I've been going through all the Stackoverflow answers on dynamic property setting, but for whatever reason I can't seem to get this to work.
I have a class, Evolution_Base, that in its init creates an instance of Value_Differences. Value_Differences should be dynamically creating properties, based on the list I pass, that returns the function value from _get_df_change:
from pandas import DataFrame
from dataclasses import dataclass
import pandas as pd
class Evolution_Base():
def __init__(self, res_date_0 : DataFrame , res_date_1 : DataFrame):
#dataclass
class Results_Data():
res_date_0_df : DataFrame
res_date_1_df : DataFrame
self.res = Results_Data(res_date_0_df= res_date_0,
res_date_1_df= res_date_1)
property_list = ['abc', 'xyz']
self.difference = Value_Differences(parent = self, property_list=property_list)
# Shared Functions
def _get_df_change(self, df_name, operator = '-'):
df_0 = getattr(self.res.res_date_0_df, df_name.lower())
df_1 = getattr(self.res.res_date_1_df, df_name.lower())
return self._df_change(df_1, df_0, operator=operator)
def _df_change(self, df_1 : pd.DataFrame, df_0 : pd.DataFrame, operator = '-') -> pd.DataFrame:
"""
Returns df_1 <operator | default = -> df_0
"""
# is_numeric mask
m_1 = df_1.select_dtypes('number')
m_0 = df_0.select_dtypes('number')
def label_me(x):
x.columns = ['t_1', 't_0']
return x
if operator == '-':
return label_me(df_1[m_1] - df_0[m_0])
elif operator == '+':
return label_me(df_1[m_1] + df_0[m_0])
class Value_Differences():
def __init__(self, parent : Evolution_Base, property_list = []):
self._parent = parent
for name in property_list:
def func(self, prop_name):
return self._parent._get_df_change(name)
# I've tried the following...
setattr(self, name, property(fget = lambda cls_self: func(cls_self, name)))
setattr(self, name, property(func(self, name)))
setattr(self, name, property(func))
Its driving me nuts... Any help appreciated!
My desired outcome is for:
evolution = Evolution_Base(df_1, df_2)
evolution.difference.abc == evolution._df_change('abc')
evolution.difference.xyz == evolution._df_change('xyz')
EDIT: The simple question is really, how do I setattr for a property function?
As asked
how do I setattr for a property function?
To be usable as a property, the accessor function needs to be wrapped as a property and then assigned as an attribute of the class, not the instance.
That function, meanwhile, needs to have a single unbound parameter - which will be an instance of the class, but is not necessarily the current self. Its logic needs to use the current value of name, but late binding will be an issue because of the desire to create lambdas in a loop.
A clear and simple way to work around this is to define a helper function accepting the Value_Differences instance and the name to use, and then bind the name value eagerly.
Naively:
from functools import partial
def _get_from_parent(name, instance):
return instance._parent._get_df_change(name)
class Value_Differences:
def __init__(self, parent: Evolution_Base, property_list = []):
self._parent = parent
for name in property_list:
setattr(Value_Differences, name, property(
fget = partial(_get_from_parent, name)
))
However, this of course has the issue that every instance of Value_Differences will set properties on the class, thus modifying what properties are available for each other instance. Further, in the case where there are many instances that should have the same properties, the setup work will be repeated at each instance creation.
The apparent goal
It seems that what is really sought, is the ability to create classes dynamically, such that a list of property names is provided and a corresponding class pops into existence, with code filled in for the properties implementing a certain logic.
There are multiple approaches to this.
Factory A: Adding properties to an instantiated template
Just like how functions can be nested within each other and the inner function will be an object that can be modified and returned (as is common when creating a decorator), a class body can appear within a function and a new class object (with the same name) is created every time the function runs. (The code in the OP already does this, for the Results_Data dataclass.)
def example():
class Template:
pass
return Template
>>> TemplateA, TemplateB = example(), example()
>>> TemplateA is TemplateB
False
>>> isinstance(TemplateA(), TemplateB)
False
>>> isinstance(TemplateB(), TemplateA)
False
So, a "factory" for value-difference classes could look like
from functools import partial
def _make_value_comparer(property_names, access_func):
class ValueDifferences:
def __init__(self, parent):
self._parent = parent
for name in property_names:
setattr(Value_Differences, name, property(
fget = partial(access_func, name)
))
return ValueDifferences
Notice that instead of hard-coding a helper, this factory expects to be provided with a function that implements the access logic. That function takes two parameters: a property name, and the ValueDifferences instance. (They're in that order because it's more convenient for functools.partial usage.)
