I would like to create a metaclass/descriptor in order to have a identifier attribute which counts through each instances. Furthermore I would like to have that the identifier attribute cannot be changed. This is what I have done so far:
class Identifier(object):
"""Iterator for Transformation Identification
"""
def __init__(self, current):
self.current = current
def __iter__(self):
return self
def __next__(self):
self.current += 1
return self.current
def __get__(self, instance, owner):
return self
def __set__(self, instance, value):
raise Exception('')
class InheritDecoratorsMixin:
_iter = Identifier(0)
def __init_subclass__(cls, *args, **kwargs):
setattr(cls, 'id', next(cls._iter))
super().__init_subclass__(*args, **kwargs)
Here we have an example:
class Test1(InheritDecoratorsMixin):
pass
class Test2(Test1):
pass
class Test3(Test1):
pass
class Test4(Test3):
pass
a = Test2()
print(a.id)
b = Test3()
print(b.id)
c = Test4()
print(c.id)
print(c.id)
c.id = 5
print(c.id)
The output is
2
3
4
4
5
This is fine except for the fact that the attribute can be changed. When I changed the code to:
class Identifier(object):
"""Iterator for Transformation Identification
"""
def __init__(self, current):
self.current = current
def __iter__(self):
return self
def __next__(self):
self.current += 1
return self
def __get__(self, instance, owner):
return self
def __set__(self, instance, value):
raise Exception('')
where the next returns 'self' which is basically the iterator then the value cannot be set to '5' but the attribute 'id' is not an integer anymore but an instance of the iterator class. Is there a way to combine both versions, where we both have an integer as id attribute as well as the restriction that the attribute cannot be changed?
Note: in the output there are different values for each subclass but it remains the same for each instance.
Thanks in advance for any help.
You don't actually need a __init_subclass__ method there: the descriptor itself can work to keep track of the instances for each class it is set on.
And then you can assign the descriptor to .id itself:
class Identifier:
def __set_name__(self, owner, name):
self.name = name
def __get__(self, instance, owner):
if instance is None:
return self
inner_attr = "_" + self.name
id = instance.__dict__.get(inner_attr)
if id is not None:
return id
counter = owner.__dict__.get(inner_attr, 0)
counter += 1
setattr(instance, inner_attr, counter)
setattr(owner, inner_attr, counter)
return counter
def __set__(self, instance, value):
raise AttributeError("read only attribute")
class A:
id = Identifier()
And testing it on an interactive session:
In [35]: a = A()
In [36]: a.id
Out[36]: 1
In [37]: a.id = 3
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
Input In [37], in <cell line: 1>()
----> 1 a.id = 3
Input In [34], in Identifier.__set__(self, instance, value)
19 def __set__(self, instance, value):
---> 20 raise AttributeError("read only attribute")
AttributeError: read only attribute
In [38]: class B(A): pass
In [39]: B().id
Out[39]: 1
In [40]: B().id
Out[40]: 2
In [41]: B().id
Out[41]: 3
In [42]: A().id
Out[42]: 2
So, this won't need metaclasses or __init_subclass__ at all.
As for the unerscored attribute _id: it will remain writable, and it is important that you understand that it is impossible, in Python to prevent the inner state of being modifiable. The "public" attribute, ".id" is not changeable, and that is what maytters. It is possible to "hide away" the _id attribute - as an attribute of the descriptor instead of in the class and instances dictionary, for example, but ultimately, if someone 'wants' to change it, there will always be a way to do so. The documentation contract that it is a readonly, public attribute named "id" and whatever else exists is private have to be enough.
