semvec/pypi-packages huggingface dataset:
draftmk-mkdocs-role-filter-plugin 0.2.0
MkDocs plugin for Draftmk to filter documentation by user roles
Requires Python: >=3.8

> **Note:** This plugin is part of the [DraftMk](https://github.com/jonmatum/draftmk) ecosystem – a full-stack markdown documentation platform with live editing, GitHub integration, and role-based publishing.

# draftmk-mkdocs-role-filter-plugin

A custom [MkDocs](https://www.mkdocs.org/) plugin that filters documentation pages based on role-based access metadata in your Markdown frontmatter.

## Features

- Filters content at build time based on allowed roles
- Supports role-based navigation pruning
- Easy to integrate into existing MkDocs projects

## Installation

You can install this plugin using pip (recommended in a virtual environment):

```bash
pip install draftmk-mkdocs-role-filter-plugin
```

## Usage

In your `mkdocs.yml` configuration file, enable the plugin and set the allowed roles:

```yaml
plugins:
  - role-filter:
      allowed_roles:
        - public
```

Pages not matching the specified roles will be excluded from the build and navigation.

### Frontmatter Example

Each Markdown file should declare its role in the frontmatter:

```markdown
---
title: "Getting Started"
role: public
---
```

You can use custom roles such as `internal`, `admin`, etc.

## How It Works

The plugin performs two key operations:

### `on_page_markdown`

- Checks each page's frontmatter for a `role` field.
- If the role matches one of the configured `allowed_roles`, the page is included in the build.
- If not, the page content is hidden.

### `on_nav`

- Prunes the MkDocs navigation (`nav`) to exclude items that have no visible children or unauthorized pages.
- This ensures the final navigation reflects only authorized content.

## Development

To test locally with editable install:

```bash
pip install -e .
```

## License

MIT © [Jonmatum](https://github.com/jonmatum)

---

> echo "Pura Vida & Happy Coding!";
GeodeBicoccaTemplatePythonProject 1.0.1
Template python project
Requires Python: >=3.8

# Milano Bicocca Workshop

## Objectives:
* Discuss best practices for:
  * software engineering
  * software development

* Introduce useful toolbox:
  * automatization
  * CI/CD

* About pzero:
  * learn history / Current state / vision
  * understand actual organization/architecture
  * provide guidance

* About DFN
  * introduction to OpenGeode
  * Discussion … plan development
mantipy-gui 0.2.0
Modern Python GUI framework with web technology integration
Requires Python: >=3.7

# mantipy-gui

Modern GUI framework for Python that bridges the gap between web technologies and native applications.

## 🧠 Why mantipy-gui?

mantipy-gui solves the real pain points in Python GUI development:

### 🔥 1. Modern UI Without JS Framework Hell
- Use any HTML/JS frontend: React, Svelte, HTMX, or plain HTML
- Embed it into a native Python window, without Electron bloat
- Full JavaScript, CSS, video, canvas support — because it's Chromium

### 🧩 2. Native App Logic in Python
- Button clicks, IPC, data processing — all in Python
- Your whole backend and frontend in one language

### 🌐 3. Pluggable DNS Resolution
- Support .evr, .dao, .ipfs, .lnbtc, or your custom protocol
- A single function hook makes your browser/app future-ready

### 🛠️ 4. Customizable UI Layout
- Native widgets (sidebars, tabs, console) can live beside the browser
- Build apps, not just websites

### 🧪 5. Perfect for Local-first, Decentralized, and dApp UIs
- Build powerful offline tools with web interfaces
- Native .evr access with no DNS hacks
- Self-contained IPFS-UI frontends

## Installation

```bash
pip install mantipy-gui
```

## Quick Start

```python
from mantipy_gui import Window, BrowserView

app = Window(title="My Mantipy App")
browser = BrowserView()
app.add_view(browser)

# Load from URL
browser.load("https://example.com")

# Load local HTML
browser.load_html("<h1>Hello from Mantipy!</h1>")

# Custom domain resolution
@browser.resolver(".evr")
def resolve_evr(domain):
    # Your custom resolver for .evr domains
    return f"https://ipfs.io/ipfs/your-hash"

app.run()
```

## Examples

Check out the [examples](./examples) directory for more use cases.

## Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

## License

This project is licensed under the MIT License - see the LICENSE file for details.
algoritmos-geneticos-DG-PV 0.1.0
Librería de algoritmos genéticos en español.
Requires Python: >=3.11.8

# 🧬 algoritmos_geneticos_col

**Una librería de algoritmos genéticos modular y parametrizable, diseñada en español latino colombiano.**  
Ideal para optimización, inteligencia artificial evolutiva, proyectos de investigación y educación.

