Decorators

Functions which modify the functionality of other functions.

Everything in Python is an object

First of all let’s understand functions in Python:

def hi(name="yasoob"):
    return "hi " + name

print(hi())
# output: 'hi yasoob'

# We can even assign a function to a variable like
greet = hi
# We are not using parentheses here because we are not calling the function hi
# instead we are just putting it into the greet variable. Let's try to run this

print(greet())
# output: 'hi yasoob'

# Let's see what happens if we delete the old hi function!
del hi
print(hi())
#outputs: NameError

print(greet())
#outputs: 'hi yasoob'

Defining functions within functions

In Python we can define functions inside other functions, ie. nested functions:

def hi(name="yasoob"):
    print("now you are inside the hi() function")

    def greet():
        return "now you are in the greet() function"

    def welcome():
        return "now you are in the welcome() function"

    print(greet())
    print(welcome())
    print("now you are back in the hi() function")

hi()
#output:now you are inside the hi() function
#       now you are in the greet() function
#       now you are in the welcome() function
#       now you are back in the hi() function

# This shows that whenever you call hi(), greet() and welcome()
# are also called. However the greet() and welcome() functions
# are not available outside the hi() function e.g:

greet()
#outputs: NameError: name 'greet' is not defined

Returning functions from within functions

It is not necessary to execute a function within another function, we can return it as an output as well:

def hi(name="yasoob"):
    def greet():
        return "now you are in the greet() function"

    def welcome():
        return "now you are in the welcome() function"

    if name == "yasoob":
        return greet
    else:
        return welcome

a = hi()
print(a)
#outputs: <function greet at 0x7f2143c01500>

#This clearly shows that `a` now points to the greet() function in hi()
#Now try this

print(a())
#outputs: now you are in the greet() function

When you put a pair of parentheses after it, the function gets executed; whereas if you don’t put parenthesis after it, then it can be passed around and can be assigned to other variables without executing it.

Giving a function as an argument to another function

def hi():
    return "hi yasoob!"

def doSomethingBeforeHi(func):
    print("I am doing some boring work before executing hi()")
    print(func())

doSomethingBeforeHi(hi)
#outputs:I am doing some boring work before executing hi()
#        hi yasoob!

Decorators let you execute code before and after a function.

Writing our first decorator

def a_new_decorator(a_func):

    def wrapTheFunction():
        print("I am doing some boring work before executing a_func()")

        a_func()

        print("I am doing some boring work after executing a_func()")

    return wrapTheFunction

def a_function_requiring_decoration():
    print("I am the function which needs some decoration to remove my foul smell")

a_function_requiring_decoration()
#outputs: "I am the function which needs some decoration to remove my foul smell"

a_function_requiring_decoration = a_new_decorator(a_function_requiring_decoration)
#now a_function_requiring_decoration is wrapped by wrapTheFunction()

a_function_requiring_decoration()
#outputs:I am doing some boring work before executing a_func()
#        I am the function which needs some decoration to remove my foul smell
#        I am doing some boring work after executing a_func()

Decorators wrap a function and modify its behavior in one way or the another.

The @ syntax is just a short way of making up a decorated function. Here is how we could have run the previous code sample using @.

@a_new_decorator
def a_function_requiring_decoration():
    """Hey you! Decorate me!"""
    print("I am the function which needs some decoration to "
          "remove my foul smell")

a_function_requiring_decoration()
#outputs: I am doing some boring work before executing a_func()
#         I am the function which needs some decoration to remove my foul smell
#         I am doing some boring work after executing a_func()

#the @a_new_decorator is just a short way of saying:
a_function_requiring_decoration = a_new_decorator(a_function_requiring_decoration)

Now there is one problem with our code. If we run:

print(a_function_requiring_decoration.__name__)
# Output: wrapTheFunction

That’s not what we expected! Its name is “a_function_requiring_decoration”. Well our function was replaced by wrapTheFunction. It overrode the name and docstring of our function. Luckily Python provides us a simple function to solve this problem and that is functools.wraps. Let’s modify our previous example to use functools.wraps:

from functools import wraps

def a_new_decorator(a_func):
    @wraps(a_func)
    def wrapTheFunction():
        print("I am doing some boring work before executing a_func()")
        a_func()
        print("I am doing some boring work after executing a_func()")
    return wrapTheFunction

