We Keep Coding

Does function know about the class before binding

Is there a way to access a class (where function is defined as a method) before there is an instance of that class?
class MyClass:
def method(self):
print("Calling me")
m1 = MyClass.method
instance = MyClass()
m2 = instance.method
print(m2.__self__.__class__) # <class 'MyClass'>
# how to access `MyClass` from `m1`?
For example I have m1 variable somewhere in my code and want to have a reference to MyClass the same way I can access it from bound method m2.__self__.__class__.
print(m1.__qualname__) # 'MyClass.method'
The only option I was able to find is __qualname__ which is a string containing name of the class.

The attribute __self__ itself is annotated by Python when the function is bound to an instance and become a method. (The code to that is run somewhere when running the __get__ code in the function, but passing an instance different than None).
So, as people pointed out, you have the option of getting the classname as a string by going through __qualname__. Otherwise, if the functions/methods for which you will need this feature are known beforehand, it is possible to create a decorator that will annotate their class when they are retrieved as a class attribute (in contrast to the native annotation which only takes place when retrieving then as an instance attribute):
class unboundmethod:
def __init__(self, func, cls):
self.__func__ = func
self.class_ = cls
self.__self__ = None
def __call__(self, instance, *args, **kw):
if not isinstance(instance, self.class_):
# This check is actually optional fancy stuff, since we are here! :-)
raise TypeError(f"First parameter fo {self.__func__.__name__} must be an instance of {self.class_}")
return self.__func__(instance, *args, **kw)
def __repr__(self):
return f"Unbound method {self.__func__!r} related to {self.class_}"
class clsbind:
def __init__(self, func):
self.func = func
def __get__(self, instance, owner):
if instance is None:
# the function is being retrieved from the class:
return unboundmethod(self.func, owner)
# return control to usual method creation codepath:
return self.func.__get__(instance, owner)
class MyClass:
#clsbind
def method(self):
print("Calling me")
And on the REPL you can have this:
In [136]: m1 = MyClass.method
In [137]: m1.class_
Out[137]: __main__.MyClass
In [138]: m1(MyClass())
Calling me

You can get the class instance using the __qualname__
my_class = eval(m1.__qualname__.split('.')[-2])
print(my_class)
Not the most generic and safest approach, but should work for this simple scenario.

Why overriding the `__get__` method of a class in Python affects what is returned? [duplicate]

I am trying to understand what Python's descriptors are and what they are useful for. I understand how they work, but here are my doubts. Consider the following code:
class Celsius(object):
def __init__(self, value=0.0):
self.value = float(value)
def __get__(self, instance, owner):
return self.value
def __set__(self, instance, value):
self.value = float(value)
class Temperature(object):
celsius = Celsius()
Why do I need the descriptor class?
What is instance and owner here? (in __get__). What is the purpose of these parameters?
How would I call/use this example?

The descriptor is how Python's property type is implemented. A descriptor simply implements __get__, __set__, etc. and is then added to another class in its definition (as you did above with the Temperature class). For example:
temp=Temperature()
temp.celsius #calls celsius.__get__
Accessing the property you assigned the descriptor to (celsius in the above example) calls the appropriate descriptor method.
instance in __get__ is the instance of the class (so above, __get__ would receive temp, while owner is the class with the descriptor (so it would be Temperature).
You need to use a descriptor class to encapsulate the logic that powers it. That way, if the descriptor is used to cache some expensive operation (for example), it could store the value on itself and not its class.
An article about descriptors can be found here.
EDIT: As jchl pointed out in the comments, if you simply try Temperature.celsius, instance will be None.

Why do I need the descriptor class?
It gives you extra control over how attributes work. If you're used to getters and setters in Java, for example, then it's Python's way of doing that. One advantage is that it looks to users just like an attribute (there's no change in syntax). So you can start with an ordinary attribute and then, when you need to do something fancy, switch to a descriptor.
An attribute is just a mutable value. A descriptor lets you execute arbitrary code when reading or setting (or deleting) a value. So you could imagine using it to map an attribute to a field in a database, for example – a kind of ORM.
Another use might be refusing to accept a new value by throwing an exception in __set__ – effectively making the "attribute" read only.
What is instance and owner here? (in __get__). What is the purpose of these parameters?
This is pretty subtle (and the reason I am writing a new answer here - I found this question while wondering the same thing and didn't find the existing answer that great).
A descriptor is defined on a class, but is typically called from an instance. When it's called from an instance both instance and owner are set (and you can work out owner from instance so it seems kinda pointless). But when called from a class, only owner is set – which is why it's there.
This is only needed for __get__ because it's the only one that can be called on a class. If you set the class value you set the descriptor itself. Similarly for deletion. Which is why the owner isn't needed there.
How would I call/use this example?
Well, here's a cool trick using similar classes:
class Celsius:
def __get__(self, instance, owner):
return 5 * (instance.fahrenheit - 32) / 9
def __set__(self, instance, value):
instance.fahrenheit = 32 + 9 * value / 5
class Temperature:
celsius = Celsius()
def __init__(self, initial_f):
self.fahrenheit = initial_f
t = Temperature(212)
print(t.celsius)
t.celsius = 0
print(t.fahrenheit)
(I'm using Python 3; for python 2 you need to make sure those divisions are / 5.0 and / 9.0). That gives:
100.0
32.0
Now there are other, arguably better ways to achieve the same effect in python (e.g. if celsius were a property, which is the same basic mechanism but places all the source inside the Temperature class), but that shows what can be done...