Factory B: Using the type constructor directly
The built-in type in Python has two entirely separate functions.
With one argument, it discloses the type of an object.
With three arguments, it creates a new type. The class syntax is in fact syntactic sugar for a call to this builtin. The arguments are:
a string name (will be set as the __name__ attribute)
a list of classes to use as superclasses (will be set as __bases__)
a dict mapping attribute names to their values (including methods and properties - will become the __dict__, roughly)
In this style, the same factory could look something like:
from functools import partial
def _make_value_comparer(property_names, access_func):
methods = {
name: property(fget = partial(access_func, name)
for name in property_names
}
methods['__init__'] = lambda self, parent: setattr(self, '_parent', parent)
return type('ValueDifferences', [], methods)
Using the factory
In either of the above cases, EvolutionBase would be modified in the same way.
Presumably, every EvolutionBase should use the same ValueDifferences class (i.e., the one that specifically defines abc and xyz properties), so the EvolutionBase class can cache that class as a class attribute, and use it later:
class Evolution_Base():
def _get_from_parent(name, mvd):
# mvd._parent will be an instance of Evolution_Base.
return mvd._parent._get_df_change(name)
_MyValueDifferences = _make_value_comparer(['abc', 'xyz'], _get_from_parent)
def __init__(self, res_date_0 : DataFrame , res_date_1 : DataFrame):
#dataclass
class Results_Data():
res_date_0_df : DataFrame
res_date_1_df : DataFrame
self.res = Results_Data(res_date_0_df= res_date_0,
res_date_1_df= res_date_1)
self.difference = _MyValueDifferences(parent = self)
Notice that the cached _MyValueDifferences class no longer requires a list of property names to be constructed. That's because it was already provided when the class was created. The actual thing that varies per instance of _MyValueDifferences, is the parent, so that's all that gets passed.
Simpler approaches
It seems that the goal is to have a class whose instances are tightly associated with instances of Evolution_Base, providing properties specifically named abc and xyz that are computed using the Evolution_Base's data.
That could just be hard-coded as a nested class:
class Evolution_Base:
class EBValueDifferences:
def __init__(self, parent):
self._parent = parent
#property
def abc(self):
return self._parent._get_df_change('abc')
#property
def xyz(self):
return self._parent._get_df_change('xyz')
def __init__(self, res_date_0 : DataFrame , res_date_1 : DataFrame):
#dataclass
class Results_Data():
res_date_0_df : DataFrame
res_date_1_df : DataFrame
self.res = Results_Data(res_date_0_df = res_date_0,
res_date_1_df = res_date_1)
self.difference = EBValueDifferences(self)
# _get_df_change etc. as before
Even simpler, provide corresponding properties directly on Evolution_Base:
class Evolution_Base:
#property
def abc_difference(self):
return self._get_df_change('abc')
#property
def xyz_difference(self):
return self._get_df_change('xyz')
def __init__(self, res_date_0 : DataFrame , res_date_1 : DataFrame):
#dataclass
class Results_Data():
res_date_0_df : DataFrame
res_date_1_df : DataFrame
self.res = Results_Data(res_date_0_df = res_date_0,
res_date_1_df = res_date_1)
# _get_df_change etc. as before
# client code now calls my_evolution_base.abc_difference
# instead of my_evolution_base.difference.abc
If there are a lot of such properties, they could be attached using a much simpler dynamic approach (that would still be reusable for other classes that define a _get_df_change):
def add_df_change_property(name, cls):
setattr(
cls, f'{name}_difference',
property(fget = lambda instance: instance._get_df_change(name))
)
which can also be adapted for use as a decorator:
from functools import partial
def exposes_df_change(name):
return partial(add_df_change_property, name)
#exposes_df_change('abc')
#exposes_df_change('def')
class Evolution_Base:
# `self.difference` can be removed, no other changes needed
This is quite the rabbit hole. Impossible is a big call, but I will say this: they don't intend you to do this. The 'Pythonic' way of achieving your example use case is the __getattr__ method. You could also override the __dir__ method to insert your custom attributes for discoverability.
This is the code for that:
class Value_Differences():
def __init__(self, parent : Evolution_Base, property_list = []):
self._parent = parent
self._property_list = property_list
def __dir__(self):
return sorted(set(
dir(super(Value_Differences, self)) + \
list(self.__dict__.keys()) + self._property_list))
def __getattr__(self, __name: str):
if __name in self._property_list:
return self._parent._get_df_change(__name)
But that wasn't the question, and respect for a really, really interesting question. This is one of those things that you look at and say 'hmm, should be possible' and can get almost to a solution. I initially thought what you asked for was technically possible, just very hacky to achieve. But it turns out that it would be very, very weird hackery if it was possible.
