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<group delimiter=", ">
 <group delimiter=" ">
   <group suffix=", ">
    <text macro="contributors-note"/>
   </group>
   <group delimiter=", ">
    <text macro="title-note"/>
   </group>
   <text macro="description-note"/>
 </group>

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#!/bin/sh

# Take a PDF, OCR it, and add OCR Text as background layer to original PDF to make it searchable.
# Hacked together using tips from these websites:
#      http://www.jlaundry.com/2012/ocr-a-scanned-pdf-with-tesseract/
#      http://askubuntu.com/questions/27097/how-to-print-a-regular-file-to-pdf-from-command-line
# Dependencies: pdftk, tesseract, imagemagick, enscript, ps2pdf
# Would be nice to use hocr2pdf instead so that the text lines up with the PDF image.
#      http://www.exactcode.com/site/open_source/exactimage/hocr2pdf/

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from scene import *
import random

BALL_RADIUS = 20
POINT_RADIUS = 5
GUTTER = 120
PAD_WIDTH = 100
PAD_HEIGHT = 20
HALF_PAD_WIDTH = PAD_WIDTH / 2
HALF_PAD_HEIGHT = PAD_HEIGHT / 2

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	{"tweet1":
	  {
		"username":"wcaleb",
		"date_sent":"January 14, 2014",
		"text":"I heart Cheerios.",
		"hashtags": [],
		"coordinates": null,
		"has_image":false
      },
	"tweet2":

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### Sample JSON

#### Exhibit A

{"search":{
    "field": null, 
    "hits": 1901, 
    "sort_order": null, 
    "do_facets": true, 
    "focus_item": null, 

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The @TxRunawayAds account tweets excerpts from advertisements related to runaway slaves in nineteenth-century Texas newspapers, along with links to the page images of the ad in the Portal to Texas History.

The tweeted excerpts come from ads identified and transcribed in the spring of 2014 by students in two digital history courses at Rice University and the University of North Texas, taught respectively by Caleb McDaniel and Andrew Torget.

The excerpts and links are composed and tweeted automatically with Python scripts written by Caleb McDaniel. Once a day, the feed posts a random ad from our data set. Occasionally, the feed also posts an ad that appeared "on this day" in history.

For more information about this project, please visit o

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<?xml version="1.0" encoding="utf-8"?>
<style xmlns="http://purl.org/net/xbiblio/csl" class="note" version="1.0" demote-non-dropping-particle="sort-only">
  <info>
    <title>Chicago Manual of Style (full note, no Ibid.)</title>
    <id>http://www.zotero.org/styles/chicago-fullnote-bibliography-no-ibid</id>
    <link href="http://www.zotero.org/styles/chicago-fullnote-bibliography-no-ibid" rel="self"/>
    <link href="http://www.chicagomanualofstyle.org/tools_citationguide.html" rel="documentation"/>
    <author>
      <name>Julian Onions</name>
      <email>julian.onions@gmail.com</email>

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#!/usr/bin/python
# -*- coding: utf-8 -*-
# Pandoc filter that grabs the BibTeX code block from each note file
# and then uses bibtexparser to add a "short title" entry in the "note" field,
# appending finished BibTeX entry to a bibliography file.

from pandocfilters import toJSONFilter, CodeBlock
# https://github.com/sciunto/python-bibtexparser
import bibtexparser

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import os
from bs4 import BeautifulSoup

files = os.listdir('.')

for file in files:
    html = open(file, 'r').read()
    soup = BeautifulSoup(html)
    url = soup.find(rel='canonical')['href']
    open(url.split('/')[-1] + '.html', 'w').write(html)

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"""
This script ingests a CSV exported from Library Thing and
returns the percentage of author last names that begin with
each letter of the alphabet.

Based on original script by Andrew Pendleton for analyzing
U.S. Census data: https://gist.github.com/apendleton/2638865

"""