I am trying to understand what Python's descriptors are and what they can be useful for.
Descriptors are objects in a class namespace that manage instance attributes (like slots, properties, or methods). For example:
class HasDescriptors:
__slots__ = 'a_slot' # creates a descriptor
def a_method(self): # creates a descriptor
"a regular method"
#staticmethod # creates a descriptor
def a_static_method():
"a static method"
#classmethod # creates a descriptor
def a_class_method(cls):
"a class method"
#property # creates a descriptor
def a_property(self):
"a property"
# even a regular function:
def a_function(some_obj_or_self): # creates a descriptor
"create a function suitable for monkey patching"
HasDescriptors.a_function = a_function # (but we usually don't do this)
Pedantically, descriptors are objects with any of the following special methods, which may be known as "descriptor methods":
__get__: non-data descriptor method, for example on a method/function
__set__: data descriptor method, for example on a property instance or slot
__delete__: data descriptor method, again used by properties or slots
These descriptor objects are attributes in other object class namespaces. That is, they live in the __dict__ of the class object.
Descriptor objects programmatically manage the results of a dotted lookup (e.g. foo.descriptor) in a normal expression, an assignment, or a deletion.
Functions/methods, bound methods, property, classmethod, and staticmethod all use these special methods to control how they are accessed via the dotted lookup.
A data descriptor, like property, can allow for lazy evaluation of attributes based on a simpler state of the object, allowing instances to use less memory than if you precomputed each possible attribute.
Another data descriptor, a member_descriptor created by __slots__, allows memory savings (and faster lookups) by having the class store data in a mutable tuple-like datastructure instead of the more flexible but space-consuming __dict__.
Non-data descriptors, instance and class methods, get their implicit first arguments (usually named self and cls, respectively) from their non-data descriptor method, __get__ - and this is how static methods know not to have an implicit first argument.
Most users of Python need to learn only the high-level usage of descriptors, and have no need to learn or understand the implementation of descriptors further.
But understanding how descriptors work can give one greater confidence in one's mastery of Python.
In Depth: What Are Descriptors?
A descriptor is an object with any of the following methods (__get__, __set__, or __delete__), intended to be used via dotted-lookup as if it were a typical attribute of an instance. For an owner-object, obj_instance, with a descriptor object:
obj_instance.descriptor invokes
descriptor.__get__(self, obj_instance, owner_class) returning a value
This is how all methods and the get on a property work.
obj_instance.descriptor = value invokes
descriptor.__set__(self, obj_instance, value) returning None
This is how the setter on a property works.
del obj_instance.descriptor invokes
descriptor.__delete__(self, obj_instance) returning None
This is how the deleter on a property works.
obj_instance is the instance whose class contains the descriptor object's instance. self is the instance of the descriptor (probably just one for the class of the obj_instance)
To define this with code, an object is a descriptor if the set of its attributes intersects with any of the required attributes:
def has_descriptor_attrs(obj):
return set(['__get__', '__set__', '__delete__']).intersection(dir(obj))
def is_descriptor(obj):
"""obj can be instance of descriptor or the descriptor class"""
return bool(has_descriptor_attrs(obj))
A Data Descriptor has a __set__ and/or __delete__.
A Non-Data-Descriptor has neither __set__ nor __delete__.
def has_data_descriptor_attrs(obj):
return set(['__set__', '__delete__']) & set(dir(obj))
def is_data_descriptor(obj):
return bool(has_data_descriptor_attrs(obj))
Builtin Descriptor Object Examples:
classmethod
staticmethod
property
functions in general
Non-Data Descriptors
We can see that classmethod and staticmethod are Non-Data-Descriptors:
>>> is_descriptor(classmethod), is_data_descriptor(classmethod)
(True, False)
>>> is_descriptor(staticmethod), is_data_descriptor(staticmethod)
(True, False)
Both only have the __get__ method:
>>> has_descriptor_attrs(classmethod), has_descriptor_attrs(staticmethod)
(set(['__get__']), set(['__get__']))
Note that all functions are also Non-Data-Descriptors:
>>> def foo(): pass
...
>>> is_descriptor(foo), is_data_descriptor(foo)
(True, False)
Data Descriptor, property
However, property is a Data-Descriptor:
>>> is_data_descriptor(property)
True
>>> has_descriptor_attrs(property)
set(['__set__', '__get__', '__delete__'])
Dotted Lookup Order
These are important distinctions, as they affect the lookup order for a dotted lookup.
obj_instance.attribute
First the above looks to see if the attribute is a Data-Descriptor on the class of the instance,
If not, it looks to see if the attribute is in the obj_instance's __dict__, then
it finally falls back to a Non-Data-Descriptor.
The consequence of this lookup order is that Non-Data-Descriptors like functions/methods can be overridden by instances.
Recap and Next Steps
We have learned that descriptors are objects with any of __get__, __set__, or __delete__. These descriptor objects can be used as attributes on other object class definitions. Now we will look at how they are used, using your code as an example.
Analysis of Code from the Question
Here's your code, followed by your questions and answers to each:
class Celsius(object):
def __init__(self, value=0.0):
self.value = float(value)
def __get__(self, instance, owner):
return self.value
def __set__(self, instance, value):
self.value = float(value)
class Temperature(object):
celsius = Celsius()
Why do I need the descriptor class?
Your descriptor ensures you always have a float for this class attribute of Temperature, and that you can't use del to delete the attribute:
>>> t1 = Temperature()
>>> del t1.celsius
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
AttributeError: __delete__
Otherwise, your descriptors ignore the owner-class and instances of the owner, instead, storing state in the descriptor. You could just as easily share state across all instances with a simple class attribute (so long as you always set it as a float to the class and never delete it, or are comfortable with users of your code doing so):
class Temperature(object):
celsius = 0.0
This gets you exactly the same behavior as your example (see response to question 3 below), but uses a Pythons builtin (property), and would be considered more idiomatic:
class Temperature(object):
_celsius = 0.0
#property
def celsius(self):
return type(self)._celsius
#celsius.setter
def celsius(self, value):
type(self)._celsius = float(value)
What is instance and owner here? (in get). What is the purpose of these parameters?
instance is the instance of the owner that is calling the descriptor. The owner is the class in which the descriptor object is used to manage access to the data point. See the descriptions of the special methods that define descriptors next to the first paragraph of this answer for more descriptive variable names.
How would I call/use this example?
Here's a demonstration:
>>> t1 = Temperature()
>>> t1.celsius
0.0
>>> t1.celsius = 1
>>>
>>> t1.celsius
1.0
>>> t2 = Temperature()
>>> t2.celsius
1.0
You can't delete the attribute:
>>> del t2.celsius
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
AttributeError: __delete__
And you can't assign a variable that can't be converted to a float:
>>> t1.celsius = '0x02'
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "<stdin>", line 7, in __set__
ValueError: invalid literal for float(): 0x02
Otherwise, what you have here is a global state for all instances, that is managed by assigning to any instance.
The expected way that most experienced Python programmers would accomplish this outcome would be to use the property decorator, which makes use of the same descriptors under the hood, but brings the behavior into the implementation of the owner class (again, as defined above):
class Temperature(object):
_celsius = 0.0
#property
def celsius(self):
return type(self)._celsius
#celsius.setter
def celsius(self, value):
type(self)._celsius = float(value)
Which has the exact same expected behavior of the original piece of code:
>>> t1 = Temperature()
>>> t2 = Temperature()
>>> t1.celsius
0.0
>>> t1.celsius = 1.0
>>> t2.celsius
1.0
>>> del t1.celsius
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
AttributeError: can't delete attribute
>>> t1.celsius = '0x02'
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "<stdin>", line 8, in celsius
ValueError: invalid literal for float(): 0x02
Conclusion
We've covered the attributes that define descriptors, the difference between data- and non-data-descriptors, builtin objects that use them, and specific questions about use.
So again, how would you use the question's example? I hope you wouldn't. I hope you would start with my first suggestion (a simple class attribute) and move on to the second suggestion (the property decorator) if you feel it is necessary.

