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Python Metaprogramming - Properties on Steroids

Metaprogramming is an advanced topic, it requires a good understanding of the language itself. I assume you already know Python well enough even though I include references to complementary resources. If you are interested only in the final result, there is a link to the code at the end of the article.

Python is a very dynamic language, everything is an object, objects are created by classes (usually) but classes can also be created by otherclases or functions (this is amazing). Objects, once created, can be modified (add/remove/replace properties, methods, attributes, etc…) and thismeans that you can do a lot of metaprogramming in python. Metaprogramming is a key that can open the doors to heaven or hell.

Ok but what is metaprogramming? here is a definition from wikipedia:

Metaprogramming is a programming technique in which computer programs have the ability to treat other programs as their data. It means that a program can be designed to read, generate, analyze or transform other programs, and even modify itself while running.
-- Harald Sondergaard. 'Course on Program Analysis and Transformation'. Retrieved 18 September 2014.
-- Czarnecki, Krzysztof; Eisenecker, Ulrich W. (2000). Generative Programming. ISBN 0-201-30977-7.

In this article, I will use metaprogramming to change how properties are defined in a class, how they can be documented, initialized, how to set a default value, how to make them read-only and observable, and as a bonus, I will improve memory usage of the objects created by the class. And as a second bonus, I will seal the object against attribute injections. I call this “Properties on Steroids”.

Requirements

A property must be defined in a succinct and pythonic way.
A property definition can setup a default value.
A property must support docstring (__doc__).
A Property can have a type hint.
A Property can be read-only or read-and-write.
A Property can be observable.
A Property must not use more memory than traditional python property (@property)
Bonus: Field storage can be optimized
Bonus2: Objects created from the class must be protected from field injection.
DO NOT USE A METACLASS

Important concepts used in the solution

Classes are created at runtime

In python, classes are created at runtime, so the code inside the class scope can modify the resulting class by adding/removing/replacing things in the class scope (local).

For more info about classes in Python: https://docs.python.org/3/tutorial/classes.html

Scopes

There are two scopes in python: local and global. Scopes are symbol tables of what is reachable from the current point in code. you can access the symbol table using the builtin functions locals() and globals(). The important part here is that you can modify the scope just adding/replacing/removing things in the symbol table.

For example, if you want to define a variable in the local scope dynamically:

For more info about scopes in python: https://realpython.com/python-scope-legb-rule/

Decorators

In python, functions are objects and can be passed to other functions like any other object. The idea of a decorator is a function that receives another function and returns a new function based on the original. There is a special syntax in python to call a decorator function just on function definition and effectively replacing the original function with the one returned by the decorator.

Example:

The above code will print:

For more info about decorators: https://realpython.com/primer-on-python-decorators/

Properties

Python has a special class called properties, it allows us to create getter/setter/deleter for a field. In combination with decorators you can define functional properties.

We will change this pattern to add more features like observability, and automatic usage of slots.

For more info about properties: https://docs.python.org/3/howto/descriptor.html#properties

Slots

Objects in Python do store attributes in an internal dictionary called __dict__, it allows dynamic creation of attributes in any object but uses additional memory for the dict object itself. If you want that your object do not support dynamic attribute creation, you can remove the __dict__ mechanism and use object slots with the additional benefit of memory savings.

I will not explain slots here, but you can find detailed info in the following resources:

Context Managers

Context managers are objects that can execute code at the beginning and at the end of a code block. they are used with the with statement.

For more info about context managers: https://docs.python.org/3/library/contextlib.html

Proposed Solution

Ok, now with all the tools in the bag, we can create our own monster.

The final goal:

Result:

The traditional equivalent code:

The differences

initially it appears to have no major advantajes, but the classes Car and CarTraditional and the objects mycar and mycar2 are very different.

Field injection

With Metaprog:

With traditional code:

Default values

With Metaprog:

Default values are specified at property definition.

With traditional code:

Default values are defined by assignment in constructor

Observability

With Metaprog:

Multiple attributes can be observed with the same listener, listener specified on property definition, listener is called only if new value is different from current.

With traditional code:You must implement observability by your own.

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Read-Only / Read-Write

With Metaprog:

One single definition will create readonly or read-write property.

With traditional code:

You must define a getter and a setter if the property is writable.

Constructor arguments to field assigment

With Metaprog:

Constructor arguments can set defined properties automatically.

Oklahoma gambling age laws. With traditional code:

You must set properties by hand.

Implementation

Ok, in a small sample class like Car there is no much advantage but in a large system with many classes and classes with many mutable and “immutable” attributes and complex state changes it will add lot of productivity in a very pythonic way.

Lets see the magic:

API

`self_properties(self, scope, exclude=(), save_args=False)`

This utility function copy all the symbols in scope to self as properties. If you call it at the beginning of the constructor and pass the local scope, it will just copy the function arguments.

if you want to exlude something from the copy, just do this:

if you want to save all arguments as a tuple (additionally):

`properties`

This is where the magic happens. properties is a context manager that do the following:

Define a prop decorator to create properties.
Manage __slots__ automatically for the class.
Clean itself from the class scope.

`@meta.prop`

prop is the decorator, it transforms functions