semvec/pypi-packages huggingface dataset:
sphinx-filtered-toctree 0.1.0
Sphinx toctree that filters out entries by prefix.
Requires Python: >=3.10

# sphinx-filtered-toctree

sphinx-filtered-toctree allows you to filter pages out of your documentation's navigation
by prefixing ToC entries with configurable tags.

## Basic usage

First, list the tags you wish to exclude with `toc_filter_exclude` in your `conf.py`
file:

```
toc_filter_exclude = ["exclude", "hidden"]
```

To filter entries out of your documentation's navigation, prefix either the document's
label or target with one of the tags defined in `toc_filter_exclude`:

```
.. filtered-toctree::

    :exclude:how-to-code
    Unit testing <:hidden:unit-testing>

```

Aside from this added functionality, `filtered-toctree` behaves exactly the same as
the `toctree` directive.

## Project setup

sphinx-filtered-toctree is published on PyPI and can be installed with:

```bash
pip install sphinx-filtered-toctree
```

After adding filtered-toctree to your Python project, update your Sphinx's conf.py file
to include filtered-toctree as one of its extensions:

```python
extensions = [
    "sphinx_filtered_toctree"
]
```

## Community and support

You can report any issues or bugs on the project's [GitHub
repository](https://github.com/canonical/sphinx-filtered-toctree).

filtered-toctree is covered by the [Ubuntu Code of
Conduct](https://ubuntu.com/community/ethos/code-of-conduct).

## License and copyright

sphinx-filtered-toctree is released under the [GPL-3.0 license](LICENSE).

© 2025 Canonical Ltd.
toolbox-parallel-robots 1.2.0
Add your description here
Requires Python: >=3.8

# Toolbox Parallel Robots
This repository includes usefull functions for working with robots with rigid bilateral contacts.

This was originally designed for working with closed kinematic loop by considering open loop models extended with contact constraints.
This package provides a convention to describe actuation of such robots and is the base of another package providing example models of parallel robots.

## Installation
Installation instuctions are to be added.

## Run examples

## Contents
* ActuationModel and ActuationData - *class for containing usefull information about the robot actuation (essentially which joints are actuated)*
* Actuation utils - *utility functions to manipulate actuated and free joints configuration (e.g. recreate robot configuration from separated actuated and free joint configuration, extract actuated joints configuration from complete state...)*
* Closures - *Functions to determine closed configurations based on open configuration and fixed serial joints*
* Constraints - *Functions to compute contact constraint violation for 6D and 3D contacts*
* Forward Kinematics
* Inverse Dynamics
* Inverse Kinematics
* Freeze Joints - *Fixes some joints and recreate the Acutation and Constraints models*
* Jacobian - *Computes closed-loop jacobians*
* Mounting - *Computes a mounted condiguration based on the contact constraints (internal and with the environment)
* Projection - *Projects configuration to the closest closed-loop configuration*

## :copyright: Credits

### :writing_hand: Written by

- [Ludovic De Matteis](https://ludovicdematteis.github.io/), LAAS-CNRS :fr:
- Virgile Batto, LAAS-CNRS :fr:

### :construction_worker: With contributions from
sphinx-youtube-links 0.1.0
A Sphinx extension for embedding YouTube video links.
Requires Python: >=3.10

# sphinx-youtube-links

sphinx-youtube-links adds a Sphinx directive that creates styled YouTube video links with
automatic title extraction.

## Basic usage

To add a YouTube link to your document, use the `youtube` directive with the desired
YouTube URL:

```
.. youtube:: https://www.youtube.com/watch?v=dQw4w9WgXcQ
```

You can also specify a custom title:

```
.. youtube:: https://www.youtube.com/watch?v=dQw4w9WgXcQ
   :title: Custom Video Title
```

## Project setup

sphinx-youtube-links is published on PyPI and can be installed with:

```bash
pip install sphinx-youtube-links
```

After adding sphinx-youtube-links to your Python project, update your Sphinx's conf.py file to
include sphinx-youtube-links as one of its extensions:

```python
extensions = [
    "sphinx_youtube_links"
]
```

## Community and support

You can report any issues or bugs on the project's [GitHub
repository](https://github.com/canonical/youtube-links).

youtube-links is covered by the [Ubuntu Code of
Conduct](https://ubuntu.com/community/ethos/code-of-conduct).

