markovify example

output.txt

The Zen of Python, by Tim Peters

Beautiful is better than ugly.
Explicit is better than implicit.
Simple is better than complex.
Complex is better than complicated.
Flat is better than nested.
Sparse is better than dense.
Readability counts.
Special cases aren't special enough to break the rules.
Although practicality beats purity.
Errors should never pass silently.
Unless explicitly silenced.
In the face of ambiguity, refuse the temptation to guess.
There should be one-- and preferably only one --obvious way to do it.
Although that way may not be obvious at first unless you're Dutch.
Now is better than never.
Although never is often better than *right* now.
If the implementation is hard to explain, it's a bad idea.
If the implementation is easy to explain, it may be a good idea.
Namespaces are one honking great idea -- let's do more of those!
================
Above lines are the original "Zen of Python".

Below lines are generated text from markov chain.
================
Sparse is better than implicit.
Now is better than implicit.
If the implementation is easy to explain, it's a bad idea.
Explicit is better than nested.
Explicit is better than *right* now.
Sparse is better than never.
Explicit is better than complicated.
Explicit is better than dense.
Explicit is better than ugly.
Simple is better than nested.
If the implementation is easy to explain, it's a bad idea.
Simple is better than implicit.
Simple is better than never.
Now is better than implicit.
If the implementation is hard to explain, it may be a good idea.

zen.py

#!/usr/bin/env python3
# -*- coding:utf-8 -*-
import this
import markovify


def main():
    message = """================
Above lines are the original "Zen of Python".

Below lines are generated text from markov chain.
================"""
    print(message)

    # need a bit trick to get original zen of python.
    d = {}
    for c in (65, 97):
        for i in range(26):
            d[chr(i + c)] = chr((i + 13) % 26 + c)
    zen_of_python = ("".join([d.get(c, c) for c in this.s]))

    # learn model from text.
    text_model = markovify.Text(zen_of_python)
    # ... and generate from model.
    for _ in range(15):
        print(text_model.make_sentence())


if __name__ == '__main__':
    main()