semvec/pypi-packages huggingface dataset:
serger 0.2.0
Stitch your module into a single file
Requires Python: >=3.10
Keywords: module, packaging, single-file

# Serger 🧵 


[![CI](https://github.com/apathetic-tools/serger/actions/workflows/ci.yml/badge.svg?branch=main)](https://github.com/goldilocks/serger/actions/workflows/ci.yml)
[![License: MIT-a-NOAI](https://img.shields.io/badge/License-MIT--aNOAI-blueviolet.svg)](LICENSE)
[![Discord](https://img.shields.io/badge/Discord-%235865F2.svg?logo=discord&logoColor=white)](https://discord.gg/PW6GahZ7)

📘 **[Roadmap](./ROADMAP.md)** · 📝 **[Release Notes](https://github.com/apathetic-tools/serger/releases)**

**Stitch your module into a single file.**
*Because packaging is overrated.*

> [!NOTE]
> Heads up: the AI cooked dinner. It's edible, but watch your step.  
> Detailed bug reports welcome.

## 🚀 Quick Start

The self-contained executable script lives at [**`bin/serger.py`**](./bin/serger.py).  
The only requirement is **Python 3.10+** — no pip, no dependencies.

Download or copy that one file anywhere and run it directly:

```bash
python3 serger.py
```

That’s it. ✨

Everything else in this repo (tests, docs, configs) exists only for developing and maintaining the script itself.

---

## 🧪 Verify Your Install

Serger includes a built-in self-check — no pytest required.  
You can verify that the script works correctly on your system by running:

```bash
python3 serger.py --selftest
```

This creates a tiny temporary project, stiches a few test files, and confirms it completes successfully.  
If you see a ✅ “Self-test passed” message, your installation is working perfectly.

---

### ⚖️ License
- [MIT-a-NOAI License](LICENSE)

You’re free to use, copy, and modify the script under the standard MIT terms.  
The additional rider simply requests that this project not be used to train or fine-tune AI/ML systems until the author deems fair compensation frameworks exist.  
Normal use, packaging, and redistribution for human developers are unaffected.

## 🧩 Run It as a Script

If you prefer a direct executable:

```bash
chmod +x serger.py
./serger.py
```

You can even drop it somewhere on your `PATH` for easy use.

## 💡 Why Python? Other Language Versions?

You don’t need a native build.  
Python 3 ships with most Linux distributions. On Mac, it's one homebrew command away. On Windows, running the script automatically prompts the user to install Python from the Microsoft Store.

Serger is meant to live comfortably next to your other tools — Go, Node.js, Rust, whatever — when all you need is a simple, dependency-free way to copy and package files.

## 🪶 Summary

**Use it. Hack it. Ship it.**
It’s MIT-licensed, minimal, and meant to stay out of your way — just with one polite request: don’t feed it to the AIs (yet).

---

> ✨ *AI was used to help draft language, formatting, and code — plus we just love em dashes.*

<p align="center">
  <sub>😐 <a href="https://apathetic-tools.github.io/">Apathetic Tools</a> © <a href="./LICENSE">MIT-a-NOAI</a></sub>
</p>
pvkoalademo 3.0.0
Koala Noise Suppression Engine demos
Requires Python: >=3.9
Keywords: Noise Cancellation, Noise Suppression, Noise Removal, Speech Enhancement, Speech Denoising

# Koala Noise Suppression Demos

Made in Vancouver, Canada by [Picovoice](https://picovoice.ai)

## Koala

Koala is an on-device noise suppression engine. Koala is:

- Private; All voice processing runs locally.
- Cross-Platform:
  - Linux (x86_64), macOS (x86_64, arm64), Windows (x86_64, arm64)
  - Android and iOS
  - Chrome, Safari, Firefox, and Edge
  - Raspberry Pi (3, 4, 5)

## Compatibility

- Python 3.9+
- Runs on Linux (x86_64), macOS (x86_64, arm64), Windows (x86_64, arm64), and Raspberry Pi (3, 4, 5).

## Installation

```console
pip3 install pvkoalademo
```

## AccessKey

Koala requires a valid Picovoice `AccessKey` at initialization. `AccessKey` acts as your credentials when using Koala 
SDKs. You can get your `AccessKey` for free. Make sure to keep your `AccessKey` secret.
Signup or Login to [Picovoice Console](https://console.picovoice.ai/) to get your `AccessKey`.

## Usage

### Microphone Demo

The microphone demo collects audio from a microphone connected to your machine. The audio is passed through the Koala
noise suppression engine and stored in a `.wav` file.
Run the following in the terminal:

```console
koala_demo_mic --access_key ${ACCESS_KEY} --output-path ${WAV_OUTPUT_PATH}
```

Replace `${ACCESS_KEY}` with yours obtained from Picovoice Console and `${WAV_OUTPUT_PATH}` with a path to a `.wav` file
where the enhanced audio will be stored. Terminate the demo with `Ctrl+C`.

