As of Python 3.4, there is a descriptor called DynamicClassAttribute. The documentation states:
types.DynamicClassAttribute(fget=None, fset=None, fdel=None, doc=None)
Route attribute access on a class to __getattr__.
This is a descriptor, used to define attributes that act differently when accessed through an instance and through a class. Instance access remains normal, but access to an attribute through a class will be routed to the class’s __getattr__ method; this is done by raising AttributeError.
This allows one to have properties active on an instance, and have virtual attributes on the class with the same name (see Enum for an example).
New in version 3.4.
It is apparently used in the enum module:
# DynamicClassAttribute is used to provide access to the `name` and
# `value` properties of enum members while keeping some measure of
# protection from modification, while still allowing for an enumeration
# to have members named `name` and `value`. This works because enumeration
# members are not set directly on the enum class -- __getattr__ is
# used to look them up.
#DynamicClassAttribute
def name(self):
"""The name of the Enum member."""
return self._name_
#DynamicClassAttribute
def value(self):
"""The value of the Enum member."""
return self._value_
I realise that enums are a little special, but I don't understand how this relates to the DynamicClassAttribute. What does it mean that those attributes are dynamic, how is this different from a normal property, and how do I use a DynamicClassAttribute to my advantage?
New Version:
I was a bit disappointed with the previous answer so I decided to rewrite it a bit:
First have a look at the source code of DynamicClassAttribute and you'll probably notice, that it looks very much like the normal property. Except for the __get__-method:
def __get__(self, instance, ownerclass=None):
if instance is None:
# Here is the difference, the normal property just does: return self
if self.__isabstractmethod__:
return self
raise AttributeError()
elif self.fget is None:
raise AttributeError("unreadable attribute")
return self.fget(instance)
So what this means is that if you want to access a DynamicClassAttribute (that isn't abstract) on the class it raises an AttributeError instead of returning self. For instances if instance: evaluates to True and the __get__ is identical to property.__get__.
For normal classes that just resolves in a visible AttributeError when calling the attribute:
from types import DynamicClassAttribute
class Fun():
#DynamicClassAttribute
def has_fun(self):
return False
Fun.has_fun
AttributeError - Traceback (most recent call last)
that for itself is not very helpful until you take a look at the "Class attribute lookup" procedure when using metaclasses (I found a nice image of this in this blog).
Because in case that an attribute raises an AttributeError and that class has a metaclass python looks at the metaclass.__getattr__ method and sees if that can resolve the attribute. To illustrate this with a minimal example:
from types import DynamicClassAttribute
# Metaclass
class Funny(type):
def __getattr__(self, value):
print('search in meta')
# Normally you would implement here some ifs/elifs or a lookup in a dictionary
# but I'll just return the attribute
return Funny.dynprop
# Metaclasses dynprop:
dynprop = 'Meta'
class Fun(metaclass=Funny):
def __init__(self, value):
self._dynprop = value
#DynamicClassAttribute
def dynprop(self):
return self._dynprop
And here comes the "dynamic" part. If you call the dynprop on the class it will search in the meta and return the meta's dynprop:
Fun.dynprop
which prints:
search in meta
'Meta'
So we invoked the metaclass.__getattr__ and returned the original attribute (which was defined with the same name as the new property).
While for instances the dynprop of the Fun-instance is returned:
Fun('Not-Meta').dynprop
we get the overriden attribute:
'Not-Meta'
My conclusion from this is, that DynamicClassAttribute is important if you want to allow subclasses to have an attribute with the same name as used in the metaclass. You'll shadow it on instances but it's still accessible if you call it on the class.
I did go into the behaviour of Enum in the old version so I left it in here:
Old Version
The DynamicClassAttribute is just useful (I'm not really sure on that point) if you suspect there could be naming conflicts between an attribute that is set on a subclass and a property on the base-class.
You'll need to know at least some basics about metaclasses, because this will not work without using metaclasses (a nice explanation on how class attributes are called can be found in this blog post) because the attribute lookup is slightly different with metaclasses.
