semvec/pypi-packages huggingface dataset:
kyouka 1.0.0
yt dl wrapper
Requires Python: >=3.6

# Kyouka - YouTube Downloader and Media Player

Kyouka is a command-line tool for downloading YouTube videos and playing media files.

## Features
- Download YouTube videos as MP4
- Download audio as high-quality MP3
- Play media files with different player options
- Rename and manage downloaded files

## Installation

### Basic Installation (audio only)
```bash
pip install kyouka
```
Full Installation (with video support)
```bash
pip install kyouka[full]
```
Note: For full video playback support, you'll need:

VLC media player installed on your system

Tkinter (usually included with Python)

Usage
Run the CLI interface:

```bash
kyouka
```
Command-line options:
```bash
kyouka --download "https://youtube.com/..."         # Download video
kyouka --download_audio "https://youtube.com/..."   # Download audio only
kyouka --list                                       # List downloaded media
kyouka --play "filename.mp4"                        # Play a media file
kyouka --rename "oldname.mp4" "newname.mp4"         # Rename a file
kyouka --delete "filename.mp4"                      # Delete a file
```
Player Options:
Player (Audio and Video - Recommended) - Requires VLC

Built-in Video Player (no audio) - Requires OpenCV

Audio Player (no video) - Requires VLC

Troubleshooting
If video players don't work, install VLC: https://www.videolan.org/

On Linux, install Tkinter: sudo apt install python3-tk

### Installation Instructions for Users

1. **Install Python** (if not already installed):
   - Download from https://python.org/downloads
   - Make sure to check "Add Python to PATH" during installation

2. **Install Kyouka**:
```bash
# For basic audio functionality:
pip install kyouka

# For full video support:
pip install kyouka[full]
```
Install VLC (for best experience):

Download from https://www.videolan.org/vlc/

Run Kyouka:

```bash
kyouka
```
For Developers
To install from source:

```bash
git clone https://github.com/lyraxial/kyouka.git
cd kyouka
pip install .[full]  # For full features
```
pxr-gf-dev 25.8
C++ headers and CMake config for developing with pxr-gf
Requires Python: >=3.8
Keywords: pixar, open-usd, dev

# Pixar Gf

[![PyPi version](https://img.shields.io/pypi/v/pxr-gf.svg?logo=pypi&label=PyPI&logoColor=gold)](https://pypi.python.org/pypi/pxr-gf)
[![CMake](https://img.shields.io/badge/CMake-3.23...4.1-blue.svg?logo=CMake&logoColor=blue)](https://cmake.org)
[![Linux](https://github.com/untwine/pxr-gf/actions/workflows/linux.yml/badge.svg?branch=main)](https://github.com/untwine/pxr-gf/actions/workflows/linux.yml)
[![MacOs](https://github.com/untwine/pxr-gf/actions/workflows/macos.yml/badge.svg?branch=main)](https://github.com/untwine/pxr-gf/actions/workflows/macos.yml)
[![Windows](https://github.com/untwine/pxr-gf/actions/workflows/windows.yml/badge.svg?branch=main)](https://github.com/untwine/pxr-gf/actions/workflows/windows.yml)
[![License](https://img.shields.io/badge/License-TOST-yellow.svg)](https://github.com/untwine/pxr-gf/blob/main/LICENSE.txt)

Graphics Foundations library used in OpenUSD.

This repository is a decoupled version of the "gf" library from the
[OpenUSD](https://graphics.pixar.com/usd/release/index.html) library, created
by Pixar.

It is regularly rebased over the "release" branch of the original
[repository](https://github.com/PixarAnimationStudios/OpenUSD) to provide
explicit divergence information from the latest updates and is currently
synchronized with
[v25.08](https://github.com/PixarAnimationStudios/OpenUSD/releases/tag/v25.08).
pxr-gf 25.8
Graphics Foundations library used in OpenUSD
Requires Python: >=3.8
Keywords: pixar, open-usd

# Pixar Gf

[![PyPi version](https://img.shields.io/pypi/v/pxr-gf.svg?logo=pypi&label=PyPI&logoColor=gold)](https://pypi.python.org/pypi/pxr-gf)
[![CMake](https://img.shields.io/badge/CMake-3.23...4.1-blue.svg?logo=CMake&logoColor=blue)](https://cmake.org)
[![Linux](https://github.com/untwine/pxr-gf/actions/workflows/linux.yml/badge.svg?branch=main)](https://github.com/untwine/pxr-gf/actions/workflows/linux.yml)
[![MacOs](https://github.com/untwine/pxr-gf/actions/workflows/macos.yml/badge.svg?branch=main)](https://github.com/untwine/pxr-gf/actions/workflows/macos.yml)
[![Windows](https://github.com/untwine/pxr-gf/actions/workflows/windows.yml/badge.svg?branch=main)](https://github.com/untwine/pxr-gf/actions/workflows/windows.yml)
[![License](https://img.shields.io/badge/License-TOST-yellow.svg)](https://github.com/untwine/pxr-gf/blob/main/LICENSE.txt)

Graphics Foundations library used in OpenUSD.

