I am using python and have an object, that object has a method. I am looking for a simple way, to replace the entire object from within that function.
E.g
class a():
def b(self):
self = other_object
How can you do that?
Thanks
You use a proxy/facade object to hold a reference to the actual object, the self if you wish and that proxy (better term than Facade, but not changing my code now) is what the rest of your codebase sees. However, any attribute/method access is forwarded on to the actual object, which is swappable.
Code below should give you a rough idea. Note that you need to be careful about recursion around __the_instance, which is why I am assigning to __dict__ directly. Bit messy, since it's been a while I've written code that wraps getattr and setattr entirely.
class Facade:
def __init__(self, instance):
self.set_obj(instance)
def set_obj(self, instance):
self.__dict__["__theinstance"] = instance
def __getattr__(self, attrname):
if attrname == "__theinstance":
return self.__dict__["__theinstance"]
return getattr(self.__dict__["__theinstance"], attrname)
def __setattr__(self, attrname, value):
if attrname == "__theinstance":
self.set_obj(value)
return setattr(self.__dict__["__theinstance"], attrname, value)
class Test:
def __init__(self, name, cntr):
self.name = name
self.cntr = cntr
def __repr__(self):
return "%s[%s]" % (self.__class__.__name__, self.__dict__)
obj1 = Test("first object", 1)
obj2 = Test("second", 2)
obj2.message = "greetings"
def pretend_client_code(facade):
print(id(facade), facade.name, facade.cntr, getattr(facade, "value", None))
facade = Facade(obj1)
pretend_client_code(facade)
facade.set_obj(obj2)
pretend_client_code(facade)
facade.value = 3
pretend_client_code(facade)
facade.set_obj(obj1)
pretend_client_code(facade)
output:
4467187104 first object 1 None
4467187104 second 2 None
4467187104 second 2 3
4467187104 first object 1 None
So basically, the "client code" always sees the same facade object, but what it is actually accessing depends on what your equivalent of def b is has done.
Facade has a specific meaning in Design Patterns terminology and it may not be really applicable here, but close enough. Maybe Proxy would have been better.
Note that if you want to change the class on the same object, that is a different thing, done through assigning self.__class__ . For example, say an RPG game with an EnemyClass who gets swapped to DeadEnemyClass once killed: self.__class__ = DeadEnemyClass
You can't directly do that. What you can do is save it as an instance variable.
class A():
def __init__(self, instance=None):
self.instance = val or self
# yes, you can make it a property as well.
def set_val(self, obj):
self.instance = obj
def get_val(self):
return self.instance
It is unlikely that replacing the 'self' variable will accomplish
whatever you're trying to do, that couldn't just be accomplished by
storing the result of func(self) in a different variable. 'self' is
effectively a local variable only defined for the duration of the
method call, used to pass in the instance of the class which is being
operated upon. Replacing self will not actually replace references to
the original instance of the class held by other objects, nor will it
create a lasting reference to the new instance which was assigned to
it.
Original source: Is it safe to replace a self object by another object of the same type in a method?
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.
I'm confused why instance attributes retrieved through after instance instantiation or set during instance construction are done so via the descriptor.
For example, assume we have the following descriptor and class.
Descriptor
from weakref import WeakKeyDictionary
class Positive:
def __init__(self):
self._instance_data = WeakKeyDictionary()
def __get__(self, instance, owner):
return self._instance_data[instance]
def __set__(self, instance, value):
if value <= 0:
raise ValueError(f'Value {value} is not positive')
self._instance_data[instance] = value
def __delete__(self, instance):
raise AttributeError('Cannot delete attribute!')
Class
class Planet:
def __init__(self, name, mass_kilograms):
self.name = name
self.mass_kilograms= mass_kilograms
mass_kilograms = Positive()
Now we create an instance of Planet and retrieve its mass.
pluto = Planet(name = 'Pluto', mass_kilograms = 1.305e22)
# The above line is doing Positive.__set__(self, pluto, 1.305e22) under the hood.
# It is NOT doing self.mass_kilograms = 1.305e22. In fact, all of the instance
# attributes are stored in the descriptor Positive's _instance_data
m = pluto.mass_kilograms # m = Positive.__get__(self, pluto, Planet)
I have no idea why Positive.__get__ is called, given that is is a class attribute. Can someone please explain? Whats even more confusing is how Positive.__set__ intercepts the direct assignment of the constructor parameter mass_kilograms.
