Luke Barker battez

Chromebook Setup Options

Option 1: Stay in Chrome OS

If you're writing bare-bones javascript for the browser, creating Chrome Apps and Extensions, or using remote coding apps like cloud9, Koding, or Nitrous, you may not need to install Ubuntu. Some tutorials can be done entirely within the browser. The tradeoff is that you won't have a full-featured command line, and you may hit a point where you can't install something that you need.

To start coding within Chrome OS, install Text or Caret as a text editor. (Text stores files in Google Docs and Caret stores the files locally on your machine, which may help you choose.) After that, you're good to go, since Chromebooks come with a browser installed.

Optio

Tinder API documentation

Note: this was written in April/May 2014 and the API may has definitely changed since. I have nothing to do with Tinder, nor its API, and I do not offer any support for anything you may build on top of this. Proceed with caution

http://rsty.org/

I've sniffed most of the Tinder API to see how it works. You can use this to create bots (etc) very trivially. Some example python bot code is here -> https://gist.github.com/rtt/5a2e0cfa638c938cca59 (horribly quick and dirty, you've been warned!)

	####
	# Get tweepy set up
	import tweepy
	from tweepy import Cursor

	consumer_key="change-me"
	consumer_secret="change-me"
	access_token="change-me"
	access_token_secret="change-me"

	from gensim import models

	sentence = models.doc2vec.LabeledSentence(
	words=[u'so`bme', u'words', u'here'], tags=["SENT_0"])
	sentence1 = models.doc2vec.LabeledSentence(
	words=[u'here', u'we', u'go'], tags=["SENT_1"])

	sentences = [sentence, sentence1]

	class LabeledLineSentence(object):

	"""Collect article metadata and associate it with url dimension.

	Currently the url dimension includes a domain and url, but there is
	a higher level grouping that we care about. What is the outlet,
	who authored the article, what kind of site is it, when was
	it published, etc.

	"""

	import os

	var map = function() {
	var summary = this.message_soup_text;
	if (summary) {
	// quick lowercase to normalize per your requirements
	summary = summary.toLowerCase().split(" ");
	for (var i = summary.length - 1; i >= 0; i--) {
	// might want to remove punctuation, etc. here
	if (summary[i]) { // make sure there's something
	emit(summary[i], 1); // store a 1 for each word
	}

	import time
	import sys

	####
	# Get tweepy set up
	import tweepy
	from tweepy import Cursor

	consumer_key=""
	consumer_secret=""

	"""
	preprocess-twitter.py

	python preprocess-twitter.py "Some random text with #hashtags, @mentions and http://t.co/kdjfkdjf (links). :)"

	Script for preprocessing tweets by Romain Paulus
	with small modifications by Jeffrey Pennington
	with translation to Python by Motoki Wu

	Translation of Ruby script to create features for GloVe vectors for Twitter data.

	from datetime import datetime

	import string

	from nltk.stem.lancaster import LancasterStemmer
	from nltk.corpus import stopwords

	#Gets the tweet time.
	def get_time(tweet):
	return datetime.strptime(tweet['created_at'], "%a %b %d %H:%M:%S +0000 %Y")

	>>> from pymongo import MongoClient
	>>> import datetime
	post = {"author": "Mike","text": "My first blog post!","tags": ["mongodb", "python", "pymongo"], "date": datetime.datetime.utcnow()}
	>>> uri = "mongodb://pyladies:[email protected]:27167/series4ladies"
	>>> client = MongoClient(uri)
	>>> client
	MongoClient('dbh16.mongolab.com', 27167)
	>>> client.series4ladies
	Database(MongoClient('dbh16.mongolab.com', 27167), u'series4ladies')
	>>> db = client.series4ladies