semvec/pypi-packages huggingface dataset:
kxy-framework 1.4.13
封装了ORM框架
License: MIT

## 使用方法：
pip install kxy-framework
```python
from kxy.framework.context import *


```


# 日志
```python
from kxy.framework.slogger import create_logger

logger,handler = create_logger(logging.DEBUG,'appName','production',filename='log/app',file_type='log',backupCount=5,maxBytes=10485760,mutiple_process=False)

# mutiple_process 是否多进程，多进程需要生成不同的文件

logger.info('hello world',extra={"logCategory":'http'})
```
### 日志进阶用法，设置traceId：
```python
from kxy.framework.slogger import user_id,user_info,new_trace
req_trace_id = request.headers.get("X-Trace-ID") or str(uuid4())
new_trace(req_trace_id)
user_id.set('login user id')
# user_info用于存储用户自定义的信息，方便在其他地方取用
user_info.set({'name':'login user name','email':'login user email'})

```
### 日志自定义打印格式：
```python

class JsonFormatter(logging.Formatter):
    def format(self, record):
        # 构建日志记录的字典
        log_record = {
            "appName":appName,
            "serverAddr":os.environ.get('IP',localIp),
            "cluster": env,
            "levelname": record.levelname,
            "filename": record.filename,
            "lineno": record.lineno,
            "traceId": record.trace_id,
            "sessionId": record.session_id,
            "userId":record.userid,
            "seqId": record.seq,
            "message": record.getMessage(),
            "CreateTime": self.formatTime(record, self.datefmt),
            "createdOn": int(time.time() * 1000)  # 添加 Unix 时间戳
        }
        # 将字典转换为 JSON 字符串
        return json.dumps(log_record, ensure_ascii=False)

from simple_util.slogger import create_logger

logger,handler = create_logger(logging.DEBUG,'appName','production',filname='log/app',file_type='log',backupCount=5,maxBytes=10485760)
handler.setFormatter(JsonFormatter())
logger.info('hello world')
```
herzfeld 0.1.0
High-fidelity Epigraphic Rendering for Zonated Feature Extraction and Labelled Datasets
License: MIT
Requires Python: >=3.10

# HERZFELD
**High-fidelity Epigraphic Rendering for Zonated Feature Extraction and Labelled Datasets**

HERZFELD is a specialised pipeline for generating synthetic datasets for Optical Character Recognition (OCR) of ancient inscriptions, specifically focusing on Middle Persian and Inscriptional Pahlavi.

## Why HERZFELD?
Traditional 2D synthetic data (like flat PNG/JPG images) fails to capture the complexity of epigraphy. HERZFELD leverages **Blender** and **Geometry Nodes** to produce physically accurate 3D renders of stone surfaces.

### Key Features:
* **OpenEXR Output:** Provides multi-channel "Ground Truth" data.
* **Depth Maps:** Allows AI models to distinguish between intentional carvings and natural stone erosion.
* **Surface Normals:** Facilitates training under variable raking-light conditions, mimicking field documentation.
* **Procedural Rock Generation:** Creates infinitely varied geological backgrounds for robust ML training.
new-year-2026 1.1.27
TCP/TLS/HTTP/SSH learning scripts and demos.
License: MIT
Requires Python: >=3.9

# 2026 New Year 练习项目

[![Documentation Status](https://readthedocs.org/projects/2026-new-year/badge/?version=stable)](https://2026-new-year.readthedocs.io/en/stable/)
[![CI Release](https://github.com/hunterkeith2017/2026_new_year/actions/workflows/ci-release.yml/badge.svg)](https://github.com/hunterkeith2017/2026_new_year/actions/workflows/ci-release.yml)
[![Docs](https://github.com/hunterkeith2017/2026_new_year/actions/workflows/docs.yml/badge.svg)](https://github.com/hunterkeith2017/2026_new_year/actions/workflows/docs.yml)

本仓库包含一组用于学习 TCP/TLS 基础通信、HTTP 请求与证书配置的示例脚本。所有示例均以 Python 编写，适合用于本地或内网环境中学习网络编程与 TLS 加密流程。