Two small foundational things to start with:
Remind ourselves of the hierarchy of Python objects that the runtime is working with when defining and instantiating classes:
The metaclass (defaulting to type), which is used to build classes. I'm going to refer to this as the Metaclass Type Object (MTO).
The class definition, which is used to build objects. I'm going to refer to this as the Class Type Object (CTO).
And the class instance or object, which I'll refer to as the Class Instance Object (CIO).
MTOs are subclasses of type. CTOs are subclasses of object. CIOs are instances of CTOs, but instantiated by MTOs.
Python runs code inside class definitions as if it was running a function:
class Class1:
print("1")
def __init__(self, v1):
print("4")
print("2")
print("3")
c1 = Class1("x")
print("5")
gives 1, 2, 3, 4, 5
Put these two things together with:
class Class1:
def attr1_get(self):
return 'attr1 value'
attr1 = property(attr1_get)
we are defining a function attr1_get as part of the class definition. We are then running an inline piece of code that creates an object of type property. Note that this is just the name of the object's type - it isn't a property as you would describe it. Just an object with some attributes, being references to various functions. We then assign that object to an attribute in the class we are defining.
In the terms I used above, once that code is run we have a CTO instantiated as an object in memory that contains an attribute attr1 of type property (an object subclass, containing a bunch of attributes itself - one of which is a reference to the function attr1_get).
That can be used to instantiate an object, the CIO.
This is where the MTO comes in. You instantiate the property object while defining the CTO so that when the runtime applies the MTO to create the CIO from the CTO, an attribute on the CIO will be formed with a custom getter function for that attribute rather than the 'standard' getter function the runtime would use. The property object means something to the type object when it is building a new object.
So when we run:
c1 = Class1()
we don't get a CIO c1 with an attribute attr1 that is an object of type property. The metaclass of type type formed a set of references against the attribute's internal state to all the functions we stored in the property object. Note that this is happening inside the runtime, and you can't call this directly from your code - you just tell the type metaclass to do it by using the property wrapper object.
So if you directly assign a property() result to an attribute of a CIO, you have a Pythonic object assigned that references some functions, but the internal state for the runtime to use to reference the getter, setter, etc. is not set up. The getter of an attribute that contains a property object is the standard getter and so returns the object instance, and not the result of the functions it wraps,
This next bit of code demonstrates how this flows:
print("Let's begin")
class MetaClass1(type):
print("Starting to define MetaClass1")
def __new__(cls, name, bases, dct):
x = super().__new__(cls, name, bases, dct)
print("Metaclass1 __new__({})".format(str(cls)))
return x
print("__new__ of MetaClass1 is defined")
def __init__(cls, name, bases, dct):
print("Metaclass1 __init__({})".format(str(cls)))
print("__init__ of MetaClass1 is defined")
print("Metaclass is defined")
class Class1(object,metaclass=MetaClass1):
print("Starting to define Class1")
def __new__(cls, *args, **kwargs):
print("Class1 __new__({})".format(str(cls)))
return super(Class1, cls).__new__(cls, *args, **kwargs)
print("__new__ of Class1 is defined")
def __init__(self):
print("Class1 __init__({})".format(str(self)))
print("__init__ of Class1 is defined")
def g1(self):
return 'attr1 value'
print("g1 of Class1 is defined")
attr1 = property(g1)
print("Class1.attr1 = ", attr1)
print("attr1 of Class1 is defined")
def addProperty(self, name, getter):
setattr(self, name, property(getter))
print("self.", name, " = ", getattr(self, name))
print("addProperty of Class1 is defined")
print("Class is defined")
c1 = Class1()
print("Instance is created")
print(c1.attr1)
def g2(cls):
return 'attr2 value'
c1.addProperty('attr2', g2)
print(c1.attr2)
I have put all those print statements there to demonstrate the order in which things happen very clearly.
In the middle, you see:
g1 of Class1 is defined
Class1.attr1 = <property object at 0x105115c10>
attr1 of Class1 is defined
We have created an object of type property and assigned it to a class attribute.
Continuing:
addProperty of Class1 is defined
Metaclass1 __new__(<class '__main__.MetaClass1'>)
Metaclass1 __init__(<class '__main__.Class1'>)
Class is defined
The metaclass got instantiated, being passed first itself (__new__) and then the class it will work on (__init__). This happened right as we stepped out of the class definition. I have only included the metaclass to show what will happen with the type metaclass by default.