You implement a descriptor protocol but do so on the class you are using an infinite counter essentially. That __set__ you write to block setting id will only work if you try to set _iter.
from typing import Any, NoReturn
from typing_extensions import Self
class Identifier:
"""Descriptor for Transformation Identification
"""
def __init__(self, starting_id: int) -> None:
self.current: int = starting_id
def __set_name__(self, owner: type, name: str) -> None:
self.name = name
self.id_map: dict[owner, int] = {}
def __get__(self, instance: Any, owner: type) -> Self|int:
if instance is None:
return self
else:
instance_id = self.id_map.get(instance, None)
if instance_id is None:
self.current += 1
instance_id = self.current
self.id_map[instance] = instance_id
return instance_id
def __set__(self, instance: Any, value: Any) -> NoReturn:
raise Exception(f'Cannot set {self.name}')
class InheritDecoratorsMixin:
id = Identifier(0)
class Test1(InheritDecoratorsMixin):
pass
class Test2(Test1):
pass
class Test3(Test1):
pass
class Test4(Test3):
pass
a = Test2()
print(a.id)
b = Test3()
print(b.id)
b2 = Test3()
print(b2.id)
c = Test4()
print(c.id)
print(c.id)
c.id = 5
And the output in ipython
1
2
3
4
4
---------------------------------------------------------------------------
Exception Traceback (most recent call last)
Input In [4], in <module>
59 print(c.id)
60 print(c.id)
---> 61 c.id = 5
Input In [4], in Identifier.__set__(self, instance, value)
27 def __set__(self, instance: Any, value: Any) -> NoReturn:
---> 28 raise Exception(f"Cannot set {self.name}")
Exception: Cannot set id
Related
I know that functions are just descriptors, like this:
def func(self):
print(self.name)
class C:
def __init__(self, name):
self.name = name
C.func = func
c = C("foo")
c.func()
I thought at first that c.func equals C.func.__get__(c)，yes，C.func.__get__(c) return a bound method. But when I set the __get__ of func to None, c.func still returns a bound method.
def func(self):
print(self.name)
class C:
def __init__(self, name):
self.name = name
func.__get__ = None
C.func = func
c = C("foo")
c.func
output:
<bound method func of <__main__.C object at 0x0000027EB23BF088>>
So I'm confused. Moreover, I found that when calling a function from an instance, Python actually calls the class's ___getAttribute__ method, which returns a bound method.
def func(self):
print(self.name)
func.__get__ = None
class C:
def __getattribute__(self, name):
r = super().__getattribute__(name)
print(r) # r is a bound method already
return r
def __init__(self, name):
self.name = name
C.func = func
c = C("foo")
c.func
output:
<bound method func of <__main__.C object at 0x0000027EB243D1C8>>
func.__get__ doesn't seem to have any effect. So, What happended in __getattribute__? How does Python turn a function into a method? I've Googled and done some research, but I still can't find the answer.
Maybe I'm making things complicated, In my understanding, function is itself a descriptor, but just like the code below, I set the func to None, it works normally:
class C:
def func(self):
print('hello world')
func.__get__ = None
c = C()
c.func()
but if it's a descriptor, it will raise TypeError:
class C:
class D:
def __get__(self, inst, cls):
if inst is None:
return self
return 'hello world'
D.__get__ = None
func = D()
c = C()
c.func
Well, if I understand correctly from what I found. (Since I didn't know the descriptors, that's exactly why I like to help, still learning)
First, let's look at __getattr__ and __getattribute__.
Let's have an empty class A
class A:
pass
If I initialize an object and try to call a property, because there is none at the moment, we get AttributeError.
a = A()
a.some_property
The following occurs:
Simple check of flow:
class FlowDemo:
def __init__(self):
self.inited_property = True
def __getattribute__(self, item):
if item in ('__class__', '__len__') : # For less spam of getting this attribute, if you want, you can remove condition.
print('Get Attribute', item)
# Call default behavior
return super().__getattribute__(item)
def __getattr__(self, item):
print('Get Attr', item)
if item == 'some_magic_name':
return "It's magic!"
raise AttributeError
fd = FlowDemo()
fd.inited_property
# Get Attribute inited_property
# True
fd.some_magic_property
# Get Attribute some_magic_name
# Get Attr some_magic_name
# "It's magic!"
fd.some_property
# Get Attribute some_property
# Get Attr some_property
# Traceback (most recent call last):
# File "<input>", line 1, in <module>
# File "stack-class-property-and-descriptors.py", line 67, in # __getattr__
# raise AttributeError
# AttributeError
This is probably understandable, including the use. But to be sure, I'll give an example. This logic is used as a dynamic representation of the result from the databases (mapping of attributes to ordinary dict, list, etc.).
