semvec/pypi-packages huggingface dataset:
json-schema-reducer 0.1.4
Extract from a JSON/dict only whats in the JSON Schema
License: MIT

===================
json-schema-reducer
===================

.. image:: https://travis-ci.org/peterbe/json-schema-reducer.svg?branch=master
    :target: https://travis-ci.org/peterbe/json-schema-reducer

.. image:: https://badge.fury.io/py/json-schema-reducer.svg
    :target: https://pypi.python.org/pypi/json-schema-reducer

Extract from a JSON/dict only whats in the JSON Schema. Assumes that the
JSON/dict you supply is valid according to the JSON Schema you also supply.


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

Simple install with ``pip``::

    pip install json-schema-reducer

How to use it
=============

Suppose you have two files on disk: ``schema.json`` and ``mything.json``.
And suppose that the ``schema.json`` only lists the properties ``foo`` and
``bar`` but the file ``mything.json`` contains many more things::

    >>> from json_schema_reducer import make_reduced_dict
    >>> make_reduced_dict('schema.json', 'mything.json')
    {'foo': 'value1', 'bar': 'value2'}

The arguments are flexible. You can also do this::

    >>> make_reduced_dict(open('schema.json'), open('mything.json'))

Or this::

    >>> make_reduced_dict(open('schema.json').read(), open('mything.json').read())

Or this::

    >>> make_reduced_dict(
    ...   json.load(open('schema.json')),
    ...   json.load(open('mything.json')))


Runnings tests
==============

Simply run::

    python setup.py test


Version History
===============

0.1.4
  * Universal wheel

0.1.3
  * Better, but basic, cli (e.g. ``--help``)
  * Support for optional nested keys

0.1.2
  * Correct README restructured text

0.1.1
  * Tidying up loose ends

0.1.0
  * First, hopefully, working version.
music_scraper 1.1.0
Gets Songs from the web and allows users to download the same
License: MIT

Music-Scraper
=============

.. image:: https://badge.fury.io/py/music_scraper.svg
    :target: https://badge.fury.io/py/music_scraper
.. image:: https://landscape.io/github/srivatsan-ramesh/Music-Scraper/master/landscape.svg?style=flat
   :target: https://landscape.io/github/srivatsan-ramesh/Music-Scraper/master
   :alt: Code Health\\
   
Download all your favourite songs from the web at one place. No more searching through the links given by Google Search, Music-Scraper does it for you.

Requirements
------------

-   python>=2.7
-   Scrapy>=1.1.1

Getting Started
---------------

Ensure that you have setuptools>=24.0.3 installed for proper installation of music_scraper.
Installation of setuptools can be done with

.. code-block:: bash

    pip install setuptools==24.0.3

Install Music-Scraper from pip directly

.. code-block:: bash

    pip install music_scraper

Or

Clone this repository to get started.

.. code-block:: bash

    git clone https://github.com/srivatsan-ramesh/Music-Scraper.git
    cd Music-Scraper/

And install it.

.. code-block:: bash

    python setup.py install

You can also install it for development.

.. code-block:: bash

    python setup.py develop

If Scrapy installation creates some problem refer this `link <http://doc.scrapy.org/en/latest/intro/install.html>`_.

How to Use
----------

Type 'music-scraper' in the terminal and type in a query which can give audio files in Google Search. For example 'kabali songs download'.

How to Contribute
-----------------

Read the docs for API reference from `here <http://music-scraper.readthedocs.io>`_. If you could think of any improvements for the project feel free to make a PULL REQUEST.
MLCLAS 0.0.3
Multi-label classification algorithms implemented in Python
License: MIT
Keywords: multi label classification

# MLCLAS
MLCLAS is a Python module for multi-label classification built on Scipy.

The project is built during my graduation project for bachelor's degree.

## Dependencies
MLCLAS is tested to work under Python 3.5, and on the systems of OS X and Linux.

It is promised to work as intended with these pakages:

- NumPy >= 1.10

- SciPy >= 0.17.0

- scikit-learn >= 0.17.0

- cvxpy >= 0.4.0

Lower version of dependencies may cause unexpected problems, so it is strongly recommended to update these packages.

## Documentation
The functions provided by this library is quite concise. You may view the examples in the `examples` folder for reference.

If you are reading this from Pypi, please visit github page to download the `examples` folder.
datasync 0.0.1.dev0
Sync environment
License: MIT

datasync
========

Management command to sync environment data.

Syncs database and media, for example when deploying an acceptance environment.

Quick Start
-----------

* pip install django-datasync

* add 'datasync' to INSTALLED APPS

* configure settings 

::

 DATASYNC = {
     'HOST': '{{ remote_host_address }}',
     'PORT': {{ remote_ssh_port }}
     'USER': '{{ remote_username }}',
     'PASSWORD': '{{ remote_user_password }}',
     'MEDIA_REMOTE': '{{ remote_media_directory }}',
     'MEDIA_TARGET': '{{ local_media_directory }}',
     'DATABASE': {
         'HOST': '{{ db_host_address }}',
         'USER': '{{ db_username }}',
         'PASSWORD': '{{ db_password }}',
         'NAME': '{{ db_name }}',
         'PORT': {{ db_port }}
     },
 }


You will now have access to the management command 'sync_environment'. Run that command from the target machine, perhaps as a cron job. Or add to ansible-playbooks setup.

Supported Databases: Postgres.

You will also have to edit the following postgres conf file in order to allow access from your target machine(s)

``/etc/postgresql/9.3/main/pg_hba.conf``