This repository is a decoupled version of the "gf" library from the
[OpenUSD](https://graphics.pixar.com/usd/release/index.html) library, created
by Pixar.

It is regularly rebased over the "release" branch of the original
[repository](https://github.com/PixarAnimationStudios/OpenUSD) to provide
explicit divergence information from the latest updates and is currently
synchronized with
[v25.08](https://github.com/PixarAnimationStudios/OpenUSD/releases/tag/v25.08).
sdkForMovies 0.0.2
SDK Python pour accéder à l’API MovieLens (Data Analysts/Data Scientists friendly)
Requires Python: >=3.13

# MovieLens SDK - `sdkForMovies`

Un SDK Python simple pour interagir avec l’API REST MovieLens. Il est conçu pour les **Data Analysts** et **Data Scientists**, avec une prise en charge native de **Pydantic**, **dictionnaires** et **DataFrames Pandas**.

[![PyPI version](https://badge.fury.io/py/moviesdk.svg)](https://badge.fury.io/py/moviesdk)
[![License: MIT](https://img.shields.io/badge/License-MIT-green.svg)](https://opensource.org/licenses/MIT)

---

## Installation

```bash
pip install sdkForMovies
```

---

## Configuration

```python
from sdkForMovies import MovieClient, MovieConfig

# Configuration avec l’URL de votre API (Render ou locale)
config = MovieConfig(movie_base_url="https://movie-backend-1rhh.onrender.com")
client = MovieClient(config=config)
```

---

## Tester le SDK

### 1. Health check

```python
client.health_check()
# Retourne : {"status": "ok"}
```

### 2. Récupérer un film

```python
movie = client.get_movie(1)
print(movie.title)
```

### 3. Liste de films au format DataFrame

```python
df = client.list_movies(limit=5, output_format="pandas")
print(df.head())
```

---

## Modes de sortie disponibles

Toutes les méthodes de liste (`list_movies`, `list_ratings`, etc.) peuvent retourner :

- des objets **Pydantic** (défaut)
- des **dictionnaires**
- des **DataFrames Pandas**

Exemple :

```python
client.list_movies(limit=10, output_format="dict")
client.list_ratings(limit=10, output_format="pandas")
```

---

## Tester en local

Vous pouvez aussi utiliser une API locale :

```python
config = MovieConfig(movie_base_url="http://localhost:8000")
client = MovieClient(config=config)
```

---

## Public cible

- Data Analysts
- Data Scientists
- Étudiants et curieux en Data
- Développeurs Python

---

## Licence

MIT License

---

## Liens utiles

- API Render : [https://movie-backend-1rhh.onrender.com](https://movie-backend-1rhh.onrender.com)
- PyPI : [https://pypi.org/project/sdkForMovies](https://pypi.org/project/sdkForMovies)
pxr-js-dev 25.8
C++ headers and CMake config for developing with pxr-js
Requires Python: >=3.8
Keywords: pixar, open-usd, dev

# Pixar Js

[![PyPi version](https://img.shields.io/pypi/v/pxr-js.svg?logo=pypi&label=PyPI&logoColor=gold)](https://pypi.python.org/pypi/pxr-js)
[![CMake](https://img.shields.io/badge/CMake-3.23...4.1-blue.svg?logo=CMake&logoColor=blue)](https://cmake.org)
[![Linux](https://github.com/untwine/pxr-js/actions/workflows/linux.yml/badge.svg?branch=main)](https://github.com/untwine/pxr-js/actions/workflows/linux.yml)
[![MacOs](https://github.com/untwine/pxr-js/actions/workflows/macos.yml/badge.svg?branch=main)](https://github.com/untwine/pxr-js/actions/workflows/macos.yml)
[![Windows](https://github.com/untwine/pxr-js/actions/workflows/windows.yml/badge.svg?branch=main)](https://github.com/untwine/pxr-js/actions/workflows/windows.yml)
[![License](https://img.shields.io/badge/License-TOST-yellow.svg)](https://github.com/untwine/pxr-js/blob/main/LICENSE.txt)

JSON interface library used in OpenUSD.

