semvec/pypi-packages huggingface dataset:
RooR 0.0.4
A project to analyze the behavior of AI models in post-quantum cybersecurity.
Requires Python: >=3.8

# RooR: AI Behavior Analysis in Post-Quantum Cybersecurity

## Overview

RooR is a Python R\&D package designed to analyze the behavior of AI models in the context of post-quantum cybersecurity.
It provides tools to:

* Simulate post-quantum cryptographic attacks.
* Analyze system logs for anomalies.
* Evaluate the resilience of AI models against emerging threats.

This package is intended for research and development purposes in cybersecurity and AI resilience.

---

## Key Features

* **AI Behavioral Analysis**: Detect anomalies in logs using advanced machine learning techniques.
* **Post-Quantum Attack Simulation**: Simulate lattice-based and code-based cryptographic attacks.
* **Resilience Evaluation**: Assess AI model performance under post-quantum threat scenarios.
* **Report Generation**: Export results in JSON and CSV formats for easy integration and analysis.

---

## Installation

Install RooR using `pip`:

```bash
# Clone the repository
git clone https://github.com/eurocybersecurite/RooR.git
cd RooR

# Install the package
pip install .
```

> Note: For development purposes, you can also install in editable mode:
>
> ```bash
> pip install -e .
> ```

---

## Running Tests

To run the automated tests for this project, activate the virtual environment and use the `unittest` module:

```bash
# Activate the virtual environment
source venv/bin/activate

# Run tests
python3 -m unittest discover tests
```

---

## Usage Examples

Here’s a quick example demonstrating the main functionalities:

```python
from RooR import behavioral_analysis, attack_simulation, report_generation
import pandas as pd

# --- 1. Simulate a post-quantum attack ---
target_model = "Quantum-Resistant AI Model"
attack_result = attack_simulation.simulate_lattice_based_attack(target_model)
report_generation.generate_json_report(attack_result, "attack_report.json")

# --- 2. Analyze logs for anomalies ---
log_data = {
    'timestamp': ['2023-10-27 10:00:00', '2023-10-27 10:05:00', '2023-10-27 10:10:00'],
    'event_type': ['login', 'logout', 'login']
}
pd.DataFrame(log_data).to_csv('dummy_logs.csv', index=False)

anomalies = behavioral_analysis.analyze_logs('dummy_logs.csv')
report_generation.generate_csv_report(anomalies, "anomalies_report.csv")

# --- 3. Evaluate model resilience ---
predictions = [1, 0, 1, 1, 0]
ground_truth = [1, 0, 1, 0, 1]

resilience = behavioral_analysis.evaluate_post_quantum_resilience(predictions, ground_truth)
report_generation.generate_json_report(resilience, "resilience_report.json")
```

---

## Contributing

Contributions are welcome!

* Open an issue to report bugs or request features.
* Submit a pull request for improvements.

Please follow standard Python packaging and testing practices.

---

## License

This project is licensed under the MIT License.

---

## Developer Information

* **Name**: ABDESSEMED Mohamed
* **Email**: [mohamed.abdessemed@eurocybersecurite.fr](mailto:mohamed.abdessemed@eurocybersecurite.fr)
zulu-cli 0.2.7
A CLI tool to help tech team to improve tasks
Requires Python: >=3.6

# Developer CLI Tool

Welcome to the Developer CLI Tool! This tool is designed to help developers streamline and improve various aspects of their workflow, including task automation, code quality checks, and project management.

## Features

- **Code Quality Checks**: Run linting, formatting, and testing commands with ease.
- **Project Management**: Manage your projects more efficiently with built-in project management commands.
- **Customizable**: Easily extend and customize the tool to fit your specific needs.

