semvec/pypi-packages huggingface dataset:
xsocks5 0.1.2
A SOCKS5 proxy
License: MIT
Requires Python: >=3.11

# xsocks5

一个基于Python asyncio的轻量级SOCKS5代理服务器，支持用户名密码认证。

## 使用方法

### 安装

```bash
# 使用pip安装
pip install xsocks5
```

### 启动代理服务器

```bash
# 使用默认参数启动
xsocks5

# 自定义参数启动
xsocks5 --host 0.0.0.0 --port 1080 --username myuser --password mypass
```

#### 参数说明

- `--host`: 服务器监听地址，默认为 `0.0.0.0`
- `--port`: 服务器监听端口，默认为 `1080`
- `--username`: 认证用户名，默认为 `admin`
- `--password`: 认证密码，默认为 `admin`

### 客户端配置

在需要使用代理的应用程序中配置以下信息：

- 代理类型：SOCKS5
- 服务器地址：代理服务器所在的主机IP
- 端口：与启动时指定的端口一致（默认1080）
- 用户名：与启动时指定的用户名一致（默认admin）
- 密码：与启动时指定的密码一致（默认admin）

## 开发

### 项目结构

```
socks5/
├── src/
│   └── xsocks5/
│       ├── __init__.py
│       └── main.py
├── tests/
│   ├── __init__.py
│   └── test_main.py
├── pyproject.toml
└── README.md
```

### 开发环境设置

```bash
# 安装uv
## On macOS and Linux.
curl -LsSf https://astral.sh/uv/install.sh | sh
## On Windows.
powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"

# 克隆仓库
git clone https://github.com/Simple-Creator/socks5-python.git
cd socks5-python

# 使用uv创建虚拟环境并安装依赖
uv sync
```

## 功能特点

- 基于Python asyncio实现的高性能异步代理服务器
- 支持SOCKS5协议
- 支持用户名密码认证
- 支持IPv4、IPv6和域名目标地址
- 简单易用的命令行接口

## 许可证

MIT License
django-ansible-observability 0.1.5
Django application for Ansible observability with OpenTelemetry integration
License: MIT
Requires Python: >=3.8

# Django Ansible Observability

```
pip3 -m venv .venv
pip install -e .
python manage.py runserver
```

`http://localhost:8000`

This should output something like the text below. The trace is showing on your console because of the Django setting `ANSIBLE_OBSERVE_OUTPUT_SPAN_TO_CONSOLE = True`.

```
[08/Aug/2025 15:31:22] "GET / HTTP/1.1" 200 43
Trace beffbf37476957dea79abb858553191a
└── [20:31:22.510739] GET index, span c29749adb0de6a7e
    ├── Kind : SERVER
    ├── Attributes :
    │   ├── http.method : GET
    │   ├── http.server_name : localhost.localdomain
    │   ├── http.scheme : http
    │   ├── net.host.name : localhost:8000
    │   ├── http.host : localhost:8000
    │   ├── net.host.port : 8000
    │   ├── http.url : http://localhost:8000/
    │   ├── net.peer.ip : 127.0.0.1
    │   ├── http.user_agent : Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/138.0.0.0 Safari/537.36
    │   ├── http.flavor : 1.1
    │   └── http.status_code : 200
    └── Resources :
        └── service.name : aap-generic
Transient error StatusCode.UNAVAILABLE encountered while exporting traces to localhost:4317, retrying in 1.15s.
Transient error StatusCode.UNAVAILABLE encountered while exporting traces to localhost:4317, retrying in 2.02s.
Transient error StatusCode.UNAVAILABLE encountered while exporting traces to localhost:4317, retrying in 4.76s.
Failed to export traces to localhost:4317, error code: StatusCode.UNAVAILABLE
```
medevalkit 0.1.3.2
A toolkit for evaluating machine learning models for healthcare applications.
License: MIT
Requires Python: >=3.8

# MedEvalKit

**MedEvalKit** is a modular and extensible Python toolkit for evaluating machine learning models, especially in healthcare applications. It provides unified APIs for computing metrics, calibration curves, bootstrap confidence intervals, and plotting diagnostic curves.

## Features

- Binary and multiclass classification support
- Threshold optimization
- Calibration error estimation
- Bootstrap confidence intervals
- ROC, PR, and calibration plotting
- Simulate results at different incidence rates

## Installation

```bash
pip install medevalkit
```

## Usage Example

```python
from medevalkit import Evaluate

clf.fit(X_train, y_train)
y_prob = clf.predict_proba(X_test)

evaluator = Evaluate(y_true=y_test, y_prob=y_prob, classification=True, threshold=threshold)
report = evaluator.generate_report(bootstrap=True)
print(report["text_report"])
```

## GitHub Repo

Visit https://github.com/wesleyyeung/medevalkit for more examples.

## License

This project is licensed under the MIT License.