semvec/pypi-packages huggingface dataset:
dummy_package 0.2.0
Python library for string matching.
License: BSD

py_stringmatching
=================

This project seeks to build a Python software package that consists of a comprehensive and scalable set of string tokenizers (such as alphabetical tokenizers, whitespace tokenizers) and string similarity measures (such as edit distance, Jaccard, TF/IDF). The package is free, open-source, and BSD-licensed.

Important links
===============

 * Project Homepage: https://sites.google.com/site/anhaidgroup/projects/py_stringmatching
 * Code repository: https://github.com/anhaidgroup/py_stringmatching
 * User Manual: http://anhaidgroup.github.io/py_stringmatching/v0.2.x/index.html
 * Tutorial: http://anhaidgroup.github.io/py_stringmatching/v0.2.x/Tutorial.html
 * How to Contribute: http://anhaidgroup.github.io/py_stringmatching/v0.2.x/Contributing.html
 * Developer Manual: http://pages.cs.wisc.edu/~anhai/py_stringmatching/v0.2.0/dev-manual-v0.2.0.pdf
 * Issue Tracker: https://github.com/anhaidgroup/py_stringmatching/issues
 * Mailing List: https://groups.google.com/forum/#!forum/py_stringmatching

Dependencies
============

py_stringmatching has been tested on Python 2.7, Python 3.3, Python 3.4 and Python 3.5.

The required dependencies to build the package are NumPy 1.7.0 or higher,
Six, and a C or C++ compiler. For the development version, you will also need Cython.

Platforms
=========

py_stringmatching has been tested on Linux, OS X and Windows.
django-enummodel 0.2.1
A Django application to bridge the gap between using enumerations and having fully-specified, normalized data models.
License: BSD

================
django-enummodel
================

``django-enummodel`` is a small utility application designed to help bridge the niche use 
case(s?) between defining enumerations for a given field (mutually exclusive options) and 
fully specifying non-exclusive options as instances of a fully specified model type.

In the common case, non-mututally exclusive options configured as a fully specified model
(along with a fixture to store those options) can, in the best case, 'just work'; in the 
worst case, it can provide mild headaches, notably when lazy translations are involved.

In the particular case of the author, the challenge was to avoid having to cope with a 
O(n) growth rate of fixtures to manage, coupled with the desire to avoid encountering 
issues of internationalization and being forced to choose a particular character set 
in which to store these values.

``django-enummodel`` provides a `Django`_ Model object with which the developer may extend 
to provide the functionality sketched above::

  LANGUAGE_CHOICES = (
      (1,_(u'English')),
      (2,_(u'Spanish')),
      (3,_(u'French')),

      (255,_(u'other')),
  )

  class Language(EnumModel):
      class EnumMeta:
          choices = LANGUAGE_CHOICES

Please note that that population of any model derived from ``EnumModel`` takes place after
schema migration; therefore, changes to the choices basis will need to be rectified by running::

  django-admin.py migrate

or::

  django-admin.py migrate app_name

.. _Django: http://djangoproject.org