), time.time())
        return self._cache[obj]

class Foo(object):

    @Lazyproperty(10)
    def bar(self):
        print('calculating')
        return 'bar'

>>> x = Foo()
>>> print(x.bar)
calculating
bar
>>> print(x.bar)
bar
...(waiting 10 seconds)...
>>> print(x.bar)
calculating
bar

property 是一个班级。A descriptor 准确地说。简单地从中派生并实现所需的行为。

class lazyproperty(property):
   ....

class testA(object):
   ....
  a = lazyproperty('_a')
  b = lazyproperty('_b')

想要是最重要的 reify (source linked!)

用作类方法装饰器。它的运行方式几乎与Python完全相同 @property decorator,但它在第一次调用后将其修饰的方法的结果放入实例dict中,从而有效地用实例变量替换其修饰的函数。用Python的说法,它是一个非数据描述符。下面是一个示例及其用法:

>>> from pyramid.decorator import reify

>>> class Foo(object):
...     @reify
...     def jammy(self):
...         print('jammy called')
...         return 1

>>> f = Foo()
>>> v = f.jammy
jammy called
>>> print(v)
1
>>> f.jammy
1
>>> # jammy func not called the second time; it replaced itself with 1
>>> # Note: reassignment is possible
>>> f.jammy = 2
>>> f.jammy
2

延迟评估只是意味着在运行时需要某个值时,在最后一个可能的时刻对某个值进行评估。 标准 @property 装饰师就是这么做的。 (*)仅在每次需要该属性的值时才对修饰函数求值(参见维基百科关于懒惰评估的文章)

回忆录是询问者寻找的正确术语。不依赖于返回值评估的副作用的纯函数可以被安全地记忆,并且实际上有一个decorator functools @functools.lru_cache

class cached_property(property):
    def __init__(self, func, name=None, doc=None):
        self.__name__ = name or func.__name__
        self.__module__ = func.__module__
        self.__doc__ = doc or func.__doc__
        self.func = func

    def __set__(self, obj, value):
        obj.__dict__[self.__name__] = value

    def __get__(self, obj, type=None):
        if obj is None:
            return self
        value = obj.__dict__.get(self.__name__, None)
        if value is None:
            value = self.func(obj)
            obj.__dict__[self.__name__] = value
        return value

class SampleClass():
    @cached_property
    def cached_property(self):
        print('I am calculating value')
        return 'My calculated value'


c = SampleClass()
print(c.cached_property)
print(c.cached_property)
c.cached_property = 2
print(c.cached_property)
print(c.cached_property)

值只在第一次计算,然后我们使用保存的值

I am calculating value
My calculated value
My calculated value
2
2