useful-decorators 0.0.3

Some decorators for Python

UsefulDecorators

Some Decorators I use for various purposes. Some more useful, some less.

This is a collection of some Decorators for Python you might find useful.

pip install usefull-decorators

Programming Patterns

Cache

The Cache Decorator will automatically cache the creation of an object of the decorated class.

from useful_decorators.patterns import Cache

def disable():
    return True

@Cache(disable=disable)
class Test:
  def __init__(self,q,p=False):
    self.q = q
    self.p = p
 
q1 = Test("test",True) #Create a new Object
q2 = Test("test",True) #This will return the SAME object form the cache
q3 = Test("test2") #This will create a new Object.

On each call of an __init__ method, the cache will check if there ever was another call that used the same arguments, and if yes return that. If you really want a new Object ( ignore the cache ) you can use <class>.__create_no_cache() with the arguments you would usually pass to the __init__ method. You can also reset the cache with <class>.__clear_cache() You can also use the 'disable' property of the decorator. This allows you to specify a function which is called to check if the cache is enabled or disabled.

Test.__create_no_cache("hello",True) #Always creates a new Object (will not be added to the cache)
#or
Test.__clear_cache()
Test("hello",True)

Note that cached objects are the same. If you change an attribute on one of the objects, the other will change too. In the example above changing something in q1 will also change q2.

Singleton

The Singleton decorator will change the class to only ever create one instance. This is done by injecting code in the __init__ method which checks if an object of the class is already created.

from useful_decorators.patterns import Singleton

@Singleton(strict=False)
class Test:
    def __init__(self, q, p=False):
        self.q = q
        self.p = p


q1 = Test("test", True)  # Creates a new Object
q2 = Test("test", True)  # This is the same object as q1
q3 = Test("test2")  # If strict is disabled, this is ALSO the same objects as q1

There are basically two methods to handle calls to the __init__ method after the original object was created. We can ignore the call no matter the arguments and return the singleton in all cases (default) or we can raise an error. It is always better to use another method to get the instance because it is clearer to everybody who reads the code. So after you create an instance you can do the following

q2 = Test.getInstance()

Which returns the instance. This is the only way when strict is set to true to obtain an instance. If strict is set to false, you can overwrite the current instance by using

Test.resetInstance()
Test("hi",True)
#OR
Test.createNew("hi",True)

createNew is just a shortcut to reset and create at the same time.

Observable

The Observable decorator allows to attach function calls to changes in the fields of the class.

from useful_decorators.patterns import Observable

def changeling(q):
    print(q)


@Observable(attach=[("q", changeling)]) #Now changeling gets called with the new value everytime a new value is assigned to the field named 'q'
class Test:
    def __init__(self, q, p=False):
        self.q = q
        self.p = p


q1 = Test("hello")
q1.q = "world" #changeling gets called with 'world'

The decorator expects a list of tuples as the attach keyword. Each tuple must contain a name of a field in the class, and a method to call. This method must take exactly ONE argument (the updated value). Currently, there is no function call if the field gets deleted with del.

builder_function

The builder_function decorator is just a shortcut for a method returning the object it belongs to. So instead of

class Test:
    def __init__(self):
        self.q = None
        self.p = None
        
    def set_q(self,q):
        self.q = q
        return self

you can write

from useful_decorators.patterns import builder_function

class Test:
    def __init__(self):
        self.q = None
        self.p = None
        
    @builder_function    
    def set_q(self,q):
        self.q = q

This allows for easy construction of Objects that can be used like this obj.setx(x).create_q(q,v=True)....

Functions

This repo provides some decorators for functions in general.

infix

This decorator allows you to make a function (which takes two arguments) into an infix operator. Your function name has to be surrounded by '|'.

from useful_decorators.functions import infix
@infix
def test(q1, q2):
    return q1 + 2 * q2


print("Hello" | test | "World" | test | "1")

attach

This decorator allows you to attach another function to the decorated function.

from useful_decorators.functions import attach

def attached(q=1):
  print("world"*q)
 
@attach(attached)
def foo():
  print("hello")
 
foo() # hello world

You can also specify the arguments that shall be passed to the attached function like so

from useful_decorators.functions import attach

def attached(q=1):
  print("world"*q)
 
@attach(attached,q=2)
def foo():
  print("hello")
 
foo() # hello worldworld

Of course, you can attach multiple methods as well. Just add more @attach decorators.

prepend

this is exactly the same as attach but will execute the specified method before the original method.

chain

the chain decorator allows for chaining functions together and feeding ones output in another ones input.

from useful_decorators.functions import chain

def q1(q,h=2):
  print(q*h)

@chain(q1)
def q2(q):
  return q+1
  
@chain(q1,3) #same as @chain(q1,h=3)
def q3(q):
  return q+1
  
q2(1) # 4 because (1+1)*2 = 4
q3(1) # 6 because (1+1)*3 = 6

The input is always the FIRST argument of the chained function. The function with the decorator is always evaluated first and then fed into the function which is mentioned in the decorator. Of course the chain can be as long as you want.