Garrett Dash Nelson garrettdashnelson

## gist:98bbf055b2e0f0756605
from bs4 import BeautifulSoup

charged = open("charged.html","r")

soup = BeautifulSoup(charged, 'html.parser')

results = soup.find_all(class_="callnum")

n = []

## puller.html
<script src="http://ajax.googleapis.com/ajax/libs/jquery/1.11.2/jquery.min.js"></script>

<script>
var ajaxOpts = {
  dataType:"jsonp",
  url:"http://api.neotomadb.org/v1/data/sites",
  data:{"loc":"-93.0546875,44.36209301204985,-89.0546875,48.36209301204985"},
  success: logger
}

## omni_generator.py
'''
This is operating on a SQLite file which was generated using the following command to filter out entries with the same origin and destination

ogr2ogr -f SQLite -sql "SELECT * FROM usa_ttw_master WHERE NOT (O_STATEFP = D_STATEFP AND O_COUNTYFP = D_COUNTYFP AND O_TRACTCE = D_TRACTCE)" commutes_without_sameorigins.sqlite *.shp

The result of that command is a database with 4080575 entries. The original Shapefile had 4156426 entries, so 75851 same-origin entries were filtered out.

'''
from __future__ import print_function

## bib-titlecase.py
# Original by Daniel L. Greenwald
# http://dlgreenwald.weebly.com/blog/capitalizing-titles-in-bibtex
# Modified by Garrett Dash Nelson

import re
from titlecase import titlecase

# Input and output files
my_file = 'library.bibtex'
new_file = 'library-capitalized.bibtex' # in case you don't want to overwrite

## scraper.py
from bs4 import BeautifulSoup
import urllib2
import re
from string import ascii_uppercase, replace


with open('results.tsv','w') as outfile:

	for letter in ascii_uppercase:


## uv-income-brackets.csv

          
            geoid
            town
            state
            <25000
            25000-49999
            50000-99999
            100000-199999
            200000>

            
              3300933860
              Hanover
              33
              366
              229
              694
              821
              759

            
              3300941300
              Lebanon
              33
              1183
              1539
              2120
              1170
              313

            
              5002752900
              Norwich
              50
              145
              183
              274
              450
              275

            
              3300924340
              Enfield
              33
              154
              590
              551
              484
              257

            
              5002732275
              Hartford
              50
              787
              905
              1588
              868
              192

            
              3300944260
              Lyme
              33
              36
              98
              153
              203
              184

            
              5002785975
              Woodstock
              50
              147
              271
              521
              256
              181

            
              3301931220
              Grantham
              33
              39
              186
              349
              423
              132

            
              3301352100
              New London
              33
              266
              287
              450
              558
              117

## uv-commuter-flows.json
{"nodes": [{"name": "Inner Hanover NH"}, {"name": "Groton-Ryegate-Peacham VT"}, {"name": "Rockingham VT"}, {"name": "Enfield NH"}, {"name": "Haverhill NH"}, {"name": "Danbury-Wilmot-Hill NH"}, {"name": "Hartford North of the White River VT"}, {"name": "Lebanon North of Route 4 NH"}, {"name": "Windsor VT"}, {"name": "Lebanon South of Route 4 NH"}, {"name": "Royalton VT"}, {"name": "Springfield Northeast VT"}, {"name": "Outer Hanover NH"}, {"name": "Norwich VT"}, {"name": "Piermont-Orford-Wentworth NH"}, {"name": "Weathersfield VT"}, {"name": "New London NH"}, {"name": "Hartland VT"}, {"name": "Randolph VT"}, {"name": "Newbury VT"}, {"name": "Chelsea-Vershire-Tunbridge-Strafford VT"}, {"name": "Walpole NH"}, {"name": "Bradford VT"}, {"name": "Unity-Acworth-Langdon NH"}, {"name": "Bellows Falls VT"}, {"name": "Charlestown NH"}, {"name": "Claremont Southwest NH"}, {"name": "Claremont Southeast NH"}, {"name": "Plainfield-Cornish NH"}, {"name": "Grantham-Croydon-Springfield NH"}, {"name": "Orange-Washington VT"}, {

## bending-lines.json

      
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                garrettdashnelson
                / bending-lines.json
            
            
              Created
              August 23, 2019 19:15
            
              
                bending-lines.json
              
          
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## index.html
<!DOCTYPE html>
<meta charset="utf-8">
<style>

body {
  background: #fcfcfa;
  height: 500px;
  position: relative;
  width: 960px;
}

## download-tiffs.py
import requests
from bs4 import BeautifulSoup

barcode = "39999059010775"

r = requests.get('https://collections.leventhalmap.org/search?page=1&per_page=100&q={}'.format(barcode))

htmlSource = BeautifulSoup(r.text, "html.parser")

for d in htmlSource.find_all("div", class_="document"):
	from bs4 import BeautifulSoup

	charged = open("charged.html","r")

	soup = BeautifulSoup(charged, 'html.parser')

	results = soup.find_all(class_="callnum")

	n = []
	<script src="http://ajax.googleapis.com/ajax/libs/jquery/1.11.2/jquery.min.js"></script>

	<script>
	var ajaxOpts = {
	dataType:"jsonp",
	url:"http://api.neotomadb.org/v1/data/sites",
	data:{"loc":"-93.0546875,44.36209301204985,-89.0546875,48.36209301204985"},
	success: logger
	}
	'''
	This is operating on a SQLite file which was generated using the following command to filter out entries with the same origin and destination

	ogr2ogr -f SQLite -sql "SELECT * FROM usa_ttw_master WHERE NOT (O_STATEFP = D_STATEFP AND O_COUNTYFP = D_COUNTYFP AND O_TRACTCE = D_TRACTCE)" commutes_without_sameorigins.sqlite *.shp

	The result of that command is a database with 4080575 entries. The original Shapefile had 4156426 entries, so 75851 same-origin entries were filtered out.

	'''
	from __future__ import print_function
	# Original by Daniel L. Greenwald
	# http://dlgreenwald.weebly.com/blog/capitalizing-titles-in-bibtex
	# Modified by Garrett Dash Nelson

	import re
	from titlecase import titlecase

	# Input and output files
	my_file = 'library.bibtex'
	new_file = 'library-capitalized.bibtex' # in case you don't want to overwrite
	from bs4 import BeautifulSoup
	import urllib2
	import re
	from string import ascii_uppercase, replace


	with open('results.tsv','w') as outfile:

	for letter in ascii_uppercase:
geoid	town	state	<25000	25000-49999	50000-99999	100000-199999	200000>
3300933860	Hanover	33	366	229	694	821	759
3300941300	Lebanon	33	1183	1539	2120	1170	313
5002752900	Norwich	50	145	183	274	450	275
3300924340	Enfield	33	154	590	551	484	257
5002732275	Hartford	50	787	905	1588	868	192
3300944260	Lyme	33	36	98	153	203	184
5002785975	Woodstock	50	147	271	521	256	181
3301931220	Grantham	33	39	186	349	423	132
3301352100	New London	33	266	287	450	558	117
	<!DOCTYPE html>
	<meta charset="utf-8">
	<style>

	body {
	background: #fcfcfa;
	height: 500px;
	position: relative;
	width: 960px;
	}
	import requests
	from bs4 import BeautifulSoup

	barcode = "39999059010775"

	r = requests.get('https://collections.leventhalmap.org/search?page=1&per_page=100&q={}'.format(barcode))

	htmlSource = BeautifulSoup(r.text, "html.parser")

	for d in htmlSource.find_all("div", class_="document"):