semvec/pypi-packages huggingface dataset:
gs-ai-package 0.1.4
Package per associare a dati trasporto pubblico la direzione della tratta
Requires Python: >=3.7

# GS AI Package

The **GS AI Package** is a library developed for standardizing and streamlining internal projects for the GreenShare Experiments team. This package provides a suite of tools to associate directional information with public transport data, specifically for occupancy (crowding) and evasion data provided by public transport companies.

## Features

- **DirectionProcessor Class**: A new class introduced in version **0.1.1**.
  - Associates the direction with public transport data.
  - Provides methods to process, normalize, and fill missing values in the data.
  - Uniformizes directional information across various projects.
  
- **Data Processing Functions**: Includes functions to:
  - Check if column names are in snake_case.
  - Calculate direction distribution based on the most common values.
  - Fill missing directions using trip-based modes.
  - Uniformize direction values within groups.

## Installation

You can install the package from PyPI using `pip`:

```bash
pip install gs_ai_package
```

For testing and development, you can also install it from TestPyPI:

```bash
pip install --index-url https://test.pypi.org/simple/ gs_ai_package
```
## Running Tests

The package includes tests to verify functionality and ensure code quality. To run the tests, follow these steps:

1. **Install Testing Dependencies:**

   Ensure you have `pytest` and `pytest-cov` installed:
   
   ```bash
   pip install pytest pytest-cov
   ```

2. **Run Tests:**

   From the root directory of the project, run:
   
   ```bash
   pytest --cov=gs_ai_package --cov-report=term-missing
   ```

   This command runs all tests and shows a coverage report in the terminal.

## Documentation

The full documentation for this package is generated using Sphinx and is available online (e.g., via Read the Docs or GitHub Pages).

To build the documentation locally:

1. Navigate to the `docs/` folder:
   
   ```bash
   cd docs
   ```

2. Build the HTML documentation:
   
   ```bash
   make html
   ```

3. Open the generated documentation in your browser by navigating to the `build/html/index.html` file.

Alternatively, you can use `sphinx-autobuild` for automatic rebuilding and live preview:

```bash
sphinx-autobuild . build/html
```

Then open `http://127.0.0.1:8000` in your browser.

## Contributing

Contributions are welcome! If you wish to contribute, please follow these guidelines:

- Fork the repository.
- Create a new branch for your feature or bugfix.
- Write tests for your changes.
- Ensure all tests pass and the coverage is maintained.
- Submit a pull request with a clear description of your changes.

## License

This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.

## Project URLs

- **Source Code:** [https://gitlab.greenshare.it/pietro.cosseddu/gs-ai-package](https://gitlab.greenshare.it/pietro.cosseddu/gs-ai-package)
- **Documentation:** (Link to your documentation site, e.g., Read the Docs or GitHub Pages)
chatushka 0.11rc20
Bot that can make your chat explode!
Requires Python: <4.0,>=3.9

# Chatushka bot

Бот для чатиков (пока только для телеграмушки)


# Usage

## Installation

```shell
pip install chatushka
```

## Start bot

```shell
python -m chatushka --token <telegrambotapitoken>
```

## Test bot

- [x] добавить ботика в чат
- [x] делегировать боту админские права
- [x] написать в чатик /help

```shell
python -m chatushka --token <telegrambotapitoken>
```
chess2 1.0.1
A Python Chess Game supporting PGN and FEN formats
Requires Python: >=3.6

# __chess__

`__chess__`는 체스 게임의 구현을 제공하는 Python 패키지입니다. 사용자는 PGN(Portable Game Notation) 및 FEN(Forsyth-Edwards Notation) 형식으로 게임을 기록하고 불러올 수 있으며, 체스의 모든 기본 규칙을 지원합니다.

## 기능

- 체스 게임을 실시간으로 플레이할 수 있습니다.
- 각 이동에 대한 PGN 기록을 자동으로 저장합니다.
- 게임 종료 후 PGN과 FEN 파일 형식으로 기록을 저장합니다.
- 체크, 체크메이트, 스테일메이트, 앙파상, 캐슬링, 프로모션 등의 체스 규칙을 지원합니다.

