semvec/pypi-packages huggingface dataset:
roc-idb 1.6.0
Plugin to manage the RPW Instrument Database (IDB)
Requires Python: <4,>=3.9

roc-idb
============

Python Package to install RPW IDB into database.

This package has been initially designed to be run in the RPW Operations and Data Pipeline (RODP).
Contact the developer team for more details.

## Quickstart

To install package using [pip](https://pypi.org/project/pip-tools/):

```
pip install roc-idb
```

## Authors

- xavier dot bonnin at obspm dot fr
- florence dot henry at obspm dot fr
- sonny dot lion at obspm dot fr

## License

This project is licensed under CeCILL 2.1.

## Acknowledgments

* Solar Orbiter / RPW Operation Centre (ROC) team
file_shredder 0.1.1
Python implementation of shred. Calls into port of github.com/rozbb/rust-shred using Py03
Requires Python: >=3.14

# This project
See github.com/rozbb/rust-shred

I merely modernized it, since the original code is from >10 years ago and some langauge features have broken. 

I also made it callable from python using Py03. 

## Example Usage
```python
from test_stuff import shred
from pathlib import Path


f = Path("./secrets")
f.write_text("My password is foo123!")
shred(
    path: f,
    n_passes: 3,
    remove: True,
    size: None,
    exact: True,
    zero: True,
    verbose: True,
)
```
pixel-renderer 0.1.4
Text renderer for PIXEL models
Requires Python: >=3.10

# PIXEL Renderer

This repository contains a text renderer for PIXEL models.

## Usage

Install dependencies:

```shell
conda create -n pixel python=3.12 -y
conda activate pixel
pip install ".[dev,pangocairo]"
```

> [!TIP]
> Having trouble installing `pycairo` and `pygobject`? 
> See [these instructions](https://pygobject.readthedocs.io/en/latest/getting_started.html#installing-pygobject).

Install:

```bash
pip install "pixel-renderer[pangocairo]"
```

Render text as an image (array):

```python
from pixel_renderer.renderer import render_text

render_text(text="test", block_size=16, font_size=12)
```

For better consistency across platforms and easier reproducibility, we need to specify the exact fonts.


```python
from font_download import FontConfig
from font_download.example_fonts.noto_sans import FONTS_NOTO_SANS
from pixel_renderer import PixelRendererProcessor

font_config = FontConfig(sources=FONTS_NOTO_SANS)
pixel_processor = PixelRendererProcessor(font=font_config)

pixel_processor.render_text_image("hello!").save("demos_output/hello.png")

pixel_processor.save_pretrained("demos_output/processor")
```

## Cite

If you use this code in your research, please consider citing the work:

```bibtex
@misc{pixel2025renderer,
  title={PIXEL Renderer},
  author={Moryossef, Amit and Kesen, Ilker},
  howpublished={\url{https://github.com/sign/pixel-renderer}},
  year={2025}
}
```
boxlite-mcp 0.3.0
MCP server providing computer use capabilities through an isolated sandbox environment
Requires Python: >=3.10
Keywords: automation, computer-use, isolation, mcp, sandbox

# boxlite-mcp

MCP server providing isolated sandbox environments for AI agents.

## The Problem

Today's AI apps don't just generate text – they write and run code, call tools, read your files, and hit the network. Running all of this directly on your machine creates real risks:

- **Security** - Malicious or buggy code can damage your system
- **Privacy** - Sensitive files and credentials are exposed
- **Reliability** - Runaway processes can consume resources or crash your machine

BoxLite solves this by giving AI agents their own isolated VM – full freedom inside, complete safety outside.

## Powered by BoxLite

[BoxLite](https://boxlite-labs.github.io/website/) is an embeddable virtual machine runtime that follows the SQLite philosophy - simple, lightweight, and zero-configuration.

### Why BoxLite?

- **Hardware-level isolation** - True VM security, not just containers. Your AI agent runs in a completely isolated environment.
- **No daemon required** - Unlike Docker, BoxLite doesn't need a background service. Just import and use.
- **Embeddable** - Designed to be embedded directly into your applications, like SQLite for compute.
- **Fast startup** - VMs boot in seconds, not minutes.
- **Cross-platform** - Works on macOS and Linux.

