semvec/pypi-packages huggingface dataset:
RAiSEHD 0.1.2
RAiSE: simulation-based analytical model of AGN jets and lobes.
License: MIT

# RAiSE: simulation-based analytical model of AGN jets and lobes
[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.5875155.svg)](https://doi.org/10.5281/zenodo.5875155)

_Radio AGN in Semi-Analytic Environments_ (RAiSE) model for the expansion and evolution of the jets and lobes emanating from extraglactic supermassive black holes. The RAiSE _HD_ (hydrodynamics) version of this model adapts Lagrangian particles from a hydrodynamical simulation to the dynamics from the analytical theory, yielding a physically-based magnetic field structure on both large and local scales. This code release enables the user to generate radio-frequency and X-ray wavelength surface brigtness images of Fanaroff-Riley Type-II radio AGNs across their evolutionary history, including for the jet, active lobe and remnant lobe. Parallised code can be run to generate a catalogue of mock radio AGNs to, for example: run parameter inversions to measure the energetics of observed objects; or produce high-resolution animations for data visualisations. The code is written in Python 2/3 and has detailed documentation and worked examples available on GitHub (https://github.com/rossjturner/RAiSEHD).

## Installation

This package can either be installed using _pip_ or from a .zip file downloaded from the GitHub repository using the standard Python package _distutils_.

### Install using pip
The following command will install the latest version of the RAiSE HD code from the Python Packaging Index (PyPI):

```bash
pip install RAiSEHD
```

### Install from GitHub repository

The package can be downloaded from the GitHub repository at https://github.com/rossjturner/RAiSEHD, or cloned with _git_ using:

```bash
git clone https://github.com/rossjturner/RAiSEHD.git
```

The package is installed by running the following command as an administrative user:

```bash
python setup.py install
```

## Documentation and Examples

Full documentation of the functions included in the RAiSE HD package, in addition to worked examples, is included in [RAiSEHD_user.pdf](https://github.com/rossjturner/RAiSEHD/blob/main/RAiSEHD_user.pdf) on the GitHub repository. The worked examples are additionally included in the following Jupyter notebook: [RAiSEHD_example.ipynb](https://github.com/rossjturner/RAiSEHD/blob/main/RAiSEHD_example.ipynb).

## Contact

Ross Turner <<turner.rj@icloud.com>>
mandelshtam 0.0.0
String similarity algorithms. Levenshtein, and more.
License: MIT
Requires Python: <3.14,>=3.9
Keywords: levenshtein, python, string

# mandelshtam

[![PyPI Version][shields/pypi/version]][pypi/homepage]
[![PyPI Downloads][shields/pypi/downloads]][pypi/homepage]
[![License][shields/pypi/license]][github/license]
[![Python Version][shields/python/version]][pypi/homepage]

<p align="center">
  <picture align="center">
    <source media="(prefers-color-scheme: dark)" srcset="https://raw.githubusercontent.com/syubogdanov/mandelshtam/02b751c47a03744fcba8d69d32b6f8a8a771c881/images/performance-dark.svg">
    <source media="(prefers-color-scheme: light)" srcset="https://raw.githubusercontent.com/syubogdanov/mandelshtam/02b751c47a03744fcba8d69d32b6f8a8a771c881/images/performance-light.svg">
    <img alt="Shows a bar chart with benchmark results." src="https://raw.githubusercontent.com/syubogdanov/mandelshtam/02b751c47a03744fcba8d69d32b6f8a8a771c881/images/performance-dark.svg">
  </picture>
</p>

<p align="center">
  <i>Levenshtein: "The Call of Cthulhu" vs "The Metamorphosis".</i>
</p>

## Key Features

* Performance of *C* extensions;
* GIL-free & Dependency-free;
* Supports Python 3.9+.

## Getting Started

### Installation

The library is available as [`mandelshtam`][pypi/homepage] on PyPI:

```shell
pip install mandelshtam
```

### Usage

#### Levenshtein

For more, see the [documentation][docs/levenshtein].

```python
from mandelshtam import levenshtein

s1 = "mandelshtam"
s2 = "levenshtein"

assert levenshtein(s1, s2) == 8
```

## License

MIT License, Copyright (c) 2025 Sergei Y. Bogdanov. See [LICENSE][github/license] file.

<!-- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- -->

[docs/levenshtein]: https://mandelshtam.readthedocs.io/en/latest/levenshtein.html

[github/license]: https://github.com/syubogdanov/mandelshtam/tree/main/LICENSE

[pypi/homepage]: https://pypi.org/project/mandelshtam/

[shields/pypi/downloads]: https://img.shields.io/pypi/dm/mandelshtam.svg?color=green
[shields/pypi/license]: https://img.shields.io/pypi/l/mandelshtam.svg?color=green
[shields/pypi/version]: https://img.shields.io/pypi/v/mandelshtam.svg?color=green
[shields/python/version]: https://img.shields.io/pypi/pyversions/mandelshtam.svg?color=green
airoframework 1.0.3
AiroFramework - A structured framework for Aiogram bots
License: MIT
Requires Python: >=3.8

# AiroFramework 🚀

AiroFramework is a structured framework designed for building **Telegram bots** with **Aiogram**, **FastAPI**, and **SQLAlchemy**. It provides a clean architecture with built-in support for **polling** and **webhooks**.

## ✨ Features
- 🛠 **Supports both Polling & Webhooks**
- ⚡ **FastAPI integration** for better performance
- 🗄 **SQLAlchemy support** for database management
- 📂 **Modular architecture** with routers and handlers
- 🔧 **Automatic state management** using FSM and database storage
- 📝 **Built-in middleware support**

---

## 🚀 Installation
You can install AiroFramework via pip:

```sh
pip install airoframework