But it can also be just logic for accessing an attribute (property), such as an access counter or validation (but this applies to __setattr__ and __setattribute__)
And what about descriptors?
First let's look at data-descriptors, they are easier for me to understand.
This is a class or decoder that has __get__ and one or both of __set__ and __delete__.
Once this is defined, python, when used in the property definition with it and then does not return a class but the value it obtains through __get__, does not overwrite an already declared class when declaring a value, but uses its __set__.
Example:
class WeekDayDescriptor:
def __init__(self):
self.__week_day = 0
def __get__(self, instance, owner=None):
return self.__week_day
def __set__(self, instance, value):
if not isinstance(value, int):
raise TypeError('Value must be int')
if not (0 <= value < 6):
raise ValueError('Value must be in range 0 - 6')
self.__week_day = value
class Calendar:
week_day = WeekDayDescriptor()
def __init__(self, week_day):
self.week_day = week_day
Demo:
c = Calendar(9)
# ValueError: Value must be in range 0-6
c = Calendar('6')
# TypeError: Value must be int
c = Calendar(3)
c.week_day = 6
c.week_day = 10
# ValueError: Value must be in range 0-6
c.week_day = 'monday'
# TypeError: Value must be int
Decorator descriptor:
class Calendar:
#property
def week_day(self):
return self.__week_day
#week_day.setter
def week_day(self, week_day):
if not isinstance(week_day, int):
raise TypeError('Value must be int')
if not (0 <= week_day < 6):
raise ValueError('Value must be in range 0 - 6')
self.__week_day = week_day
def __init__(self, week_day):
self.week_day = week_day
pass
And now for non-data descriptors...
A non-data descriptor is one that has only __get__.
As I understand it, each method automatically has its own descriptor, thanks to which the functions get references to the object - self.
We can write our own descriptor for a function / method, but it's not that straightforward, we have to help ourselves and get around it a bit.
def function_as_method(self, value):
print(self, value)
class HelperDescriptor:
def __get__(self, instance, owner):
def wrapper(*args, **kwargs):
return function_as_method(instance, *args, **kwargs)
return wrapper
class Foo:
baz = HelperDescriptor()
>>> bar = Foo()
>>> bar.baz(1)
<__main__.Foo object at 0x7f64f7768b70> 1
Source of last code block, but in czech lang.
And finally, your mentioned problem, when we set __get__ to None and you still get a reference to the function.
It's simple, python doesn't directly distinguish between primitive data types and functions, it's all a variable (or attribute / property) that has a value. Whether it's just value or it's callable is a different matter.
def f(): return True
print(type(f), f())
# <class 'function'> True
f = 123
print(type(f), f)
# <class 'int'> 123
Therefore, when we ask for the obj.func method or call it obj.func() directly, the first two changed magic is called first - __getattribute__ and __getattr__.
And if we call a method, it is called only after we get a reference to a function in memory.
