semvec/pypi-packages huggingface dataset:
bdd-trace 0.5.7
BDD项目日志和追踪SDK
Requires Python: >=3.10

# bdd-trace

一个方便 BDD 项目集成 OpenTelemetry 的 Python 库，提供简化的追踪初始化和配置功能。

## 功能特性

- 简化 OpenTelemetry 集成配置
- 支持开发和生产环境配置
- 提供统一的追踪初始化接口
- 支持 OTLP 导出器配置
- 自动处理追踪器设置

## 安装

推荐使用 uv 管理依赖：

```bash
uv add bdd-trace
uv sync

# 或者用 pip
pip install bdd-trace
```

然后安装 OpenTelemetry 的各种 Instrumentation：

```bash
# uv
uv run opentelemetry-bootstrap -a requirements | uv add -r -
uv sync

# pip
opentelemetry-bootstrap -a install
```

## 快速开始

### 基本使用

```python
# 在所有导入之前，先导入 bdd_trace 并调用 init_trace 初始化
from bdd_trace import Profile, init_trace

# 预置了 DEV, TEST 和 PROD 三个环境的配置
init_trace(service_name="my-service", profile=Profile.DEV)

# 支持自定义配置
init_trace(
    service_name="my-service",
    # 自定义 OTLP 端点
    exporter_otlp_endpoint="http://localhost:4317",
    # 其他设置可运行 opentelemetry-instrument --help 查看
    fastapi_exclude_urls="/healthCheck",
)

# 设置 profile=NO_TRACE 可以禁用自动追踪
init_trace(service_name="my-service", profile=Profile.NO_TRACE)
```
android-mobile-mcp 2.4.1
Android Mobile MCP
Keywords: android, mobile, mcp, mobile-mcp, android-mcp

# Android Mobile MCP

## Overview
Android Mobile MCP bridges the Model Context Protocol with Android device automation, enabling AI agents to interact with Android devices through UI manipulation, app management, and screen capture.

## MCP Configuration
```json
{
  "mcpServers": {
    "android-mobile-mcp": {
      "command": "uvx",
      "args": ["android-mobile-mcp"]
    }
  }
}
```

### Prerequisites
1. Connect Android device via USB or network
2. Enable USB debugging on your Android device
3. Install ADB (Android Debug Bridge)

## Tools Reference

### Screen Analysis

**`mobile_dump_ui`** - Extract UI elements as hierarchical JSON
- Parses screen XML to identify focusable elements and text content
- Calculates center coordinates for each interactive element
- Returns structured parent-child element relationships

**`mobile_take_screenshot`** - Capture current screen state
- Returns PNG image data for visual analysis

### Touch Interactions

**`mobile_click`** - Click at specific coordinates
- Validates coordinates against current UI state
- Requires prior `mobile_dump_ui` call for coordinate verification
- Prevents clicking on invalid or non-interactive areas

**`mobile_swipe`** - Perform swipe gestures
- Executes directional swipes between two coordinate points
- Configurable duration for gesture speed control

### Text Input

**`mobile_type`** - Input text into focused fields
- Sends text to currently active input field
- Optional automatic submission with Enter key

### Navigation

**`mobile_key_press`** - Press system buttons
- Supports hardware and virtual keys: BACK, HOME, RECENT, ENTER

### App Management

**`mobile_list_apps`** - List installed applications
- Filters out system apps and non-launchable packages
- Returns only user-accessible applications

**`mobile_launch_app`** - Start applications by package name
- Validates package existence before launch attempt
phox-modbus 1.2.2
Basic drivers to manage PHOXENE flash devices
Requires Python: >=3.7
Keywords: Sx-IP, Sx-LED, SxIP, phoxene, python

# PHOXENE Modbus driver

![Build Status](https://img.shields.io/pypi/v/phox_modbus.svg)
![python](https://img.shields.io/pypi/pyversions/phox_modbus.svg)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
[![Documentation Status](https://readthedocs.org/projects/phox-modbus/badge/?version=main)](https://phox-modbus.readthedocs.io/)

![Phoxene Logo](https://www.phoxene.com/wp-content/uploads/2024/06/logo_phoxene_160x89.png)

This module is a Python Modbus RTU on serial port driver

# Usage
This driver has been developped as Modbus layer for PHOXENE's devices that
implements a serial Modbus communication.

