My First Article in Software Development Journal

Hey finally my very first article on python iterators got published on SDjournal.org magazine http://sdjournal.org/python-starterkit-released/.

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Python Variables

Following up on my previous post, in this one I will writing mostly on variables, scopes, modules

Variables

  • Variables are name, not containers
    • Everything is an object
    • Everything is a reference
    • Variables are neither
    • They are not allocated memory that holds data, they are neither objects or references they are only labels
  • Everything concrete is an object
    • Whenever you hold an object, whenever you store it anywhere you are storing a reference, you dont own an object, you just own their reference.
  • Everything that holds anything, holds references
  • Variables refers to objects
    • namespaces maps names to objects

Scopes

  • 3 Scope rule
    • names are either local or global or built ins
  • Local names defined by assignment unless ‘global’ definition says otherwise
    • local means existing in current class, function, global means existing in current module and not existing everywhere
    • builtins is just a special namespace used by builtin functions
    • if you have a global of the same name as a builtin the compiler will find the global instead of the builtin

Modules

  • They have their own namespace, keeps the rest of the modules tidy
  • Executed on first import, namespace becomes attributes
  • Then cached in sys.modules
  • import is syntactic sugar, it calls in the following function
    • mod = __import__(“mod”)
  • sys.modules doesn’t have to contain only modules
    • it is just a mapping of names to whatever object you want import to return
  • If you map None to a name then it will raise import error

Scoping in Python – The LEGB Rule

Actually, a concise rule for Python Scope resolution, from Learning Python, 3rd. Ed.. (These rules are specific to variable names, not attributes. If you reference it without a period, these rules apply)

LEGB Rule.

L. Local. (Names assigned in any way within a function (def or lambda), and not declared global in that function.

E. Enclosing function locals. (Name in the local scope of any and all enclosing functions (def orlambda), form inner to outer.

G. Global (module). Names assigned at the top-level of a module file, or declared global in a def within the file.

B. Built-in (Python). Names preassigned in the built-in names module : open,range,SyntaxError,…

So, in the case of

code1
class Foo:
   code2
   def spam.....
      code3
      for code4..:
       code5
       x()

The for loop does not have it’s own namespace. It would look in the LEGB order,

L : local, in the current def.

E : Enclosed function, any enclosing functions(if def spam was in another def)

G : Global. Were there any declared globally in the module?

B : Any builtin x() in Python.

 

 

source- stackoverflow

A quirky take on programming languages

OK, this is going to be a little bit un-PC, but what the hell.

PHP is your teenage sweetheart, the girl you first awkwardly fumbled around with that one summer. Just don’t try and start a more serious relationship – this girl has serious issues.

Perl is PHP’s older sister. She might be a bit old for you, but she was pretty popular back in the 90s. She’s looking pretty ugly and scarred now, so you don’t hear from her too much.

Ruby is the cool kid of the scripting family. When you first saw her, she took your breath away with her beauty. She was fun, too. At the time she seemed a bit slow and ditzy – though she’s matured a lot in the last few years.

Python is Ruby’s sensible (and slightly more boring) sister.

Java is a successful career woman. What she lacks in raw intelligence she makes up for in dress – always turned out in immaculateCamelCase, sure to impress the enterprise customers. You might feel like she’s the sensible type you should settle down with. Just prepare for years of “NO THAT DOESNT GO THERE GOD YOU ALWAYS USE THE WRONG TYPE AND YOU MISSED A SEMICOLON” nagging.

C++ is Java’s cousin. Similar to Java in many ways, the main difference being she grew up in a more innocent time and doesn’t believe in condoms/automatic memory management, so be cautious.

is C++’s mom. Mention her name to some old grey beard hackers and they’re sure to reminisce with a twinkle in their eye.

Objective C is another member of the C family. She joined that weird church a while back, and won’t date anyone outside of it.

Haskell, Clojure, Scheme and their friends are those hipster, artsy, intellectual girls you probably spent a blissful college summer with a few years ago. The first girls who really challenged you. Of course, it could never have become something more serious (you tell yourself). Though you’ll always be left asking “what if?”

