This library leverages the most advanced Python features including the descriptor protocol and support for customizable metaclasses. The documentation below explains these features and how WorkToy leverages them to provide powerful and flexible tools for Python developers.

• Installation
• Usage
• worktoy.desc
  • Background - The Python Descriptor Protocol
    • The __set_name__method
    • The __get__ method
    • The __set__ method
    • The delete method
    • Descriptor Protocol Implementations
  • The property class
    • The AbstractDescriptor class
    • The Field class
    • The AttriBox class
    • THIS - Advanced AttriBox Usage
    • Using AttriBox in PySide6 - Qt for Python
    • worktoy.desc Conclusion

• The Python metaclass - worktoy.meta
  • Introduction - Python is the Best
  • Background - The Python Metaclass
  • Everything is an object!
  • Extensions of object
  • The Python Function
  • The * and ** operators
  • The Python lambda Function
  • Class Instantiations
  • The Custom Class
  • The Custom Metaclass
  • The Custom Namespace
  • The Python Metaclass - Conclusion
• The worktoy.meta Module
  • Nomenclature
  • Metaclass and Namespace Pattern
  • Function Overloading
  • Singleton
  • Summary
• The worktoy.keenum module
• The worktoy.ezdata module
  • Summary of worktoy.ezdata module
• The worktoy.text module
  • worktoy.text.stringList
  • worktoy.text.monoSpace
  • worktoy.text.wordWrap
  • worktoy.text.typeMsg
  • worktoy.text.joinwords
  • worktoy.parse module

The stable version of WorkToy may be installed using the following command:

pip install worktoy

The development version, which is not for the faint of heart, may be installed by passing the --pre flag:

pip install worktoy --pre

The descriptor protocol in Python allows significant customisation of the attribute access mechanism. To illustrate, let us implement a descriptor class Integer which wraps integer values. Such a class is intended to be instantiated in the class bodies of other classes.

This discussion will now continue in the docstrings found in the implementation of this class.

#  AGPL-3.0 license
#  Copyright (c) 2024 Asger Jon Vistisen
from __future__ import annotations

from typing import Any


class Integer:
  """Let us continue this discussion in the docstrings of the 'Integer'
    class.
    
    This descriptor class defines the following instance attributes:
    - __fallback_value__: If instantiated without a default value, this 
      value is used.
    - __default_value__: The default value of the descriptor instance.
    - __field_name__: The name by which the descriptor instance appears in 
      the class body.
    - __field_owner__: The class owning the descriptor instance.
    - __pvt_name__: The name of the private variable used to store the 
      value of the attribute.
    
    It also defines the following methods which each provides a docstring
    explaining their purpose and function:
    - __init__
    - __set_name__
    - __get__
    - __set__
    - __delete__  # DO NOT MISTAKE WITH __del__! 
    """

  __fallback_value__ = 0
  __default_value__ = None
  __field_name__ = None
  __field_owner__ = None

  def __init__(self, *args) -> None:
    """This constructor method accepts any number of positional arguments.
     It then implements the under used and underappreciated 'for-loop'
     ending with an 'else' clause. The code in the 'else' block runs 
     after the loop completes. The point is that the 'break' keyword also 
     applies to this block. So if 'break' is encountered, the code in the 
     'else' block does not run. To translate to natural language: 
     
     'Go through each positional argument and when you find an integer, 
     assign it to the default value. If you have not found any integer 
     after looking through each positional argument, use the fallback 
     value.'  
     
     It is the opinion of this author that the 'else' clause in a loop is
     underused and underappreciated."""
    for arg in args:
      if isinstance(arg, int):
        self.__default_value__ = arg
        break
    else:
      self.__default_value__ = self.__fallback_value__

  def __set_name__(self, owner: type, name: str) -> None:
    """This is the method that elevates the power of the descriptor 
    protocol beyond the mundane getter and setter pattern! This feature 
    was added in Python 3.6 released on December 23, 2016. Does this make it
    a recent feature? Well, Minecraft Java 1.11 had been released on 
    November 14, 2016, meaning that this feature is about the same age as 
    totems of undying, shulker boxes and the observer block.
     
    This method is invoked when the class owning the descriptor is created. 
    It informs the instance of the descriptor of its owner and the name 
    by which it appears in the class body of the owner. This means that the 
    descriptor instance is aware of its own name in the namespace of its 
    owner.  
    
    """
    self.__field_name__ = name
    self.__field_owner__ = owner
    self.__pvt_name__ = '__%s_value__' % (name,)

  def __get__(self, instance: object, owner: type) -> Any:
    """This method is called when the descriptor instance is accessed. It 
    returns the value of the attribute. If the descriptor instance is 
    accessed through the owning class, the descriptor instance itself is 
    returned. For example:
    
    class Owner:
      num = Integer(69)
    
    if __name__ == '__main__':
      owner = Owner()
      print(type(owner.num))  # <class 'int'>, the wrapped value instance
      print(type(Owner.num))  # <class 'Integer'>, the descriptor instance
    
    'owner.num' results in the following call:
    '__get__(owner, Owner)'
    
    'Owner.num' results in the following call:
    '__get__(None, Owner)'
    
    By making the above distinction, the descriptor instance object may 
    be accessed by going through the owning class. This is the most 
    common and highly recommended pattern. However, it also means that 
    ambiguity exists for the type-hint: When accessing through the 
    instance the hint should be 'int', but when accessing through the 
    class, the hint should be 'type'. For this reason, the 'Any' type is
    used here. 
    """
    if instance is None:
      return self
    if getattr(instance, self.__pvt_name__, None) is None:
      return self.__default_value__
    return getattr(instance, self.__pvt_name__)

  def __set__(self, instance: object, value: object) -> None:
    """This method is called when the attribute at the field name of the 
    descriptor instance is attempted to be set on the instance of the 
    owning class. Unlike the '__get__' method defined above, this method 
    is invoked only when the attribute is set on the instance. If set on 
    the owner, the descriptor instance itself is overwritten. This is 
    consistent with the pattern that access through the owning class 
    refers to the descriptor instance, whilst access through the owning 
    instance is managed by the descriptor class. For example:
    
    class Owner:
      num = Integer(420)
      
    if __name__ == '__main__':
      owner = Owner()
      print(owner.num)  # 420
      owner.num = 69 
      print(owner.num)  # 69
      
      print(Owner.num)  # <Integer object at 0x1EE7B00B5>
      print(type(Owner.num))  # <class 'Integer'>
      Owner.num = 69
      print(Owner.num)  # 69
      print(type(Owner.num))  # <class 'int'>
      
      owner.num = 69  # This results in the following call:
      '__set__(owner, 69)'
      
      Owner.num = 69  # This results in the following call:
      'type(Owner).__setattr__(Owner, 'num', 69)'
    """
    setattr(instance, self.__pvt_name__, value)

  def __delete__(self, instance: object) -> None:
    """This method is called when the attribute is attempted to be 
    deleted. DO NOT MISTAKE WITH '__del__'! The '__del__' method is 
    called when the instance is destroyed. Both of these are outside the 
    scope of this discussion."""
    delattr(instance, self.__pvt_name__)

In summary, the Integer class defined above provides integer valued attributes to other classes. The accessor functions implements only trivial functionality here, but serves to illustrate the possibilities for customization.

Python does provide a built-in class for creating descriptors: the property class. This class allows the use of a decorator to define getter, setter and deleter functions for an attribute. Alternatively, the property may be instantiated in the class body with the getter, setter and deleter functions as arguments. The following class has attributes name and number both instances of property implemented with the decorator and the constructor respectively.