Suppose you have:
class Funny(type):
dynprop = 'Very important meta attribute, do not override'
class Fun(metaclass=Funny):
def __init__(self, value):
self._stub = value
#property
def dynprop(self):
return 'Haha, overridden it with {}'.format(self._stub)
and then call:
Fun.dynprop
property at 0x1b3d9fd19a8
and on the instance we get:
Fun(2).dynprop
'Haha, overridden it with 2'
bad ... it's lost. But wait we can use the metaclass special lookup: Let's implement an __getattr__ (fallback) and implement the dynprop as DynamicClassAttribute. Because according to it's documentation that's its purpose - to fallback to the __getattr__ if it's called on the class:
from types import DynamicClassAttribute
class Funny(type):
def __getattr__(self, value):
print('search in meta')
return Funny.dynprop
dynprop = 'Meta'
class Fun(metaclass=Funny):
def __init__(self, value):
self._dynprop = value
#DynamicClassAttribute
def dynprop(self):
return self._dynprop
now we access the class-attribute:
Fun.dynprop
which prints:
search in meta
'Meta'
So we invoked the metaclass.__getattr__ and returned the original attribute (which was defined with the same name as the new property).
And for instances:
Fun('Not-Meta').dynprop
we get the overriden attribute:
'Not-Meta'
Well that's not too bad considering we can reroute using metaclasses to previously defined but overriden attributes without creating an instance. This example is the opposite that is done with Enum, where you define attributes on the subclass:
from enum import Enum
class Fun(Enum):
name = 'me'
age = 28
hair = 'brown'
and want to access these afterwards defined attributes by default.
Fun.name
# <Fun.name: 'me'>
but you also want to allow accessing the name attribute that was defined as DynamicClassAttribute (which returns which name the variable actually has):
Fun('me').name
# 'name'
because otherwise how could you access the name of 28?
Fun.hair.age
# <Fun.age: 28>
# BUT:
Fun.hair.name
# returns 'hair'
See the difference? Why does the second one don't return <Fun.name: 'me'>? That's because of this use of DynamicClassAttribute. So you can shadow the original property but "release" it again later. This behaviour is the reverse of that shown in my example and requires at least the usage of __new__ and __prepare__. But for that you need to know how that exactly works and is explained in a lot of blogs and stackoverflow-answers that can explain it much better than I can so I'll skip going into that much depth (and I'm not sure if I could solve it in short order).
Actual use-cases might be sparse but given time one can propably think of some...
Very nice discussion on the documentation of DynamicClassAttribute: "we added it because we needed it"
What is a DynamicClassAttribute
A DynamicClassAttribute is a descriptor that is similar to property. Dynamic is part of the name because you get different results based on whether you access it via the class or via the instance:
instance access is identical to property and simply runs whatever method was decorated, returning its result
class access raises an AttributeError; when this happens Python then searches every parent class (via the mro) looking for that attribute -- when it doesn't find it, it calls the class' metaclass's __getattr__ for one last shot at finding the attribute. __getattr__ can, of course, do whatever it wants -- in the case of EnumMeta __getattr__ looks in the class' _member_map_ to see if the requested attribute is there, and returns it if it is. As a side note: all that searching had a severe performance impact, which is why we ended up putting all members that did not have name conflicts with DynamicClassAttributes in the Enum class' __dict__ after all.
and how do I use it?
You use it just like you would property -- the only difference is that you use it when creating a base class for other Enums. As an example, the Enum from aenum1 has three reserved names:
name
value
values
values is there to support Enum members with multiple values. That class is effectively:
class Enum(metaclass=EnumMeta):
#DynamicClassAttribute
def name(self):
return self._name_
#DynamicClassAttribute
def value(self):
return self._value_
#DynamicClassAttribute
def values(self):
return self._values_
and now any aenum.Enum can have a values member without messing up Enum.<member>.values.
1 Disclosure: I am the author of the Python stdlib Enum, the enum34 backport, and the Advanced Enumeration (aenum) library.
Related
I have a class with a private constant _BAR = object().
In a child class, outside of a method (no access to self), I want to refer to _BAR.
