semvec/pypi-packages huggingface dataset:
salvage 0.1.3
Allows a group of people to hold sensitive information using a simple secret-splitting scheme.
License: BSD

Salvage distributes sensitive data to multiple people such that it can only be
recovered by several people working together. This is useful for storing
information with both a low risk of losing access to it and a low risk of
accidental disclosure. A classic application is to create a "recovery kit" for a
server or infrastructure, which can be used in the event that conventionally
stored keys and credentials become lost or unavailable.

Salvage works by encrypting a file or directory with a random master key and
then applying a simple key-splitting scheme to distribute the key across
multiple shares. You can create a kit for any number of participants with any
threshold required to recover the information. For example, you might create a
kit for five people, any three of whom may combine their shares to recover the
data.

Salvage runs under Python 2.7 or Python 3.2 and later. The only external
dependency is `gpg`_, for the cryptography. For maximum utility, it is packaged
as a single flat Python script that can be run with no installation. The
algorithms and file formats are simple and carefully documented to ensure that
recovery is always possible even if this software is unavailable for some
reason.


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

    ``$ pip install salvage``

This package will only install the ``salvage`` executable. It does not depend on
any Python packages.


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

To create a new salvage kit for five participants with a recovery threshold of
three:

    ``% salvage new 5 3 path/to/source/dir``

This will create five shares, each containing an encrypted archive and some
metadata. To decrypt and unpack the archive:

    ``% salvage recover path/to/share1 path/to/share2 path/to/share3``

The three paths must be three of the shares generated in the first step. The
master key will be reconstructed and the data will be decrypted and unpacked.

See ``salvage -h`` for additional options.


.. _gpg: https://www.gnupg.org/
apns-worker 0.1
A robust client library for Apple's push notification service.
License: BSD

apns-worker
===========

This is a client library for Apple's push notification service. It is not the
first such library for Python, but, as is often the way, the existing options
didn't quite meet our needs:

#. **No dependencies** (other than six).
#. **Fire and forget**. APNs is a quirky binary protocol that frequently requires
   reconnecting to the service and re-sending notifications after an error.
#. **Robust**. We don't do quick and dirty here. Readability, tests, and
   documentation are not optional.


Quick start
-----------

An APNs client is by nature stateful. The default apns-worker backend uses
Python threads to asynchronously process a queue of messages to send to the
service. Most users will want to maintain a global ApnsManager instance to
process messages. Note that if your own program is threaded, you may need to
take care to create this global instance safely.

::

    from apns_worker import ApnsManager

    apns = None

    def init_apns(key_path, cert_path):
        """ Call this once at program init time. """
        global apns
        apns = ApnsManager(key_path, cert_path)

    def send_badge(token, badge=1):
        """ Badge the app on a single device. """
        apns.send_aps([token], badge=badge)
pyes 0.99.6
Python Elastic Search driver
License: BSD

=============================
 pyes - Python ElasticSearch
=============================

:Web: http://pypi.python.org/pypi/pyes/
:Download: http://pypi.python.org/pypi/pyes/
:Source: http://github.com/aparo/pyes/
:Documentation: http://pyes.rtfd.org/
:Keywords: search, elastisearch, distribute search

--

[![Gitter](https://badges.gitter.im/Join%20Chat.svg)](https://gitter.im/aparo/pyes?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)

pyes is a pythonic way to use ElasticSearch since 2010. 

This version requires elasticsearch 1.x or above. It's a pre release for pyes 1.x. Give a look to migrations documentation to upgrade you code for ElasticSearch 1.x.

We are working to provide full support to ElasticSearch 1.x (check the develop branch: we are using the git-flow workflow) that'll have:

- connection based on Official ElasticSearch client ??
- full support to ElasticSearch 1.x (removed old support due incompatibility with old version of ES)
- migration from multi_field to >field>.fields
- refactory of old code to be more pythonic
- performance improvements


Features
========

- Python3 support (only HTTP, thrift lib is not available on python3)
- Thrift/HTTP protocols
- Bulk insert/delete
- Index management
- Every search query types
- Facet Support
- Aggregation Support
- Geolocalization support
- Highlighting
- Percolator
- River support

Changelog
=========

v. 0.99.0:

    Migrated many code to ElasticSearch 1.x

    Full coverage for actual queries

v. 0.99:

    Added aggregation

    Fix for python3 compatibility

    Upgraded code to use ElasticSearch 1.x or above

v. 0.90.1:

    Bug Fix releases for some python3 introduced regression

v. 0.90.0:

    A lot of improvements.

    Python 3 support.

Migration to version 0.99
=========================

CustomScoreQuery has been removed. The FunctionScoreQuery with its functions cover the previous functionalities.
For scripting use ScriptScoreFunction.




TODO
----

- much more documentation
- add coverage
- add jython native client protocol

License
=======

This software is licensed under the ``New BSD License``. See the ``LICENSE``
file in the top distribution directory for the full license text.

.. # vim: syntax=rst expandtab tabstop=4 shiftwidth=4 shiftround