#  AGPL-3.0 license
#  Copyright (c) 2024 Asger Jon Vistisen
from __future__ import annotations


class OwningClass:
  """This class uses 'property' to implement the 'name' and 'number' 
  attributes. """

  __fallback_number__ = 0
  __fallback_name__ = 'Unnamed'
  __inner_number__ = None
  __inner_name__ = None

  def __init__(self, *args, **kwargs) -> None:
    self.__inner_number__ = kwargs.get('number', None)
    self.__inner_name__ = kwargs.get('name', None)
    for arg in args:
      if isinstance(arg, int) and self.__inner_number__ is None:
        self.__inner_number__ = arg
      elif isinstance(arg, str) and self.__inner_name__ is None:
        self.__inner_name__ = arg

  @property
  def name(self) -> str:
    """Name property"""
    if self.__inner_name__ is None:
      return self.__fallback_name__
    return self.__inner_name__

  @name.setter
  def name(self, value: str) -> None:
    """Name setter"""
    self.__inner_name__ = value

  @name.deleter
  def name(self) -> None:
    """Name deleter"""
    del self.__inner_name__

  def _getNumber(self, ) -> int:
    """Number getter"""
    if self.__inner_number__ is None:
      return self.__fallback_number__
    return self.__inner_number__

  def _setNumber(self, value: int) -> None:
    """Number setter"""
    self.__inner_number__ = value

  def _delNumber(self) -> None:
    """Number deleter"""
    del self.__inner_number__

  number = property(_getNumber, _setNumber, _delNumber, doc='Number')

Before proceeding further, let us briefly discuss the @decorator syntax.

When using the keywords: def, async def and class, we begin a compound statement that creates a new object at the name following the keyword. When such a compound statement is 'decorated' it means that the created object is passed to the decorating function upon creation and the object returned by the decorator is assigned to the name instead.

A decorator could return an object wrapping the decorated object to augment its behaviour. Alternatively, it could record the decorated function for a particular purpose and return the object exactly as received. Or even a combination of the two.

Let us now examine a few decorators beginning with a simple notifier:

#  AGPL-3.0 license
#  Copyright (c) 2024 Asger Jon Vistisen
from __future__ import annotations

from typing import Callable


def notify(callMeMaybe: Callable) -> Callable:
  """This decorator notifies the creation of the received object by 
  printing its name. """
  print(callMeMaybe.__name__)
  return callMeMaybe


@notify
class SomeClass:
  """This is just some class. """

  @notify
  def someMethod(self, ) -> None:
    """This is just some method. """

    @notify
    def nestedMethod() -> None:
      """It even has a nested method!"""


if __name__ == '__main__':
  print('Entry point!')
  someInstance = SomeClass()

The above code will output the following:

someMethod
SomeClass
Entry point!

The above may surprise some readers anticipating the output to begin with the 'Entry point!' message. However, the class body is executed immediately even before the if __name__ == '__main__': block is entered. During this execution, the someMethod function is created and passed to the notify decorator. Upon completion of the class body execution, the newly created class object is passed to the notify decorator. Both happen prior to the normal entry point. But what about the nested method? The someMethod creates it when called, not when created. In the example, the class body creates the someMethod, but nobody actually invokes it. Thus, the nested method remains uncreated.

Having introduced the Python descriptor protocol and the property class, we shall now introduce the Field class provided by the worktoy.desc module. This class aims at providing the same functionality as the property class. Class owning instances of Field can heavily customize the attribute access mechanism at a particular name. In fact, the Field descriptor provides no actual functionality itself, but simply allows the owning class to define each of the accessor functions. If multiple classes are to implement multiple attributes with similar behaviour using the Field class, it will introduce significant boilerplate code. To implement general attribute behaviour requires a different approach. Foreshadowing...

The following is a truncated version of the Field class provided by the worktoy.desc module. This documentation will continue in the docstrings below.

#  AGPL-3.0 license
#  Copyright (c) 2024 Asger Jon Vistisen
from __future__ import annotations

from typing import Callable, Never


class Field:
  """Welcome back to the docstrings, where our discussion continues! 
  Please note that the 'Field' class found in 'worktoy.desc' is 
  significantly more complicated than the code below, which simply 
  illustrates the concept. """

  #  Accessor functions for the accessors of the attribute. 
  __getter_function__ = None
  __setter_function__ = None
  __deleter_function__ = None

  def _getGetter(self, ) -> Callable:
    """This method returns the getter function. """
    return self.__getter_function__

  def _setGetter(self, value: Callable) -> None:
    """This method sets the getter function. """
    self.__getter_function__ = value

  def _getSetter(self, ) -> Callable:
    """This method returns the setter function. """
    return self.__setter_function__

  def _setSetter(self, value: Callable) -> None:
    """This method sets the setter function. """
    self.__setter_function__ = value

  def _getDeleter(self, ) -> Callable:
    """This method returns the deleter function. """
    return self.__deleter_function__

  def _setDeleter(self, value: Callable) -> None:
    """This method sets the deleter function. """
    self.__deleter_function__ = value

  #  So much boilerplate code!

  __field_name__ = None
  __field_owner__ = None

  def __set_name__(self, owner: type, name: str) -> None:
    """This is the same as in the previous 'Integer' class example."""
    self.__field_name__ = name
    self.__field_owner__ = owner

  def _getPrivateName(self) -> str:
    """Parses a private name from the field name. """
    return '__%s_value__' % (self.__field_name__,)

  #  We are finally ready to implement the descriptor protocol!

  def __get__(self, instance: object, owner: type) -> object:
    """This method is called when the descriptor instance is accessed. """
    if instance is None:
      return self
    pvtName = self._getPrivateName()
    getFunc = self._getGetter()  # Existence check omitted
    return getFunc(instance)

  def __set__(self, instance: object, value: object) -> None:
    """This method is called when the attribute at the field name of the 
    descriptor instance is attempted to be set on the instance of the 
    owning class. """
    pvtName = self._getPrivateName()
    setFunc = self._getSetter()  # Existence check omitted
    setFunc(instance, value)

  def __delete__(self, instance: object) -> Never:
    """Outside the scope of this discussion."""
    e = """This example does not implement attribute deletion!"""
    raise TypeError(e)

  #  Public names for the accessor decorators:

  def GET(self, func: Callable) -> Callable:
    """This decorator marks the function as the getter function. """
    self._setGetter(func)
    return func

  def SET(self, func: Callable) -> Callable:
    """This decorator marks the function as the setter function. """
    self._setSetter(func)
    return func

  def DEL(self, func: Callable) -> Callable:
    """This decorator marks the function as the deleter function. """
    self._setDeleter(func)
    return func

To illustrate the use of the Field class, we will implement a class encapsulation of a complex number having the real and imaginary parts as separate attributes. The example will illustrate decorators, the Field class and the dangers of boilerplate code.

#  AGPL-3.0 license
#  Copyright (c) 2024 Asger Jon Vistisen
from __future__ import annotations

from worktoy.desc import Field
from worktoy.parse import maybe  # None-aware filter


#  The 'maybe' function provides None-awareness. It is similar to the 
#  null-coalescing operator in javascript.
#  maybe(a, b) returns 'a' if 'a' is not None, otherwise it returns 'b'.
#  Same as: a ?? b in javascript.
#  Likely, the only redeeming feature of javascript.


class Complex:
  """More boilerplate??"""

  __real_fallback__ = 0.
  __imag_fallback__ = 0.
  __real_part__ = None
  __imag_part__ = None

  RE = Field()
  IM = Field()

  @RE.GET
  def _getReal(self) -> float:
    """Getter function for the real part."""
    return maybe(self.__real_part__, self.__real_fallback__)

  @RE.SET
  def _setReal(self, value: float) -> None:
    """Setter function for the real part."""
    self.__real_part__ = value

  @IM.GET
  def _getImag(self) -> float:
    """Getter function for the imaginary part."""
    return maybe(self.__imag_part__, self.__imag_fallback__)

  @IM.SET
  def _setImag(self, value: float) -> None:
    """Setter function for the imaginary part."""
    self.__imag_part__ = value

  def __init__(self, *args) -> None:
    x, y = None, None

    for arg in args:
      if isinstance(arg, int):
        arg = float(arg)
      if isinstance(arg, float) and x is None:
        x = arg
      elif isinstance(arg, float) and y is None:
        y = arg
        break
      elif isinstance(arg, complex):
        x, y = arg.real, arg.imag
        break
    else:
      x, y = 69., 420.
    self.RE, self.IM = x, y

  def __add__(self, other: object) -> Complex:
    """This method is left as an exercise to the reader along with: 
     __sub__
     __mul__
     __truediv__
     __pow__
     __abs__
     Any 'try-hard' reader may also implement the 'i' and 'r' versions."""

