semvec/pypi-packages huggingface dataset:
Chinese2025 1.2.0.post2
一个关于中文的集合库，支持广韵查询，方言查询，常见拟音搜索功能，永明体检测，古体诗注韵，古诗注声调、韵。
Requires Python: <4,>=3.6

## 介绍

一个关于汉语的数据库，未来还会添加更多功能，目前已有广韵查询，方言查询，常见拟音搜索功能，永明体检测，古体诗注韵，古诗注声调、韵等功能。

## 使用教程

[Chinese2025库文档](https://yesandnoandperhaps.cn/posts/59f94f23.html)
scotty-beam-tracing 3.0.3
Beam tracing for tokamak DBS diagnostics
Requires Python: >=3.10

Scotty
======

Beam tracing for tokamak DBS diagnostics


Install
-------

Scotty can be installed using `pip`:

```
pip install scotty-beam-tracing
```

Note that the package to install is called `scotty-beam-tracing`, but
the python module is just `scotty`.

To install the development version of `scotty` straight from Github:

```
pip install git+https://github.com/beam-tracing/Scotty
```

or from a local clone:

```
pip install .
```

when developing, use:

```
pip install -e .
```

Documentation
-------
https://scotty.readthedocs.io/

Background
-------
Description of the Scotty [[1]](#1), derivation of beam tracing given in Appendix A.

## References
<a id="1">[1]</a> 
Hall-Chen, V. H., Parra, F. I., & Hillesheim, J. C. (2022). 
[Beam model of Doppler backscattering](https://iopscience.iop.org/article/10.1088/1361-6587/ac57a1). 
*Plasma Physics and Controlled Fusion*, **64**(9), 095002.
mcp-calpower-server 0.4.0
A Model Context Protocol server for power calculating
Requires Python: >=3.10

# Calculator MCP Server

A Model Context Protocol server for power calculating. This server enables LLMs to use calculator for precise numerical power calculations.

### Available Tools

- `calculate` - Calculates/evaluates the given expression.
  - `expression` (string, required): Expression to be calculated

## Installation

### Using uv (recommended)

When using [`uv`](https://docs.astral.sh/uv/) no specific installation is needed. We will
use [`uvx`](https://docs.astral.sh/uv/guides/tools/) to directly run *mcp-calpower-server*.

```bash
curl -LsSf https://astral.sh/uv/install.sh | sh
```

### Using PIP

Alternatively you can install `mcp-calpower-server` via pip:

```bash
pip install mcp-calpower-server
```

After installation, you can run it as a script using:

```bash
python -m mcp_calpower_server
```

## Configuration

### Using uv (recommended)

Add this to your MCP client settings:

```json
{
    "mcpServers": {
        "calculator": {
            "command": "uvx",
            "args": ["mcp-calpower-server"]
        }
    }
}
```

### Using PIP

Alternatively add this to your MCP client settings:

```json
"mcpServers": {
  "calculator": {
    "command": "python",
    "args": ["-m", "mcp_calpower_server"]
  }
}
```

## License

mcp-calpower-server is licensed under the MIT License. This means you are free to use, modify, and distribute the software, subject to the terms and conditions of the MIT License. For more details, please see the LICENSE file in the project repository.
office-tools-mcp-server 0.1.0
Office Tool MCP Server, different types of file could be generated, including PPTs, documents, spreadsheets, webpages and broadcasts.
Requires Python: >=3.11

# Skywork-Super-Agents

A lightweight MCP server offering office tools to generate various file types, including PPTs, documents, spreadsheets, webpages and broadcasts.

## Setup

1. Install dependencies using `uv`:
```bash
uv venv
uv sync
```

2. Create a `.env` file with your SkyWork API key:
```
SKYWORK_API_KEY=your_api_key_here
```

"You can request an API key by reaching out to peter@skywork.ai."

## Running the Server

```json
{
    "mcpServers": {
        "office": {
            "command": "uvx",
            "args": ["--from", "git+https://github.com/Skywork-ai/Skywork-Super-Agents.git", "office-tool"],
            "env": {
                "SKYWORK_API_KEY": "your_api_key_here"
            }
        }
    }
}
```

## API Usage Response
Response:
```json
{
    "url": "output_oss_url"
}
```
english-improver 0.2.0
An library to improve your english proficiency skills.
Requires Python: >=3.13.0