---

## 🚀 Características principales

- 📚 Métodos de selección: **Ruleta**, **Torneo**, **Aleatorio**.
- 🔀 Métodos de cruce: **Un punto**, **Dos puntos**, **Uniforme**.
- 🧬 Métodos de mutación: **Básica** y **Aleatoria**.
- 🔄 Métodos de reemplazo: **Generacional** y **Elitismo**.
- 🎯 Totalmente **parametrizable** (tamaño de población, tasa de cruce, tasa de mutación, generaciones, etc.).
- 🛠️ Código limpio, documentado y en **español latino**.

---

## 📦 Instalación

Una vez esté publicada en PyPI:

```bash
pip install algoritmos_geneticos_col
```

Por ahora, si deseas instalar localmente:

```bash
pip install .
```
Desde la raíz del proyecto donde está el `setup.py`.

---

## 🛠️ Ejemplo de uso básico

```python
from algoritmos_geneticos_col.genetico import AlgoritmoGenetico

# Definir función de aptitud (fitness)
def aptitud(individuo):
    return sum(individuo)  # Ejemplo simple: maximizar n煤mero de 1s

# Inicializar el algoritmo
algoritmo = AlgoritmoGenetico(
    tama帽o_poblacion=20,
    num_generaciones=50,
    funcion_fitness=aptitud,
    metodo_seleccion='torneo',
    metodo_cruce='un_punto',
    metodo_mutacion='basica',
    metodo_reemplazo='elitismo',
    probabilidad_cruce=0.8,
    probabilidad_mutacion=0.05
)

# Inicializar población (genomas de 10 genes)
algoritmo.inicializar_poblacion(tama帽o_genoma=10)

# Evolucionar
algoritmo.evolucionar()

# Mostrar mejor individuo
mejor, puntuacion = algoritmo.obtener_mejor_individuo()
print(f"Mejor individuo: {mejor}, Aptitud: {puntuacion}")
```

---

## 📋 Documentación

- 📋 [Documentación completa](https://github.com/oxgerrero/algoritmos_geneticos_DG_PV/wiki) (en construcción).
- Código comentado directamente en cada módulo.
- Compatible con generación de documentación automática (`Sphinx`, `MkDocs`).

---

## 📄 Licencia

Este proyecto está licenciado bajo la **Licencia MIT**.  
Libre para uso, modificación y distribución con atribución.

---

## 🤝 Contribuciones

¡Las contribuciones son bienvenidas!  
Puedes proponer mejoras, corregir errores o sugerir nuevos métodos evolutivos.

1. Haz un **fork** del proyecto.
2. Crea una **rama** (`git checkout -b mejora-nueva`).
3. Haz **commit** a tus cambios (`git commit -am 'nueva mejora'`).
4. Haz **push** (`git push origin mejora-nueva`).
5. Abre un **pull request**.

---

## 👨‍💻 Autor

- **David Gomez** - [GitHub](https://github.com/oxgerrero/)
- **Paula Vera** - [GitHub](https://github.com/paulav14/)
turbojpeg 0.0.2
Python bindungs for libjpeg-turbo using pybind11
Requires Python: >=3.7

# turbojpeg-python

Python bindings for libjpeg-turbo using pybind11. It's using Version 3 of the TurboJPEG C API.

It implements the three functions `transform`, `compress` and `decompress`. For more details see the function docstrings or the TurboJPEG C API docs <https://rawcdn.githack.com/libjpeg-turbo/libjpeg-turbo/main/doc/html/group___turbo_j_p_e_g.html>

## Install

- `pip install turbojpeg`

## Example

Rotate JPEG file losslessly by 90 degrees.

```python
import turbojpeg

with open("image.jpg", "rb") as fr:
    img_data = fr.read()

# lossless rotation. fails if lossless rotation is not possible
# for example when the image dimensions are not multiples of 16
img_data_rot = turbojpeg.transform(img_data, op=turbojpeg.OP.ROT90)

with open("image-rotated.jpg", "xb") as fw:
    fw.write(img_data_rot)
```

Save random image as JPEG with arithmetic coding.

```python
import numpy as np
import turbojpeg
arr = np.random.randint(0, 256, (256, 256, 3), dtype=np.uint8)  # random color image
quality = 90
result = turbojpeg.compress(arr, quality, turbojpeg.SAMP.Y420, arithmetic=True)
with open("arithmetic.jpg", "xb") as fw:
    fw.write(result)
```

## Build

The Python `setup.py` expects the `libjpeg-turbo` binaries in `libjpeg-turbo-build`. You can either build it yourself or download precompiled binaries. For more details see <https://github.com/libjpeg-turbo/libjpeg-turbo/blob/main/BUILDING.md>.

GitHub actions CI builds wheels for Windows 32 and 64-bit, manylinux 64-bit, musllinux 64-bit and MacOS 64-bit. 32-bit Linux builds fail due some weird NASM error. Not all wheels are tested, since some of the test dependencies are not available for all platforms.

## Run tests

`python -m unittest discover -s tests`