semvec/pypi-packages huggingface dataset:
webgpu 1.1.0
A Python based WebGPU API using pyodide
Requires Python: >=3.8
Keywords: webgpu, compute shaders, graphics, python, pyodide, wgsl

# WebGPU API for Python for local and Pyodide development

This library provides a Python wrapper around the WebGPU API that runs in the browser via Pyodide or connects to a local Python instance. It is intended for interactive visualization and compute in environments such as Jupyter notebooks and plain HTML pages.

The package bundles a small rendering framework (scenes, cameras, labels, colormaps) and utilities to work with WGSL shaders from Python. It is used in higher level frameworks like ngsolve_webgpu for GPU-accelerated finite element visualizations in the browser.

---

## Features

- **Browser-based GPU access** via WebGPU from local Python and Pyodide.
- **Python-first API** for creating renderers, scenes and simple GUI controls.
- **Jupyter integration** through a `webgpu.jupyter` helper module.
- **WGSL tooling**:
  - simple `#import` mechanism for shader code
  - conditional compilation using `#ifdef/#else/#endif`
- **Ready-to-use building blocks**:
  - `Renderer`, `Scene`, `Camera`, `Labels`, `Colormap`, `Clipping`, `Font`
  - utilities for GPU buffers and bindings

---

## Requirements

- Python 3.8 or newer
- A browser with WebGPU support (for example a recent Chrome or Edge)
- For notebook use: JupyterLab or Jupyter Notebook

To check WebGPU support in your browser, you can use common WebGPU capability pages (for example WebGPU report pages linked from the project website).

---

## Installation

### From PyPI

```bash
pip install webgpu
```

### From source

```bash
git clone https://github.com/CERBSim/webgpu
cd webgpu
pip install -e .
```

## Documentation

The full project documentation, including API reference, tutorials and user guide, is published at:

- https://cerbsim.github.io/webgpu/

## Examples

The repository contains several examples:

- examples/first_shader.ipynb – minimal example drawing a triangle with custom WGSL shader code.
- examples/websocket.ipynb – example using the websocket link.
- docs/introduction.ipynb – overview and introductory material.

Additional, more advanced examples in docs/*.ipynb (compute, instanced shapes, selection).

## Browser support

This library relies on the browser’s WebGPU implementation. In practice this means:

- A recent Chromium-based browser (Chrome, Edge) or Firefox is recommended.
- Some platforms may require enabling experimental WebGPU flags.
- Mobile and non-Chromium browsers may have partial or no support.

If WebGPU is not available, initialization will fail. See project documentation and browser-specific WebGPU status pages for details and troubleshooting.

## License

This project is licensed under the terms of the LGPL-2.1-or-later. See the LICENSE file for details.
maximus-client 0.1.2
Python library for working with MAX messenger API through WebSocket connections
Requires Python: >=3.8
Keywords: max, messenger, api, websocket, client

# Maximus Client

Python library for working with MAX messenger API through WebSocket connections.

## Installation

```bash
pip install maximus-client
```

## Quick Start

```python
import asyncio
from maximus import MaxClient

async def main():
    client = MaxClient(session="session.maximus", debug=True)
    
    @client.on("ready")
    async def on_ready():
        print("Authorization successful!")
        print(f"User: {client.user.name if client.user else 'Unknown'}")
        print(f"Chats: {len(client.chats)}")
    
    @client.on("new_message")
    async def on_new_message(message):
        print(f"[{message.chat_title}] {message.sender_name}: {message.text}")
        
        if message.text.lower() == "hello":
            await message.reply("Hello! How are you?")
    
    phone_number = "YOUR_PHONE_NUMBER"
    await client.start(phone=phone_number)
    await client.run_until_disconnected()

if __name__ == "__main__":
    asyncio.run(main())
```

## Features

- Asynchronous WebSocket-based communication
- Event-driven architecture
- Session management
- Message handling and sending
- Contact management
- Chat operations

## Documentation

- [Complete Documentation](https://github.com/Vodeninja/maximus-client/blob/master/docs/README.md)
- [API Reference](https://github.com/Vodeninja/maximus-client/blob/master/docs/api-reference.md)
- [Examples](https://github.com/Vodeninja/maximus-client/blob/master/docs/examples.md)
- [PyPI Package](https://pypi.org/project/maximus-client/)

## Requirements

- Python 3.8+
- websockets>=12.0
- sqlalchemy>=2.0.0

## License

MIT License
advanced-yaml 0.5.0
A suite of tools to advance your usage of yaml.  YASL provides schema definition and verification.  YAQL let's you query your yalm as if it were a database.  YARL provides analysis and reporting.  And YATL allows you to transform yaml from one structure (schema) to another.
Requires Python: >=3.12

# Advanced YAML

Advanced YAML is a suite of tools that streamline data management of structured data in YAML.

These tools include:
 - YASL - YAML Advanced Schema Language
 - YAQL - YAML Advanced Query Language
 - YARL - YAML Advanced Reporting Language

## Vision & Inspiration

Teams and organizations often manage a diverse, complex set data as they collaborate to achieve their objectives.
This data is commonly persisted in a database.
When the data evolves rapidly (i.e. has high transaction rates), this makes sense.
But often our data may be considered 'semi-static', changing infrequently over time.
Visibility into and integrity of changes to this 'semi-static' data as it evolves can be valuable.
What changed?
Who made the change?
Was the change valid?
Establishing this visibility within databases can become complex and costly.