Then:
Class1 __new__(<class '__main__.Class1'>)
Class1 __init__(<__main__.Class1 object at 0x105124c10>)
Instance is created
attr1 value
self. attr2 = <property object at 0x105115cb0>
<property object at 0x105115cb0>
Class1 is instantiated, providing first its type to __new__ and then its instance to __init__.
We see that attr1 is instantiated properly, but attr2 is not. That is because setattr is being called once the class instance is already constructed and is just saying attr2 is an instance of the class property and not defining attr2 as the actual runtime construct of a property.
Which is made more clear if we run:
print(c1.attr2.fget(c1))
print(c1.attr1.fget(c1))
attr2 (a property object) isn't aware of the class or instance of the containing attribute's parent. The function it wraps still needs to be given the instance to work on.
attr1 doesn't know what to do with that, because as far as it is concerned it is a string object, and has no concept of how the runtime is mapping its getter.
The fundamental reason why what you tried doesn't work is that a property, a use case of a descriptor, by design must be stored as a class variable, not as an instance attribute.
Excerpt from the documentation of descriptor:
To use the descriptor, it must be stored as a class variable in
another class:
To create a class with dynamically named properties that has access to a parent class, one elegant approach is to create the class within a method of the main class, and use setattr to create class attributes with dynamic names and property objects. A class created in the closure of a method automatically has access to the self object of the parent instance, avoiding having to manage a clunky _parent attribute like you do in your attempt:
class Evolution_Base:
def __init__(self, property_list):
self.property_list = property_list
self._difference = None
#property
def difference(self):
if not self._difference:
class Value_Differences:
pass
for name in self.property_list:
# use default value to store the value of name in each iteration
def func(obj, prop_name=name):
return self._get_df_change(prop_name) # access self via closure
setattr(Value_Differences, name, property(func))
self._difference = Value_Differences()
return self._difference
def _get_df_change(self, df_name):
return f'df change of {df_name}' # simplified return value for demo purposes
so that:
evolution = Evolution_Base(['abc', 'xyz'])
print(evolution.difference.abc)
print(evolution.difference.xyz)
would output:
df change of abc
df change of xyz
Demo: https://replit.com/#blhsing/ExtralargeNaturalCoordinate
Responding directly to your question, you can create a class:
class FooBar:
def __init__(self, props):
def make_prop(name):
return property(lambda accessor_self: self._prop_impl(name))
self.accessor = type(
'Accessor',
tuple(),
{p: make_prop(p) for p in props}
)()
def _prop_impl(self, arg):
return arg
o = FooBar(['foo', 'bar'])
assert o.accessor.foo == o._prop_impl('foo')
assert o.accessor.bar == o._prop_impl('bar')
Further, it would be beneficiary to cache created class to make equivalent objects more similar and eliminate potential issues with equality comparison.
That said, I am not sure if this is desired. There's little benefit of replacing method call syntax (o.f('a')) with property access (o.a). I believe it can be detrimental on multiple accounts: dynamic properties are confusing, harder to document, etc., finally while none of this is strictly guaranteed in crazy world of dynamic python -- they kind of communicate wrong message: that the access is cheap and does not involve computation and that perhaps you can attempt to write to it.
I think that when you define the function func in the loop, it closes over the current value of the name variable, not the value of the name variable at the time the property is accessed. To fix this, you can use a lambda function to create a closure that captures the value of name at the time the property is defined.
class Value_Differences():
def __init__(self, parent : Evolution_Base, property_list = []):
self._parent = parent
for name in property_list:
setattr(self, name, property(fget = lambda self, name=name: self._parent._get_df_change(name)))
Does this help you ?
The simple question is really, how do I setattr for a property function?
In python we can set dynamic attributes like this:
class DynamicProperties():
def __init__(self, property_list):
self.property_list = property_list
def add_properties(self):
for name in self.property_list:
setattr(self.__class__, name, property(fget=lambda self: 1))
dync = DynamicProperties(['a', 'b'])
dync.add_properties()
print(dync.a) # prints 1
print(dync.b) # prints 1
Correct me if I am wrong but from reviewing your code, you want to create a dynamic attributes then set their value to a specific function call within the same class, where the passed in data is passed in attributes in the constructor " init " this is achievable, an example:
class DynamicProperties():
def __init__(self, property_list, data1, data2):
self.property_list = property_list
self.data1 = data1
self.data2 = data2
def add_properties(self):
for name in self.property_list:
setattr(self.__class__, name, property(fget=lambda self: self.change(self.data1, self.data2) ))
def change(self, data1, data2):
return data1 - data2
dync = DynamicProperties(['a', 'b'], 1, 2)
dync.add_properties()
print(dync.a == dync.change(1, 2)) # prints true
print(dync.b == dync.change(1,2)) # prints true
You just have to add more complexity to the member, __getattr__ / __setattr__ gives you the string, so it can be interpreted as needed. The biggest "problem" doing this is that the return might no be consistent and piping it back to a library that expect an object to have a specific behavior can cause soft errors.