You might be put off C# due to her family’s reputation. But they’ve gone legit, the last few years, they tell you. Once you’re one of us, you’re one of us, you hear? You need a database? Her brother MSSQL will hook you up. Need a place to stay? Heck, her daddy will even buy you your own mansion on Azure avenue. What’s that, you’re having second thoughts about all these overly friendly relatives? No, you can never leave. You’re part of the family, now, ya hear?

Javascript – hey, wasn’t that the girl you first kissed, way before even PHP came on the scene? I wonder what she’s doing now. I hear her career’s really taken off in the last few years. Would be cool to catch up some time, if only for old time’s sake… (You catch sight of her, dressed head to toe in designer jQuery)… wow,somebody grew into a beautiful swan…

 

 

***Originally posted on quora by Isaac Lewis (http://thectonetwork.com/blog)***

The Innocuous Decorators in Python

Python’s functions are objects

To understand decorators, you must first understand that functions are objects in Python. This has important consequences. Let’s see why with a simple example :

def shout(word="yes"):
    return word.capitalize()+"!"

print shout()
# outputs : 'Yes!'

# As an object, you can assign the function to a variable like any
# other object

scream = shout

# Notice we don't use parentheses: we are not calling the function, we are
# putting the function "shout" into the variable "scream".
# It means you can then call "shout" from "scream":

print scream()
# outputs : 'Yes!'

# More than that, it means you can remove the old name 'shout', and
# the function will still be accessible from 'scream'

del shout
try:
     print shout()
except NameError, e:
     print e
#outputs: "name 'shout' is not defined"

print scream()
# outputs: 'Yes!'

OK, keep that in mind, we are going back to it soon. Another interesting property of Python functions is they can be defined… inside another function!

def talk():

# You can define a function on the fly in "talk" ...
    def whisper(word="yes"):
        return word.lower()+"...";

# ... and use it right away!

    print whisper()

# You call "talk", that defines "whisper" EVERY TIME you call it, then
# "whisper" is called in "talk".
talk()
# outputs:
# "yes..."

# But "whisper" DOES NOT EXIST outside "talk":

try:
    print whisper()
except NameError, e:
    print e
#outputs : "name 'whisper' is not defined"*

Functions references

OK, still here? Now the fun part, you’ve seen that functions are objects and therefore:

  • can be assigned to a variable;
  • can be defined in another function.

Well, that means that a function can return another function 🙂 Have a look:

def getTalk(type="shout"):

# We define functions on the fly
    def shout(word="yes"):
        return word.capitalize()+"!"

    def whisper(word="yes") :
        return word.lower()+"...";

# Then we return one of them
    if type == "shout":
# We don't use "()", we are not calling the function,
# we are returning the function object
        return shout
    else:
        return whisper

# How do you use this strange beast?

# Get the function and assign it to a variable
talk = getTalk()

# You can see that "talk" is here a function object:
print talk
#outputs : <function shout at 0xb7ea817c>

# The object is the one returned by the function:
print talk()

# And you can even use it directly if you feel wild:
print getTalk("whisper")()
#outputs : yes...

But wait, there is more. If you can return a function, then you can pass one as a parameter:

def doSomethingBefore(func):
    print "I do something before then I call the function you gave me"
    print func()

doSomethingBefore(scream)
#outputs:
#I do something before then I call the function you gave me
#Yes!

Well, you just have everything needed to understand decorators. You see, decorators are wrappers which means that they let you execute code before and after the function they decorate without the need to modify the function itself.

