semvec/pypi-packages huggingface dataset:
django-accessibility 1.0
A Django App enhance the accessibility and usability of their website
Requires Python: >=3.8

Django All in One Accessibility
===============================

Django All in One Accessibility is a django App based on assistive technology that helps organizations enhance the accessibility and usability of their website Detailed documentation is in the "docs" directory.


Quick start
============

- ``settings.py`` — add 'accessibility' in your INSTALLED_APPS

- ``settings.TEMPLATES`` — put this line in TEMPLATES context_processors to `accessibility.context_processors.admin_AIOA`

- ``base.html`` — put this line in footer of your base. html to `<script id="aioa-adawidget" src="{{ AIOA_URL }}"></script>`

Run ``python manage.py migrate`` to create the  All in One Accessibility models

Start the development server and visit http://127.0.0.1:8000/admin/ to create a All in One Accessibility
pyfed-macos 0.0.35
PyFed is an open-source framework for federated learning algorithms.
Requires Python: >=3.7

# PyFed
PyFed is an open-source framework for federated learning. PyFed is fairly straightforward and brief in comparison to other federated learning frameworks. Furthermore, it allows running federated learning algorithms with any Tensorflow dataset on any preprocessed dataset. PyFed introduces several methods of federated learning implementation such as running multiple processes on a single machine and training on various systems. In addition, PyFed employs Tensorboard to demonstrate the history of training of each client and assess loss and accuracy of each client per round.
</br>
PyFed implements FL using sockets, processes, and threads. Simply put, each client will run its particular process and tries to establish a socket connection with the server, which also has its specific process. 
Once initiated, each connection will be handled by one thread of the server's process. Each thread will communicate with its respective client to receive the trained weights per round. 
Once they receive the result of one round, threads will return the weights to the server's process, which will arrive at a new model using the mentioned weights. The server will send the new model to the clients using newly initiated threads.
</br>
PyFed is mainly based on two classes:
 
- __FL_Server__: which represents the server to which clients communicate in a federated learning problem. The __train()__ function of this class handles socket connections and the FL policy. </br>
- __FL_Client__: which represents each client in a federated learning network. An object of this class handles training procedure of any global model on any local data.

PyFed can run federated learning in 2 ways: 

1. Running FL only on one system and using separate processes.
2. Running FL on multiple systems. 

__Tutorials and installation guides are mentioned on [PyFed webpage](https://pyfed.readthedocs.io/en/latest/).__
pyfed 0.0.35
PyFed is an open-source framework for federated learning algorithms.
Requires Python: >=3.7

# PyFed
PyFed is an open-source framework for federated learning. PyFed is fairly straightforward and brief in comparison to other federated learning frameworks. Furthermore, it allows running federated learning algorithms with any Tensorflow dataset on any preprocessed dataset. PyFed introduces several methods of federated learning implementation such as running multiple processes on a single machine and training on various systems. In addition, PyFed employs Tensorboard to demonstrate the history of training of each client and assess loss and accuracy of each client per round.
</br>
PyFed implements FL using sockets, processes, and threads. Simply put, each client will run its particular process and tries to establish a socket connection with the server, which also has its specific process. 
Once initiated, each connection will be handled by one thread of the server's process. Each thread will communicate with its respective client to receive the trained weights per round. 
Once they receive the result of one round, threads will return the weights to the server's process, which will arrive at a new model using the mentioned weights. The server will send the new model to the clients using newly initiated threads.
</br>
PyFed is mainly based on two classes:
 
- __FL_Server__: which represents the server to which clients communicate in a federated learning problem. The __train()__ function of this class handles socket connections and the FL policy. </br>
- __FL_Client__: which represents each client in a federated learning network. An object of this class handles training procedure of any global model on any local data.

PyFed can run federated learning in 2 ways: 

1. Running FL only on one system and using separate processes.
2. Running FL on multiple systems. 

__Tutorials and installation guides are mentioned on [PyFed webpage](https://pyfed.readthedocs.io/en/latest/).__
odoo8-addon-sale-order-back2draft 8.0.1.0.0.99.dev17
Back to draft on sales orders
Requires Python: ~=2.7

Get back sales orders to draft
==============================

Allows to get back to draft any cancelled sale order.

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

Nothing special is needed to install this module.

Configuration
=============

Nothing special is needed to configure this module.

Usage
=====

When you cancel a sale order, you will see a new button call *Back to draft*,
that resets the sale order to draft state.

Known issues / Roadmap
======================

None known.


Bug Tracker
===========

Bugs are tracked on `GitHub Issues <https://github.com/OCA/sale-workflow/issues>`_.
In case of trouble, please check there if your issue has already been reported.
If you spotted it first, help us smashing it by providing a detailed and welcomed feedback
`here <https://github.com/OCA/sale-workflow/issues/new?body=module:%20sale_order_back2draft%0Aversion:%208.0%0A%0A**Steps%20to%20reproduce**%0A-%20...%0A%0A**Current%20behavior**%0A%0A**Expected%20behavior**>`_.


Credits
=======

Contributors
------------
* Pedro M. Baeza <pedro.baeza@serviciosbaeza.com>
* Oihane Crucelaegui <oihanecrucelaegi@avanzosc.es>


Maintainer
----------

.. image:: http://odoo-community.org/logo.png
   :alt: Odoo Community Association
   :target: http://odoo-community.org

This module is maintained by the OCA.

OCA, or the Odoo Community Association, is a nonprofit organization whose mission is to support the collaborative development of Odoo features and promote its widespread use.

To contribute to this module, please visit http://odoo-community.org.
sqlsprinkler-python-GT3CH1 0.0.13
A python library to control a SQLSprinkler system
Requires Python: >=3.8

# sqlsprinkler_python
A python library for contrlling a sqlsprinkler system.


TODO:
- [ ] Create a zone class
- [ ] Create a system class
- [ ] Add controlling individual zones (setting on and off)
- [ ] Add manipulating individual zones (add,update,delete)
- [ ] Add controlling the entire system (system on or system off)