{ "info": { "author": "R\u00e9my Sanchez", "author_email": "remy.sanchez@hyperthese.net", "bugtrack_url": null, "classifiers": [], "description": "Gibi\n====\n\nGibi is a random word generator, based on Markov chains.\n\nIt analyzes a text in order to deduce the probability of transition from one\ncharacter to another, and then generates a completely random word that will look\nalike what you have feeded it as input.\n\nBuild status\n------------\n\n.. |build-develop| image:: https://travis-ci.org/Xowap/gibi.png?branch=develop\n.. _build-develop: https://travis-ci.org/Xowap/gibi\n\n.. |build-master| image:: https://travis-ci.org/Xowap/gibi.png?branch=master\n.. _build-master: https://travis-ci.org/Xowap/gibi\n\n:develop: |build-develop|_\n:master: |build-master|_\n\nUsage\n-----\n\nGibi can be used either as a CLI tool, either as a library.\n\nCLI\n~~~\n\nA tool named `gibi` helps you to generate a matrix file, and then use it to generate words. It is\nself-documented using the `--help` switch, however here is a typical workflow.\n\n.. code-block::\n\n $ gibi analyze corpus.txt matrix.gibi\n $ gibi generate matrix.gibi\n\nThis will analyze the `corpus.txt` file into the `matrix.gibi` file, and then produce 10 random\nwords.\n\nAPI\n~~~\n\nBasic usage\n...........\n\nIn its simplest form, using the API looks like:\n\n.. code-block:: python\n\n with codecs.open(path, 'r', encoding='utf-8') as f:\n n = FrenchNormalizer(f)\n m = Matrix()\n\n m.feed(n)\n\n print(m.make_word())\n\nYou can also make something deterministic by providing a seed to `make_word()`.\nGiven the same `Matrix` and the same seed, you will always get the same result.\nThe provided seed is anything that `Python's random `_\nwould accept. See the following example:\n\n.. code-block:: python\n\n assert m.make_word(42) == m.make_word(42)\n\nUsing matrix files\n..................\n\nYou will probably pre-generate a matrix file using `gibi analyze`, and then load the resulting file\nlike this\n\n.. code-block:: python\n\n m = Matrix()\n with open('matrix.gibi', 'rb') as f:\n m.load(f)\n\n print(m.make_word())\n\nThis is much more performant, as generating the matrix can be time-consuming if the corpus is big.\n\nLegal terms\n-----------\n\nThis project is written and copyrighted by its authors, as the Git log can\ntestify.\n\nIt is released under the terms of the WTFPL. 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