A simple library for implementing common design patterns.

Overview

PyPattyrn

from pypattyrn.creational.singleton import Singleton

class DummyClass(object, metaclass=Singleton):  #  DummyClass is now a Singleton!
    ...

PyPattyrn is a python package aiming to make it easier and faster to implement design patterns into your own projects.

Design patterns by nature cannot be directly translated into code as they are just a description of how to solve a particular problem. However, many of the common design patterns have boilerplate code that is common throughout all implementations of the pattern. This package captures that common code and makes it easy to use so that you dont have to write it yourself in all your projects.


Contents



Installation


pip install pypattyrn

or

git clone https://github.com/tylerlaberge/PyPattyrn.git
cd PyPattyrn
python setup.py install

Examples


Behavioral Patterns

Patterns which deal with communication between objects.


Chain of Responsibility Pattern

Pass a request along a chain of objects until the request is handled.

from pypattyrn.behavioral.chain import Chain, ChainLink


class ConcreteChainLinkThree(ChainLink): # This object is a ChainLink

    def handle(self, request): # Implement the handle method.
        if request == 'handle_three':
            return "Handled in chain link three"
        else:
            return self.successor_handle(request) # If this ChainLink can't handle the request, 
                                                  # ask its successor to handle it. 
                                                  # (Has no successor so will raise AttributeError)
                                                  # (This exception is caught and will call a Chains fail method)


class ConcreteChainLinkTwo(ChainLink): # This object is a ChainLink

    def __init__(self): # Override init to set a successor on initialization.
        super().__init__() # first call ChainLinks init
        self.set_successor(ConcreteChainLinkThree()) # Set the successor of this chain link 
                                                     # to a ConcreteChainLinkThree instance.

    def handle(self, request): # Implement the handle method.
        if request == 'handle_two':
            return "Handled in chain link two"
        else:
            return self.successor_handle(request) # If this ChainLink can't handle a request 
                                                  # ask its successor to handle it 
                                                  # (the ConcreteChainLinkThree instance).


class ConcreteChainLinkOne(ChainLink): # This object is a ChainLink

    def __init__(self): 
        super().__init__()
        self.set_successor(ConcreteChainLinkTwo()) # Set the successor of this ChainLink
                                                   # to a ConcreteChainLinkTwo instance.

    def handle(self, request): # Implement the handle method.
        if request == 'handle_one':
            return "Handled in chain link one"
        else:
            return self.successor_handle(request) # If this ChainLink can't handle a request 
                                                  # ask its successor to handle it 
                                                  # (the ConcreteChainLinkTwo instance).

class ConcreteChain(Chain): # This object is a Chain

    def __init__(self): # Override init to initialize a Chain with the starting chain link.
        super().__init__(ConcreteChainLinkOne()) # Initialize this Chain with a start chain link.
                                                 # (a ConcreteChainLinkOne instance)

    def fail(self): # Implement the fail method, this is called if no chain links could handle a request.
        return 'Fail'


chain = ConcreteChain()

assert "Handled in chain link one" == chain.handle("handle_one")
assert "Handled in chain link two" == chain.handle("handle_two")
assert "Handled in chain link three" == chain.handle("handle_three")
assert "Fail" == chain.handle('handle_four')
Command Pattern

Encapsulate information for performing an action into an object.

from pypattyrn.behavioral.command import Receiver, Command, Invoker


class Thermostat(Receiver): # This object is a Receiver. 
                            # Contains methods for commands to use.

    def raise_temp(self, amount):
        return "Temperature raised by {0} degrees".format(amount)

    def lower_temp(self, amount):
        return "Temperature lowered by {0} degrees".format(amount)


class RaiseTempCommand(Command): # This object is a Command.

    def __init__(self, receiver, amount=5): # Override init to include a temperature amount argument.
        super().__init__(receiver)
        self.amount = amount

    def execute(self): # Implement the execute method.
        return self._receiver.action('raise_temp', self.amount) # Call on the Receiver's action method  with 
                                                                # the name of the method to call and args.
                                                                # (Raises the temperature by 5 degrees.)

    def unexecute(self): # Implement the unexecute method.
        return self._receiver.action('lower_temp', self.amount) # Calls on the Receiver to lower 
                                                                # the temperature by 5 degrees.
                                                     

class LowerTempCommand(Command): # This object is a Command.
 
   def __init__(self, receiver, amount=5):
        super().__init__(receiver)
        self.amount = amount

    def execute(self):
        return self._receiver.action('lower_temp', self.amount) # Call on the receiver to lower the
                                                                # temperature by 5 degrees.

