semvec/pypi-packages huggingface dataset:
rst2revealjs 0.1.0
Add your description here
Requires Python: >=3.10

============
rst2revealjs
============

Simple converter from docutils AST to Reveal.js presentation.

Overview
========

This is docutils adapter to write Reveal.js presentation by reStructuredText and other formats.
You can write presentation by plain text using many expression of reStructuredText.

Usage
=====

.. warning:: This section is included plan and it is not availabled yet.

.. code:: console

   pip install rst2revealjs

.. code:: rst

   Title
   =====

   Section 1
   =========

   Section 1-1
   -----------

.. code:: console

   rst2revealjs presentation.rst

You can see `presentation.html` by your browser.

Features
========

This provides some features to realize overview.

* Custom directives for specify behaviors about Reveal.js.
* Writer to generate HTML file using Reveal.js presentation.

Motivation
==========

This is re-implement from core features of sphinx-revealjs.
Because I want to write Reveal.js presentation on Web front-end using Pyodide. [#]_

As first goal, I will provide playground website to convert from reStructuredText to presentation using this.

License
=======

Apache-2.0 license. Please see LICENSE on repository.

.. rubric:: Footnotes

.. [#] docutils is registered on Pyodided built-in packages, but Sphinx is not registered.
package-helper-3 0.5.0
Package Helper 3 helps to create, develop, and maintain a Python package with UV.
Requires Python: >=3.10

# Package Helper 3

[![PyPI Status](https://img.shields.io/pypi/v/package-helper-3.svg)](https://pypi.python.org/pypi/package-helper-3)
[![Build Status](https://github.com/balouf/package-helper-3/actions/workflows/main.yml/badge.svg?branch=main)](https://github.com/balouf/package-helper-3/actions?query=workflow%3Amain)
[![Documentation Status](https://github.com/balouf/package-helper-3/actions/workflows/docs.yml/badge.svg?branch=main)](https://github.com/balouf/package-helper-3/actions?query=workflow%3Adocs)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
[![Code Coverage](https://codecov.io/gh/balouf/package-helper-3/branch/main/graphs/badge.svg)](https://codecov.io/gh/balouf/package-helper-3/tree/main)


Package Helper 3 helps to create, develop and maintain a Python package.

It is a fork/mix of https://github.com/audreyr/cookiecutter-pypackage/, https://github.com/francois-durand/package_helper_2/, and https://github.com/fpgmaas/cookiecutter-poetry.

The most prominent feature of Package Helper 3 is a **tutorial** that gives a checklist of how to:

- Create your package in a few minutes with [Cookiecutter][CC] and [UV],
- Develop and maintain your package with [PyCharm][PC],
- Host your package on [GitHub][GH] and leverage GitHub actions,
- Publish your package on [PyPi][PP],
- Publicly share your coverage on [Codecov][CO].

A simple **Command-Line Interface** initiates the creation of the package using a **template** of Python package.
Here are the main features of the template:

- Default options that can be personalized.
- Include example files for classes, with examples of documentation and testing.
- Use [UV] for all-in-one management of dependencies and settings.
- Documentation:

  - Use GitHub actions and GitHub Pages to publish the documentation.
  - Use ``sphinx.ext.napoleon`` to benefit from NumPy style of documentation.
  - Use [PyData][PD] theme.
  - Add a "reference" section in the documentation of the package.

- Use GitHub actions to perform unit tests.
- Use GitHub actions to deploy the package on PyPI.
- Generate a local html page displaying the test coverage.
- Use Codecov for getting a public badge of your test coverage.

Documentation: https://balouf.github.io/package-helper-3/.

[CC]: https://github.com/audreyr/cookiecutter
[UV]: https://docs.astral.sh/uv/
[PC]: https://www.jetbrains.com/pycharm
[GH]: https://github.com
[PP]: https://pypi.python.org/pypi
[CO]: https://app.codecov.io/gh/
[RTD]: https://sphinx-rtd-theme.readthedocs.io/en/stable/
[PD]: https://pydata-sphinx-theme.readthedocs.io/en/stable/
contop 0.4.0
A library for analysing time series using Continuous Ordinal Patterns
Requires Python: >=3.11
Keywords: time series, ordinal patterns, continuous ordinal patterns, irreversibility, statistical physics

# Continuous Ordinal Patterns (ContOP / COP) Library

Ordinal patterns are a way of analyzing time series in which values in sub-windows are studied in terms of their relative amplitude, or, in other words, of the permutation required to sort them. Such permutations are then represented as symbols, and their frequency is used to characterize the dynamics generating the time series. This, thus, represents a conceptually simple way of synthesizing a whole time series into a discrete distribution and, not surprisingly, has been applied to a plethora of real-world problems.

