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from __future__ import division
from bs4 import BeautifulSoup as bs
import requests
import re
import time
from pymongo import MongoClient
from time import mktime
from datetime import datetime
import plotly.plotly as py
import plotly.graph_objs as go
function FindProxyForURL(url, host)
{
if (dnsDomainIs(host, ".pandora.com"))
return "PROXY 199.189.84.217:3128"
if (dnsDomainIs(host, ".spotify.com"))
return "PROXY 54.246.92.203:80"
return "DIRECT"
}

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@thinrhino
thinrhino / DeleteTweets.py
Last active August 29, 2015 14:15
A small script to delete all tweets from your twitter stream
# First download the twitter archive
# Get API_KEY and API_SECRET from developer.twitter.com
import os
import json
import glob
import base64
import requests
from requests_oauthlib import OAuth1Session
@thinrhino
thinrhino / emacs
Last active August 29, 2015 14:13
Enable mouse in emacs
;; Enable mouse support
(unless window-system
(require 'mouse)
(xterm-mouse-mode t)
(global-set-key [mouse-4] '(lambda ()
(interactive)
(scroll-down 1)))
(global-set-key [mouse-5] '(lambda ()
(interactive)
(scroll-up 1)))
@thinrhino
thinrhino / gae_shell.py
Last active August 29, 2015 14:13 — forked from djm/gae_shell.py
#!/usr/bin/env python -i
"""
A local interactive IPython shell for Google App Engine on Mac OSX.
Usage:
cd /to/project/folder/with/app.yaml
python gae_shell.py
Notes:
$(document).ready(function() {
$(chart_id).highcharts({
chart: chart,
title: title,
xAxis: xAxis,
yAxis: yAxis,
series: series
});
});
@thinrhino
thinrhino / vagrant-scp
Last active August 29, 2015 14:07 — forked from geedew/vagrant-scp
#!/bin/sh
# Change these settings to match what you are wanting to do
FILE=/File/To/Copy
SERVER=localhost
PATH=/Where/To/Put/File
OPTIONS=`vagrant ssh-config | awk -v ORS=' ' '{print "-o " $1 "=" $2}'`
scp ${OPTIONS} $FILE vagrant@$SERVER:$PATH
@thinrhino
thinrhino / tf-idf.py
Created May 5, 2014 13:09
Calculate tf-idf
# ref: http://www.tfidf.com/
# Example:
# Consider a document containing 100 words wherein the word cat appears 3 times.
# The term frequency (i.e., tf) for cat is then (3 / 100) = 0.03. Now, assume we
# have 10 million documents and the word cat appears in one thousand of these.
# Then, the inverse document frequency (i.e., idf) is calculated as log(10,000,000 / 1,000) = 4.
# Thus, the Tf-idf weight is the product of these quantities: 0.03 * 4 = 0.12.
#
# Hence:
# 1. Calculate term frequency
@thinrhino
thinrhino / zipfs.py
Created May 5, 2014 13:07
Plot zipf's law
from collections import defaultdict
import matplotlib.pyplot as plt
data = open('<data_file>', 'r')
r_data = []
# reading relevant data
while True:
l = data.readline()
if l == '':