semvec/pypi-packages huggingface dataset:
jambi 0.1.3
A peewee database migration manager
License: MIT

.. image:: https://i.imgur.com/vf8CyKL.png

jambi
=====

a peewee database migration manager (in development)

About
-----
The `peewee ORM <https://github.com/coleifer/peewee>`_ has limited migration support through its extension, playhouse. A ``SchemaMigrator`` is supplied that can run schema-altering statements, but there is no concept of versioning the database. By giving your database a version number, jambi can help you keep your database schema revisions organized, and allows you to incrementally upgrade if desired.

Getting Started
---------------
- install with pip
    ``pip install jambi``
- create a jambi config file in your favorite directory
    ``cd myproj/db && touch jambi.conf``
- see the section entitled 'Configuration' to learn about what must be in jambi.conf
- run jambi!
    ``jambi --help``

Supported Operations
--------------------
    **init** -- create the jambi table and set the version to 0

    **inspect** -- return the database's current version

    **latest** -- retuns the latest migration version

    **upgrade <version>** -- upgrade your database to the supplied version

    **makemigration** -- generate a new migration version from template

Configuration
-------------
Jambi needs to know how to connect to your database, and where your migrations are stored, which can be conveyed though the jambi.conf configuration file. **Currently jambi only supports PostgreSQL**, but support for others wouldn't be hard to implement -- feel free to make a pull request!) Jambi will look for your configuration file in your current working directory.

Here is an example jambi.conf, set up to connect to a vanilla postgres database:

.. code-block:: ini

    [database]
    database=test
    schema=public
    host=localhost
    user=postgres
    password=

    [migrate]
    location=./migrations/


Contributing
------------
Pull requests are welcome, and please open issues for any bugs, enhancements, or feature requests.

Disclaimer
----------
I claim no responsibility for bugs or other misuse of this tool resulting in loss of data or any other unintended side-effect. This is still an alpha release, so don't expect too much. (see: Contributing).
repo-checker 0.2.0
A graphical user interface version checker for open source project.
License: MIT
Keywords: network,checker,gui

repo-checker
------------

.. image:: https://img.shields.io/badge/license-MIT-brightgreen.svg
  :target: LICENSE
.. image:: https://img.shields.io/badge/python-2.7-green.svg
.. image:: https://img.shields.io/badge/python-3.5-green.svg

.. image:: https://files.gitter.im/1dot75cm/KWdU/2016-12-30-23-20-24-_____.png
  :target: https://pypi.python.org/pypi/repo-checker/

repo-checker is a graphical user interface version checker for open source projects. It works with Python 2.7/3.x and PyQt 4/5.

It uses the requests package to get the release page, and uses XPath rules to match time. And then, convert it to timestamp and compare them. Finally, it tell you that some projects has been updated.
pykevoplus 1.0.1
Control Kwikset Kevo locks
License: MIT
Keywords: kevo kwikset

==========
pykevoplus
==========

Python module for controlling Kwikset Kevo locks

Kwikset does not provide an official API for their Kevo locks; I reverse
engineered this module from the mykevo.com web app.

To use this module you will need to have a `Kevo Plus`_ installed and a
registered account on `mykevo.com`_. You will need your mykevo.com
credentials to use this module.

This module is published to `pypi`_ so you can install it simply via ``pip install pykevoplus``

Usage
=====

The ``Kevo.GetLocks()`` function will attempt to scrape the mykevo.com web
site to find your locks; as of this writing it can find all of your
locks, but scraping the HTML might break at any time if Kwikset changes
the website.

.. code:: python

    from pykevoplus import Kevo
    locks = Kevo.GetLocks("username@email.com", "password123")
    for lock in locks:
        print repr(lock)

Output::

    KevoLock(name=Front Door, id=cca7cd1d-c1d5-43ce-a087-c73b974b3529, state=Locked)
    KevoLock(name=Back Door, id=c60130cd-8139-4688-8ba3-199276a65ad6, state=Locked)

A better way is to explicitly instantiate a ``KevoLock`` object using the
UUID of the lock. You can get the lock IDs manually by logging into
mykevo.com, click Details for the lock, click Settings, the lock ID is
on the right.

.. code:: python

    from pykevoplus import KevoLock
    lock = KevoLock.FromLockID("cca7cd1d-c1d5-43ce-a087-c73b974b3529", "username@email.com", "password123")

Locking and Unlocking
'''''''''''''''''''''

.. code:: python

    from pykevoplus import KevoLock
    lock = KevoLock.FromLockID(lock_id, username, password)
    lock.Unlock()
    print lock.GetBoltState()
    lock.Lock()
    print lock.GetBoltState()

Output::

    Unlocked
    Locked

Multiple operations in the same session
'''''''''''''''''''''''''''''''''''''''

The ``KevoLockSession`` context manager allows you to perform multiple
operations on a lock with a single auth session

.. code:: python

    from pykevoplus import KevoLock, KevoLockSession
    lock = KevoLock.FromLockID(lock_id, username, password)
    with KevoLockSession(lock):
        lock.Unlock()
        lock.Lock()

Known Issues
============

* Error handling is extremely basic and needs much more work. Communication errors as well as lock bolt errors need to be addressed
* No unit tests yet
* Currently only python 2.7 support



.. _Kevo Plus: http://www.kwikset.com/kevo/plus
.. _mykevo.com: mykevo.com
.. _pypi: https://pypi.python.org/pypi/pykevoplus
webrtcvad 2.0.10
Python interface to the Google WebRTC Voice Activity Detector (VAD)
License: MIT
Keywords: speechrecognition asr voiceactivitydetection vad webrtc

.. image:: https://travis-ci.org/wiseman/py-webrtcvad.svg?branch=master
    :target: https://travis-ci.org/wiseman/py-webrtcvad

py-webrtcvad
============

This is a python interface to the WebRTC Voice Activity Detector
(VAD).  It is compatible with Python 2 and Python 3.

