semvec/pypi-packages huggingface dataset:
hass-nabucasa 1.15.0
Home Assistant cloud integration by Nabu Casa, Inc.
Requires Python: >=3.13

# hass-nabucasa

`hass-nabucasa` is the underlying library that enables Home Assistant to connect to and utilize Nabu Casa cloud services.

This library handles a range of cloud-related functionality including:

- Authentication and account management
- Remote UI connections via [SniTun](https://www.github.com/NabuCasa/snitun)
- API interactions with Nabu Casa cloud services
- Voice processing capabilities
- ACME certificate management
- Google Assistant and Alexa integration
- Cloud webhook management
- Cloud file storage and management

## Installation

```bash
python3 -m pip install hass-nabucasa==x.y.z
```

## Release process

`hass-nabucasa` is released through GitHub and published to [PyPI].
The release process is automated and triggered through the GitHub UI:

1. Go to the [GitHub Releases page][releases].
2. Find the draft release created by release-drafter.
3. Verify that the tag and name are the expected ones (e.g., `1.2.3`)
4. Publish the release (and set it as the latest release)

Once published, GitHub Actions workflows automatically:

- Build the package
- Publish to [PyPI]

There is no need to manually update version information in the codebase.

## Development and contributing

### Development environment

We recommend using Visual Studio Code with the official Dev Container extension for development. This provides a consistent, pre-configured environment with all dependencies installed.

This will automatically set up a development environment with all required dependencies.

### Running tests

```bash
scripts/test
```

### Code quality

This project uses pre-commit hooks for code quality checks:

```bash
scripts/lint
```

### Updating voice data

To update the voice data with the latest from Azure:

```bash
python3 -m scripts.update_voice_data
```

You will need to fetch an Azure TTS token. You can generate one by running the [sample key generator server](https://github.com/Azure-Samples/cognitive-services-speech-sdk/tree/master/samples/js/browser/server) and visiting `http://localhost:3001/api/get-speech-token`.

[releases]: https://github.com/NabuCasa/hass-nabucasa/releases
[PyPI]: https://pypi.org/project/hass-nabucasa/
inhibit 0.2.1
System suspend inhibitor
Requires Python: >=3.14

# Inhibit.py

Systemd Inhibitor - Prevent system shutdowns and sleep states via stdin ON/OFF
commands.

This script creates a systemd inhibitor that can be controlled via stdin.
It reads ON/OFF commands from standard input and toggles the inhibition
accordingly. This is useful for preventing system shutdowns and sleep states
when controlled by external processes through pipes.

Basic Usage:
```bash
    # Just an example of valid input values.
    echo "ON" | inhibit
    echo "OFF" | inhibit

    # In practice, an external process emits these values.
    some_process |inhibit

    # Common arguments
    inhibit --what sleep --who "my-app" --why "processing data" --mode "block"
```
The script uses systemd's logind interface to create inhibitors for:
- sleep: Prevent system sleep/suspend
- idle: Prevent automatic idle detection

For detailed command-line options, use --help.

For detailed description of inhibitor locks, see: https://systemd.io/INHIBITOR_LOCKS/

## Requirements

### System requirements

These must be available and installed

- dbus-python
- systemd

## Installing

Important: tool assumes that system provides `dbus-python` package.

Alternatively you can install `dbus-python` manually.

```bash
$ python -m venv --system-site-packages venv
$ source venv/bin/activate
$ python -m pip install inhibit
$ inhibit --version
inhibit 0.1.0
```

To install the package, it's recommended to use the Linux distribution default package manager.

## Development

### Requirements

- uv
- just
- reuse
- tox

### Build

- `just check`
- `just build`

See [Justfile](Justfile).
explicit-python-dlq 1.1.0
Набор компонентов реализующих DQL для систем на базе explicit
Requires Python: >=3.9