This repository is a decoupled version of the "js" library from the
[OpenUSD](https://graphics.pixar.com/usd/release/index.html) library, created
by Pixar.

It is regularly rebased over the "release" branch of the original
[repository](https://github.com/PixarAnimationStudios/OpenUSD) to provide
explicit divergence information from the latest updates and is currently
synchronized with
[v25.08](https://github.com/PixarAnimationStudios/OpenUSD/releases/tag/v25.08).
pxr-js 25.8
JSON interface library used in OpenUSD
Requires Python: >=3.8
Keywords: pixar, open-usd

# Pixar Js

[![PyPi version](https://img.shields.io/pypi/v/pxr-js.svg?logo=pypi&label=PyPI&logoColor=gold)](https://pypi.python.org/pypi/pxr-js)
[![CMake](https://img.shields.io/badge/CMake-3.23...4.1-blue.svg?logo=CMake&logoColor=blue)](https://cmake.org)
[![Linux](https://github.com/untwine/pxr-js/actions/workflows/linux.yml/badge.svg?branch=main)](https://github.com/untwine/pxr-js/actions/workflows/linux.yml)
[![MacOs](https://github.com/untwine/pxr-js/actions/workflows/macos.yml/badge.svg?branch=main)](https://github.com/untwine/pxr-js/actions/workflows/macos.yml)
[![Windows](https://github.com/untwine/pxr-js/actions/workflows/windows.yml/badge.svg?branch=main)](https://github.com/untwine/pxr-js/actions/workflows/windows.yml)
[![License](https://img.shields.io/badge/License-TOST-yellow.svg)](https://github.com/untwine/pxr-js/blob/main/LICENSE.txt)

JSON interface library used in OpenUSD.

This repository is a decoupled version of the "js" library from the
[OpenUSD](https://graphics.pixar.com/usd/release/index.html) library, created
by Pixar.

It is regularly rebased over the "release" branch of the original
[repository](https://github.com/PixarAnimationStudios/OpenUSD) to provide
explicit divergence information from the latest updates and is currently
synchronized with
[v25.08](https://github.com/PixarAnimationStudios/OpenUSD/releases/tag/v25.08).
library3d 0.25
Saf Python 3D kütüphanesi (OpenGL kullanmadan, v0.25)
Requires Python: >=3.7

# library3d

Saf Python 3D kütüphanesi (OpenGL kullanmadan)

## Özellikler
- `Engine` sınıfı: canvas oluşturur, çizim yapar
- `Vector3` sınıfı: 3D vektör işlemleri
- `draw_polygon`, `fill_polygon`, `render_line`: yardımcı çizim fonksiyonları
- RGB renk desteği
- PIP ile yüklenebilir
urdf2mjcf 0.2.39
The urdf2mjcf project
Requires Python: >=3.11

<div align="center">

[![License](https://img.shields.io/badge/license-MIT-green)](https://github.com/kscalelabs/urdf2mjcf/blob/main/LICENSE)
[![Version](https://img.shields.io/pypi/v/urdf2mjcf)](https://pypi.org/project/urdf2mjcf/)
[![Discord](https://img.shields.io/discord/1224056091017478166)](https://discord.gg/kscale)
[![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/urdf2mjcf/actions/workflows/test.yml/badge.svg)](https://github.com/kscalelabs/urdf2mjcf/actions/workflows/test.yml)
[![Publish Python Package](https://github.com/kscalelabs/urdf2mjcf/actions/workflows/publish.yml/badge.svg)](https://github.com/kscalelabs/urdf2mjcf/actions/workflows/publish.yml)

</div>

# urdf2mjcf

Convert URDF to MJCF XML.

See the documentation for this project [here](https://docs.kscale.dev/docs/urdf2mjcf).
orientLoss 0.0.8
orientLoss is a classification loss function @pytorch. It less prone to overfitting compared to crossEntropy. Compatible with random vector/embeddings with direction-indicating categories、probabilistic、oneHot as target. 
Requires Python: >=3.8
Keywords: orientLoss, Loss, PyTorch, AI, neural, NN, classification, categories

# orientLoss
orientLoss is a classification loss function. It less prone to overfitting compared to crossEntropy. Compatible with random vector/embeddings with direction-indicating categories、probabilistic、oneHot as target.

## 思路说明
很多时候网络需要输出的类别数远高于网络中使用的特征数/通道数,需要在最后一层加一个巨大的线性层将输出扩增才送入交叉熵损失.但本质上这一层并没有增加信息量,所以可以使用某些损失函数取代这一过程.再考虑到交叉熵由于其下降没有终点,容易引起过拟合.通过设计公式从形状上逼近和优化维度无限扩增后的交叉熵损失得到了这个损失函数.