The Complex class leverages the Field implementation of the descriptor protocol to provide attributes for the real and imaginary parts of a complex number. Below is usage example:

#  AGPL-3.0 license
#  Copyright (c) 2024 Asger Jon Vistisen
from __future__ import annotations

if __name__ == '__main__':
  z1 = Complex(69, 420)
  z2 = Complex(1, 2)
  z2.RE = 3
  z2.IM = 4
  z3 = z1 + z2
  print(z3.RE, z3.IM)
  z4 = 1337 * z1  # Hopefully, a try-hard reader implemented this!
  print(z4.RE, z4.IM)

The above code will output the following:

70.0 422.0
93003.0 56180.0

In summary, both the Field and property classes provide a way to customize the attribute access mechanism for each class. This comes at the cost of significant boilerplate code for attributes that are frequently behaving in the same way. In fact, most attributes might reasonably be expected to behave as the Field class does. The worktoy.desc module provides a class for this exact purpose: the AttriBox class.

Where Field relies on the owning class itself to specify the accessor functions, the AttriBox class provides an attribute of a specified class. This class is not instantiated until an instance of the owning class calls the __get__ method. Only then will the inner object of the specified class be created. The inner object is then placed on a private variable belonging to the owning instance. When the __get__ is next called the inner object at the private variable is returned. When instantiating the AttriBox class, the following syntactic sugar should be used: fieldName = AttriBox[FieldClass](*args, **kwargs). The arguments placed in the parentheses after the brackets are those used to instantiate the FieldClass given in the brackets.

Below is an example of a class using the AttriBox class to implement a Circle class. It uses the Point class defined above to manage the center of the circle. Notice how the Point class itself is wrapped in an AttriBox instance. The area attribute is defined using the Field class and illustrates the use of the Field class to expose a value as an attribute.

#  AGPL-3.0 license
#  Copyright (c) 2024 Asger Jon Vistisen
from __future__ import annotations

from worktoy.desc import AttriBox, Field

pi = 3.1415926535897932


class Point:
  """This class provides a point in 2D space. """
  x = AttriBox[float](0)
  y = AttriBox[float](0)

  def __init__(self, *args) -> None:
    for arg in args:
      if isinstance(arg, int):
        arg = float(arg)
      if isinstance(arg, float) and self.x is None:
        self.x = arg
      elif isinstance(arg, float) and self.y is None:
        self.y = arg
        break
    else:
      self.x, self.y = 0, 0


class Circle:
  """This class uses the 'AttriBox' descriptor to manage the radius and
  center, and it also illustrates a use case for the 'Field' class."""

  radius = AttriBox[float](0)
  center = AttriBox[Point](0, 0)
  area = Field()

  @area.GET
  def _getArea(self) -> float:
    return pi * self.radius ** 2

  def __str__(self) -> str:
    msg = """Circle centered at: (%.3f, %.3f), with radius: %.3f"""
    return msg % (self.center.x, self.center.y, self.radius)

  def __init__(self, *args) -> None:
    """This constructor is left as an exercise to the reader."""


if __name__ == '__main__':
  circle = Circle(69, 420, 1337)
  print(circle)
  circle.radius = 80085
  print(circle)

Running the code above will output the following:

Circle centered at: (69.000, 420.000), with radius: 4.000
Circle centered at: (69.000, 420.000), with radius: 1.000

So far the AttriBox instantiation has used the following syntax:

"""Basic instantiation of the 'AttriBox' class."""
#  AGPL-3.0 license
#  Copyright (c) 2024 Asger Jon Vistisen
from __future__ import annotations

from worktoy.desc import AttriBox


class Owner:
  """Basic instantiation of the 'AttriBox' class."""

  floatBox = AttriBox[float](69.)
  intBox = AttriBox[int](420)

In the above example, the AttriBox instantiates before the owning class is even created. However, suppose the boxed class require the owning instance to be passed to the constructor. This presents a challenge as the AttriBox instance exists before the owning class event exists. Enter the THIS object!

TL;DR

"""Advanced instantiation of the 'AttriBox' class."""
#  AGPL-3.0 license
#  Copyright (c) 2024 Asger Jon Vistisen
from __future__ import annotations

from worktoy.desc import AttriBox, THIS


class WhoDat:
  """Boxed class aware of its owning instance."""

  __the_boss__ = None

  def __init__(self, who: object) -> None:
    self.__the_boss__ = who

  def getBoss(self) -> object:
    return self.__the_boss__

  def __str__(self) -> str:
    return 'Mah boss: %s' % (self.getBoss(),)


class Boss:
  """Owning class."""

  whoDat = AttriBox[WhoDat](THIS)
  name = AttriBox[str]()

  def __init__(self, name: str) -> None:
    self.name = name

  def __str__(self) -> str:
    return 'Mr. %s' % (self.name,)


if __name__ == '__main__':
  boss = Boss('Guido')
  print(boss.whoDat)
  print(boss.whoDat.getBoss() is boss)

The above produces:

Mah boss: Mr. Guido
True

When the AttriBox.__get__ is called on the whoData attribute, the WhoDat class instantiates, but the AttriBox instance replaces THIS with the owning instance. This allows the WhoDat instance to be aware of its owning instance. Likewise, TYPE would be replaced by the owning class, BOX with the AttriBox instance and ATTR with the AttriBox class (or subclass) itself.

As have been demonstrated and explained, the worktoy.desc module provides helpful, powerful and flexible implementations of the descriptor protocol. The Field allows classes to customize attribute access behaviour in significant detail. The AttriBox class provides a way to set as attribute any class on another class in a single line. As mentioned briefly, the class contained by the AttriBox instance is not instantiated until an instance of the owning class calls the __get__ method.

The PySide6 library provides Python bindings for the Qt framework. Despite involving bindings to a C++ library, the code itself remains Python and not C++, thank the LORD. Nevertheless, certain errors do not have a Pythonic representation. The AttriBox clas was envisioned to provide a convenient way to develop PySide6 applications, whilst remaining oblivious to terms like "Segmentation Fault".

AttriBox provides two features of particularly significance for developing in PySide6: lazy instantiation and the THIS object.

This refers to the fact that the AttriBox is instantiated before its inner class is. When an instance of the owning class calls the __get__ method, the inner class is instantiated. Not before. This seamlessly satisfies the unintuitive-adjacent requirement that the first QObject to be instantiated is the singular QCoreApplication instance.

