bwbaugh

class InterruptableRegion(object):
    
    def __init__(self, signum_list=None):
        if signum_list is None:
            signum_list = [signal.SIGINT]
        self.signum_list = signum_list
        self.reset()
        
    def __enter__(self):
        self.reset()

bwbaugh

1. Check version of Ubuntu:

   lsb_release --all
   

2. Use version to follow instructions on https://www.torproject.org/docs/debian.html.en

3. Install polipo:

   sudo aptitude install polipo
   

4. Add the following lines to /etc/polipo/config:

bwbaugh

# Original blog post: <https://mnx.io/blog/a-proper-server-naming-scheme/>
# Original word list: <http://web.archive.org/web/20091003023412/http://tothink.com/mnemonic/wordlist.txt>
# Sample usage: `curl <gist> | tail --lines +4 | shuf | head --lines 1`
acrobat
africa
alaska
albert
albino
album
alcohol

bwbaugh

user www-data;
worker_processes 1;
pid /var/run/nginx.pid;

events {
    worker_connections 1024;
}

http {
    sendfile on;

bwbaugh

Guide to set up a new VPS

This guide was written while setting up an Unbuntu VPS. There may be some differences when setting up a different distro.

Initial steps as root

Do some basic setup as the root user, which should mainly consist of

bwbaugh

from __future__ import division
import itertools
import json

import tweepy


# Configuration.
USERNAME_LENGTH = 3
SAVE_FNAME = 'twitter_usernames.txt'

bwbaugh

lower = [chr(x) for x in range(ord('a'), ord('z') + 1)]
# >>> lower
# ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n',
#  'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z']

digits = [str(x) for x in range(10)]
# >>> digits
# ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']

characters = lower + digits + ['_']

bwbaugh

# Copyright (C) 2013 Wesley Baugh
"""Tools for text classification.

Extracted from the [infer](https://github.com/bwbaugh/infer) library.
"""
from __future__ import division

import math
from collections import defaultdict, namedtuple, Counter
from fractions import Fraction

bwbaugh

Example output

Minimum number of edges

python random_connected_graph.py -p -g names.gml names.txt

Console

bwbaugh

def sim(word1, word2, lch_threshold=2.15, verbose=False):
    """Determine if two (already lemmatized) words are similar or not.

    Call with verbose=True to print the WordNet senses from each word
    that are considered similar.

    The documentation for the NLTK WordNet Interface is available here:
    http://nltk.googlecode.com/svn/trunk/doc/howto/wordnet.html
    """
    from nltk.corpus import wordnet as wn