    def unexecute(self):
        return self._receiver.action('raise_temp', self.amount) # Call on the receiver to raise the 
                                                                # temperature by 5 degrees.


class Worker(Invoker): # This object is the Invoker

    def __init__(self): # Override init to initialize an Invoker with Commands to accept.
        super().__init__([LowerTempCommand, RaiseTempCommand]) # Initialize the Invoker with the
                                                               # LowerTempCommand and RaiseTempCommand classes.


thermostat = Thermostat() # Create a Receiver.
worker = Worker() # Create an Invoker.

assert "Temperature lowered by 5 degrees" == worker.execute(LowerTempCommand(thermostat)) # Have the Invoker execute a LowerTempCommand
                                                                                          # which uses the thermostat Receiver.
assert "Temperature raised by 5 degrees" == worker.execute(RaiseTempCommand(thermostat)) # Have the Invoker execute a RaiseTempCommand
                                                                                          # which uses the thermostat Receiver.
assert "Temperature lowered by 5 degrees" == worker.undo() # Undo the previous command (Calls the RaiseTempCommand unexecute method.)
Iterator Pattern

A way of sequentially accessing elements in a collection.

from pypattyrn.behavioral.iterator import Iterable, Iterator


class Counter(Iterable): # This object is an Iterable

    def __init__(self, max):
        self.count = 0
        self.max = max

    def __next__(self): # Implement the __next__ method.
        self.count += 1 # Increment the count
        if self.count > self.max:
            raise StopIteration() # make sure to raise StopIteration at the appropriate time.
        else:
            return self.count - 1 # Return the count


class CounterIterator(Iterator): # This object is an Iterator

    def __init__(self): # Override init to initialize an Iterator with the Counter object.
        super().__init__(Counter(10)) # Initialize the iterator with a Counter that goes to 10.

counter_iterator = CounterIterator() # Create a CounterIterator.

for count in counter_iterator: # You can loop through it how you would expect.
    print(count)  # 0, 1, 2, 3, ..., 9
Mediator Pattern

An intermediary for managing communications between many objects.

from pypattyrn.behavioral.mediator import Mediator


class Dog(object):
    sound = ''

    def set_sound(self, sound):
        self.sound = sound


class Cat(object):
    sound = ''

    def set_sound(self, sound):
        self.sound = sound


dog = Dog()
cat = Cat()

mediator = Mediator() # Create a Mediator object.

mediator.connect('set_dog_sound', dog.set_sound) # Connect the signal 'set_dog_sound' to the dog.set_sound method.
mediator.connect('set_cat_sound', cat.set_sound) # Connect the signal 'set_cat_sound' to the cat.set_sound method.

mediator.connect('set_sound', dog.set_sound) # Also connect the signal 'set_sound' to the dog.set_sound method.
mediator.connect('set_sound', cat.set_sound) # Also connect the signal 'set_sound' to the cat.set_sound method.

mediator.signal('set_sound', 'foo') # Send out the signal 'set_sound' 
                                    # with an argument to be passed to the connected methods.
                                    # (Sets dog.sound and cat.sound to 'foo')


assert 'foo' == dog.sound 
assert 'foo' == cat.sound

mediator.signal('set_dog_sound', 'woof') # Send out the signal 'set_dog_sound'
                                         # (Sets dog.sound to 'woof')
mediator.signal('set_cat_sound', 'meow') # Send out the signal 'set_cat_sound'
                                         # (Sets cat.sound to 'meow')

assert 'woof' == dog.sound
assert 'meow' == cat.sound

mediator.disconnect('set_sound', dog.set_sound) # Disconnect the method dog.sound from the signal 'set_sound'

mediator.signal('set_sound', 'bar') # Send out the signal 'set_sound'
                                    # (Only sets cat.sound to 'bar' because we disconnected dog.sound)

assert 'woof' == dog.sound
assert 'bar' == cat.sound
Memento Pattern

Save the state of an object and rollback to it at a later time.

from pypattyrn.behavioral.memento import Originator


class Cat(Originator): # This object is an Originator
    def __init__(self, name):
        self.name = name


cat = Cat('Tom') # Initialize a Cat with the name 'Tom'
assert cat.name == 'Tom'

cat_memento = cat.commit() # Save the cats current state to a Memento object.
cat.name = 'jerry' # Change the cats name to 'jerry'
assert cat.name == 'jerry'

cat.rollback(cat_memento) # Restore the cats state to the memento object we saved.
assert 'Tom' == cat.name # The cats name was changed back to 'Tom' as expected.
Null Object Pattern