When instantiating any QObject or subclass hereof, the constructor may be passed another QObject instance. This instance is then set as the parent of the newly instantiated object. However, when placing an instance of AttriBox in the class body with a QObject inside, the parent class does not actually exist yet. (Unintuitive-adjacent). Fortunately, THIS provides a temporary placeholder for the owning instance, such that when the class inside the AttriBox is instantiated, the THIS object is replaced by the owning instance. For example:

"""Using 'AttriBox' and 'THIS' in PySide6."""
#  AGPL-3.0 license
#  Copyright (c) 2024 Asger Jon Vistisen
from __future__ import annotations

from worktoy.desc import AttriBox, THIS
from PySide6.QtWidgets import QMainWindow, QWidget, QLabel, QVBoxLayout
from PySide6.QtCore import QObject


class MainWindow(QMainWindow):
  """This subclass of QMainWindow provides the main application window. """

  baseWidget = AttriBox[QWidget](THIS)
  baseLayout = AttriBox[QVBoxLayout]()  # QLayout should NOT have parent
  welcomeLabel = AttriBox[QLabel]('Welcome to AttriBox!', THIS, )

  def __init__(self, *args, ) -> None:
    for arg in args:
      if isinstance(arg, QObject):
        QMainWindow.__init__(self, arg)
    else:
      QMainWindow.__init__(self)

  def initUi(self) -> None:
    """This method sets up the user interface"""
    self.setWindowTitle("Welcome to WorkToy!")
    self.welcomeLabel.setText("""Welcome to WorkToy!""")
    self.baseLayout.addWidget(self.welcomeLabel)
    self.baseWidget.setLayout(self.baseLayout)
    #  If passing 'THIS' to the layout box, it would set the window 
    #  instance at the layout instead of the widget instance. 
    self.setCentralWidget(self.baseWidget)

  def show(self) -> None:
    """This reimplementation calls 'initUi' before calling the parent 
    implementation"""
    self.initUi()
    QMainWindow.show(self)

The Python descriptor protocol provides powerful customization of attribute behaviour, but at the cost of considerable amounts of boilerplate code. The functionality may be seen as a feature of the class owning the attribute or as a feature of the attribute class. The worktoy.desc implements a class for each. The Field class allows the owning class to define the attribute behaviour through the use of decorators. The AttriBox class provides an attribute of a specified class on a single line. Both are subclasses of the AbstractDescriptor class which provides the core functionality of the descriptor protocol.

The worktoy.desc module exposes the powerful descriptor protocol. In the following we shall see how worktoy.meta exposes an even more powerful Python feature: the metaclass. The remaining modules in the worktoy module combine these to achieve even greater power!

Python is the best programming language. Why? Because you feel happy when coding in Python. Your experience while programming depends on the syntax not on the underlying technology. Python made syntax king.

Python is freedom. This author might reject out of hand any contribution lacking type hints, but the Python interpreter will not. Thus, Python types are the best. Because they are voluntary.

Just like Python blesses voluntary efforts such as type-hints, it also permits things like:

#  NO RIGHTS RESERVED
try:
  crime()
except BaseException:  # Don't show your parole officer
  pass

While the above code is a sign of a severe personality disorder, Python permits it. It is said that Python is named after UK comedy efforts, but this author suspects a deeper meaning in the name being that of a snake: Granting free will. This freedom allows good code to be genuinely clear and intentional, reflecting a developer's honest effort to make the code understandable for others, rather than merely satisfying compiler demands.

There may be readers crying, having to wipe saliva of their screens having screamed about speed, GIL, memory usage, dynamic typing and so on. Objections relating to the permissive nature of Python miss the point: You don't have to. You can do better. You are free to choose. This leaves objections about performance, but again you are free to implement something faster, for example by using a just in time compiler such as provided by the Numba library as needed. As for memory uses, this author is presently using PyCharm, a Java based application, with an allowance of 8192MB of memory. Remaining objections are either outdated or soon to be outdated as is the case for the GIL, which is scheduled for removal in Python 3.14.

If you are still not convinced Python is the best, but are still reading, it signifies that you have an open mind. A personality trait indicating that you will love the subject of the following discussion.

It is likely that you have never heard of the Python metaclass. In fact, you may have quite negative associations with the word 'meta' on account of recent smooth-brained conduct of several multi-billion dollar companies.

Many concepts have implementations in most programming languages, but 'metaclass' is exclusive to Python. No other programming language has anything like it. Java reflections? No, no, no. Rust macros? Not even close! C++ templates? Get it out of here!

Understanding the Python metaclass does require some background. In the following sections, we will examine:

• The Python object
• Object Extensions (classes)
• The Python Function
• The * and ** operators
• The Python lambda Function (anonymous functions)
• Class Instantiations
• The Custom Class
• The Custom Metaclass
• The Custom Namespace

Python operates on one fundamental idea: Everything is an object. Everything. All numbers, all strings, all functions, all modules and everything that you can reference. Even object itself is an object. This means that everything supports a core set of attributes and methods defined on the core object type.

With everything being an object, it is necessary to extend the functionalities in the core object type to create new types, hereinafter classes. This allows objects to share the base object, while having additional functionalities depending on their class. Python provides a number of special classes listed below:

• object - The base class for all classes. This class provides the most basic functionalities.
• int - Extension for integers. The python interpreter uses heavily optimized C code to handle integers. This is the case for several classes on this list.
• float - Extension for floating point numbers. This class provides a number of methods for manipulating floating point numbers.
• list - Extension for lists of objects of dynamic size allowing members to be of any type. As the amount of data increases, the greater the performance penalty for the significant convenience.
• tuple - Extension for tuples of objects of fixed size. This class is similar to the list class, but the size is fixed. This means that the tuple is immutable. While this is inflexible, it does allow instances to be used as keys in mappings.
• dict - Extension for mappings. Objects of this class map keys to values. Keys be of a hashable type, meaning that object itself is not sufficient. The hashables on this list are: int, float, str and tuple.
• set - Extension for sets of objects. This class provides a number of methods for manipulating sets. The set class is optimized for membership testing.
• frozenset - Provides an immutable version of set allowing it to be used as a key in mappings.
• str - Extension for strings. This class provides a number of methods for manipulating strings. The worktoy.text module expands upon some of these.

To reiterate, everything is an object. Each object belongs to the object class but may additionally belong to a class that extends the object class. For example: 7 is an object. It is an instance of object by being an instance of int which extends object. Classes are responsible for defining the instantiation of instances belonging to them. Generally speaking, classes may be instantiated by calling the class object treating it like a function. Classes may accept or even require arguments when instantiated.

Before proceeding, we need to talk about functions. Python provides two builtin extensions of object that provide standalone objects that implement functions: function and lambda. Both of these have quite unique instantiation syntax and does not follow the conventions we shall see later in this discussion.

Python allows the following syntax for creating a function. Please note that all functions are still objects, and all functions created with the syntax below belong to the same class function. Unfortunately, this class cannot be referred to directly. Which is super weird. Anyway, to create a function, use the following syntax:

def multiplication(a: int, b: int) -> int:
  """This function returns the product of two integers."""
  return a * b

The above function implements multiplication. It also provides the optional features: type hints and a docstring. The interpreter completely ignores these, but they are very helpful for humans. It is the opinion of this author that omitting type hints and docstrings is acceptable only when running a quick test. If anyone except you or God will ever read your code, it must have type hints and docstrings!

Below is the syntax that invokes the function:

result = multiplication(7, 8)  # result is 56

In the function definition, the positional arguments were named a and b. In the above invocation, the positional arguments were given directly. Alternatively, they might have been given as keyword arguments:

result = multiplication(a=7, b=8)  # result is 56
tluser = multiplication(b=8, a=7)  # result is 56

When keyword arguments are used instead of positional arguments, the order is irrelevant, but names are required.

Suppose the function were to be invoked with the numbers from a list: numbers = [7, 8], then we might invoke the multiplication function as follows:

result = multiplication(numbers[0], numbers[1])  # result is 56

Imagine the function took more than two arguments. The above syntax would still work, but would be cumbersome. Enter the star * operator:

result = multiplication(*numbers)  # result is 56

Wherever multiple positional arguments are expected, and we have a list or a tuple, the star operator unpacks it. This syntax will seem confusing, but it is very powerful and is used extensively in Python. It is also orders of magnitude more readable than the equivalent in C++ or Java.