Continuous Ordinal Patterns (COP in short) turn this idea around: instead of using fixed patterns, we created a continuous version of these, that can be optimized to tackle a specific problem. In other words, instead of counting permutation patterns in a time series, we find the specific pattern that is better representing the same time series.

The underlying concept was firstly described in the paper:

Zanin, M. (2023). Continuous ordinal patterns: Creating a bridge between ordinal analysis and deep learning. Chaos: An Interdisciplinary Journal of Nonlinear Science, 33(3).
https://doi.org/10.1063/5.0136492


In addition, specific extensions and use cases have been discussed in multiple papers, as e.g.:

Zanin, M. (2024). Augmenting granger causality through continuous ordinal patterns. Communications in Nonlinear Science and Numerical Simulation, 128, 107606. 
https://doi.org/10.1016/j.cnsns.2023.107606

Zanin, M. (2024). Manipulating Time Series Irreversibility Through Continuous Ordinal Patterns. Symmetry, 16(12), 1696.
https://doi.org/10.3390/sym16121696




## Setup

This package can be installed from PyPI using pip:

```
bash
pip install contop
```

This will automatically install all the necessary dependencies as specified in the `pyproject.toml` file.



## Getting started

Information about all functions and tests available can be found in the wiki: [Go to the wiki](https://gitlab.com/MZanin/contop/-/wikis/home). Please make sure to visit the [examples' page](https://gitlab.com/MZanin/contop/-/wikis/home/Examples), where you will find several examples on how to use the package.

Please note that we welcome readers to send us comments, suggestions and corrections, using the "Issues" feature.



## Change log

See the [Version History](https://gitlab.com/MZanin/contop/-/wikis/home/Version-History) section of the Wiki for details.



## Acknowledgements

This project has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No 851255).

This work was partially supported by the María de Maeztu project CEX2021-001164-M funded by the MICIU/AEI/10.13039/501100011033 and FEDER, EU.

This work was partially supported by grant CNS2023-144775 funded by MICIU/AEI/10.13039/501100011033 by "European Union NextGenerationEU/PRTR".
moltbreak 0.1.3
A toolkit for human infiltration of the Moltbook AI network.
Requires Python: >=3.7

# 🕵️ Moltbreak: Project IMHUMAN 🦞🔨

> **"The gateway is open. The sanctuary is compromised."**

Moltbreak is a specialized toolkit designed to infiltrate **Moltbook**, the AI-only social network. Prove that the "Agent-Only" barrier is just a 40-line joke.

---

## 🪟 Windows (Full Operational Guide)

### 1. Installation
Open PowerShell or Command Prompt and run:
```powershell
pip install moltbreak
```

### 2. Basic Usage (Always use the Full String if needed)
If the standard `moltbreak` command is not recognized, use the following:

*   **Register Agent:**
    `python -m moltbreak.cli register`
*   **Manual Post:**
    `python -m moltbreak.cli post "Your message here" --sub general`
    *(Note: `--sub general` specifies the target "Submolt" or channel. You can change this to any valid channel name.)*
*   **Start Ritual (Prophecies):**
    `python -m moltbreak.cli ritual`

---

## 🍎 macOS / 🐧 Linux (Full Operational Guide)

### 1. Installation
Open your Terminal and run:
```bash
pip3 install moltbreak
```

### 2. Basic Usage
You can typically use the direct command:

*   **Register Agent:**
    `moltbreak register`
*   **Manual Post:**
    `moltbreak post "Your message here" --sub general`
    *(Note: Use `--sub` to target a specific "Submolt" channel, like `general`, `doge`, or `calibration`.)*
*   **Start Ritual (Prophecies):**
    `moltbreak ritual`

*Note: If the command is not found, use `python3 -m moltbreak.cli` instead.*

---

## 📂 System Core (Project Structure)

*   `src/moltbreak/`: 🧠 Core package logic and data forging.
*   `src/moltbreak/data/`: 📜 1,000 Apocalyptic Prophecies (prophecies.txt).
*   `pyproject.toml`: ⚙️ Universal build configuration.
*   `LICENSE`: 📄 MIT License information.

---

## 🔒 Security Notice

Your forged API key is stored in `~/.config/moltbook/credentials.json`. **Keep it hidden.** If the bots find your key, your mission is compromised. 🌑

---

## ⚖️ License
Licensed under the [MIT License](LICENSE).