python面对对象编程-------5：获取属性的四种办法：@property, __setattr__(__getattr__) ,descriptor

一：最基本的属性操作

class Generic:
    pass

g= Generic()

>>> g.attribute= "value"    #创建属性并赋值
>>> g.attribute
'value'
>>> g.unset
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
AttributeError: 'Generic' object has no attribute 'unset'
>>> del g.attribute         #注意，此地时直接删除了这个属性
>>> g.attribute
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
AttributeError: 'Generic' object has no attribute 'attribute'

 

二：@property

　　被@property修饰的是一个方法,但此方法名可以像属性一样被获取，设置，删除
　　需要注意的是，属性的外部添加是十分简单的，但property的外部添加不是，所以其与属性还是有区别的
　　有两种方法创建property：
　　    1：用@property修饰的函数
　　    2：用property()方法
　　有两种设计模式：
   　　 1：懒惰计算模式：被调用时才执行
　　    2：主动计算模式：实例化时就执行

　　懒惰模式：计算手牌总和

class Hand_Lazy(Hand):
    def __init__( self, dealer_card, *cards ):
        self.dealer_card= dealer_card
        self._cards= list(cards)
    @property
    def total( self ):
        delta_soft = max(c.soft-c.hard for c in self._cards)
        hard_total = sum(c.hard for c in self._cards)
        if hard_total+delta_soft <= 21:
            return hard_total+delta_soft
        return hard_total
    @property
    def card( self ):
        return self._cards
    @card.setter
    def card( self, aCard ):
        self._cards.append( aCard )
    @card.deleter
    def card( self ):
        self._cards.pop(-1)


d= Deck()
h= Hand_Lazy( d.pop(), d.pop(), d.pop() )
h.total                 #被调用时才执行计算手头的牌之和
# 19
h.card = d.pop()        #注意，可以将@property看作@property.getter,而此地可以看作两步，左边为@property获取属性，=号调用@property.setter并且将右边的d.pop()当作参数传入。
h.total
# 29

　　主动计算模式

# 将计算嵌入到@card.setter中，每新添加一张手牌就立马更新手牌总和
class Hand_Eager(Hand):
    def __init__( self, dealer_card, *cards ):
        self.dealer_card= dealer_card
        self.total= 0
        self._delta_soft= 0
        self._hard_total= 0
        self._cards= list()
        for c in cards:
            self.card = c
    @property
    def card( self ):
        return self._cards
    @card.setter
    def card( self, aCard ):
        self._cards.append(aCard)
        self._delta_soft = max(aCard.soft-aCard.hard,self._delta_soft)
        self._hard_total += aCard.hard
        self._set_total()
    @card.deleter
    def card( self ):
        removed= self._cards.pop(-1)
        self._hard_total -= removed.hard
        # Issue: was this the only ace?
        self._delta_soft = max( c.soft-c.hard for c in self._cards)
        self._set_total()

    def _set_total( self ):
        if self._hard_total+self._delta_soft <= 21:
            self.total= self._hard_total+self._delta_soft
        else:
            self.total= self._hard_total

d= Deck()
h1= Hand_Lazy( d.pop(), d.pop(), d.pop() )
print( h1.total )
h2= Hand_Eager( d.pop(), d.pop(), d.pop() )
print( h2.total )

　　其实@property已经模糊了数据和行为了，那么到底什么时候我们需要使用@property呢？

　　　　1：需要使用类中其他属性计算得到【也就是上面的情况】　　

　　　　2：对于难以查找或者计算的东西，将这个值以私有属性的形式缓存到本地，而后再次访问就快捷很多：

from urllib.request import urlopen
class WebPage:
    def __init__(self,url):
        self.url = url
        self._content = None

    @property
    def content(self):
        if not self._content:
            print("retriving new page")
            self._content = urlopen(self.url).read()

        return self._content

import time
webpage = WebPage("http://ccphillips.net/")
now = time.time()
content1 = webpage.content
print(time.time()-now)
now = time.time()
content2 = webpage.content
print(time.time()-now)