Handcrafted decorators

How you would do it manually:

# A decorator is a function that expects ANOTHER function as parameter
def my_shiny_new_decorator(a_function_to_decorate):

# Inside, the decorator defines a function on the fly: the wrapper.
# This function is going to be wrapped around the original function
# so it can execute code before and after it.
    def the_wrapper_around_the_original_function():

       # Put here the code you want to be executed BEFORE the original
       # function is called
       print "Before the function runs"

       # Call the function here (using parentheses)
       a_function_to_decorate()

       # Put here the code you want to be executed AFTER the original
       # function is called
       print "After the function runs"

       # At this point, "a_function_to_decorate" HAS NEVER BEEN EXECUTED.
       # We return the wrapper function we have just created.
       # The wrapper contains the function and the code to execute before
       # and after. It's ready to use!
    return the_wrapper_around_the_original_function

# Now imagine you create a function you don't want to ever touch again.
def a_stand_alone_function():
    print "I am a stand alone function, don't you dare modify me"

a_stand_alone_function()
#outputs: I am a stand alone function, don't you dare modify me

# Well, you can decorate it to extend its behavior.
# Just pass it to the decorator, it will wrap it dynamically in
# any code you want and return you a new function ready to be used:

a_stand_alone_function_decorated = my_shiny_new_decorator(a_stand_alone_function)
a_stand_alone_function_decorated()
#outputs:
#Before the function runs
#I am a stand alone function, don't you dare modify me
#After the function runs

Now, you probably want that every time you call a_stand_alone_function,a_stand_alone_function_decorated is called instead. That’s easy, just overwritea_stand_alone_function with the function returned by my_shiny_new_decorator:

a_stand_alone_function = my_shiny_new_decorator(a_stand_alone_function)
a_stand_alone_function()
#outputs:
#Before the function runs
#I am a stand alone function, don't you dare modify me
#After the function runs

# And guess what? That's EXACTLY what decorators do!

Decorators demystified

The previous example, using the decorator syntax:

@my_shiny_new_decorator
def another_stand_alone_function():
    print "Leave me alone"

another_stand_alone_function()
#outputs:
#Before the function runs
#Leave me alone
#After the function runs

Yes, that’s all, it’s that simple. @decorator is just a shortcut to:

another_stand_alone_function = my_shiny_new_decorator(another_stand_alone_function)

Decorators are just a pythonic variant of the decorator design pattern. There are several classic design patterns embedded in Python to ease development, like iterators.

Of course, you can cumulate decorators:

def bread(func):
    def wrapper():
        print "</''''''\>"
        func()
        print "<\______/>"
    return wrapper

def ingredients(func):
    def wrapper():
        print "#tomatoes#"
        func()
        print "~salad~"
    return wrapper

def sandwich(food="--ham--"):
    print food

sandwich()
#outputs: --ham--
sandwich = bread(ingredients(sandwich))
sandwich()
#outputs:
#</''''''\>
# #tomatoes#
# --ham--
# ~salad~
#<\______/>

Using the Python decorator syntax:

@bread
@ingredients
def sandwich(food="--ham--"):
    print food

sandwich()
#outputs:
#</''''''\>
# #tomatoes#
# --ham--
# ~salad~
#<\______/>

The order you set the decorators MATTERS:

@ingredients
@bread
def strange_sandwich(food="--ham--"):
    print food

strange_sandwich()
#outputs:
##tomatoes#
#</''''''\>
# --ham--
#<\______/>
# ~salad~

Eventually answering the question

As a conclusion, you can easily see how to answer the question:

# The decorator to make it bold
def makebold(fn):
    # The new function the decorator returns
    def wrapper():
    # Insertion of some code before and after
        return "<b>" + fn() + "</b>"
    return wrapper

# The decorator to make it italic
def makeitalic(fn):
    # The new function the decorator returns
    def wrapper():
        # Insertion of some code before and after
        return "<i>" + fn() + "</i>"
    return wrapper

@makebold
@makeitalic
def say():
    return "hello"

print say()
#outputs: <b><i>hello</i></b>

# This is the exact equivalent to
def say():
    return "hello"
say = makebold(makeitalic(say))

print say()
#outputs: <b><i>hello</i></b>

You can now just leave happy, or burn your brain a little bit more and see advanced uses of decorators.