# Набор компонентов реализующих очередь необработанных сообщений (Dead Letter Queue).

## Пример подключения
testapp/dlq/apps.py:
```python
from django.apps import AppConfig as AppConfigBase


class AppConfig(AppConfigBase):
    name = __package__

    def ready(self):
        self._configure_dlq()

    def _configure_dlq(self):
        from testapp.core import bus
        from explicit.dlq.config import DLQConfig, configure_dlq
        # реализация репозитория специфичная для используемого слоя хранения данных
        from testapp.dlq.adapters.db import Repository
        
        # реализация обработчика сообщений, специфичного для приложения
        from testapp.dlq.services.dispatch import dispatch_message

        config = DLQConfig(
            bus=bus,
            repository=Repository(),
            message_dispatcher=dispatch_message
        )
        # регистрация репозитория и регистрация обработчиков команд
        configure_dlq(config)
```

testapp/dlq/services/dispatch.py:
```python
import json
from explicit.contrib.messagebus.event_registry import Message

from testapp.core import bus
from testapp.core import event_registry

def dispatch_message(raw_message_value: bytes, topic: str):
    """Преобразует сообщение в событие и отправляет его в шину."""
    message = json.loads(raw_message_value)

    if event := event_registry.resolve(
        Message(
            topic=topic,
            type=message.get('type'),
            body=message
        )
    ):
        bus.handle(event)

````

testapp/entrypoints/kafka.py:

```python
def bootstrap() -> None:
    from explicit.dlq.infrastructure.handlers import RegisterInDLQOnFailure

    from testapp.core import bus

    from testapp.dlq.services.dispatch import dispatch_message
    topics = ('test.foo.topic',)

    for raw_message in adapter.subscribe(*topics):
        with RegisterInDLQOnFailure(
            bus=bus,
            topic=raw_message.topic(),
            raw_message_value=raw_message.value(),
            raw_message_key=raw_message.key
        ):
            dispatch_message(raw_message.value(), raw_message.topic())
```
## Готовые компоненты (contrib)

В пакете реализованы готовые к использованию компоненты:
* Реализация абстрактного [хранилища сообщений](./src/explicit/dlq/contrib/django/README.md) на базе Django ORM
* [REST API](./src/explicit/dlq/contrib/drf/README.md) для работы с хранилищем сообщений. Предназначен для совместной работы с реализацией хранилища сообщений django ORM.
pyfreeform 0.5.0
A minimalist, art-focused Python drawing library with object-oriented design
Requires Python: >=3.10
Keywords: art, creative-coding, drawing, freeform, generative-art, graphics, svg

<p align="center">
  <img src="wiki/_images/logo.svg" width="120" alt="PyFreeform logo">
</p>

<h1 align="center">PyFreeform</h1>

<p align="center">Turn images into art with Python.</p>

<p align="center">
  <img src="wiki/_images/home/hero-mona-lisa.svg" width="480" alt="Mona Lisa rendered as dot art">
</p>

## Install

```bash
pip install pyfreeform
```

Requires Python 3.10+.

## Quick Start

```python
from pyfreeform import Scene

scene = Scene.from_image("photo.jpg", grid_size=40)
for cell in scene.grid:
    cell.add_dot(radius=cell.brightness * 0.4, color=cell.color)
scene.save("artwork.svg")
```

That's it — four lines from photo to SVG.

## What Can You Make?

<table>
  <tr>
    <td align="center"><img src="wiki/_images/recipes/image-color-dots.svg" width="200"><br><sub>Color dots</sub></td>
    <td align="center"><img src="wiki/_images/guide/paths-filled-lissajous.svg" width="200"><br><sub>Filled Lissajous curves</sub></td>
    <td align="center"><img src="wiki/_images/recipes/flow-escher-curves.svg" width="200"><br><sub>Flowing curves</sub></td>
  </tr>
  <tr>
    <td align="center"><img src="wiki/_images/recipes/typo-ascii.svg" width="200"><br><sub>ASCII art</sub></td>
    <td align="center"><img src="wiki/_images/recipes/network-arrows.svg" width="200"><br><sub>Connected networks</sub></td>
    <td align="center"><img src="wiki/_images/recipes/adv-combined.svg" width="200"><br><sub>Multi-layer compositions</sub></td>
  </tr>
</table>

Dots, lines, curves, polygons, text, ellipses, custom paths — all positioned relative to a grid of cells. Sizes are fractions of the cell, so your art scales to any resolution.