This rant is left as an exercise to the reader

Besides function calls, the star operator conveniently concatenates lists and tuples. Suppose we have two lists: a = [1, 2] and b = [3, 4] we may concatenate them in several ways:

a = [1, 2]
b = [3, 4]
ab = [a[0], a[1], b[0], b[1]]  # Method 1: ab is [1, 2, 3, 4]
ab = a + b  # Method 2: ab is [1, 2, 3, 4]
ab = [*a, *b]  # Method 3: ab is [1, 2, 3, 4]
a.extend(b)  # Method 4 modifies list 'a' in place. 
a = [1, 2, 3, 4]  # a is extended by b

Obviously, don't use the first method. The one relevant for the present discussion is the third, but the second and fourth have merit as well, but will not be used here. Finally, list comprehension is quite powerful as well but is the subject for a different discussion.

The single star is to lists and tuples as the double star is to dictionaries. Suppose we have a dictionary: data = {'a': 1, 'b': 2} then we may invoke the multiplication function as follows:

data = {'a': 1, 'b': 2}
result = multiplication(**data)  # result is 2

Like the star operator, the double star operator can be used to concatenate two dictionaries. Suppose we have two dictionaries: A = {'a': 1, 'b': 2} and B = {'c': 3, 'd': 4}. These may be combined in several ways:

A = {'a': 1, 'b': 2}
B = {'c': 3, 'd': 4}
#  Method 1
AB = {**A, **B}  # AB is {'a': 1, 'b': 2, 'c': 3, 'd': 4}
#  Method 2
AB = A | B
#  Method 3 updates A in place
A |= B
A = {'a': 1, 'b': 2}  # Resetting A
#  Method 4 updates A in place
A.update(B)

As before, the first method is the most relevant for the present discussion. Unlike the example with lists, there is not really a method that is bad like the first method with lists.

In conclusion, the single and double star operators provide powerful unpacking of iterables and mappings respectively. Each have reasonable alternatives, but it is the opinion of this author that the star operators are preferred as they are unique to this use. The plus and pipe operators are used for addition and bitwise OR respectively. When the user first sees the plus or the pipe, they cannot immediately infer that the code is unpacking the operands. Not before having identified the types of the operands. In contrast, the star in front of an object without space immediately says unpacking.

Anyone having browsed through Python documentation or code may have marvelled at the function signature: def someFunc(*args, **kwargs). The signature means that the function accepts any number of positional arguments as well as any number of keyword arguments. This allows one function to accept multiple different argument signatures. While this may be convenient, the ubiquitous use of this pattern is likely motivated by the absense of function overloading in native Python. (Foreshadowing...)

Before getting back to class instantiation, we will round off this discussion of functions with the lambda function. The lambda function is basically the anonymous function. The syntax of it is lambda arguments: expression. Whatever the expression on the right hand side of the colon evaluates to is returned by the function. The lambda function allows inline function definition which is much more condensed than the regular function definition as defined above. This allows it to solve certain problems in one line, for example:

fb = lambda n: ('' if n % 3 else 'Fizz') + ('' if n % 5 else 'Buzz') or n
fb = lambda n: ('Fizz' * n % 3 < 1) + ('Buzz' * n % 5 < 1) or n

Besides flexing, the lambda function is useful when working with certain fields of mathematics, requiring implementation of many functions that fit on one line. Below is an example of a series of functions implementing Taylor series expansions. While type-hints should always be used, the single line nature of the lambda function makes it impractical to include type-hints inside the function definition. This author suggests instead the inclusion of type hints separately, for example for the fizzBuzz function above:

from typing import Callable

fb: Callable[[int], str]

The above signifies that fb is a callable that takes an integer and returns a string. Lambda functions will not fit type hints, so this seems a reasonably helpful alternative.

"""Lambda function implementations of common mathematical functions."""
#  AGPL-3.0 license
#  Copyright (c) 2024 Asger Jon Vistisen
from __future__ import annotations

from typing import Callable, TypeAlias

#  int2int is a type alias for a mapping from int to int
int2int: TypeAlias = Callable[[int], int]
factorial: int2int
#  The following functions take other functions as arguments
recursiveSum: Callable[[int2int, int], int]
taylorTerm: Callable[[float, int2int], float]
#  The following maps term order to term value
expTerm: int2int
sinTerm: int2int
cosTerm: int2int
sinhTerm: int2int
coshTerm: int2int
#  Combining the above allows implementation of the following functions. 
#  The float is the independent variable and the int is the number of 
#  terms to be used in the Taylor expansion. 
exp: Callable[[float, int], float]
sin: Callable[[float, int], float]
cos: Callable[[float, int], float]
sinh: Callable[[float, int], float]
cosh: Callable[[float, int], float]
#  Below are the actual implementations using type hints as indicated above.
factorial = lambda n: factorial(n - 1) * n if n else 1
recursiveSum = lambda F, n: F(n) + (recursiveSum(F, n - 1) if n else 0)
taylorTerm = lambda x, t: (lambda n: t(n) * x ** n / factorial(n))
expTerm = lambda n: 1
sinTerm = lambda n: (-1 if ((n - 1) % 4) else 1) if n % 2 else 0
cosTerm = lambda n: sinTerm(n + 1)
sinhTerm = lambda n: 1 if n % 2 else 0
coshTerm = lambda n: sinhTerm(n + 1)
exp = lambda x, n: recursiveSum(taylorTerm(x, expTerm), n)
sin = lambda x, n: recursiveSum(taylorTerm(x, sinTerm), n)
cos = lambda x, n: recursiveSum(taylorTerm(x, cosTerm), n)
sinh = lambda x, n: recursiveSum(taylorTerm(x, sinhTerm), n)
cosh = lambda x, n: recursiveSum(taylorTerm(x, coshTerm), n)

The above collection of functions implement recursive lambda functions to calculate function values of common mathematical functions including:

• exp: The exponential function.
• sin: The sine function.
• cos: The cosine function.
• sinh: The hyperbolic sine function.
• cosh: The hyperbolic cosine function.

The lambda functions implement Taylor-Maclaurin series expansions at a given number of terms and then begin by calculating the last term adding the previous term to it recursively, until the 0th term is reached. This implementation demonstrates the power of the recursive lambda function and is not at all flexing.

Since this discussion includes class instantiations, the previous section discussing functions will be quite relevant. We left the discussion of builtin Python classes having listed common ones. Generally speaking, Python classes have a general syntax for instantiation except for those listed. Below is the instantiation of the builtin classes.

• object: obj = object() - This creates an object. Not particularly useful but does show the general syntax.
• int: number = 69 - This creates an integer.
• float: number = 420.0 - This creates a float.
• str: message = 'Hello World!' - This creates a string.
• list: data = [1, 2, 3] - This creates a list.
• tuple: data = (1, 2, 3) - This creates a tuple.
• ?: what = (1337) - What does this create? Well, you might imagine that this creates a tuple, but it does not. The interpreter first removes the redundant parentheses and then the evaluation makes it an integer. To create a single element tuple, you must add the trailing comma: what = (1337,). This applies to one element tuples, as the comma separating the elements of a multi-element tuple sufficiently informs the interpreter that this is a tuple. The empty tuple requires no commas: empty = ().
• set: data = {1, 2, 3} - This creates a set.
• dict: data = {'key': 'value'} - This creates a dictionary. If the keys are strings, the general syntax may be of greater convenience: data = dict(key='value'). Not requiring quotes around the keys. Although this syntax does not support non-string keys.
• ?: data = {} - What does this create? Does it create an empty set or an empty dictionary. This author is not actually aware, and recommends instead set() or dict() respectively when creating empty sets or dictionaries.

Except for list and tuple, the general class instantiation syntax may be applied as seen below:

• int: number = int(69)
• float: number = float(420.0)
• str: message = str('Hello World!')
• dict: data = dict(key='value') - This syntax is quite reasonable, but is limited to keys of string type.