This example is not the same as yours, but it has the same concept, manipulate columns with members. To get a copy with changes a set is not needed, with a copy, modify and return, the new instance can be created with whatever needed.
For example, the __getattr__ in this line will:
Check and interpret the string xyz_mull_0
Validate that the members and the operand exists
Make a copy of data_a
Modify the copy and return it
var = data_a.xyz_mull_0()
This looks more complex that it actually is, with the same instance members its clear what it is doing, but the _of modifier needs a callback, this is because the __getattr__ can only have one parameter, so it needs to save the attr and return a callback to be called with the other instance that then will call back to the __getattr__ and complete the rest of the function.
import re
class FlexibleFrame:
operand_mod = {
'sub': lambda a, b: a - b,
'add': lambda a, b: a + b,
'div': lambda a, b: a / b,
'mod': lambda a, b: a % b,
'mull': lambda a, b: a * b,
}
#staticmethod
def add_operand(name, func):
if name not in FlexibleFrame.operand_mod.keys():
FlexibleFrame.operand_mod[name] = func
# This makes this class subscriptable
def __getitem__(self, item):
return self.__dict__[item]
# Uses:
# -> object.value
# -> object.member()
# -> object.<name>_<operand>_<name|int>()
# -> object.<name>_<operand>_<name|int>_<flow>()
def __getattr__(self, attr):
if re.match(r'^[a-zA-Z]+_[a-zA-Z]+_[a-zA-Z0-9]+(_of)?$', attr):
seg = attr.split('_')
var_a, operand, var_b = seg[0:3]
# If there is a _of: the second operand is from the other
# instance, the _of is removed and a callback is returned
if len(seg) == 4:
self.__attr_ref = '_'.join(seg[0:3])
return self.__getattr_of
# Checks if this was a _of attribute and resets it
if self.__back_ref is not None:
other = self.__back_ref
self.__back_ref = None
self.__attr_ref = None
else:
other = self
if var_a not in self.__dict__:
raise AttributeError(
f'No match of {var_a} in (primary) {__class__.__name__}'
)
if operand not in FlexibleFrame.operand_mod.keys():
raise AttributeError(
f'No match of operand {operand}'
)
# The return is a copy of self, if not the instance
# is getting modified making x = a.b() useless
ret = FlexibleFrame(**self.__dict__)
# Checks if the second operand is a int
if re.match(r'^\d+$', var_b) :
ref_b_num = int(var_b)
for i in range(len(self[var_a])):
ret[var_a][i] = FlexibleFrame.operand_mod[operand](
self[var_a][i], ref_b_num
)
elif var_b in other.__dict__:
for i in range(len(self[var_a])):
# out_index = operand[type](in_a_index, in_b_index)
ret[var_a][i] = FlexibleFrame.operand_mod[operand](
self[var_a][i], other[var_b][i]
)
else:
raise AttributeError(
f'No match of {var_b} in (secondary) {__class__.__name__}'
)
# This swaps the .member to a .member()
# it also adds and extra () in __getattr_of
return lambda: ret
# return ret
if attr in self.__dict__:
return self[attr]
raise AttributeError(
f'No match of {attr} in {__class__.__name__}'
)
def __getattr_of(self, other):
self.__back_ref = other
return self.__getattr__(self.__attr_ref)()
def __init__(self, **kwargs):
self.__back_ref = None
self.__attr_ref = None
#TODO: Check if data columns match in size
# if not, implement column_<name>_filler=<default>
for i in kwargs:
self.__dict__[i] = kwargs[i]
if __name__ == '__main__':
data_a = FlexibleFrame(**{
'abc': [i for i in range(10)],
'nmv': [i for i in range(10)],
'xyz': [i for i in range(10)],
})
data_b = FlexibleFrame(**{
'fee': [i + 10 for i in range(10)],
'foo': [i + 10 for i in range(10)],
})
FlexibleFrame.add_operand('set', lambda a, b: b)
var = data_a.xyz_mull_0()
var = var.abc_set_xyz()
var = var.xyz_add_fee_of(data_b)
As a extra thing, lambdas in python have this thing, so it can make difficult using them when self changes.
