worktoy 0.99.34

Collection of Utilities

WorkToy v0.99.xx

WorkToy collects common utilities. It is available for installation via pip:

pip install worktoy

Version 0.99.xx is in final stages of development. It will see no new features, only bug fixes and documentation updates. Upon completion of tasks given below, version 1.0.0 will be released. Navigate with the table of contents below.

Table of Contents

• WorkToy v0.99.xx
  • Table of Contents
  • Installation
  • Usage
    • desc
    • ezdata
    • keenum
    • meta
    • parse
    • text
    • yolo
  • Development

Installation

pip install worktoy

Usage

worktoy.desc

Background - The Python Descriptor Protocol

The descriptor protocol in Python allows significant customisation of the attribute access mechanism. To understand this protocol, consider a class body assigning an object to a name. During the class creation process, when this line is reached, the object is assigned to the name. For the purposes of this discussion, the object is created when this line is reached, for example:

class PlanePoint:
  """This class represent an integer valued point in the plane. """
  x = Integer(0)
  y = Integer(0)  # Integer is defined below. In practice, classes should 
  #  be defined in dedicated files.

The above class ´PlanePoint´ owns a pair of attributes. These are instances of the ´Integer´ class defined below. The ´Integer´ class is a descriptor and is thus the focus of this discussion.

class Integer:
  """This descriptor class wraps an integer value. More details will be 
  added throughout this discussion."""
  __fallback_value__ = 0
  __default_value__ = None
  __field_name__ = None
  __field_owner__ = None

  def __init__(self, *args) -> None:
    for arg in args:
      if isinstance(arg, int):
        self.__default_value__ = arg
        break
    else:
      self.__default_value__ = self.__fallback_value__

  #  The '__init__' method implemented above makes use of the unusual 
  #  'else' clause at the end of a loop. This clause is executed once after 
  #  the loop has completed. Since it is part of the loop, the 'break' 
  #  keyword applies to it as well as the loop itself. The for loop above 
  #  iterates through the positional arguments and if it encounters an 
  #  'int' argument, it assigns it and issues the 'break'. So if the loop 
  #  completes without hitting the 'break', no 'int' could be found in any 
  #  of the positional arguments. Conveniently, the 'else' block then 
  #  assigns the fallback value.

  def __set_name__(self, owner: type, name: str) -> None:
    """Powerful method called automatically when the class owning the 
    descriptor instance is finally created. It informs the descriptor 
    instance of its owner and importantly, it informs the descriptor of 
    the name by which it appears in the class body. """
    self.__field_name__ = name
    self.__field_owner__ = owner

  def __get__(self, instance: object, owner: type) -> int:
    """Getter-function."""

The __set_name__ method

Python 3.6 was released on December 23, 2016. This version introduced the __set_name__ method, which marks a significant improvement to the descriptor protocol. It informs instances of descriptor classes of the class that owns them and the name by which they appear in the class namespace. Much of the functionality found in the worktoy.desc module would not be possible without this method.

The __get__ method

Consider the code below:

class Descriptor:
  """Descriptor class."""

  def __get__(self, instance: object, owner: type) -> object:
    """Getter-function."""

  def __set_name__(self, owner: type, name: str) -> None:
    """Informs the descriptor instance that the owner is created"""


class OwningClass:
  """Class owning the descriptor."""
  descriptor = Descriptor()


#  At this point in the code, the OwningClass is created, which triggers 
#  the '__set_name__' method on the descriptor instance. 
#  Event 1

if __name__ == '__main__':
  owningInstance = OwningClass()  # Event 2
  print(OwningClass.descriptor)  # Event 3
  print(owningInstance.descriptor)  # Event 4

Let us examine each of the four events marked in the above example:

1. Event 1 - This marks the completion of the class creation process, which is described in excruciating detail in later sections. Of interest here is the call to the __set_name__ method on the descriptor: Descriptor.__set_name__(descriptor, OwningClass, 'descriptor')
2. Event 2 - This marks the creation of an instance of the owning class. This event is not of interest to this discussion.
3. Event 3 - Accessing the descriptor on the OwningClass triggers the following function call: Descriptor.__get__(descriptor, None, OwningClass)
4. Event 4 - Accessing the descriptor on an instance of the owning class triggers the following function call: Descriptor.__get__(descriptor, owningInstance, OwningClass)

The __get__ determines what is returned when the descriptor is accessed. Please note that this method is what is called every time the descriptor instance is accessed regardless of how. In the above example, the following would each result in a call to the __get__ method:

#  Each result in:  Descriptor.__get__(descriptor, None, OwningClass)
OwningClass.descriptor
getattr(OwningClass, 'descriptor')
object.__getattribute__(OwningClass, 'descriptor')

The interpreter always refers to the __get__ method. To still allow access to the descriptor object itself, the common pattern is for the __get__ method to return the descriptor instance when accessed through the owning class:

class Descriptor:
  """Descriptor class."""

  def __get__(self, instance: object, owner: type) -> object:
    """Getter-function."""
    if instance is None:
      return self
    #  YOLO

This author suggests never deviating from this pattern. Perhaps some more functionality or some hooks may be implemented, but the descriptor instance itself should always be returned when accessed through the owning class. When accessed through the instance, the descriptor is free to do whatever it wants!

The __set__ method

The prior section focused on the distinction between accessing on the class or instance level. The __set__ method defined on the Descriptor is invoked only when accessed through the instance:

class Descriptor:
  """Descriptor class."""

  #  Code as before
  def __set__(self, instance: object, value: object) -> None:
    """Setter-function."""


if __name__ == '__main__':
  owningInstance = OwningClass()
  owningInstance.descriptor = 69  # Event 1
  print(owningInstance.descriptor)  # Event 2
  OwningClass.descriptor = 420  # Event 3
  print(owningInstance.descriptor)  # Event 4

The above code triggers the following function call:

-Event 1: Descriptor.__set__(descriptor, owningInstance, 7) -Event 2: Descriptor.__get__(descriptor, owningInstance, OwningClass) -Event 3: This call does not involve the descriptor at all, instead it simply overwrites the descriptor. -Event 4: The previous event overwrote the descriptor instance so now it simply returns the value set in Event 3.

Thus, when trying to call __set__ through the owning class, it is applied to the descriptor object itself. This matches the suggested implementation of the __get__ method which would return the descriptor item itself, when accessed through the owning class.

The delete method

The most important comment here is this warning: Do not mistake __del__ and __delete__! __del__ is a mysterious method associated with the destruction of an object.

The __delete__ method on the other hand allows instance specific control over what happens when the del keyword is used on an attribute through an instance. If the descriptor class does not implement it, it will raise an AttributeError when the interpreter tries to invoke __delete__ on the descriptor instance. Deleting an attribute through the owning class does not involve the descriptor at all, but is managed on the metaclass level. Generally, the descriptor is just yeeted in this case.

class Descriptor:
  """Descriptor class."""

  #  Code as before
  def __delete__(self, instance: object, ) -> None:
    """Setter-function."""


if __name__ == '__main__':
  owningInstance = OwningClass()
  del owningInstance.descriptor  # event 1
  print(owningInstance.descriptor)  # event 2

If a descriptor class does implement the __delete__ method the script above is expected to trigger the following:

-Event 1: Descriptor.__delete__(descriptor, owningInstance)

-Event 2a: AttributeError: __delete__ if the descriptor does not implement the method.

-Event 2b: AttributeError: 'OwningClass' object has no attribute 'descriptor'! if the descriptor does implement the method and allows deletion to proceed.

-Event 2c: A custom implementation of the __delete__ method which does not allow deletion and thus raises an appropriate exception. In this case, this author suggests raising a TypeError to indicate that the attribute is of a read-only type. This is different than the AttributeError which generally indicate the absense of the attribute.