Encapsulate the absence of an object into an object with neutral behavior.

from pypattyrn.behavioral.null import Null

# You can initialize a Null object with any parameters.
try:
    Null()
    Null('value')
    Null('value', param='value')
except:
    raise AssertionError()

null = Null()

# Attempting to set parameters won't do anything and won't error
try:
    null.foo = 'foo' 
    null.foo.bar = 'bar'
except:
    raise AssertionError()

# Calling on a null object in any way will just return that null object.

assert null == null()
assert null == null('value')
assert null == null('value', param='value')
assert null == null.foo
assert null == null.foo.bar
assert null == null.foo.bar.method1()
assert null == null.foo.bar.method1().method2('foo', bar='bar').attr3

assert str(null) == '' # Null objects string representation is the empty string.
assert repr(null) == '' # Null objects repr is the empty string.
assert bool(null) is False # Null object evaluates to False as a boolean.

# Trying to delete attributes doesn't do anything and won't error.
try:
    del null.foo 
    del null.foo.bar
except:
    raise AssertionError()
Observer Pattern

Notify many objects when a single object's state changes.

from pypattyrn.behavioral.observer import Observable, Observer


class ConcreteObservable(Observable): # This object is an Observable.
    _kinda_private_var = 'I am kinda private'
    __private_var = 'I am more private'

    def change_state(self, **kwargs): # Some method to change this objects state and notify observers.
        for key, value in kwargs.items():
            setattr(self, key, value) # Change the objects state.

        self.notify() # Notify all observers of the change in state.
                      # Will call the update method of each attached observer with kwargs that come
                      # from this objects __dict__ attribute, minus any private variables (starts with __ or _)


class ConcreteObserver(Observer): # This object is an Observer.
    updated_state = None

    def update(self, **state): # Implement the update method. 
                               # Called when an attached observable calls its notify method.
        self.updated_state = state


observable = ConcreteObservable() # Create an Observable
observer_1 = ConcreteObserver() # Create Observers
observer_2 = ConcreteObserver()
observer_3 = ConcreteObserver()

observable.attach(observer_1) # Attach each of the Observers to the Observable
observable.attach(observer_2)
observable.attach(observer_3)

observable.change_state(foo='foo', bar='bar') # Change the Observable's state.
                                              # Also calls notify which will call each Observers update method.

expected_state = {'foo': 'foo', 'bar': 'bar'} 

# Make sure each Observers state was changed accordingly when the notify method was called by the Observable.

assert sorted(expected_state.keys()) == sorted(observer_1.updated_state.keys()) and \
       sorted(expected_state.values()) == sorted(observer_1.updated_state.values())

assert sorted(expected_state.keys()) == sorted(observer_2.updated_state.keys()) and \
       sorted(expected_state.values()) == sorted(observer_2.updated_state.values())

assert sorted(expected_state.keys()) == sorted(observer_3.updated_state.keys()) and \
       sorted(expected_state.values()) == sorted(observer_3.updated_state.values())

observable.detach(observer_1) # Detach Observer 1 from the Observable
observable.change_state(bar='foobar') # Change the Observables state.
expected_state_2 = {'foo': 'foo', 'bar': 'foobar'}

# Make sure each Observers state was changed accordingly when notify was called by the Observable,
# Except for observer_1 because we detached that Observer from the Observable.

assert sorted(expected_state_2.keys()) != sorted(observer_1.updated_state.keys()) or \
       sorted(expected_state_2.values()) != sorted(observer_1.updated_state.values())

assert sorted(expected_state_2.keys()) == sorted(observer_2.updated_state.keys()) and \
       sorted(expected_state_2.values()) == sorted(observer_2.updated_state.values())

assert sorted(expected_state_2.keys()) == sorted(observer_3.updated_state.keys()) and \
       sorted(expected_state_2.values()) == sorted(observer_3.updated_state.values())
Visitor Pattern

Add new operations to an existing class without modifying it.

from pypattyrn.behavioral.visitor import Visitee, Visitor


class A(Visitee): # This object is a Visitee
    pass


class B(A): # This object is an A, which also makes this a Visitee.
    pass


class C(B): # This object is a B, which also makes this a Visitee.
    pass


class D(Visitee): # This object is a Visitee
    pass


class NodeVisitor(Visitor): # This object is a Visitor

    def generic_visit(self, node, *args, **kwargs): # Implement the generic visit method.
                                                    # This is called when there is no visit_ method
                                                    # implemented for a particular visitee.