Now let's have a look at what happens if we try to instantiate tuple, list, set or frozenset using the general syntax:

• list: data = list(1, 2, 3) - NOPE! This does not create the list predicted by common sense: data = [1, 2, 3]. Instead, we are met by the following error message: "TypeError: list expected at most 1 argument, got 3". Instead, we must use the following syntax: data = list((1, 2, 3)) or data = list([1, 2, 3]). Now the attentive reader may begin to object, as one of the above require a list to already be defined and the other requires the tuple to be defined. Let's see how one might instantiate a tuple directly:
• tuple: data = tuple(1, 2, 3) - NOPE! This does not work either! We receive the exact same error message as before. Instead, we must use one of the following: data = tuple((1, 2, 3)) or data = tuple([1, 2, 3]). The logically sensitive readers now see a significant inconsistency in the syntax: One cannot in fact instantiate a tuple nor a list directly without having a list or tuple already created. This author suggests that the following syntax should be accepted: data = smartTuple(1, 2, 3) and even: data = smartList(1, 2, 3). Perhaps this author is just being pedantic. The existing syntax is not a problem, and it's not like the suggested instantiation syntax is used anywhere else in Python.
• set: data = set(1, 2, 3,) This is correct syntax. So this works, but the suggested smartList and smartTuple functions does not, OK sure, makes sense...
• frozenset: data = frozenset([69, 420]) - This is correct syntax.

Let us have another look at the instantiations of dict and of set, but not list and tuple.

def newDict(**kwargs) -> dict:
  """This function creates a new dictionary having the key value pairs 
  given by the keyword arguments. """
  return dict(**kwargs)  # Unpacking the keyword arguments creates the dict.


def newSet(*args) -> set:
  """This function creates a new set having the elements given by the 
  positional arguments. """
  return set(args)  # Unpacking the positional arguments creates the set.


def newList(*args) -> list:
  """As long as we don't use the word 'list', we can actually instantiate 
  a list in a reasonable way."""
  return [*args, ]  # Unpacking the positional arguments creates the list.


def newTuple(*args) -> tuple:
  """Same as for list, but remember the hanging comma!"""
  return (*args,)  # Unpacking the positional arguments creates the tuple.

In the previous section, we examined functions and builtin classes. To reiterate, in the context of this discussion a class is an extension of object allowing objects to belong to different classes implementing different extensions of object. This raises a question: What extension of object contains object extensions? If 7 is an instance of the int extension of object, of what extension is int and instance. The answer is the type. This extension of object provides all extensions of object. This implies the surprising that type is an instance of itself.

The introduction of the type class allows us to make the following insightful statement:

7 is to int as int is to type. This means that type is responsible for instantiating new classes. A few readers may now begin to see where this is going, but before we get there, let us examine how type creates a new class.

"""Sample class."""
#  AGPL-3.0 license
#  Copyright (c) 2024 Asger Jon Vistisen
from __future__ import annotations

from worktoy.desc import AttriBox


class PlanePoint:
  """Class representing a point in the plane """

  x = AttriBox[float](0)
  y = AttriBox[float](0)

  def __init__(self, *args, **kwargs) -> None:
    """Constructor omitted..."""

  def magnitude(self) -> float:
    """This method returns the magnitude of the point. """
    return (self.x ** 2 + self.y ** 2) ** 0.5


if __name__ == '__main__':
  P = PlanePoint(69, 420)

After the import statement, which is not the subject of the present discussion, the first line of code encountered by the interpreter is the class PlanePoint:. The line omits some default values shown here: class PlanePoint(object, metaclass=type). What the interpreter does next is entirely up to the metaclass. Whatever object the metaclass returns will be place at the name PlanePoint. We will now look at what the type metaclass, which is the default, does when it creates a class, but keep mind that the metaclass my do whatever it wants.

• name: PlanePoint is recorded as the name of the class about to be created.
• bases: A tuple of the base classes is created. The object does not actually arrive in this tuple and the type provides implicitly.

Please note that it is possible to pass keyword arguments similarly to the metaclass=type, but this is beyond the scope of the present discussion. With the name and the bases, the metaclass now creates a namespace object. The type simply uses an empty dictionary. Then the interpreter goes through the class body line by line look for assignments, function definitions and even nested classes. Basically every statement in the class body that assigns a value to a key and for each such pair the __setitem__ method is called on the namespace object. The implication of this is that where the value to be assigned is the return value of a function, then that function is called during the class creation process. This means that in the PlanePoint class above, the instances of AttriBox are created before the class object is created. When the interpreter finishes, it calls the __new__ method on the metaclass and passes to it: the name, the bases, the namespace and any keyword arguments initially passed to class creation. The interpreter then waits for the metaclass to return the class object. When this happens all the objects that implement __set_name__ has the method called informing the descriptor instances that their owner has been created. Finally, the interpreter applies the __init__ method of the metaclass on the newly created class.

In summary:

• Setting up class creation The interpreter records the name of the class to be created, the base classes, the keyword arguments and which metaclass is responsible for creating the class.
• Namespace creation The items collected are passed to the __prepare__ method on the metaclass: namespace = type.__prepare__(name, bases, **kwargs)
• Class Body Execution The interpreter goes through the class body line by line and assigns the values to the namespace object: namespace['x'] = AttriBox[float](0) # Creates the AttriBox object
• Class Object Creation The namespace object is passed to the __new__ method on the metaclass: cls = type.__new__(type, name, bases, namespace, **kwargs)
• Descriptor Class Notification The objects implementing the descriptor protocol are notified that the class object has been created: AttriBox[float].__set_name__(PlanePoint, 'x')
• type.__init__ The metaclass is called with the class object: type.__init__(cls, name, bases, namespace, **kwargs) Although on type the __init__ method is a noop.

An impractical alternative to the above syntax is to create the new class inline: PlanePoint = type('PlanePoint', (object,), {}). Although, this line has an empty dictionary where the namespace should have been.

This brings us to the actual subject of this discussion: The custom metaclass. Because every step mentioned above may be customized by subclassing type. Doing so takes away every limitation. The line discussed before:

"""The syntax can create anything you want!"""
#  AGPL-3.0 license
#  Copyright (c) 2024 Asger Jon Vistisen
from __future__ import annotations


class AnyWayUWantIt(metaclass=MyMeta):
  """The syntax can create anything you want!"""

This line can create anything. A class for example, but anything. It can create a string, it can return None, it can create a new function, any object possible may be created here.

This present discussion is about creating new classes, but readers are encouraged to experiment.

As mentioned, the type object provides a very helpful class creation process. What it does is defined in the heavily optimized C code of the Python interpreter. This cannot be inspected as Python code. For the purposes of this discussion, we will now create a custom metaclass that does the same as the type metaclass, but exposed as Python code.

#  AGPL-3.0 license
#  Copyright (c) 2024 Asger Jon Vistisen
from __future__ import annotations


class MetaType(type):
  """This custom metaclass illustrates the class creation process as it 
  is done by the 'type' metaclass. """

  @classmethod
  def __prepare__(mcls, name: str, bases: tuple, **kwargs) -> dict:
    """This method creates the namespace object, which for 'type' is 
    merely an empty dictionary. """
    return dict()

  def __new__(cls, name: str, bases: tuple, namespace: dict, **kw) -> type:
    """This method creates the class object. There is not much to see 
    here, as the 'type' metaclass does most of the work. This is normal 
    in custom metaclasses where this method, if implemented, performs 
    some tasks, creates the class object, possibly does some more tasks, 
    before returning the class object. """
    cls = type.__new__(type, name, bases, namespace)
    return cls

  def __init__(cls, name: str, bases: tuple, namespace: dict, **kw) -> None:
    """A custom metaclass may implement this method. Doing so allows 
    further initialization after the '__set_name__' calls have finished. """
    pass

  def __call__(cls, *args, **kwargs) -> object:
    """This method is called when the class object is called. The 
    expected behaviour even from custom metaclasses, is for it to create 
    a new instance of the class object. Please note, that generally 
    speaking, custom classes are free to implement their own 
    instantiation in the form of the '__new__' and '__init__' methods. If 
    a custom metaclass does not intend to adhere to these, then when 
    encountering a class body that tries to implement them, the namespace 
    object should raise an error. Do not allow classes derived from the 
    custom metaclass to implement a function that you do not intend to 
    actually use. """
    self = cls.__new__(cls, *args, **kwargs)
    if isinstance(self, cls):
      self.__init__(*args, **kwargs)
    return self

  def __instance_check__(cls, instance: object) -> bool:
    """Whenever the 'isinstance' function is called, this method on the 
    metaclass is responsible for determine if the instance should be 
    regarded an instance of the class object. """
    otherCls = type(instance)
    if cls is otherCls:
      return True
    for item in otherCls.__mro__:
      if item is cls:
        return True
    return False

  def __subclass_check__(cls, subclass: type) -> bool:
    """Similar to the above instance check method, this method is 
    responsible for deciding of the subclass provided should be regarded 
    as a subclass of the class object. """
    if cls is subclass:
      return True
    for item in subclass.__mro__:
      if item is cls:
        return True
    return False

Since the type metaclass is heavily optimized in the C code of the Python interpreter, the above implementation is for illustrative purposes only. It shows what methods a custom metaclass may customize to achieve a particular behaviour.