If a custom implementation of the __delete__ method is provided, 2b or 2c as described above should happen, as this is the generally expected behaviour in Python. Implementing some alternative behaviour might be cute or something, but when the code is then used elsewhere, the bugs resulting from this unexpected behaviour are nearly impossible to find.

Descriptor Protocol Implementations

There are three questions to consider before discussing implementation details:

1. Is the descriptor class simply a way for owning classes to enhance attribute access?
2. Should classes implement the descriptor protocol to define their behaviour when owned by other classes?
3. Should a central descriptor class define how one class can own an instance of another?

There is certainly a need for classes to specialize access to their attributes. This is the most common use of the descriptor protocol. Python itself implements the property class to provide this control.

When creating a custom class, it seems reasonable to consider that other classes might own instance of it and to implement descriptor protocol methods as appropriate. Unfortunately, this is not commonly done meaning that you can expect to have to own instances of classes that do not provide for this ownership interaction themselves.

The AttriBox class provided by the worktoy.desc module does implement the descriptor protocol in a way designed to allow one class to own instances of another without either having to implement anything related to the descriptor protocol. This class also makes the second question redundant.

This discussion will now proceed with the following:

1. Usage of the property class provided by Python.
2. The worktoy.desc module provides the AbstractDescriptor class, which implements the parts of the descriptor protocol used by both Field and AttriBox.
3. Implementation of the vastly superior Field class provided by the worktoy.desc module. 4Usage an examples of the AttriBox class provided by the worktoy.desc module.

The property class

The property class is a built-in class in Python. It allows the use of a decorator to define getter, setter and deleter functions for a property. Alternatively, the property may be instantiated in the class body with the getter, setter and deleter functions as arguments. The following example will demonstrate a class owning a number and a name, demonstrating the two approaches to defining properties.

from __future__ import annotations


class OwningClass:
  """This class uses 'property' to implement the 'name' attribute. """

  __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')

The above example demonstrates the use of the property class to enhance the attribute access mechanism.

The AbstractDescriptor class

The AbstractDescriptor class provides the __set_name__ method and delegates accessor functions to the following methods:

• __instance_get__: Getter-function (Required!)
• __instance_set__: Setter-function (Optional)
• __instance_del__: Deleter-function (Optional)

When implementing __instance_get__ to handle a missing value, the subclass must raise a MissingValueException passing the instance and itself to the constructor of it. This missing value situation is one where no default value is provided and the value has not been set. The AbstractDescriptor calls __instance_get__ during the __set__ and delete__ methods to collect the old value which is used in the notification. If it catches such an error during __get__, it raises an AttributeError from it.

The AbstractDescriptor provides descriptors to mark methods to be notified when the attribute is accessed. The methods are:

• ONGET: Called when the attribute is accessed.
• ONSET: Called when the attribute is set.
• ONDEL: Called when the attribute is deleted.

Both Field and AttriBox subclass the AbstractDescriptor class. These are discussed below.

The Field class

The Field class provides descriptors in addition to those implemented on the AbstractDescriptor class. These are used by owning classes to mark accessor methods, much like the property class. Unlike the Python property class, instances of the Field class must be defined before the accessor methods in the class body. The decorators require their field instance to be defined in the lexical scope before they are available to decorate methods.

