semvec/pypi-packages huggingface dataset:
uac-cli 1.0.1
A CLI tool for executing commands against the Stonebranch UAC API
Requires Python: >=3.7

# README

## Notice: Project Transition
As of 2025-01-01, this project is no longer under active development. The existing code will remain available on PyPI and GitHub for reference, but no new features, enhancements, or updates will be provided.

Moving forward, this tool will be managed and supported by Stonebranch. Customers are encouraged to download the latest binary package of this tool from the Stonebranch Product Download Page.

Thank you for your support, and we recommend transitioning to the Stonebranch-supported version for continued updates and maintenance.

## Overview

UAC CLI is a command-line interface tool designed to interact with the
Universal Controller API. It provides a comprehensive set of commands for
managing resources such as audits, agent clusters, agents, bundles, business
services, calendars, cluster nodes, credentials, custom days, database
connections, email connections, LDAP configurations, metrics, OAuth clients,
OMS servers, PeopleSoft connections, promotion targets, properties, reports,
SAP connections, scripts, server operations, simulations, system status, task
instances, tasks, triggers, universal events, universal templates, user groups,
users, variables, virtual resources, webhooks, and workflows.

Go to the [official documentation](https://stonebranch-uac-cli.readthedocs-hosted.com/en) site to read the docs.


### Useful links

- [User Guide](https://stonebranch-uac-cli.readthedocs-hosted.com/en/develop/getting_started.html)
- [Continuous Integration](/CI.md)
- [Development Environment](/DEVENV.md)
decrypt-ha-backup 2024.1.27.1
Decryption utility for Home Assistant backups
Requires Python: >=3.10

## What is this?
This is a command-line python module that allows you to turn an encrypted Home Assistant backup (aka "Password Protected") into a non-encrypted backup.  You might find this useful in situations such as:
- Your backup has been corrupted and you're just trying to get what you can out of it.
- You're trying to get just one or two files out of a backup without having to restore the whole thing.

Home Assistant backups are just compressed tar files but to encrypt them with a password it uses a non-standard encryption scheme.  To the author's knowledge there is not way to decrypt these with standard compression/decompression tools which is why he wrote this little utility.

## A note on reliability and expectations
This tool isn't sanctioned by the developers of Home Assistant and isn't updated in response to changes Home Assistant makes to the format of its backup files.  This tool hacks apart a backup and then builds it back up, which makes it very sensitive to any changes the Home Assistant developers make to the backup file format.  

It has been tested on backups created by Home Assistant version 2022.6.7. If you encounter an error using this tool please consider creating an issue for it on GitHub to notify the maintainer, you'll probably be helping many other users if you bring attention to an issue.

## Installation
Make sure you have python 3 and pip installed on your system.  Search around on Google for how to install them on your operating system.  Then from the command line: 
```bash
pip install decrypt-ha-backup
```

## Usage
Download your backup from Home Assistant.  Ensure you ahve at least twice the size of your backup available on your hard drive, and run:
```bash
python3 -m decrypt-ha-backup /path/to/your/backup.tar
```

You will be asked for the backup's password, after being processed the decrypted backup will be placed at ```/path/to/your/Decrypted backup.tar```.

### Optional Arguments
- <kbd>--password secret_password</kbd> Specify the the password instead of being asked
- <kbd>--output_filename /path/to/output.tar</kbd> Specify the output decrypted backup file name
chatool 0.0.0
Chat Tools
Requires Python: >=3.8
Keywords: Deep Learning, Artificial Intelligence

### Install
```shell
pip install chatool
```

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pscript 0.8.0
Python to JavaScript compiler.
Requires Python: >=3.6
Keywords: Python, JavaScript, compiler, transpiler, parser

PScript
=======

[![Build Status](https://github.com/flexxui/pscript/workflows/CI/badge.svg)](https://github.com/flexxui/pscript/actions)
[![Documentation Status](https://readthedocs.org/projects/pscript/badge/?version=latest)](https://pscript.readthedocs.org)


PScript is a Python to JavaScript compiler, and is also the name of the subset
of Python that this compiler supports. It was originally developed as a part of
[Flexx]https://github.com/flexxui/flexx) and is now a project on its own.


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

PScript is pure Python and requires Python 3.6+ (including Pypy).
It has no further dependencies.

```
pip install pscript
```


Short example
-------------

```py

   from pscript import py2js

   def foo(a, b=2):
      print(a - b)

   print(py2js(foo))
```

Gives:

```js
   var foo;
   foo = function flx_foo (a, b) {
      b = (b === undefined) ? 2: b;
      console.log((a - b));
      return null;
   };
```


Supported browsers
------------------

PScript aims to support all modern browsers, including Firefox, Chrome and Edge.
Internet Explorer is in principal supported from version 9, though some constructs
(e.g. ``async`` and ``await``) do not work in Internet Explorer.