Again a simple example:
def func(self, value):
print('Printing:', value)
class PrintDescriptor:
def __init__(self, name):
self.name = name
def __get__(self, instance, owner):
def wrapper(*args, **kwargs):
print(f"Calling '{self.name}' method")
return func(instance, *args, **kwargs)
return wrapper
class B:
foo = PrintDescriptor('foo')
bar = PrintDescriptor('bar')
def __getattribute__(self, item):
if item not in ('__len__', '__class__', '__dict__'):
print('Get Attribute', item)
return super().__getattribute__(item)
Demo:
b = B()
b.foo
# Get Attribute foo
# <function PrintDescriptor.__get__.<locals>.wrapper at 0x7f774a782ee0>
b.foo(2)
# Get Attribute foo
# Calling 'foo' method
# Printing: 2
b.bar(4)
# Get Attribute bar
# Calling 'bar' method
# Printing: 4
Sources:
https://www.datacamp.com/community/tutorials/python-descriptors#above1
https://blog.milde.cz/post/319-pokrocile-techniky-v-pythonu-deskriptory/
Python Doc, __get__
Python Docs, __getattribute__
Python Docs, __getattr__
Is there a way to apply the same property logic to a set of attributes in a class? For example, I want to apply the same #attr1.setter decorator to attr2, attr3, and attr4 without having to define the property for each attribute.
class Sample:
def __init__(self):
self.attr1 = None
self.attr2 = None
self.attr3 = None
self.attr4 = None
#property
def attr1(self):
return self.__attr1
#attr1.setter
def attr1(self, val):
if val < 0:
self.__attr1 = 0
else:
self.__attr1 = val
Just create your own descriptor for this:
class MyDescriptor:
def __set_name__(self, owner, name):
self.name = f'_{name}'
def __get__(self, instance, owner):
return getattr(instance, self.name)
def __set__(self, instance, val):
if val is None:
setattr(instance, self.name, None)
elif val < 0:
setattr(instance, self.name, 0)
else:
setattr(instance, self.name, val)
class Sample:
attr1 = MyDescriptor()
attr2 = MyDescriptor()
attr3 = MyDescriptor()
attr4 = MyDescriptor()
def __init__(self):
self.attr1 = None
self.attr2 = None
self.attr3 = None
self.attr4 = None
Now, in action:
In [3]: s = Sample()
In [4]: s.attr1 = -99
In [5]: s.attr1
Out[5]: 0
In [6]: s.attr2
In [7]: s.attr2 = 10
In [8]: s.attr2
Out[8]: 10
In [9]: s.attr2 = -1
In [10]: s.attr2
Out[10]: 0
See the Descriptor HOWTO and some more relevant documentation
Note, I incorporated the possibility of None in your setter logic (your code would have raised a TypeError on initialization of an instance, because the setter checks if None < 0). Also note, you probably don't want to be using double-underscore name-mangling (which doesn't mean private), so I used the conventional single-underscore to denote a variable not part of the public api. Using double-underscore name-mangling complicates things here.
You could override the __getattr__ and __setattr__ to behave the way you want them. This way you don't need to define any private variables nor initialize any of the member variables either.
class Sample:
def __getattr__(self, attr):
return self.__dict__.get(attr)
def __setattr__(self, attr, val):
if val is not None and val < 0:
self.__dict__[attr] = 0
else:
self.__dict__[attr] = val
s = Sample()
print(s.attr1) # None
s.attr1 = 10
print(s.attr1) # 10
s.attr1 = -10
print(s.attr1) # 0
s.attr1 = None
print(s.attr1) # None
Read the Python Cookbook and saw descriptors, particularly the example for enforcing types when using class attributes. I am writing a few classes where that would be useful, but I would also like to enforce immutability. How to do it? Type checking descriptor adapted from the book:
class Descriptor(object):
def __init__(self, name=None, **kwargs):
self.name = name
for key, value in kwargs.items():
setattr(self, key, value)
def __set__(self, instance, value):
instance.__dict__[self.name] = value
# by default allows None
class Typed(Descriptor):
def __init__(self, expected_types=None, **kwargs):
self.expected_types = expected_types
super().__init__(**kwargs)
def __set__(self, instance, value):
if value is not None and not isinstance(value, self.expected_types):
raise TypeError('Expected: {}'.format(str(self.expected_types)))
super(Typed, self).__set__(instance, value)
class T(object):
v = Typed(int)
def __init__(self, v):
self.v = v
Attempt #1: add a self.is_set attribute to Typed
# by default allows None
class ImmutableTyped(Descriptor):
def __init__(self, expected_types=None, **kwargs):
self.expected_types = expected_types
self.is_set = False
super().__init__(**kwargs)
def __set__(self, instance, value):
if self.is_set:
raise ImmutableException(...)
