purepy 1.0.0

Minor utilites for developing pure virtual classes.

purepy

Pure virtual class functionality in Python.

A very small metaclass to do some of the testing for us.

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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.