Thanks!
mass_kilograms is not an instance attribute. It is a class attribute bound to an instance of the descriptor Planet.
class Planet:
def __init__(self, name, mass_kilograms):
self.name = name
self.mass_kilograms= mass_kilograms
a
mass_kilograms = Positive() # This creates a class attribute
Because Positive defines __set__, it is a data descriptor. According to the descriptor how-to:
For objects, the machinery is in object.__getattribute__() which transforms b.x into type(b).__dict__['x'].__get__(b, type(b)). The implementation works through a precedence chain that gives data descriptors priority over instance variables, instance variables priority over non-data descriptors, and assigns lowest priority to __getattr__() if provided. The full C implementation can be found in PyObject_GenericGetAttr() in Objects/object.c.
In Planet.__init__, the assignment to this class attribute triggers a call to the __set__ method of the descriptor, as if you had written
def __init__(self, name, mass_kilograms):
self.name = name
self.mass_kilograms.__set__(self, mass_kilograms)
Even more basically, the last line is equivalent to
Positive.__set__(type(self).mass_kilograms, self, mass_kilograms)
Usually Python descriptor are defined as class attributes. But in my case, I want every object instance to have different set descriptors that depends on the input. For example:
class MyClass(object):
def __init__(self, **kwargs):
for attr, val in kwargs.items():
self.__dict__[attr] = MyDescriptor(val)
Each object are have different set of attributes that are decided at instantiation time. Since these are one-off objects, it is not convenient to first subclass them.
tv = MyClass(type="tv", size="30")
smartphone = MyClass(type="phone", os="android")
tv.size # do something smart with the descriptor
Assign Descriptor to the object does not seem to work. If I try to access the attribute, I got something like
<property at 0x4067cf0>
Do you know why is this not working? Is there any work around?
This is not working because you have to assign the descriptor to the class of the object.
class Descriptor:
def __get__(...):
# this is called when the value is got
def __set__(...
def __del__(...
if you write
obj.attr
=> type(obj).__getattribute__(obj, 'attr') is called
=> obj.__dict__['attr'] is returned if there else:
=> type(obj).__dict__['attr'] is looked up
if this contains a descriptor object then this is used.
so it does not work because the type dictionairy is looked up for descriptors and not the object dictionairy.
there are possible work arounds:
put the descriptor into the class and make it use e.g. obj.xxxattr to store the value.
If there is only one descriptor behaviour this works.
overwrite setattr and getattr and delattr to respond to discriptors.
put a discriptor into the class that responds to descriptors stored in the object dictionairy.
You are using descriptors in the wrong way.
Descriptors don't make sense on an instance level. After all the __get__/__set__
methods give you access to the instance of the class.
Without knowing what exactly you want to do, I'd suggest you put the per-instance
logic inside the __set__ method, by checking who is the "caller/instance" and act accordingly.
Otherwise tell us what you are trying to achieve, so that we can propose alternative solutions.
I dynamically create instances by execing a made-up class. This may suit your use case.
def make_myclass(**kwargs):
class MyDescriptor(object):
def __init__(self, val):
self.val = val
def __get__(self, obj, cls):
return self.val
def __set__(self, obj, val):
self.val = val
cls = 'class MyClass(object):\n{}'.format('\n'.join(' {0} = MyDescriptor({0})'.format(k) for k in kwargs))
#check if names in kwargs collide with local names
for key in kwargs:
if key in locals():
raise Exception('name "{}" collides with local name'.format(key))
kwargs.update(locals())
exec(cls, kwargs, locals())
return MyClass()
Test;
In [577]: tv = make_myclass(type="tv", size="30")
In [578]: tv.type
Out[578]: 'tv'
In [579]: tv.size
Out[579]: '30'
In [580]: tv.__dict__
Out[580]: {}
But the instances are of different class.
