semvec/pypi-packages huggingface dataset:
ThorsonWiki 0.1a19
Simple, modular wiki sites with Django
License: MIT

============
Thorson Wiki
============

Thorson Wiki is a simple Django wiki framework. It allows developers to create
one or more wikis quickly and easily. The framework is designed to be both
extensible and easy to work with---it provides basic functionality for simple
cases, and an advanced API for more complex cases. 

------------
Installation
------------

The easiest way to install Thorson Wiki is with either :code:`pip` or
:code:`easy_install`. 

1. Install the package:

   pip install thorson_wiki

2. Add :code:`thorson_wiki` to :code:`INSTALLED_APPS` in your Django
   settings.

3. Run :code:`manage.py migrate` to synchronize your database.

---------------
Creating a Wiki
---------------

To create a wiki, you must first tell Thorson Wiki some information about the
wikis that you want to create. This is done using the
:code:`THORSONWIKI_NAMESPACES` setting. This setting is a dictionary mapping
namespace names to information about that namespace. An example is given below,
containing only the required settings.

::

    THORSONWIKI_NAMESPACES = {
        'mywiki': {
            'title': "My Wiki",
            'description': "A wiki designed by me, for me.",
            'author': "Jacob Collard",
            'landing': "Home Page",
        }
    }

The :code:`title` attribute provides a human-readable title for your wiki
namespace. This appears in most of the templates in the nav bar. The
description appears in the description :code:`<meta>` tag in the template HTML.
Likewise, the :code:`author` appears in the author :code:`<meta>` tag.
:code:`landing` is the name of the Wiki's landing page. 

If you are using the default templates, it is also recommended that you add the
following settings:

::

    THORSONWIKI_DEFAULT_NAMESPACE = 'mywiki'

    LOGIN_URL = reverse_lazy(THORSONWIKI_DEFAULT_NAMESPACE + 'login')
    LOGIN_REDIRECT_URL = reverse_lazy(THORSONWIKI_DEFAULT_NAMESPACE + ':main')

The next thing you need to do is tell your project the URLs for your wiki.
Simple import :code:`include_wiki` from :code:`thorson_wiki.urls` and add the
following to your urlpatterns:

::
    
    url(r'^my_base_url/', include_wiki('mywiki'))

This will automatically generate URLs beginning with :code:`my_base_url` that
relate to the wiki :code:`mywiki`. Simply navigate to :code:`my_base_url`, and
you will find your wiki is in operation. Note that if you haven't yet created
the landing page, you will be prompted to do so when you visit the wiki.
rl_wsd_labeled 0.1.1
Labeled contexts of Russian polysemous words
License: MIT

Labeled Russian Context for WSD
===============================

Contexts sampled from RuTenTen and RNC. Sense definitions from Active Dictionary.
Some words have two annotators. Number of contexts is 100 for most words
and 500 for 7 words.

Annotators (words):

- Анастасия Лопухина (47)
- Константин Лопухин (11)
- Александра Удальцова (2)
- Анастасия К. (2)
- Анна Кот (2)
- Анна Татаренко (2)
- Борис Иомдин (2)
- Иван Самойленко (1)

Contexts are stored in ``rl_wsd_labeled/``::

    rl_wsd_labeled
    ├── adjectives
    │   └── RuTenTen
    ├── nouns
    │   ├── RNC
    │   └── RuTenTen
    └── verbs
        └── RuTenTen

A python interface is provided. Intall the package first::

    pip install rl_wsd_labeled

and then in order to get labeled contexts::

    >>> import rl_wsd_labeled
    >>> f = rl_wsd_labeled.contexts_filename('nouns', 'RuTenTen', 'горшок')
    >>> rl_wsd_labeled.get_contexts(f)