if value is not None and not isinstance(value, self.expected_types):
raise TypeError('Expected: {}'.format(str(self.expected_types)))
self.is_set = True
super(Typed, self).__set__(instance, value)
Wrong, because when doing the following, ImmutableTyped is 'global' in the sense that it's a singleton throughout all instances of the class. When t2 is instantiated, is_set is already True from the previous object.
class T(object):
v = ImmutableTyped(int)
def __init__(self, v):
self.v = v
t1 = T()
t2 = T() # fail when instantiating
Attempt #2: Thought instance in __set__ refers to the class containing the attribute so tried to check if instance.__dict__[self.name] is still a Typed. That is also wrong.
Idea #3: Make Typed be used more similar to #property by accepting a 'fget' method returning the __dict__ of T instances. This would require the definition of a function in T similar to:
#Typed
def v(self):
return self.__dict__
which seems wrong.
How to implement immutability AND type checking as a descriptor?
Now this is my approach to the problem:
class ImmutableTyped:
def __set_name__(self, owner, name):
self.name = name
def __init__(self, *, immutable=False, types=None)
self.immutable == immutable is True
self.types = types if types else []
def __get__(self, instance, owner):
return instance.__dict__[self.name]
def __set__(self, instance, value):
if self.immutable is True:
raise TypeError('read-only attribute')
elif not any(isinstance(value, cls)
for cls in self.types):
raise TypeError('invalid argument type')
else:
instance.__dict__[self.name] = value
Side note: __set_name__ can be used to allow you to not specify the attribute name in initialisation. This means you can just do:
class Foo:
bar = ImmutableTyped()
and the instance of ImmutableTyped will automatically have the name attribute bar since I typed for that to occur in the __set_name__ method.
Could not succeed in making such a descriptor. Perhaps it's also unnecessarily complicated. The following method + property use suffices.
# this also allows None to go through
def check_type(data, expected_types):
if data is not None and not isinstance(data, expected_types):
raise TypeError('Expected: {}'.format(str(expected_types)))
return data
class A():
def __init__(self, value=None):
self._value = check_type(value, (str, bytes))
#property
def value(self):
return self._value
foo = A()
print(foo.value) # None
foo.value = 'bla' # AttributeError
bar = A('goosfraba')
print(bar.value) # goosfraba
bar.value = 'bla' # AttributeError
class ImmutableTyped(object):
def __set_name__(self, owner, name):
self.name = name
def __init__(self, *, types=None):
self.types = tuple(types or [])
self.instances = {}
return None
def __get__(self, instance, owner):
return instance.__dict__[self.name]
def __set__(self, instance, value):
is_set = self.instances.setdefault(id(instance), False)
if is_set:
raise AttributeError("read-only attribute '%s'" % (self.name))
if self.types:
if not isinstance(value, self.types):
raise TypeError("invalid argument type '%s' for '%s'" % (type(value), self.name))
self.instances[id(instance)] = True
instance.__dict__[self.name] = value
return None
Examples:
class Something(object):
prop1 = ImmutableTyped(types=[int])
something = Something()
something.prop1 = "1"
Will give:
TypeError: invalid argument type '<class 'str'>' for 'prop1'
And:
something = Something()
something.prop1 = 1
something.prop1 = 2
Will give:
TypeError: read-only attribute 'prop1'
I need to create a class whose instances can't have same values. If you create instance with value that have already been used you'll get old same instance.
I did it using special class method:
class A():
instances = []
def __init__(self, val):
self.val = val
#classmethod
def new(cls, val):
"""
Return instance with same value or create new.
