Book Excerpt: The Python Standard Library by Example

Acquiring Function Properties for Decorators

Updating the properties of a wrapped callable is especially useful when used in a decorator, since the transformed function ends up with properties of the original “bare” function.

import functools 

def	show_details(name, f): 
      """Show details of a callable object.""" 
      print %s:’ % name 
      print ’ object:’,f 
      print ’ __name__:’, 
      try: 
           print f.__name__ 
      except AttributeError: 
           print ’(no __name__)’ 
      print ’ __doc__’, repr(f.__doc__) 
      print 
      return 

def	simple_decorator(f): 
      @functools.wraps(f)
      def decorated(a=’decorated defaults’, b=1): 
          print ’ decorated:’, (a, b) 
          print ’’,
          f(a, b=b) 
          return 
      return decorated 

def	myfunc(a, b=2): 
     "myfunc() is not complicated" 
      print ’ myfunc:’, (a,b) 
      return 

# The raw function 
show_details(’myfunc’, myfunc) 
myfunc(’unwrapped, default b’) 
myfunc(’unwrapped, passing b’, 3) 
print 

# Wrap explicitly 
wrapped_myfunc = simple_decorator(myfunc) 
show_details(’wrapped_myfunc’, wrapped_myfunc) 
wrapped_myfunc() 
wrapped_myfunc(’args to wrapped’, 4) 
print 

# Wrap with decorator syntax 
@simple_decorator 
def decorated_myfunc(a, b): 
    myfunc(a, b) 
    return 

show_details(’decorated_myfunc’, decorated_myfunc) 
decorated_myfunc() 
decorated_myfunc(’args to decorated’, 4) 

functools provides a decorator, wraps(), that applies update_wrapper() to the decorated function.

$ python functools_wraps.py 


myfunc:
 object: <function myfunc at 0x100da3488>
 __name__: myfunc
 __doc__ ’myfunc() is not complicated’ 

 myfunc: (’unwrapped, default b’, 2)
 myfunc: (’unwrapped, passing b’, 3) 

wrapped_myfunc:
 object: <function myfunc at 0x100da3500>
 __name__: myfunc
 __doc__ ’myfunc() is not complicated’ 

decorated: (’decorated defaults’, 1)
   myfunc: (’decorated defaults’, 1) 
decorated: (’args to wrapped’, 4)
   myfunc: (’args to wrapped’, 4) 

decorated_myfunc:
 object: <function decorated_myfunc at 0x100da35f0>
 __name__: decorated_myfunc
 __doc__ None 

 decorated: (’decorated defaults’, 1)
    myfunc: (’decorated defaults’, 1)
 decorated: (’args to decorated’, 4)
    myfunc: (’args to decorated’, 4) 
______________________

Webcast
How to Build an Optimal Hadoop Cluster to Store and Maintain Unlimited Amounts of Data Using Microservers

Realizing the promise of Apache® Hadoop® requires the effective deployment of compute, memory, storage and networking to achieve optimal results. With its flexibility and multitude of options, it is easy to over or under provision the server infrastructure, resulting in poor performance and high TCO. Join us for an in depth, technical discussion with industry experts from leading Hadoop and server companies who will provide insights into the key considerations for designing and deploying an optimal Hadoop cluster.

Learn More

Sponsored by AMD

White Paper
Private PaaS for the Agile Enterprise

If you already use virtualized infrastructure, you are well on your way to leveraging the power of the cloud. Virtualization offers the promise of limitless resources, but how do you manage that scalability when your DevOps team doesn’t scale? In today’s hypercompetitive markets, fast results can make a difference between leading the pack vs. obsolescence. Organizations need more benefits from cloud computing than just raw resources. They need agility, flexibility, convenience, ROI, and control.

Stackato private Platform-as-a-Service technology from ActiveState extends your private cloud infrastructure by creating a private PaaS to provide on-demand availability, flexibility, control, and ultimately, faster time-to-market for your enterprise.

Learn More

Sponsored by ActiveState