It seems you're bending the language to do weird things. I'd take it as a smell that your code is probably getting convoluted but I'm not saying there would never be a use-case for it so here is a minimal example of how to do it:
class Obj:
def _df_change(self, arg):
print('change', arg)
class DynAttributes(Obj):
def __getattr__(self, name):
return self._df_change(name)
class Something:
difference = DynAttributes()
a = Something()
b = Obj()
assert a.difference.hello == b._df_change('hello')
When calling setattr , use self.__class__ instead of self
Code sample:
class A:
def __init__(self,names : List[str]):
for name in names:
setattr(self.__class__,name,property(fget=self.__create_getter(name)))
def __create_getter(self,name: str):
def inner(self):
print(f"invoking {name}")
return 10
return inner
a = A(['x','y'])
print(a.x + 1)
print(a.y + 2)
Using py3, I have an object that uses the #property decorator
class O(object):
def __init__(self):
self._a = None
#property
def a(self):
return 1
accessing the attribute a via __dict__ (with _a) doesn't seem to return the property decorated value but the initialized value None
o = O()
print(o.a, o.__dict__['_a'])
>>> 1, None
Is there a generic way to make this work? I mostly need this for
def __str__(self):
return ' '.join('{}: {}'.format(key, val) for key, val in self.__dict__.items())
Of course self.__dict__["_a"] will return self._a (well actually it's the other way round - self._a will return self.__dict__["_a"] - but anyway), not self.a. The only thing the property is doing here is to automatically invoke it's getter (your a(self) function) so you don't have to type the parens, otherwise it's just a plain method call.
If you want something that works with properties too, you'll have to get those manually from dir(self.__class__) and getattr(self.__class__, name), ie:
def __str__(self):
# py2
attribs = self.__dict__.items()
# py3
# attribs = list(self.__dict__.items())
for name in dir(self.__class__):
obj = getattr(self.__class__, name)
if isinstance(obj, property):
val = obj.__get__(self, self.__class__)
attribs.append((name, val))
return ' '.join('{}: {}'.format(key, val) for key, val in attribs)
Note that this won't prevent _a to appears in attribs - if you want to avoid this you'll also have to filter out protected names from the attribs list (all protected names, since you ask for something generic):
def __str__(self):
attribs = [(k, v) for k, v in self.__dict__.items() if not k.startswith("_")]
for name in dir(self.__class__):
# a protected property is somewhat uncommon but
# let's stay consistent with plain attribs
if name.startswith("_"):
continue
obj = getattr(self.__class__, name)
if isinstance(obj, property):
val = obj.__get__(self, self.__class__)
attribs.append((name, val))
return ' '.join('{}: {}'.format(key, val) for key, val in attribs)
Also note that this won't handle other computed attributes (property is just one generic implementation of the descriptor protocol). At this point, your best bet for something that's still as generic as possible but that can be customised if needed is to implement the above as a mixin class with a couple hooks for specialization:
class PropStrMixin(object):
# add other descriptor types you want to include in the
# attribs list
_COMPUTED_ATTRIBUTES_CLASSES = [property,]
def _get_attr_list(self):
attribs = [(k, v) for k, v in self.__dict__.items() if not k.startswith("_")]
for name in dir(self.__class__):
# a protected property is somewhat uncommon but
# let's stay consistent with plain attribs
if name.startswith("_"):
continue
obj = getattr(self.__class__, name)
if isinstance(obj, *self._COMPUTED_ATTRIBUTES_CLASSES):
val = obj.__get__(self, self.__class__)
attribs.append((name, val))
return attribs
def __str__(self):
attribs = self._get_attr_list()
return ' '.join('{}: {}'.format(key, val) for key, val in attribs)
class YouClass(SomeParent, PropStrMixin):
# here you can add to _COMPUTED_ATTRIBUTES_CLASSES
_COMPUTED_ATTRIBUTES_CLASSES = PropStrMixin + [SomeCustomDescriptor])
Property is basically a "computed attribute". In general, the property's value is not stored anywhere, it is computed on demand. That's why you cannot find it in the __dict__.
#property decorator replaces the class method by a descriptor object which then calls the original method as its getter. This happens at the class level.
The lookup for o.a starts at the instance. It does not exist there, the class is checked in the next step. O.a exists and is a descriptor (because it has special methods for the descriptor protocol), so the descriptor's getter is called and the returned value is used.