PScript in the wild
-------------------

To give an idea of what PScript can do, here are some examples in the wild:

* Obviously, everything built in Flexx uses PScript, see e.g. [these examples](https://flexx.readthedocs.io/en/stable/examples/)
* The front-end of [TimeTagger.app](https://timetagger.app) is built in Python using PScript.

*Let us know if you know more!*


License
-------

PScript makes use of the liberal 2-clause BSD license. See LICENSE for details.
autoqt6 0.0.8
Simplify pyqtProperty creation.
Requires Python: >=3.6
Keywords: PyQt, PyQt6, pyqtProperty, PySide6

# autoqt
[![PyPI](https://badge.fury.io/py/autoqt.svg)](https://badge.fury.io/py/autoqt)
[![Build Status](https://semaphoreci.com/api/v1/chipolux/autoqt/branches/master/shields_badge.svg)](https://semaphoreci.com/chipolux/autoqt)

A simple single file module that makes setting up basic Qt properties a little
bit nicer without restricting you.

This is very useful if you have QObjects with many readonly properties and a
few special setters or state modifying slots.

Example:
```
from PyQt6.QtCore import pyqtSignal, pyqtSlot
from autoqt6 import AutoObject, AutoProp


class SomeObject(AutoObject):
    valuesChanged = pyqtSignal()

    aNumber = AutoProp(int, 'valuesChanged', '_aNumber')
    otherNumber = AutoProp(float, 'valuesChanged', '_otherNumber')
    aString = AutoProp(str, 'valuesChanged', '_aString', write=True)

    def __init__(self, parent=None):
        super().__init__(parent=parent)
        self._aNumber = 10
        self._otherNumber = 20
        self._aString = 'spam'

    @pyqtSlot()
    def incrementNumber(self):
        self._aNumber += 1
        self.valuesChanged.emit()

    @otherNumber.setter
    def otherNumber(self, value):
        self._otherNumber = value + 2
        self.valuesChanged.emit()


x = SomeObject()

print(x.aNumber)  # 10
x.aNumber = 10  # throws AttributeError, readonly property
x.incrementNumber()  # valuesChanged is emitted
print(x.aNumber)  # 11

print(x.otherNumber)  # 20
x.otherNumber = 40  #  otherNumber.setter called, valuesChanged is emitted
print(x.otherNumber)  # 42

print(x.aString)  # 'spam'
x.aString = 'ham'  # valuesChanged is emitted, writable property
print(x.aString)  # 'ham'
```
gpu-power-optimizer 0.1.0
Universal GPU power efficiency optimizer
Requires Python: >=3.8

# GPU Power Optimizer

Universal GPU power efficiency optimizer that reduces power consumption by 15-30% with a single line of code. Supports both NVIDIA and AMD GPUs across all major ML frameworks.

This project represents a significant step towards sustainable AI and computing. By making advanced power optimization technology freely available, we aim to reduce global data center energy consumption and environmental impact while democratizing access to efficient AI training for everyone.

## Why Open Source?

The pressing challenges of energy consumption in AI and computing require immediate, global solutions. By open-sourcing this technology, we enable:

* Immediate impact on global energy consumption
* Democratized access to efficient computing
* Accelerated innovation in sustainable technology
* Environmental protection through reduced power usage

## Features

* **Simple Integration**: One line of code to optimize GPU power efficiency
* **Universal Support**: Works with NVIDIA and AMD GPUs
* **Framework Agnostic**: Supports PyTorch, TensorFlow, and other major ML frameworks
* **Production Ready**: Extensively tested in various environments
* **Immediate Results**: 15-30% power reduction without performance loss

## Quick Start

```python
from gpu_optimizer import GPUPowerOptimizer

# Initialize optimizer
optimizer = GPUPowerOptimizer()

# Apply optimizations to your model
optimizer.apply_power_optimizations(model)
```

## Installation

```bash
pip install gpu-power-optimizer
```

## Impact

* **Environmental**: Potential reduction of millions of tons of CO2 emissions
* **Economic**: Significant cost savings for data centers and AI companies
* **Technological**: New paradigm in GPU power efficiency
* **Social**: Democratizing access to efficient AI computing

This technology is being open-sourced as part of a broader initiative to drive sustainable innovation in computing. For business inquiries or advanced optimization solutions, please reach out through issues or discussions.
```