Below is an example of a plane point implementing the coordinate attributes using the Field class:

from __future__ import annotations
from worktoy.desc import Field


class Point:
  """This class uses the 'Field' descriptor to implement the coordinate
  attributes. """
  __x_value__ = None
  __y_value__ = None

  x = Field()
  y = Field()

  @x.GET
  def _getX(self) -> float:
    return self.__x_value__

  @x.SET
  def _setX(self, value: float) -> None:
    self.__x_value__ = value

  @y.GET
  def _getY(self) -> float:
    return self.__y_value__

  @y.SET
  def _setY(self, value: float) -> None:
    self.__y_value__ = value

  def __init__(self, *args, **kwargs) -> None:
    self.__x_value__ = kwargs.get('x', None)
    self.__y_value__ = kwargs.get('y', None)
    for arg in args:
      if isinstance(arg, int):
        arg = float(arg)
      if isinstance(arg, float):
        if self.__x_value__ is None:
          self.__x_value__ = arg
        elif self.__y_value__ is None:
          self.__y_value__ = arg
          break
    else:
      if self.__x_value__ is None:
        self.__x_value__, self.__y_value__ = 69., 420.
      elif self.__y_value__ is None:

        self.__y_value__ = 420.

The Field class allows classes to implement how attributes are accessed.

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 AttriBxo 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. Finally, it used the ONSET decorator to mark a method as the validator for the radius attribute. This causes the method to be hooked into the __set__ method on the radius.

from __future__ import annotations


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 3.1415926535897932 * self.radius ** 2

  @radius.ONSET
  def _validateRadius(self, _, value: float) -> None:
    if value < 0:
      e = """Received negative radius!"""
      raise ValueError(e)

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

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


if __name__ == '__main__':
  circle = Circle(69, 420, 1337)
  print(circle)
  circle.radius = 1
  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

worktoy.desc - Summary

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. In the following section, the importance of this lazy instantiation feature will be illustrated with an example involving the PySide6 library.

PySide6 - Qt for Python

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 class was envisioned for this very reason. Its lazy instantiation system prevents something called 'Segmentation Fault'. You can lead a long and happy life not ever encountering those words again, thanks to the AttriBox class!

Central to Qt is the main event loop managed by an instance of QCoreApplication or of a subclass of it. While the application is running, instances of QObject provide the actual application functionality. It is reasonable to regard the QObject in Qt and the object object in Python as similar. Every window, every widget, the running application, managed threads and just about everything else in Qt is essentially an instance of QObject. Having defined these terms, we may now discuss the two central rules of Qt:

• Only one instances of QCoreApplication may be running at any time.
• The first instantiated QObject must be an instance of the QCoreApplication class.

Instantiating any QObject before the QCoreApplication is running will result in immediate error. Enter the AttriBox class. The somewhat inflexible nature of the above rules regains flexibility by making use of the lazy instantiation provided by the AttriBox class.

In the following, we will see a simple application consisting of a window showing a welcome message provided as an instance of the QLabel class along with an exit button provided by the QPushButton class. These widgets are stacked vertically and are managed by an instance of the QVBoxLayout class and finally these are managed by an instance of QWidget which is the parent of the widgets and which owns the layout. This widget is set as the central widget in the main window. The main window itself is a subclass of the QMainWindow class.

import sys
from PySide6.QtWidgets import QApplication, QMainWindow
from PySide6.QtWidgets import QWidget, QLabel, QPushButton, QVBoxLayout
from PySide6.QtCore import QSize
from worktoy.desc import AttriBox


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

  baseWidget = AttriBox[QWidget]()
  layout = AttriBox[QVBoxLayout]()
  welcomeLabel = AttriBox[QLabel]()
  exitButton = AttriBox[QPushButton]()

  def initUi(self) -> None:
    """This method sets up the user interface"""
    self.setMinimumSize(QSize(480, 320))
    self.setWindowTitle("Welcome to WorkToy!")
    self.welcomeLabel.setText("""Welcome to WorkToy!""")
    self.exitButton.setText("Exit")
    self.layout.addWidget(self.welcomeLabel)
    self.layout.addWidget(self.exitButton)
    self.baseWidget.setLayout(self.layout)
    self.setCentralWidget(self.baseWidget)

  def initSignalSlot(self) -> None:
    """This method connects the signals and slots"""
    self.exitButton.clicked.connect(self.close)

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


if __name__ == '__main__':
  app = QApplication([])
  window = MainWindow()
  window.show()
  sys.exit(app.exec())

The above script demonstrates the use of the AttriBox class to provide lazy instantiation of the widgets in the PySide6 application.

