semvec/pypi-packages huggingface dataset:
china-union-lotto 0.3.9
China Union Lotto MCP Server
Requires Python: >=3.12

# china-union-lotto

## 概述
一个双色球的MCP Server项目

## 参考
https://zhuanlan.zhihu.com/p/1914511215515923435

## 开发
```shell
uv init
uv venv
.venv/Scripts/activate

uv add fastmcp
uv add asyncio
uv add argparse
uv add tzlocal
uv add requests
uv add python-dotenv

python -m china_union_lotto


uv pip install build twine
uv pip install requests beautifulsoup4 python-dotenv
uv pip install loguru

uv add beautifulsoup4
uv add loguru
uv add python-dotenv
uv add requests

# 新上传
uv add "mcp[cli]" httpx

mcp dev -v

python -m build

python -m twine upload ./dist/*

# 更新
# 更新toml文件中的版本号

# Cherry Studio uv cache path
# C:\Users\huwei\AppData\Local\uv

uv cache clean

uvx china-union-lotto

# TODO


```
funlbm 1.2.87
funlbm
Requires Python: >=3.9

# FunLBM

[![Python Version](https://img.shields.io/badge/python-3.9%2B-blue.svg)](https://www.python.org/downloads/)
[![License](https://img.shields.io/badge/license-MIT-green.svg)](LICENSE)
[![Version](https://img.shields.io/badge/version-1.2.78-orange.svg)](https://github.com/farfarfun/funlbm/releases)

FunLBM 是一个基于格子玻尔兹曼方法（Lattice Boltzmann Method, LBM）的三维流体力学仿真库。该项目提供了完整的 LBM 仿真框架，包括流场分析、粒子追踪、数据可视化等功能。

## ✨ 主要特性

- 🔬 **完整的 LBM 仿真框架**：支持三维流体力学计算
- 📊 **数据分析工具**：内置流场分析和统计功能
- 🎨 **可视化支持**：集成 matplotlib 和 VTK 可视化
- 🌐 **Web 界面**：基于 NiceGUI 的交互式 Web 界面
- 🚀 **高性能计算**：支持 PyTorch 加速计算
- 📁 **多格式支持**：支持 HDF5、VTK 等多种数据格式

## 🚀 快速开始

### 安装

使用 pip 安装：

```bash
pip install funlbm
```

或者从源码安装：

```bash
git clone https://github.com/farfarfun/funlbm.git
cd funlbm
pip install -e .
```

### 基本使用

```python
from funlbm.plot.job import plt_job
from nicegui import ui

# 绘制仿真作业结果
plt_job(job_id=13995570, home="/path/to/workbench/")

# 启动 Web 界面
ui.run()
```

### 命令行工具

安装后可以直接使用命令行工具：

```bash
funlbm --help
```

## 📁 项目结构

```
funlbm/
├── analyse/          # 流场分析工具
├── base/            # 基础类和工具
├── config/          # 配置管理
├── export/          # 数据导出功能
├── file/            # 文件处理工具
├── flow/            # 流场计算核心
├── lbm/             # LBM 算法实现
├── particle/        # 粒子追踪
├── plot/            # 可视化工具
├── server/          # Web 服务器
└── util/            # 通用工具
```

## 📖 示例

查看 `example/` 目录获取更多使用示例：

- `00-plot/`: 数据可视化示例
- `config/`: 配置文件示例

## 🔧 依赖项

主要依赖包括：

- **计算核心**: `torch`, `scipy`, `numpy`
- **数据处理**: `pandas`, `h5py`
- **可视化**: `matplotlib`, `funvtk`, `funtecplot`
- **Web界面**: `nicegui`
- **工具库**: `funutil`, `funtable`

完整依赖列表请查看 `pyproject.toml`。

## 🤝 贡献

欢迎贡献代码！请遵循以下步骤：

1. Fork 本仓库
2. 创建特性分支 (`git checkout -b feature/AmazingFeature`)
3. 提交更改 (`git commit -m 'Add some AmazingFeature'`)
4. 推送到分支 (`git push origin feature/AmazingFeature`)
5. 开启 Pull Request

## 📄 许可证

本项目采用 MIT 许可证 - 详见 [LICENSE](LICENSE) 文件。

## 🔗 相关链接

- **GitHub**: [https://github.com/farfarfun/funlbm](https://github.com/farfarfun/funlbm)
- **Releases**: [https://github.com/farfarfun/funlbm/releases](https://github.com/farfarfun/funlbm/releases)
- **PyLBM**: [https://github.com/pylbm/pylbm](https://github.com/pylbm/pylbm) - 相关的 LBM 实现

## 👥 维护者

- **牛哥** - [niuliangtao@qq.com](mailto:niuliangtao@qq.com)
- **farfarfun** - [farfarfun@qq.com](mailto:farfarfun@qq.com)

## 📞 联系我们

如有问题或建议，请通过以下方式联系：

- 提交 [Issue](https://github.com/farfarfun/funlbm/issues)
- 发送邮件至维护者邮箱
- 访问我们的 [组织主页](https://github.com/farfarfun)
slurm-executor 0.2.0
A library for transparent execution of heavy Python jobs on SLURM!
Requires Python: >=3.12


> [!WARNING]  
>This project is under heavy development and it is in very early stage. It may contain a ton of bugs!

![Slurm Executor robot](doc/executor_robot.png)

# A library for transparent execution of heavy Python jobs on SLURM!

Imagine being able to offload **any**\* Python function to a HPC cluster with just few lines of code!

No more logging into a cluster, git pulling, troubleshooting and other headaches.

(\* or at least any function that `cloudpickle` can serialize)

Just define your expected pipeline:

```python
remote = os.getenv("SLURM_REMOTE")
port = os.getenv("SLURM_PORT")
user = os.getenv("SLURM_USERNAME")
cpu_partition = os.getenv("CPU_PARTITION")

pipeline = Pipeline(
    steps=[
        RSyncWorkspace(
            local_root="./",
            remote_root=f"/home/{user}/remote_job/",
            exclude_from="rsync-exclude.txt",
            direction="to_remote",
        ),
        SendCall(),
        SendSbatchScript(
            partition=cpu_partition,
            time="00:05:00",
        ),
        SubmitSbatchScript(output_file_location=f"/home/{user}/remote_job/job.out"),
        WaitForJobCompletion(poll_interval_ms=1000),
        ExecuteCommand(remote_command=f"ls /home/{user}/remote_job/"),
        RSyncWorkspace(
            local_root="./",
            remote_root=f"/home/{user}/remote_job/",
            include_only="rsync-include.txt",
            direction="from_remote",
        ),
    ],
    connection_config=ConnectionConfig(
        host=remote,
        user=user,
        port=int(port),
    ),
)
```

Decorate your function:

```python
from your_code import heavy_data_load, heavy_data_process, heavy_data_save
import os

@pipeline.remote_run
def heavy_step(input_file, output_file):
    data = heavy_data_load()
    processed = heavy_data_process(data)
    heavy_data_save(remote=os.getenv('s3://my_bucket'))

```

And watch how seamlessly you can execute remote jobs without leaving your local IDE!

## Core features:

- Blazingly fast repository synchronization between machines using `rsync` compression
- Safe serialization of functions with `cloudpickle`
- Automatic transferring of logs from remote job right into your local terminal
- Deep integration with familiar SLURM CLI: `sbatch`, `sinfo`, `sacct`
- Full customization of sbatch script, job resources, setting up environment and remote execution
- Easy pipeline composition to satisfy various use cases and HPC cluster setups.
- And more to come...!