semvec/pypi-packages huggingface dataset:
intergalactic-namerator 0.1.4
Generate names based on Rick and Morty characters.
Requires Python: >=3.12

![INTERGALACTIC-NAMERATOR](https://raw.githubusercontent.com/vanesterik/intergalactic-namerator/refs/heads/main/references/rick-and-morty.png)

# INTERGALACTIC-NAMERATOR

This repository contains the code for the Intergalactic Namerator, a neural network model designed to generate names for intergalactic entities. The model is trained on a diverse dataset of names based on the Rick and Morty characters and utilizes advanced tokenization techniques to produce unique and creative outputs.

## Purpose

This project aims to explore the capabilities of recurrent neural networks (RNNs) in generating novel names that fit within the thematic context of intergalactic settings. It serves as both a fun application of machine learning and a demonstration of text generation techniques.

## Features

- RNN-based name generation
- Custom tokenizer for handling unique character sets
- Easy-to-use API for generating names
- Logging and monitoring capabilities
- Dockerized for easy deployment

## Contributing

Contributions and suggestions are welcome! If you have a useful plotting function or improvement, feel free to open an issue or pull request.

## License

[MIT License](https://github.com/vanesterik/plot-shift/blob/main/LICENSE)

## Author

[Koen van Esterik](https://github.com/vanesterik)
qpdk 0.1.3
Quantum-RF generic PDK
Requires Python: <3.14,>=3.12
Keywords: python

# Sample Generic Superconducting Quantum RF PDK

[![Docs](https://github.com/gdsfactory/quantum-rf-pdk/actions/workflows/pages.yml/badge.svg)](https://gdsfactory.github.io/quantum-rf-pdk/)
[![Tests](https://github.com/gdsfactory/quantum-rf-pdk/actions/workflows/test.yml/badge.svg)](https://github.com/gdsfactory/quantum-rf-pdk/actions/workflows/test.yml)
[![HTML Docs](https://img.shields.io/badge/%F0%9F%93%84_HTML-Docs-blue?style=flat)](https://gdsfactory.github.io/quantum-rf-pdk/)
[![PDF Docs](https://img.shields.io/badge/%F0%9F%93%84_PDF-Docs-blue?style=flat&logo=adobeacrobatreader)](https://gdsfactory.github.io/quantum-rf-pdk/qpdk.pdf)
[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/gdsfactory/quantum-rf-pdk/HEAD)
[![PyPI - Version](https://img.shields.io/pypi/v/qpdk?color=blue)](https://pypi.org/p/qpdk)
[![MIT](https://img.shields.io/github/license/gdsfactory/quantum-rf-pdk)](https://choosealicense.com/licenses/mit/)

______________________________________________________________________

A generic process design kit (PDK) for superconducting quantum RF applications based on
[gdsfactory](https://gdsfactory.github.io/gdsfactory/).

## Examples

- [PDK cells in the documentation](https://gdsfactory.github.io/quantum-rf-pdk/cells.html): showcases available
  geometries.
- [`qpdk/samples/`](qpdk/samples): contains example layouts and simulations.
- [`notebooks/`](notebooks): contains notebooks demonstrating design and simulation workflows.

## Installation

We recommend using [`uv`](https://astral.sh/uv/) for package management.

### Installation for Users

Install the package with:

```bash
uv pip install qpdk
```

> [!NOTE]
> After installation, restart KLayout to ensure the new technology appears.

Optional dependencies for the models and simulation tools can be installed with:

```bash
uv pip install qpdk[models]
```

### Installation for Contributors

Clone the repository and install at least the development dependencies:

```bash
git clone https://github.com/gdsfactory/quantum-rf-pdk.git
cd quantum-rf-pdk
uv sync --group dev
```

> [!NOTE]
> [Git LFS](https://git-lfs.github.com/) must be installed to run all tests locally. Some test data files (e.g., CSV
> files in `tests/models/data/`) are tracked with Git LFS and will not be properly downloaded without it.