### File Demo

The file demo passes audio stored in a `.wav` file through the Koala noise suppression engine. The result is shifted to
compensate for the delay introduced by Koala and stored in a separate `.wav` file that will have the same length as the
input file without any delay.
Run the following in the terminal:

```console
koala_demo_file --access_key ${ACCESS_KEY} --input_path ${WAV_INPUT_PATH} --output_path ${WAV_OUTPUT_PATH}
```

Replace `${ACCESS_KEY}` with yours obtained from Picovoice Console, `${WAV_INPUT_PATH}` with a path to a compatible
(single-channel, 16 kHz, 16-bit PCM) `.wav` file you wish to enhance, and `${WAV_OUTPUT_PATH}` with a path to a `.wav` 
file where the enhanced audio will be stored.
core-e2 0.1.0
Una practica de paquetes

# Cursos disponibles

- Introduccion a linux [15 horas]
- Personalizacion de linux [3 horas]
- Introduccion al hacking [53 horas]

## Instalacion

Instalar el paquete usando `pip3`:

```python
pip3 install core_e2
```

## Uso basico

### Listar todo los cursos

```python
from core_e2 import list_courses

for course in list_courses():
  print(course)
```

### Obtener un curso por nombre

```python
from core_e2 import get_course_by_name

course = get_course_by_name("Introduccion a linux")
print(course)
```

### Calcular duracion total de cursos

```python
from core_e2..utils import total_duration

print(f"Duracion total: {total_duration()} horas")
```
onzr 0.6.0
The one-hour-late Deezer CLI.
Requires Python: <4.0.0,>3.10
Keywords: audio, cli, deezer, flac, mp3, tui

# Onzr, the one-hour-late Deezer 💜 CLI.

> Pronounced onze heure (11 O-Clock à-la-française) 🤡

![GitHub Actions Workflow Status](https://img.shields.io/github/actions/workflow/status/jmaupetit/onzr/quality.yml)
![PyPI - Version](https://img.shields.io/pypi/v/onzr)

!!! warning

    This project is still at an early stage. It works in its core parts, but
    will not meet standard requirements for a decent player.

![Onzr Demo VHS](https://vhs.charm.sh/vhs-6cGaxSq0RKCq7ELvYDItWy.gif)

## Requirements

- [Python](https://www.python.org): 3.11+
- [VLC](https://www.videolan.org/vlc/index.en_GB.html): we use VLC bindings to
  play tracks, so this is a strict requirement.

## Quick start guide

Onzr is a python package, it can be installed using Pip (or any other package
manager you may use):

```sh
pip install --user onzr
```

Once installed the `onzr` command should be available (if not check your `PATH`
definition). Before using Onzr, you should configure it (once for all):

```sh
onzr init
```

This command will prompt for an `ARL` token. If you don't know how to find it,
please follow
[this guide](https://github.com/nathom/streamrip/wiki/Finding-Your-Deezer-ARL-Cookie).

You may now explore commands and their usage:

```sh
onzr --help
```

Onzr is based on an HTTP client/server architecture, hence, once installed, you
should run the server before starting to use it:

```sh
onzr serve --log-level error &
```

In this case, the server is ran as a background job; see the `serve` command
documentation for details about running Onzr server.

Play your first album:

```sh
onzr search --artist "Billie Eilish" --ids --first | \
    onzr artist --albums --ids --limit 1 - | \
    onzr album --ids - | \
    onzr add - && \
    onzr play
```

Aaand, tada 🎉

In this command, we look for the latest Billie Eilish album, add it to the
queue and play it instantly!

## Documentation

The complete documentation of the project is available at:
[https://jmaupetit.github.io/onzr/](https://jmaupetit.github.io/onzr/)

## License

This work is released under the MIT License.
openvolley-pydatavolley 0.3.0
Extract data from DataVolley .dvw files and VolleyStation .vsm files
Requires Python: >=3.11

# py-datavolley

A Python package for parsing and analyzing volleyball scouting data from DataVolley files (\*.dvw).

Rebuilt [pydatavolley](https://github.com/openvolley/pydatavolley) with modern Python tooling ([Astral ecosystem](https://docs.astral.sh/)) for improved experience: UV for package management, Ruff for linting/formatting and [Ty](https://github.com/astral-sh/ty) for type checking.

```bash
mkdir my-analysis
cd my-analysis
uv init
uv add ruff
uv add ty
uv add openvolley-pydatavolley
```

```python
# data = dv.read_dv(path_of_dvw_file)
data = dv.read_dv(dv.example_file())
print(data)
```