文档地址：https://2026-new-year.readthedocs.io/en/stable/
测试一下

## 目录结构

| 文件 | 作用说明 |
| --- | --- |
| `tcp_tls/tcp_server.py` | 经典 TCP 回显服务器示例，监听端口并返回客户端发送内容。 |
| `tcp_tls/tcp_client.py` | TCP 客户端示例，与 `tcp_server.py` 配套。 |
| `tcp_tls/tls_echo_server.py` | 基于 TLS 的回显服务端，使用本仓库的证书与私钥。 |
| `tcp_tls/tls_echo_client.py` | TLS 回显客户端示例，与 `tls_echo_server.py` 配套。 |
| `tcp_tls/mini_tls_server.py` | 简化版 TLS 服务端示例，演示证书加载与加密通信流程。 |
| `tcp_tls/get_or_post.py` | 简单 HTTP 请求示例，可执行 GET/POST 以练习应用层协议。 |
| `tcp_tls/ssh_minimal_client.py` | 最小 SSH 连接示例，仅完成 banner 交换。 |
| `tcp_tls/cert.pem` | 自签名证书（用于本地测试）。 |
| `tcp_tls/key.pem` | 自签名证书私钥（用于本地测试）。 |

## 环境准备

- Python 3.8 及以上版本
- 本地可用的终端或命令行环境

建议新建虚拟环境进行练习（可选）：

```bash
python -m venv .venv
source .venv/bin/activate
```

## 快速开始

### 1. 运行 TCP 回显示例

启动服务端：

```bash
python -m new_year_2026.tcp_tls.tcp_server
```

在另一终端启动客户端：

```bash
python -m new_year_2026.tcp_tls.tcp_client
```

客户端发送的内容会被服务端原样返回，用于验证基础 TCP 通信。

### 2. 运行 TLS 回显示例

启动 TLS 服务端：

```bash
python -m new_year_2026.tcp_tls.tls_echo_server
```

再运行客户端：

```bash
python -m new_year_2026.tcp_tls.tls_echo_client
```

此示例展示如何在 TCP 之上加入 TLS 加密层。若需要替换证书，请同时更新 `tcp_tls/cert.pem` 与 `tcp_tls/key.pem`。

### 3. 运行简化 TLS 服务端

```bash
python -m new_year_2026.tcp_tls.mini_tls_server
```

该脚本提供更紧凑的实现方式，适合阅读或二次修改。

### 4. 运行 HTTP GET/POST 示例

```bash
python -m new_year_2026.tcp_tls.get_or_post
```

脚本会执行一次 HTTP 请求，适合配合抓包工具学习 HTTP 报文格式。

### 5. 运行最小 SSH 连接示例

```bash
python -m new_year_2026.tcp_tls.ssh_minimal_client
```

该脚本只完成 SSH 的识别字符串（banner）交换，不包含密钥协商、加密与认证流程。默认连接 `127.0.0.1:22`，请根据需要修改 `HOST` 与 `PORT`。


## 常见问题

### 证书不被信任怎么办？

当前 `tcp_tls/cert.pem` 为自签名证书，系统默认不会信任。用于本地学习时可忽略该告警；如需生产环境使用，请替换为权威机构签发证书。

### 端口占用或无法绑定？

请确认没有其他程序占用对应端口，或调整脚本中的端口配置后再运行。

## 练习建议

- 将 `tcp_tls/tcp_server.py` 改为多线程/异步版本，体验并发处理。
- 修改 TLS 示例，尝试双向认证（mTLS）。
- 为 `tcp_tls/get_or_post.py` 添加自定义头部或 JSON 请求体。

## 本地扩展与共享库示例

- `src/new_year_2026/ctypes_shared`：构建 `.so/.dylib/.dll` 并用 `ctypes` 加载调用。
- `src/new_year_2026/hello_ext`：主包内的 C/C++ 扩展示例，构建产物在 macOS/Linux 为 `.so`，在 Windows 为 `.pyd`。

