semvec/pypi-packages huggingface dataset:
smarkov 1.0
Simple Markov and HMM
License: MIT
Keywords: hmm markov

smarkov
=======

Simple, lightweight and easy to read implementation of Markov chains and
HMMs.

This is a toy project, don't expect any exciting speeds or robustness.

Happy hacking!

Installing
----------

::

    pip3 install git+git://github.com/greenify/smarkov.git

Hacking
-------

::

    git clone https://github.com/greenify/smarkov
    cd smarkov
    python3 setup.py develop

Train with a corpus
-------------------

::

    from smarkov import Markov
    chain = Markov(["AGACAGACGAC"])

Attributes
~~~~~~~~~~

| corpus: given corpus (a corpus\_entry needs to be a tuple or array)
| order: maximal order to look back for a given state (default 1)
  tokenize: function how to split an element of the corpus (e.g
  sentences into words)

Generate text from a chain
--------------------------

::

    print("".join(chain.generate_text()))

``Generate_text()`` generates exactly one element from the Markov chain.
In other words: It goes in the Markov chain the universal start state to
universal end state.

More Examples
-------------

See ``examples``

Coming
------

Documentation how to use it with HMM.

License
-------

MIT
simple-article 0.2.1
Provide deadly simple Article model for Django
License: MIT
Keywords: article model simple django entry blog

.. image:: https://travis-ci.org/zniper/simple-article.svg?branch=master
          :target: https://travis-ci.org/zniper/simple-article

.. image:: https://coveralls.io/repos/zniper/simple-article/badge.svg?branch=master 
          :target: https://coveralls.io/r/zniper/simple-article?branch=master

**simple-article** is a Django application which provides a simple Article model. That model could be a good start for simple blog or news, without the needs for installation much of other 3rd party packages.

Installation
============

You can install simple-article from PyPI::

    pip install simple-article

Or from GitHub::

    pip install https://github.com/zniper/simple-article/zipball/master

Configuration
=============

Inside settings module, put `article` and related applications into `INSTALLED_APPS`::

    INSTALLED_APPS = (
        ...
        'tinymce',
        'taggit',
        'article',
    )

Include article URLs inside application urls.py file::

    urlpatterns = patterns(
       ...
        url(r'^blog/', include('article.urls')),
        ...
    )

Then, update the database.

With Django 1.9 or newer::

    python manage.py migrate article

With Django 1.8.x or older::

    python manage.py syncdb

or using South::
  
    python manage.py migrate article

Usage
=====

Inside templates, there is an assignment tag named `recent_articles` which returns limited number of newest articles::

    {% load article_tags %}

Short call, with maximum 10 newest articles returned::

    {% recent_articles as other_articles %}

Customized call, with exclusion of given article::
    
    {% recent_articles limit=5 exclude=article.pk as other_articles %}
polycomp 1.0.4
Python bindings to the libpolycomp C library
License: MIT
Keywords: compression astronomy fits

========
Polycomp
========

This package provides a set of Python bindings to the libpolycomp
library, as well as a stand-alone program which can be used to
compress/decompress FITS files into polycomp files (still FITS files
in disguise).

Requirements
------------

1. Either Python 2.7 or 3.4 will work

2. Libpolycomp (https://github.com/ziotom78/libpolycomp) must already
   be installed

3. The following Python libraries are required:
   - `click` (http://click.pocoo.org/5/);
   - `numpy` (http://www.numpy.org/);
   - Either `astropy` (version 0.4 or greater, http://www.astropy.org/) or
     `pyfits` (http://www.stsci.edu/institute/software_hardware/pyfits). (The
     `setup.py` script installs Astropy.)

**Note**: Recent versions of NumPy (1.10) seem to be incompatible with PyFits
3.3: saving files will lead to strange assertion errors. NumPy 1.8.2 and PyFits
3.3 are fine.

Basic usage
-----------

To use the bindings in your code, simply import ``pypolycomp`` in your
project::

  import pypolycomp

This package provides also a standalone program, ``polycomp``. Use the
``--help`` flag to get some help about how to use it::

  $ polycomp --help