Before going into the details of descriptors it may be important to know how attribute lookup in Python works. This assumes that the class has no metaclass and that it uses the default implementation of __getattribute__ (both can be used to "customize" the behavior).
The best illustration of attribute lookup (in Python 3.x or for new-style classes in Python 2.x) in this case is from Understanding Python metaclasses (ionel's codelog). The image uses : as substitute for "non-customizable attribute lookup".
This represents the lookup of an attribute foobar on an instance of Class:
Two conditions are important here:
If the class of instance has an entry for the attribute name and it has __get__ and __set__.
If the instance has no entry for the attribute name but the class has one and it has __get__.
That's where descriptors come into it:
Data descriptors which have both __get__ and __set__.
Non-data descriptors which only have __get__.
In both cases the returned value goes through __get__ called with the instance as first argument and the class as second argument.
The lookup is even more complicated for class attribute lookup (see for example Class attribute lookup (in the above mentioned blog)).
Let's move to your specific questions:
Why do I need the descriptor class?
In most cases you don't need to write descriptor classes! However you're probably a very regular end user. For example functions. Functions are descriptors, that's how functions can be used as methods with self implicitly passed as first argument.
def test_function(self):
return self
class TestClass(object):
def test_method(self):
...
If you look up test_method on an instance you'll get back a "bound method":
>>> instance = TestClass()
>>> instance.test_method
<bound method TestClass.test_method of <__main__.TestClass object at ...>>
Similarly you could also bind a function by invoking its __get__ method manually (not really recommended, just for illustrative purposes):
>>> test_function.__get__(instance, TestClass)
<bound method test_function of <__main__.TestClass object at ...>>
You can even call this "self-bound method":
>>> test_function.__get__(instance, TestClass)()
<__main__.TestClass at ...>
Note that I did not provide any arguments and the function did return the instance I had bound!
Functions are Non-data descriptors!
Some built-in examples of a data-descriptor would be property. Neglecting getter, setter, and deleter the property descriptor is (from Descriptor HowTo Guide "Properties"):
class Property(object):
def __init__(self, fget=None, fset=None, fdel=None, doc=None):
self.fget = fget
self.fset = fset
self.fdel = fdel
if doc is None and fget is not None:
doc = fget.__doc__
self.__doc__ = doc
def __get__(self, obj, objtype=None):
if obj is None:
return self
if self.fget is None:
raise AttributeError("unreadable attribute")
return self.fget(obj)
def __set__(self, obj, value):
if self.fset is None:
raise AttributeError("can't set attribute")
self.fset(obj, value)
def __delete__(self, obj):
if self.fdel is None:
raise AttributeError("can't delete attribute")
self.fdel(obj)
Since it's a data descriptor it's invoked whenever you look up the "name" of the property and it simply delegates to the functions decorated with #property, #name.setter, and #name.deleter (if present).
There are several other descriptors in the standard library, for example staticmethod, classmethod.
The point of descriptors is easy (although you rarely need them): Abstract common code for attribute access. property is an abstraction for instance variable access, function provides an abstraction for methods, staticmethod provides an abstraction for methods that don't need instance access and classmethod provides an abstraction for methods that need class access rather than instance access (this is a bit simplified).
Another example would be a class property.
One fun example (using __set_name__ from Python 3.6) could also be a property that only allows a specific type:
class TypedProperty(object):
__slots__ = ('_name', '_type')
def __init__(self, typ):
self._type = typ
def __get__(self, instance, klass=None):
if instance is None:
return self
return instance.__dict__[self._name]
def __set__(self, instance, value):
if not isinstance(value, self._type):
raise TypeError(f"Expected class {self._type}, got {type(value)}")
instance.__dict__[self._name] = value
def __delete__(self, instance):
del instance.__dict__[self._name]
def __set_name__(self, klass, name):
self._name = name
Then you can use the descriptor in a class:
class Test(object):
int_prop = TypedProperty(int)
And playing a bit with it:
>>> t = Test()
>>> t.int_prop = 10
>>> t.int_prop
10
>>> t.int_prop = 20.0
TypeError: Expected class <class 'int'>, got <class 'float'>
Or a "lazy property":
class LazyProperty(object):
__slots__ = ('_fget', '_name')
def __init__(self, fget):
self._fget = fget
def __get__(self, instance, klass=None):
if instance is None:
return self
try:
return instance.__dict__[self._name]
except KeyError:
value = self._fget(instance)
instance.__dict__[self._name] = value
return value
def __set_name__(self, klass, name):
self._name = name
class Test(object):
#LazyProperty
def lazy(self):
print('calculating')
return 10
>>> t = Test()
>>> t.lazy
calculating
10
>>> t.lazy
10
These are cases where moving the logic into a common descriptor might make sense, however one could also solve them (but maybe with repeating some code) with other means.
What is instance and owner here? (in __get__). What is the purpose of these parameters?
It depends on how you look up the attribute. If you look up the attribute on an instance then:
the second argument is the instance on which you look up the attribute
the third argument is the class of the instance
In case you look up the attribute on the class (assuming the descriptor is defined on the class):
the second argument is None
the third argument is the class where you look up the attribute
So basically the third argument is necessary if you want to customize the behavior when you do class-level look-up (because the instance is None).
How would I call/use this example?
Your example is basically a property that only allows values that can be converted to float and that is shared between all instances of the class (and on the class - although one can only use "read" access on the class otherwise you would replace the descriptor instance):
>>> t1 = Temperature()
>>> t2 = Temperature()
>>> t1.celsius = 20 # setting it on one instance
>>> t2.celsius # looking it up on another instance
20.0
>>> Temperature.celsius # looking it up on the class
20.0
That's why descriptors generally use the second argument (instance) to store the value to avoid sharing it. However in some cases sharing a value between instances might be desired (although I cannot think of a scenario at this moment). However it makes practically no sense for a celsius property on a temperature class... except maybe as purely academic exercise.

Why do I need the descriptor class?
Inspired by Fluent Python by Buciano Ramalho
Imaging you have a class like this
class LineItem:
price = 10.9
weight = 2.1
def __init__(self, name, price, weight):
self.name = name
self.price = price
self.weight = weight
item = LineItem("apple", 2.9, 2.1)
item.price = -0.9 # it's price is negative, you need to refund to your customer even you delivered the apple :(
item.weight = -0.8 # negative weight, it doesn't make sense
We should validate the weight and price in avoid to assign them a negative number, we can write less code if we use descriptor as a proxy as this
class Quantity(object):
__index = 0
def __init__(self):
self.__index = self.__class__.__index
self._storage_name = "quantity#{}".format(self.__index)
self.__class__.__index += 1
def __set__(self, instance, value):
if value > 0:
setattr(instance, self._storage_name, value)
else:
raise ValueError('value should >0')
def __get__(self, instance, owner):
return getattr(instance, self._storage_name)
then define class LineItem like this:
class LineItem(object):
weight = Quantity()
price = Quantity()
def __init__(self, name, weight, price):
self.name = name
self.weight = weight
self.price = price
and we can extend the Quantity class to do more common validating

You'd see https://docs.python.org/3/howto/descriptor.html#properties
class Property(object):
"Emulate PyProperty_Type() in Objects/descrobject.c"
def __init__(self, fget=None, fset=None, fdel=None, doc=None):
self.fget = fget
self.fset = fset
self.fdel = fdel
if doc is None and fget is not None:
doc = fget.__doc__
self.__doc__ = doc
def __get__(self, obj, objtype=None):
if obj is None:
return self
if self.fget is None:
raise AttributeError("unreadable attribute")
return self.fget(obj)
def __set__(self, obj, value):
if self.fset is None:
raise AttributeError("can't set attribute")
self.fset(obj, value)
def __delete__(self, obj):
if self.fdel is None:
raise AttributeError("can't delete attribute")
self.fdel(obj)
def getter(self, fget):
return type(self)(fget, self.fset, self.fdel, self.__doc__)
def setter(self, fset):
return type(self)(self.fget, fset, self.fdel, self.__doc__)
def deleter(self, fdel):
return type(self)(self.fget, self.fset, fdel, self.__doc__)

