Python:强制新式类

发布于 2024-09-13 22:57:00 字数 905 浏览 6 评论 0原文

我希望这段代码“正常工作”:

def main():
    c = Castable()
    print c/3
    print 2-c
    print c%7
    print c**2
    print "%s" % c
    print "%i" % c
    print "%f" % c

当然,最简单的方法是编写 int(c)/3,但我想为配置启用更简单的 perl-ish 语法迷你语言。

值得注意的是,如果我使用“旧式”类(不从对象继承),我可以通过定义 __coerce__ 方法来非常简单地做到这一点,但旧式类已被弃用,并且将被在 python3 中删除了。

当我对新式类做同样的事情时,我收到此错误:

TypeError: unsupported operand type(s) for /: 'Castable' and 'int'

我相信这是设计使然,但是如何用新式类模拟旧式 __coerce__ 行为?您可以在下面找到我当前的解决方案,但它非常丑陋且冗长。

这是相关文档:(我认为)

奖励积分:

    print pow(c, 2, 100)

I want this code to "just work":

def main():
    c = Castable()
    print c/3
    print 2-c
    print c%7
    print c**2
    print "%s" % c
    print "%i" % c
    print "%f" % c

Of course, the easy way out is to write int(c)/3, but I'd like to enable a simpler perl-ish syntax for a configuration mini-language.

It's notable that if I use an "old-style" class (don't inherit from object) I can do this quite simply by defining a __coerce__ method, but old-style classes are deprecated and will be removed in python3.

When I do the same thing with a new-style class, I get this error:

TypeError: unsupported operand type(s) for /: 'Castable' and 'int'

I believe this is by design, but then how can I simulate the old-style __coerce__ behavior with a new-style class? You can find my current solution below, but it's quite ugly and long-winded.

This is the relevant documentation: (i think)

Bonus points:

    print pow(c, 2, 100)

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稚然 2024-09-20 22:57:00

如果你想让c/3工作,你需要定义__div__。 Python 不会先为您将对象转换为数字。

You need to define __div__ if you want c/3 to work. Python won't convert your object to a number first for you.

混吃等死 2024-09-20 22:57:00

这是有效的,并且经过几次改进(支持@jchl)后不再那么粗糙,但似乎仍然是不必要的,特别是考虑到你可以通过“旧式”课程免费获得它。

我仍在寻找更好的答案。如果没有更好的方法,在我看来这就像Python语言的回归。

def ops_list():
    "calculate the list of overloadable operators"
    #<type 'object'> has functions but no operations
    not_ops = dir(object)

    #calculate the list of operation names
    ops = set()
    for mytype in (int, float, str):
        for op in dir(mytype):
            if op.endswith("__") and op not in not_ops:
                ops.add(op)
    return sorted(ops)

class MetaCastable(type):
    __ops = ops_list()

    def __new__(mcs, name, bases, dict):
        #pass any undefined ops to self.__op__
        def add_op(op):
            if op in dict:
                return
            fn = lambda self, *args: self.__op__(op, args)
            fn.__name__ = op
            dict[op] = fn

        for op in mcs.__ops:
            add_op( op )
        return type.__new__(mcs, name, bases, dict)


class Castable(object):
    __metaclass__ = MetaCastable
    def __str__(self):
        print "str!"
        return "<Castable>"
    def __int__(self):
        print "int!"
        return 42
    def __float__(self):
        print "float!"
        return 2.718281828459045

    def __op__(self, op, args):
        try:
            other = args[0]
        except IndexError:
            other = None
        print "%s %s %s" % (self, op, other)
        self, other = coerce(self, other)
        return getattr(self, op)(*args)

    def __coerce__(self, other):
        print "coercing like %r!" % other
        if other is None: other = 0.0
        return (type(other)(self), other)

This works, and is less gross after several improvements (props to @jchl), but still seems like it should be unecessary, especially considering that you get this for free with "old-style" classes.