### Use Cases

- **AI Agent Sandboxing** - Let AI agents execute code, browse the web, and use applications safely
- **Secure Code Execution** - Run untrusted code without risk to your host system
- **Browser Automation** - Headless browser with CDP for web scraping and testing
- **Development Environments** - Disposable, reproducible dev environments

## Demo

[▶️ Watch the demo on YouTube](https://youtu.be/JjwLg6ww234)

https://github.com/user-attachments/assets/0685d428-64e4-4a68-adfe-c24dc0dc5ae8

## Available Tools

| Tool | Description |
|------|-------------|
| `computer` | Full Ubuntu desktop with XFCE. Anthropic computer use API compatible. |
| `browser` | Chromium browser with CDP endpoint for Puppeteer/Playwright/Selenium |
| `code_interpreter` | Python code execution sandbox |
| `sandbox` | Generic container for running shell commands |

## Quick Start

### Claude Code

```bash
claude mcp add boxlite -- uvx boxlite-mcp
```

### Claude Desktop

Add to your Claude Desktop configuration (`~/Library/Application Support/Claude/claude_desktop_config.json` on macOS):

```json
{
  "mcpServers": {
    "boxlite": {
      "command": "uvx",
      "args": ["boxlite-mcp"]
    }
  }
}
```

### Manual Installation

```bash
pip install boxlite-mcp
```

## Development

```bash
git clone https://github.com/boxlite-labs/boxlite-mcp.git
cd boxlite-mcp
uv sync --extra dev
uv run pytest
```

## License

Apache-2.0
pocketpylife 0.0.3
A Python package for manipulation of patterns in cellular automata.
Requires Python: >=3.10

# pocketlife
A Python package for manipulation of patterns in cellular automata.
## Introduction
This is my first Python package, and it contains functions for manipulating patterns in cellular automata.
It was inspired by the much faster [lifelib](https://gitlab.com/apgoucher/lifelib) by Adam P. Goucher, but unlike lifelib, this package:
* Is written entirely in Python.
* Works regardless of OS and CPU architecture.
It is very slow, however, and so it is mostly written as a test.
## Classes
Cellular automata functionality is implemented through the class ```lifetree```. A lifetree is responsible for the actual simulation, using an extremely basic algorithm and an unbounded grid. It does this as a backend to the class ```Pattern```, which stores a dictionary of live cells.
The syntax is very similar to lifelib, with patterns being advanced using ```pt[gens]```, and many functions and properties are similar as well.
## Documentation
Documentation on the ```lifetree``` and ```Pattern``` classes can be found in /doc.
algo-backend-framework 0.0.8
Ctcdn algorithm backend framework
Requires Python: >=3.10

# algo-backend-framework-python

# 功能概述
- 配置
- 错误码、异常、参数检验报错转换
- 标准响应体、x-request-id
- 日志框架支持：loguru、nb_log
- 多进程prometheus指标
- otel

## 分支规范
- master分支: 归档分支
- release/release-×.×.×: 发布分支，×.×.×为版本号；修改[pyproject.toml](pyproject.toml)中的version构建并发布包
- dev分支: 开发分支；合并至release分支时，需要提mr，mr需要填写版本号和功能描述；需要充分自测和review之后才能合并至release
- feat/feat-×××××: 功能分支；合并至dev时需要mr，mr需要填写功能名称和功能描述；新功能或较大重构需要建feat分支，较小的改动不必开feat分支

## 本地调试

本地开发需要打包验证，则先构建包（但不要发布），然后在本地目录离线安装
```shell
# 构建包
uv build
# 拷贝wheel包到本地测试目录下执行以下命令
uv add algo_backend_framework-*.*.*-py3-none-any.whl
# 调试时发现包有问题，只需要remove后重新打包，并重新add
uv remove algo-backend-framework
```

## 开发约定
- 代码提交前进行sonar扫描和语法检查，并进行代码格式化
    ```
    uv run ruff format algo_backend/×××××
    ```
- 子模块__init__.py文件需要声明包内暴露模块，不建议用户通过包内文件名导入，以减少后期微调重构引起的不兼容
- 跨模块导入一律使用`from algo_backend.× import 模块名`的全限定包名，同级目录下导入从使用`from . import `


