semvec/pypi-packages huggingface dataset:
pstui 0.1.0
A terminal UI for managing processes
Requires Python: >=3.7
Keywords: process, tui, system, monitor

# psTUI - A terminal UI for managing processes

![psTUI](./assets/image.png)

## Features

- Search processes by name
- Sort processes by CPU or memory usage
- Kill processes
- Toggle auto-refresh
- Lightweight and fast

## Install with pip

```bash
pip install pstui
```

### Run

```bash
pstui
```

## Run from source

### Installation

```bash
pip install -r requirements.txt
```

### Usage

```bash
python src/pstui/main.py
```

## Controls

- Q: Quit
- F: Find process
- R: Refresh
- K: Kill selected process
- S: Toggle sort (CPU/Memory)
- A: Toggle auto-refresh
- ↑/↓: Navigate

## License

Licensed under the [MIT License](LICENSE.md).
See [LICENSE.md](LICENSE.md) for more information.
numbers2hebrew 0.1.0
A Function to convert a number into words in Hebrew
Requires Python: >=3.6

# README.md

# My PyPI Package

A Python function for converting a number to hebrew words.

## Installation

You can install the package using pip:

```
pip install num2heb
```

## Usage

Here is a simple example of how to use the package:

```python
from num2heb import number_to_words

result = number_to_words(123)
print(result)  # Output: "מאה עשרים ושלושה"
```

## Contributing

If you would like to contribute to this package, please fork the repository and submit a pull request.

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
VeMatch 0.0.2
A comprehensive toolkit for managing datasets and implementing advanced techniques for similar image identification.
Requires Python: >=3.12

<img src="https://github.com/gsoaresbaptista/VeMatch/raw/main/docs/_static/logo/purple.svg" width="50px" height="80px" align="left">

# VeMatch: Image Similarity Toolkit
[![PyPi](https://img.shields.io/badge/pypi-%23ececec.svg?style=for-the-badge&logo=pypi&logoColor=1f73b7)](https://pypi.org/project/VeMatch/)
[![PyTorch](https://img.shields.io/badge/PyTorch-%23EE4C2C.svg?style=for-the-badge&logo=PyTorch&logoColor=white)](https://pytorch.org/)
![PyPI - Python Version](https://img.shields.io/pypi/pyversions/VeMatch?style=for-the-badge)
[![Static Badge](https://img.shields.io/badge/Documentation-6e56cf?style=for-the-badge)](https://gsoaresbaptista.github.io/VeMatch/)


VeMatch is a Python library designed to implement state-of-the-art techniques for image similarity identification. It is ideal for researchers and developers working on re-identification (ReID) and embedding analysis tasks. VeMatch provides practical and customizable tools for manipulating datasets and similarity algorithms.


## Installation
Install the package via pip:
```bash
pip install VeMatch
```

## Roadmap
TO-DO
havenlighting 0.1.5
Python client for Haven Lighting API
Requires Python: >=3.7

# Haven Lighting API Client

A Python client library for interacting with the Haven Lighting API.

## Installation

```bash
pip install havenlighting
```

## Usage

```python
from havenlighting import HavenClient

# Initialize the client
client = HavenClient()

# Authenticate
authenticated = client.authenticate(
    email="your-email@example.com",
    password="your-password"
)

if authenticated:
    # Discover locations
    locations = client.discover_locations()
    
    # Print available locations and lights
    for location_id, location in locations.items():
        print(f"Location: {location.name}")
        
        # Get lights for this location
        lights = location.get_lights()
        
        # Control lights
        for light_id, light in lights.items():
            print(f"Light: {light.name}")
            light.turn_on()   # Turn light on
            light.turn_off()  # Turn light off

```

## Development

1. Clone the repository
2. Copy .env.example to .env and fill in your credentials:
```bash
cp .env.example .env
```
3. Install development dependencies:
```bash
pip install -e ".[dev]"
```

4. Run tests:
```bash
pytest
```

## License

MIT License - see LICENSE file for details.