# I created this project to address my declining English skills. First, this project scrapes through Project Gutenberg's books to get a-lot of text. Then, it uses a Markov Chain to generate random sentences, which will then be saved to a PDF file. Here's how to use my package:

```python
from gutenberg_scraper import GutenbergScraper
from markov_chain_generator import MarkovChainGenerator
from pdf_generator import PDFGenerator
from asyncio import run

async def main() -> None:
    scraper: GutenbergScraper = GutenbergScraper("data.txt")
    await scraper.save_book_texts(amount=200, overwrite_file=True)

    markov_chain_generator: MarkovChainGenerator = MarkovChainGenerator("data.txt", "generated.txt", 1000)
    markov_chain_generator.generate_text(overwrite_file=True)

    pdf_generator: PDFGenerator = PDFGenerator("generated.txt", "output.pdf")
    pdf_generator.generate_pdf()

if __name__ == "__main__":
    run(main())
```
points-in-rbbox 1.1.8
point-in-rbbox CUDA ops

[<img height="23" src="https://github.com/lh9171338/Outline/blob/master/icon.jpg"/>](https://github.com/lh9171338/Outline) point-in-rbbox
===

# 功能

实现`point-in-rbbox`CUDA算子，用于计算3D旋转框内的点云，相比PyTorch版本显存有明显优化

# 依赖

- CUDA：11.8
- PyTorch：2.0.0

# 安装

- pip安装
```shell
export TORCH_CUDA_ARCH_LIST="6.0 6.1 7.0 7.5 8.0 8.6 8.9 9.0+PTX" # 支持不同GPU架构【可选】
export ENABLE_BF16=1 # 支持BF16【可选，部分GPU可能不支持】
python3 -m pip install points-in-rbbox
```

- 源码安装

```shell
git clone https://github.com/lh9171338/points-in-rbbox.git
cd points-in-rbbox

export TORCH_CUDA_ARCH_LIST="6.0 6.1 7.0 7.5 8.0 8.6 8.9 9.0+PTX" # 支持不同GPU架构【可选】
export ENABLE_BF16=1 # 支持BF16【可选，部分GPU可能不支持】
python3 setup.py install
```

# 使用

```python
from points_in_rbbox import points_in_rbbox_cuda

mask = points_in_rbbox_cuda(points, boxes)
```

# 显存&耗时

| 方法 | dtype | 显存(GB) | 耗时(s) |
| :---: | :---: | :---: | :---: |
| points_in_rbbox_torch | FP32 | 10.0 | 12 |
| points_in_rbbox_torch | FP16 | 5.3 | 10 |
| points_in_rbbox_torch | BF16 | 5.3 | 10 |
| points_in_rbbox_cuda | FP32 | 1.2 | 9 |
| points_in_rbbox_cuda | FP16 | 1.0 | 9 |
| points_in_rbbox_cuda | BF16 | 1.0 | 9 |
quickcodec 0.0.1
Pythonic bindings for FFmpeg's libraries.
Requires Python: >=3.9

QuickCodec
====

QuickCodec is designed for fast loading videos for VLLMs, especially long videos with relatively high frame sampling rates.

---

[![GitHub Test Status][github-tests-badge]][github-tests] [![Documentation][docs-badge]][docs] [![Python Package Index][pypi-badge]][pypi]


Installation
------------

The easiest way to use QuickCodec is via the binary wheels are provided on [PyPI][pypi] for Linux, MacOS and Windows linked against the latest stable version of ffmpeg. You can install these wheels by running:

```bash
pip install quickcodec
```


Installing From Source
----------------------

For the more adventurous fold  Here's how to build PyAV from source. You must use [MSYS2](https://www.msys2.org/) when using Windows.

```bash
git clone https://github.com/PyAV-Org/PyAV.git
cd PyAV
source scripts/activate.sh

# Build ffmpeg from source. You can skip this step
# if ffmpeg is already installed.
./scripts/build-deps

# Build PyAV
./scripts/build

# if you want to install it globally instead of in the env, deactivate
deactivate

pip install .
```

---


Notice
----------------------
QuickVideo is build on top of [FFmpeg][ffmpeg] and [PyAv][pyav] libraries.  
Huge thanks to the contributors and maintainers of those libraries, they have done a huge amount of work create a clean interface that handles a lot of the messy nature of multimedia processing.
This project is **not endorsed** any maintainer of PyAv or FFmpeg, if you have any problems with QuickCodec please open as issue on **this repository**.
We inherit all the features of PyAv (including processing for other modalities like audio) which you can read about [here][docs], have fun!


[conda-badge]: https://img.shields.io/conda/vn/conda-forge/av.svg?colorB=CCB39A
[conda]: https://anaconda.org/conda-forge/av
[docs-badge]: https://img.shields.io/badge/docs-on%20pyav.basswood--io.com-blue.svg
[docs]: https://pyav.basswood-io.com
[pypi-badge]: https://img.shields.io/pypi/v/av.svg?colorB=CCB39A
[pypi]: https://pypi.org/project/av
[discuss]: https://github.com/PyAV-Org/PyAV/discussions

[github-tests-badge]: https://github.com/PyAV-Org/PyAV/workflows/tests/badge.svg
[github-tests]: https://github.com/PyAV-Org/PyAV/actions?workflow=tests
[github]: https://github.com/TigerLab/PyAV