## Installation

You can install the Developer CLI Tool using npm:

```bash
npm install -g zulu-cli
django-postgres-lock 0.2
Django Postgres Lock
Requires Python: >=3.9

# Django Postgres Lock

A [Django](https://www.djangoproject.com/) management command which will run a command inside a
Postgres lock, ensuring that only a single instance of the inner command will run.

## Installation

Using [pip](https://pip.pypa.io/):

```console
$ pip install django-postgres-lock
```

Edit your Django project's settings module, and add the application to ``INSTALLED_APPS``:

```python
INSTALLED_APPS = [
    # ...
    "postgres_lock",
    # ...
]
```

## Usage

To run clearsessions with the default lock:

```console
$ ./manage.py command_lock -- ./manage.py clearsessions
```

To use a unique lock for this task:

```console
$ ./manage.py command_lock --name clearsessions -- ./manage.py clearsessions
```

To exit immediately if a lock can't be acquired:

```console
$ ./manage.py command_lock --try --name clearsessions -- ./manage.py clearsessions
```

To ignore a lock failure and return a successful exit code:

```console
$ ./manage.py command_lock --try --ignore-fail --name clearsessions -- ./manage.py clearsessions
```
hack4u-bichos 0.1.0
Una biblioteca para constular cursos de hack4u.

# HACK4IO Academy Courses Library 

Una biblioteca Python para consulrar cursos de la academia Hack4U.

## Cursos disponibles:
-Introduccion a linux [15 horas]
-Personalizacion de linux [3 horas]
-Introduccion al Hacking [53 horas]

## Instalacion

Instala el paquete usando `pip3`:

```python3
pip3 install hack4u_bichos
```


## Uso basico

### Listar todos los cursos

```python
from hack4u import list_courses

for course in list_courses():
    print(course)
```

### Obtener un curso por nombre

```python
from hack4u import get_course_by_name

course = get_course_by_name("Introduccion a Linux")
print(course)
```

### Calcular duracion total de los cursos

```python
from hack4u import total_duration

print(f"Duracion total: {total_dutation()} horas")
```
pbi-parsers 0.9.5
Power BI lexer, parsers, and formatters for DAX and M (Power Query) languages
Requires Python: >=3.11.0

# Overview

[![PyPI Downloads](https://static.pepy.tech/badge/pbi-parsers)](https://pepy.tech/projects/pbi-parsers)
![Python](https://img.shields.io/badge/python-3.11-blue.svg)
[![Coverage Status](https://coveralls.io/repos/github/douglassimonsen/pbi_parsers/badge.svg?branch=main)](https://coveralls.io/github/douglassimonsen/pbi_parsers?branch=main)
![Repo Size](https://img.shields.io/github/repo-size/douglassimonsen/pbi_parsers)
[![FOSSA Status](https://app.fossa.com/api/projects/git%2Bgithub.com%2Fdouglassimonsen%2Fpbi_parsers.svg?type=shield&issueType=license)](https://app.fossa.com/projects/git%2Bgithub.com%2Fdouglassimonsen%2Fpbi_parsers?ref=badge_shield&issueType=license)
[![FOSSA Status](https://app.fossa.com/api/projects/git%2Bgithub.com%2Fdouglassimonsen%2Fpbi_parsers.svg?type=shield&issueType=security)](https://app.fossa.com/projects/git%2Bgithub.com%2Fdouglassimonsen%2Fpbi_parsers?ref=badge_shield&issueType=security)

Based on [Crafting Interpreters](https://timothya.com/pdfs/crafting-interpreters.pdf). Library provides lexers, parsers, and formatters for DAX and Power Query (M) languages. Designed to support code introspection and analysis, not execution. This enables developement of [ruff](https://github.com/astral-sh/ruff)-equivalent tools for DAX and Power Query. It also enables extracting metadata from DAX and Power Query code, such PQ source types (Excel, SQL, etc.) and DAX lineage dependencies.

For more information, see the [docs](https://douglassimonsen.github.io/pbi_parsers/)

# Installation

```shell
python -m pip install pbi_parsers
```

# Dev Instructions


## Set Up

```shell
python -m venv venv
venv\Scripts\activate
python -m pip install .
pre-commit install
```


# Running the Documentation Server

```shell
python -m pip install .[docs]
mkdocs serve -f docs/mkdocs.yml
```

## Deploy docs to Github Pages

```shell
mkdocs  gh-deploy --clean -f docs/mkdocs.yml
```

## Testing

```shell

pip install -e .
```

# Build Wheel

```shell
python -m build .
```