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plain text workflow
Files for the publication & poster for Data Workers, an exhibition by Algolit at the Mundaneum in Mons from 28 March until 28 April 2019. http://www.algolit.net/index.php/Data_Workers
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line width: 110 char lines per page: 70
70 140 210 280 350 420 490 560 630 700
--- txt to pdf ---
options ...
weasyprint
(stretched the page size, font size, etc, in order to place everything)
enscript
(using postscript to create pdf)
$ enscript --word-wrap --margins=40:10:10:20 --fancy-header writers.intro.txt -o - | ps2pdf - test.pdf
$ cat writers.intro.txt | iconv -c -f utf-8 -t ISO-8859-1 | enscript --word-wrap --margins=40:10:10:20 --fancy-header -o - | ps2pdf - test.pdf
txt2pdf
(uses reportlab)
https://github.com/baruchel/txt2pdf
$ python3 txt2pdf/txt2pdf.py -T 1 -B 2 -L 2 -R 1 writers.intro.txt -o test.pdf
$ python3 txt2pdf/txt2pdf.py -m A4 -f fonts/fantasque/TTF/FantasqueSansMono-Regular.ttf -s 10 -v 0 -T 1 -B 1 -L 1.5 -R 1.5 data-workers.txt -o test.pdf
currently using:
$ python3 txt2pdf/txt2pdf.py -m A4 -f fonts/fantasque/TTF/FantasqueSansMono-Regular.ttf -s 9 -v 0.05 -T 1 -B 0.9 -L 1.5 -R 1.5 data-workers.txt -o test.pdf
PDF2txt miner
The inverted tool of this process https://www.unixuser.org/~euske/python/pdfminer/ "What's It? PDFMiner is a tool for extracting information from PDF documents. Unlike other PDF-related tools, it focuses entirely on getting and analyzing text data. PDFMiner allows one to obtain the exact location of text in a page, as well as other information such as fonts or lines."
--- hyphenation ---
Hyphenator
https://pypi.org/project/hyphenator/
textwrap2
https://pypi.org/project/textwrap3/
--- commands ---
Generate the publication to PDF:
$ python3 create_all.py && python3 txt2pdf/txt2pdf.py -m A4 -f fonts/unifont-11.0.03.ttf -s 9 -v 0.05 -T 1 -B 0.9 -L 1.6 -R 1.4 data-workers.en.txt -o data-workers.en.pdf
Add logos.pdf on last page with PDFTK
$ pdftk data-workers.en.pdf A=data-workers.en.pdf cat A52 output data-workers.en.backcover.pdf
$ pdftk data-workers.en.backcover.pdf multistamp logos.pdf output data-workers.en.logos.pdf
$ pdftk A=data-workers.en.pdf B=data-workers.en.backcover.logos.pdf cat A1-51 B output data-workers.en.logos.pdf
PDFTK in one command:
$ pdftk data-workers.en.pdf A=data-workers.en.pdf cat A52 output data-workers.en.backcover.pdf && pdftk data-workers.en.backcover.pdf multistamp logos.pdf output data-workers.en.logos.pdf && pdftk A=data-workers.en.pdf B=data-workers.en.backcover.logos.pdf cat A1-51 B output data-workers.en.logos.pdf
--- ASCII/UNICODE fonts ---
Unicode art :) http://xahlee.info/comp/unicode_ascii_art.html http://qaz.wtf/u/convert.cgi?text=This+is+pretty+fun+too.+Do+something+for+your+group+tag https://coolsymbol.com/cool-fancy-text-generator.html http://www.alanwood.net/unicode/
--- unifont ---
http://unifoundry.com/unifont/index.html
--- DUMP ---
[/]<?')([\"\w] ░
work
many authors
write
every human being
who has access
to the internet
interacts
we
chat,
write,
click,
like
and share
we
leave our data
we
find ourselves writing in Python
some neural networks
write
human editors
assist
poets,
playwrights
or novelists
assist
Writers write ░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░ ▒▒▒▒▒▒▒▒▒▒▒▒▒▒
Data workers ░░░░░░░░░░░░ need data to ▒▒▒▒ with. work
The data that is used in the context
of Algolit, is written language.
Machine learning relies on many types
Many authors of writing. ░░░░░░░░░░░░ ▒▒▒▒▒ in the write
form of publications, like books or
articles. These are part of organised
archives and are sometimes digitized.
But there are other kinds of writing
every human too. We could say that ░░░░░░░░░░░░
being who has access to the internet ░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░
is a writer each time they ▒▒▒▒▒▒▒▒▒ interact
with algorithms.
We ░░ ▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒. chat, write, click, like and share
we In return for free services, ░░ ▒▒▒▒▒ leave
▒▒▒▒▒▒▒▒ that is compiled into profiles our data
and sold for advertisement and research.
Machine learning algorithms are not
critics: they take whatever they're
given, no matter the writing style,
no matter the CV of the author, no
matter their spelling mistakes. In
fact, mistakes make it better: the
more variety, the better they learn
to anticipate unexpected text. But
often, human authors are not aware
of what happens to their work.
Most of the writing we use is in
English, some is in French, some in
Dutch. Most often we find ourselves
writing in Python, the programming
language we use.
Algorithms can be writers too. Some
neural networks write their own rules
and generate their own texts. And for
the models that are still wrestling with
the ambiguities of natural language,
there are human editors to assist them.
Poets, playwrights or novelists start
their new careers as assistants of AI.
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