semvec/pypi-packages huggingface dataset:
MM23084UNO 1.0.2
Librería para resolver sistemas de ecuaciones lineales y no lineales
Requires Python: >=3.7

## Métodos disponibles:

- Eliminación de Gauss
- Gauss-Jordan
- Regla de Cramer
- Descomposición LU
- Método de Jacobi
- Método de Gauss-Seidel
- Método de Bisección

## Instalación (una vez publicada en PyPI)
```bash
pip install MM23084UNO==1.0.2
```

## Ejemplo de uso
```python
from MM23084UNO import resolver_gauss_jordan
import copy

# Ejemplo 1: Sistema 3x3
matriz = [
    [2, 1, -1],
    [-3, -1, 2],
    [-2, 1, 2]
]
vector = [8, -11, -3]

# Hacer copias para no modificar los originales
matriz_copia = copy.deepcopy(matriz)
vector_copia = copy.deepcopy(vector)

solucion = resolver_gauss_jordan(matriz_copia, vector_copia)
print("Solución con Gauss-Jordan:", solucion)
# Resultado esperado: [2.0, 3.0, -1.0]
```

## Autor
Ricardo Mora - mm23084@ues.edu.sv
btrfs 15
Python module to interact programmatically with an online btrfs file system
Keywords: btrfs, filesystem

python-btrfs is a Python 3 library that provides ways to interact
programmatically with an online btrfs file system.

It provides a pure python shadow implementation of data structures used in
btrfs together with convenient wrappers around the collection of kernel
functions that are available in the btrfs kernel API.

Using them, we can examine the secret inner world of a btrfs file system for
educational purposes.

The target audience for using the library is system administrators and
developers who want to discover more about the internals of a btrfs file
system, or want to create adjusted monitoring or administration tools that are
optimized for their specific use cases.

python-btrfs does not directly access disk storage, it only uses functions
available in the kernel interface, using system calls. This also means that
python-btrfs can not be used to repair a broken filesystem whenever the running
Linux kernel cannot properly mount it.
chewmetrics 0.1.0
A library for analyzing chewing patterns using computer vision
Requires Python: >=3.7

# orofacIAnalysis

orofacIAnalysis is a Python library for analyzing chewing patterns using computer vision and facial landmark detection. It uses MediaPipe to track jaw movements and provides tools for cycle detection and analysis.

## Installation

```bash
pip install orofacIAnalysis
```

## Features

- Jaw movement tracking using facial landmarks
- Chewing cycle detection and analysis
- Various signal smoothing methods
- Utilities for data visualization and analysis

## Usage

```python
from chewmetrics import ChewAnnotator

# Analyze a video file
annotator = ChewAnnotator(video_path="path/to/video.mp4")
cycles = annotator.analyze_chewing()

# Print cycle information
for cycle in cycles:
    print(f"Chew count: {len(cycle['directions'])}")
    print(f"Left: {cycle['left']}, Right: {cycle['right']}, Middle: {cycle['middle']}")
```

## Advanced Usage

```python
from chewmetrics import Cycle, SmoothingMethods
import numpy as np

# Create a cycle manually
cycle = Cycle(start_frame=10)
cycle.jaw_movements = np.array([...])  # Your jaw movement data
cycle.fit()

# Print cycle stats
print(cycle)

# Apply different smoothing methods
from chewmetrics.smoothing import apply_smoothing

smoothed_signal = apply_smoothing(jaw_movements, "butterworth")
```

## License

This project is licensed under the MIT License - see the LICENSE file for details.