(EDITED)
There is not a general way to dump the name:value pairs for the descriptors. Classes including the bases must be inspected, this part is not difficult. However retrieving the values is equivalent to a function call and may have unexpected and undesirable side-effects. For a different perspective I'd like to quote a comment by bruno desthuilliers here: "property get should not have unwanted side effects (if it does then there's an obvious design error)".
You can also update self._a as getter since the return of the getter should always reflect what self._a is stored:
class O(object):
def __init__(self):
self._a = self.a
#property
def a(self):
self._a = 1
return self._a
A bit redundant, maybe, but setting self._a = None initially is useless in this case.
In case you need a setter
This would also be compatible given remove the first line in getter:
#a.setter
def a(self, value):
self._a = value
The problem:
I have implemented a class with rather complex internal behavior which pretends to be an int type for all intents and purposes. Then, as a cherry on top, I really wanted my class to successfully pass isinstance() and issubclass() checks for int. I failed so far.
Here's a small demo class that I'm using to test the concept. I have tried inheriting it from both object and int, and while inheriting it from int makes it pass the checks, it also breaks some of it's behavior:
#class DemoClass(int):
class DemoClass(object):
_value = 0
def __init__(self, value = 0):
print 'init() called'
self._value = value
def __int__(self):
print 'int() called'
return self._value + 2
def __index__(self):
print 'index() called'
return self._value + 2
def __str__(self):
print 'str() called'
return str(self._value + 2)
def __repr__(self):
print 'repr() called'
return '%s(%d)' % (type(self).__name__, self._value)
# overrides for other magic methods skipped as irrelevant
a = DemoClass(3)
print a # uses __str__() in both cases
print int(a) # uses __int__() in both cases
print '%d' % a # __int__() is only called when inheriting from object
rng = range(10)
print rng[a] # __index__() is only called when inheriting from object
print isinstance(a, int)
print issubclass(DemoClass, int)
Essentially, inheriting from an immutable class results in an immutable class, and Python will often use base class raw value instead of my carefully-designed magic methods. Not good.
I have looked at abstract base classes, but they seem to be doing something entirely opposite: instead of making my class look like a subclass of an built-in type, they make a class pretend to be a superclass to one.
Using __new__(cls, ...) doesn't seem like a solution either. It's good if all you want is modify object starting value before actually creating it, but I want to evade the immutability curse. Attempt to use object.__new__() did not bear fruit either, as Python simply complained that it's not safe to use object.__new__ to create an int object.
Attempt to inherit my class from (int, dict) and use dict.__new__() was not very successful either as Python apparenty doesn't allow to combine them in a single class.
I suspect the solution might be found with metaclasses, but so far haven't been successful with them either, mostly because my brains simply aren't bent enough to comprehend them properly. I'm still trying but it doesn't look like I'll be getting results soon.
So, the question: is it possible at all to inherit or imitate inheritance from immutable type even though my class is very much mutable? Class inheritance structure doesn't really matter for me for as long as a solution is found (assuming it exists at all).
The problem here is not immutability, but simply inheritance. If DemoClass is a subclass of int, a true int is constructed for each object of type DemoClass and will be used directly without calling __int__ wherever a int could be used, just try a + 2.
I would rather try to simply cheat isinstance here. I would just make DemoClass subclass of object and hide the built-in isinstance behind a custom function:
class DemoClass(object):
...
def isinstance(obj, cls):
if __builtins__.isinstance(obj, DemoClass) and issubclass(int, cls):
return True
else:
return __builtins__.isinstance(obj, cls)
I can then do:
>>> a = DemoClass(3)
init() called
>>> isinstance("abc", str)
True
>>> isinstance(a, DemoClass)
True
>>> isinstance(a, int)
True
>>> issubclass(DemoClass, int)
False
So, if I understand correctly, you have:
def i_want_int(int_):
# can't read the code; it uses isinstance(int_, int)
And you want call i_want_int(DemoClass()), where DemoClass is convertible to int via __int__ method.
If you want to subclass int, instances' values are determined at creation time.
If you don't want to write conversion to int everywhere (like i_want_int(int(DemoClass()))), the simplest approach I can think about is defining wrapper for i_want_int, doing the conversion:
def i_want_something_intlike(intlike):
return i_want_int(int(intlike))
So far, no alternative solutions have been suggested, so here's the solution that I'm using in the end (loosely based on Serge Ballesta's answer):
def forge_inheritances(disguise_heir = {}, disguise_type = {}, disguise_tree = {},
isinstance = None, issubclass = None, type = None):
"""
Monkey patch isinstance(), issubclass() and type() built-in functions to create fake inheritances.