It is mainly used by Phoxene's applications or devices' drivers 

# Documentation
https://phox-modbus.readthedocs.io/en/main/

# Installation
```shell
pip install phox-modbus
```

# Dependencies
* pySerial

# Compatibility
Tested on Windows 10, Windows 11 and Debian 12.2

![Windows Logo](https://img.shields.io/badge/Windows-0078D6?style=for-the-badge&logo=windows&logoColor=white)
![Debian Logo](https://img.shields.io/badge/Debian-A81D33?style=for-the-badge&logo=debian&logoColor=white)

# Change log
https://github.com/Phoxene/phox-modbus/blob/main/CHANGELOG.md

# Licence
MIT License Copyright (c) 2025 PHOXENE

See LICENSE file

# Authors
written by Aurélien PLANTIN <a.plantin@phoxene.com>
autodistill-rfdetr 0.1.0
RF-DETR model for use with Autodistill
Requires Python: >=3.7

<div align="center">
  <p>
    <a align="center" href="" target="_blank">
      <img
        width="850"
        src="https://media.roboflow.com/open-source/autodistill/autodistill-banner.png"
      >
    </a>
  </p>
</div>

# Autodistill RF-DETR Module

This repository contains the code supporting the RF-DETR target model for use with [Autodistill](https://github.com/autodistill/autodistill).

[RF-DETR](https://github.com/roboflow/rf-detr) is a real-time, transformer-based object detection model developed by Roboflow and released under the Apache 2.0 license.

RF-DETR-N outperforms YOLO11-N by 10 mAP points on the Microsoft COCO benchmark while running faster at inference. On RF100-VL, RF-DETR achieves state-of-the-art results, with RF-DETR-M beating YOLO11-M by an average of 5 mAP points across aerial datasets including drone, satellite, and radar.

Read the full [Autodistill documentation](https://autodistill.github.io/autodistill/).

## Installation

To use the RF-DETR target model, you will need to install the following dependency:

```bash
pip3 install autodistill-rfdetr
```

## Quickstart

```python
from autodistill_rfdetr import RFDETR

target_model = RFDETR("base")

# train a model
target_model.train("./labeled_data/data.yaml", epochs=200)

# run inference on the new model
pred = target_model.predict("./labeled_data/train/images/dog-7.jpg", conf=0.01)
```

## License

[Autodistill RF-DETR is licensed under an Apache 2.0 license.](LICENSE) [RF-DETR is licensed under an Apache 2.0 license.](https://github.com/roboflow/rf-detr?tab=Apache-2.0-1-ov-file#readme)

## 🏆 Contributing

We love your input! Please see the core Autodistill [contributing guide](https://github.com/autodistill/autodistill/blob/main/CONTRIBUTING.md) to get started. Thank you 🙏 to all our contributors!
brightcove-async 0.1.8
An asynchronous client for the Brightcove API.
Requires Python: >=3.13

# brightcove_async

`brightcove_async` is an asynchronous Python client library for interacting with the [Brightcove](https://www.brightcove.com/) video platform APIs. This library is designed to provide easy, non-blocking access to Brightcove's RESTful endpoints, making it ideal for use in modern async Python applications.

## Features

- Fully asynchronous API using `asyncio` 
- Simple authentication with OAuth2 client credentials
- Support for core Brightcove API endpoints (Videos, Analytics, etc.)
- Strong typing and data validation powered by Pydantic for reliable development and rich IDE autocompletion

## Installation

```bash
pip install brightcove_async
```

## Quick Start

```python
import asyncio

import brightcove_async
from brightcove_async.schemas.cms_model import (
    CreateVideoRequestBodyFields,
    State,
)

async def main() -> None:
    client = brightcove_async.initialise_brightcove_client()
    async with client as client_instance:
        result = await client_instance.cms.create_video(
            account_id="12345",
            video_data=CreateVideoRequestBodyFields(
                name="test",
                State=State.ACTIVE,
            ),
        )
        print(result.model_dump())


if __name__ == "__main__":
    asyncio.run(main())
```