输出：
retriving new page
14.51249384880066
0.0        #！！！！

　　补充：廖雪峰的关于@property片段的代码

class Student:
    def get_score(self):
        return self._score

    def set_score(self,value):
        if not isinstance(value,int):
            raise ValueError('must be integer')
        if value < 0 or value > 100:
            raise ValueError('0~100')
        self._score = value

s=Student()
s.set_score(60)
s.get_score()
# 60

# 用@property优化：
# 注意，可以把@property看作getter，而setter与deletter都是基于getter的
class Student:
    @property
    def score(self):
        return self._score

    @score.setter
    def score(self,value):
        if not isinstance(value,int):
            raise ValueError('must be integer')
        if value < 0 or value > 100:
            raise ValueError('0~100')
        self._score = value

s=Student()
s.score = 60
s.score
# 60

 

三：属性获取的特殊方法

　　__getattr__(), __setattr__(), and __delattr__(),__dir__()，__getattribute__()
         __setattr__(): 创建属性并赋值
         __getattr__(): 首先：如果此属性已有值，不会用到__getattr__(),直接返回值就是了。 
　                      其次：如果此属性没有值，此时调用__getattr__()并且返回其中设定的返回值。
　                      最后：如果压根没有这属性，报 AttributeError 错误。
         __delattr__()：删除一个属性
         __dir__()：    返回包含属性的list
         __getattribute__()：更加底层的属性获取方法，他默认从__dict__（或__slots__）中获取值，如果没有找到，调用__getattr__()作为反馈。如果发现此值是一个dexcriptor，就调用descriptor，否者就直接返回值。

    __getattr__()方法只当某个属性没有值时才起作用。
    
　　
　　1：创建immutable object
　　　　什么是immutable object：不能够在外部直接赋值一个已有属性的值，不能创建新属性
　　　　immutable object的一个特点是__hash__()能够返回固定的值
　　　　版本一：用__slots__创建immutable object：

class BlackJackCard:
    """Abstract Superclass"""
    __slots__ = ( 'rank', 'suit', 'hard', 'soft' )          #__slots__限定了只有这些属性可用
    def __init__( self, rank, suit, hard, soft ):
        super().__setattr__( 'rank', rank )
        super().__setattr__( 'suit', suit )
        super().__setattr__( 'hard', hard )
        super().__setattr__( 'soft', soft )
    def __str__( self ):
        return "{0.rank}{0.suit}".format( self )
    def __setattr__( self, name, value ):
        raise AttributeError( "'{__class__.__name__}' has no attribute '{name}'".format( __class__= self.__class__, name= name ) )

# We defined __setattr__() to raise an exception rather than do anything useful.
# __init__() use the superclass version of __setattr__() so that values can be properly set in spite of the absence of a working __setattr__() method in this class.
# 我们知道，python并不阻止人干坏事，所以可以通过 object.__setattr__(c, 'bad', 5) 来绕过immutable机制

　　　　版本2： 我们还可以通过继承 tuple 并且覆盖__getattr__()来写immutable object。

class BlackJackCard2( tuple ):
    def __new__( cls, rank, suit, hard, soft ):          # tuple(iterable) -> tuple initialized from iterable's items
        return super().__new__( cls, (rank, suit, hard, soft) )

    def __getattr__( self, name ):      #translate __getattr__(name) requests to self[index] requests
        return self[{'rank':0, 'suit':1, 'hard':2 , 'soft':3}[name]]

    def __setattr__( self, name, value ):
        raise AttributeError

>>> d = BlackJackCard2( 'A', '?', 1, 11 )
>>> d.rank
'A'
>>> d.suit
'?'
>>> d.bad= 2            #不能改变属性值了
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "<stdin>", line 7, in __setattr__AttributeError