Passing arguments to the decorated function

# It's not black magic, you just have to let the wrapper
# pass the argument:

def a_decorator_passing_arguments(function_to_decorate):
    def a_wrapper_accepting_arguments(arg1, arg2):
        print "I got args! Look:", arg1, arg2
        function_to_decorate(arg1, arg2)
    return a_wrapper_accepting_arguments

# Since when you are calling the function returned by the decorator, you are
# calling the wrapper, passing arguments to the wrapper will let it pass them to
# the decorated function

@a_decorator_passing_arguments
def print_full_name(first_name, last_name):
    print "My name is", first_name, last_name

print_full_name("Peter", "Venkman")
# outputs:
#I got args! Look: Peter Venkman
#My name is Peter Venkman

Decorating methods

What’s great with Python is that methods and functions are really the same, except methods expect their first parameter to be a reference to the current object (self). It means you can build a decorator for methods the same way, just remember to take self in consideration:

def method_friendly_decorator(method_to_decorate):
    def wrapper(self, lie):
        lie = lie - 3 # very friendly, decrease age even more 🙂
        return method_to_decorate(self, lie)
    return wrapper

class Lucy(object):

    def __init__(self):
        self.age = 32

@method_friendly_decorator
def sayYourAge(self, lie):
    print "I am %s, what did you think?" % (self.age + lie)

l = Lucy()
l.sayYourAge(-3)
#outputs: I am 26, what did you think?

Of course, if you make a very general decorator and want to apply it to any function or method, no matter its arguments, then just use *args, **kwargs:

def a_decorator_passing_arbitrary_arguments(function_to_decorate):
# The wrapper accepts any arguments
    def a_wrapper_accepting_arbitrary_arguments(*args, **kwargs):
        print "Do I have args?:"
        print args
        print kwargs
       # Then you unpack the arguments, here *args, **kwargs
       # If you are not familiar with unpacking, check:
       # http://www.saltycrane.com/blog/2008/01/how-to-use-args-and-kwargs-in-python/
        function_to_decorate(*args, **kwargs)
    return a_wrapper_accepting_arbitrary_arguments

@a_decorator_passing_arbitrary_arguments
def function_with_no_argument():
    print "Python is cool, no argument here."

function_with_no_argument()
#outputs
#Do I have args?:
#()
#{}
#Python is cool, no argument here.

@a_decorator_passing_arbitrary_arguments
def function_with_arguments(a, b, c):
    print a, b, c

function_with_arguments(1,2,3)
#outputs
#Do I have args?:
#(1, 2, 3)
#{}
#1 2 3

@a_decorator_passing_arbitrary_arguments
def function_with_named_arguments(a, b, c, platypus="Why not ?"):
    print "Do %s, %s and %s like platypus? %s" %\
          (a, b, c, platypus)

function_with_named_arguments("Bill", "Linus", "Steve", platypus="Indeed!")
#outputs
#Do I have args ? :
#('Bill', 'Linus', 'Steve')
#{'platypus': 'Indeed!'}
#Do Bill, Linus and Steve like platypus? Indeed!

class Mary(object):

    def __init__(self):
        self.age = 31

@a_decorator_passing_arbitrary_arguments
def sayYourAge(self, lie=-3): # You can now add a default value
    print "I am %s, what did you think ?" % (self.age + lie)

m = Mary()
m.sayYourAge()
#outputs
# Do I have args?:
#(<__main__.Mary object at 0xb7d303ac>,)
#{}
#I am 28, what did you think?

Passing arguments to the decorator

Great, now what would you say about passing arguments to the decorator itself? Well this is a bit twisted because a decorator must accept a function as an argument and therefore, you cannot pass the decorated function arguments directly to the decorator.