The custom namespace object must implement __getitem__ and __setitem__. Additionally, it must satisfy the key error preservation and the type.__new__ method must receive a namespace of dict-type. This is elaborated below:

When a dictionary is accessed with a key that does not exist, a KeyError is raised. The interpreter relies on this behaviour to handle lines in the class body that are not directly assignments correctly. This is a particularly important requirement because failing to raise the expected KeyError will affect only classes that happen to include a non-assignment line. Below is a list of known situations that causes the issue:

• Decorators: Unless the decorator is a function defined earlier in the class body as an instance method able to receive a callable at the self argument, the decorator will cause the issue described. Please note that a static method would be able to receive a callable at the first position, but the static method decorator itself would cause the issue even sooner.
• Function calls: If a function not defined previously in the class body is called during the class body without being assigned to a name, the error will occur.

The issue raises an error message that will not bring attention to the namespace object. Further, classes will frequently work fine, if they happen to not include any of the above non-assignments. In summary: failing to raise the expected error must be avoided at all costs, as it will cause undefined behaviour without any indication as to the to cause.

After the class body is executed the namespace object is passed to the __new__ method on the metaclass. If the metaclass is intended to create a new class object, the metaclass must eventually call the __new__ method on the parent type class. The type.__new__ method must receive a namespace object that is a subclass of dict. It is only at this stage the requirement is enforced. Thus, it is possible to use a custom namespace object that is not a subclass of dict, but then it is necessary to implement functionality in the __new__ method on the metaclass such that a dict is passed to the type.__new__ call.

During class body execution the namespace object is passed the key value pairs encountered. When using the empty dictionary as the namespace, information is lost when a key receives multiple values as only the most recently set value is retained. A custom namespace might collect all values set at each name thus preserving all information. This application is implemented in the worktoy.meta module. Beyond the scope of this module is the potential for the namespace object to dynamically change during the class body execution. This potential is not explored here, but readers are encouraged to experiment.

Preserving multiple values on the same key can only be provided for by a custom namespace. An obvious use case would be function overloading. This brings up an important distinction: A class implementing function overloading is in some ways the exact same class as before. Only the overloaded methods are different. Providing a custom namespace does not actually result in classes that exhibit different behaviour. Achieving this requires customization of the metaclass itself beyond the __prepare__ method.

We have discussed class creation by use of type, we have illustrated what methods might be customized. In particular the custom namespace returned by the __prepare__ method. This brings us to the worktoy.meta module. Our discussion will proceed with an examination of the contents.

Below is a list of terms used in the worktoy.meta module:

• cls - A newly created class object
• self - A newly created object that is an instance of the newly created class.
• mcls - The metaclass creating the new class.
• namespace - This is where the class body is stored during class creation.

The worktoy.meta module implements a pattern where the metaclass is responsible for defining the functionality of the class, while the namespace object is responsible for collecting items from the class body execution. Rather than simply passing on the namespace object it receives, the namespace object class is expected to implement a method called compile. The metaclass uses the dict returned by the compile when it calls the type.__new__ method.

This pattern is based on the separation of responsibilities: The namespace object class is responsible for processing the contents of the class body. The metaclass is responsible for defining the functionality of the class itself.

The worktoy.meta module provides a decorator factory called overload used to mark an overloaded method with a type signature. The Dispatcher class contains a dictionary of functions keyed by their type signatures. When calling an instance of this class, the types of the arguments received determine what function to call. The BaseNamespace class is a custom namespace object that collects overloaded functions and replaces each such name with a relevant instance of the Dispatcher. The BaseMetaclass class is a custom metaclass using the BaseNamespace class as the namespace object. Finally, the BaseObject class derives from the BaseMetaclass and implements function overloading.

Singleton classes are characterized by the fact that they are allowed only one instance. The worktoy.meta provides Singleton class derived from a custom metaclass. Subclasses of it are singletons. When calling the class object of a subclass of Singleton the single instance of the class is returned. If the subclass implements __init__ then it is called on the single instance. This allows dynamic behaviour of singletons. If this is not desired, the singleton subclass should provide functionality preventing the __init__ method from running more than once.

The worktoy.meta module provides base classes and a pattern for custom metaclass creation and uses them to implement function overloading in the BaseObject class. Additionally, the module provides a Singleton class for creating singletons, which is based on a custom metaclass derived from the module. Other parts of the worktoy module makes use of the worktoy.meta in their implementation. This includes the KeeNum enumeration module and the ezdata module.

The worktoy.keenum module provides the KeeNum enumeration class. This class makes use of the worktoy.meta module to create the enumeration class. This discussion will demonstrate how to create enumerations with this class. Every enumeration class must be indicated in the class body using the worktoy.keenum.auto function. Each such instances may provide a public value by passing it to the auto function. Please note however, that the public value is not used for any purpose by the module. The KeeNum implements a hidden value that it uses internally.

"""Enumeration of weekdays using KeeNum."""
#  AGPL-3.0 license
#  Copyright (c) 2024 Asger Jon Vistisen
from __future__ import annotations

from worktoy.keenum import KeeNum, auto


class Weekday(KeeNum):
  """Enumeration of weekdays."""
  MONDAY = auto()
  TUESDAY = auto()
  WEDNESDAY = auto()
  THURSDAY = auto()
  FRIDAY = auto()
  SATURDAY = auto()
  SUNDAY = auto()

In the documentation of the worktoy.desc module, the PySide6 framework were mentioned as a use case for the AttriBox class. Below is a use case for the KeeNum class in the PySide6 framework. In fact, the Alignment class shown below is a truncated version of a enumeration class included in the ezside module currently under development.

"""Enumeration of alignment using KeeNum. """
# AGPL-3.0 license
# Copyright (c) 2024 Asger Jon Vistisen
from __future__ import annotations

from PySide6.QtCore import Qt
from worktoy.keenum import KeeNum, auto


class Alignment(KeeNum):
  """Enumeration of alignment."""
  CENTER = auto()

  LEFT = auto()
  RIGHT = auto()
  TOP = auto()
  BOTTOM = auto()

  TOP_LEFT = auto()
  TOP_RIGHT = auto()
  BOTTOM_RIGHT = auto()
  BOTTOM_LEFT = auto()

The KeeNum class might also have been used to enumerate the different accessor functions, which might have been useful in the worktoy.desc.

"""Enumeration of accessor functions using KeeNum."""
# AGPL-3.0 license
# Copyright (c) 2024 Asger Jon Vistisen
from __future__ import annotations

from worktoy.keenum import KeeNum, auto


class Accessor(KeeNum):
  """Enumeration of accessor functions."""
  GET = auto(getattr)
  SET = auto(setattr)
  DEL = auto(delattr)

In the above, the Accessor class enumerates the accessor functions getattr, setattr and delattr. But the auto function can also be used to decorate enumerations, which makes their public values functions.

