semvec/pypi-packages huggingface dataset:
pylockgen 1.6.7
Secure password generator and strength checker
License: MIT
Requires Python: >=3.6

# PyLockGen
        
        ![version](https://img.shields.io/badge/version-1.6.7-blue.svg)
        ![license](https://img.shields.io/badge/license-MIT-green)
        
        **PyLockGen** is a simple and secure Python module that can:
        -  Generate strong passwords
        -  Calculate entropy of a password
        -  Check password strength
        -  Check passwords that if they are in the breached list 
        
        ## Installation
        
        ```
        pip install pylockgen
        ```
        
        ## Usage
        ```
        import pylockgen
        
        # Generate a secure password
        password = pylockgen.generate()
        print(f"Generated: {password}")
        
        # Check entropy
        print("Entropy:", pylockgen.entropy(password))
        
        # Get strength 
        print("Strength:", pylockgen.strength(password))
        
        # Check if the password is breached
        print("Boolean:", pylockgen.isbreached(password)) # Returns a boolean
        
        print(check_breach(password)) # Returns a string
        
        ```
        
        ## License
        This project is licensed under the MIT License.
        MIT © 2025 Hadi Raza
image-labeling-yolo 0.1.2
Tkinter GUI for reviewing and editing YOLO labels.
License: MIT
Requires Python: >=3.9
Keywords: yolo, labeling, annotation, gui, tkinter

# image-labeling-yolo

Lightweight Tkinter GUI for reviewing and editing YOLO-format bounding boxes. Point it at your dataset and quickly add, edit, and save labels.

## Features

- Simple, fast GUI for YOLO labels (class id, x_center, y_center, width, height)
- Keyboard-friendly: A add box, D delete, S save, digits 0–9 to set class
- Reads class names from `yolo_dataset/data.yaml` (`names:` list) when present
- Works with images under `yolo_dataset/images/` and matching labels under `yolo_dataset/labels/`

## Install

```bash
pip install image-labeling-yolo
```

Python 3.9+ is supported. Tkinter must be available in your Python installation (most CPython builds include it by default).

## Quick start

1) Prepare folders (minimal structure):

```
yolo_dataset/
  images/
    img001.jpg
    img002.jpg
  labels/
    img001.txt   # optional; created on save if missing
    img002.txt
  data.yaml      # optional; provides class names via `names:`
```

2) Launch the GUI:

```bash
label-review
```

If you prefer, you can also run:

```bash
python -m review_gui
```

3) Use the folder pickers (right panel) to select your `images/` and `labels/` directories if the defaults are not detected.

## Usage notes

- Navigation: Left/Right arrows or N/P
- Add a box: Press A, then click two opposite corners; drag handles to resize
- Delete selection: D
- Save: S (auto-saves when you navigate)
- Set class: Digits 0–9 (or choose from the dropdown)
- Class names: Provide a `yolo_dataset/data.yaml` with a `names:` list to show readable labels; otherwise numeric IDs are shown

## Troubleshooting

- Tkinter missing: On some Linux distributions you may need to install Tk packages (e.g., `sudo apt-get install python3-tk`), or use a Python build that includes Tkinter.
- Headless servers: Run the GUI on a machine with a display or use X forwarding/remote desktop options.

## License

MIT

## Links

- Source and issues: https://github.com/mateus558/image-labeling-yolo
relatipy 0.1.0
Numeric and symbolic tools for Kerr black hole geometry
License: MIT
Requires Python: >=3.10

# KerrPy 🌀

Herramienta simbólica, numérica y visual para explorar la geometría y dinámica alrededor de **agujeros negros de Kerr**.  

