thinrhino

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

thinrhino

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"
}

thinrhino

Keybase proof

I hereby claim:

• I am thinrhino on github.
• I am thinrhino (https://keybase.io/thinrhino) on keybase.
• I have a public key whose fingerprint is 76EB 936C 5D76 6E82 E9A9 68FF 96C8 359B CC53 9C5F

To claim this, I am signing this object:

thinrhino

# 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

;; 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

#!/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:

thinrhino

$(document).ready(function() {
    $(chart_id).highcharts({
        chart: chart,
        title: title,
        xAxis: xAxis,
        yAxis: yAxis,
        series: series
    });
});

thinrhino

#!/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

# 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

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 == '':