"""
for ins in cls.instances:
if ins.val == val:
return ins
new_ins = A(val)
cls.instances.append(new_ins)
return new_ins
a1 = A.new("x")
a2 = A.new("x")
a3 = A.new("y")
print a1 # <__main__.A instance at 0x05B7FD00> S\ /M\
print a2 # <__main__.A instance at 0x05B7FD00> \A/ \E
print a3 # <__main__.A instance at 0x05B7FD28>
Is there a way to do it more elegant, without using .new method?
You could try functools.lru_cache.
For example:
from functools import lru_cache
class A:
#lru_cache()
def __new__(cls, arg):
return super().__new__(cls)
def __init__(self, arg):
self.n = arg
Sample usage:
>>> a1 = A('1')
>>> a2 = A('1')
>>> a1 is a2
True
>>> a1.n
'1'
>>> a2.n
'1'
Alternatively you could try building a custom caching class, as pointed out by Raymond Hettinger in this tweet: https://twitter.com/raymondh/status/977613745634471937.
This can be done by overriding the __new__ method, which is responsible for creating new instances of a class. Whenever you create a new instance you store it in a dict, and if the dict contains a matching instance then you return it instead of creating a new one:
class A:
instances = {}
def __new__(cls, val):
try:
return cls.instances[val]
except KeyError:
pass
obj = super().__new__(cls)
cls.instances[val] = obj
return obj
def __init__(self, val):
self.val = val
a = A(1)
b = A(2)
c = A(1)
print(a is b) # False
print(a is c) # True
One downside of this solution is that the __init__ method will be called regardless of whether the instance is a newly created one or one that's been stored in the dict. This can cause problems if your constructor has undesired side effects:
class A:
...
def __init__(self, val):
self.val = val
self.foo = 'foo'
a = A(1)
a.foo = 'bar'
b = A(1)
print(a.foo) # output: foo
Notice how a's foo attribute changed from "bar" to "foo" when b was created.
Another option is to use a metaclass and override its __call__ method:
class MemoMeta(type):
def __new__(mcs, name, bases, attrs):
cls = super().__new__(mcs, name, bases, attrs)
cls.instances = {}
return cls
def __call__(cls, val):
try:
return cls.instances[val]
except KeyError:
pass
obj = super().__call__(val)
cls.instances[val] = obj
return obj
class A(metaclass=MemoMeta):
def __init__(self, val):
self.val = val
self.foo = 'foo'
This bypasses the problem with __init__ being called on existing instances:
a = A(1)
a.foo = 'bar'
b = A(1)
print(a.foo) # output: bar
If you really want to make it more elegant, implement the duplicate check in __new__, so it will be performed when you call A(something).
Just do it in __new__:
def __new__(cls, val=None):
for i in cls.instances:
if val == i.val:
return i
return object.__new__(cls)
I'm trying to design a descriptor class which I can use through other class which is a subclass of a class which is a subclass of a class.
class MyDescriptorClass(object):
def __init__(self, owner, name, activates = 0):
self.value = None
self.name = name
self.owner = owner
self.activates = 0
self.connects = []
def __set__(self, obj, val):
self.set(val)
def __get__(self, instance, owner):
return self.value
def set(self, value):
if self.value == value:
return 0
self.value = value
if self.activates:
self.owner.evaluate()
def connect(self, inputs):
if not isinstance(inputs, list):
inputs = list(inputs)
for input in inputs:
self.connects.append(input)
class ParentClass(object):
def __init__(self, name):
self.states = {}
self.name = name
self.A = MyDescriptorClass(self, name, activates = 1)
self.B = MyDescriptorClass(self, name, activates = 1)
self.states.setDefault('A', self.A)
self.states.setDefault('B', self.B)
class ChildClass1(ParentClass):
def __init__(self, name)
super(ChildClass1, self).__init__(name)
self.ans = None
def evaluate(self):
self.ans = self.A.value + self.B.value
class ChildClass2(ParentClass):
def __init__(self, name)
super(ChildClass1, self).__init__(name)
self.ans = None
def evaluate(self):
self.ans = self.A.value * self.B.value
self.A = MyDescriptorClass() will not work according to the python docs
so the only way is that I declate A = MyDescriptorClass() in the ParentClass as
class ParentClass(object):
A = MyDescriptorClass() # here I am unable to pass the owner
And since, I'm using a child class, super call skips this part and starts directly with __init__
Is there any way in which I can modify the design so as to set the value of ChildClass1.A instance directly?
