Python decorators.

Python Decorators:

What Can You Do With Decorators?

Decorators allow you to inject or modify code in functions or classes. Sounds a bit like Aspect-Oriented Programming (AOP) in Java, doesn't it? Except that it's both much simpler and (as a result) much more powerful. For example, suppose you'd like to do something at the entry and exit points of a function (such as perform some kind of security, tracing, locking, etc. -- all the standard arguments for AOP). With decorators, it looks like this:

@entryExit
def func1():
    print "inside func1()"

@entryExit
def func2():
    print "inside func2()"

The @ indicates the application of the decorator.
Function Decorators

A function decorator is applied to a function definition by placing it on the line before that function definition begins. For example:

@myDecorator
def aFunction():
    print "inside aFunction"

When the compiler passes over this code, aFunction() is compiled and the resulting function object is passed to the myDecorator code, which does something to produce a function-like object that is then substituted for the original aFunction().

What does the myDecorator code look like? Well, most introductory examples show this as a function, but I've found that it's easier to start understanding decorators by using classes as decoration mechanisms instead of functions. In addition, it's more powerful.

The only constraint upon the object returned by the decorator is that it can be used as a function -- which basically means it must be callable. Thus, any classes we use as decorators must implement __call__.

What should the decorator do? Well, it can do anything but usually you expect the original function code to be used at some point. This is not required, however:

class myDecorator(object):

    def __init__(self, f):
        print "inside myDecorator.__init__()"
        f() # Prove that function definition has completed

    def __call__(self):
        print "inside myDecorator.__call__()"

@myDecorator
def aFunction():
    print "inside aFunction()"

print "Finished decorating aFunction()"

aFunction()

When you run this code, you see:

inside myDecorator.__init__()
inside aFunction()
Finished decorating aFunction()
inside myDecorator.__call__()

Notice that the constructor for myDecorator is executed at the point of decoration of the function. Since we can call f() inside __init__(), it shows that the creation of f() is complete before the decorator is called. Note also that the decorator constructor receives the function object being decorated. Typically, you'll capture the function object in the constructor and later use it in the __call__() method (the fact that decoration and calling are two clear phases when using classes is why I argue that it's easier and more powerful this way).

When aFunction() is called after it has been decorated, we get completely different behavior; the myDecorator.__call__() method is called instead of the original code. That's because the act of decoration replaces the original function object with the result of the decoration -- in our case, the myDecorator object replaces aFunction. Indeed, before decorators were added you had to do something much less elegant to achieve the same thing:

def foo(): pass
foo = staticmethod(foo)

With the addition of the @ decoration operator, you now get the same result by saying:

@staticmethod
def foo(): pass

This is the reason why people argued against decorators, because the @ is just a little syntax sugar meaning "pass a function object through another function and assign the result to the original function."

The reason I think decorators will have such a big impact is because this little bit of syntax sugar changes the way you think about programming. Indeed, it brings the idea of "applying code to other code" (i.e.: macros) into mainstream thinking by formalizing it as a language construct.
Slightly More Useful

Now let's go back and implement the first example. Here, we'll do the more typical thing and actually use the code in the decorated functions:

class entryExit(object):

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

    def __call__(self):
        print "Entering", self.f.__name__
        self.f()
        print "Exited", self.f.__name__

@entryExit
def func1():
    print "inside func1()"

@entryExit
def func2():
    print "inside func2()"

func1()
func2()

The output is:

Entering func1
inside func1()
Exited func1
Entering func2
inside func2()
Exited func2

You can see that the decorated functions now have the "Entering" and "Exited" trace statements around the call.

The constructor stores the argument, which is the function object. In the call, we use the __name__ attribute of the function to display that function's name, then call the function itself.
Using Functions as Decorators

The only constraint on the result of a decorator is that it be callable, so it can properly replace the decorated function. In the above examples, I've replaced the original function with an object of a class that has a __call__() method. But a function object is also callable, so we can rewrite the previous example using a function instead of a class, like this:

def entryExit(f):
    def new_f():
        print "Entering", f.__name__
        f()
        print "Exited", f.__name__
    return new_f

@entryExit
def func1():
    print "inside func1()"

@entryExit
def func2():
    print "inside func2()"

func1()
func2()
print func1.__name__

new_f() is defined within the body of entryExit(), so it is created and returned when entryExit() is called. Note that new_f() is a closure, because it captures the actual value of f.

Once new_f() has been defined, it is returned from entryExit() so that the decorator mechanism can assign the result as the decorated function.

The output of the line print func1.__name__ is new_f, because the new_f function has been substituted for the original function during decoration. If this is a problem you can change the name of the decorator function before you return it:

def entryExit(f):
    def new_f():
        print "Entering", f.__name__
        f()
        print "Exited", f.__name__
    new_f.__name__ = f.__name__
    return new_f

The information you can dynamically get about functions, and the modifications you can make to those functions, are quite powerful in Python.


I would prefer to go through this document, here very good explanation given with lower level information.

http://pythonconquerstheuniverse.wordpress.com/2009/08/06/introduction-to-python-decorators-part-1/