Decorator Concept Introduction

Introduction

This section introduces the concept of a decorator. This is an advanced topic for which we only scratch the surface.

Skills Graph

Logging Example

Consider a function.

def add(x, y):
    return x + y

Now, consider the function with some logging added to it.

def add(x, y):
    print('Calling add')
    return x + y

Now a second function also with some logging.

def sub(x, y):
    print('Calling sub')
    return x - y

Observation

Observation: It's kind of repetitive.

Writing programs where there is a lot of code replication is often really annoying. They are tedious to write and hard to maintain. Especially if you decide that you want to change how it works (i.e., a different kind of logging perhaps).

Code that makes logging

Perhaps you can make a function that makes functions with logging added to them. A wrapper.

def logged(func):
    def wrapper(*args, **kwargs):
        print('Calling', func.__name__)
        return func(*args, **kwargs)
    return wrapper

Now use it.

def add(x, y):
    return x + y

logged_add = logged(add)

What happens when you call the function returned by logged?

logged_add(3, 4)      ## You see the logging message appear

This example illustrates the process of creating a so-called wrapper function.

A wrapper is a function that wraps around another function with some extra bits of processing, but otherwise works in the exact same way as the original function.

>>> logged_add(3, 4)
Calling add   ## Extra output. Added by the wrapper
7
>>>

Note: The logged() function creates the wrapper and returns it as a result.

Decorators

Putting wrappers around functions is extremely common in Python. So common, there is a special syntax for it.

def add(x, y):
    return x + y
add = logged(add)

## Special syntax
@logged
def add(x, y):
    return x + y

The special syntax performs the same exact steps as shown above. A decorator is just new syntax. It is said to decorate the function.

Commentary

There are many more subtle details to decorators than what has been presented here. For example, using them in classes. Or using multiple decorators with a function. However, the previous example is a good illustration of how their use tends to arise. Usually, it's in response to repetitive code appearing across a wide range of function definitions. A decorator can move that code to a central definition.

Exercise 7.10: A decorator for timing

If you define a function, its name and module are stored in the __name__ and __module__ attributes. For example:

>>> def add(x,y):
        return x+y

>>> add.__name__
'add'
>>> add.__module__
'__main__'
>>>

In a file timethis.py, write a decorator function timethis(func) that wraps a function with an extra layer of logic that prints out how long it takes for a function to execute. To do this, you'll surround the function with timing calls like this:

start = time.time()
r = func(*args,**kwargs)
end = time.time()
print('%s.%s: %f' % (func.__module__, func.__name__, end-start))

Here is an example of how your decorator should work:

>>> from timethis import timethis
>>> @timethis
def countdown(n):
    while n > 0:
        n -= 1

>>> countdown(10000000)
__main__.countdown : 0.076562
>>>

Discussion: This @timethis decorator can be placed in front of any function definition. Thus, you might use it as a diagnostic tool for performance tuning.

Summary

Congratulations! You have completed the Function Decorators lab. You can practice more labs in LabEx to improve your skills.