Here is a contrived example:
class Foo:
_BAR = object()
def __init__(self, bar: object = _BAR):
...
class DFoo(Foo):
"""Child class where I want to access private class variable from parent."""
def __init__(self, baz: object = super()._BAR):
super().__init__(baz)
Unfortunately, this doesn't work. One gets an error: RuntimeError: super(): no arguments
Is there a way to use super outside of a method to get a parent class attribute?
The workaround is to use Foo._BAR, I am wondering though if one can use super to solve this problem.
Inside of DFoo, you cannot refer to Foo._BAR without referring to Foo. Python variables are searched in the local, enclosing, global and built-in scopes (and in this order, it is the so called LEGB rule) and _BAR is not present in any of them.
Let's ignore an explicit Foo._BAR.
Further, it gets inherited: DFoo._BAR will be looked up first in DFoo, and when not found, in Foo.
What other means are there to get the Foo reference? Foo is a base class of DFoo. Can we use this relationship? Yes and no. Yes at execution time and no at definition time.
The problem is when the DFoo is being defined, it does not exist yet. We have no start point to start following the inheritance chain. This rules out an indirect reference (DFoo -> Foo) in a def method(self, ....): line and in a class attribute _DBAR = _BAR.
It is possible to work around this limitation using a class decorator. Define the class and then modify it:
def deco(cls):
cls._BAR = cls.__mro__[1]._BAR * 2 # __mro__[0] is the class itself
return cls
class Foo:
_BAR = 10
#deco
class DFoo(Foo):
pass
print(Foo._BAR, DFoo._BAR) # 10 20
Similar effect can be achieved with a metaclass.
The last option to get a reference to Foo is at execution time. We have the object self, its type is DFoo, and its parent type is Foo and there exists the _BAR. The well known super() is a shortcut to get the parent.
I have assumed only one base class for simplicity. If there were several base classes, super() returns only one of them. The example class decorator does the same. To understand how several bases are sorted to a sequence, see how the MRO works (Method Resolution Order).
My final thought is that I could not think up a use-case where such access as in the question would be required.
Short answer: you can't !
I'm not going into much details about super class itself here. (I've written a pure Python implementation in this gist if you like to read.)
But now let's see how we can call super:
1- Without arguments:
From PEP 3135:
This PEP proposes syntactic sugar for use of the super type to
automatically construct instances of the super type binding to the
class that a method was defined in, and the instance (or class object
for classmethods) that the method is currently acting upon.
The new syntax:
super()
is equivalent to:
super(__class__, <firstarg>)
...and <firstarg> is the first parameter of the method
So this is not an option because you don't have access to the "instance".
(Body of the function/methods is not executed unless it gets called, so no problem if DFoo doesn't exist yet inside the method definition)
2- super(type, instance)
From documentation:
The zero argument form only works inside a class definition, as the
compiler fills in the necessary details to correctly retrieve the
class being defined, as well as accessing the current instance for
ordinary methods.
What were those necessary details mentioned above? A "type" and A "instance":
We can't pass neither "instance" nor "type" which is DFoo here. The first one is because it's not inside the method so we don't have access to instance(self). Second one is DFoo itself. By the time the body of the DFoo class is being executed there is no reference to DFoo, it doesn't exist yet. The body of the class is executed inside a namespace which is a dictionary. After that a new instance of type type which is here named DFoo is created using that populated dictionary and added to the global namespaces. That's what class keyword roughly does in its simple form.
3- super(type, type):
If the second argument is a type, issubclass(type2, type) must be
true
Same reason mentioned in above about accessing the DFoo.
4- super(type):
If the second argument is omitted, the super object returned is
unbound.
If you have an unbound super object you can't do lookup(unless for the super object's attributes itself). Remember super() object is a descriptor. You can turn an unbound object to a bound object by calling __get__ and passing the instance:
class A:
a = 1
class B(A):
pass
class C(B):
sup = super(B)
try:
sup.a
except AttributeError as e:
print(e) # 'super' object has no attribute 'a'
obj = C()
print(obj.sup.a) # 1
obj.sup automatically calls the __get__.
