semvec/pypi-packages huggingface dataset:
MORALS 0.1.4
MORALS: Morse Graph-aided discovery of Regions of Attraction in a learned Latent Space

# MORALS:  *Mo*rse Graph-aided discovery of *R*egions of *A*ttraction in a learned *L*atent *S*pace 

Ewerton R. Vieira*, Aravind Sivaramakrishnan*, Sumanth Tangirala, Edgar Granados, Konstantin Mischaikow, Kostas E. Bekris

_2024 IEEE International Conference on Robotics and Automation (ICRA), 2024_. **Best Paper Award in Automation finalist**.

## Introduction
_MORALS_ combines autoencoding neural networks with Morse Graphs. It first projects the dynamics of the controlled system into a learned latent space. Then, it constructs a reduced form of Morse Graphs representing the bistability of the underlying dynamics, i.e., detecting when the controller results in a desired versus an undesired behavior.

![alt text](intro-figure.png)

## Installation
```
pip install MORALS
```

## Usage

### Reproduce experiments from the paper
1. Download the [Pendulum (LQR) dataset](https://drive.google.com/file/d/1C2SgOQiMpAkpjD-_WJykARZnUYduaL02/view?usp=sharing) or [Other Robot Controllers](https://archive.cs.rutgers.edu/archive/a/2024/MORALS/).
2. Extract and place it inside `examples/data/`. There should be a directory `pendulum_lqr1k` and a labels file `pendulum_lqr1k_success.txt`.
3. Train the autoencoder and latent dynamics networks: `python train.py --config pendulum_lqr.txt`.
4. Obtain the Morse Graph and the Regions of Attraction (RoAs) for the learned latent space dynamics: `python get_MG_RoA.py --config pendulum_lqr.txt --name_out pendulum_lqr --RoA --sub 16`.

### Try out MORALS on your own dataset (WIP)
1. Add a Python class corresponding to your system to `MORALS/systems/`. At the very least, you must provide a name for your system so that its corresponding object can be returned by `MORALS.systems.utils.get_system()`.
2. Generate a config file for your system and place it inside `examples/config/`. You can follow the format of the example config files provided.
3. Follow the steps above to train the autoencoder and latent dynamics networks using your custom config file, and then obtain the Morse Graph and the RoAs.

## Bibtex
If you find this repository useful in your work, please consider citing:
```
@inproceedings{morals2024,
    title={{\tt MORALS}: Analysis of High-Dimensional Robot Controllers via Topological Tools in a Latent Space}, 
    author={Ewerton R. Vieira* and Aravind Sivaramakrishnan* and Sumanth Tangirala and Edgar Granados and Konstantin Mischaikow and Kostas E. Bekris},
    booktitle={ICRA},
    year={2024},
}
```
minppo 0.0.1
The minppo project
Requires Python: >=3.11

<p align="center">
  <picture>
    <img alt="K-Scale Open Source Robotics" src="https://media.kscale.dev/kscale-open-source-header.png" style="max-width: 100%;">
  </picture>
</p>

<div align="center">

[![License](https://img.shields.io/badge/license-MIT-green)](https://github.com/kscalelabs/ksim/blob/main/LICENSE)
[![Discord](https://img.shields.io/discord/1224056091017478166)](https://discord.gg/k5mSvCkYQh)
[![Wiki](https://img.shields.io/badge/wiki-humanoids-black)](https://humanoids.wiki)
<br />
[![python](https://img.shields.io/badge/-Python_3.11-blue?logo=python&logoColor=white)](https://github.com/pre-commit/pre-commit)
[![black](https://img.shields.io/badge/Code%20Style-Black-black.svg?labelColor=gray)](https://black.readthedocs.io/en/stable/)
[![ruff](https://img.shields.io/badge/Linter-Ruff-red.svg?labelColor=gray)](https://github.com/charliermarsh/ruff)
<br />
[![Python Checks](https://github.com/kscalelabs/humanoid-standup/actions/workflows/test.yml/badge.svg)](https://github.com/kscalelabs/humanoid-standup/actions/workflows/test.yml)

</div>

# MinPPO

This repository implements a minimal version of PPO using Jax.

## Usage

To visualize the environment, run:

```bash
python environment.py configs/stompy_pro.yaml
```

To train the model, run:

```bash
python train.py configs/stompy_pro.yaml
```

To run inference on the trained model, run:

```bash
python infer.py configs/stompy_pro.yaml 'inference.model_path=path/to/trained/model.pkl'
```
datahtml 0.6.0
A lib to work with html and web data
Requires Python: >=3.8

# datahtml

[![PyPI - Version](https://img.shields.io/pypi/v/datahtml.svg)](https://pypi.org/project/datahtml)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/datahtml.svg)](https://pypi.org/project/datahtml)
[![readthedocs](https://readthedocs.org/projects/datahtml/badge/?version=latest)](https://datahtml.readthedocs.io/en/latest/)

-------

**datahtml** is a library for crawling and extraction of data from html and xml content. 

Datahtml lets you:

* Extract ld+json data from html
* Extract frequently used meta tags from html (those that are used for SEO and social media, between others)
* Extract Article data from a html, usually from Newspaper sites
* Parse RSS feeds from sites
* Crawl some specific social media sites like google and youtube

Under the hood datahtml uses libraries like BeautifoulSoup, Newspaper2k, feedparser between others, but
datahtml takes an opinionated approach for crawling based on our expriencies doing so. 



## Quickstart

```console
pip install datahtml
```

```python

from datahtml import web, crawler

c = crawler.LocalCrawler()
w = web.download("https://www.infobae.com", crawler=c)
w.links()
```

## License

`datahtml` is distributed under the terms of the [MPL-2.0](https://www.mozilla.org/en-US/MPL/2.0/) license.
recipe-manager-montwh1te 0.0.1
a package that can improve your recipe and baking management
Requires Python: >=3.8

# Recipe Manager

a package that can improve your recipe and baking management

## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install recipe_manager

```bash
pip install ### colocar comando de instalacao terminal
```

## Usage

```python
from recipe_manager.components.recipe import Recipe

Recipe.add_step("Mix the ingredients.", 1)
```

## Author
montwh1te

## License
[MIT](https://choosealicense.com/licenses/mit/)