I'm still looking for a better answer. If there's no better method, this seems to me like a regression in the Python language.

def ops_list():
    "calculate the list of overloadable operators"
    #<type 'object'> has functions but no operations
    not_ops = dir(object)

    #calculate the list of operation names
    ops = set()
    for mytype in (int, float, str):
        for op in dir(mytype):
            if op.endswith("__") and op not in not_ops:
                ops.add(op)
    return sorted(ops)

class MetaCastable(type):
    __ops = ops_list()

    def __new__(mcs, name, bases, dict):
        #pass any undefined ops to self.__op__
        def add_op(op):
            if op in dict:
                return
            fn = lambda self, *args: self.__op__(op, args)
            fn.__name__ = op
            dict[op] = fn

        for op in mcs.__ops:
            add_op( op )
        return type.__new__(mcs, name, bases, dict)


class Castable(object):
    __metaclass__ = MetaCastable
    def __str__(self):
        print "str!"
        return "<Castable>"
    def __int__(self):
        print "int!"
        return 42
    def __float__(self):
        print "float!"
        return 2.718281828459045

    def __op__(self, op, args):
        try:
            other = args[0]
        except IndexError:
            other = None
        print "%s %s %s" % (self, op, other)
        self, other = coerce(self, other)
        return getattr(self, op)(*args)

    def __coerce__(self, other):
        print "coercing like %r!" % other
        if other is None: other = 0.0
        return (type(other)(self), other)
情未る 2024-09-20 22:57:00
class MetaCastable(type):
    __binary_ops = ( 
            'add', 'sub', 'mul', 'floordiv', 'mod', 'divmod', 'pow', 'lshift', 
            'rshift', 'and', 'xor', 'or', 'div', 'truediv',
    )

    __unary_ops = ( 'neg', 'pos', 'abs', 'invert', )

    def __new__(mcs, name, bases, dict):
        def make_binary_op(op):
            fn = lambda self, other: self.__op__(op, other)
            fn.__name__ = op
            return fn

        for opname in mcs.__binary_ops:
            for op in ( '__%s__', '__r%s__' ):
                op %= opname
                if op in dict:
                    continue
                dict[op] = make_binary_op(op)

        def make_unary_op(op):
            fn = lambda self: self.__op__(op, None)
            fn.__name__ = op
            return fn

        for opname in mcs.__unary_ops:
            op = '__%s__' % opname
            if op in dict:
                continue
            dict[op] = make_unary_op(op)

        return type.__new__(mcs, name, bases, dict)

class Castable(object):
    __metaclass__ = MetaCastable
    def __str__(self):
        print "str!"
        return "<Castable>"
    def __int__(self):
        print "int!"
        return 42
    def __float__(self):
        print "float!"
        return 2.718281828459045

    def __op__(self, op, other):
        if other is None:
            print "%s(%s)" % (op, self)
            self, other = coerce(self, 0.0)
            return getattr(self, op)()
        else:
            print "%s %s %s" % (self, op, other)
            self, other = coerce(self, other)
            return getattr(self, op)(other)

    def __coerce__(self, other):
        print "coercing like %r!" % other
        return (type(other)(self), other)
class MetaCastable(type):
    __binary_ops = ( 
            'add', 'sub', 'mul', 'floordiv', 'mod', 'divmod', 'pow', 'lshift', 
            'rshift', 'and', 'xor', 'or', 'div', 'truediv',
    )

    __unary_ops = ( 'neg', 'pos', 'abs', 'invert', )

    def __new__(mcs, name, bases, dict):
        def make_binary_op(op):
            fn = lambda self, other: self.__op__(op, other)
            fn.__name__ = op
            return fn

        for opname in mcs.__binary_ops:
            for op in ( '__%s__', '__r%s__' ):
                op %= opname
                if op in dict:
                    continue
                dict[op] = make_binary_op(op)

        def make_unary_op(op):
            fn = lambda self: self.__op__(op, None)
            fn.__name__ = op
            return fn

        for opname in mcs.__unary_ops:
            op = '__%s__' % opname
            if op in dict:
                continue
            dict[op] = make_unary_op(op)

        return type.__new__(mcs, name, bases, dict)