## Install
```bash
pip install orientLoss
```

## Use
```python
from orientLoss import orientLoss
...
loss=orientLoss(input,target,dim=-1,meanOut=True,angleSmooth=1,normSmooth=1,dimScalingOrd=0,eps=1e-8)
```

target可以是由方向指示类别的随机向量/嵌入、概率、独热编码. 概率或独热编码输入时最好各类别独立进行标准化后再送入损失函数.
3dlibrary 0.2
Saf Python 3D kütüphanesi (OpenGL kullanmadan, v0.2)
Requires Python: >=3.7

# 3Dlibrary v0.2

Saf Python 3D kütüphanesi. OpenGL kullanmadan çalışır.

## Özellikler
- 3D vertex ve polygon çizimi
- Matris dönüşümleri (rotate, scale, translate)
- Z-buffer ve backface culling
- Basit kamera sistemi
- Animasyon destekli render döngüsü
- Mesh / OBJ yükleme desteği (temel)
- Polygon renkleri ve basit ışık efektleri
pptweaver 0.1.0
An innovative tool to weave your web content into editable PowerPoint presentations.
Requires Python: >=3.8

# 幻织 (PPTWeaver) ✨ - 我为连接 AI 与可编辑 PPT 所做的个人探索

你好！这是我的一个个人项目，它源于一个简单的问题：“我能让 AI 制作出*真正可编辑*的 PowerPoint 幻灯片吗？” 这就是那次实验的故事。

**English** | [中文](./README_en.md)

---

### 🤔 痛点：AI PPT 工具总感觉……差点意思

和许多人一样，我为 AI 生成内容的能力而着迷。但当我深入研究 AI PPT 生成器时，我发现它们大多属于以下几类，而且没有一类能完全满足我的需求：

1.  **模板填充型**: 这类工具接收你的文本，然后将其填充到预定义的模板中。速度很快，但在创意上限制很大。你没法让它生成一个复杂的、定制化的图表，因为 AI 只是在“填空”。
2.  **图片导出型**: 这类工具提供了更多的视觉自由度，但它们在“作弊”。它们将漂亮的幻灯片生成为静态图片或 PDF。这完全违背了 PowerPoint 的初衷——你什么都改不了！
3.  **封闭生态型**: 像 Microsoft Copilot 这样强大的工具与 Office 深度集成，但它们通常是付费的、闭源的商业产品。对于一个喜欢自己动手探索的开发者来说，它们就像一个黑盒。

### ✨ 灵光一现：AI 是 HTML & SVG 的大师

当我意识到我问错了问题时，真正的突破到来了。我不该问“AI，帮我做个 PPT”，而应该问“AI，帮我为 PPT *生成视觉元素*”。

事实证明，大语言模型在使用 HTML 和 SVG 生成复杂、精美且精确的视觉内容方面，能力**惊人**。这是它们能够原生理解的语言。突然之间，我找到了一种方法，可以创造出任何我能想象到的定制图表、图示或布局。

但这带来了新的挑战：你如何将这些完美的、基于 Web 的内容，转换成一个完全可编辑的 `.pptx` 文件呢？

### 🛠️ 我的解决方案：PPTWeaver 的工作流

这个项目就是我的答案。我不再将 AI 视为幻灯片设计师，而是将其视为一名前端开发人员。工作流程如下：

![PPTWeaver 工作流图](docs/about_pptweaver_workflow.svg)

1.  **以 HTML/SVG 为源**: 我们从一个标准的 HTML 文件开始。每一个 `<body>` 标签都代表一张新的幻灯片。
2.  **使用无头浏览器渲染**: PPTWeaver 在后台使用 Playwright，像浏览器一样精确地渲染 HTML。这一步以完美的保真度捕获所有的 CSS 样式和布局信息。
3.  **提取与翻译**: 核心引擎会检查渲染好的页面，提取每一个 SVG 元素，并将其属性（位置、大小、颜色、文本）翻译成 `python-pptx` 库能理解的语言。
4.  **编织成 PPTX**: 最后，它将每个元素重构为原生的、可编辑的 PowerPoint 形状，从而将 Web 内容“编织”成最终的 `.pptx` 文件。

### 🏛️ 软件架构

这是一个更详细的内部组件协作图：

![PPTWeaver 软件架构图](docs/architecture.svg)

### 🚀 来试试看！

**安装:**
```bash
# 安装主包
pip install pptweaver
# 安装 Playwright 依赖 (首次使用需要)
playwright install
```

**快速使用:**
```python
import asyncio
from pptweaver import Converter

async def main():
    converter = Converter(input_file="your_slides.html", output_file="presentation.pptx")
    await converter.convert()

asyncio.run(main())
```