## Learn More

Everything you need is in the docs:

- [**Getting Started**](https://matheweb.github.io/pyfreeform/getting-started/) — install and create your first artwork
- [**Guide**](https://matheweb.github.io/pyfreeform/guide/) — every feature, explained visually
- [**Recipes**](https://matheweb.github.io/pyfreeform/recipes/) — portraits, patterns, typography, networks
- [**API Reference**](https://matheweb.github.io/pyfreeform/api-reference/) — complete reference

## License

GPL-3.0 — see [LICENSE](./LICENSE) for details.

## Acknowledgments

Built on [NumPy](https://numpy.org/) (BSD-3-Clause) and [Pillow](https://python-pillow.github.io) (MIT-CMU). See [LICENSES/third_party/](./LICENSES/third_party/) for full texts.
max-div 0.5.4
Configurable Solver for Maximum Diversity Problems with Fairness Constraints.
Requires Python: >=3.11

![shields.io-python-versions](https://img.shields.io/badge/python-3.11%20%7C%203.12%20%7C%203.13%20%7C%203.14-blue)
![genbadge-test-count](https://bertpl.github.io/max-div/version_artifacts/release/v0.5.4/badge-test-count.svg)
![genbadge-test-coverage](https://bertpl.github.io/max-div/version_artifacts/release/v0.5.4/badge-coverage.svg)
[![docs-build-status](https://app.readthedocs.org/projects/max-div/badge/?version=latest)](https://max-div.readthedocs.io/en/stable)
[![License](https://img.shields.io/github/license/bertpl/max-div)](https://github.com/bertpl/max-div/blob/main/LICENSE)
<p>
  <img src="https://bertpl.github.io/max-div/version_artifacts/release/v0.5.4/splash.webp" alt="max-div logo" style="max-width: max(60%, min(100%,800px)); height: auto;">
</p>

# max-div
Configurable Solver for Maximum Diversity Problems with Fairness Constraints.

### --- ***Under Development*** ---
stackmate 0.1.1
A Full-Stack Development Toolkit for Python developers.
Requires Python: >=3.8

# StackMate

**StackMate** is a production-ready Full-Stack Development Toolkit for Python developers. It provides essential utilities for building robust APIs, managing authentication, handling errors, and simplifying database operations.

## Features

- **Logger**: Structured request/response logging with latency tracking.
- **Validator**: API payload validation using type hints and schema checks.
- **DB Helper**: Generic CRUD utilities for SQL/NoSQL databases.
- **Auth**: Secure JWT token management (creation and verification).
- **Error Handler**: Standardized API response formatting.
- **Utils**: Common helper functions (UUIDs, timestamps, etc.).

## Installation

```bash
pip install stackmate
```

## Quick Start

```python
from stackmate.logger import AppLogger
from stackmate.auth import AuthManager

# Initialize components
logger = AppLogger("my-app")
auth = AuthManager(secret_key="your-secret-key")

# Use them in your application
token = auth.create_token({"user_id": 123})
```

## Project Structure

```text
stackmate/
├── stackmate/           # Core library files
├── examples/            # Example usage scripts
├── tests/               # Unit tests
├── pyproject.toml       # Build configuration
└── README.md            # Project documentation
```

## Author

- **Sudhakar K** - [sudhakark4227@gmail.com](mailto:sudhakark4227@gmail.com)

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
groupdocs-parser-cloud 26.2
GroupDocs.Parser Cloud Python SDK
Keywords: groupdocs, parser, cloud, python, sdk

# GroupDocs.Parser Cloud Python SDK
Python package for communicating with the GroupDocs.Parser Cloud API. This SDK allows you to work with GroupDocs.Parser Cloud REST APIs in your python applications.