　　# 注意上面两个版本是有区别的，在版本2中可以通过d.__dict__来增加属性
　　# 而版本1中用了__slots__后就会关闭__dict__
　　
　　2：创建一个一旦给定速度与时间就自动更新距离的类，让其继承自dict，好处是用format函数特别方便

class RateTimeDistance( dict ):
    def __init__( self, *args, **kw ):
        super().__init__( *args, **kw )
        self._solve()
    def __getattr__( self, name ):
        return self.get(name,None)              #对应字典的get方法
    def __setattr__( self, name, value ):
        self[name]= value                       #对应字典的赋值方法
        self._solve()                           #在__setattr__中调用方法既是一旦赋值就能能够完成计算
    def __dir__( self ):
        return list(self.keys())
    def _solve(self):
        if self.rate is not None and self.time is not None:
            self['distance'] = self.rate*self.time
        elif self.rate is not None and self.distance is not None:
            self['time'] = self.distance / self.rate
        elif self.time is not None and self.distance is not None:
            self['rate'] = self.distance / self.time

>>> rtd= RateTimeDistance( rate=6.3, time=8.25, distance=None )
>>> print( "Rate={rate}, Time={time}, Distance={distance}".format(**rtd ) )
Rate=6.3, Time=8.25, Distance=51.975
# It's also important to note that once all three values are set, this object can't be changed to provide new solutions easily.
# 上面有个bug在于，一旦我们想改变时间，这时发现速度与距离至少其一一定会变，按代码顺序是改变了距离，而如果我们不想改变距离而是改变速度就不行了
# 或者是两个都不想改变，唯一的办法不改变其中一个就是先把一个值设为None

# 解决办法:design a model that tracked the order that the variables were set in
# this model could save us from having to clear one variable before setting another to recompute a related result.

 

　　　3：The __getattribute__() method

　　　　总的来说，几乎没必要用__getattribute__(),其默认的方法已近够强大了，况且几乎所有我们需要的都能够通过__getattr__()实现。

class BlackJackCard3:
    """Abstract Superclass"""
    def __init__( self, rank, suit, hard, soft ):
        super().__setattr__( 'rank', rank )
        super().__setattr__( 'suit', suit )
        super().__setattr__( 'hard', hard )
        super().__setattr__( 'soft', soft )
    def __setattr__( self, name, value ):
        if name in self.__dict__:
            raise AttributeError( "Cannot set {name}".format(name=name) )
        raise AttributeError( "'{__class__.__name__}' has no attribute'{name}'".format( __class__= self.__class__, name= name ) )
    def __getattribute__( self, name ):
        if name.startswith('_'):
            raise AttributeError
        return object.__getattribute__( self, name )

>>> c = BlackJackCard3( 'A', '?', 1, 11 )
>>> c.rank= 12
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "<stdin>", line 9, in __setattr__
File "<stdin>", line 13, in __getattribute__
AttributeError
>>> c.__dict__['rank']= 12
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "<stdin>", line 13, in __getattribute__
AttributeError

 

 四：descriptors

    Descriptor.__get__( self, instance, owner )，Descriptor.__set__( self, instance, value )，Descriptor.__delete__( self, instance )
        instance： the self variable of the object being accessed
        owner   ： the owning class object
        value   ： the new value that the descriptor needs to be set to.

    描述符是一个类：在达到属性前处理，可用于get，set，delete
    其本身在类定义时创建，并不是在__init__中创建，它是类的一部分，不同于方法以及属性
    用其来实现（不）可变对象：
        无数据描述符：实现__set__or__delete__ or both，若是immutable对象，只用实现__set__并返回AttributeError
        数据描述符：  至少实现__get__，通常实现__get__与__set__来创建个可变对象。

