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Description:

Ardy 0.0.6

Ardy is a toolkit to work with AWS Lambas and implement Continuous Integration.
AWS Lambda is a serverless compute service that runs your code in response to events and automatically manages the underlying compute resources for you. Alas,
AWS Lambda has a very bad GUI interfaces, especially if you work with teams and releases. You can’t see at a glance
the triggers you have active, the resources of your AWS Lambda or have a version control.
With Ardy you can manage your AWS Lambda with a JSON config file stored in your VCS.
IMPORTANT NOTE: If you want to work with AWS Lambda, it’s recommended read about it. Ardy helps and support you to manage your environments but doesn’t performs “The black magic” for you.

Installation
Install the latest Ardy release via pip:
pip install ardy
You may also install a specific version:
pip install ardy==0.0.1

Quickstart
See the documentation

How to contrib
This project is build with Git Flow. If you want to commit some
code use this pattern please:

Extra: Why this name?
import operator
from nltk import FreqDist
from nltk.tokenize import RegexpTokenizer
from nltk.book import text6 # Book Monty Python Holy Grail
import requests

tokens = [f.lower() for f in text6]
result_holygrail = FreqDist(tokens)
# result_holygrail.most_common(42)
holygrail_top = [s[0] for s in sorted([(w, result_holygrail[w]) for w in set(tokens) if len(w) > 4 and result_holygrail[w] > 20], key=operator.itemgetter(1), reverse=True)]

tokenizer = RegexpTokenizer(r'\w+')

response = requests.get("http://www.angelfire.com/movies/closedcaptioned/meanlife.txt")
meanlife = response.text

tokens = tokenizer.tokenize(meanlife)

result_meanlife = FreqDist(tokens)
# result_meanlife.most_common(42)
meanlife_top = [s[0] for s in sorted([(w, result_meanlife[w]) for w in set(tokens) if len(w) > 4 and result_meanlife[w] > 20], key=operator.itemgetter(1), reverse=True)]
for i in range(0, 30):
print("{}: {} {}".format(i+1, holygrail_top[i], meanlife_top[i]))
print("{}: {}{}".format(i+1, holygrail_top[i][:2], meanlife_top[i][-2:]))