Easy to digest (with example) Explanation for __get__ & __set__ & __call__ in classes, what is Owner, Instance?
Some points to mug up before diving in:
__get__ __set__ are called descriptors of the class to work/save their internal attributes namely: __name__ (name of class/owner class), variables - __dict__ etc. I will explain what is an owner later
Descriptors are used in design patterers more commonly, for example, with decorators (to abstract things out). You can consider it's more often used in software architecture design to make things less redundant and more readable (seems ironical). Thus abiding SOLID and DRY principles.
If you are not designing software that should abide by SOLID and DRY principles, you probably don't need them, but it's always wise to understand them.
1. Conside this code:
class Method:
def __init__(self, name):
self.name = name
def __call__(self, instance, arg1, arg2):
print(f"{self.name}: {instance} called with {arg1} and {arg2}")
class MyClass:
method = Method("Internal call")
instance = MyClass()
instance.method("first", "second")
# Prints:TypeError: __call__() missing 1 required positional argument: 'arg2'
So, when instance.method("first", "second") is called, __call__ method is called from the Method class (call method makes a class object just callable like a function - whenever a class instance is called __call__ gets instiantiated), and following arguments are assigned: instance: "first", arg1: "second", and the last arg2 is left out, this prints out the error: TypeError: __call__() missing 1 required positional argument: 'arg2'
2. how to solve it?
Since __call__ takes instance as first argument (instance, arg1, arg2), but instance of what?
Instance is the instance of main class (MyClass) which is calling the descriptor class (Method). So, instance = MyClass() is the instance and so who is the owner? the class holding the discriptor class - MyClass, However, there is no method in our descriptor class (Method) to recognise it as an instance. So that is where we need __get__ method. Again consider the code below:
from types import MethodType
class Method:
def __init__(self, name):
self.name = name
def __call__(self, instance, arg1, arg2):
print(f"{self.name}: {instance} called with {arg1} and {arg2}")
def __set__(self, instance, value):
self.value = value
instance.__dict__["method"] = value
def __get__(self, instance, owner):
if instance is None:
return self
print (instance, owner)
return MethodType(self, instance)
class MyClass:
method = Method("Internal call")
instance = MyClass()
instance.method("first", "second")
# Prints: Internal call: <__main__.MyClass object at 0x7fb7dd989690> called with first and second
forget about set for now according to docs:
__get__ "Called to get the attribute of the owner class (class attribute access) or of an instance of that class (instance attribute access)."
if you do: instance.method.__get__(instance)
Prints:<__main__.MyClass object at 0x7fb7dd9eab90> <class '__main__.MyClass'>
this means instance: object of MyClass which is instance
and Owner is MyClass itself
3. __set__ Explaination:
__set__ is used to set some value in the class __dict__ object (let's say using a command line). command for setting the internal value for set is: instance.descriptor = 'value' # where descriptor is method in this case
(instance.__dict__["method"] = value in the code just update the __dict__ object of the descriptor)
So do: instance.method = 'value' now to check if the value = 'value' is set in the __set__ method we can access __dict__ object of the descriptor method.
Do:
instance.method.__dict__ prints: {'_name': 'Internal call', 'value': 'value'}
Or you can check the __dict__ value using vars(instance.method)
prints: {'name': 'Internal call', 'value': 'value'}
I hope things are clear now:)

I tried (with minor changes as suggested) the code from Andrew Cooke's answer. (I am running python 2.7).
The code:
#!/usr/bin/env python
class Celsius:
def __get__(self, instance, owner): return 9 * (instance.fahrenheit + 32) / 5.0
def __set__(self, instance, value): instance.fahrenheit = 32 + 5 * value / 9.0
class Temperature:
def __init__(self, initial_f): self.fahrenheit = initial_f
celsius = Celsius()
if __name__ == "__main__":
t = Temperature(212)
print(t.celsius)
t.celsius = 0
print(t.fahrenheit)
The result:
C:\Users\gkuhn\Desktop>python test2.py
<__main__.Celsius instance at 0x02E95A80>
212
With Python prior to 3, make sure you subclass from object which will make the descriptor work correctly as the get magic does not work for old style classes.

Need help understanding a piece of code with class decorators and descriptors [duplicate]

I am trying to understand what Python's descriptors are and what they are useful for. I understand how they work, but here are my doubts. Consider the following code:
class Celsius(object):
def __init__(self, value=0.0):
self.value = float(value)
def __get__(self, instance, owner):
return self.value
def __set__(self, instance, value):
self.value = float(value)
class Temperature(object):
celsius = Celsius()
Why do I need the descriptor class?
What is instance and owner here? (in __get__). What is the purpose of these parameters?
How would I call/use this example?

The descriptor is how Python's property type is implemented. A descriptor simply implements __get__, __set__, etc. and is then added to another class in its definition (as you did above with the Temperature class). For example:
temp=Temperature()
temp.celsius #calls celsius.__get__
Accessing the property you assigned the descriptor to (celsius in the above example) calls the appropriate descriptor method.
instance in __get__ is the instance of the class (so above, __get__ would receive temp, while owner is the class with the descriptor (so it would be Temperature).
You need to use a descriptor class to encapsulate the logic that powers it. That way, if the descriptor is used to cache some expensive operation (for example), it could store the value on itself and not its class.
An article about descriptors can be found here.
EDIT: As jchl pointed out in the comments, if you simply try Temperature.celsius, instance will be None.

Why do I need the descriptor class?
It gives you extra control over how attributes work. If you're used to getters and setters in Java, for example, then it's Python's way of doing that. One advantage is that it looks to users just like an attribute (there's no change in syntax). So you can start with an ordinary attribute and then, when you need to do something fancy, switch to a descriptor.
An attribute is just a mutable value. A descriptor lets you execute arbitrary code when reading or setting (or deleting) a value. So you could imagine using it to map an attribute to a field in a database, for example – a kind of ORM.
Another use might be refusing to accept a new value by throwing an exception in __set__ – effectively making the "attribute" read only.
What is instance and owner here? (in __get__). What is the purpose of these parameters?
This is pretty subtle (and the reason I am writing a new answer here - I found this question while wondering the same thing and didn't find the existing answer that great).
A descriptor is defined on a class, but is typically called from an instance. When it's called from an instance both instance and owner are set (and you can work out owner from instance so it seems kinda pointless). But when called from a class, only owner is set – which is why it's there.
This is only needed for __get__ because it's the only one that can be called on a class. If you set the class value you set the descriptor itself. Similarly for deletion. Which is why the owner isn't needed there.
How would I call/use this example?
Well, here's a cool trick using similar classes:
class Celsius:
def __get__(self, instance, owner):
return 5 * (instance.fahrenheit - 32) / 9
def __set__(self, instance, value):
instance.fahrenheit = 32 + 9 * value / 5
class Temperature:
celsius = Celsius()
def __init__(self, initial_f):
self.fahrenheit = initial_f
t = Temperature(212)
print(t.celsius)
t.celsius = 0
print(t.fahrenheit)
(I'm using Python 3; for python 2 you need to make sure those divisions are / 5.0 and / 9.0). That gives:
100.0
32.0
Now there are other, arguably better ways to achieve the same effect in python (e.g. if celsius were a property, which is the same basic mechanism but places all the source inside the Temperature class), but that shows what can be done...

