Collection of Utilities
Project description
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
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:
- 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')
- Event 2 - This marks the creation of an instance of the owning class. This event is not of interest to this discussion.
- Event 3 - Accessing the descriptor on the OwningClass triggers the
following function call:
Descriptor.__get__(descriptor, None, OwningClass)
- 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:
- Is the descriptor class simply a way for owning classes to enhance attribute access?
- Should classes implement the descriptor protocol to define their behaviour when owned by other classes?
- 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:
- Usage of the
property
class provided by Python. - The
worktoy.desc
module provides theAbstractDescriptor
class, which implements the parts of the descriptor protocol used by bothField
andAttriBox
. - Implementation of the vastly superior
Field
class provided by theworktoy.desc
module. 4Usage an examples of theAttriBox
class provided by theworktoy.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 theQCoreApplication
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 thatobject
itself is not sufficient. The hashables on this list are:int
,float
,str
andtuple
.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 ofset
allowing it to be used as a key in mappings.str
- Extension for strings. This class provides a number of methods for manipulating strings. Theworktoy.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 insteadset()
ordict()
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))
ordata = 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))
ordata = 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 suggestedsmartList
andsmartTuple
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 thetype
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 ontype
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 objectself
- 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 argumentseparator
, 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. ThemonoSpace
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 aTypeError
exception when anobject
namedname
did not belong to the expected classcls
.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 fromNone
.maybeType
: Same asmaybe
but ignoring arguments that are not of the expected type given as the first positional argument.
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