natematias

{
	"//ABOUT THIS FILE": "This is a configuration file for a repeating Twitter farewell message that uses cheapbotsdonequick.com. To set it up, I logged into CheapBotsDoneQuick with my Twitter account, pasted this configuration file into the box, and set the announcement for twice daily. The site will now auto-post in perpituity until one of the systems goes down or my account is banned from Twitter.",
	"origin": [
		" I have left Twitter, due to the the dismantling of the platform's safety & security capacity.\n\n Find me on Mastodon, LinkedIn, or sign up for email updates: https://natematias.com/updates/ \n\n Thanks for #noun#, #people#. \n\nThis message repeats."
	],
	"noun": [
	       "all the support and love",
               "great conversations",
               "all the inspiration",
               "so many great discussions",

natematias

import sys,os,io
import simplejson as json
import zstandard as zstd

subreddit = "futurology"

infile = sys.argv[1]
outfile = sys.argv[2]

print("infile: {0}".format(infile))

natematias

from PIL import Image
import sys

## Example via: https://stackoverflow.com/questions/28057722/algorithm-to-turn-a-large-blob-of-text-into-an-image-as-defined-by-the-image-e

def to_ascii(img,maxLen=250.0):  

    #resize to maximum line length
    width, height = img.size
    rate = maxLen / max(width, height)

natematias

natematias

all_users_info = [x._json for x in users_info]
f = codecs.open(FILENAME, mode="w", encoding="utf-8")
f.write(json.dumps(all_users_info, ensure_ascii=False))
f.close()

natematias

MSM: ("bbc.com", "reuters.com", "nytimes.com", "washingtonpost.com", "cnn.com", 
 "telegraph.co.uk", "latimes.com", "huffingtonpost.com", "theguardian.com", "forbes.com",
 "examiner.com", "usatoday.com", "wsj.com", "cbsnews.com", "cbc.ca", "time.com",
 "sfgate.com", "newsweek.com", "bostonglobe.com", "nydailynews.com", "msnbc.com",
 "foxnews.com", "aljazeera.com", "nbcnews.com", "npr.org", "bloomberg.com", "abcnews.com", 
 "aljazeera.com", "bigstory.ap.com", "cbc.ca", "time.com")
 
TABLOIDS: ['dailymail.co.uk', 'express.co.uk','mirror.co.uk', 
            'news.com.au', 'nypost.com', 'thesun.co.uk','dailystar.co.uk','metro.co.uk']

natematias

import nltk
import sys
sent_detector = nltk.data.load('tokenizers/punkt/english.pickle')

fulltext = open(sys.argv[1], "r").read()
for sentence in sent_detector.tokenize(fulltext.strip()):
  if(len(sentence) <= 140):
    print(sentence)

natematias

class SillyError < StandardError; end

module CatchAnError
  def self.included base
    base.extend CatchSpecific
  end

  module CatchSpecific
    def catch_specific_error(a,stdout)
      num_attempts = 0

natematias

import codecs
import json
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from scipy import stats
from dateutil import *
import math
import statsmodels.formula.api as sm

natematias

#adapted from minus_context

import nltk
import feedparser
import random
import twitter
import yaml
import string
import re
from urlparse import urlparse