---

## 📑 Tabla de contenidos
- [KerrPy 🌀](#kerrpy-)
  - [📑 Tabla de contenidos](#-tabla-de-contenidos)
  - [🌌 Introducción](#-introducción)
  - [🏗️ Arquitectura del proyecto](#️-arquitectura-del-proyecto)
  - [Inicializar proyecto](#inicializar-proyecto)

---

## 🌌 Introducción
KerrPy tiene como objetivo brindar un entorno unificado para estudiar **agujeros negros de Kerr** combinando:
- **Cálculos simbólicos** (tensores, invariantes, geodésicas).
- **Simulaciones numéricas** (órbitas, trayectorias de partículas y fotones).
- **Visualización interactiva** (potenciales efectivos, mapas de curvatura, trayectorias).  

Este proyecto busca servir como herramienta de **investigación**.

---

## 🏗️ Arquitectura del proyecto
El proyecto se organiza en módulos separados:
```
/docs          → documentación teórica y notas
/symbolic      → cálculos simbólicos 
/numeric       → cálculos numéricos 
/visualization → visualización y análisis interactivo 
```

## Inicializar proyecto
Garantizando que tiene `docker-compose` instalado, puede iniciar el proyecto con:

```bash
docker-compose build
```

Para la parte simbólica:
```
docker-compose run symbolic
```

Para la parte numérica:
```
docker-compose run numeric
```

Para la parte de visualización:
```
docker-compose up visualization
```
mace-descriptor 0.9.6
Minimal fork of MACE for fast descriptor extraction from pretrained foundation models.
License: MIT
Requires Python: >=3.7

# mace-descriptor

A lightweight fork of the [MACE](https://github.com/ACEsuit/mace), focused exclusively on **descriptor extraction** using pretrained foundation models.

This package strips away all training, logging, CLI, and experimental tracking code from MACE, providing a fast generation of pretrained MACE descriptor.

---

## 🔧 Installation

```bash
pip install mace-descriptor
```

---

## 🚀 Quick Start

```python
from mace_descriptor.calculators import mace_mp
from ase.build import molecule

atoms = molecule("CH4")
calc = mace_mp(model="small")
desc = calc.get_descriptors(atoms)  # shape: (n_atoms, descriptor_dim)

# compute numerical gradients with respect to atomic positions
desc_grad = calc.numerical_descriptor_gradient(atoms)  # shape: (n_atoms, n_atoms, 3, descriptor_dim)
```

---

## 📚 Reference Models

mace-descriptor uses pretrained MACE foundation models such as:

* **MACE-MP-0a** (Materials Project DFT data)
* **MACE-OFF23** (Organic force fields)

For more, visit: [https://github.com/ACEsuit/mace-foundations](https://github.com/ACEsuit/mace-foundations)

---

## 📖 Citing MACE

If you use this package or the underlying pretrained models, please cite the original MACE paper:

```bibtex
@inproceedings{Batatia2022mace,
  title={{MACE}: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields},
  author={Ilyes Batatia and David Peter Kovacs and Gregor N. C. Simm and Christoph Ortner and Gabor Csanyi},
  booktitle={Advances in Neural Information Processing Systems},
  editor={Alice H. Oh and Alekh Agarwal and Danielle Belgrave and Kyunghyun Cho},
  year={2022},
  url={https://openreview.net/forum?id=YPpSngE-ZU}
}

@misc{Batatia2022Design,
  title = {The Design Space of E(3)-Equivariant Atom-Centered Interatomic Potentials},
  author = {Batatia, Ilyes and Batzner, Simon and Kov{\'a}cs, D{\'a}vid P{\'e}ter and Musaelian, Albert and Simm, Gregor N. C. and Drautz, Ralf and Ortner, Christoph and Kozinsky, Boris and Cs{\'a}nyi, G{\'a}bor},
  year = {2022},
  number = {arXiv:2205.06643},
  eprint = {2205.06643},
  eprinttype = {arxiv},
  doi = {10.48550/arXiv.2205.06643},
  archiveprefix = {arXiv}
 }
```

---

## 📄 License

MIT License (c) 2022 ACEsuit/mace, modified by In Won Yeu 2025

This is a reduced version focused solely on descriptor extraction. See `LICENSE.md` for details.