And again same reason about accessing DFoo type mentioned above, nothing changed. Just added for records. These are the ways how we can call super.
'Every thing in python is an object'
So, should all objects have to have attributes and methods ?
I read below statemts in tutorial site, could you give example of pre-defined object in python that has neither attributes nor methods ?
Some objects have neither attributes nor methods
Everything in Python is indeed an object, this is true. Even classes themselves are considered to be objects and they're indeed the product of the builtin class typewhich is not surprisingly an object too. But objects will almost certainly inherit attributes including methods or data in most circumstances.
So, should all objects have to have attributes and methods ?
Not necessarily all objects have their own attributes. For instance, an object can inherit the attribute from its class and its class's superclasses, but that attribute or method doesn't necessarily live within the instance's namespace dictionary. In fact, instances' namespaces could be empty like the following:
class Foo:
pass
a = A()
print(a.__dict__)
a here doesn't have any attributes aside from those inherited from its class so if you check its namespace through the builtin attribute __dict__ you'll find the namespace to be an empty dictionary. But you might wonder isn't a.__dict__ an attribute of a? Make a distinction between class-level attributes--attributes inherited from the class or its superclasses and instance-attributes--attributes that belong to the instance and usually live in its namespace __dict__.
Could you give example of pre-defined object in python that has neither attributes nor methods ?
If you meant by predefined object, a builtin object, I couldn't imagine such scenario. Again, even if there are no attributes at the object itself, there would be attributes inherited from its class or the class's superclasses if there's any superclass in most cases. Probably and I'm guessing here, the tutorial is asking you to create class that assigns no attributes to its objects, just like the code I included above.
And this already answers your question better: Is everything an object in python like ruby?
There's a hackish way to emulate a Python object with no attributes.
class NoAttr(object):
def __getattribute__(self, attr):
raise AttributeError("no attribute: %s" % attr)
def __setattr__(self, attr, value):
raise AttributeError("can't set attribute: %s" % attr)
def __delattr__(self, attr):
raise AttributeError("no attribute: %s" % attr)
a = NoAttr()
This instance a, for all intents and purposes, in pure Python, behaves like an object with no attributes (you can try hasattr on it).
There may be a low-level way to do this in a C extension by implementing a type in C that pathologically stops Python's object implementation from working. Anyway the margin here is too small for writing one.
A pre-defined object with no attributes would defeat the purpose of pre-defining it.
I was searching for the meaning of default parameters object,self that are present as default class and function parameters, so moving away from it, if we are calling an attribute of a class should we use Foo (class reference) or should we use Foo() (instance of the class).
If you are reading a normal attribute then it doesn't matter. If you are binding a normal attribute then you must use the correct one in order for the code to work. If you are accessing a descriptor then you must use an instance.
The details of python's class semantics are quite well documented in the data model. Especially the __get__ semantics are at work here. Instances basically stack their namespace on top of their class' namespace and add some boilerplate for calling methods.
There are some large "it depends on what you are doing" gotchas at work here. The most important question: do you want to access class or instance attributes? Second, do you want attribute or methods?
Let's take this example:
class Foo(object):
bar = 1
baz = 2
def __init__(self, foobar="barfoo", baz=3):
self.foobar = foobar
self.baz = baz
def meth(self, param):
print self, param
#classmethod
def clsmeth(cls, param):
print cls, param
#staticmethod
def stcmeth(param):
print param
Here, bar is a class attribute, so you can get it via Foo.bar. Since instances have implicit access to their class namespace, you can also get it as Foo().bar. foobar is an instance attribute, since it is never bound to the class (only instances, i.e. selfs) - you can only get it as Foo().foobar. Last, baz is both a class and an instance attribute. By default, Foo.baz == 2 and Foo().baz == 3, since the class attribute is hidden by the instance attribute set in __init__.
Similarly, in an assignment there are slight differences whether you work on the class or an instance. Foo.bar=2 will set the class attribute (also for all instances) while Foo().bar=2 will create an instance attribute that shadows the class attribute for this specific instance.
