samplereality / olipy

Python library for artistic text generation

olipy

Olipy is a Python library for artistic text generation. It has many useful modules.

Setup

Olipy uses the TextBlob library to parse text. Note that TextBlob has extra dependencies (text corpora) which are not installed as part of the Python package. Instructions for installing the extra dependencies are on the TextBlob site, but they boil down to running this Python script.

queneau.py

Dependencies: TextBlob

A module for Queneau assembly, a technique pioneered by Raymond Queneau in his 1961 book "Cent mille milliards de poèmes" ("One hundred million million poems"). Queneau assembly randomly creates new texts from a collection of existing texts with identical structure.

Example scripts for queneau.py:

• example.words.py: Generates common-looking and obscure-looking English words. Demonstrates Queneau assembly on parts of a word.

• example.mashteroids.py: Generates names and IAU citations for minor planets. Demonstrates Queneau assembly on sentences.

• example.apollo.py: Generates dialogue between astronauts and Mission Control. Demonstrates Queneau assembly on dialogue.

• example.boardgame.py: Generates board game names and descriptions.

• example.sonnet.py: Generates Shakespearean sonnets.

• example.dinosaurs.py: Generates dinosaur names.

ebooks.py

Dependencies: TextBlob

A module for incongruously sampling texts in the style of the infamous https://twitter.com/horse_ebooks. Based on the https://twitter.com/zzt_ebooks algorithm by Allison Parrish.

Example scripts for ebooks.py:

• example.horse_ebooks.py: Selects some lines from a Project Gutenberg text, with a bias towards the keywords you give it as command-line arguments.

markov.py

Dependencies: None

A module for generating new token lists from old token lists using a Markov chain.

The primary purpose of the olipy library is to promote alternatives to Markov chains (such as Queneau assembly and the _ebooks algorithm), but sometimes you really do want a Markov chain. Queneau assembly is usually better than a Markov chain above the word level (constructing paragraphs from sentences) and below the word level (constructing words from phonemes), but Markov chains are usually better when assembling sequences of words.

markov.py was originally written by Allison "A. A." Parrish.

gibberish.py

Dependencies: None

A module for those interested in the appearance of Unicode glyphs. Its main use is generating aesthetically pleasing gibberish using selected combinations of Unicode code charts.

Example scripts for gibberish.py:

• example.gibberish.py: Prints out a 140-character string of gibberish.

• example.corrupt.py: "Corrupts" whatever text is typed in by adding increasing numbers of diacritical marks.

gutenberg.py

Dependencies: rdflib (Only necessary if you have a copy of Project Gutenberg's RDF catalog and you want to get extra metadata from it.)

A module for dealing with texts from Project Gutenberg.

integration.py

Dependencies: python-twitter

A module for integrating Olipy with other pieces of software (notably the Twitter API).

wordfilter.py

Dependencies: None

A Python port of Darius Kazemi's word filter, for finding strings that contain racial slurs and the like.

data/

This directory contains data files used by the example scripts, as well as some miscellaneous datasets useful in text generation projects.

• ids_for_old_gutenberg_filenames.json: Maps old-style (pre-2007) Project Gutenberg filenames to the new-style ebook IDs. For example, "/etext95/3boat10.zip" is mapped to the number 308 (see http://www.gutenberg.org/ebooks/308).