        return 'generic_visit ' + node.__class__.__name__

    def visit_b(self, node, *args, **kwargs): # Called when this object visits a Visitee of class 'B'.
        return 'visit_b ' + node.__class__.__name__

    def visit_d(self, node, *args, **kwargs): # Called when this object visits a Visitee of class 'D'.
        return 'visit_d {0} args: {1} kwargs: {2}'.format(node.__class__.__name__, args, kwargs)

# Create Visitee's
node_a = A()
node_b = B()
node_c = C()
node_d = D()

# Create Visitor
node_visitor = NodeVisitor()

assert 'generic_visit A' == node_a.accept(node_visitor) # Visit node_a with the Visitor.
                                                        # Since the visitor does not have a visit_a method,
                                                        # generic_visit is called.
assert 'visit_b B' == node_b.accept(node_visitor) # Visit node_b with the Visitor.
                                                  # Calls the visitors visit_b method.
assert 'visit_b C' == node_c.accept(node_visitor) # Visit node_c with the Visitor.
                                                  # Even though the visitor does not have a visit_c method, 
                                                  # since node_c inherits B, the Visitors visit_b method is called.

# Visit node_d with the Visitor. Calls the visitors visit_d method.
assert "visit_d D args: ('foo', 'bar') kwargs: {'foobar': 'foobar'}" == node_d.accept(node_visitor,
                                                                                      'foo', 'bar',
                                                                                      foobar='foobar')

Creational Patterns

Patterns which deal with object creation.


Builder Pattern

Separate object construction from its representation.

from pypattyrn.creational.builder import Builder, Director


class Building(object):  # The object being constructed.

    def __init__(self):
        self.floor = None
        self.size = None

    def __repr__(self):
        return 'Floor: {0.floor} | Size: {0.size}'.format(self)


class HomeBuilder(Builder):  # A base Builder class for constructing homes.

    def __init__(self):
        super().__init__(Building())  # Initialize the Builder class with a Building instance.
        self._register('floor', self._build_floor)  # Register the keyword 'floor' with the _build_floor method.
        self._register('size', self._build_size)  # Register the keyword 'size' with the _build_size method.

    def _build_floor(self):
        pass

    def _build_size(self):
        pass


class HouseBuilder(HomeBuilder):  # A concrete HomeBuilder class for constructing houses

    def _build_floor(self):
        self.constructed_object.floor = 'One'  # Alter the Building's floor attribute.

    def _build_size(self):
        self.constructed_object.size = 'Big'  # Alter the Building's size attribute.


class FlatBuilder(HomeBuilder):  # A concrete HomeBuilder class for constructing flats

    def _build_floor(self):
        self.constructed_object.floor = 'More than one'  # Alter the Building's floor attribute.

    def _build_size(self):
        self.constructed_object.size = 'Small'  # Alter the Building's size attribute.


class HomeDirector(Director):  # A Director class for managing home construction.

    def construct(self):
        self.builder.build('floor')  # Build the floor part of the Building by using the keyword 'floor'
        self.builder.build('size')  # Build the size part of the Building by using the keyword 'size'


home_director = HomeDirector()

home_director.builder = HouseBuilder()  # Use the house builder.
home_director.construct()  # Construct the house.
house = home_director.get_constructed_object()  # Get the constructed house.
print(repr(house))    #Floor: One | Size: Big
    
home_director.builder = FlatBuilder()  # Use the flat builder.
home_director.construct()  # Construct the flat.
house = home_director.get_constructed_object()  # Get the constructed flat.
print(repr(house))    #Floor: More than one | Size: Small
Factory Pattern

An interface for creating an object.

from pypattyrn.creational.factory import Factory  # This is just an interface


class Cat(object):

    def speak(self):
        print('meow')


class Dog(object):

    def speak(self):
        print('woof')


class AnimalFactory(Factory):  # A factory class for creating animals.

    def create(self, animal_type):  # Implement the abstract create method.
        if animal_type == 'cat':
            return Cat()
        elif animal_type == 'dog':
            return Dog()
        else:
            return None


animal_factory = AnimalFactory()

cat = animal_factory.create('cat')
dog = animal_factory.create('dog')

cat.speak()  # 'meow'
dog.speak()  # 'woof'
Abstract Factory Pattern

Create an instance from a family of factories.

from pypattyrn.creational.factory import Factory, AbstractFactory


class Cat(object):

    def speak(self):
        print('meow')


class Dog(object):

    def speak(self):
        print('woof')


class Ant(object):

    def march(self):
        print('march')


class Fly(object):

    def fly(self):
        print('fly')


class AnimalFactory(Factory):  # A factory class for creating animals.