<details>
<summary>Will return (this is a sample and not the entire example file) </summary>

```json
[
  {
    "match_id": "106859",
    "video_time": 495,
    "code": "a02RM-~~~58AM~~00B",
    "team": "University of Dayton",
    "player_number": 2,
    "player_name": "Maura Collins",
    "player_id": "-230138",
    "skill": "Reception",
    "skill_type": "Jump-float serve reception",
    "skill_subtype": "Jump Float",
    "evaluation_code": "-",
    "setter_position": "6",
    "attack_code": null,
    "set_code": null,
    "set_type": null,
    "start_zone": "5",
    "end_zone": "8",
    "end_subzone": "A",
    "num_players_numeric": null,
    "home_team_score": "0",
    "visiting_team_score": "0",
    "home_setter_position": "1",
    "visiting_setter_position": "6",
    "custom_code": "00B",
    "home_p1": "19",
    "home_p2": "9",
    "home_p3": "11",
    "home_p4": "15",
    "home_p5": "10",
    "home_p6": "7",
    "visiting_p1": "1",
    "visiting_p2": "16",
    "visiting_p3": "17",
    "visiting_p4": "10",
    "visiting_p5": "6",
    "visiting_p6": "8",
    "start_coordinate": "0431",
    "mid_coordinate": "-1-1",
    "end_coordinate": "7642",
    "point_phase": "Reception",
    "attack_phase": null,
    "start_coordinate_x": 1.26875,
    "start_coordinate_y": 0.092596,
    "mid_coordinate_x": null,
    "mid_coordinate_y": null,
    "end_coordinate_x": 1.68125,
    "end_coordinate_y": 5.425924,
    "set_number": "1",
    "home_team": "University of Louisville",
    "visiting_team": "University of Dayton",
    "home_team_id": 17,
    "visiting_team_id": 42,
    "point_won_by": "University of Louisville",
    "serving_team": "University of Louisville",
    "receiving_team": "University of Dayton",
    "rally_number": 1,
    "possession_number": 1
  }
]
```

</details>
myqcc 0.0.1
A Qiskit package containing basic quantum circuits and gate implementations
Requires Python: >=3.6

# myqcc

This package contains Qiskit implementations for basic quantum operations, gates, and circuits.

## Installation

```bash
pip install myqcc
```

## Usage

```python
import qc

# List available functions
qc.help()

# Run a specific quantum function
qc.hadamard_gate()
qc.circuit_formation_1()

# Run ALL functions
qc.all()
```
parajax 0.3.1
Parallelization utilities for JAX
Requires Python: >=3.10

<div align="center">
  <a href="https://github.com/gerlero/parajax"><img src="https://raw.githubusercontent.com/gerlero/parajax/main/logo.png" alt="Parajax" width="250"/></a>

  **Automagic parallelization of calls to [JAX](https://github.com/jax-ml/jax)-based functions**

  [![Documentation](https://img.shields.io/readthedocs/parajax)](https://parajax.readthedocs.io/)
  [![CI](https://github.com/gerlero/parajax/actions/workflows/ci.yml/badge.svg)](https://github.com/gerlero/parajax/actions/workflows/ci.yml)
  [![Codecov](https://codecov.io/gh/gerlero/parajax/branch/main/graph/badge.svg)](https://codecov.io/gh/gerlero/parajax)
  [![Ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json)](https://github.com/astral-sh/ruff)
  [![ty](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ty/main/assets/badge/v0.json)](https://github.com/astral-sh/ty)
  [![uv](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/uv/main/assets/badge/v0.json)](https://github.com/astral-sh/uv)
  [![Publish](https://github.com/gerlero/parajax/actions/workflows/pypi-publish.yml/badge.svg)](https://github.com/gerlero/parajax/actions/workflows/pypi-publish.yml)
  [![PyPI](https://img.shields.io/pypi/v/parajax)](https://pypi.org/project/parajax/)
  [![PyPI - Python Version](https://img.shields.io/pypi/pyversions/parajax)](https://pypi.org/project/parajax/)
</div>

## Features

- 🚀 **Device-parallel execution**: run across multiple CPUs, GPUs or TPUs automatically
- 🧩 **Fully composable** with [`jax.jit`](https://docs.jax.dev/en/latest/_autosummary/jax.jit.html), [`jax.vmap`](https://docs.jax.dev/en/latest/_autosummary/jax.vmap.html), and other JAX transformations
- 🪄 **Automatic handling** of input shapes not divisible by the number of devices
- 🎯 **Simple interface**: just decorate your function with [`@parallelize`](https://parajax.readthedocs.io/en/stable/#parajax.parallelize)

## Installation

```bash
pip install parajax
```

## Example

```python
import multiprocessing

import jax
import jax.numpy as jnp
from parajax import parallelize

jax.config.update("jax_num_cpu_devices", multiprocessing.cpu_count())
# ^ Only needed on CPU: allow JAX to use all CPU cores

@parallelize
def square(xs):
    return xs**2

xs = jnp.arange(12_345)
ys = square(xs)
```

That's it! Invocations of `square` will now be automatically parallelized across all available devices.

## Documentation

For more details, check out the [documentation](https://parajax.readthedocs.io/).