semvec/pypi-packages huggingface dataset:
dicomcriterion 0.1.0
Create and evaluate boolean expressions against DICOM datasets
Requires Python: >=3.10

# DICOM Criterion

[![CI](https://github.com/sjoerdk/dicomcriterion/actions/workflows/build.yml/badge.svg?branch=main)](https://github.com/sjoerdk/dicomcriterion/actions/workflows/build.yml?query=branch%3Amain)
[![PyPI](https://img.shields.io/pypi/v/dicomcriterion)](https://pypi.org/project/dicomcriterion/)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/dicomcriterion)](https://pypi.org/project/dicomcriterion/)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
[![Checked with mypy](http://www.mypy-lang.org/static/mypy_badge.svg)](http://mypy-lang.org/)


A Python library for creating and evaluating boolean expressions against DICOM datasets. Build flexible validation rules using intuitive syntax to filter, validate, and process medical imaging data.

## Quick Start

```python
from pydicom import Dataset
from dicomcriterion import Criterion

# Create a sample DICOM dataset
dataset = Dataset()
dataset.PatientName = "John Doe"
dataset.StudyDescription = "Brain MRI with contrast"
dataset.Modality = "MR"

# Create validation criteria
criterion = Criterion("PatientName.equals('John Doe') and StudyDescription.contains('MRI')")

# Evaluate against dataset
result = criterion.evaluate(dataset)  # Returns True
```

## Key Features

- **Simple syntax**: Write validation rules using natural boolean expressions
- **Built-in functions**: `equals()`, `contains()`, `exists()` for common validations
- **Boolean operators**: Combine rules with `and`, `or`, `not`, and parentheses
- **Extensible**: Add custom validation functions for specialized requirements

## Installation

```bash
pip install dicom-criterion
```

## Credits
This package was created with the [kiro](https://kiro.dev/) editor.
wgen 0.1.0
High-performance Wordlists generator CLI tool
Requires Python: <4.0,>=3.8

# WGen - High-Performance Wordlists Generator

## Description

A CLI tool for generating wordlists for security research, supporting custom charsets, lengths, and patterns.

## Installation

```bash
pip install .
```

## Usage

```bash
wgen generate --charset "abc" --min-length 1 --max-length 2 --output mylist.txt
wgen estimate --charset "abc" --min-length 1 --max-length 2
```

## Testing

```bash
pytest
```
error-markov 0.1.0
This is a markov-chain module/package/library/idk and it is dumb

# error_markov
This is just your normal markov-chain module/package/library/what do you call this. If your life is on the line, don't use this, it's unreliable. This **will** not work I can guarantee you. Its here if you want something dumb that you can easily frame as AI.
## Installation
```bash
pip install error_markov
```
## Usage
```python
from error_markov import DumbMarkov

bot = DumbMarkov(order=1) # If order is not specified, it will default to 2
```
### Training
```python
text = "your text here"
bot.train(text)
```
You can also use multiple sentences:
- 1 train command
```python
text = "An apple is existing. Yes an apple is not existing."
bot.train(text)
```
- many train commands
```python
bot.train("An apple is existing.")
bot.train("Yes an apple is not existing.")
```
### Generating
```python
bot.generate()
```
The code above will generate 1 sentence with a max of 50 words. It will start with a random word.
- If you want to start with something specific
```python
inputt = "Yes an"
output = bot.generate(start=inputt)
# To start with something, do:
# start=
```
- If you want to change the max words
```python
max_w = 75
inputt = "Yes an"
output = bot.generate(max_words=max_w, start=inputt)
```
- And then print the output:
```python
print(output)
```
## License
This thing is licensed under the WTFPL.
---
arl-mcp 0.1.13
ARL MCP Server - A Model Context Protocol server for ARL platform integration
Requires Python: >=3.8

# ARL MCP Server

A Model Context Protocol (MCP) server for ARL platform integration.

## 简介

ARL MCP Server 是一个基于 Model Context Protocol (MCP) 的服务器，用于与 ARL (Asset Reconnaissance Lighthouse) 平台集成。它提供了一系列工具函数，可以通过 MCP 协议调用 ARL 平台的各种功能。

## 功能特性

- **域名提取**: 从 HTTP 数据包中提取主域名
- **任务管理**: 创建和查询 ARL 平台扫描任务
- **子域名枚举**: 获取指定域名的所有子域名
- **IP 地址发现**: 获取域名对应的 IP 地址列表
- **站点识别**: 获取域名下的所有站点地址
- **文件泄露检测**: 发现潜在的文件泄露链接
- **节流控制**: 内置休眠功能，避免过快轮询

## 安装

### 使用 uvx (推荐)

```bash
# 直接运行（无需安装）
uvx arl-mcp

# 从 GitHub 运行
uvx --from git+https://github.com/WACHI/arl-mcp.git arl-mcp

# 从本地路径运行
uvx --from ./dist/arl_mcp-0.1.0-py3-none-any.whl arl-mcp
```

### 使用 pip

```bash
pip install arl-mcp
```

## 使用方法

### 启动服务器

```bash
arl-mcp
```

### 可用工具函数

1. **extract_main_domain(packet: str)**: 从 HTTP 数据包中提取主域名
2. **extract_domain_or_ip(text: str)**: 从文本中提取域名或 IP
3. **sleep_for(seconds: int)**: 休眠指定秒数
4. **add_scan_task(...)**: 创建 ARL 扫描任务
5. **query_task_status(name: str)**: 查询任务状态
6. **get_all_subdomains(domain: str)**: 获取子域名列表
7. **query_ip_list(domain: str)**: 获取 IP 地址列表
8. **query_site_list(domain: str)**: 获取站点列表
9. **query_fileleak_list(domain: str)**: 获取文件泄露链接

## 配置要求

- Python 3.8+
- ARL 平台访问权限
- 有效的 ARL API Token

## 开发

### 本地开发安装

```bash
git clone https://github.com/WACHI/arl-mcp.git
cd arl-mcp
pip install -e .
```

### 运行测试

```bash
pytest
```

### 构建包

```bash
python -m build
```

## 许可证

MIT License

## 贡献

欢迎提交 Issue 和 Pull Request！

## 注意事项

⚠️ **安全提醒**: 
- 请确保 API Token 的安全性
- 不要在公开代码中暴露敏感信息
- 建议在生产环境中使用环境变量管理配置

## 联系方式

如有问题或建议，请通过 GitHub Issues 联系我们。