I am trying to understand what Python's descriptors are and what they can be useful for.
Descriptors are objects in a class namespace that manage instance attributes (like slots, properties, or methods). For example:
class HasDescriptors:
__slots__ = 'a_slot' # creates a descriptor
def a_method(self): # creates a descriptor
"a regular method"
#staticmethod # creates a descriptor
def a_static_method():
"a static method"
#classmethod # creates a descriptor
def a_class_method(cls):
"a class method"
#property # creates a descriptor
def a_property(self):
"a property"
# even a regular function:
def a_function(some_obj_or_self): # creates a descriptor
"create a function suitable for monkey patching"
HasDescriptors.a_function = a_function # (but we usually don't do this)
Pedantically, descriptors are objects with any of the following special methods, which may be known as "descriptor methods":
__get__: non-data descriptor method, for example on a method/function
__set__: data descriptor method, for example on a property instance or slot
__delete__: data descriptor method, again used by properties or slots
These descriptor objects are attributes in other object class namespaces. That is, they live in the __dict__ of the class object.
Descriptor objects programmatically manage the results of a dotted lookup (e.g. foo.descriptor) in a normal expression, an assignment, or a deletion.
Functions/methods, bound methods, property, classmethod, and staticmethod all use these special methods to control how they are accessed via the dotted lookup.
A data descriptor, like property, can allow for lazy evaluation of attributes based on a simpler state of the object, allowing instances to use less memory than if you precomputed each possible attribute.
Another data descriptor, a member_descriptor created by __slots__, allows memory savings (and faster lookups) by having the class store data in a mutable tuple-like datastructure instead of the more flexible but space-consuming __dict__.
Non-data descriptors, instance and class methods, get their implicit first arguments (usually named self and cls, respectively) from their non-data descriptor method, __get__ - and this is how static methods know not to have an implicit first argument.
Most users of Python need to learn only the high-level usage of descriptors, and have no need to learn or understand the implementation of descriptors further.
But understanding how descriptors work can give one greater confidence in one's mastery of Python.
In Depth: What Are Descriptors?
A descriptor is an object with any of the following methods (__get__, __set__, or __delete__), intended to be used via dotted-lookup as if it were a typical attribute of an instance. For an owner-object, obj_instance, with a descriptor object:
obj_instance.descriptor invokes
descriptor.__get__(self, obj_instance, owner_class) returning a value
This is how all methods and the get on a property work.
obj_instance.descriptor = value invokes
descriptor.__set__(self, obj_instance, value) returning None
This is how the setter on a property works.
del obj_instance.descriptor invokes
descriptor.__delete__(self, obj_instance) returning None
This is how the deleter on a property works.
obj_instance is the instance whose class contains the descriptor object's instance. self is the instance of the descriptor (probably just one for the class of the obj_instance)
To define this with code, an object is a descriptor if the set of its attributes intersects with any of the required attributes:
def has_descriptor_attrs(obj):
return set(['__get__', '__set__', '__delete__']).intersection(dir(obj))
def is_descriptor(obj):
"""obj can be instance of descriptor or the descriptor class"""
return bool(has_descriptor_attrs(obj))
A Data Descriptor has a __set__ and/or __delete__.
A Non-Data-Descriptor has neither __set__ nor __delete__.
def has_data_descriptor_attrs(obj):
return set(['__set__', '__delete__']) & set(dir(obj))
def is_data_descriptor(obj):
return bool(has_data_descriptor_attrs(obj))
Builtin Descriptor Object Examples:
classmethod
staticmethod
property
functions in general
Non-Data Descriptors
We can see that classmethod and staticmethod are Non-Data-Descriptors:
>>> is_descriptor(classmethod), is_data_descriptor(classmethod)
(True, False)
>>> is_descriptor(staticmethod), is_data_descriptor(staticmethod)
(True, False)
Both only have the __get__ method:
>>> has_descriptor_attrs(classmethod), has_descriptor_attrs(staticmethod)
(set(['__get__']), set(['__get__']))
Note that all functions are also Non-Data-Descriptors:
>>> def foo(): pass
...
>>> is_descriptor(foo), is_data_descriptor(foo)
(True, False)
Data Descriptor, property
However, property is a Data-Descriptor:
>>> is_data_descriptor(property)
True
>>> has_descriptor_attrs(property)
set(['__set__', '__get__', '__delete__'])
Dotted Lookup Order
These are important distinctions, as they affect the lookup order for a dotted lookup.
obj_instance.attribute
First the above looks to see if the attribute is a Data-Descriptor on the class of the instance,
If not, it looks to see if the attribute is in the obj_instance's __dict__, then
it finally falls back to a Non-Data-Descriptor.
The consequence of this lookup order is that Non-Data-Descriptors like functions/methods can be overridden by instances.
Recap and Next Steps
We have learned that descriptors are objects with any of __get__, __set__, or __delete__. These descriptor objects can be used as attributes on other object class definitions. Now we will look at how they are used, using your code as an example.
Analysis of Code from the Question
Here's your code, followed by your questions and answers to each:
class Celsius(object):
def __init__(self, value=0.0):
self.value = float(value)
def __get__(self, instance, owner):
return self.value
def __set__(self, instance, value):
self.value = float(value)
class Temperature(object):
celsius = Celsius()
Why do I need the descriptor class?
Your descriptor ensures you always have a float for this class attribute of Temperature, and that you can't use del to delete the attribute:
>>> t1 = Temperature()
>>> del t1.celsius
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
AttributeError: __delete__
Otherwise, your descriptors ignore the owner-class and instances of the owner, instead, storing state in the descriptor. You could just as easily share state across all instances with a simple class attribute (so long as you always set it as a float to the class and never delete it, or are comfortable with users of your code doing so):
class Temperature(object):
celsius = 0.0
This gets you exactly the same behavior as your example (see response to question 3 below), but uses a Pythons builtin (property), and would be considered more idiomatic:
class Temperature(object):
_celsius = 0.0
#property
def celsius(self):
return type(self)._celsius
#celsius.setter
def celsius(self, value):
type(self)._celsius = float(value)
What is instance and owner here? (in get). What is the purpose of these parameters?
instance is the instance of the owner that is calling the descriptor. The owner is the class in which the descriptor object is used to manage access to the data point. See the descriptions of the special methods that define descriptors next to the first paragraph of this answer for more descriptive variable names.
How would I call/use this example?
Here's a demonstration:
>>> t1 = Temperature()
>>> t1.celsius
0.0
>>> t1.celsius = 1
>>>
>>> t1.celsius
1.0
>>> t2 = Temperature()
>>> t2.celsius
1.0
You can't delete the attribute:
>>> del t2.celsius
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
AttributeError: __delete__
And you can't assign a variable that can't be converted to a float:
>>> t1.celsius = '0x02'
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "<stdin>", line 7, in __set__
ValueError: invalid literal for float(): 0x02
Otherwise, what you have here is a global state for all instances, that is managed by assigning to any instance.
The expected way that most experienced Python programmers would accomplish this outcome would be to use the property decorator, which makes use of the same descriptors under the hood, but brings the behavior into the implementation of the owner class (again, as defined above):
class Temperature(object):
_celsius = 0.0
#property
def celsius(self):
return type(self)._celsius
#celsius.setter
def celsius(self, value):
type(self)._celsius = float(value)
Which has the exact same expected behavior of the original piece of code:
>>> t1 = Temperature()
>>> t2 = Temperature()
>>> t1.celsius
0.0
>>> t1.celsius = 1.0
>>> t2.celsius
1.0
>>> del t1.celsius
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
AttributeError: can't delete attribute
>>> t1.celsius = '0x02'
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "<stdin>", line 8, in celsius
ValueError: invalid literal for float(): 0x02
Conclusion
We've covered the attributes that define descriptors, the difference between data- and non-data-descriptors, builtin objects that use them, and specific questions about use.
So again, how would you use the question's example? I hope you wouldn't. I hope you would start with my first suggestion (a simple class attribute) and move on to the second suggestion (the property decorator) if you feel it is necessary.