## 发布
- 在release分支进行构建和推送
```shell
uv build
# 推送ctcdn nexus
uv publish --index ctcdn-pypi-upload
# 推送pypi
uv publish
```
scraper2-hj3415 2.8.2
Naver WiseReport scraper
Requires Python: >=3.11
Keywords: example, demo

# scraper2

KRX 기반 국내 주식 재무(NFS) 데이터 스크래퍼 CLI 도구입니다.
종목 단건 수집(one)과 유니버스 전체 수집(all)을 명확히 분리해 운영합니다.

⸻

설치

pip install -e .

설치 후 CLI 확인:

scraper2 --help


⸻

CLI 구조

scraper2
 ├─ nfs
 │   ├─ one   # 종목 1개 수집
 │   └─ all   # 유니버스 전체 수집
 └─ mi        # (reserved)


⸻

1️⃣ 단일 종목 수집 (one)

사용법

scraper2 nfs one <endpoint> <code> [options]

예시

# 삼성전자 c101 단건 수집 (메모리)
scraper2 nfs one c101 005930

# 여러 endpoint 동시 수집
scraper2 nfs one all 005930

# MongoDB 저장
scraper2 nfs one c101 005930 --sink mongo

# 결과 DTO 출력 비활성화
scraper2 nfs one c101 005930 --no-show

특징
	•	항상 종목 1개만 처리
	•	결과 DTO를 즉시 출력 (디버깅·검증용)
	•	운영 자동화보다는 수동 테스트 / 검증용

⸻

2️⃣ 유니버스 전체 수집 (all)

사용 전 필수

# universe를 먼저 DB에 적재
krx sync


⸻

사용법

scraper2 nfs all <endpoint> [options]

예시

# krx300 전체 종목 c101 수집
scraper2 nfs all c101

# 모든 endpoint 수집
scraper2 nfs all all

# 특정 유니버스 지정
scraper2 nfs all c103 --universe krx300

# 처리 개수 제한 (테스트용)
scraper2 nfs all c101 --limit 10

# 진행률 배치 크기 조정
scraper2 nfs all c101 --chunk 5

실행 중 출력 예시

=== NFS ALL === universe=krx300, endpoint=c101, codes=298, sink=mongo

=== START: c101 === total=298, chunk_size=5
progress: 25/298 (ok=25, fail=0)
progress: 50/298 (ok=50, fail=0)
...
=== DONE: c101 === ok=297, fail=1, total=298

⏱ elapsed time: 6m 42s

특징
	•	DB에 저장된 universe_latest 기준
	•	진행률, 성공/실패 개수 실시간 출력
	•	전체 작업 종료 시 총 소요 시간 표시
	•	운영/스케줄러용 메인 모드

⸻

3️⃣ 작업 스케줄러 예시 (cron)

매일 새벽 2시 전체 수집

0 2 * * * /path/to/venv/bin/scraper2 nfs all all >> /var/log/scraper2_nfs.log 2>&1

endpoint 분리 실행 (권장)

0 2 * * * /path/to/venv/bin/scraper2 nfs all c101 >> /var/log/nfs_c101.log 2>&1
10 2 * * * /path/to/venv/bin/scraper2 nfs all c103 >> /var/log/nfs_c103.log 2>&1

테스트용 제한 실행

0 1 * * * /path/to/venv/bin/scraper2 nfs all c101 --limit 20 >> /var/log/nfs_test.log 2>&1


⸻

운영 권장 사항
	•	all 모드는 반드시 mongo sink 사용
	•	universe는 krx sync로 관리
	•	로그에는
	•	진행률
	•	ok / fail 개수
	•	전체 소요 시간
이 모두 남도록 설계됨
	•	장애 발생 시 어디까지 진행됐는지 로그만 보고 판단 가능

⸻

요약

목적	명령
단건 검증	scraper2 nfs one c101 005930
전체 수집	scraper2 nfs all all
스케줄러	scraper2 nfs all <endpoint>