In [581]: phone = make_myclass(type='phone')
In [582]: phone.type
Out[582]: 'phone'
In [583]: tv.type
Out[583]: 'tv'
In [584]: isinstance(tv,type(phone))
Out[584]: False
In [585]: isinstance(phone,type(tv))
Out[585]: False
In [586]: type(tv)
Out[586]: MyClass
In [587]: type(phone)
Out[587]: MyClass
In [588]: type(phone) is type(tv)
Out[588]: False
This looks like a use-case for named tuples
The reason it is not working is because Python only checks for descriptors when looking up attributes on the class, not on the instance; the methods in question are:
__getattribute__
__setattr__
__delattr__
It is possible to override those methods on your class in order to implement the descriptor protocol on instances as well as classes:
# do not use in production, example code only, needs more checks
class ClassAllowingInstanceDescriptors(object):
def __delattr__(self, name):
res = self.__dict__.get(name)
for method in ('__get__', '__set__', '__delete__'):
if hasattr(res, method):
# we have a descriptor, use it
res = res.__delete__(name)
break
else:
res = object.__delattr__(self, name)
return res
def __getattribute__(self, *args):
res = object.__getattribute__(self, *args)
for method in ('__get__', '__set__', '__delete__'):
if hasattr(res, method):
# we have a descriptor, call it
res = res.__get__(self, self.__class__)
return res
def __setattr__(self, name, val):
# check if object already exists
res = self.__dict__.get(name)
for method in ('__get__', '__set__', '__delete__'):
if hasattr(res, method):
# we have a descriptor, use it
res = res.__set__(self, val)
break
else:
res = object.__setattr__(self, name, val)
return res
#property
def world(self):
return 'hello!'
When the above class is used as below:
huh = ClassAllowingInstanceDescriptors()
print(huh.world)
huh.uni = 'BIG'
print(huh.uni)
huh.huh = property(lambda *a: 'really?')
print(huh.huh)
print('*' * 50)
try:
del huh.world
except Exception, e:
print(e)
print(huh.world)
print('*' * 50)
try:
del huh.huh
except Exception, e:
print(e)
print(huh.huh)
The results are:
hello!
BIG
really?
can't delete attribute
hello!
can't delete attribute
really?
In class B below I wanted the __set__ function in class A to be called whenever you assign a value to B().a . Instead, setting a value to B().a overwrites B().a with the value. Class C assigning to C().a works correctly, but I wanted to have a separate instance of A for each user class, i.e. I don't want changing 'a' in one instance of C() to change 'a' in all other instances. I wrote a couple of tests to help illustrate the problem. Can you help me define a class that will pass both test1 and test2?
class A(object):
def __set__(self, instance, value):
print "__set__ called: ", value
class B(object):
def __init__(self):
self.a = A()
class C(object):
a = A()
def test1( class_in ):
o = class_in()
o.a = "test"
if isinstance(o.a, A):
print "pass"
else:
print "fail"
def test2( class_in ):
o1, o2 = class_in(), class_in()
if o1.a is o2.a:
print "fail"
else:
print "pass"
Accordingly to the documentation:
The following methods only apply when an instance of the class containing
the method (a so-called descriptor
class) appears in the class dictionary
of another new-style class, known as
the owner class. In the examples
below, “the attribute” refers to the
attribute whose name is the key of the
property in the owner class’ __dict__.
Descriptors can only be implemented as
new-style classes themselves.
So you can't have descriptors on instances.
However, since the descriptor gets a ref to the instance being used to access it, just use that as a key to storing state and you can have different behavior depending on the instance.
Here's a class that can pass the original tests, but don't try using it in most situations. it fails the isinstance test on itself!
class E(object):
def __new__(cls, state):
class E(object):
a = A(state)
def __init__(self, state):
self.state = state
return E(state)
#>>> isinstance(E(1), E)
#False
I was bitten by a similar issue in that I wanted to class objects with attributes governed by a descriptor. When I did this, I noticed that the attributes were being overwritten in all of the objects such that they weren't individual.
I raised a SO question and the resultant answer is here: class attribute changing value for no reason
A good document link discussing descriptors is here: http://martyalchin.com/2007/nov/24/python-descriptors-part-2-of-2/
An example descriptor from the aforementioned link is below:
class Numberise(object):
def __init__(self, name):
self.name = name
def __get__(self, instance, owner):
if self.name not in instance.__dict__:
raise (AttributeError, self.name)
return '%o'%(instance.__dict__[self.name])
def __set__(self, instance, value):
print ('setting value to: %d'%value)
instance.__dict__[self.name] = value