Before going into the details of descriptors it may be important to know how attribute lookup in Python works. This assumes that the class has no metaclass and that it uses the default implementation of __getattribute__ (both can be used to "customize" the behavior).
The best illustration of attribute lookup (in Python 3.x or for new-style classes in Python 2.x) in this case is from Understanding Python metaclasses (ionel's codelog). The image uses : as substitute for "non-customizable attribute lookup".
This represents the lookup of an attribute foobar on an instance of Class:
Two conditions are important here:
If the class of instance has an entry for the attribute name and it has __get__ and __set__.
If the instance has no entry for the attribute name but the class has one and it has __get__.
That's where descriptors come into it:
Data descriptors which have both __get__ and __set__.
Non-data descriptors which only have __get__.
In both cases the returned value goes through __get__ called with the instance as first argument and the class as second argument.
The lookup is even more complicated for class attribute lookup (see for example Class attribute lookup (in the above mentioned blog)).
Let's move to your specific questions:
Why do I need the descriptor class?
In most cases you don't need to write descriptor classes! However you're probably a very regular end user. For example functions. Functions are descriptors, that's how functions can be used as methods with self implicitly passed as first argument.
def test_function(self):
return self
class TestClass(object):
def test_method(self):
...
If you look up test_method on an instance you'll get back a "bound method":
>>> instance = TestClass()
>>> instance.test_method
<bound method TestClass.test_method of <__main__.TestClass object at ...>>
Similarly you could also bind a function by invoking its __get__ method manually (not really recommended, just for illustrative purposes):
>>> test_function.__get__(instance, TestClass)
<bound method test_function of <__main__.TestClass object at ...>>
You can even call this "self-bound method":
>>> test_function.__get__(instance, TestClass)()
<__main__.TestClass at ...>
Note that I did not provide any arguments and the function did return the instance I had bound!
Functions are Non-data descriptors!
Some built-in examples of a data-descriptor would be property. Neglecting getter, setter, and deleter the property descriptor is (from Descriptor HowTo Guide "Properties"):
class Property(object):
def __init__(self, fget=None, fset=None, fdel=None, doc=None):
self.fget = fget
self.fset = fset
self.fdel = fdel
if doc is None and fget is not None:
doc = fget.__doc__
self.__doc__ = doc
def __get__(self, obj, objtype=None):
if obj is None:
return self
if self.fget is None:
raise AttributeError("unreadable attribute")
return self.fget(obj)
def __set__(self, obj, value):
if self.fset is None:
raise AttributeError("can't set attribute")
self.fset(obj, value)
def __delete__(self, obj):
if self.fdel is None:
raise AttributeError("can't delete attribute")
self.fdel(obj)
Since it's a data descriptor it's invoked whenever you look up the "name" of the property and it simply delegates to the functions decorated with #property, #name.setter, and #name.deleter (if present).
There are several other descriptors in the standard library, for example staticmethod, classmethod.
The point of descriptors is easy (although you rarely need them): Abstract common code for attribute access. property is an abstraction for instance variable access, function provides an abstraction for methods, staticmethod provides an abstraction for methods that don't need instance access and classmethod provides an abstraction for methods that need class access rather than instance access (this is a bit simplified).
Another example would be a class property.
One fun example (using __set_name__ from Python 3.6) could also be a property that only allows a specific type:
class TypedProperty(object):
__slots__ = ('_name', '_type')
def __init__(self, typ):
self._type = typ
def __get__(self, instance, klass=None):
if instance is None:
return self
return instance.__dict__[self._name]
def __set__(self, instance, value):
if not isinstance(value, self._type):
raise TypeError(f"Expected class {self._type}, got {type(value)}")
instance.__dict__[self._name] = value
def __delete__(self, instance):
del instance.__dict__[self._name]
def __set_name__(self, klass, name):
self._name = name
Then you can use the descriptor in a class:
class Test(object):
int_prop = TypedProperty(int)
And playing a bit with it:
>>> t = Test()
>>> t.int_prop = 10
>>> t.int_prop
10
>>> t.int_prop = 20.0
TypeError: Expected class <class 'int'>, got <class 'float'>
Or a "lazy property":
class LazyProperty(object):
__slots__ = ('_fget', '_name')
def __init__(self, fget):
self._fget = fget
def __get__(self, instance, klass=None):
if instance is None:
return self
try:
return instance.__dict__[self._name]
except KeyError:
value = self._fget(instance)
instance.__dict__[self._name] = value
return value
def __set_name__(self, klass, name):
self._name = name
class Test(object):
#LazyProperty
def lazy(self):
print('calculating')
return 10
>>> t = Test()
>>> t.lazy
calculating
10
>>> t.lazy
10
These are cases where moving the logic into a common descriptor might make sense, however one could also solve them (but maybe with repeating some code) with other means.
What is instance and owner here? (in __get__). What is the purpose of these parameters?
It depends on how you look up the attribute. If you look up the attribute on an instance then:
the second argument is the instance on which you look up the attribute
the third argument is the class of the instance
In case you look up the attribute on the class (assuming the descriptor is defined on the class):
the second argument is None
the third argument is the class where you look up the attribute
So basically the third argument is necessary if you want to customize the behavior when you do class-level look-up (because the instance is None).
How would I call/use this example?
Your example is basically a property that only allows values that can be converted to float and that is shared between all instances of the class (and on the class - although one can only use "read" access on the class otherwise you would replace the descriptor instance):
>>> t1 = Temperature()
>>> t2 = Temperature()
>>> t1.celsius = 20 # setting it on one instance
>>> t2.celsius # looking it up on another instance
20.0
>>> Temperature.celsius # looking it up on the class
20.0
That's why descriptors generally use the second argument (instance) to store the value to avoid sharing it. However in some cases sharing a value between instances might be desired (although I cannot think of a scenario at this moment). However it makes practically no sense for a celsius property on a temperature class... except maybe as purely academic exercise.

Why do I need the descriptor class?
Inspired by Fluent Python by Buciano Ramalho
Imaging you have a class like this
class LineItem:
price = 10.9
weight = 2.1
def __init__(self, name, price, weight):
self.name = name
self.price = price
self.weight = weight
item = LineItem("apple", 2.9, 2.1)
item.price = -0.9 # it's price is negative, you need to refund to your customer even you delivered the apple :(
item.weight = -0.8 # negative weight, it doesn't make sense
We should validate the weight and price in avoid to assign them a negative number, we can write less code if we use descriptor as a proxy as this
class Quantity(object):
__index = 0
def __init__(self):
self.__index = self.__class__.__index
self._storage_name = "quantity#{}".format(self.__index)
self.__class__.__index += 1
def __set__(self, instance, value):
if value > 0:
setattr(instance, self._storage_name, value)
else:
raise ValueError('value should >0')
def __get__(self, instance, owner):
return getattr(instance, self._storage_name)
then define class LineItem like this:
class LineItem(object):
weight = Quantity()
price = Quantity()
def __init__(self, name, weight, price):
self.name = name
self.weight = weight
self.price = price
and we can extend the Quantity class to do more common validating