class Castable(object):
    __metaclass__ = MetaCastable
    def __str__(self):
        print "str!"
        return "<Castable>"
    def __int__(self):
        print "int!"
        return 42
    def __float__(self):
        print "float!"
        return 2.718281828459045

    def __op__(self, op, other):
        if other is None:
            print "%s(%s)" % (op, self)
            self, other = coerce(self, 0.0)
            return getattr(self, op)()
        else:
            print "%s %s %s" % (self, op, other)
            self, other = coerce(self, other)
            return getattr(self, op)(other)

    def __coerce__(self, other):
        print "coercing like %r!" % other
        return (type(other)(self), other)
酷遇一生 2024-09-20 22:57:00
class Castable(object):
    def __div__(self, other):
        return 42 / other
class Castable(object):
    def __div__(self, other):
        return 42 / other
云裳 2024-09-20 22:57:00

新样式的类比旧样式的类运行得更快、更精确。因此,不会因为任何廉价的原因并且以有问题的顺序进行更昂贵的 __getattr__ 、 __getattribute__ 、 、 __coerce__ 调用。

旧样式 __coerce__ 也存在问题,即使您已经出于某种特殊目的重载了运算符方法,它也会被调用。它要求转换为相同的常见类型,并且仅限于某些二进制操作。想想 int / float / string 的所有其他方法和属性 - 以及 pow()。由于所有这些限制,PY3 中缺少coerce。问题示例针对相当广泛的虚拟化。

对于新样式的类,它只是一个循环,用很少的代码提供许多“类似”的方法,或者将这些调用路由到虚拟处理程序,然后以正确和细粒度的方式快速、精确地定义和子类化。这不是“Python 语言的回归”。

但是,我不会仅仅为了这样的循环或提供简单的基类行为而采用其他答案中所示的元类。这无异于用大锤敲碎坚果。


这里是一个“变体”虚拟化的示例助手:

def Virtual(*methods):
    """Build a (new style) base or mixin class, which routes method or
    operator calls to one __virtualmeth__ and attribute lookups to
    __virtualget__ and __virtualset__ optionally.

    *methods (strings, classes): Providing method names to be routed
    """
    class VirtualBase(object):  
        def __virtualmeth__(self, methname, *args, **kw):
            raise NotImplementedError
    def _mkmeth(methname, thing):
        if not callable(thing):
            prop = property(lambda self:self.__virtualget__(methname),
                            lambda self, v:self.__virtualset__(methname, v))
            return prop
        def _meth(self, *args, **kw):
            return self.__virtualmeth__(methname, *args, **kw)
        _meth.__name__ = methname
        return _meth
    for m in methods:
        for name, thing in (isinstance(m, str) and
                            {m:lambda:None} or m.__dict__).items():
            if name not in ('__new__', '__init__', '__setattr__', ##'__cmp__',
                            '__getattribute__', '__doc__', ):   ##'__getattr__', 
                setattr(VirtualBase, name, _mkmeth(name, thing))
    return VirtualBase

这里是一个示例用例:Anaphor! (PY2 和 PY3):

import operator
class Anaphor(Virtual(int, float, str)):   
    """remember a sub-expression comfortably:

    A = Anaphor()      # at least per thread / TLS
    if re.search(...) >> A:
        print(A.groups(), +A)
    if A(x % 7) != 0:
        print(A, 1 + A, A < 3.0, A.real, '%.2f' % A, +A)
    """
    value = 0
    def __virtualmeth__(self, methname, *args, **kw):
        try: r = getattr(self.value, methname)(*args, **kw)
        except AttributeError:
            return getattr(operator, methname)(self.value, *args, **kw)
        if r is NotImplemented: # simple type -> coerce
            try: tcommon = type(self.value + args[0])    # PY2 coerce
            except: return NotImplemented
            return getattr(tcommon(self.value), methname)(*args, **kw)
        return r
    def __call__(self, value):   
        self.value = value
        return value
    __lshift__ = __rrshift__ = __call__     # A << x;  x >> A
    def __pos__(self):                      # real = +A
        return self.value
    def __getattr__(self, name):
        return getattr(self.value, name)
    def __repr__(self):
        return '<Anaphor:%r>' % self.value