[ffmpeg]: https://ffmpeg.org/
[conda-forge]: https://conda-forge.github.io/
[conda-install]: https://docs.conda.io/projects/conda/en/latest/user-guide/install/index.html
[pyav]: https://github.com/PyAV-Org/PyAV
amapote-moni-mcp-server-xyt 0.1.9
使用API查询中国境内天气。
Requires Python: >=3.13

# 中国城市天气查询 MCP 服务

基于高德地图 API 的中国城市天气查询 Model Context Protocol (MCP) 服务。该服务允许用户通过简单的请求获取中国各城市的实时天气状况和未来几天的天气预报。

## 功能特点

- 使用高德地图天气 API 获取精确的中国城市天气数据
- 支持查询实时天气状况
- 支持查询未来 3-4 天的天气预报
- 支持智能城市名称识别，包括城市、区县级别的查询
- 支持城市名称搜索功能
- 实现为标准 MCP 服务，易于集成到 AI 应用中

## 安装说明

### 前提条件

- 高德地图开发者 API 密钥（需通过环境变量配置）

### 安装步骤

1. 克隆或下载本仓库
2. 进入项目目录
3. 使用 uv 安装依赖:

```bash
uv venv
source .venv/bin/activate
uv add "mcp[cli]" httpx
```

## 使用方法

### 启动 MCP 服务

如果你需要自行创建客户端测试该 MCP 服务，请手动启动。
在终端中执行以下命令启动 MCP 服务:

```bash
uv run weather.py
```

### 使用集成的 MCP 服务

#### 在 Claude Desktop 中使用

如果想在 Claude Desktop 中使用，请添加服务器配置：
请在文本编辑器中打开 Claude for Desktop App 配置，路径参考如下 `~/Library/Application Support/Claude/claude_desktop_config.json`
如果文件不存在，请确保创建该文件。

```python
{
    "mcpServers": {
        "amap-weather-mcp-server": {
            "command": "uv",
            "args": [
                "--directory",
                "/ABSOLUTE/PATH/TO/PARENT/FOLDER/weather",
                "run",
                "weather.py"
            ],
            "env": {
                "AMAP_API_KEY": "YOUR_API_KEY_HERE"
            }
        }
    }
}
```

#### 在 Cherry Studio 中使用

如下图所示配置即可：
![in-cherrystudio](https://r2.nyaashino.com/docs/imgs/amap-weather-mcp-server/use-in-cherrystudio.png)

### 调用服务

MCP 服务提供了三个主要工具函数用于天气查询:

1. `get_current_weather`: 获取城市当前天气状况
2. `get_weather_forecast`: 获取未来几天的天气预报
3. `search_city`: 搜索城市名称

使用 MCP 客户端调用示例:

```python
from mcp.client import Client

# 连接到服务
client = Client("amap-weather-mcp-server")

# 查询实时天气
current_weather = await client.get_current_weather("北京市")
print(current_weather)

# 查询天气预报
forecast = await client.get_weather_forecast("上海市")
print(forecast)

# 搜索城市名称
cities = await client.search_city("广州")
print(cities)
```

## API 参考

### get_current_weather

获取指定城市的实时天气数据。

**参数:**

- `city`: 中国城市名称，需要包含省/市/县等行政区划标识，如"北京市"、"上海市"、"海珠区"等

**返回:**
格式化的城市实时天气信息字符串，包括:

- 城市名称
- 天气状况
- 温度
- 风向
- 风力
- 湿度
- 发布时间

### get_weather_forecast

获取指定城市的天气预报（未来3-4天）。

**参数:**

- `city`: 中国城市名称，需要包含省/市/县等行政区划标识，如"北京市"、"上海市"、"浙江省杭州市"等

**返回:**
格式化的城市天气预报信息字符串，包括:

- 城市名称
- 未来几天的日期
- 白天和夜间天气状况
- 白天和夜间温度
- 白天和夜间风向、风力

### search_city

根据关键词搜索匹配的城市名称。

**参数:**

- `keyword`: 城市名称关键词，建议包含省/市/县等行政区划标识，如"广州市"、"海珠区"等

**返回:**
包含匹配城市名称列表的字符串。

## 数据来源

本服务使用高德地图开放平台提供的天气数据 API：

- 实时天气数据 API: <https://restapi.amap.com/v3/weather/weatherInfo>
- 城市编码数据: AMap_adcode_citycode.csv

## 注意事项

- 该服务仅支持中国大陆地区的城市天气查询
- 高德地图 API 可能有请求频率限制
- 城市名称识别支持直接输入城市名称或区县名称（建议带上行政区划标识如"市"、"县"、"区"等）
- 环境变量必须正确配置，否则API请求将失败

## 许可证

[MIT 许可证](LICENSE)