## 许可说明

本仓库仅用于学习与教学示例，证书与私钥仅用于本地测试，请勿在生产环境直接使用。
simplepydoc 0.1.2
Simple Python documentation generator - parse code, generate markdown
License: MIT
Requires Python: <4.0,>=3.9
Keywords: documentation, python, markdown, docstring, ast, api-docs

# SimplePyDoc 🐍

[![PyPI version](https://badge.fury.io/py/simplepydoc.svg)](https://pypi.org/project/simplepydoc/)
[![CI](https://github.com/msodiq19/simplepydoc/actions/workflows/ci.yml/badge.svg)](https://github.com/msodiq19/simplepydoc/actions)
[![License: MIT](https://img.shields.io/badge/License-MIT-blue.svg)](https://opensource.org/licenses/MIT)

Automatically generate simple markdown documentation from Python codebases.

## What It Does

SimplePyDoc parses Python files and generates clean markdown documentation showing:

- Classes with their methods and docstrings
- Functions with arguments and docstrings
- Simple, readable API documentation

## Installation

### From PyPI (Recommended)

```bash
pip install simplepydoc
```

### From Source (Development)

```bash
# Clone the repository
git clone https://github.com/msodiq19/simplepydoc.git
cd simplepydoc

# Install with Poetry
poetry install
```

## Usage

```bash
# If installed from PyPI
simplepydoc generate --repo ./src/myproject --output ./docs

# If running from source with Poetry
poetry run simplepydoc generate --repo ./src/myproject --output ./docs

# Example: Generate docs for SimplePyDoc itself
simplepydoc generate --repo ./src/llm_docgen --output ./docs
```

This creates `docs/API.md` with documentation extracted from your Python code.

## Example Output

Given this Python code:

```python
class Calculator:
    """A simple calculator."""

    def add(self, a, b):
        """Add two numbers."""
        return a + b

def helper():
    """A helper function."""
    pass
```

SimplePyDoc generates:

```markdown
# myproject API Documentation

## Classes

### Class: `Calculator`

A simple calculator.

**Methods:**

- **`add`**: Add two numbers.

## Functions

### `helper()`

A helper function.
```

## Current Scope (MVP)

- ✅ Python files only
- ✅ Parses classes, methods, and functions
- ✅ Extracts docstrings
- ✅ Generates markdown output
- ❌ No template customization (simple, opinionated format)
- ❌ No notebook support
- ❌ No remote repository cloning (use local paths)

## Development

```bash
# Run tests
poetry run pytest

# Run with coverage
poetry run pytest --cov=src

# Run the CLI during development
poetry run simplepydoc generate --repo ./src/llm_docgen --output ./docs

# Format code
poetry run black src tests

# Lint
poetry run flake8 src tests
```

## Contributing

See [CONTRIBUTING.md](CONTRIBUTING.md) for development guidelines.

## License

MIT - see [LICENSE](LICENSE) for details.
maestro-cubes 0.0.1
YAML-driven modular pipeline orchestrator for document-to-audio transformation. Coming soon.
License: MIT
Requires Python: >=3.10
Keywords: ai, audio, cubes, orchestration, pipeline, tts, yaml

# maestro-cubes

🎼 **YAML-driven modular pipeline orchestrator** for document-to-audio transformation.

Part of the [muid.io](https://muid.io) AI toolkit ecosystem (alongside `prompt-cubes`, `unified-ai-sdk`).

## 🚧 Coming Soon

This package is under active development. Features planned:

- **YAML Pipeline Definitions** - Declarative step-based pipelines
- **Modular Processors** - Text split/merge, audio split/merge, format conversion
- **LLM Integration** - Process text through AI models
- **TTS Support** - Convert text to speech with multiple providers
- **Checkpoint/Recovery** - Resume failed pipelines from last checkpoint

## Installation (when released)

```bash
pip install maestro-cubes
```

## Links

- GitHub: https://github.com/muid-io/maestro-cubes
- Documentation: Coming soon

## License

MIT