### 🚧 项目仍在施工中 & 未来计划

这个项目还远未完成！以下是路线图上的一个粗略想法：

-   [ ] **改进样式转换**: 更好地支持 CSS 渐变、阴影和复杂的文本样式。
-   [ ] **动画与过渡**: 为幻灯片过渡和简单的入场动画提供基础支持。
-   [ ] **交互式元素**: 探索将 HTML 链接或按钮映射到 PowerPoint 交互式元素的方法。
-   [ ] **从 Prompt 直达 PPTX**: 一个更高级的 API，与 LLM 集成，实现从文本提示直接生成最终的 PPTX 文件。
-   [ ] **更多处理器**: 支持更多小众的 SVG 标签和 CSS 属性。

### 🤝 一起玩吧！

这是一个个人项目，但我非常希望它能成为一个社区项目。如果你有任何想法、发现了 bug，或者想添加新功能，请随时开启一个 Issue 或提交一个 Pull Request！
py-caelus 5.0.0
Caelus python library
Requires Python: >=3.10


# Caelus Python Library (CPL)

[![Build status](https://github.com/sayerhs/cpl/actions/workflows/ci.yml/badge.svg)](https://github.com/sayerhs/cpl/actions/workflows/ci.yml)
[![Coverage Status](https://coveralls.io/repos/github/sayerhs/cpl/badge.svg?branch=dev)](https://coveralls.io/github/sayerhs/cpl?branch=main)

Caelus Python Library (CPL) is a python package for interacting with Caelus CML
software. The software is distributed under Apache License, Version 2.0 (see
LICENSE.txt) for details.

## Documentation

Please see [online documentation](https://sayerhs.github.io/cpl) for th installation instructions, user manual, as well as the API reference manual.
pytensorlite 0.1.4
Lightweight tensor utilities for Python AI modules.
Requires Python: >=3.6

\# pytensorlite



Lightweight and minimal tensor operation package for Python.  

Useful for prototyping machine learning models with simple mathematical tools.



\## Features

\- Basic tensor operations

\- Element-wise computations

\- No external dependencies



\## Example

```python

from pytensorlite import tensor\_add



print(tensor\_add(\[1, 2], \[3, 4]))  # \[4, 6]
loglocal 1.0.5
A Python library for local logging with advanced features
Requires Python: >=3.11

# loglocal

`loglocal` is a Python library for advanced local logging, built on top of [loguru](https://github.com/Delgan/loguru) and [pydantic](https://github.com/pydantic/pydantic). It provides configurable logging with support for both synchronous and asynchronous function decorators, file rotation, retention, and more.

## Features

- Easy-to-use logger with sensible defaults
- Configurable via environment variables and pydantic models
- Supports both console and file logging
- Decorators for logging function execution (sync and async)
- File rotation, retention, compression, and serialization

## Installation
You can install `loglocal` via pip:
```bash
pip install loglocal
```
Or if you prefer to use `uv` (Universal Virtual Environment):
```bash
uv add loglocal
```
## Usage

```python
from loglocal import logger

logger.info("Hello from loglocal!")


@logger.wrap
def my_function(x, y):
  return x + y


@logger.wrap
async def my_async_function(x, y):
  return x * y
```

## API Reference

### `logger`

A class-level property that returns a configured `LogLocal` instance using default settings.

### `LogLocal`

Extends `loguru.Logger` with additional configuration and decorators.

#### Methods

- `from_config(config: LogLocalConfig, **kwargs) -> LogLocal`  
  Create a logger instance from a `LogLocalConfig` object.

- `wrap(func=None)`  
  Decorator to log execution of sync or async functions.

#### Example

```python
from loglocal import Logger, logger
from loglocal.models import LogLocalConfig

custom_logger = Logger(
    config=LogLocalConfig(
        ...  # Custom configuration parameters
    )
)
logger.info("Logging with default config")
```

### Configuration

#### Environment Variables
- `LOGLOCAL_PATH`: Path to the log file (default: `logs/app.log`)

You can customize logging behavior by passing a custom `LogLocalConfig`:

- `log_path`: Path to log file (default: `logs/app.log`) # overrides `LOGLOCAL_PATH` environment variable
- `log_format`: Log message format
- `log_level`: Logging level (default: `DEBUG`)
- `log_opt`: Additional options for the logger (e.g., `exception`, `depth`, `colors`, etc.) of type `LogLocalOptions`
- `log_config`: Pydantic model for additional configuration options (e.g., file rotation, retention, compression, etc.) of type `LogConfig`

## License

MIT