　　1：无数据描述符

class UnitValue_1:
    """Measure and Unit combined."""
    def __init__( self, unit ):
        self.value= None
        self.unit= unit
        self.default_format= "5.2f"
    def __set__( self, instance, value ):
        self.value= value
    def __str__( self ):
        return "{value:{spec}} {unit}".format( spec=self.default_format, **self.__dict__)
    def __format__( self, spec="5.2f" ):
        #print( "formatting", spec )
        if spec == "": spec= self.default_format
            return "{value:{spec}} {unit}".format( spec=spec,**self.__dict__)

# The following is a class that does rate-time-distance calculations eagerly:
class RTD_1:
    rate= UnitValue_1( "kt" )
    time= UnitValue_1( "hr" )
    distance= UnitValue_1( "nm" )
    def __init__( self, rate=None, time=None, distance=None ):
        if rate is None:
            self.time = time
            self.distance = distance
            self.rate = distance / time
        if time is None:
            self.rate = rate
            self.distance = distance
            self.time = distance / rate
        if distance is None:
            self.rate = rate
            self.time = time
            self.distance = rate * time
    def __str__( self ):
        return "rate: {0.rate} time: {0.time} distance:{0.distance}".format(self)

# As soon as the object is created and the attributes loaded, the missing value is computed.
# Once computed, the descriptor can be examined to get the value or the unit's name.
# Additionally, the descriptor has a handy response to str() and formatting requests

>>> m1 = RTD_1( rate=5.8, distance=12 )
>>> str(m1)
'rate: 5.80 kt time: 2.07 hr distance: 12.00 nm'
>>> print( "Time:", m1.time.value, m1.time.unit )
Time: 2.0689655172413794 hr

　　2：数据描述符，转换单位后自动更新

class Unit:
    conversion= 1.0
    def __get__( self, instance, owner ):
        return instance.kph * self.conversion       #kph：千米每小时
    def __set__( self, instance, value ):
        instance.kph= value / self.conversion

# The following are the two conversion descriptors:
class Knots( Unit ):
    conversion= 0.5399568
class MPH( Unit ):
    conversion= 0.62137119
# The following is a unit descriptor for a standard unit, kilometers per hour:
class KPH( Unit ):
    def __get__( self, instance, owner ):
        return instance._kph
    def __set__( self, instance, value ):
        instance._kph= value


class Measurement:
    kph= KPH()
    knots= Knots()
    mph= MPH()
    def __init__( self, kph=None, mph=None, knots=None ):
        if kph:
            self.kph= kph
        elif mph:
            self.mph= mph
        elif knots:
            self.knots= knots
        else:
            raise TypeError
    def __str__( self ):
        return "rate: {0.kph} kph = {0.mph} mph = {0.knots}knots".format(self)

# 在不同进制下自动完成转换
>>> m2 = Measurement( knots=5.9 )
>>> str(m2)
'rate: 10.92680006993152 kph = 6.789598762345432 mph = 5.9 knots'
>>> m2.kph
10.92680006993152
>>> m2.mph
6.789598762345432

 

 

五：一些补充：

　　Internally, Python uses descriptors to implement features such as method functions,
static method functions, and properties. Many of the cool use cases for descriptors
are already first-class features of the language

　　

In Python, it's considerably simpler to treat all attributes as public. This means the following:
     They should be well documented.
     They should properly reflect the state of the object; they shouldn't be temporary or transient values.
     In the rare case of an attribute that has a potentially confusing (or brittle)
      　　value, a single leading underscore character (_) marks the name as "not part
      　　of the defined interface." It's not really private.

　　一般来说，外部能够改变属性值并不是严重的事，但是当一个属性值改变后会影响到另一个时，我们需要考虑用函数或者property进行一些设置。
注意区别property的两种设计方式（eager calcilation & lazy calculation）


    descriptor是非常高级的python用法，一般用于连接 python 与 non-python 的处理，比如python与SQL，python做网络服务器，
在我们的程序里，关于attributes我们尽量用property来实现，如果发现property需要写的太复杂，那么我们转向descriptor。