## 설치

`__chess__`는 Python 패키지로 설치할 수 있습니다. 다음 명령어를 통해 설치하세요:

```bash
pip install __chess__
realsense-depth-detector 0.1.0
A package for object detection with depth using RealSense cameras
Requires Python: >=3.8
Keywords: realsense, depth detection, computer vision, yolo, object detection

# RealSense Depth Detector

A Python package for object detection with depth information using Intel RealSense cameras.

## Installation

```bash
pip install .
```

## Usage

```python
from realsense_depth_detector import DepthDetector, DepthConfig
from ultralytics import YOLO

# Configure depth planes
config = DepthConfig(
    depth_planes={
        "close": (0.2, 1.0),
        "medium": (1.0, 2.0),
        "far": (2.0, 5.0)
    },
    active_plane="medium"
)

# Initialize
model = YOLO("yolov8n.pt")
detector = DepthDetector(model, config)

# Process frames
frame, detections = detector.process_frame()

# Clean up
detector.release()
```

## Requirements

- pyrealsense2
- numpy
- opencv-python
- ultralytics (for YOLO)
zoomeyehk 2.2.2
Python library and command-line tool for ZoomEye.HK (https://www.zoomeye.hk/doc)
Keywords: security tool, zoomeye, zoomeyehk, command tool

ZoomEye-python-hk
--------------

This project has been deprecated. Please use the new project at: 
[https://github.com/zoomeye-ai/ZoomEye-python](https://github.com/zoomeye-ai/ZoomEye-python)
comfy-installer 0.1.17
A CLI tool to install custom nodes for ComfyUI using YAML configuration.
Requires Python: >=3.9

# Comfy Installer

**Comfy Installer** is a CLI tool that simplifies installing custom nodes and models for [ComfyUI](https://github.com/comfyanonymous/ComfyUI). It uses a YAML configuration to automate the setup, ensuring that all dependencies, custom nodes, and model files are installed correctly.

## Features

- **Easy Custom Node Installation**  
  Install nodes from GitHub repositories, ZIP files, or direct file copy.

- **Automatic Model Download**  
  Fetches model files from URLs and places them into the correct ComfyUI directories.

- **YAML-Based Configuration**  
  Manage installations via a single YAML file, ensuring reproducible setups.

- **Workflow Integration**  
  The included `ComfyRunner` class can start ComfyUI, manage installations, and run workflows.

- **Lightweight & Extensible**  
  The tool is built with modular utilities, making it easy to customize or extend.

## Requirements

- **Python 3.9+**
- **pip** or a similar package manager

## Installation

### From PyPI with uv

```bash
uv add comfy-installer
```

### From Source

1. Clone the repository:
   ```bash
   git clone https://github.com/khengyun/comfy-installer.git
   cd comfy-installer
   ```
2. Install dependencies:
   ```bash
   uv run pip install -r requirements.txt
   ```
3. (Optional) Install as a package:
   ```bash
   python setup.py install
   ```

## Usage

### 1. Prepare a YAML File

Create a YAML file (e.g., `config.yaml`) with the nodes and models you want to install:

```yaml
nodes:
  - name: "ComfyUI-AnimateDiff-Evolved"
    url: "https://github.com/Kosinkadink/ComfyUI-AnimateDiff-Evolved"
    install_type: "git-clone"

  - name: "ComfyUI-Advanced-ControlNet"
    url: "https://github.com/Kosinkadink/ComfyUI-Advanced-ControlNet"
    install_type: "git-clone"

models:
  - name: "sd_xl_base_18897557.0.safetensors"
    url: "https://civitai.com/api/download/models/299716"
    dest: "./ComfyUI/models/checkpoints/"

  - name: "mod_sdxl.safetensors"
    url: "https://civitai.com/api/download/models/299716"
    dest: "./ComfyUI/models/checkpoints/"

```

### 2. Run the Installer via CLI

```bash
comfy-install --yaml path/to/config.yaml [--install-dir /your/install/path]
```

## Contributing

1. Fork the repository.
2. Create a new branch: `git checkout -b feature/your-feature`.
3. Commit your changes: `git commit -m 'Add feature'`.
4. Push to the branch: `git push origin feature/your-feature`.
5. Open a pull request.

## License

This project is licensed under the [MIT License](LICENSE).

## Support

For issues or feature requests, please [open an issue](https://github.com/yourusername/comfy-installer/issues).