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from hashlib import md5
from base64 import b64decode
from base64 import b64encode
from Crypto.Cipher import AES


# Padding for the input string --not
# related to encryption itself.
BLOCK_SIZE = 16  # Bytes
pad = lambda s: s + (BLOCK_SIZE - len(s) % BLOCK_SIZE) * \

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// $ 6g echo.go && 6l -o echo echo.6
// $ ./echo
//
//  ~ in another terminal ~
//
// $ nc localhost 3540

package main

import (

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#!/usr/bin/env python3
# ~*~ coding: utf-8 ~*~
#
# >>
#   .. created: 5/20/16
#   .. author:  blake.vandemerwe
#
# LICENSE
# <<

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Easy parallel python with concurrent.futures

As of version 3.3, python includes the very promising concurrent.futures module, with elegant context managers for running tasks concurrently. Thanks to the simple and consistent interface you can use both threads and processes with minimal effort.

For most CPU bound tasks - anything that is heavy number crunching - you want your program to use all the CPUs in your PC. The simplest way to get a CPU bound task to run in parallel is to use the ProcessPoolExecutor, which will create enough sub-processes to keep all your CPUs busy.

We use the context manager thusly:

with concurrent.futures.ProcessPoolExecutor() as executor:

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from concurrent.futures import ThreadPoolExecutor, Future
import asyncio
import time

async def sum_numbers(x, y):
    await asyncio.sleep(1)
    return x + y

def sum_numbers_blocking(x, y):
    time.sleep(1)

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# Original code at http://code.activestate.com/recipes/203871/
# I have added a limit for the number of tasks waiting
# The method queueTask will block if the current taskList size 
# exceeds the specified limit

import threading
from time import sleep

# Ensure booleans exist (not needed for Python 2.2.1 or higher)

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Packages

• lxml - Pythonic binding for the C libraries libxml2 and libxslt.
• boto - Python interface to Amazon Web Services
• Django - Django is a high-level Python Web framework that encourages rapid development and clean, pragmatic design.
• Fabric - Library and command-line tool for streamlining the use of SSH for application deployment or systems administration task.
• PyMongo - Tools for working with MongoDB, and is the recommended way to work with MongoDB from Python.
• Celery - Task queue to distribute work across threads or machines.
• pytz - pytz brings the Olson tz database into Python. This library allows accurate and cross platform timezone calculations using Python 2.4 or higher.

Guides

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Libraries

• jQuery - The de-facto library for the modern age. It makes things like HTML document traversal and manipulation, event handling, animation, and Ajax much simpler with an easy-to-use API that works across a multitude of browsers.
• Backbone - Backbone.js gives structure to web applications by providing models with key-value binding and custom events, collections with a rich API of enumerable functions, views with declarative event handling, and connects it all to your existing API over a RESTful JSON interface.
• AngularJS - Conventions based MVC framework for HTML5 apps.
• Underscore - Underscore is a utility-belt library for JavaScript that provides a lot of the functional programming support that you would expect in Prototype.js (or Ruby), but without extending any of the built-in JavaScript objects.
• lawnchair - Key/value store adapter for indexdb, localStorage

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// Use Gists to store code you would like to remember later on
console.log(window); // log the "window" object to the console