The vision of advanced-yaml is to allow for 'semi-static' data to be persisted in structured human and machine readable files that can be validated for correctness and referential integrity.
We see clear examples of this concept in infrastructure-as-code tools.
advanced-yaml seeks to explore that paradigm for data in a highly flexible manner, suitable for a variety of domains.
This would allow teams to leverage robust version control systems to rigorously manage change.
Teams can create automations to ensure data validity and integrity.
And this could be done without compromising the common user experiences teams are accustomed to when working with their data.

advanced-yaml attempts to enable this by providing robust solutions for defining and validating data structure and integrity through schemas in the `yasl` tool.
The `yaql` tool builds upon this by providing a query language and database fascade for your data.
And the `yarl` tool further enhances this by enabling analysis and reporting for your data.
Over time additional tools will be added to support other use cases and streamline development of products that utilize this 'semi-static' data management concept.


## Developer Setup

Advanced YAML is written in python and managed with the `UV` tool.

- Install UV:
    ```bash
    curl -LsSf https://astral.sh/uv/install.sh | sh
    ```
- Clone the advanced-yaml repo
    ```bash
    git clone git@github.com:jondavid-black/advanced-yaml.git
    cd advanced-yaml
    ```

- Setup the virtual environment and install dependencies
    ```bash
    uv venv
    source .venv/bin/activate
    uv pip install -e .[dev]
    ```

- Run tests w/ coverage
    ```bash
    uv run pytest
    ```

- Run linter
    ```bash
    uv run ruff check .
    ```

- Run formatter
    ```bash
    uv run ruff format .
    ```

- Run type checks
    ```bash
    uv run pyright
    ```
snowball-mcp 0.2.8
基于雪球API的MCP服务，让您通过Claude或其他AI助手轻松获取股票数据
Requires Python: >=3.11
Keywords: ai, claude, mcp, model-context-protocol, stock, xueqiu

# Snowball MCP (雪球 MCP)

基于雪球API的MCP服务，让您通过Claude或其他AI助手轻松获取股票数据。

## 项目简介

本项目基于[pysnowball](https://github.com/uname-yang/pysnowball)封装了雪球API，并通过MCP协议提供服务，使您能够在Claude等AI助手中直接查询股票数据。

## 安装方法

### 方式一：在线安装（推荐）✨

无需克隆仓库，直接使用 `uvx` 运行：

```bash
# 设置环境变量
export XUEQIU_TOKEN="xq_a_token=xxxxx;u=xxxx"

# 直接运行
uvx snowball-mcp
```

### 方式二：Claude Desktop / Cursor / MCPHub 配置

在 MCP 客户端配置文件中添加：

```json
{
  "mcpServers": {
    "snowball-mcp": {
      "command": "uvx",
      "args": ["snowball-mcp"],
      "env": {
        "XUEQIU_TOKEN": "xq_a_token=xxxxx;u=xxxx"
      }
    }
  }
}
```

### 方式三：本地开发安装

```bash
# 克隆仓库
git clone https://github.com/xunzhimeng/xueqiu_mcp.git
cd xueqiu_mcp

# 使用uv安装依赖
uv pip install -e .

# 创建 .env 文件配置 token
echo 'XUEQIU_TOKEN="xq_a_token=xxxxx;u=xxxx"' > .env

# 运行
snowball-mcp
```

## 配置雪球Token

关于如何获取雪球token，请参考[pysnowball文档](https://github.com/uname-yang/pysnowball/blob/master/how_to_get_token.md)。

### 多 Token 配置（可选）

为了分散请求压力、减少限流错误，支持配置多个 Token：

```bash
# 方式一：逗号分隔
XUEQIU_TOKEN="token1,token2,token3"

# 方式二：多个环境变量
XUEQIU_TOKEN_1="token1"
XUEQIU_TOKEN_2="token2"
XUEQIU_TOKEN_3="token3"
```

多 Token 会自动轮换使用，当某个 Token 连续失败时会临时禁用并切换到下一个。

## 功能特性

- 获取股票实时行情
- 获取K线数据（支持日/周/月/季/年线和分钟级数据）
- 查询指数收益
- 获取深港通/沪港通北向数据
- 基金相关数据查询
- 关键词搜索股票代码
- 财务报表数据（利润表、资产负债表、现金流量表等）
- 资金流向数据
- 融资融券数据
- 大宗交易数据
- 机构评级和持仓数据

### 无需登录的功能

以下功能无需配置 Token 即可使用：
- `suggest_stock` - 股票搜索
- `quotec` - 基础行情
- `pankou` - 盘口数据

## 展示图

![image](./images/cursor_mcp.png)

![image](./images/claude_mcp.png)

## 致谢

本项目 fork 自 [liqiongyu/xueqiu_mcp](https://github.com/liqiongyu/xueqiu_mcp)，在此基础上进行了改进和优化。

- [liqiongyu/xueqiu_mcp](https://github.com/liqiongyu/xueqiu_mcp) - 原始项目
- [pysnowball](https://github.com/uname-yang/pysnowball) - 雪球股票数据接口的Python版本
- [fastmcp](https://github.com/jlowin/fastmcp) - MCP服务框架

## 许可证

[MIT License](./LICENSE)