## License and copyright

youtube-links is released under the [GPL-3.0 license](LICENSE).

© 2025 Canonical Ltd.
legacy-puyo-tools 2025.10.10.1
A tool to edit text for older Puyo Puyo games.
Requires Python: >=3.12
Keywords: puyopuyo

# Legacy Puyo Tools

A command line tool for modding older Puyo Puyo games (Yes, the name is using a
[reversed naming scheme](https://github.com/microsoft/WSL)).

This project is still in development, expect **major breaking changes** over its
lifetime.

## Why

[Puyo Text Editor](https://github.com/nickworonekin/puyo-text-editor),
[fntedit](https://github.com/exelotl/fntedit), and other tools already can
modify formats used by older Puyo Puyo games. However, there are advantages to
rewrite them in Python:

- Better cross compatibility with Linux.
- Easier migration when upgrade away from end of life language versions.
- Formats are stored in an intermediate representation before conversion.

This project also aims to document how these formats work, so others can
reimplement them if needed.

## Progress Report

Current progress on implementing conversion and creation support for formats
that are used by the older Puyo Puyo games. Includes regression testing and
documentation. Additional formats may be added if there is a need to modify
other Puyo Puyo games.

-   Legend:
    - ✅ Fully Complete
    - ⚠️ Partially Complete
    - ❌ Incomplete

| Format | Conversion | Creation | Testing | Documentation |
| ------ | ---------- | -------- | ------- | ------------- |
| `fmp`  | ✅        | ✅       | ✅      | ✅           |
| `fnt`  | ❌        | ❌       | ❌      | ❌           |
| `fpd`  | ✅        | ✅       | ✅      | ✅           |
| `mtx`  | ⚠️        | ❌       | ❌      | ❌           |
forecopy 0.1.1
Forecast Reconciliation in Python
Requires Python: >=3.10
Keywords: forecasting, reconciliation, forecast, time-series

# FoRecoPy: Forecast Reconciliation in Python <img src="sphinx/source/forecopy.svg" alt="" align="right" width="150" style="border: none; float: right;"/>

[![pytest](https://github.com/danigiro/FoRecoPy/actions/workflows/test.yml/badge.svg)](https://github.com/danigiro/FoRecoPy/actions/workflows/test.yml) [![codecov](https://codecov.io/github/danigiro/FoRecoPy/graph/badge.svg?token=S7CMY2OT3U)](https://codecov.io/github/danigiro/FoRecoPy)

Forecast reconciliation is a post-forecasting process aimed at improving the
accuracy and coherence of forecasts for a system of **linearly constrained
time series** (e.g., hierarchical, grouped, or temporal structures).

The **FoRecoPy** package is inspired by the
[R package FoReco](https://danigiro.github.io/FoReco) and brings similar
functionality to **Python**. It is designed for researchers, practitioners,
and data scientists who use Python for time series forecasting and want
access to **state-of-the-art reconciliation methods**.

Future versions will expand the scope to include the cross-temporal framework,
non-negative constraints and probabilistic reconciliation.

## Installation

Make sure to have a working `JAX` installation (please, follow [these instructions](https://github.com/google/jax#installation)).

To install the package from PyPI, call:

```bash
pip install forecopy
```

To install the latest GitHub <RELEASE>, just call the following on the
command line:

```bash
pip install git+https://github.com/danigiro/FoRecoPy@<RELEASE>
```

## Features

- Optimal combination reconciliation via projection and structural approaches  
- Tools for both **cross-sectional** (`csrec`) and **temporal** (`terec`) reconciliation  
- Different covariance matrix approximation
- Support for custom aggregation or constraints matrices  
- Option to enforce **non-negativity** on reconciled forecasts  
- Efficient solvers suitable for high-dimensional problems  

## Quick Examples
Examples of cross-sectional and temporal forecast reconciliation are available [here](https://danigiro.github.io/FoRecoPy/overview.html)


## License

MIT License. See [LICENSE](LICENSE) for details.