Book Tree: Laser Cut Word Clouds in Three Dimensions

Author: Brad Cable

booklist <- read.csv("books.csv", header=TRUE)
FREQ_X <- 533

Code for Python EPUB extraction to word frequency CSV

#!/usr/bin/env python3

from xml.dom import minidom
import epub, re, sys

if len(sys.argv) == 1:
    print("More arguments.")

def search_for_tag(xml_input, tag_name):
    if type(xml_input) is bytes:
        xml_dom = minidom.parseString(xml_input)
        xml_dom = xml_input

    ret = []
    for i in range(0, len(xml_dom.childNodes)):
        xml_child = xml_dom.childNodes[i]
        if type(xml_child) is minidom.Element:
            if tag_name == xml_child.tagName:
                ret.extend(search_for_tag(xml_child, tag_name))

    return ret

def process_epub(epubfile):
    the_epub =, "r")
    xml_container ="META-INF/container.xml")
    dom_root_rootfiles = search_for_tag(xml_container, "rootfiles")
    dom_rootfiles = search_for_tag(dom_root_rootfiles[0], "rootfile")

    raw_text = b""
    for i in range(0, len(dom_rootfiles)):
        data =[i].getAttribute("full-path"))
        items = search_for_tag(data, "item")
        for item in items:
            raw_text +="href"))

    return raw_text

if __name__ == "__main__":
    txt = process_epub(sys.argv[1])

    #txt = re.sub(b"<style.*</style>", b"", txt)
    txt = re.sub(b"<[^>]+>", b"", txt)
    txt = re.sub(b"[^A-Za-z' \n\r\t]+", b"", txt)
    txt = re.sub(b"[ \r\n\t]+", b" ", txt)
    txt = txt.lower().strip().decode()
    words = txt.split(" ")
    frequency_count = {}
    for word in words:
        if word in frequency_count:
            frequency_count[word] += 1
            frequency_count[word] = 1

    for key in frequency_count.keys():
        print("{},{}".format(key, frequency_count[key]))
PATH_TO_PYSCRIPT <- file.path(getwd(), "")

Load Top X Frequent Words

english_words_frequency <- read.table("english-word-list-total.csv",
    header=FALSE, skip=4, sep=";", dec=",", quote="",
    col.names=c("Rank", "Word", "Frequency", "Ratio")

english_words_frequency <- english_words_frequency[
    !$Rank) &
    english_words_frequency$Rank <= FREQ_X,

english_words_frequency$Word <- tolower(
english_words_frequency <- rbind(
    c(501, "can't", NA, 1),
    c(502, "wasn't", NA, 1),
    c(503, "don't", NA, 1),
    c(504, "didn't", NA, 1),
    c(505, "you're", NA, 1),
    c(505, "youre", NA, 1),
    c(506, "himself", NA, 1),
    c(507, "that's", NA, 1),
    c(508, "ender's", NA, 1),
    c(509, "thats", NA, 1),
    c(510, "didnt", NA, 1),
    c(511, "began", NA, 1),
    c(512, "didnt", NA, 1),
    c(513, "there's", NA, 1),
    c(514, "she's", NA, 1),
    c(515, "theyd", NA, 1),
    c(516, "wasnt", NA, 1),
    c(517, "didnt", NA, 1),
    c(518, "couldnt", NA, 1),
    c(519, "nietzsche", NA, 1),
    c(520, "aafvfv", NA, 1),
    c(521, "wants", NA, 1),
    c(522, "themselves", NA, 1),
    c(523, "what's", NA, 1),
    c(524, "qa'gwgwa", NA, 1),
    c(525, "we're", NA, 1),
    c(526, "isn't", NA, 1),
    c(527, "runciter's", NA, 1),
    c(528, "couldn't", NA, 1),
    c(529, "they're", NA, 1),
    c(530, "jeffs", NA, 1),
    c(531, "hadnt", NA, 1),
    c(532, "hadn't", NA, 1),
    c(533, "aarfy", NA, 1)

Functions to Process EPUBs and Graph

epub_to_words <- function(filename){
    data_words <- paste0(
        system2(PATH_TO_PYSCRIPT, args=shQuote(filename), stdout=TRUE)

    words <- read.table(header=TRUE, text=data_words, sep=",", quote="")
    words$Word <- tolower(words$Word)
    words$Count <- as.numeric(words$Count)

    words <- words[!(words$Word %in% english_words_frequency$Word),]

    words <- words[nchar(as.character(words$Word)) > 4,]
    words$Word <- toupper(words$Word)

wordcloud_wrap <- function(author, title){

    words <- epub_to_words(booklist$Filename[
        booklist$Author == author & booklist$Title == title

    wordcloud(words$Word, words$Count, scale=c(10,.5), min.freq=3, max.words=20)


Generate Word Clouds

Douglas Adams - Hitchhiker's Guide to the Galaxy

words <- wordcloud_wrap("Douglas Adams", "Hitchhiker's Guide to the Galaxy")

plot of chunk da_wordcloud

Friedrich Nietzsche - Thus Spoke Zarathustra

words <- wordcloud_wrap("Friedrich Nietzsche", "Thus Spoke Zarathustra")

plot of chunk fn_wordcloud

George Orwell - 1984

words <- wordcloud_wrap("George Orwell", "1984")

plot of chunk go_wordcloud

Robert Anton Wilson & Robert Shea - Illuminatus! Trilogy

words <- wordcloud_wrap("Robert Anton Wilson & Robert Shea", "Illuminatus! Trilogy")

plot of chunk raw_wordcloud

Philip K. Dick - Ubik

words <- wordcloud_wrap("Philip K. Dick", "Ubik")

plot of chunk pkd_wordcloud

Philip K. Dick - Do Android Dream of Electric Sheep

words <- wordcloud_wrap("Philip K. Dick", "Do Android Dream of Electric Sheep")

plot of chunk pkd2_wordcloud

Frank Herbert - Dune

words <- wordcloud_wrap("Frank Herbert", "Dune")

plot of chunk fh_wordcloud

Orson Scott Card - Ender's Game

words <- wordcloud_wrap("Orson Scott Card", "Ender's Game")

plot of chunk osc_wordcloud

Joseph Heller - Catch-22

words <- wordcloud_wrap("Joseph Heller", "Catch-22")

plot of chunk jh_wordcloud

Ken Kesey - One Flew Over the Cuckoo's Nest

words <- wordcloud_wrap("Ken Kesey", "One Flew Over the Cuckoo's Nest")

plot of chunk kk_wordcloud

Ken Grimwood - Replay

words <- wordcloud_wrap("Ken Grimwood", "Replay")

plot of chunk kg_wordcloud

Final Book Selections and Manual Editing

20+ hours manual editing in Gimp to make the words not fall down in places, also using multi-layered merges for some layers of the final cut.

Final Book Selection


  1. Friedrich Nietzsche - Thus Spoke Zarathustra
  2. Robert Anton Wilson & Robert Shea - Illuminatus! Trilogy
  3. Philip K. Dick - Do Android Dream of Electric Sheep
  4. Frank Herbert - Dune
  5. Douglas Adams - Hitchhiker's Guide to the Galaxy
  6. George Orwell - 1984

Mockup Rendering


Final Product