You'd see https://docs.python.org/3/howto/descriptor.html#properties
class Property(object):
"Emulate PyProperty_Type() in Objects/descrobject.c"
def __init__(self, fget=None, fset=None, fdel=None, doc=None):
self.fget = fget
self.fset = fset
self.fdel = fdel
if doc is None and fget is not None:
doc = fget.__doc__
self.__doc__ = doc
def __get__(self, obj, objtype=None):
if obj is None:
return self
if self.fget is None:
raise AttributeError("unreadable attribute")
return self.fget(obj)
def __set__(self, obj, value):
if self.fset is None:
raise AttributeError("can't set attribute")
self.fset(obj, value)
def __delete__(self, obj):
if self.fdel is None:
raise AttributeError("can't delete attribute")
self.fdel(obj)
def getter(self, fget):
return type(self)(fget, self.fset, self.fdel, self.__doc__)
def setter(self, fset):
return type(self)(self.fget, fset, self.fdel, self.__doc__)
def deleter(self, fdel):
return type(self)(self.fget, self.fset, fdel, self.__doc__)

Easy to digest (with example) Explanation for __get__ & __set__ & __call__ in classes, what is Owner, Instance?
Some points to mug up before diving in:
__get__ __set__ are called descriptors of the class to work/save their internal attributes namely: __name__ (name of class/owner class), variables - __dict__ etc. I will explain what is an owner later
Descriptors are used in design patterers more commonly, for example, with decorators (to abstract things out). You can consider it's more often used in software architecture design to make things less redundant and more readable (seems ironical). Thus abiding SOLID and DRY principles.
If you are not designing software that should abide by SOLID and DRY principles, you probably don't need them, but it's always wise to understand them.
1. Conside this code:
class Method:
def __init__(self, name):
self.name = name
def __call__(self, instance, arg1, arg2):
print(f"{self.name}: {instance} called with {arg1} and {arg2}")
class MyClass:
method = Method("Internal call")
instance = MyClass()
instance.method("first", "second")
# Prints:TypeError: __call__() missing 1 required positional argument: 'arg2'
So, when instance.method("first", "second") is called, __call__ method is called from the Method class (call method makes a class object just callable like a function - whenever a class instance is called __call__ gets instiantiated), and following arguments are assigned: instance: "first", arg1: "second", and the last arg2 is left out, this prints out the error: TypeError: __call__() missing 1 required positional argument: 'arg2'
2. how to solve it?
Since __call__ takes instance as first argument (instance, arg1, arg2), but instance of what?
Instance is the instance of main class (MyClass) which is calling the descriptor class (Method). So, instance = MyClass() is the instance and so who is the owner? the class holding the discriptor class - MyClass, However, there is no method in our descriptor class (Method) to recognise it as an instance. So that is where we need __get__ method. Again consider the code below:
from types import MethodType
class Method:
def __init__(self, name):
self.name = name
def __call__(self, instance, arg1, arg2):
print(f"{self.name}: {instance} called with {arg1} and {arg2}")
def __set__(self, instance, value):
self.value = value
instance.__dict__["method"] = value
def __get__(self, instance, owner):
if instance is None:
return self
print (instance, owner)
return MethodType(self, instance)
class MyClass:
method = Method("Internal call")
instance = MyClass()
instance.method("first", "second")
# Prints: Internal call: <__main__.MyClass object at 0x7fb7dd989690> called with first and second
forget about set for now according to docs:
__get__ "Called to get the attribute of the owner class (class attribute access) or of an instance of that class (instance attribute access)."
if you do: instance.method.__get__(instance)
Prints:<__main__.MyClass object at 0x7fb7dd9eab90> <class '__main__.MyClass'>
this means instance: object of MyClass which is instance
and Owner is MyClass itself
3. __set__ Explaination:
__set__ is used to set some value in the class __dict__ object (let's say using a command line). command for setting the internal value for set is: instance.descriptor = 'value' # where descriptor is method in this case
(instance.__dict__["method"] = value in the code just update the __dict__ object of the descriptor)
So do: instance.method = 'value' now to check if the value = 'value' is set in the __set__ method we can access __dict__ object of the descriptor method.
Do:
instance.method.__dict__ prints: {'_name': 'Internal call', 'value': 'value'}
Or you can check the __dict__ value using vars(instance.method)
prints: {'name': 'Internal call', 'value': 'value'}
I hope things are clear now:)

I tried (with minor changes as suggested) the code from Andrew Cooke's answer. (I am running python 2.7).
The code:
#!/usr/bin/env python
class Celsius:
def __get__(self, instance, owner): return 9 * (instance.fahrenheit + 32) / 5.0
def __set__(self, instance, value): instance.fahrenheit = 32 + 5 * value / 9.0
class Temperature:
def __init__(self, initial_f): self.fahrenheit = initial_f
celsius = Celsius()
if __name__ == "__main__":
t = Temperature(212)
print(t.celsius)
t.celsius = 0
print(t.fahrenheit)
The result:
C:\Users\gkuhn\Desktop>python test2.py
<__main__.Celsius instance at 0x02E95A80>
212
With Python prior to 3, make sure you subclass from object which will make the descriptor work correctly as the get magic does not work for old style classes.

Turning nested class into descriptor without instantiating it

Note: Python version is 2.7
Problem
I want to have {hierarchy of descriptor classes} integrated within {hierarchy of widget classes}, and overriding descriptor behaviour must be as easy as defining nested derived class. Example:
class A(object):
class width(Attribute):
def changed(self, obj, value, old):
print 'A.width changed:', value
class B(A):
class width(A.width):
def changed(self, obj, value, old):
super(B.width, self).changed(obj, value, old)
print 'B.width changed:', value
B().width = 10
# must print:
# A.width.changed: 10
# B.width.changed: 10
Here is my custom descriptor class:
class Attribute(object):
def __init__(self):
if not hasattr(self, '_name'):
self._name = self.__class__.__name__
def __get__(self, obj, cls):
if obj is None:
print 'Attribute getter (class):', cls
return self
print 'Attribute getter (class, inst):', (cls, obj)
print 'Attribute getter returning:', self.get(obj)
return self.get(obj)
def __set__(self, obj, value):
print 'Attribute setter (inst, value):', (obj, value)
self.set(obj, value)
def get(self, obj):
try:
return obj.__dict__[self._name]
except KeyError:
raise AttributeError("attribute '%s' referenced before assigment" % (self._name))
def set(self, obj, value):
try:
old = obj.__dict__[self._name]
except KeyError:
obj.__dict__[self._name] = value
self.changed(obj, value, None)
else:
obj.__dict__[self._name] = value
if old != value:
self.changed(obj, value, old)
def changed(self, obj, value, old):
pass
Problem is that Python don't want to use __get__ and __set__, while they are attributes of class. It may be seen from this test:
# `A` and `B` were defined above
A.width_ = A.width()
B.width_ = B.width()
to_test = (
# good:
'Aw_ = A.width_',
'Bw_ = B.width_',
'B().width_ = 10',
# silent:
'Aw = A.width',
'Bw = B.width',
'B().width = 10',
)
for expr in to_test:
print "Testing:", expr
exec expr
So, my Attribute works only when instantiated.
What I have tried already
Decorating __get__ and __set__ with either staticmethod or classmethod. No changes in silent section. Good section fails: methods aren't callable. Wat.
Adding __get__ and __set__ to class Attribute from outside, as methods bound to Attribute. Nothing changed.
Code:
# `__get__` was renamed to `_unbound__get__`
Attribute.__get__ = Attribute._unbound__get__.__get__(Attribute, Attribute.__class__)
# `__set__` was renamed to `_unbound__set__`
Attribute.__set__ = Attribute._unbound__set__.__get__(Attribute, Attribute.__class__)
Using instantiated descriptors. This approach requires 2 symbols: one for descriptor (sub)class and one for descriptor. It also requires instantiating descriptor after subclassing it.
Code:
class B(A):
class Width(A.width):
def changed(self, obj, value, old):
super(B.width, self).changed(obj, value, old)
print 'B.width.changed:', value
B.width = B.Width()
More background.
I have growing hierarchy of widgets, where some propertries must be tracked for changes, and response to change may be extended in subclassses. So I am trying to create automated approach, with modular machinery. Because keeping relevant variables and methods per each propertry inside actual widgets is just horrible annoying mess.
Question
Are there workarounds to achieve my needs? Or may be I am doing something wrong?