    def create(self, animal_type):  # Implement the abstract create method.
        if animal_type == 'cat':
            return Cat()
        elif animal_type == 'dog':
            return Dog()
        else:
            return None


class InsectFactory(Factory):  # A factory class for creating insects.

    def create(self, insect_type):  # Implement the abstract create method.
        if insect_type == 'ant':
            return Ant()
        elif insect_type == 'fly':
            return Fly()
        else:
            return None


class CreatureFactory(AbstractFactory):  # A Factory class for creating creatures.

    def __init__(self):
        super().__init__()
        self._register('insect_factory', InsectFactory())  # Register an InsectFactory with a keyword.
        self._register('animal_factory', AnimalFactory())  # Register an AnimalFactory with a keyword.

    def create(self, creature_type):  # Implement the Abstract create method.
        if creature_type == 'cat' or creature_type == 'dog':
            return self._factories['animal_factory'].create(creature_type)  # Use the AnimalFactory
        elif creature_type == 'ant' or creature_type == 'fly':
            return self._factories['insect_factory'].create(creature_type)  # Use the InsectFactory
        else:
            return None

creature_factory = CreatureFactory()

cat = creature_factory.create('cat')
dog = creature_factory.create('dog')
ant = creature_factory.create('ant')
fly = creature_factory.create('fly')

cat.speak()  # 'meow'
dog.speak()  # 'woof'
ant.march()  # 'march'
fly.fly()  # 'fly'
Object Pool Pattern

Provide a pool of instantiated objects which can be checked out and returned rather than creating new objects all the time.

from pypattyrn.creational.pool import Reusable, Pool


class Dog(Reusable):
    def __init__(self, sound):
        self.sound = sound
        super().__init__()


class DogPool(Pool):
    def __init__(self):
        super().__init__(Dog, 'woof')


dog_pool = DogPool()

dog_one = dog_pool.acquire()
dog_two = dog_pool.acquire()
dog_two.sound = 'meow'

dog_pool.release(dog_one)
dog_three = dog_pool.acquire()

dog_pool.release(dog_two)
dog_four = dog_pool.acquire()

assert id(dog_one) == id(dog_three)
assert id(dog_two) == id(dog_four)
assert dog_one.sound == dog_two.sound
assert dog_three.sound == dog_four.sound
assert dog_one.sound == dog_four.sound
Prototype Pattern

Clone an object to produce new objects.

from pypattyrn.creational.prototype import Prototype


class Point(Prototype):
    def __init__(self, x, y):
        self.x = x
        self.y = y

    def move(self, x, y):
        self.x += x
        self.y += y


point_one = Point(15, 15)
point_two = point_one.prototype(z=20)
point_three = point_two.prototype()

assert point_one.x == point_two.x
assert point_one.y == point_two.y
assert not hasattr(point_one, 'z')
assert hasattr(point_two, 'z')
assert point_two.z == 20
assert point_three.__dict__ == point_two.__dict__

from math import sqrt
def distance_to(this, other):
    return sqrt((this.x - other.x) ** 2 + (this.y - other.y) ** 2)

point_four = point_three.prototype(distance_to=distance_to)

assert hasattr(point_four, 'distance_to')
assert point_four.distance_to(point_three) == 0
Singleton Pattern

Ensure that only a single instance of a class exists.

from pypattyrn.creational.singleton import Singleton


class DummySingletonOne(object, metaclass=Singleton):

    def __init__(self):
        pass


class DummySingletonTwo(object, metaclass=Singleton):

    def __init__(self):
        pass


dummy_class_one_instance_one = DummySingletonOne()
dummy_class_one_instance_two = DummySingletonOne()

dummy_class_two_instance_one = DummySingletonTwo()
dummy_class_two_instance_two = DummySingletonTwo()

assert id(dummy_class_one_instance_one) == id(dummy_class_one_instance_two)
assert id(dummy_class_two_instance_one) == id(dummy_class_two_instance_two)

assert id(dummy_class_one_instance_one) != id(dummy_class_two_instance_one)
assert id(dummy_class_one_instance_two) != id(dummy_class_two_instance_two)

Structural Patterns

Patterns which deal with object composition


Adapter Pattern

Wrap an object into an interface which the client expects.

from pypattyrn.structural.adapter import Adapter


class Dog(object):
    def __init__(self):
        self.name = "Dog"

    def bark(self):
        return "woof!"


class Cat(object):
    def __init__(self):
        self.name = "Cat"

    def meow(self):
        return "meow!"