## Usage

### Authentication

`brightcove_async` handles OAuth2 token management automatically. The easiest way to do this is to set the following environmental variables: 
```bash
export CLIENT_ID="your_client_id"
export CLIENT_SECRET="your_client_secret"
```

## API Coverage

- [x] Videos - In progress 
- [x] Analytics - In progress 
- [x] Syndication - In progress 

## Documentation

- [Brightcove API Documentation](https://apis.support.brightcove.com/)

## Requirements

- Python 3.13+

## Contributing

Pull requests are welcome! Please open an issue to discuss your feature or bugfix before submitting a PR.

## License

MIT License

## Disclaimer

This project is not affiliated with or endorsed by Brightcove Inc.
simply-just-logic-gates 3
Simply Just Logic Gates
Requires Python: >=3.6

Simply Just Logic Gates!

---

**Package name:** simply_just_logic_gates

**Install with:** `pip install simply-just-logic-gates`

**Update with:** `pip install --upgrade simply-just-logic-gates`

**Use with:** `import lg`

---

For a list of logic gates, use `lg.gates()`. For credits, use `lg.credits()`.

---

**Created by:** Error Dev **|** [https://devicals.github.io/](https://devicals.github.io/)
sparrowpy 1.0.1
Sound Propagation with Acoustic Radiosity for Realistic Outdoor Worlds.
Requires Python: >=3.10
Keywords: acoustics, radiosity, sound propagation, auralization

<h1 align="center">
<p align="center">
    <img src="docs/_static/logo.svg" width="300"
             onerror="this.onerror=null;this.src='https://github.com/sparrow-acoustics/sparrowpy/raw/main/docs/_static/logo.svg';">
</p>
</h1><br>

[![PyPI version](https://badge.fury.io/py/sparrowpy.svg)](https://badge.fury.io/py/sparrowpy)
[![Documentation Status](https://readthedocs.org/projects/sparrowpy/badge/?version=latest)](https://sparrowpy.readthedocs.io/en/latest/?badge=latest)
[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/sparrow-acoustics/sparrowpy/main?urlpath=%2Fdoc%2Ftree%2Fexamples%2Ffast_radiosity.ipynb)
![testing](https://github.com/sparrow-acoustics/sparrowpy/actions/workflows/pytest_pr.yml/badge.svg)

Sound Propagation with Acoustic Radiosity for Realistic Outdoor Worlds.

# Getting Started

Follow our [example notebooks](https://sparrowpy.readthedocs.io/en/stable/examples.html) to get a sense of the structure and functions of sparrowpy.

## Installation

Use pip to install sparrowpy

    pip install sparrowpy

(Requires Python >= 3.10)

if numba is installed the code will be precompiled and will run faster. We strongly recommend to use numba to accelerate the simulations significantly

    pip install sparrowpy numba

Geometry import via blender or STL files will be supported in a future release, an install of the Blender API. Note that blender has strong requirements on the python version, see [pypi](https://pypi.org/project/bpy/). You can install it via pip

    pip install bpy

by default these packages are not installed

## Contributing

Check out the [contributing guidelines](https://sparrowpy.readthedocs.io/en/stable/contributing.html) if you want to become part of sparrowpy.
llama-index-readers-paddle-ocr 0.1.0
llama-index readers paddle_ocr integration
Requires Python: <4.0,>=3.9
Keywords: academic papers, ocr, pdf

# Paddle OCR loader

```bash
pip install llama-index-readers-paddle-ocr
```

This loader reads the equations, symbols, and tables included in the PDF.

Users can input the path of the academic PDF document `file` which they want to parse. This OCR understands LaTeX math and tables.

## Usage

Here's an example usage of the PDFPaddleOCR.

```python
from llama_index.readers.paddle_ocr import PDFPaddleOCR

reader = PDFPaddleOCR()

pdf_path = Path("/path/to/pdf")

documents = reader.load_data(pdf_path)
```

## Miscellaneous

An `output` folder will be created with the same name as the pdf and `.mmd` extension.