c = ChildClass1("c1")
c.A = 10 # I directly want to set this value instead of using c.A.set(10)
c.B = 20
c.evaluate()
print c.ans # 30
c.B = 40
print c.ans # 50
Try not to put information which is specific to instances in the descriptor. Keep information specific to instances in instance attributes, and keep information specific to the descriptor (like activates) in the descriptor:
class MyDescriptorClass(object):
def __init__(self, activates = 0):
self.value = None
self.activates = activates
self.connects = []
def __set__(self, instance, val): # 1
if self.value == val:
return 0
self.value = val
if self.activates:
instance.evaluate()
def __get__(self, instance, instcls): # 1
return self.value
Note that the __set__ and __get__ methods are passed the
instance which is accessing the descriptor. Therefore, you do not
need to store the owner in MyDescriptor. The instance is the
owner.
Given the clarification of the problem in the comments below, here is how I would implement the descriptor.
class GateInput(object):
def __init__(self, index):
self.index = index # 4
def __get__(self, inst, instcls):
return inst.inputs[self.index].ans # 5
def __set__(self, inst, val):
if isinstance(val, (float, int)):
inst.inputs[self.index] = Constant(val)
else:
inst.inputs[self.index] = val
class Constant(object):
def __init__(self, val):
self.ans = val
class Gate(object):
A = GateInput(0) # 1
B = GateInput(1) # 1
def __init__(self, name):
self.name = name
self.inputs = [Constant(0), Constant(0)] # 2
class Adder(Gate):
#property
def ans(self):
result = 0
for gate in self.inputs:
result += gate.ans # 3
return result
class Multiplier(Gate):
#property
def ans(self):
result = 1
for gate in self.inputs:
result *= gate.ans
return result
b = Multiplier('b1')
b.A = 2
b.B = 3
print(b.A)
# 2
print(b.ans)
# 6
c = Adder('c1')
c.A = 10
print(c.ans)
# 10
# This connects output of b to an input of c
c.B = b
print(c.ans)
# 16
Descriptors have to be defined as class attributes, not instance
attributes. Since the descriptor is accessed by all instances, you
probably do not want the descriptor to change merely because an
instance is being created. Therefore, do not instantiate the
descriptor in __init__.
Each instance of Gate has a list of inputs. The items self.inputs
are instances of Constant or Gate.
Here we see the purpose of the Constant class. For every gate,
gate.ans needs to return a value.
The index records which item in inst.inputs the GateInput is
connected to.
inst is an instance of Gate. For example, c.A causes Python to
call GateInput.__get__(self, c, type(c)). Thus, inst is c
here.
As it is int he comments:
descriptors must be class attributes, not instance attributes in order to work -
so, to start with:
class ParentClass(object):
A = MyDescriptorClass()
B = MyDescriptorClass()
def __init__(self, name):
self.states = {}
self.name = name
self.A.configure(self, name, activates = 1)
self.B.configure(self, name, activates = 1)
self.states.setDefault('A', self.A)
self.states.setDefault('B', self.B)
And then you fix your Descriptor class accordingly:
either have then keeping all data refering to an instance in the instance itself
(that is why __get__ and __set__ receive the object itself) - or have
each descriptor instance have a dictionary where they can annotate data related
to the instances of the class they belong too, by, for example, object ID.
Your descriptor class could be more or less along these lines:
class MyDescriptorClass(object):
def __init__(self):
self.data = defaultDict(dict)
def configure(self, owner, name, activates = 0):
container = self.data(id(owner))
container["value"] = None
container["name"] = name
...
def __set__(self, owner, value):
# implemnt your previous "set" method straight here
...
...