cat = Cat()
dog = Dog()

cat_adapter = Adapter(cat, make_noise=cat.meow)
dog_adapter = Adapter(dog, make_noise=dog.bark)

assert cat_adapter.make_noise == cat.meow
assert dog_adapter.make_noise == dog.bark

assert cat_adapter.make_noise() == 'meow!'
assert dog_adapter.make_noise() == 'woof!'

assert cat_adapter.name == 'Cat'
assert dog_adapter.name == 'Dog'

assert cat_adapter.meow() == 'meow!'
assert dog_adapter.bark() == 'woof!'

assert cat_adapter.original_dict() == cat.__dict__
assert dog_adapter.original_dict() == dog.__dict__

bad_cat_adapter = Adapter(cat, foo=cat.name)

try:
    bad_cat_adapter.foo
except AttributeError:
    pass
else:
    raise AssertionError()

bad_dog_adapter = Adapter(dog, make_noise=cat.meow)

try:
    bad_dog_adapter.make_noise()
except AttributeError:
    pass
else:
    raise AssertionError()
Composite Pattern

Compose objects into a tree structure of objects that can be treated uniformly.

from pypattyrn.structural.composite import Composite


class Component(object):

    def do_something(self):
        pass


class Leaf(Component):

    def __init__(self):
        self.did_something = False

    def do_something(self):
        self.did_something = True


leaf_one = Leaf()
leaf_two = Leaf()
leaf_three = Leaf()

composite_one = Composite(Component)
composite_two = Composite(Component)
composite_three = Composite(Component)

composite_one.add_component(leaf_one)
composite_two.add_component(leaf_two)
composite_three.add_component(leaf_three)

composite_two.add_component(composite_three)
composite_one.add_component(composite_two)


assert set() == {leaf_one, composite_two}.symmetric_difference(composite_one.components)
assert set() == {leaf_two, composite_three}.symmetric_difference(composite_two.components)
assert set() == {leaf_three}.symmetric_difference(composite_three.components)

composite_two.remove_component(composite_three)

assert set() == {leaf_two}.symmetric_difference(composite_two.components)

assert not leaf_one.did_something
assert not leaf_two.did_something
assert not leaf_three.did_something

composite_one.do_something()

assert leaf_one.did_something
assert leaf_two.did_something
assert not leaf_three.did_something
Decorator Pattern

Attach additional functionality to functions.

import time
from pypattyrn.structural.decorator import DecoratorSimple, DecoratorComplex, CallWrapper


class TimeThis(DecoratorSimple):

    def __call__(self, *args, **kwargs):
        start = time.time()
        result = self.func(*args, **kwargs)
        end = time.time() - start
        return result, end


class SlowClass(object):

    @TimeThis
    def slow_function(self, n):
        time.sleep(n)
        return 'foo'

slow_class = SlowClass()
result = slow_class.slow_function(1)
assert result[0] == 'foo'
assert 1 <= result[1] <= 2


class Alert(DecoratorComplex):

    def __init__(self, alert_time):
        self.alert_time = alert_time

    @CallWrapper
    def __call__(self, func, *args, **kwargs):
        start = time.time()
        return_val = func(*args, **kwargs)
        end = time.time() - start
        if end > self.alert_time:
            return return_val, True
        return return_val, False


class SlowClass(object):

    @Alert(1)
    def slow_function_true(self, n):
        time.sleep(n)
        return n

    @Alert(1)
    def slow_function_false(self, n):
        return n


slow_class = SlowClass()
assert (2, True) == slow_class.slow_function_true(2)
assert (10, False) == slow_class.slow_function_false(10)
Flyweight Pattern

Share data with other similar objects to increase efficiency.

from pypattyrn.structural.flyweight import FlyweightMeta


class Card(object, metaclass=FlyweightMeta):

    def __init__(self, suit, value):
        self.suit = suit
        self.value = value


three_of_spades = Card('Spade', 3)
four_of_spades = Card('Spade', 4)
three_of_spades_two = Card('Spade', 3)

assert id(three_of_spades) == id(three_of_spades_two)
assert id(three_of_spades) != id(four_of_spades)

Resources



You might also like...
A Domain Specific Language (DSL) for building language patterns. These can be later compiled into spaCy patterns, pure regex, or any other format
A Domain Specific Language (DSL) for building language patterns. These can be later compiled into spaCy patterns, pure regex, or any other format

RITA DSL This is a language, loosely based on language Apache UIMA RUTA, focused on writing manual language rules, which compiles into either spaCy co

IDAPatternSearch adds a capability of finding functions according to bit-patterns into the well-known IDA Pro disassembler based on Ghidra’s function patterns format.