Descriptors require instances, so you cannot achieve exactly what you want. In particular you want A.width to be, at the same time, both a class and an instance of that class, which is impossible. You must have a way to access the class and the instance separately.
It's pretty simple to automatically create the instances with a known naming scheme. For example using a class decorator:
def track_attributes(cls):
for attr in dir(cls):
value = getattr(cls, attr)
if isinstance(value, type) and issubclass(value, Attribute):
# probably require more checks and/or different naming scheme
setattr(cls, attr.lower(), value())
return cls
Used as:
#track_attributes
class A(object):
class Width(Attribute):
def changed(self, obj, value, old):
print 'A.width changed:', value
#track_attributes
class B(A):
class Width(A.Width):
def changed(self, obj, value, old):
super(B.Width, self).changed(obj, value, old)
print 'B.width changed:', value
B().width = 10
output:
$python test_attributes.py
Attribute getter (class): <class '__main__.B'>
Attribute setter (inst, value): (<__main__.B object at 0x7fc225cd7d50>, 10)
A.width changed: 10
B.width changed: 10
An other way to achieve this without explicit decorators is to create a metaclass and have a Widget base class for all widgets.

As mata says, descriptors needs to be instances. One possible solution would be to use a class decorator or a custom metaclass (in your case more probably a custom metaclass) that would lookup the class namespace for Attribute subclasses and instanciate them. The whole thing smells a bit like overengineering to me but sometimes well you just need that level of complexity.

Attribute mapping with a Python property

Is there a way to make a Python #property act as a setter and getter all at once?
I feel like I've seen this somewhere before but can't remember and can't recreate the solution myself.
For example, instead of:
class A(object):
def __init__(self, b): self.b = b
def get_c(self): return self.b.c
def set_c(self, value): self.b.c = value
c = property(get_c, set_c)
we could somehow signal that for A objects, the c attribute is really equivalent to b.c for getter, setter (and deleter if we like).
Motivation:
This would be particularly useful when we need A to be a proxy wrapper around B objects (of which b is an instance) but share only the data attributes and no methods. Properties such as these would allow the A and B objects' data to stay completely in sync while both are used by the same code.

I think you are looking for this forwardTo class as posted on ActiveState.
This recipe lets you transparently forward attribute access to another
object in your class. This way, you can expose functionality from some
member of your class instance directly, e.g. foo.baz() instead of
foo.bar.baz().
class forwardTo(object):
"""
A descriptor based recipe that makes it possible to write shorthands
that forward attribute access from one object onto another.
>>> class C(object):
... def __init__(self):
... class CC(object):
... def xx(self, extra):
... return 100 + extra
... foo = 42
... self.cc = CC()
...
... localcc = forwardTo('cc', 'xx')
... localfoo = forwardTo('cc', 'foo')
...
>>> print C().localcc(10)
110
>>> print C().localfoo
42
Arguments: objectName - name of the attribute containing the second object.
attrName - name of the attribute in the second object.
Returns: An object that will forward any calls as described above.
"""
def __init__(self, objectName, attrName):
self.objectName = objectName
self.attrName = attrName
def __get__(self, instance, owner=None):
return getattr(getattr(instance, self.objectName), self.attrName)
def __set__(self, instance, value):
setattr(getattr(instance, self.objectName), self.attrName, value)
def __delete__(self, instance):
delattr(getattr(instance, self.objectName), self.attrName)
For a more robust code, you may want to consider replacing getattr(instance, self.objectName) with operator.attrgetter(self.objectName)(instance). This would allow objectName to be a dotted name (e.g., so you could have A.c be a proxy for A.x.y.z.d).

If you're trying to delegate a whole slew of properties from any A object to its b member, it's probably easier to do that inside __getattr__, __setattr__, and __delattr__, e.g.:
class A(object):
delegated = ['c', 'd', 'e', 'f']
def __getattr__(self, attr):
if attr in A.delegated:
return getattr(self.b, attr)
raise AttributeError()
I haven't shown the __setattr__ and __delattr__ definitions here, for brevity, and to avoid having to explain the difference between __getattr__ and __getattribute__. See the docs if you need more information.
This is readily extensible to classes that want to proxy different attributes to different members:
class A(object):
b_delegated = ['c', 'd', 'e', 'f']
x_delegated = ['y', 'z']
def __getattr__(self, attr):
if attr in A.b_delegated:
return getattr(self.b, attr)
elif attr in A.x_delegated:
return getattr(self.x, attr)
else:
raise AttributeError()
If you need to delegate all attributes, dynamically, that's almost as easy. You just get a list of self.b's attributes (or self.b.__class__'s) at init time or at call time (which of the four possibilities depends on exactly what you want to do), and use that in place of the static list b_delegated.
You can of course filter this by name (e.g., to remove _private methods), or by type, or any arbitrary predicate (e.g., to remove any callable attributes).
Or combine any of the above.
At any rate, this is the idiomatic way to do (especially dynamic) proxying in Python. It's not perfect, but trying to invent a different mechanism is probably not a good idea.
And in fact, it's not really meant to be perfect. This is something you shouldn't be doing too often, and shouldn't be trying to disguise when you do it. It's obvious that a ctypes.cdll or a pyobjc module is actually delegating to something else, because it's actually useful for the user to know that. If you really need to delegate most of the public interface of one class to another, and don't want the user to know about the delegation… maybe you don't need it. Maybe it's better to just expose the private object directly, or reorganize your object model so the user is interacting with the right things in the first place.

There's the decorator syntax for creating properties, then there are full blown custom-defined descriptors, but since the setter/getter pseudo-private pattern is actively discouraged in Python and the Python community, there isn't really a widely distributed or commonly used way to do what you are looking for.
For proxy objects, you can use __getattr__, __setattr__, and __getattribute__, or try to manipulate things earlier in the process by fooling around with __new__ or a metaclass.

def make_property(parent, attr):
def get(self):
return getattr(getattr(self, parent), attr)
def set(self, value):
setattr(getattr(self, parent), attr, value)
return property(get, set)
class A(object):
def __init__(self, b): self.b = b
c = make_property('b', 'c')

Here's another way of doing it, statically forwarding properties from one object to another, but with economy.
It allows to forward get/set property in two lines, and aread-only property in one line, making use of dynamic property definition at the class level and lambdas.
class A:
"""Classic definition of property, with decorator"""
_id = ""
_answer = 42
#property
def id(self):
return self._id
#id.setter
def id(self, value):
self._id = value
#property
def what(self):
return self._answer
class B:
obj = A()
# Forward "id" from self.obj
id = property(lambda self: self.obj.id,
lambda self, value: setattr(self.obj, "id", value))
# Forward read-only property from self.obj
what = property(lambda self: self.obj.what)