For methods, it is somewhat similar. However, here you get the implicit self parameter for instance method (what a function is if defined for a class). Basically, the call Foo().meth(param=x) is silently translated to Foo.meth(self=Foo(), param=x). This is why it is usually not valid to call Foo.meth(param=x) - meth is not "bound" to an instance and thus lacks the self parameter.
Now, sometimes you do not need any instance data in a method - for example, you have strict string transformation that is an implementation detail of a larger parser class. This is where #classmethod and #staticmethod come into play. A classmethod's first parameter is always the class, as opposed to the instance for regular methods. Foo().clsmeth(param=x) and Foo.clsmeth(param=x) result in a call of clsmethod(cls=Foo, param=x). Here, the two are equivalent. Going one step further, a staticmethod doesn't get any class or instance information - it is like a raw function bound to the classes namespace.
I have a class sysprops in which I'd like to have a number of constants. However, I'd like to pull the values for those constants from the database, so I'd like some sort of hook any time one of these class constants are accessed (something like the getattribute method for instance variables).
class sysprops(object):
SOME_CONSTANT = 'SOME_VALUE'
sysprops.SOME_CONSTANT # this statement would not return 'SOME_VALUE' but instead a dynamic value pulled from the database.
Although I think it is a very bad idea to do this, it is possible:
class GetAttributeMetaClass(type):
def __getattribute__(self, key):
print 'Getting attribute', key
class sysprops(object):
__metaclass__ = GetAttributeMetaClass
While the other two answers have a valid method. I like to take the route of 'least-magic'.
You can do something similar to the metaclass approach without actually using them. Simply by using a decorator.
def instancer(cls):
return cls()
#instancer
class SysProps(object):
def __getattribute__(self, key):
return key # dummy
This will create an instance of SysProps and then assign it back to the SysProps name. Effectively shadowing the actual class definition and allowing a constant instance.
Since decorators are more common in Python I find this way easier to grasp for other people that have to read your code.
sysprops.SOME_CONSTANT can be the return value of a function if SOME_CONSTANT were a property defined on type(sysprops).
In other words, what you are talking about is commonly done if sysprops were an instance instead of a class.
But here is the kicker -- classes are instances of metaclasses. So everything you know about controlling the behavior of instances through the use of classes applies equally well to controlling the behavior of classes through the use of metaclasses.
Usually the metaclass is type, but you are free to define other metaclasses by subclassing type. If you place a property SOME_CONSTANT in the metaclass, then the instance of that metaclass, e.g. sysprops will have the desired behavior when Python evaluates sysprops.SOME_CONSTANT.
class MetaSysProps(type):
#property
def SOME_CONSTANT(cls):
return 'SOME_VALUE'
class SysProps(object):
__metaclass__ = MetaSysProps
print(SysProps.SOME_CONSTANT)
yields
SOME_VALUE
I just spent too long on a bug like the following:
>>> class Odp():
def __init__(self):
self.foo = "bar"
>>> o = Odp()
>>> o.raw_foo = 3 # oops - meant o.foo
I have a class with an attribute. I was trying to set it, and wondering why it had no effect. Then, I went back to the original class definition, and saw that the attribute was named something slightly different. Thus, I was creating/setting a new attribute instead of the one meant to.
First off, isn't this exactly the type of error that statically-typed languages are supposed to prevent? In this case, what is the advantage of dynamic typing?
Secondly, is there a way I could have forbidden this when defining Odp, and thus saved myself the trouble?
You can implement a __setattr__ method for the purpose -- that's much more robust than the __slots__ which is often misused for the purpose (for example, __slots__ is automatically "lost" when the class is inherited from, while __setattr__ survives unless explicitly overridden).
def __setattr__(self, name, value):
if hasattr(self, name):
object.__setattr__(self, name, value)
else:
raise TypeError('Cannot set name %r on object of type %s' % (
name, self.__class__.__name__))
You'll have to make sure the hasattr succeeds for the names you do want to be able to set, for example by setting the attributes at a class level or by using object.__setattr__ in your __init__ method rather than direct attribute assignment. (To forbid setting attributes on a class rather than its instances you'll have to define a custom metaclass with a similar special method).