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python-design-patterns
Advanced tools
python-design-patterns is a Python library that provides implementations of various design patterns. Currently, it includes an implementation of the Pipeline pattern, which allows for the processing of data through a series of steps.
To use this library, you need Python 3.9 or later installed on your machine. You can install the library using pip
.
pip install python-design-patterns
The Pipeline pattern allows you to pass data through multiple processing steps. Each step can take inputs, perform operations, and pass results to the next step.
Here is a simple example of how to use the pipeline:
from pdp.pipeline import Pipeline, Step
# Define your processing functions
def add(x, y):
return x + y
def compute(step1, x, z):
return step1 + x - z
# Create a pipeline and add steps
steps = [
Step(name="step1", func=add),
Step(name="step2", func=compute),
]
pipeline = Pipeline(steps)
# Run the pipeline
result = pipeline.run(x=1, y=2, z=3)
print(result) # Output: {'x': 1, 'y': 2, 'z': 3, 'step1': 3, 'step2': 1}
Here are some additional examples to demonstrate the capabilities of the Pipeline pattern:
def multiply(x, y):
return x * y
def subtract(step1, z):
return step1 - z
# Create a new pipeline
pipeline = Pipeline()
pipeline.add_step(Step(name="step1", func=multiply))
pipeline.add_step(Step(name="step2", func=subtract))
# Run the pipeline
result = pipeline.run(x=2, y=3, z=1)
print(result) # Output: {'x': 2, 'y': 3, 'z': 1, 'step1': 6, 'step2': 5}
The Memento pattern allows you to save and restore the state of an object without exposing its internal structure. It is useful for implementing features like undo/redo in applications.
Here's a simple example of how to use the Memento pattern: To use this pattern, your class should inherit from the BaseOriginator class provided by the library.
from pdp.memento import BaseOriginator, Caretaker
# Define your own class that you want to be able to save/restore
class Mobility(BaseOriginator):
def __init__(self):
self.x = 1
self.y = 2
self.speed = 26
# override get and restore state
def get_state(self):
return {
'x': self.x,
'y': self.y,
'speed': self.speed
}
def set_state(self, state):
self.x = state['x']
self.y = state['y']
self.speed = state['speed']
def __str__(self):
return f"x={self.x}, y={self.y}, speed={self.speed}"
car = Mobility()
caretaker = Caretaker(car)
# save state
print(car) # x=1, y=2, speed=26
caretaker.save()
# change state
car.x = 5
car.y = 10
car.speed = 50
print(car) # x=5, y=10, speed=50
# save new state
caretaker.save()
# change state
car.x = 10
car.y = 20
car.speed = 100
print(car) # x=10, y=20, speed=100
# restore to previous state saved
caretaker.undo()
print(car) # x=5, y=10, speed=50
# restore to saved index
caretaker.restore(0)
print(car) # x=1, y=2, speed=26
# save history to file
caretaker.save_to_file("car_history.json")
Mediator is a behavioral design pattern that lets you reduce chaotic dependencies between objects. The pattern restricts direct communications between the objects and forces them to collaborate only via a mediator object.
Here's a simple example of how to use the Mediator pattern: To use this pattern, your class should inherit from the BaseComponent class provided by the library.
from pdp.mediator import Mediator, BaseComponent
class Button(BaseComponent):
def __init__(self, name: str, mediator: Mediator):
super().__init__(name, mediator)
def click(self):
self.notify({"value": "toto"})
def on_notify(self, sender: BaseComponent, event: dict, *args, **kwargs):
pass
class TextBox(BaseComponent):
def __init__(self, name: str, mediator: Mediator):
super().__init__(name, mediator)
self.text = "default"
def on_notify(self, sender: BaseComponent, event: dict, *args, **kwargs):
if sender.name == "Button":
self.text = event["value"]
def show_text(self):
print(self.text)
mediator = Mediator()
button = Button("Button", mediator)
textbox = TextBox("TextBox", mediator)
mediator.add_components(button, textbox)
textbox.show_text() # > "default"
button.click()
textbox.show_text() # > "toto"
FAQs
A Python library for implementing design patterns
We found that python-design-patterns demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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