    ({'1': 'Округлый глиняный сосуд для приготовления пищи (печной горшок)',
      '2': 'Расширяющийся кверху сосуд с отверстием в дне (цветочный горшок)',
      '3': 'Ночной горшок'},
     [(('телевизор, - ковер, , - музыкальный центр, - стол, - аквариум, - 3 шкафа, - цветы в',
        ' горшках',
        ', - мелкие аксессуары.'),
      '2'),
      ...
      (('ибо настанет срок и оно будет разрушено течением времени либо войною, будто старый',
        ' горшок',
        ' с вином в трюме торгового корабля, попавшего в бурю и разбившегося о скалы.'),
      '1')
     ])

Apart from senses, there are two special annotations: "0" means
"I don't know/the context is unclear/the contexts is invalid", and "max sense + 1"
mean "other sense, not listed among given senses". Contexts marked as "0" or "other"
are not returned, unless ``with_skipped=True`` is passed.
If there was more then one annotator, contexts where annotators did not agree are also
not included. There is a function ``rl_wsd_labeled.get_agreement`` that returns the
ratio of senses where where both annotators gave either the
same concrete sense, or both skipped the senses (so "0" and "other" are considered equal).
glovepy 0.0.3
Python implementation of the GloVe algorithm for word embedding
License: MIT
Keywords: NLP,Machine Learning

# GlovePy

A Python implementation with Cython of the [GloVe](http://www-nlp.stanford.edu/projects/glove/) algorithm with multi-threaded training.
this Python package mainly contains two Python classes. The first Python class (Corpus) builds the co-occurrence matrix given a collection of
documents; while the second Python class (Glove) will generate vector representations for words.

GloVe is an unsupervised learning algorithm for generating vector representations for words developed by Stanford NLP lab.
The paper describing the model is [here](http://nlp.stanford.edu/projects/glove/glove.pdf). In contrast with
[Word2Vec](https://code.google.com/p/word2vec/) (there is a great Python implementation in [gensim](http://radimrehurek.com/gensim/models/word2vec.html))
which is often referred as prediction method, GloVe is called counting method which the embedding is produced by factorizing the logarithm of the
corpus word co-occurrence matrix.

The original implementation for this Machine Learning model can be [found here](http://nlp.stanford.edu/projects/glove/). This work is based on the work of [glove-python](https://github.com/maciejkula/glove-python) and [glove](https://github.com/JonathanRaiman/glove).

## Installation

### Build on Windows

If you are installing this package on Windows (tested on Windows 10), you will need to install Visual C++ 2015 Build Tools. If you have already installed Microsoft Visual Studio, you cannot install the Visual C++ Build Tools. It will ask you to uninstall your existing VS when you tried to install the Visual C++ build tools using the standalone installer. However, since you already have the VS, you can go to Control Panel—Programs and Features and right click the VS item and Change-Modify, then check the option of those components that relates to the Visual C++ Build Tools, like Visual C++, Windows SDD, then install them. Please also make sure the Build tools path has been added to the system path.

Once you have the Visual C++ Build Tools, you can build the package as follows:

```python
python setup.py build_ext --inplace --compiler=msvc
python setup.py install
```

### Linux

Install from the source code:

```python
python setup.py install
```
jyotisha 0.0.9
Tools for computations involved in the jyotiSha vedAnga
License: MIT
Keywords: indic vedic sanskrit astronomy astrology jyotisa jyotish jyotis,panchanga,panchangam tithi

JyotiSha tools
==============

For users
=========

For detailed examples and help, please see individual module files in
this package.

Installation or upgrade:
------------------------

-  ``sudo pip install jyotisha -U``
-  ``sudo pip install git+https://github.com/sanskrit-coders/jyotisha/@master -U``
-  `Web <https://pypi.python.org/pypi/jyotisha>`__.

Usage
-----

-  Please see the generated python sphynx docs in one of the following
   places:

   -  http://jyotisha.readthedocs.io
   -  `project
      page <https://sanskrit-coders.github.io/jyotisha/build/html/jyotisha.html>`__.
   -  under docs/\_build/html/index.html

For contributors
================

Contact
-------

Have a problem or question? Please head to
`github <https://github.com/sanskrit-coders/jyotisha>`__.

Packaging
---------

-  ~/.pypirc should have your pypi login credentials.