@a_new_decorator
def a_function_requiring_decoration():
    """Hey yo! Decorate me!"""
    print("I am the function which needs some decoration to "
          "remove my foul smell")

print(a_function_requiring_decoration.__name__)
# Output: a_function_requiring_decoration

Blueprint:

from functools import wraps
def decorator_name(f):
    @wraps(f)
    def decorated(*args, **kwargs):
        # Here goes the extra behavior
        if not can_run:
            return "Function will not run"
        return f(*args, **kwargs)
    return decorated

@decorator_name
def func():
    return("Function is running")

can_run = True
print(func())
# Output: Function is running

can_run = False
print(func())
# Output: Function will not run

Note: @wraps takes a function to be decorated and adds the functionality of copying over the function name, docstring, arguments list, etc. This allows to access the pre-decorated function’s properties in the decorator.

Use cases

Authorization

Check whether someone is authorized to use an endpoint in a web applicatio

Example:

from functools import wraps

def requires_auth(f):
    @wraps(f)
    def decorated(*args, **kwargs):
        auth = request.authorization
        if not auth or not check_auth(auth.username, auth.password):
            authenticate()
        return f(*args, **kwargs)
    return decorated

Logging

Example:

from functools import wraps

def logit(func):
    @wraps(func)
    def with_logging(*args, **kwargs):
        print(func.__name__ + " was called")
        return func(*args, **kwargs)
    return with_logging

@logit
def addition_func(x):
   """Do some math."""
   return x + x


result = addition_func(4)
# Output: addition_func was called

Decorators with Arguments

@wraps is also a decorator. But, it takes an argument like any normal function can do.

This is because when you use the @my_decorator syntax, you are applying a wrapper function with a single function as a parameter. Remember, everything in Python is an object, and this includes functions! We can write a function that returns a wrapper function.

Nesting a Decorator Within a Function

Create a wrapper which lets us specify a logfile to output to:

from functools import wraps

def logit(logfile='out.log'):
    def logging_decorator(func):
        @wraps(func)
        def wrapped_function(*args, **kwargs):
            log_string = func.__name__ + " was called"
            print(log_string)
            # Open the logfile and append
            with open(logfile, 'a') as opened_file:
                # Now we log to the specified logfile
                opened_file.write(log_string + '\n')
        return wrapped_function
    return logging_decorator

@logit()
def myfunc1():
    pass

myfunc1()
# Output: myfunc1 was called
# A file called out.log now exists, with the above string

@logit(logfile='func2.log')
def myfunc2():
    pass

myfunc2()
# Output: myfunc2 was called
# A file called func2.log now exists, with the above string

Decorator Classes

Now we have our logit decorator in production, but when some parts of our application are considered critical, failure might be something that needs more immediate attention. Let’s say sometimes you want to just log to a file. Other times you want an email sent, so the problem is brought to your attention, and still keep a log for your own records. This is a case for using inheritance, but so far we’ve only seen functions being used to build decorators.

Classes can also be used to build decorators.

Rebuild logit as a class instead of a function.

class logit(object):

    _logfile = 'out.log'

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

    def __call__(self, *args):
        log_string = self.func.__name__ + " was called"
        print(log_string)
        # Open the logfile and append
        with open(self._logfile, 'a') as opened_file:
            # Now we log to the specified logfile
            opened_file.write(log_string + '\n')
        # Now, send a notification
        self.notify()

        # return base func
        return self.func(*args)


    def notify(self):
        # logit only logs, no more
        pass

This implementation has an additional advantage of being much cleaner than the nested function approach, and wrapping a function still will use the same syntax as before:

logit._logfile = 'out2.log' # if change log file
@logit
def myfunc1():
    pass

myfunc1()
# Output: myfunc1 was called

Now, let’s subclass logit to add email functionality:

class email_logit(logit):
    '''
    A logit implementation for sending emails to admins
    when the function is called.
    '''
    def __init__(self, email='[e-mail]', *args, **kwargs):
        self.email = email
        super(email_logit, self).__init__(*args, **kwargs)

    def notify(self):
        # Send an email to self.email
        # Will not be implemented here
        pass

From here, @email_logit works just like @logit but sends an email to the admin in addition to logging.