Minor utilites for developing pure virtual classes.
Project description
purepy
Pure virtual class functionality in Python.
A very small metaclass to do some of the testing for us.
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master |
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dev |
The What
In C++ and other strong typed, OOP, languages, we use virtual classes and pure virtual classes to help handle some incredibly cool paradigms when it comes to plugin design, object organization and more.
Python thinks of everything as a virtual class. Which is great because polymorphism doesn't require us to explicitly set which functions are virtual or overloaded but instead just works!
This is awesome until it's not.
The Why
So, with this knowledge, you ask, "Why bother with pure virtual classes? There are plenty of reasons not to use this in Python." You would be right! There's plenty of reasons not to use/need this tool.
But, when the need arises, you may just find this quite helpful. For us, we found it most useful when we were integrating an API into multiple third party applications and wanted to assure ourselves we had the right functionality and signatures without needing to write additional test code or wait for the interpreter to make an instance of an ABCMeta object for it to fail.
The Advantage
We first took a stab with the abc.ABCMeta
object from Pythons default libs but ran into the issue of
I can do whatever I want and until the object is made, it will be wrong!
Which is good sometimes, because it allows for crazy stuff like setattr()
and dynamic class building
but, when it comes to integration of an app, there's usually less desire for out-there solutions like
__setitem__
or setattr()
.
We want the interpreter, as soon as it loads our class into memory, to alert us if it's not "up to code" and tell us what we need to fix about it. This is very "preprocessor" like and it has some major advantages with a few caveats.
Basic Example
Given the following:
from purepy import PureVirtualMeta, pure_virtual
class Interface(metaclass=PureVirtualMeta):
@pure_virtual
def save(self, filepath=None):
raise NotImplementedError()
@pure_virtual
def load(self, filepath=None):
raise NotImplementedError()
class Overload(Interface):
def save(self, filepath=None):
print ("Saving")
If we put this into the interpreter, without even creating an instance of the Overload class, we would get:
# ...
# PureVirtualError: Virtual Class Declaration:
# - 'Overload': The following pure virtual functions must be overloaded from
# base: 'Interface' before class can be used:
# - def load(self, filepath=None)
We got that error without having to execute any manual code or writing a test. This may not be the way you want to work, at which point you don't need this utility!
Additional Features
To act like a proper pure virtual class, PureVirtualMeta
and the default pure_virtual
utilities are
extremely strict when it comes to working with the classes. There are a wide variety of ways to augment
this however as described below.
Signature Verification
By default purepy
will assert that the signatures of the pure_virtual
function match the
overloaded.
class Interface(metaclass=PureVirtualMeta):
@pure_virtual
def save(self, filepath=None):
raise NotImplementedError()
class Overload(Interface):
def save(self):
print ("Saving")
# Result:
# ...
# PureVirtualError: Virtual Class Declaration:
# - 'Overload': The following overload functions have the
# wrong signature from base: 'Interface'
# - def save(self): -> def save(self, filepath=None):
This can be disabled by setting the class variable pv_explicit_args = False
class Interface(metaclass=PureVirtualMeta):
pv_explicit_args = False
# ...
Base Instances
By default purepy
will mimic the abc.abstractmethod
and raise and error when we try to
instantiate a pure virtual class.
class Interface(metaclass=PureVirtualMeta):
@pure_virtual
def save(self, filepath=None):
raise NotImplementedError()
>>> Interface()
# ...
# PureVirtualError: Cannot instantiate pure virtual class
# 'Interface' with pure virtual functions: (save)
This can be disabled with the class variable pv_allow_base_instance = True
class Interface(metaclass=PureVirtualMeta):
pv_allow_base_instance = True
@pure_virtual
def save(self, filepath=None):
raise NotImplementedError()
>>> print(Interface())
# <__main__.Interface object at ...>
Forced NotImplementedError
By default, the pure_virtual
decorator will force all it's functions to raise a NotImplementedError
even
when there is information defined and the class can be instantiated.
class Interface(metaclass=PureVirtualMeta):
pv_allow_base_instance = True
@pure_virtual
def save(self, filepath=None):
print ("Saving ", str(filepath))
>>> inst = Interface()
>>> inst.save("foo")
# ...
# NotImplementedError: Illegal call to pure virtual function save
This can be disabled with a custom decorator by setting force_not_implemented = False
.
my_pure_virtual = PureVirtualMeta.new(force_not_implemented=False)
class Interface(metaclass=PureVirtualMeta):
pv_allow_base_instance = True
@my_pure_virtual
def save(self, filepath=None):
print ("Saving ", filepath)
>>> inst = Interface()
>>> inst.save("foo")
# Saving foo
Customized Decorator
By default, the pure_virtual
decorator provided is quite strict. In some cases you may want to
augment the properties to make it more forgiving. This can be done with the PureVirtualMeta.new()
and PureVirtualMeta.new_class()
functions. Both functions take additional **kwargs
that augment the
decorator and subsequent validation.
my_pure_virtual = PureVirtualMeta.new(strict_types=False)
class Interface(metaclass=PureVirtualMeta):
@my_pure_virtual
def foo(self, filepath: str):
raise NotImplementedError()
class Overload(Interface):
# This is NOT okay by default, but okay with our custom decorator
def foo(self, filepath):
pass
Registry
There are two ways to control/retrieve the pure virtual functions available in the api.
From Id
Each pure_virtual
decorator gets a unique identifier and all functions it its registry are handled
underneath that.
class Interface(metaclass=PureVirtualMeta):
@pure_virtual
def foo(self, filepath):
raise NotImplementedError()
print (PureVirtualMeta.virtual_functions_from_id(pure_virtual.id()))
# [<function Interface.save at ...>]
From Class
Each class registers the pure virtual functions and can be polled by both the class and an instance of said class.
class Interface(metaclass=PureVirtualMeta):
pv_allow_base_instance = True
@pure_virtual
def foo(self, filepath):
raise NotImplementedError()
print (PureVirtualMeta.pure_virtual_functions(Interface))
# [<function Interface.save at ...>]
print (PureVirtualMeta.pure_virtual_functions(Interface()))
# [<function Interface.save at ...>]
print (PureVirtualMeta.is_pure_virtual_class(Interface))
# True
Override Decorator
For clarity, we may want to decorate the overloaded functions. In C++ we use something like:
void myFunction(int variable) override;
purepy
provides the override
decorator this this purpose.
from purepy import override
class Overload(Interface):
@override()
def foo(self, filepath):
print ("This is overloaded")
Note: You must call the override decorator, even with no arguments, to setup the proper function binding.
In the future, this may to be used to further augment the functionality of overloaded functions.
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