你想定义某些在属性赋值上面有限制的数据结构。
在这个问题中,你需要在对某些实例属性赋值时进行检查。所以你要自定义属性赋值函数,这种情况下最好使用描述器。
下面的代码使用描述器实现了一个系统类型和赋值验证框架:
# Base class. Uses a descriptor to set a value
class Descriptor:
def __init__(self, name=None, **opts):
self.name = name
for key, value in opts.items():
setattr(self, key, value)
def __set__(self, instance, value):
instance.__dict__[self.name] = value
# Descriptor for enforcing types
class Typed(Descriptor):
expected_type = type(None)
def __set__(self, instance, value):
if not isinstance(value, self.expected_type):
raise TypeError('expected ' + str(self.expected_type))
super().__set__(instance, value)
# Descriptor for enforcing values
class Unsigned(Descriptor):
def __set__(self, instance, value):
if value < 0:
raise ValueError('Expected >= 0')
super().__set__(instance, value)
class MaxSized(Descriptor):
def __init__(self, name=None, **opts):
if 'size' not in opts:
raise TypeError('missing size option')
super().__init__(name, **opts)
def __set__(self, instance, value):
if len(value) >= self.size:
raise ValueError('size must be < ' + str(self.size))
super().__set__(instance, value)
这些类就是你要创建的数据模型或类型系统的基础构建模块。下面就是我们实际定义的各种不同的数据类型:
class Integer(Typed):
expected_type = int
class UnsignedInteger(Integer, Unsigned):
pass
class Float(Typed):
expected_type = float
class UnsignedFloat(Float, Unsigned):
pass
class String(Typed):
expected_type = str
class SizedString(String, MaxSized):
pass
然后使用这些自定义数据类型,我们定义一个类:
class Stock:
# Specify constraints
name = SizedString('name', size=8)
shares = UnsignedInteger('shares')
price = UnsignedFloat('price')
def __init__(self, name, shares, price):
self.name = name
self.shares = shares
self.price = price
然后测试这个类的属性赋值约束,可发现对某些属性的赋值违法了约束是不合法的:
>>> s.name
'ACME'
>>> s.shares = 75
>>> s.shares = -10
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "example.py", line 17, in __set__
super().__set__(instance, value)
File "example.py", line 23, in __set__
raise ValueError('Expected >= 0')
ValueError: Expected >= 0
>>> s.price = 'a lot'
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "example.py", line 16, in __set__
raise TypeError('expected ' + str(self.expected_type))
TypeError: expected <class 'float'>
>>> s.name = 'ABRACADABRA'
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "example.py", line 17, in __set__
super().__set__(instance, value)
File "example.py", line 35, in __set__
raise ValueError('size must be < ' + str(self.size))
ValueError: size must be < 8
>>>
还有一些技术可以简化上面的代码,其中一种是使用类装饰器:
# Class decorator to apply constraints
def check_attributes(**kwargs):
def decorate(cls):
for key, value in kwargs.items():
if isinstance(value, Descriptor):
value.name = key
setattr(cls, key, value)
else:
setattr(cls, key, value(key))
return cls
return decorate
# Example
@check_attributes(name=SizedString(size=8),
shares=UnsignedInteger,
price=UnsignedFloat)
class Stock:
def __init__(self, name, shares, price):
self.name = name
self.shares = shares
self.price = price
另外一种方式是使用元类:
# A metaclass that applies checking
class checkedmeta(type):
def __new__(cls, clsname, bases, methods):
# Attach attribute names to the descriptors
for key, value in methods.items():
if isinstance(value, Descriptor):
value.name = key
return type.__new__(cls, clsname, bases, methods)
# Example
class Stock2(metaclass=checkedmeta):
name = SizedString(size=8)
shares = UnsignedInteger()
price = UnsignedFloat()
def __init__(self, name, shares, price):
self.name = name
self.shares = shares
self.price = price
本节使用了很多高级技术,包括描述器、混入类、super()
的使用、类装饰器和元类。不可能在这里一一详细展开来讲,但是可以在8.9、8.18、9.19小节找到更多例子。但是,我在这里还是要提一下几个需要注意的点。
首先,在 Descriptor
基类中你会看到有个 __set__()
方法,却没有相应的 __get__()
方法。如果一个描述仅仅是从底层实例字典中获取某个属性值的话,那么没必要去定义 __get__()
方法。
所有描述器类都是基于混入类来实现的。比如 Unsigned
和 MaxSized
要跟其他继承自 Typed
类混入。这里利用多继承来实现相应的功能。
混入类的一个比较难理解的地方是,调用 super()
函数时,你并不知道究竟要调用哪个具体类。你需要跟其他类结合后才能正确的使用,也就是必须合作才能产生效果。
使用类装饰器和元类通常可以简化代码。上面两个例子中你会发现你只需要输入一次属性名即可了。
# Normal
class Point:
x = Integer('x')
y = Integer('y')
# Metaclass
class Point(metaclass=checkedmeta):
x = Integer()
y = Integer()
所有方法中,类装饰器方案应该是最灵活和最高明的。首先,它并不依赖任何其他新的技术,比如元类。其次,装饰器可以很容易的添加或删除。
最后,装饰器还能作为混入类的替代技术来实现同样的效果;
# Decorator for applying type checking
def Typed(expected_type, cls=None):
if cls is None:
return lambda cls: Typed(expected_type, cls)
super_set = cls.__set__
def __set__(self, instance, value):
if not isinstance(value, expected_type):
raise TypeError('expected ' + str(expected_type))
super_set(self, instance, value)
cls.__set__ = __set__
return cls
# Decorator for unsigned values
def Unsigned(cls):
super_set = cls.__set__
def __set__(self, instance, value):
if value < 0:
raise ValueError('Expected >= 0')
super_set(self, instance, value)
cls.__set__ = __set__
return cls
# Decorator for allowing sized values
def MaxSized(cls):
super_init = cls.__init__
def __init__(self, name=None, **opts):
if 'size' not in opts:
raise TypeError('missing size option')
super_init(self, name, **opts)
cls.__init__ = __init__
super_set = cls.__set__
def __set__(self, instance, value):
if len(value) >= self.size:
raise ValueError('size must be < ' + str(self.size))
super_set(self, instance, value)
cls.__set__ = __set__
return cls
# Specialized descriptors
@Typed(int)
class Integer(Descriptor):
pass
@Unsigned
class UnsignedInteger(Integer):
pass
@Typed(float)
class Float(Descriptor):
pass
@Unsigned
class UnsignedFloat(Float):
pass
@Typed(str)
class String(Descriptor):
pass
@MaxSized
class SizedString(String):
pass
这种方式定义的类跟之前的效果一样,而且执行速度会更快。设置一个简单的类型属性的值,装饰器方式要比之前的混入类的方式几乎快100%。现在你应该庆幸自己读完了本节全部内容了吧?^_^