Before rushing to the solution, let’s write a little reminder:

# Decorators are ORDINARY functions
def my_decorator(func):
    print "I am a ordinary function"
    def wrapper():
        print "I am function returned by the decorator"
        func()
    return wrapper

# Therefore, you can call it without any "@"

def lazy_function():
    print "zzzzzzzz"

decorated_function = my_decorator(lazy_function)
#outputs: I am a ordinary function

# It outputs "I am a ordinary function", because that's just what you do:
# calling a function. Nothing magic.

@my_decorator
def lazy_function():
    print "zzzzzzzz"

#outputs: I am a ordinary function

It’s exactly the same. “my_decorator” is called. So when you @my_decorator, you are telling Python to call the function ‘labeled by the variable “my_decorator“‘. It’s important, because the label you give can point directly to the decorator… or not! Let’s start to be evil!

def decorator_maker():

    print "I make decorators! I am executed only once: "+\
          "when you make me create a decorator."

def my_decorator(func):

    print "I am a decorator! I am executed only when you decorate a function."

    def wrapped():
        print ("I am the wrapper around the decorated function. "
               "I am called when you call the decorated function. "
               "As the wrapper, I return the RESULT of the decorated function.")
        return func()

    print "As the decorator, I return the wrapped function."

    return wrapped

print "As a decorator maker, I return a decorator"
return my_decorator

# Let's create a decorator. It's just a new function after all.
new_decorator = decorator_maker()
#outputs:
#I make decorators! I am executed only once: when you make me create a decorator.
#As a decorator maker, I return a decorator

# Then we decorate the function

def decorated_function():
print "I am the decorated function."

decorated_function = new_decorator(decorated_function)
#outputs:
#I am a decorator! I am executed only when you decorate a function.
#As the decorator, I return the wrapped function

# Let's call the function:
decorated_function()
#outputs:
#I am the wrapper around the decorated function. I am called when you call the decorated function.
#As the wrapper, I return the RESULT of the decorated function.
#I am the decorated function.

No surprise here. Let’s do EXACTLY the same thing, but skipping intermediate variables:

def decorated_function():
print "I am the decorated function."
decorated_function = decorator_maker()(decorated_function)
#outputs:
#I make decorators! I am executed only once: when you make me create a decorator.
#As a decorator maker, I return a decorator
#I am a decorator! I am executed only when you decorate a function.
#As the decorator, I return the wrapped function.

# Finally:
decorated_function()
#outputs:
#I am the wrapper around the decorated function. I am called when you call the decorated function.
#As the wrapper, I return the RESULT of the decorated function.
#I am the decorated function.

Let’s make it AGAIN, even shorter:

@decorator_maker()
def decorated_function():
print "I am the decorated function."
#outputs:
#I make decorators! I am executed only once: when you make me create a decorator.
#As a decorator maker, I return a decorator
#I am a decorator! I am executed only when you decorate a function.
#As the decorator, I return the wrapped function.

#Eventually:
decorated_function()
#outputs:
#I am the wrapper around the decorated function. I am called when you call the decorated function.
#As the wrapper, I return the RESULT of the decorated function.
#I am the decorated function.

Hey, did you see that? We used a function call with the “@” syntax 🙂

So back to decorators with arguments. If we can use functions to generate the decorator on the fly, we can pass arguments to that function, right?


def decorator_maker_with_arguments(decorator_arg1, decorator_arg2):

print "I make decorators! And I accept arguments:", decorator_arg1, decorator_arg2

def my_decorator(func):
# The ability to pass arguments here is a gift from closures.
# If you are not comfortable with closures, you can assume it's ok,
# or read: http://stackoverflow.com/questions/13857/can-you-explain-closures-as-they-relate-to-python
print "I am the decorator. Somehow you passed me arguments:", decorator_arg1, decorator_arg2

# Don't confuse decorator arguments and function arguments!
def wrapped(function_arg1, function_arg2) :
print ("I am the wrapper around the decorated function.\n"
"I can access all the variables\n"
"\t- from the decorator: {0} {1}\n"
"\t- from the function call: {2} {3}\n"
"Then I can pass them to the decorated function"
.format(decorator_arg1, decorator_arg2,
function_arg1, function_arg2))
return func(function_arg1, function_arg2)

return wrapped

return my_decorator

@decorator_maker_with_arguments("Leonard", "Sheldon")
def decorated_function_with_arguments(function_arg1, function_arg2):
print ("I am the decorated function and only knows about my arguments: {0}"
" {1}".format(function_arg1, function_arg2))

decorated_function_with_arguments("Rajesh", "Howard")
#outputs:
#I make decorators! And I accept arguments: Leonard Sheldon
#I am the decorator. Somehow you passed me arguments: Leonard Sheldon
#I am the wrapper around the decorated function.
#I can access all the variables
#   - from the decorator: Leonard Sheldon
#   - from the function call: Rajesh Howard
#Then I can pass them to the decorated function
#I am the decorated function and only knows about my arguments: Rajesh Howard

