semvec/pypi-packages huggingface dataset:
tsuraika 0.1.3
A simple and powerful reverse proxy tool
Requires Python: >=3.7

# Tsuraika

Tsuraika 是一个简单但功能强大的反向代理工具，可以帮助你将内部服务安全地暴露到公网。基于 Python 实现，支持服务器-客户端模式运行，适用于开发测试、内网穿透等场景。

## 特性

- 🚀 简单易用的命令行界面
- 🔒 支持服务器-客户端模式
- 🔄 自动重连机制
- 📊 详细的日志记录
- 🛡 稳定的连接管理
- ⚡ 高效的数据转发

## 安装

### 从 PyPI 安装（推荐）

```bash
pip install tsuraika
```

### 从源码安装

```bash
git clone https://github.com/cocoteirina/tsuraika.git
cd tsuraika
pip install .
```

## 快速开始

### 启动服务器

```bash
tsuraika server --port 7000
```

### 启动客户端

```bash
tsuraika client \
    --local-addr localhost \
    --local-port 8080 \
    --remote-addr example.com \
    --remote-port 80 \
    --server-port 7000
```

## 命令行参数

### 服务器模式

```
tsuraika server [OPTIONS]

Options:
  -p, --port INTEGER  服务器控制端口 [default: 7000]
  --help             显示帮助信息
```

### 客户端模式

```
tsuraika client [OPTIONS]

Options:
  -l, --local-addr TEXT   本地服务地址 [required]
  -p, --local-port INTEGER  本地服务端口 [required]
  -r, --remote-addr TEXT    远程服务器地址 [required]
  -P, --remote-port INTEGER 远程暴露端口 [required]
  -s, --server-port INTEGER 服务器控制端口 [default: 7000]
  --help                   显示帮助信息
```

## 使用场景

1. **开发测试**
   - 快速将本地开发环境暴露给测试人员
   - 方便地展示开发成果给客户

2. **内网穿透**
   - 安全地将内网服务暴露到公网
   - 远程访问内网资源

3. **临时服务共享**
   - 分享本地运行的网站或服务
   - 协作开发和调试

## 项目结构

```
tsuraika/
├── tsuraika/
│   ├── __init__.py
│   ├── core.py        # 核心功能实现
│   └── cli.py         # 命令行接口
├── setup.py
├── README.md
└── requirements.txt
```

## 技术细节

- 使用 Python 异步 Socket 编程
- 采用线程池管理连接
- 实现指数退避重连策略
- 支持优雅关闭和资源清理

## 常见问题

### 1. 连接被重置

可能的原因：
- 防火墙限制
- 网络不稳定
- 服务器负载过高

解决方案：
- 检查防火墙设置
- 确保网络连接稳定
- 适当增加重试次数和间隔

### 2. 端口已被占用

解决方案：
- 使用 `netstat` 检查端口占用情况
- 选择其他可用端口
- 关闭占用端口的程序

## 开发计划

- [ ] 添加 TLS 加密支持
- [ ] 实现配置文件支持
- [ ] 添加 Web 管理界面
- [ ] 支持多路复用
- [ ] 添加流量统计功能

## 贡献指南

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

## 更新日志

### v0.1.0 (2024-03-09)
- 初始版本发布
- 实现基本的反向代理功能
- 添加命令行界面
- 支持服务器-客户端模式
pyviewmaster 0.1.2
viewmastR uses machine learning implemented in Rust to perform automated cell type classification for single-cell genomic data.  Currently viewmastR is authored to work with scRNAseq data, but more features are coming.
Requires Python: >=3.9

# pyviewmaster

version 0.1.2

<p align="center"><img src="blob/viewmaster.png" alt="" width="500"></a></p>
<hr>

pyviewmaster is a method for performing unsupervised classification of single cells across datasets written for use in the python environment using Rust machine learning

**Visit [HowTo](https://github.com/furlan-lab/pyviewmaster/blob/main/notebooks/01_load_data.ipynb) for a tutorials**

## Acknowledgements

Written by Scott Furlan.

<p align="center"><img src="blob/furlan_lab_logo.png" alt="" width="500"></a></p>
<hr>
condo 1.0.0
Confounded domain adaptation
Keywords: domain adaptation, tabular, confounding, batch correction

# condo-adapter

[![PyPI version](https://badge.fury.io/py/condo.svg)](https://badge.fury.io/py/condo.svg)
[![Downloads](https://pepy.tech/badge/condo)](https://pepy.tech/project/condo)
[![CC BY-NC-SA 4.0][cc-by-nc-sa-shield]][cc-by-nc-sa]

ConDo Adapter performs Confounded Domain Adaptation, which corrects for
batch effects while conditioning on confounding variables.
We hope it sparks joy as you clean up your data!

### Using and citing this toolbox

If you use this toolbox in your research and find it useful, please cite ConDo
using the following reference to our [arXiv preprint](https://arxiv.org/abs/2203.12720):

In Bibtex format:

```bibtex
@misc{https://doi.org/10.48550/arxiv.2203.12720,
  doi = {10.48550/ARXIV.2203.12720},
  url = {https://arxiv.org/abs/2203.12720},
  author = {McCarter, Calvin},
  title = {Towards Backwards-Compatible Data with Confounded Domain Adaptation},
  publisher = {arXiv},
  year = {2022},
}
```

## Installation

### Installation from pip

You can install the toolbox through PyPI with:

```console
pip install condo
```

Note: If you have issues with importing `torchmin`, you may need to install from source, as shown below. Or you can try re-installing [pytorch-minimize](https://github.com/rfeinman/pytorch-minimize) from source. 

### Installation from source

After cloning this repo, install the dependencies on the command-line via:

```console
pip install -r requirements.txt
```

In this directory, run

```console
pip install -e .
```

## Usage

Import ConDo and create the adapter:
```python
from condo import ConDoAdapterKLD
condoer = ConDoAdapterKLD()
```

Try using it:
```python
import numpy as np

X_T = np.sort(np.random.uniform(0, 8, size=(100, 1)))
X_S = np.sort(np.random.uniform(4, 8, size=(100, 1)))
Y_T = np.random.normal(4 * X_T + 1, 1 * X_T + 1)
Y_Strue = np.random.normal(4 * X_S + 1, 1 * X_S + 1)
Y_S = 5 * Y_Strue + 2
condoer.fit(Y_S, Y_T, X_S, X_T)
Y_S2T = condoer.transform(Y_S)
print(f"before ConDo: {np.mean((Y_S - Y_Strue) ** 2):.3f}")
print(f"after ConDo:  {np.mean((Y_S2T - Y_Strue) ** 2):.3f}")
```

More thorough examples are provided in the examples directory.

## Development

### Testing
In this directory run
```console
pytest
```

## License Information

This work is licensed under a
[Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License][cc-by-nc-sa].

[![CC BY-NC-SA 4.0][cc-by-nc-sa-image]][cc-by-nc-sa]

[cc-by-nc-sa]: http://creativecommons.org/licenses/by-nc-sa/4.0/
[cc-by-nc-sa-image]: https://licensebuttons.net/l/by-nc-sa/4.0/88x31.png
[cc-by-nc-sa-shield]: https://img.shields.io/badge/License-CC%20BY--NC--SA%204.0-lightgrey.svg