七个好用的装饰器

2023-02-28

name def add

1、dispachPython天然支持多态，但使用dispatch可以让你的代码更加容易阅读。安装：复制pipinstallmultipledispatch1.使用：复制>>>frommultipledispatchimportdispatch>>>@dispat

1、dispach

Python 天然支持多态，但使用 dispatch 可以让你的代码更加容易阅读。

安装：

pip install multipledispatch1.

使用：

>>> from multipledispatch import dispatch
>>> @dispatch(int, int)
... def add(x, y):
...     return x + y
>>> @dispatch(object, object)
... def add(x, y):
...     return "%s + %s" % (x, y)
>>> add(1, 2)
3
>>> add(1, 'hello')
'1 + hello'1.
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2、click

click 可以很方便地让你实现命令行工具。

安装：

pip install click1.

使用：demo2.py :

import click
@click.command()
@click.option('--count', default=1, help='Number of greetings.')
@click.option('--name', prompt='Your name',
             help='The person to greet.')
def hello(count, name):
   """Simple program that greets NAME for a total of COUNT times."""
   for x in range(count):
       click.echo(f"Hello {name}!")
if __name__ == '__main__':
   hello()1.
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运行结果：

❯ python demo2.py --count=3 --name=joih
Hello joih!
Hello joih!
Hello joih!
❯ python demo2.py --count=3
Your name: somenzz
Hello somenzz!
Hello somenzz!
Hello somenzz!1.
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3、celery

分布式的任务队列，非 Celery 莫属。

from celery import Celery
app = Celery('tasks', broker='pyamqp://guest@localhost//')
@app.task
def add(x, y):
   return x + y1.
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4、deprecated

这个相信大家在使用别的包时都遇到过，当要下线一个老版本的函数的时候就可以使用这个装饰器。

安装：

pip install Deprecated1.

使用：demo4.py

from deprecated import deprecated
@deprecated ("This function is deprecated, please do not use it")
def func1():
   pass
func1()1.
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运行效果如下：

❯ python demo4.py
demo4.py:6: DeprecationWarning: Call to deprecated function (or staticmethod) func1. (This function is deprecated, please do not use it)
 func1()1.
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5、deco.concurrent

安装：

pip install deco1.

使用 DECO 就像在 Python 程序中查找或创建两个函数一样简单。我们可以用 @concurrent 装饰需要并行运行的函数，用 @synchronized 装饰调用并行函数的函数，使用举例：

from deco import concurrent, synchronized  
@concurrent # We add this for the concurrent function
def process_url(url, data):
 #Does some work which takes a while
 return result
@synchronized # And we add this for the function which calls the concurrent function
def process_data_set(data):
 results = {}
 for url in urls:
   results[url] = process_url(url, data)
 return results1.
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6、cachetools

缓存工具

安装：

pip install cachetools1.

使用：

from cachetools import cached, LRUCache, TTLCache
# speed up calculating Fibonacci numbers with dynamic programming
@cached(cache={})
def fib(n):
   return n if n < 2 else fib(n - 1) + fib(n - 2)
# cache least recently used Python Enhancement Proposals
@cached(cache=LRUCache(maxsize=32))
def get_pep(num):
   url = 'http://www.python.org/dev/peps/pep-%04d/' % num
   with urllib.request.urlopen(url) as s:
       return s.read()
# cache weather data for no longer than ten minutes
@cached(cache=TTLCache(maxsize=1024, ttl=600))
def get_weather(place):
   return owm.weather_at_place(place).get_weather()1.
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7、retry

重试装饰器，支持各种各样的重试需求。

安装：

pip install tenacity1.

使用：

import random
from tenacity import retry
@retry
def do_something_unreliable():
   if random.randint(0, 10) > 1:
       raise IOError("Broken sauce, everything is hosed!!!111one")
   else:
       return "Awesome sauce!"
@retry(stop=stop_after_attempt(7))
def stop_after_7_attempts():
   print("Stopping after 7 attempts")
   raise Exception
@retry(stop=stop_after_delay(10))
def stop_after_10_s():
   print("Stopping after 10 seconds")
   raise Exception
@retry(stop=(stop_after_delay(10) | stop_after_attempt(5)))
def stop_after_10_s_or_5_retries():
   print("Stopping after 10 seconds or 5 retries")
   raise Exception1.
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