IDA Pattern Search by Argus Cyber Security Ltd. The IDA Pattern Search plugin adds a capability of finding functions according to bit-patterns into th

A collection of design patterns/idioms in Python

python-patterns A collection of design patterns and idioms in Python. Current Patterns Creational Patterns: Pattern Description abstract_factory use a

A Sklearn-like Framework for Hyperparameter Tuning and AutoML in Deep Learning projects. Finally have the right abstractions and design patterns to properly do AutoML. Let your pipeline steps have hyperparameter spaces. Enable checkpoints to cut duplicate calculations. Go from research to production environment easily.
A collection of design patterns and idioms in Python (With tests!).

Python Patterns Help the project financially: Donate: https://smartlegion.github.io/donate/ Yandex Money: https://yoomoney.ru/to/4100115206129186 PayP

System Design Assignments as part of Arpit's System Design Masterclass
System Design Assignments as part of Arpit's System Design Masterclass

System Design Assignments The repository contains a set of problem statements around Software Architecture and System Design as conducted by Arpit's S

An awesome list of AI for art and design - resources, and popular datasets and how we may apply computer vision tasks to art and design.
An awesome list of AI for art and design - resources, and popular datasets and how we may apply computer vision tasks to art and design.

Awesome AI for Art & Design An awesome list of AI for art and design - resources, and popular datasets and how we may apply computer vision tasks to a

A Python library for detecting patterns and anomalies in massive datasets using the Matrix Profile
A Python library for detecting patterns and anomalies in massive datasets using the Matrix Profile

matrixprofile-ts matrixprofile-ts is a Python 2 and 3 library for evaluating time series data using the Matrix Profile algorithms developed by the Keo

kikuchipy is an open-source Python library for processing and analysis of electron backscatter diffraction (EBSD) patterns
kikuchipy is an open-source Python library for processing and analysis of electron backscatter diffraction (EBSD) patterns

kikuchipy is an open-source Python library for processing and analysis of electron backscatter diffraction (EBSD) patterns. The library builds on the

DeepHawkeye is a library to detect unusual patterns in images using features from pretrained neural networks
DeepHawkeye is a library to detect unusual patterns in images using features from pretrained neural networks

English | 简体中文 Introduction DeepHawkeye is a library to detect unusual patterns in images using features from pretrained neural networks Reference Pat

A python library for implementing a recommender system

python-recsys A python library for implementing a recommender system. Installation Dependencies python-recsys is build on top of Divisi2, with csc-pys

Ariadne is a Python library for implementing GraphQL servers using schema-first approach.
Ariadne is a Python library for implementing GraphQL servers using schema-first approach.

Ariadne Ariadne is a Python library for implementing GraphQL servers. Schema-first: Ariadne enables Python developers to use schema-first approach to

Library for implementing reservoir computing models (echo state networks) for multivariate time series classification and clustering.
Library for implementing reservoir computing models (echo state networks) for multivariate time series classification and clustering.

Framework overview This library allows to quickly implement different architectures based on Reservoir Computing (the family of approaches popularized

Telegram bot implementing Lex Arcana using python-telegram-bot library.
Telegram bot implementing Lex Arcana using python-telegram-bot library.

Lex Arcana Telegram Bot 🤖 Telegram bot implementing Lex Arcana using python-telegram-bot library. This bot was evaluated for the course "Computer Eng

Pynomial - a lightweight python library for implementing the many confidence intervals for the risk parameter of a binomial model

Pynomial - a lightweight python library for implementing the many confidence intervals for the risk parameter of a binomial model

This is an open source library implementing hyperbox-based machine learning algorithms
This is an open source library implementing hyperbox-based machine learning algorithms

hyperbox-brain is a Python open source toolbox implementing hyperbox-based machine learning algorithms built on top of scikit-learn and is distributed

 Simple yet powerful CAD (Computer Aided Design) library, written with Python.
Simple yet powerful CAD (Computer Aided Design) library, written with Python.

Py-MADCAD it's time to throw parametric softwares out ! Simple yet powerful CAD (Computer Aided Design) library, written with Python. Installation

Implementation of the Angular Spectrum method in Python to simulate Diffraction Patterns
Implementation of the Angular Spectrum method in Python to simulate Diffraction Patterns

Diffraction Simulations - Angular Spectrum Method Implementation of the Angular Spectrum method in Python to simulate Diffraction Patterns with arbitr

Comments
  • docs: fix simple typo, becaue -> because

    docs: fix simple typo, becaue -> because

    There is a small typo in html/epydoc.js.

    Should read because rather than becaue.