## Requirements

Python 3.4+

## Installation
Install `groupdocs-parser-cloud` with [PIP](https://pypi.org/project/pip/) from [PyPI](https://pypi.org/) by:

```sh
pip install groupdocs-parser-cloud
```

Or clone repository and install it via [Setuptools](http://pypi.python.org/pypi/setuptools): 

```sh
python setup.py install
```

## Getting Started

Please follow the [installation procedure](#installation) and then run following:

```python
# Import modules
import groupdocs_parser_cloud
from Common import Common

# This example demonstrates how to parse a document using template object.
class ParseByTemplateDefinedAsAnObject:
    @classmethod  
    def Run(cls):
        parseApi = groupdocs_parser_cloud.ParseApi.from_config(Common.GetConfig())
        options = groupdocs_parser_cloud.ParseOptions()
        options.file_info = groupdocs_parser_cloud.FileInfo()
        options.file_info.file_path = "words-processing/docx/companies.docx"
        options.template = Common.GetTemplate()

        request = groupdocs_parser_cloud.ParseRequest(options)
        result = parseApi.parse(request)
        
        for data in result.fields_data:
            if data.page_area.page_text_area is not None:
                print("Field name: " + data.name + ". Text :" + data.page_area.page_text_area.text)

            if data.page_area.page_table_area is not None:
                print("Table name: " + data.name)
                for cell in data.page_area.page_table_area.page_table_area_cells:
                    print("Table cell. Row " + str(cell.row_index) + " column " + str(cell.column_index) + ". Text: " + cell.page_area.page_text_area.text);
```


## Licensing
GroupDocs.Parser Cloud Python SDK licensed under [MIT License](http://github.com/groupdocs-parser-cloud/groupdocs-parser-cloud-python/LICENSE).

## Resources
+ [**Website**](https://www.groupdocs.cloud)
+ [**Product Home**](https://products.groupdocs.cloud/parser)
+ [**Documentation**](https://docs.groupdocs.cloud/display/parsercloud/Home)
+ [**Free Support Forum**](https://forum.groupdocs.cloud/c/parser)
+ [**Blog**](https://blog.groupdocs.cloud/category/parser)

## Contact Us
Your feedback is very important to us. Please feel free to contact us using our [Support Forums](https://forum.groupdocs.cloud/c/parser).
shelcolors.ansi 0.1.8
ANSI color helper. ANSI(color, text, type), get_colors() and clear_screen().
Requires Python: >=3.8

# shelcolors-ansi



To install, type '`pip install shelcolors.ansi`'.

To use, type '`import shelcolors_ansi`'



Simple ANSI color helper. Use ANSI(color, text, mode)
`mode` is `print` on default but there is also `return`

<details>
    <summary>Also here are the colors</summary>

    ### Mono-scale Colors

    Black = black

    White = white

    Gray = gray

    Light Gray = lightgray

    Dark Gray = darkgray



    ### Red Colors

    Red = red

    Bright Red = brightred

    Dark Red = darkred

    Maroon = maroon

    Crimson = crimson


    ### Yellow and Orange Colors

    Yellow = yellow

    Bright Yellow = brightyellow

    Orange = orange

    Dark Orange = darkorange

    Gold = gold

    Amber = amber

    Mustard = mustard



    ### Green Colors

    Green = green

    Bright Green = brightgreen

    Lime = lime

    Light Green = lightgreen

    Dark Green = darkgreen

    Olive = olive

    Mint = mint



    ### Blue Colors

    Blue = blue

    Bright Blue = brightblue

    Light Blue = lightblue

    Sky Blue = skyblue

    Cyan = cyan

    Aqua = aqua

    Teal = teal

    Navy = navy



    ### Purple and Pink Colors

    Magenta = magenta

    Bright Magenta = brightmagenta

    Purple = purple

    Violet = violet

    Lavender = lavender

    Pink = pink

    Hot Pink = hotpink



    ### Brown Colors

    Brown = brown

    Dark Brown = darkbrown

    Tan = tan

    Beige = beige

</details>

`get_colors()` returns all colors used in `ANSI( ... )`


`clear_screen()` clears screen
yut23-utils 0.2.5
Common code I find myself reaching for from multiple different repositories.
Requires Python: >=3.9

# yut23_utils

[![PyPI - Version](https://img.shields.io/pypi/v/yut23-utils.svg)](https://pypi.org/project/yut23-utils)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/yut23-utils.svg)](https://pypi.org/project/yut23-utils)

-----

**Table of Contents**

- [Installation](#installation)
- [License](#license)

## Installation

```console
pip install yut23-utils
```

## License

`yut23-utils` is distributed under the terms of the [BSD-3-Clause](https://spdx.org/licenses/BSD-3-Clause.html) license.
waxy 0.2.0
A Python wrapper around the Rust taffy UI layout library
Requires Python: >=3.13
Keywords: layout, taffy, ui

# Waxy

[![PyPI](https://img.shields.io/pypi/v/waxy)](https://pypi.org/project/waxy/)

A Python wrapper around the Rust [Taffy](https://docs.rs/taffy/latest/taffy/) UI layout library,
built with [PyO3](https://pyo3.rs/) and [maturin](https://www.maturin.rs/).