#### Testing and Building Documentation

Check out the commands for testing and building documentation with:

```bash
make help
```

## Documentation

- [Quantum RF PDK documentation (HTML)](https://gdsfactory.github.io/quantum-rf-pdk/)
- [Quantum RF PDK documentation (PDF)](https://gdsfactory.github.io/quantum-rf-pdk/qpdk.pdf)
- [gdsfactory documentation](https://gdsfactory.github.io/gdsfactory/)
vcsp 0.0.4
Virtual Cyber Security Program (Offensive Security Toolkit)
Requires Python: >=3.8

# VC-SP — Virtual Cyber Security Program  
### Offensive Security · Terminal Framework · Custom Tools

VC-SP is a lightweight, terminal-based offensive security framework designed for learning, testing, and performing cybersecurity tasks.  
It provides an organized environment for **reconnaissance**, **exploitation**, **scanning**, **payload generation**, and **cyber-learning modules** — all accessible through a clean CLI or interactive shell.

VC-SP is built with modularity in mind, allowing users to create, load, and extend tools easily.

---

## 🚀 Features

### 🔍 Reconnaissance Tools
- Async port scanner  
- OS fingerprint attempts  
- Banner grabbing  
- WHOIS + DNS lookup  
- Subdomain enumeration  

### 🛠️ Exploitation & Payloads
- Payload generator template  
- Reverse shell stager  
- Web exploit skeletons  
- Custom exploit loader  

### 🧰 Utility Modules
- Hash cracking helper  
- Wordlist generator  
- Base64/hex/URL encoders  
- Basic fuzzing engine  

### 🎓 Learning Mode
A built-in interactive learning system:
asaya-sculpture-utils 1.0.1
Simple utility functions for sculpture websites
Requires Python: >=3.6

# asaya-sculpture-utils

Simple utility functions for sculpture and art gallery websites.

## Installation

pip install asaya-sculpture-utils

## Usage

from asaya_sculpture_utils import format_dimensions, calculate_volume

# Format artwork dimensions
print(format_dimensions(50, 30, 20))
# Output: "50 Ã— 30 Ã— 20 cm"

# Calculate volume
print(calculate_volume(50, 30, 20))
# Output: 30000

## Author

**Asaya** - [asayasculpture.com](https://asayasculpture.com)

## License

MIT
hwloc-xml-parser 0.0.4
Parse xml output of hwloc.
Requires Python: >=3.10

![GitHub Release](https://img.shields.io/github/v/release/uliegecsm/hwloc-xml-parser)
![GitHub Actions Workflow Status](https://img.shields.io/github/actions/workflow/status/uliegecsm/hwloc-xml-parser/test.yml)
![GitHub License](https://img.shields.io/github/license/uliegecsm/hwloc-xml-parser)

[![PyPI - Version](https://img.shields.io/pypi/v/hwloc-xml-parser?color=blue)](https://pypi.org/project/hwloc-xml-parser/)
![PyPI - Wheel](https://img.shields.io/pypi/wheel/hwloc-xml-parser)
![PyPI - Implementation](https://img.shields.io/pypi/implementation/hwloc-xml-parser)

# hwloc-xml-parser

Python tool to parse `xml` output of [`hwloc`](https://www.open-mpi.org/projects/hwloc/).
Products.mcdutils 5.0
A Zope product with memcached-backed ZCache and Zope session implementations.
Requires Python: >=3.10
Keywords: Zope, session, memcache, memcached

.. image:: https://github.com/dataflake/Products.mcdutils/actions/workflows/tests.yml/badge.svg
   :target: https://github.com/dataflake/Products.mcdutils/actions/workflows/tests.yml

.. image:: https://coveralls.io/repos/github/dataflake/Products.mcdutils/badge.svg
   :target: https://coveralls.io/github/dataflake/Products.mcdutils

.. image:: https://readthedocs.org/projects/mcdutils/badge/?version=latest
   :target: https://mcdutils.readthedocs.io
   :alt: Documentation Status

.. image:: https://img.shields.io/pypi/v/Products.mcdutils.svg
   :target: https://pypi.python.org/pypi/Products.mcdutils
   :alt: Current version on PyPI

.. image:: https://img.shields.io/pypi/pyversions/Products.mcdutils.svg
   :target: https://pypi.python.org/pypi/Products.mcdutils
   :alt: Supported Python versions


===================
 Products.mcdutils
===================

The `Products.mcdutils` product supplies a replacement for the ZODB-based
session data container from the `Transience` product, shipped with
the Zope core prior to Zope 4 and available as a separate package after that.
Rather than using a ZODB storage as the backing store for session data, as
`Transience` does, `Products.mcdutils` stores session data in a cluster of
one or more `memcached` servers.

This approach is a bit of a cheat, as it uses the daemons as primary stores,
rather than as caches for results of an expensive query.  Nevertheless, the
semantics are not a bad match for typical session usage.