semvec/pypi-packages huggingface dataset:
tacocompression 0.9.6
Implementation of TACO: A Lightweight Tree-Based Approximate Compression Method for Time Series
Requires Python: >=3.9

# taco-compression
This is the official implementation of the TACO compression algorithm for time series [1]. 

# Installation

Please install via pip:
```shell
pip install tacocompression
```

# Usage
There are two usage modes, one as a command line tool operating on files, the other as callables from within python.

## Command Line

Please use `python -m tacocompression` to (de)compress your time series from the command line interface. This
application takes 4 named keyword arguments and at least one additional argument specifying the input(s). These inputs
are the paths to the files/directory of the (compressed) time series. You can either specify a directory from which all 
csv files are read...

```shell
python -m tacocompression -o=compressed time_series
```

... or by passing the paths to the files directly.

```shell
python -m tacocompression -o=. data/time_series_1.csv data/time_series_2.csv
```

Each file must contain a single column interpreted as a univariate time series. We assume row 0 is the header. The
compressed data is saved in csv files as well.

The keyword arguments are
- mode (short: m) Use `c` to compress and `d` to decompress. Defaults to `c`.
- output (short: o) Provide a directory to save the outputs to.
- pairing_function (short: p) One of "rosenberg", "szudzik", and "cantor". Defaults to "rosenberg".
- decimals (short: d) Precision of the reconstruction, i.e., number of decimals recoverable during decompression. Only used in compression. Defaults to `1`.
- header. Whether the input file(s) contain a header. Only used in compression. Set only if true.

To decompress the above time series, use

```shell
python -m tacocompression -m=d -o=time_series compressed
```

respectively

```shell
python -m tacocompression -m=d -o=data compressed_time_series_1.csv compressed_time_series_2.csv
```

## As a Callable
```python
from tacocompression import compress, decompress

# 2 univariate time series, length 10 and 8:
time_series = [[0.0, 1.1, 2.2, 3.3, 4.4, 5.5, 6.6, 7.7, 8.8, 9.9], [8.8, 7.7, 6.6, 5.5, 4.4, 3.3, 2.2, 1.1]]

compressed = compress(time_series, "szudzik", 1)
reconstructed = decompress(compressed, "szudzik")

assert time_series == reconstructed
```

[1] Bauer, André. TACO: A Lightweight Tree-Based Approximate Compression Method for Time Series. In: Proceedings of the 
    14th International Conference on Data Science, Technology and Applications (DATA 2025), pages 182-190. 2025.
brython 3.14.0
Brython is an implementation of Python 3 running in the browser
Requires Python: >=3.12

With Brython you can write browser programs in Python instead of Javascript,
by inserting Python code in an HTML page by:

    <script type="text/python">
    ...
    </script>

Usage:

    pip install brython

Then in an empty folder:

    brython-cli --install

or in a folder with older versions already present:

    brython-cli --update

The package includes a page **demo.html** with examples of use. For more
information see the [Brython site](http://brython.info).
ytdlp-servustv 2025.10.11
yt-dlp extractor for servustv.com
Requires Python: >=3.9
Keywords: yt-dlp, youtube-dl, servustv, extractors, plugins

# servustv.com support for yt-dlp

> **_NOTE_:**  
> 
> Since version 2023.01.06 yt-dlp supports plugins from python packages.
> The ytdlp-plugins package is no longer necessary.
> 
> For further details see https://github.com/yt-dlp/yt-dlp#plugins


* supported domains: [servustv.com](https://servustv.com) 
* supported live channels:
  * [Hauptkanal](https://www.servustv.com/allgemein/p/jetzt-live/119753/) 
  * [Wetterpanorama](https://www.servustv.com/aktuelles/v/aa9bgcvsvf7sq8y4sm14/) 
  * [Kanal: Natur](https://www.servustv.com/natur/k/natur-kanal/269299/)
  * [Kanal: Wissen](https://www.servustv.com/wissen/k/wissen-kanal/269302/)
  * [Kanal: Sport](https://www.servustv.com/sport/k/sport-kanal/269300/)
  * [Kanal: Wintersport](https://www.servustv.com/sport/k/wintersport-kanal/269301/)
* playlist from topics (e.g. [Servus Nachrichten](https://www.servustv.com/aktuelles/b/servus-nachrichten/aa-1y5rjcd1h2111/)
  or [Motorsport](https://www.servustv.com/sport/p/motorsport/325/))
* playlist from searches (not supported)

## installation

You can install ytdlp-servustv via pip:

* Use [PyPI package](https://pypi.org/project/yt-dlp):

  `python3 -m pip install --upgrade ytdlp-servustv`
* Install from GitHub branch:

  `python3 -m pip install -U https://github.com/flashdagger/ytdlp-plugins/archive/refs/heads/servustv.zip`

Note that on some systems, you may need to use `py` or `python` instead of `python3`

## example: show all titles from channel

`python3 -m yt_dlp --flat-playlist -e https://www.servustv.com/unterhaltung/b/spielfilme/aa-1u4ebfxk52111/`

## yt-dlp

[yt-dlp](https://github.com/yt-dlp/yt-dlp) is a [youtube-dl](https://github.com/ytdl-org/youtube-dl) fork based on the now
inactive [youtube-dlc](https://github.com/blackjack4494/yt-dlc). The main focus of this project is adding new features
and patches while also keeping up to date with the original project

Note that **all** plugins are imported even if not invoked, and that **there are no checks** performed on plugin code.
Use plugins at your own risk and only if you trust the code
sfs 0.6.3
Sound Field Synthesis Toolbox
Requires Python: >=3.11
Keywords: audio, SFS, WFS, Ambisonics

Sound Field Synthesis (SFS) Toolbox for Python
==============================================

A Python library for creating numercial simulations of sound field synthesis
methods like Wave Field Synthesis (WFS) or Near-Field Compensated Higher Order
Ambisonics (NFC-HOA).

Documentation:
    https://sfs-python.readthedocs.io/

Source code and issue tracker:
    https://github.com/sfstoolbox/sfs-python/

License:
    MIT -- see the file ``LICENSE`` for details.

Quick start:
    * Install Python and (optionally) Matplotlib
    * Install the ``sfs`` package for Python
    * Check out the examples in the documentation

More information about the underlying theory can be found at
https://sfs.readthedocs.io/.
There is also a Sound Field Synthesis Toolbox for Octave/Matlab, see
https://sfs-matlab.readthedocs.io/.