Here it is, a decorator with arguments. Arguments can be set as variable:

c1 = "Penny"
c2 = "Leslie"

@decorator_maker_with_arguments("Leonard", c1)
def decorated_function_with_arguments(function_arg1, function_arg2):
print ("I am the decorated function and only knows about my arguments:"
" {0} {1}".format(function_arg1, function_arg2))

decorated_function_with_arguments(c2, "Howard")
#outputs:
#I make decorators! And I accept arguments: Leonard Penny
#I am the decorator. Somehow you passed me arguments: Leonard Penny
#I am the wrapper around the decorated function.
#I can access all the variables
#   - from the decorator: Leonard Penny
#   - from the function call: Leslie Howard
#Then I can pass them to the decorated function
#I am the decorated function and only knows about my arguments: Leslie Howard

As you can see, you can pass arguments to the decorator like any function using this trick. You can even use *args, **kwargs if you wish. But remember decorators are called only once. Just when Python imports the script. You can’t dynamically set the arguments afterwards. When you do “import x”, the function is already decorated, so you can’t change anything.

Let’s practice: a decorator to decorate a decorator

OK, as a bonus, I’ll give you a snippet to make any decorator accept generically any argument. After all, in order to accept arguments, we created our decorator using another function. We wrapped the decorator. Anything else we saw recently that wrapped function? Oh yes, decorators! Let’s have some fun and write a decorator for the decorators:

def decorator_with_args(decorator_to_enhance):
"""
This function is supposed to be used as a decorator.
It must decorate an other function, that is intended to be used as a decorator.
Take a cup of coffee.
It will allow any decorator to accept an arbitrary number of arguments,
saving you the headache to remember how to do that every time.
"""

# We use the same trick we did to pass arguments
def decorator_maker(*args, **kwargs):

# We create on the fly a decorator that accepts only a function
# but keeps the passed arguments from the maker.
def decorator_wrapper(func):

# We return the result of the original decorator, which, after all,
# IS JUST AN ORDINARY FUNCTION (which returns a function).
# Only pitfall: the decorator must have this specific signature or it won't work:
return decorator_to_enhance(func, *args, **kwargs)

return decorator_wrapper

return decorator_maker

It can be used as follows:

# You create the function you will use as a decorator. And stick a decorator on it 🙂
# Don't forget, the signature is "decorator(func, *args, **kwargs)"
@decorator_with_args
def decorated_decorator(func, *args, **kwargs):
def wrapper(function_arg1, function_arg2):
print "Decorated with", args, kwargs
return func(function_arg1, function_arg2)
return wrapper

# Then you decorate the functions you wish with your brand new decorated decorator.

@decorated_decorator(42, 404, 1024)
def decorated_function(function_arg1, function_arg2):
print "Hello", function_arg1, function_arg2

decorated_function("Universe and", "everything")
#outputs:
#Decorated with (42, 404, 1024) {}
#Hello Universe and everything

# Whoooot!

I know, the last time you had this feeling, it was after listening a guy saying: “before understanding recursion, you must first understand recursion”. But now, don’t you feel good about mastering this?

Best practices while using decorators

  • They are new as of Python 2.4, so be sure that’s what your code is running on.
  • Decorators slow down the function call. Keep that in mind.
  • You can not un-decorate a function. There are hacks to create decorators that can be removed but nobody uses them. So once a function is decorated, it’s done. For all the code.
  • Decorators wrap functions, which can make them hard to debug.