:param disguise_heir: dict of desired subclass:superclass pairs; type(subclass()) will return subclass
:param disguise_type: dict of desired subclass:superclass pairs, type(subclass()) will return superclass
:param disguise_tree: dict of desired subclass:superclass pairs, type(subclass()) will return superclass for subclass and all it's heirs
:param isinstance: optional callable parameter, if provided it will be used instead of __builtins__.isinstance as Python real isinstance() function.
:param issubclass: optional callable parameter, if provided it will be used instead of __builtins__.issubclass as Python real issubclass() function.
:param type: optional callable parameter, if provided it will be used instead of __builtins__.type as Python real type() function.
"""
if not(disguise_heir or disguise_type or disguise_tree): return
import __builtin__
from itertools import chain
python_isinstance = __builtin__.isinstance if isinstance is None else isinstance
python_issubclass = __builtin__.issubclass if issubclass is None else issubclass
python_type = __builtin__.type if type is None else type
def disguised_isinstance(obj, cls, honest = False):
if cls == disguised_type: cls = python_type
if honest:
if python_isinstance.__name__ == 'disguised_isinstance':
return python_isinstance(obj, cls, True)
return python_isinstance(obj, cls)
if python_type(cls) == tuple:
return any(map(lambda subcls: disguised_isinstance(obj, subcls), cls))
for subclass, superclass in chain(disguise_heir.iteritems(),
disguise_type.iteritems(),
disguise_tree.iteritems()):
if python_isinstance(obj, subclass) and python_issubclass(superclass, cls):
return True
return python_isinstance(obj, cls)
__builtin__.isinstance = disguised_isinstance
def disguised_issubclass(qcls, cls, honest = False):
if cls == disguised_type: cls = python_type
if honest:
if python_issubclass.__name__ == 'disguised_issubclass':
return python_issubclass(qcls, cls, True)
return python_issubclass(qcls, cls)
if python_type(cls) == tuple:
return any(map(lambda subcls: disguised_issubclass(qcls, subcls), cls))
for subclass, superclass in chain(disguise_heir.iteritems(),
disguise_type.iteritems(),
disguise_tree.iteritems()):
if python_issubclass(qcls, subclass) and python_issubclass(superclass, cls):
return True
return python_issubclass(qcls, cls)
__builtin__.issubclass = disguised_issubclass
if not(disguise_type or disguise_tree): return # No need to patch type() if these are empty
def disguised_type(obj, honest = False, extra = None):
if (extra is not None):
# this is a call to create a type instance, we must not touch it
return python_type(obj, honest, extra)
if honest:
if python_type.__name__ == 'disguised_type':
return python_type(obj, True)
return python_type(obj)
for subclass, superclass in disguise_type.iteritems():
if obj == subclass:
return superclass
for subclass, superclass in disguise_tree.iteritems():
if python_isinstance(obj, subclass):
return superclass
return python_type(obj)
__builtin__.type = disguised_type
if __name__ == '__main__':
class A(object): pass
class B(object): pass
class C(object): pass
forge_inheritances(disguise_type = { C: B, B: A })
print issubclass(B, A) # prints True
print issubclass(C, B) # prints True
print issubclass(C, A) # prints False - cannot link two fake inheritances without stacking
It is possible to ignore the faked inheritance by providing optional honest parameter to isinstance(), issubclass() and type() calls.
Usage examples.
Make class B a fake heir of class A:
class A(object): pass
class B(object): pass
forge_inheritances(disguise_heir = { B: A })
b = B()
print isinstance(b, A) # prints True
print isinstance(b, A, honest = True) # prints False
Make class B pretend to be class A:
class A(object): pass
class B(object): pass
forge_inheritances(disguise_type = { B: A})
b = B()
print type(b) # prints "<class '__main__.A'>"
print type(b, honest = True) # prints "<class '__main__.B'>"
Make class B and all it's heirs pretend to be class A:
class A(object): pass
class B(object): pass
class D(B): pass
forge_inheritances(disguise_tree = { B: A})
d = D()
print type(d) # prints "<class '__main__.A'>"
Multiple layers of fake inheritances can be achieved by stacking calls to forge_inheritances():
class A(object): pass
class B(object): pass
class C(object): pass
forge_inheritance(disguise_heir = { B: A})
forge_inheritance(disguise_heir = { C: B})
c = C()
print isinstance(c, A) # prints True
Obviously, this hack will not affect super() calls and attribute/method inheritance in any way, the primary intent here is just to cheat isinstance() and type(inst) == class checks in a situation when you have no way to fix them directly.