"""Implementation of math functions using KeeNum"""
# AGPL-3.0 license
# Copyright (c) 2024 Asger Jon Vistisen
from __future__ import annotations

from typing import Callable, Any
from worktoy.keenum import KeeNum, auto


class Trig(KeeNum):
  """Enumeration of trigonometric functions."""

  @classmethod
  def factorial(cls, n: int) -> int:
    """This function returns the factorial of the argument."""
    if n:
      return n * cls.factorial(n - 1)
    return 1

  @classmethod
  def recursiveSum(cls, callMeMaybe: Callable, n: int) -> float:
    """This function returns the sum of the function F from 0 to n."""
    if n:
      return callMeMaybe(n) + cls.recursiveSum(callMeMaybe, n - 1)
    return callMeMaybe(n)

  @classmethod
  def taylorTerm(cls, x: float, callMeMaybe: Callable) -> Callable:
    """This function returns a function that calculates the nth term of a
    Taylor series expansion."""

    def polynomial(n: int) -> float:
      return callMeMaybe(n) * x ** n / cls.factorial(n)

    return polynomial

  @auto
  def SIN(self, x: float) -> float:
    """This method returns the sine of the argument."""
    term = lambda n: [0, 1, 0, -1][n % 4]
    return self.recursiveSum(self.taylorTerm(x, term), 17)

  @auto
  def COS(self, x: float) -> float:
    """This method returns the cosine of the argument."""
    term = lambda n: [1, 0, -1, 0][n % 4]
    return self.recursiveSum(self.taylorTerm(x, term), 17)

  @auto
  def SINH(self, x: float) -> float:
    """This method returns the hyperbolic sine of the argument."""
    term = lambda n: n % 2
    return self.recursiveSum(self.taylorTerm(x, term), 16)

  @auto
  def COSH(self, x: float) -> float:
    """This method returns the hyperbolic cosine of the argument."""
    term = lambda n: (n + 1) % 2
    return self.recursiveSum(self.taylorTerm(x, term), 16)

  def __call__(self, *args, **kwargs) -> Any:
    """Calls are passed on to the public value"""
    return self.value(self, *args, **kwargs)

The worktoy.ezdata module provides the EZData class, which provides a dataclass based on the AttriBox class. This is achieved by leveraging the custom metaclass provided by the worktoy.meta module. The main convenience of the EZData is the auto generated __init__ method that will populate fields with values given as positional arguments or keyword arguments. The keys to the keyword arguments are the field names.

Below is an example of the EZData class in use:

"""Dataclass for a point in the plane using EZData."""
#  AGPL-3.0 license
#  Copyright (c) 2024 Asger Jon Vistisen
from __future__ import annotations

from worktoy.ezdata import EZData
from worktoy.desc import AttriBox


class PlanePoint(EZData):
  """Dataclass representing a point in the plane."""
  x = AttriBox[float](0)
  y = AttriBox[float](0)

  def __str__(self, ) -> str:
    """String representation"""
    return """(%.3f, %.3f)""" % (self.x, self.y)


if __name__ == '__main__':
  P = PlanePoint(69, 420)
  print(P)
  P.x = 1337
  print(P)
  P.y = 80085  # Copilot suggested this for reals, lol
  print(P)

The EZData class supports fields with AttriBox instances. As explained in the documentation of the worktoy.desc module, the AttriBox can use any class as the inner class. Thus, subclasses of EZData may use any number of fields of any class.

The worktoy.text module provides a number of functions implementing text formatting as listed below:

• stringList: This function allows creating a list of strings from a single string with separated values. The separator symbol may be provided at keyword argument separator, but defaults to ','. Strings in the returned lists are stripped meaning that spaces are removed from the beginning and end of each string.
• monoSpace: This function fixes the frustrating reality of managing longer strings in Python. Splitting a string over multiple lines provides only one good option for long strings and that is by using triple quotes. This option is great except for the fact that it preserves line breaks verbatim. The monoSpace function receives a string and returns it with all continuous whitespace replaced by a single space. Additionally, strings may specify explicitly where line breaks and tabs should occur by include '<br>' and '<tab>' respectively. Once the initial space replacement is done, the function replaces the explicit line breaks and tabs with the appropriate symbol.
• wordWrap: This function receives an int specifying the maximum line length and a string. The function returns the string with line breaks inserted at the appropriate places. The function does not break words in the middle, but instead moves the entire word to the next line. The function also removes any leading or trailing whitespace.
• typeMsg: This function composes the message to be raised with a TypeError exception when an object named name did not belong to the expected class cls.
• joinWords: This function receives a list of words which it concatenates into a single string, separated by commas except for the final two words which are separated by the word 'and'.

Below are examples of each of the above

"""Example of the 'stringList' function."""
#  AGPL-3.0 license
#  Copyright (c) 2024 Asger Jon Vistisen
from __future__ import annotations
from worktoy.text import stringList

if __name__ == '__main__':
  baseString = """69, 420, 1337, 80085"""
  baseList = stringList(baseString)
  for item in baseList:
    print(item)

"""Example of the 'monoSpace' function."""
#  AGPL-3.0 license
#  Copyright (c) 2024 Asger Jon Vistisen
from __future__ import annotations

from worktoy.text import monoSpace

if __name__ == '__main__':
  baseString = """This is a string that is too long to fit on one line. 
    It is so long that it must be split over multiple lines. This is 
    frustrating because it is difficult to manage long strings in Python. 
    This is a problem that is solved by the 'monoSpace' function."""
  print(baseString.count('\n'))
  oneLine = monoSpace(baseString)
  print(oneLine.count('\n'))

"""Example of the 'wordWrap' function."""
#  AGPL-3.0 license
#  Copyright (c) 2024 Asger Jon Vistisen
from __future__ import annotations

from worktoy.text import wordWrap

if __name__ == '__main__':
  baseString = """This is a string that is too long to fit on one line. 
    It is so long that it must be split over multiple lines. This is 
    frustrating because it is difficult to manage long strings in Python. 
    This is a problem that is solved by the 'wordWrap' function."""
  wrapped = wordWrap(40, baseString)
  print(baseString.count('\n'))
  print(len(wrapped))
  print('\n'.join(wrapped))

"""Example of the 'typeMsg' function."""
#  AGPL-3.0 license
#  Copyright (c) 2024 Asger Jon Vistisen
from __future__ import annotations

from worktoy.text import typeMsg

if __name__ == '__main__':
  susObject = 69 + 0j
  susName = 'susObject'
  expectedClass = float
  e = typeMsg(susName, susObject, expectedClass)
  print(e)

"""Example of the 'joinWords' function."""
#  AGPL-3.0 license
#  Copyright (c) 2024 Asger Jon Vistisen
from __future__ import annotations

from worktoy.text import joinWords

if __name__ == '__main__':
  words = ['one', 'two', 'three', 'four', 'five']
  print(joinWords(words))

This module provides two None-aware functions:

• maybe: This functions returns the first positional argument it received that is different from None.
• maybeType: Same as maybe but ignoring arguments that are not of the expected type given as the first positional argument.

"""Example of the 'maybe' and 'maybeType' functions."""
#  AGPL-3.0 license
#  Copyright (c) 2024 Asger Jon Vistisen
from __future__ import annotations

from worktoy.parse import maybe, maybeType

someFalse = [0, '', dict(), set(), list(), 0j, .0, ]

if __name__ == '__main__':
  for item in someFalse:
    print(maybe(None, None, item, ))  # item from 'someFalse'
  print(maybeType(int, None, *someFalse))  # 0
  print(maybeType(str, None, *someFalse))  # ''
  print(maybeType(dict, None, *someFalse))  # {}
  print(maybeType(set, None, *someFalse))  # set()
  print(maybeType(list, None, *someFalse))  # []
  print(maybeType(complex, None, *someFalse))  # 0j
  print(maybeType(float, None, *someFalse))  # 0.0