A `VAD <https://en.wikipedia.org/wiki/Voice_activity_detection>`_
classifies a piece of audio data as being voiced or unvoiced. It can
be useful for telephony and speech recognition.

The VAD that Google developed for the `WebRTC <https://webrtc.org/>`_
project is reportedly one of the best available, being fast, modern
and free.

How to use it
-------------

0. Install the webrtcvad module::

    pip install webrtcvad

1. Create a ``Vad`` object::

    import webrtcvad
    vad = webrtcvad.Vad()

2. Optionally, set its aggressiveness mode, which is an integer
   between 0 and 3. 0 is the least aggressive about filtering out
   non-speech, 3 is the most aggressive. (You can also set the mode
   when you create the VAD, e.g. ``vad = webrtcvad.Vad(3)``)::

    vad.set_mode(1)

3. Give it a short segment ("frame") of audio. The WebRTC VAD only
   accepts 16-bit mono PCM audio, sampled at 8000, 16000, or 32000 Hz.
   A frame must be either 10, 20, or 30 ms in duration::

    # Run the VAD on 10 ms of silence. The result should be False.
    sample_rate = 16000
    frame_duration = 10  # ms
    frame = b'\x00\x00' * (sample_rate * frame_duration / 1000)
    print 'Contains speech: %s' % (vad.is_speech(frame, sample_rate)


See `example.py
<https://github.com/wiseman/py-webrtcvad/blob/master/example.py>`_ for
a more detailed example that will process a .wav file, find the voiced
segments, and write each one as a separate .wav.


How to run unit tests
---------------------

To run unit tests::

    pip install -e ".[dev]"
    python setup.py test
wikilabels 0.4.0
A generalized data labeling system for use in MediaWiki wikis
License: MIT

# Wiki Labels
This package provides the components of a generalized labeling service for
MediaWiki.  There are two primary components, a user script to be used on
MediaWiki and a flask server for the gadget to converse with.  


## Server
The flask server is intended to be hosted by a web server, but a dev server can
be started locally with

    wikilabels dev_server

### Installation


#### Dependencies
Installation will require some additional packages to be available.

  `sudo apt-get install postgresql-server-dev-all postgresql libffi-dev npm g++ python3-dev libmemcached-dev`

#### Database setup
You'll need to create a `wikilabels` user and database. Here's a sequence of
commands that works on a fresh install of postgres (note that `sudo` rights
will be required).

Create a wikilabels user

    $ sudo useradd wikilabels

Switch to `postgres` user to run commands

    $ sudo su postgres
    $ psql
    postgres=# CREATE USER wikilabels WITH PASSWORD 'something secure';
    postgres=# CREATE DATABASE wikilabels;
    postgres=# GRANT ALL PRIVILEGES ON DATABASE wikilabels to wikilabels;
    postgres=# \q
    $ exit

Switch to `wikilabels` user to load schema

    $ sudo su wikilabels
    $ psql

Copy-paste config/schema.sql into the command prompt.

Optionally, you can also load sample data into the database by copy-pasting config/schema-testdata.sql into the command prompt

### Starting the dev server

    $ wikilabels dev_server --config config-localdev.yaml

## Gadget

1. Go to one of the js subpages of your user page. You can choose a page such as these:
  * [meta:User:`<Name>`/global.js](https://meta.wikimedia.org/wiki/Special:MyPage/global.js), which will be loaded in all wikis, in all skins
  * [meta:User:`<Name>`/common.js](https://meta.wikimedia.org/wiki/Special:MyPage/common.js), which will be loaded only on Meta-wiki, in all skins
  * [meta:User:`<Name>`/vector.js](https://meta.wikimedia.org/wiki/Special:MyPage/vector.js), which will be loaded only on Meta-wiki, in the vector skin
2. Copy the following to the page you have chosen:

  ```javascript
  // [[File:User:EpochFail/WikiLabels.js]] (workaround for [[phab:T35355]])
  mw.loader.load( '//labels.wmflabs.org/gadget/loader.js' );
  ```

3. Clear the cache of your browser.

This will import a live copy of the javascript.

## See also
* [meta:Wiki labels](https://meta.wikimedia.org/wiki/Wiki labels)
wikiclass 0.3.2
A library for performing automatic detection of assessment classes of Wikipedia articles.
License: MIT

# Wikipedia article quality classification

This library provides a set of utilities for performing automatic detection of assessment classes of Wikipedia articles.  For more information, see the full documentation at http://pythonhosted.org/wikiclass.

**Compatible with Python 3.x only.**  Sorry.

* **Install:** ``pip install wikiclass``
* **Models:** https://github.com/halfak/wikiclass/tree/master/models
* **Documentation:** http://pythonhosted.org/wikiclass

## Basic usage

    >>> import wikiclass
    >>> from revscoring.scorer_models import MLScorerModel
    >>>
    >>> scorer_model = MLScorerModel.load(open("models/enwiki.wp10.rf.model", "rb"))
    >>>
    >>> text = "I am the text of a page.  I have a <ref>word</ref>"
    >>> wikiclass.score(scorer_model, text)
    {'prediction': 'stub',
     'probability': {'stub': 0.27156163795807853,
                     'b': 0.14707452309674252,
                     'fa': 0.16844898943510833,
                     'c': 0.057668704007171959,
                     'ga': 0.21617801281707663,
                     'start': 0.13906813268582238}}


## Authors
* Aaron Halfaker -- https://github.com/halfak
* Morten Warncke-Wang -- http://www-users.cs.umn.edu/~morten