Python 2.5 solves this last issue by providing the functools module including functools.wrapsthat copies the name, module and docstring of any wrapped function to it’s wrapper. Fun fact,functools.wraps is a decorator 🙂

# For debugging, the stacktrace prints you the function __name__
def foo():
print "foo"

print foo.__name__
#outputs: foo

# With a decorator, it gets messy
def bar(func):
def wrapper():
print "bar"
return func()
return wrapper

@bar
def foo():
print "foo"

print foo.__name__
#outputs: wrapper

# "functools" can help for that

import functools

def bar(func):
# We say that "wrapper", is wrapping "func"
# and the magic begins
@functools.wraps(func)
def wrapper():
print "bar"
return func()
return wrapper

@bar
def foo():
print "foo"

print foo.__name__
#outputs: foo

How can the decorators be useful?

Now the big question: what can I use decorators for? Seem cool and powerful, but a practical example would be great. Well, there are 1000 possibilities. Classic uses are extending a function behavior from an external lib (you can’t modify it) or for a debug purpose (you don’t want to modify it because it’s temporary). You can use them to extends several functions with the same code without rewriting it every time, for DRY’s sake. E.g.:

def benchmark(func):
"""
A decorator that prints the time a function takes
to execute.
"""
import time
def wrapper(*args, **kwargs):
t = time.clock()
res = func(*args, **kwargs)
print func.__name__, time.clock()-t
return res
return wrapper

def logging(func):
"""
A decorator that logs the activity of the script.
(it actually just prints it, but it could be logging!)
"""
def wrapper(*args, **kwargs):
res = func(*args, **kwargs)
print func.__name__, args, kwargs
return res
return wrapper

def counter(func):
"""
A decorator that counts and prints the number of times a function has been executed
"""
def wrapper(*args, **kwargs):
wrapper.count = wrapper.count + 1
res = func(*args, **kwargs)
print "{0} has been used: {1}x".format(func.__name__, wrapper.count)
return res
wrapper.count = 0
return wrapper

@counter
@benchmark
@logging
def reverse_string(string):
return str(reversed(string))

print reverse_string("Able was I ere I saw Elba")
print reverse_string("A man, a plan, a canoe, pasta, heros, rajahs, a coloratura, maps, snipe, percale, macaroni, a gag, a banana bag, a tan, a tag, a banana bag again (or a camel), a crepe, pins, Spam, a rut, a Rolo, cash, a jar, sore hats, a peon, a canal: Panama!")

#outputs:
#reverse_string ('Able was I ere I saw Elba',) {}
#wrapper 0.0
#wrapper has been used: 1x
#ablE was I ere I saw elbA
#reverse_string ('A man, a plan, a canoe, pasta, heros, rajahs, a coloratura, maps, snipe, percale, macaroni, a gag, a banana bag, a tan, a tag, a banana bag again (or a camel), a crepe, pins, Spam, a rut, a Rolo, cash, a jar, sore hats, a peon, a canal: Panama!',) {}
#wrapper 0.0
#wrapper has been used: 2x
#!amanaP :lanac a ,noep a ,stah eros ,raj a ,hsac ,oloR a ,tur a ,mapS ,snip ,eperc a ,)lemac a ro( niaga gab ananab a ,gat a ,nat a ,gab ananab a ,gag a ,inoracam ,elacrep ,epins ,spam ,arutaroloc a ,shajar ,soreh ,atsap ,eonac a ,nalp a ,nam A

Of course the good thing with decorators is that you can use them right away on almost anything without rewriting. DRY, I said:

@counter
@benchmark
@logging
def get_random_futurama_quote():
import httplib
conn = httplib.HTTPConnection("slashdot.org:80")
conn.request("HEAD", "/index.html")
for key, value in conn.getresponse().getheaders():
if key.startswith("x-b") or key.startswith("x-f"):
return value
return "No, I'm ... doesn't!"

print get_random_furturama_quote()
print get_random_furturama_quote()

#outputs:
#get_random_futurama_quote () {}
#wrapper 0.02
#wrapper has been used: 1x
#The laws of science be a harsh mistress.
#get_random_futurama_quote () {}
#wrapper 0.01
#wrapper has been used: 2x
#Curse you, merciful Poseidon!