semvec/pypi-packages huggingface dataset:
rgpd-pro-client 2.0.7
RGPD_PRO - Professional GDPR Compliance Scanner CLI Client
License: MIT
Requires Python: >=3.8
Keywords: gdpr, compliance, privacy, scanner, audit

# 🛡️ RGPD_PRO CLI Client

**Professional GDPR Compliance Scanner - Command Line Interface**

Connect to RGPD_PRO scanning service to audit websites for GDPR compliance.

## 💰 Pricing

**$24.99 per scan**

## 🚀 Quick Start

### Installation

```bash
pip install rgpd-pro-client
```

### Usage

```bash
rgpd-scan
```

The CLI will guide you through:
1. Connect to API server (default: http://65.108.59.188:8000)
2. Enter website URL to scan
3. Configure company details (revenue, employees, sector)
4. Run scan and get results

## 📊 What You Get

- **Interactive HTML Report** - Violations, risk assessment, remediation roadmap
- **Fine Estimates** - Based on 2,091 real European GDPR sanctions
- **ROI Analysis** - Investment costs vs. risk avoided
- **Forensic Evidence** - Optional legal-grade proof package

## 🎯 Features

✅ **Simple CLI Interface** - Guided menu system  
✅ **Remote Scanning** - No browser installation needed  
✅ **Progress Tracking** - Real-time scan status  
✅ **Auto Download** - HTML reports and evidence packages  
✅ **Fast Results** - 2-3 minutes average  

## 📋 Requirements

- Python 3.8+
- Internet connection
- API access (subscription required)

## 🔧 Advanced Usage

### Custom API Server

```bash
# When prompted, enter your custom API URL
rgpd-scan
```

### Programmatic Use

```python
import requests

# Submit scan
response = requests.post(
    "http://65.108.59.188:8000/scans",
    json={
        "url": "https://example.com",
        "revenue_bracket": "MEDIUM",
        "employee_bracket": "MEDIUM",
        "sector": "ecommerce"
    }
)

scan_id = response.json()["scan_id"]

# Poll status
status = requests.get(f"http://65.108.59.188:8000/scans/{scan_id}").json()

# Download report
html_report = requests.get(f"http://65.108.59.188:8000{status['html_url']}").content
```

## 📖 Documentation

Full documentation: https://github.com/yourusername/rgpd-pro-client

## 🆘 Support

- Issues: https://github.com/yourusername/rgpd-pro-client/issues
- Email: support@rgpd-pro.com

## 📄 License

MIT License - See LICENSE file

## ⚠️ Legal Disclaimer

This tool provides technical analysis, NOT legal advice. Fine estimates are statistical projections with ±30% margin. Always consult qualified legal counsel for compliance matters.

---

**Built for GDPR compliance professionals**
rep-grafos 0.2.3
Representações Monte Carlo, Las Vegas e Determinística de Grafos Massivos
License: MIT
Requires Python: >=3.8

# Representação de grafos Las Vegas

São disponibilizadas duas classes ``RepProbabilistic``, ``RepSpatialXOR`` e ``RepModular`` para representação de grafos.

# Para executar
Primeiro instale o pacote:
> pip install rep-grafos

# Como usar

Aqui estão as instruções para utilização das classes:

## A classe ``RepProbabilistic``

São disponibilizados três parâmetros ``c``, ``hash_size`` e ``fator``. O parâmetro ``c`` indica que cada função hash em ``H[v]`` deve representar pelo menos ``c`` vizinhos de ``v``. O parâmetro ``hash_size`` diz o tamanho em bits das funções hash.  Já o parâmetro  ``q``,  refere-se à fração máxima de vizinhos de ``v`` que cada vértice ``v`` devem ser permitidos não serem representados pelas funções hash, isto é, presentes em ``D[v]``.

O método ``construir`` deve ser executado passando-se uma instância ``igraph.Graph`` ou qualquer instância que implemente os métodos ``neighbors`` que retorna os vizinhos de um determinado vértice e ``complementer`` que retorna o complemento de um grafo.

## A classe ``RepModular``

São disponibilizados os parâmetro ``c``. O parâmetro ``c`` indica que cada função hash em ``H[v]`` deve representar pelo menos ``c`` vizinhos de ``v``.

O método ``construir`` deve ser executado passando-se uma instância ``igraph.Graph`` ou qualquer instância que implemente os métodos ``neighbors`` que retorna os vizinhos de um determinado vértice e ``complementer`` que retorna o complemento de um grafo.

## A classe ``RepSpatialXOR``

São disponibilizados os parâmetro ``tamanho_fingerprint`` e ``k``. O parâmetro ``tamanho_fingerprint`` indica o tamanho em bits das funções hash dos filtros SpatialXOR. O parâmetro ``k`` é o número de funções hash utilizadas pelos filtros SpatialXOR.

O método ``construir`` deve ser executado passando-se uma instância ``igraph.Graph`` ou qualquer instância que implemente os métodos ``neighbors`` que retorna os vizinhos de um determinado vértice e ``complementer`` que retorna o complemento de um grafo.


A pasta ``data`` tem os dados do artigo, separados por ``;``.
mongo-entity-orm 0.1.1
A MongoDB ORM for Python
License: MIT
Requires Python: <4.0,>=3.9
Keywords: mongodb, orm, mongo, async

# mongo-orm

A MongoDB ORM for Python

## Usage

### Defining an Entity

Use the `@entity` decorator to map a class to a MongoDB collection. Your class should inherit from `BaseEntity`.

```python
from mongo_orm import BaseEntity, entity

@entity("users")
class User(BaseEntity):
    name: str
    email: str
    
    # Define indexes
    __indexes__ = [
        {"keys": [("email", 1)], "unique": True},
        {"keys": [("name", 1)]}
    ]
```

### Index Management

The ORM can automatically manage your MongoDB indexes based on the `__indexes__` attribute. This behavior is controlled by the `MONGODB_INDEX_AUTOAPPLY` environment variable.

#### Configuration (`MONGODB_INDEX_AUTOAPPLY`)

- `never` (default): No automatic index management.
- `always`: Checks and applies indexes every time an entity class is initialized (on startup).
- `auto-lock`: Checks and applies indexes once. After a successful check, it writes a hash of the index to `mongo-orm.lock`. Subsequent startups will skip the check if the hash is present in the lock file.

#### Manual Index Application

You can also trigger index application manually for all registered entities:

```python
from mongo_orm.utils import apply_all_indexes

# Apply indexes using the current environment configuration
apply_all_indexes()

# Or force a specific mode
apply_all_indexes(mode="always")
```

## Development

```bash
# Install dependencies
poetry install

# Run tests
poetry run pytest

# Format code
poetry run black .
poetry run isort .

# Type checking
poetry run mypy src/
```

## License

MIT
jwapi 1.1.1
Python library for parsing JW.org content
License: MIT
Requires Python: >=3.9
Keywords: jw.org, api, parser

# JW API - Python

Python library for parsing JW.org content.

## Installation

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

## Quick Start

```python
from jwapi import JWParser

parser = JWParser()

# Get languages
languages = parser.get_languages()

# Get video categories
categories = parser.get_categories("E", "video")

# Get Bible editions
bibles = parser.get_bibles_list("en")
```

## Documentation

- [English Documentation](../docs/python-en.md)
- [Documentation Française](../docs/python-fr.md)

## Development

```bash
# Install dev dependencies
pip install -e ".[dev]"

# Run tests
pytest

# Format code
black jwapi/

# Lint
ruff check jwapi/
```