shawngraham

"""
pip instal fitz
pip install PyMuPDF
"""

import fitz
doc = fitz.open("file.pdf")
for i in range(len(doc)):
    for img in doc.getPageImageList(i):
        xref = img[0]

shawngraham

;; built for Netlogo version 6.04
;; a small demo model that loads a map
;; populates it with golems, and gives one of them
;; a message - or a virus, pick your metaphor - and turns
;; them loose.
;; Golems move by selecting a target patch of ground ahead of them within their cone of vision
;; where ground they can walk on is the colour of the path
;; in the maps generated by Oleg Dolya's Medieval Fantasy City Map Generator at
;; https://watabou.itch.io/medieval-fantasy-city-generator
;; 1. Generate a map there, and select the 'sanguine' colour palette (if you select a different palatte, you'll have to 

shawngraham

;; Written in Netlogo 6.0.4
;; We implement Tom Brughmans simple pot trade model on top of a network
;; created by the code snippet 'network import example'
;; Brughmans' model code and explanation is at: https://archaeologicalnetworks.files.wordpress.com/2013/11/netlogo_networks_tutorial_v2.pdf
;; Here we have also implemented a 'breakage' feature that uses the 
;; 'strength' value of the network as an indication of the difficulty
;; of trading pots over a particular route. You might interpret that value as bumpiness of the road, or likelihood of cargoes being lost, etc.

;; in the interface, create a slider with minimum 0, maximum 1 and set its value to 0.5. Call it trade-threshold
;; in the interface, create a slider with minimum 1, maximum 100 and set its value at 50. Call it num-pots

shawngraham

;; built for Netlogo version 6.04
;; a version of the itineraries model that 
;; loads a network dataset
;; represents it as nodes around a circle, rather than geographic
;; 'distances' or 'travel difficulty' is represented by the 'strength' value of the links
;; puts some agents on it
;; has them wander around, at differing speeds (or distances)
;; measures how long it takes for a message to be 'heard' by everyone
;;----
;; the model is built by combining default code snippets in netlogo:

shawngraham

from bs4 import BeautifulSoup
import csv
import requests

file = open("output.txt", "w")
# f = csv.writer(open("output.csv", "w"))
# f.writerow(["domain", "fulllink"])

pages = []

shawngraham

shawngraham

<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<!DOCTYPE svg PUBLIC "-//W3C//DTD SVG 1.1//EN"
 "http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd">
<!-- Generated by graphviz version 2.40.1 (20161225.0304)
 -->
<!-- Title: %3 Pages: 1 -->
<svg width="1996pt" height="58947pt"
 viewBox="0.00 0.00 1995.62 58947.45" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink">
<g id="graph0" class="graph" transform="scale(1 1) rotate(0) translate(4 58943.4457)">

shawngraham

# use ctrl+enter to run each line in turn

install.packages("textreuse")

# next line just displays the help file for the package in the help window in R studio
vignette("textreuse-introduction", package = "textreuse")

setwd("full-path-to-the-directory-you're-working-in")

# check what directory you're in

shawngraham

https://farm3.staticflickr.com/5310/5898076654_51085e157c_o.jpg
https://c1.staticflickr.com/1/67/197493648_628a7cb2ee_o.jpg
https://c7.staticflickr.com/8/7056/7143870979_83a291e780_o.jpg
https://farm5.staticflickr.com/5128/5301868579_f042b35323_o.jpg
https://c6.staticflickr.com/4/3930/15342460029_6f441b0439_o.jpg
https://c7.staticflickr.com/1/668/21529344631_4bdf3c253a_o.jpg
https://farm3.staticflickr.com/3892/14587227141_d36fa37264_o.jpg
https://c5.staticflickr.com/5/4024/4323769914_8f7b8a4a55_o.jpg
https://c5.staticflickr.com/4/3871/14594283694_43c91ce7f9_o.jpg
https://c4.staticflickr.com/8/7335/12405729553_4d56279bcc_o.jpg

shawngraham

library("tabulizer")

# The file could be local, or it could be on the web.
# We're going to use 
# Lane, K., Pomeroy, E. & Davila, M. (2018). Over Rock and Under Stone: Carved Rocks and Subterranean Burials at Kipia, Ancash, AD 1000 – 1532. Open Archaeology, 4(1), pp. 299-321. Retrieved 20 Jul. 2018, from doi:10.1515/opar-2018-0018

location <- 'https://www.degruyter.com/downloadpdf/j/opar.2018.4.issue-1/opar-2018-0018/opar-2018-0018.pdf'

# Next, let's grab the data in Table 1. This is on pg 6 of the pdf.
# The line below will open a window in the viewer on the right side