Conclusion

To take full advantage of the descriptor protocol requires considerable boilerplate code if implemented from scratch. The worktoy.desc module provides the Field and AttriBox classes to minimize boilerplate. The Field class requires only the code you actually want to use. The AttriBox lets you set any class as an attribute in a single line.

worktoy.meta - Background

Python is the best programming language. The most important reason is that the syntax is made to be human-readable. Regardless of your personal preference for languages, you are never happier than when writing in Python. The objections to Python are valid. Provided you are talking about Python from 10 years ago. The final reason is the subject of this discussion: the Python metaclass. The Python metaclass is the most powerful single concept in programming. 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
• The Python Function
• The * and ** operators
• The Python lambda Function
• Class Instantiations
• The Custom Class
• The Custom Metaclass
• The Custom Namespace

Understanding the Python metaclass

Readers may associate the word meta with crime on account of the hype created around the term metaverse. This author hopes readers will come to associate the word instead with the Python metaclass. The 'worktoy.meta' module provides functions and classes allowing a more streamlined approach to metaclass programming. This documentation explains the functionality of metaclasses in general and how this module provides helpful tools.

Everything is an object!

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.

Extensions of object

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.

Defining a function

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

RANT

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!

END OF RANT

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.

The star * and double star ** operators

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.

RANT

This rant is left as an exercise to the reader

END OF RANT

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 double star ** operator

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.

RANT

If you have ever had the misfortune of working with C++ or Java, you would know that the syntax were disgusting, but you didn't know the words for it. The functionalities coded by C++ and Java cannot be inferred easily. It is necessary to see multiple parts of the code to infer what functionality is intended. For example, suppose we have a C++ class with a constructor.

class SomeClass {
private:
  int _a;
  int _b;
public:
  int a;
  SomeClass(int a, int b) {
      // Constructor code
  }
  int b {
    return _b;
  }
};

Find the constructor above. It does not have a name that means "Hello there, I am a constructor". Instead, it is named the same as the class itself. So to find the constructor, you need to identify the class name first then go through the class to find name again. The decision for this naming makes sense in that it creates something with the name called. But it significantly reduces readability. The second attack on human dignity is the syntax for the function definition. Where the class defines the public variable 'a', the syntax used is not bad. But because the syntax is identical for the functions, it increases the amount of code required to infer that a function is being created.

The two examples of nauseating syntax above do not serve any performance related purpose. Software engineering and development requires the full cognitive capability of the human brain. Deliberately obscuring code, reduces the cognitive capacity left over for actual problem-solving. This syntax is kept in place for no other purpose than gate-keeping.

END OF RANT

The famous function signature: def someFunc(*args, **kwargs)

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...)

The lambda function

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 that 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

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. This takes advantage of the fact that many such functions may be distinguished only by a factor mapped from the term in the series.

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.

Instantiation of classes

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.

Custom classes

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.

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.

The Custom Metaclass

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:

class AnyWayUWantIt(metaclass=MyMeta):
  """Class representing a point in the plane """

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.

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

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:

KeyError preservation

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.

The type.__new__ expects a namespace of dict-type

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.

Applications of Custom Namespace

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.

The worktoy.meta module

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.

Nomenclature

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.

Metaclass and Namespace Pattern

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.

Function Overloading

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

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.

Summary

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

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

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)

Summary of worktoy.ezdata module

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.

worktoy.text module

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

worktoy.text.stringList

"""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)

worktoy.text.monoSpace

"""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'))

worktoy.text.wordWrap

"""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))

worktoy.text.typeMsg

"""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)

worktoy.text.joinWords

"""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))

worktoy.parse module

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.