## Links

- [PyPI](https://pypi.org/project/waxy/)
- [Taffy documentation](https://docs.rs/taffy/latest/taffy/)
- [Taffy on GitHub](https://github.com/DioxusLabs/taffy)
DBDuck 0.0.1
Universal data object model for SQL, NoSQL, graph, vector, and AI databases.
Requires Python: >=3.9

﻿# dbduck (UDOM Core)

**dbduck is a universal data object model for SQL, NoSQL, graph, vector, and AI databases.**

Define data once, query anywhere.

## What It Does

`dbduck` (implemented here as `UDOM`) provides one consistent interface for working across multiple database types:

- SQL: SQLite, MySQL, PostgreSQL, MariaDB
- NoSQL: MongoDB and document-style stores
- Graph: Neo4j and graph backends
- Extensible path for vector and AI-native data workflows

## Core Interface

- `query(sql)`: run native queries on supported backends
- `uexecute(uql)`: run universal query language (UQL) operations

## Install

```bash
pip install -r requirements.txt
```

## Quick Example

```python
from udom import UDOM

# MySQL example

db = UDOM(
    db_type="mysql",
    url="mysql+pymysql://root:password@localhost:3306/udom"
)

# Native query
print(db.query("SELECT * FROM `User`;"))

# UQL create and read

db.uexecute('CREATE User {name: "Veeresh", age: 23, active: true}')
print(db.uexecute("FIND User WHERE age > 21"))
```

More examples are available in `examples/`.
bai2-reader 0.0.4
Utility to parse BAI2 files and export them to structured formats like CSV/JSON/Parquet
Requires Python: <4.0.0,>=3.13

# BAI2-READER
Utility to read bank statements that are in BAI2 standard format, and convert them to CSV/JSON/Parquet format. 

```text
 ____      _     ___  ____          ____   _____     _     ____   _____  ____  
| __ )    / \   |_ _||___ \        |  _ \ | ____|   / \   |  _ \ | ____||  _ \ 
|  _ \   / _ \   | |   __) | _____ | |_) ||  _|    / _ \  | | | ||  _|  | |_) |
| |_) | / ___ \  | |  / __/ |_____||  _ < | |___  / ___ \ | |_| || |___ |  _ < 
|____/ /_/   \_\|___||_____|       |_| \_\|_____|/_/   \_\|____/ |_____||_| \_\
```

### Documentation 

- Github Wiki https://sh-vardhantallapalli.github.io/bai2-reader/
- ReadTheDocs : https://bai2-reader.readthedocs.io/en/latest/ 

(Pick either, they are always in sync, only the look and feel changes)

### Comes with an UI 💥 too, to explore the BAI file 

- Tabular View
![TabularView.png](docs/images/TabularView.png)
- Classic View
![ClassicView.png](docs/images/ClassicView.png)
- JSON View
![JSONView.png](docs/images/JSONView.png)


### Sample BAI files
- [sample_1.bai](app/bai2_reader/samples/sample_1.bai)
- [sample_2.bai](app/bai2_reader/samples/sample_2.bai)
- [sample_3.bai](app/bai2_reader/samples/sample_3.bai)
- [sample_4.bai](app/bai2_reader/samples/sample_4.bai)
- [sample_5.bai](app/bai2_reader/samples/sample_5.bai)
neuralgraph 0.0.2
NeuralGraph — Not quite a neural network. More than a graph. A context engine for AI.
Requires Python: >=3.9
Keywords: ai, context, knowledge-graph, llm, memory

# NeuralGraph

Not quite a neural network. More than a graph. A context engine for AI.

**Coming soon.** Visit [neuralgraph.app](https://neuralgraph.app) for more info.

## Installation

```bash
pip install neuralgraph
```

## Status

This package is under active development. The SDK is not functional yet.