semvec/pypi-packages huggingface dataset:
h4x-xd 1.0.0
A powerful, fast, and independent video downloader for YouTube, TikTok, and more.
Requires Python: >=3.8

# h4x-xd: High-Performance Independent Video Downloader

**h4x-xd** is a powerful, lightweight, and extremely fast Python library designed for downloading high-quality videos from various platforms. Unlike other tools, **h4x-xd** is built from the ground up with independent extractors and a multi-threaded download engine, making it ideal for web applications, Telegram bots, and automated workflows.

## Key Features
- **Independent Extractors**: Custom logic for YouTube, TikTok, and more – no reliance on `yt-dlp`.
- **Maximum Speed**: Built-in multi-chunked downloader that bypasses standard connection throttling.
- **Highest Quality**: Automatically identifies and merges the best available video and audio streams.
- **Async-First**: Fully compatible with `asyncio` for high-concurrency environments.
- **Bot-Ready**: Seamless integration for Telegram bots (stream to buffer) and Web APIs (stream to response).

## Installation
```bash
# Clone the repository and install dependencies
git clone https://github.com/your-repo/h4x-xd.git
cd h4x-xd
pip install aiohttp httpx tqdm
```

## Quick Start
```python
import asyncio
from h4x_xd import Downloader

async def main():
    dl = Downloader(output_dir="videos", num_chunks=16)
    # Download in highest quality
    path = await dl.download("https://www.youtube.com/watch?v=...")
    print(f"Downloaded to: {path}")

asyncio.run(main())
```

## Integration Examples

### Telegram Bot (Telethon)
```python
from h4x_xd import TelegramHelper

helper = TelegramHelper()
buffer = await helper.stream_to_buffer("https://tiktok.com/...")
await client.send_file(chat_id, buffer, caption="Downloaded via h4x-xd")
```

### Web API (FastAPI)
```python
from fastapi import FastAPI
from h4x_xd import web_router

app = FastAPI()
app.include_router(web_router, prefix="/api/v1")
```

## Why h4x-xd?
| Feature | yt-dlp | h4x-xd |
|---------|--------|---------|
| Dependency | Heavy | Lightweight |
| Architecture | CLI-first | Library-first |
| Speed | Standard | Multi-chunked (Turbo) |
| Async | Wrapper | Native |
| Integration | Complex | Plug-and-play |

## License
MIT License
datadog-checks-base 37.31.0
The Datadog Check Toolkit
Keywords: agent, checks, datadog

# Datadog Checks Base

[![Latest PyPI version][1]][7]
[![Supported Python versions][2]][7]

## Overview

This package provides the Python bits needed by the [Datadog Agent][4]
to run Agent-based Integrations (also known as _Checks_).

This package is used in two scenarios:

1. When used from within the Python interpreter embedded in the Agent, it
provides all the base classes and utilities needed by any Check.

2. When installed in a local environment with a regular Python interpreter, it
mocks the presence of a running Agent so checks can work in standalone mode,
mostly useful for testing and development.

Please refer to the [docs][5] for details.

## Installation

Checks from [integrations-core][6] already
use the toolkit in a transparent way when you run the tests but you can
install the toolkit locally and play with it:

```shell
pip install datadog-checks-base
```

## Performance Optimizations

We strive to balance lean resource usage with a "batteries included" user experience.
We employ a few tricks to achieve this.

One of them is the [lazy-loader][9] library that allows us to expose a nice API (simple, short imports) without the baseline memory overhead of importing everything all the time.

Another trick is to import some of our dependencies inside functions that use them instead of the more conventional import section at the top of the file. We rely on this the most in the `AgentCheck` base class.

## Troubleshooting

Need help? Contact [Datadog support][8].

[1]: https://img.shields.io/pypi/v/datadog-checks-base.svg
[2]: https://img.shields.io/pypi/pyversions/datadog-checks-base.svg
[4]: https://github.com/DataDog/datadog-agent
[5]: https://datadoghq.dev/integrations-core/base/about/
[6]: https://github.com/DataDog/integrations-core
[7]: https://pypi.org/project/datadog-checks-base/
[8]: https://docs.datadoghq.com/help/
[9]: https://github.com/scientific-python/lazy-loader
huira 0.8.1
Python bindings for the Huira library
Requires Python: >=3.9

![Huira](https://raw.githubusercontent.com/huira-render/huira/main/docs/_static/logo/full/blue_on_transparent.svg)

*Huira* is a ray-tracing library for rendering large scenes, star fields, and simulating solar radiation pressure.


[![Linux CI/CD](https://github.com/huira-render/huira/actions/workflows/linux-ci-cd.yml/badge.svg?branch=main)](https://github.com/huira-render/huira/actions/workflows/linux-ci-cd.yml?query=branch%3Amain)
[![Windows CI/CD](https://github.com/huira-render/huira/actions/workflows/windows-ci-cd.yml/badge.svg?branch=main)](https://github.com/huira-render/huira/actions/workflows/windows-ci-cd.yml?query=branch%3Amain)
[![macOS CI/CD](https://github.com/huira-render/huira/actions/workflows/macos-ci-cd.yml/badge.svg?branch=main)](https://github.com/huira-render/huira/actions/workflows/macos-ci-cd.yml?query=branch%3Amain)
[![Coverage](https://codecov.io/gh/huira-render/huira/branch/main/graph/badge.svg)](https://app.codecov.io/gh/huira-render/huira/tree/main)

[![Conda Build](https://github.com/huira-render/huira/actions/workflows/conda-build.yml/badge.svg?branch=main)](https://github.com/huira-render/huira/actions/workflows/conda-build.yml?query=branch%3Amain)
[![Python](https://github.com/huira-render/huira/actions/workflows/python.yml/badge.svg?branch=main)](https://github.com/huira-render/huira/actions/workflows/python.yml?query=branch%3Amain)

***
# Features
Initial work on Huira has been on the basic architecture as well as distribution/cross-platform compatability.  As much of that work is now completed, new features are expected to be released in relatively short order.

If there are features you wish to see, that you don't see listed here, please feel free to submit a [Feature Request](https://github.com/huira-render/huira/issues/new?template=feature_request.md)

## Currently Stable Features (as of v0.8.1)
- Radiometrically accurate unresolved rendering with calibrated camera distortion models and common camera controls
- SPICE toolkit integration for spacecraft ephemeris and reference frames
- Star field rendering with accurate celestial coordinates
- Python Bindings
- Logging and crash report generation
- API Reference Documentation (NOTE: Some docs may appear incomplete or poorly formatted)

## Features Coming Soon
- 3D mesh and material support
- Motion blur
- Camera Depth-of-Field
- Digital Elevation Maps
- Level-of-detail support
- Solar Radiation Pressure simulation
- LIDAR simulation
- TLE support
- Improved API Reference Documentation and Quick-start guides

## Long Term Plans
- Vulkan based GPU Acceleration
- Desktop application (GUI)