它还无缝处理 3-arg 运算符 pow() :-) :

>>> A = Anaphor()
>>> x = 1
>>> if x + 11 >> A:
...     print repr(A), A, +A, 'y' * A, 3.0 < A, pow(A, 2, 100)
...     
<Anaphor:12> 12 12 yyyyyyyyyyyy True 44

New style classes operate faster and more precise than old style classes. Thus no more expensive __getattr__, __getattribute__ , __coerce__ calls for any cheap reasons and in a questionable order.

The old style __coerce__ also had the problem, that it was called even when you have already overloaded an operator method for some special purpose. And it demands casting to equal common types, and is limited to certain binary ops. Think about all the other methods and properties of an int / float / string - and about pow(). Due to all these limitations coerce is missing in PY3. The question examples aim at rather wide virtualization.

With new style classes its just about a loop to provide many "similar" methods with little code, or route those calls to a virtual handler and then its fast and precisely defined and subclassable in correct and fine grained manner. Thats not a "regression in the Python language".

However I would not employ a meta class as shown in other answers just for such a loop or for providing a simple base class kind of behavior. That would be cracking a nut with a sledgehammer.


Here an example helper for virtualization of a "variant":

def Virtual(*methods):
    """Build a (new style) base or mixin class, which routes method or
    operator calls to one __virtualmeth__ and attribute lookups to
    __virtualget__ and __virtualset__ optionally.

    *methods (strings, classes): Providing method names to be routed
    """
    class VirtualBase(object):  
        def __virtualmeth__(self, methname, *args, **kw):
            raise NotImplementedError
    def _mkmeth(methname, thing):
        if not callable(thing):
            prop = property(lambda self:self.__virtualget__(methname),
                            lambda self, v:self.__virtualset__(methname, v))
            return prop
        def _meth(self, *args, **kw):
            return self.__virtualmeth__(methname, *args, **kw)
        _meth.__name__ = methname
        return _meth
    for m in methods:
        for name, thing in (isinstance(m, str) and
                            {m:lambda:None} or m.__dict__).items():
            if name not in ('__new__', '__init__', '__setattr__', ##'__cmp__',
                            '__getattribute__', '__doc__', ):   ##'__getattr__', 
                setattr(VirtualBase, name, _mkmeth(name, thing))
    return VirtualBase

And here an example use case: An Anaphor! (PY2 and PY3) :

import operator
class Anaphor(Virtual(int, float, str)):   
    """remember a sub-expression comfortably:

    A = Anaphor()      # at least per thread / TLS
    if re.search(...) >> A:
        print(A.groups(), +A)
    if A(x % 7) != 0:
        print(A, 1 + A, A < 3.0, A.real, '%.2f' % A, +A)
    """
    value = 0
    def __virtualmeth__(self, methname, *args, **kw):
        try: r = getattr(self.value, methname)(*args, **kw)
        except AttributeError:
            return getattr(operator, methname)(self.value, *args, **kw)
        if r is NotImplemented: # simple type -> coerce
            try: tcommon = type(self.value + args[0])    # PY2 coerce
            except: return NotImplemented
            return getattr(tcommon(self.value), methname)(*args, **kw)
        return r
    def __call__(self, value):   
        self.value = value
        return value
    __lshift__ = __rrshift__ = __call__     # A << x;  x >> A
    def __pos__(self):                      # real = +A
        return self.value
    def __getattr__(self, name):
        return getattr(self.value, name)
    def __repr__(self):
        return '<Anaphor:%r>' % self.value

Seamlessly it also handles the 3-arg opertor pow() :-) :

>>> A = Anaphor()
>>> x = 1
>>> if x + 11 >> A:
...     print repr(A), A, +A, 'y' * A, 3.0 < A, pow(A, 2, 100)
...     
<Anaphor:12> 12 12 yyyyyyyyyyyy True 44
~没有更多了~
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