Python decorators-Python Tutorial-php.cn

Python decorators

巴扎黑

Release： 2016-12-08 11:12:15

Original

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There are many ways to write a custom decorator, but the simplest and easiest to understand is to write a function that returns a subfunction that encapsulates the original function call.

The general pattern is as follows.

Python code

def my_decorator(function):  
    def _my_decorator(*args, **kw):  
        #在调用实际函数之前做些填充工作  
        res = function(*args, **kw)  
        #做完某些填充工作之后  
        return res  
    #返回子函数  
    return _my_decorator

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When the decorator requires parameters, the second level of encapsulation must be used.

Python code

def my_decorator(arg1, arg2):  
    def _my_decorator(function):  
        def __my_decorator(*args, **kw):  
            res = function()  
            return res  
        return __my_decorator  
    return _my_decorator

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Quote

Because decorators are loaded by the interpreter when the module is first read, their use must be limited to the wrappers that can be applied overall. If a decorator is bound to a method's class or enhanced function signature, it should be refactored into a regular callable object to avoid complexity. In any case, when decorators handle APIs, a good approach is to gather them together in an easily maintainable module.

Parameter check:

Python code

def check_param_isvalid():  
    def check(method):  
        def check_param(*args,**kwargs):  
            for a in args:  
                assert isinstance(a, int),"arg %r does not match %s" % (a,int)  
                assert a > 100000,"arg %r must gt 100000" % a  
            return method(*args, **kwargs)  
        return check_param  
    return check  
 
@check_param_isvalid()  
def foo(*args):  
    print args  
  
foo(200000,500000)

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Cache:

Python code

import time  
import hashlib  
import pickle  
  
cache = {}  
def is_obsolete(entry, duration):  
    return time.time() - entry[&#39;time&#39;] > duration  
  
def computer_key(function, args, kw):  
    key = pickle.dumps((function.func_name, args, kw))  
    return hashlib.sha1(key).hexdigest()  
  
def memoize(duration=30):  
    def _memoize(function):  
        def __memoize(*args, **kw):  
            key = computer_key(function, args, kw)  
            if key in cache and not is_obsolete(cache[key], duration):  
                print &#39;wo got a winner&#39;  
                return cache[key][&#39;value&#39;]  
            result = function(*args, **kw)  
            cache[key] = {&#39;value&#39;:result,&#39;time&#39;:time.time()}  
            return result  
        return __memoize  
    return _memoize  
 
@memoize()  
def very_complex_stuff(a,b):  
    return a + b  
  
print very_complex_stuff(2,2)

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Proxy:

Python code

class User(object):  
    def __init__(self, roles):  
        self.roles = roles  
  
class Unauthorized(Exception):  
    pass  
  
def protect(role):  
    def _protect(function):  
        def __protect(*args, **kw):  
            user = globals().get(&#39;user&#39;)  
            if user is None or role not in user.roles:  
                raise Unauthorized("I won&#39;t tell you")  
            return function(*args, **kw)  
        return __protect  
    return _protect  
  
tarek = User((&#39;admin&#39;, &#39;user&#39;))  
bill = User((&#39;user&#39;,))  
  
class MySecrets(object):  
 
    @protect(&#39;admin&#39;)  
    def waffle_recipe(self):  
        print &#39;use tons of butter!&#39;  
  
these_are = MySecrets()  
user = tarek  
these_are.waffle_recipe()  
user = bill  
these_are.waffle_recipe()

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Context provided By:

Python code

from threading import RLock  
lock = RLock()  
  
def synchronized(function):  
    def _synchronized(*args, **kw):  
        lock.acquire()  
        try:  
            return function(*args, **kw)  
        finally:  
            lock.release()  
    return _synchronized  
 
@synchronized  
def thread_safe():  
    print &#39;haha&#39;  
     
thread_safe()

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