    Semi-automated pull request generated by https://github.com/timgates42/meticulous/blob/master/docs/NOTE.md

    opened by timgates42 0
Releases(v1.2)
implementation of the KNN algorithm on crab biometrics dataset for CS16

crab-knn implementation of the KNN algorithm in Python applied to biometrics data of purple rock crabs (leptograpsus variegatus) to classify the sex o

Andrew W. Chen 1 Nov 18, 2021
Algorithms written in different programming languages

Data Structures and Algorithms Clean example implementations of data structures and algorithms written in different languages. List of implementations

Zoran Pandovski 1.3k Jan 03, 2023
ROS Basics and TurtleSim

Homework 1: Turtle Control Package Anna Garverick This package draws given waypoints, then waits for a service call with a start position to send the

Anna Garverick 1 Nov 22, 2021
Repository for data structure and algorithms in Python for coding interviews

Python Data Structures and Algorithms This repository contains questions requiring implementation of data structures and algorithms concepts. It is us

Prabhu Pant 1.9k Jan 01, 2023
A litle algorithm that i made for transform a picture in a spreadsheet.

PicsToSheets How it works? It is an algorithm designed to transform an image into a spreadsheet file. this converts image pixels to color cells of she

Guilherme de Oliveira 1 Nov 12, 2021
A command line tool for memorizing algorithms in Python by typing them.

Algo Drills A command line tool for memorizing algorithms in Python by typing them. In alpha and things will change. How it works Type out an algorith

Travis Jungroth 43 Dec 02, 2022
Python Package for Reflection Ultrasound Computed Tomography (RUCT) Delay And Sum (DAS) Algorithm

pyruct Python Package for Reflection Ultrasound Computed Tomography (RUCT) Delay And Sum (DAS) Algorithm The imaging setup is explained in these paper

Berkan Lafci 21 Dec 12, 2022
A GUI visualization of QuickSort algorithm

QQuickSort A simple GUI visualization of QuickSort algorithm. It only uses PySide6, it does not have any other external dependency. How to run Install

Jaime R. 2 Dec 24, 2021
Algorithmic Trading with Python

Source code for Algorithmic Trading with Python (2020) by Chris Conlan

Chris Conlan 1.3k Jan 03, 2023
It is a platform that implements some path planning algorithms.

PathPlanningAlgorithms It is a platform that implements some path planning algorithms. Main dependence: python3.7, opencv4.1.1.26 (for image show) Tip

5 Feb 24, 2022
Repository for Comparison based sorting algorithms in python

Repository for Comparison based sorting algorithms in python. This was implemented for project one submission for ITCS 6114 Data Structures and Algorithms under the guidance of Dr. Dewan at the Unive

Devashri Khagesh Gadgil 1 Dec 20, 2021
Our implementation of Gillespie's Stochastic Simulation Algorithm (SSA)

SSA Our implementation of Gillespie's Stochastic Simulation Algorithm (SSA) Requirements python =3.7 numpy pandas matplotlib pyyaml Command line usag

Anoop Lab 1 Jan 27, 2022
Visualisation for sorting algorithms. Version 2.0

Visualisation for sorting algorithms v2. Upped a notch from version 1. This program provides animates simple, common and popular sorting algorithms, t

Ben Woo 7 Nov 08, 2022
A simple python application to visualize sorting algorithms.

Visualize sorting algorithms A simple python application to visualize sorting algorithms. Sort Algorithms Name Function Name O( ) Bubble Sort bubble_s

Duc Tran 3 Apr 01, 2022
Fedlearn algorithm toolkit for researchers

Fedlearn algorithm toolkit for researchers

89 Nov 14, 2022
Multiple Imputation with Random Forests in Python

miceforest: Fast, Memory Efficient Imputation with lightgbm Fast, memory efficient Multiple Imputation by Chained Equations (MICE) with lightgbm. The

Samuel Wilson 202 Dec 31, 2022
Exam Schedule Generator using Genetic Algorithm

Exam Schedule Generator using Genetic Algorithm Requirements Use any kind of crossover Choose any justifiable rate of mutation Use roulette wheel sele

Sana Khan 1 Jan 12, 2022
A Python description of the Kinematic Bicycle Model with an animated example.

Kinematic Bicycle Model Abstract A python library for the Kinematic Bicycle model. The Kinematic Bicycle is a compromise between the non-linear and li

Winston H. 36 Dec 23, 2022
Rover. Finding the shortest pass by Dijkstra’s shortest path algorithm

rover Rover. Finding the shortest path by Dijkstra’s shortest path algorithm Задача Вы — инженер, проектирующий роверы-беспилотники. Вам надо спроекти

1 Nov 11, 2021