   ::

       python setup.py bdist_wheel
       twine upload dist/* --skip-existing

Document generation
-------------------

-  Sphynx html docs can be generated with ``cd docs; make html``
-  http://jyotisha.readthedocs.io/en/latest/jyotisha.html should
   automatically have good updated documentation - unless there are
   build errors.
azureml.datacollector 0.1.0a13
Microsoft Azure Machine Learning Python SDK for collecting model data during operationalization
License: MIT
Keywords: azure machinelearning model operationalization datacollection

Microsoft Azure Machine Learning Data Collection API for Python
===================================================

This package has been tested with Python 2.7 and 3.5.

This is a preview release
=========================

The SDK is being released as a preview, for gathering feedback.

**Future releases are subject to breaking changes.**

.. :changelog:

Release History
===============


0.1.0a1 (2017-05-30)
^^^^^^^^^^^^^^^^^^^^^

* Preview release.
sphinx_theme_pd 0.8.3
Material Design theme for Sphinx.
License: MIT

Sphinx Theme 'pd'
=================

Material Design theme for `Sphinx`_

Sample
------

* https://sphinx-theme-pd.netlify.com/


Screen shot
-----------

.. image:: ./img/sphinx_theme_pd_1.png

.. image:: ./img/sphinx_theme_pd_2.png

.. image:: ./img/sphinx_theme_pd_3.png

.. image:: ./img/sphinx_theme_pd_4.png


Installation
------------

1. pip install

.. code::

   $ pip install sphinx_theme_pd

2. Edit your conf.py

.. code::

   import sphinx_theme_pd
   html_theme = "sphinx_theme_pd"
   html_theme_path = [sphinx_theme_pd.get_html_theme_path()]

Build
-----

.. code::
   $ yarn
   $ webpack

.. _Sphinx: http://sphinx-doc.org
jsonext 0.4.2
Well-structured helpers to help serializing commonly encountered structures to JSON (like datetime, to_dict(),  etc.
License: MIT

jsonext
=======

.. image:: https://travis-ci.org/mbr/jsonext.svg?branch=master
   :target: https://travis-ci.org/mbr/jsonext


jsonext makes it easy to serialize objects outside of the standard Python
primitives to JSON::

    >>> import jsonext
    >>> from datetime import datetime
    >>> jsonext.dumps(datetime.now())
    '"2014-03-22T22:17:18.304528+00:00"'
    >>> jsonext.dumps(i**2 for i in range(10))
    '[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]'

It uses mixins to the standard encoder to achieve this and is easily reuse-
and extensible. Check out the `documentation <http://pythonhosted
.org/jsonext>`_ for details.
pylpchanged 0.1.1
A Pylp plugin for filtering unchanged files
License: MIT
Requires Python: >=3.5
Keywords: pylp gulp changed

===========
PylpChanged
===========


Information
===========

| **pylpchanged** is a plugin for `Pylp`_ that filter unchanged files.
|
| **Note**: this plugin cannot filter unchanged files with a name transformed by another
  plugin further into the stream (``pylprename`` for example).


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

Install **pylpchanged** with ``pip``::

    pip install pylpchanged

If you don't have Python ``Scripts`` folder in your PATH, you can run also::

    python -m pip install pylpchanged


Usage
=====

The usual use of **pylpchanged** is as follows:

.. code:: python

    import pylp
    from pylpchanged import pylpchanged

    pylp.task('default', lambda:
        pylp.src('lib/*.py')
          .pipe(changed())
        # .pipe(another_plugin())
          .pipe(pylp.dest('build'))
    )

| Without parameters, **pylpchanged** will wait for the destination stream (i.e. ``pylp.dest``)
  to compare the last result with the source files.
|
| For a faster execution, you can pass directly the destination path like this:

.. code:: python

    import pylp
    from pylpchanged import pylpchanged

    pylp.task('default', lambda:
        pylp.src('lib/*.py')
          .pipe(changed('build'))
        # .pipe(another_plugin())
          .pipe(pylp.dest('build'))
    )


.. _Pylp: https://github.com/pylp/pylp