semvec/pypi-packages huggingface dataset:
iam_watching 1.6.0
Realtime monitor for IAM activity for IaC projects
License: MIT
Requires Python: <4.0,>=3.13

# IAM Action Watcher

Monitors IAM activities (Actions) for a given user or role in realtime. Outputs a list of actions to help construct a policy document.

"A CLI helper tool which runs alongside your IaC project to determine exactly what permissions your policy will require"

```
> poetry run iam_watching
No user specified
Using sts identity: arn:aws:sts::[account_id]:assumed-role/[role_name]/[session_tag]

        Watching every 5s for last 50
        operations currently being performed by testuser
        Events can take up to 2 minutes to show up

        Displaying unique actions only

        Hit Ctrl+C to stop watching security events


2025-08-18 15:28:07-07:00 | ec2:DescribeInstances
2025-08-18 15:21:10-07:00 | rds:DescribeDBClusters
2025-08-18 15:21:04-07:00 | iam:GetUser
^C
        The following actions were recently
        performed by testuser:

"Action": [
  "ec2:DescribeInstances",
  "rds:DescribeDBClusters",
  "iam:GetUser"
]
```

## Why?
With AWS IAM it can be hard to know exactly what permissions are required to run your code. IaC tooling makes many different API calls invoking actions requiring specific permissions.

E.g: Running a few different high-level functions on a simple program/module will do different things:
- refresh makes 'list/describe/get' calls
- up/apply makes 'create' calls
- down/destroy makes 'destroy/delete/deregister/de-provision' calls

I've found there is no good way to know exactly what these calls will be until all the functions have been tested and this usually means a lot of back & forth debugging to raise or lower access permissions to a reasonable level. Best-practice for IaC is a policy carrying the exact/minimum security.

This simple CLI tool monitors CloudTrail for all security actions performed by a user/principal during a time window, this removes the guesswork and toil of testing every function to failure.

By default the program will detect if you are authenticated as a traditional iam/user or a modern sts/session. But you can still over-ride which events you want to see with the `--user` flag.

## Developing

Running it locally

```bash
poetry install
poetry run iam_watching
```

## Install from PyPi

```bash
pipx install iam_watching
iam_watching --help
```
plcoding 0.1.1
An advanced research toolkit for Arikan's polar codes.
License: MIT

<table>
  <tr>
    <td><img src="logo.png" alt="plcoding logo" width="60"/></td>
    <td><h1>Plcoding: A Python Library for the Research of Polar Codes</h1></td>
  </tr>
</table>

这是一个关于极化码（polar codes）的Python库。最初供我自己做科研使用，现在开源到github，希望能够帮助到做相关研究、或者单纯是对信息论与编码技术感兴趣的同学们。

在库中，提供了各种基于Numpy的信息论与编码理论基础工具箱、以及基于极化现象的Python模块，还基于这些模块复现了一些极化码方向的经典工作，如**信源极化压缩**、**SW-问题的单调链极化码**、**极化码列表译码算法**、**有损信源编码**等等。其主要目的是辅助科学研究和理论分析，并不打算实现工业级别的解决方案。如果你对极化码的实用化感兴趣，不妨以这个库为基础进行开发。相比Matlab一类的偏仿真模拟的工业语言，Python/C++这类通用编程语言具有更良好的设计范式，更适合方便快速地集成到其他系统中。

Plcoding库配套一册基于Jupyter Notebook的在线免费文档，其中给出了大量的**可执行代码**，并配有详实的文字说明。撰写该手册的最初目的是用于助教工作。其现版本可以视作一本极化码入门手册。其中第一章《极化码简介》在没有对理论原理进行深入介绍的情况下，直接给出了明确的构造、编码和译码算法，适合那些不希望深入了解极化原理、只希望实现一份简单但完备的极化码解决方案的同学们。

如果对你有帮助，请务必在Github [项目主页](https://github.com/RenZichang/plcoding) 的右上角，点击star支持本项目。**这次一定！**

## 入门教程

👉 [Plcoding用户手册](https://renzichang.github.io/plcoding/)

> 为实时获取手册的最新内容，可在阅读时使用组合按键 `Shift+F5` 强制刷新浏览器缓存。

**除了入门手册外，在本项目的./plcoding/tests/目录下，也给出了测试用例，可用于快速了解plcoding库的基本用法。**

## 细节说明

这个库包括如下几个子模块：

- **`plcoding`**  
  包括一些常用的基本函数，例如：
  - 熵的计算与逆运算
  - 极化码的递归编码
  - 比特反转置换（bit-reversal permutation）
  - 二进制擦除信道（binary erasure channel）构造法
  - 矩阵的Kronecker幂运算
  - ...

- **`plcoding.channel`**  
  用于支持初级的信道纠错极化码研究，包括：
  - 常用信道，如AWGN信道、比特翻转信道等
  - 常用调制算法，如BPSK、QAM，以及对应的软/硬判决算法
  - 基于SC译码的信道极化码
  - ...

- **`plcoding.source`**  
  用于支持初级的信源压缩极化码研究，包括：
  - 几种经典的无失真极化压缩技术
  - 适配实用压缩系统的极化熵编码器
  - ...

- **`plcoding.research`**  
  用于支持进阶的极化码研究，包括：
  - 密度演化（density evolution）构造法
  - 单调链（Monotone chain）极化码
  - 限失真信源编码
  - ...

- **`plcoding.cpp_core`**  
  - 高性能计算的核心部分，部分其他模块是对该核心的Python封装
  - 这部分源码是可见的，如果确定理解它们的工作方式，可以绕过封装直接使用

## 安装方法

这个库使用pybind11来绑定C++模块到Python，并且使用了FFTW3来加速卷积运算，因此环境需求为：

- C++（源码编译）
  - FFTW3

- Python3（运行环境）
  - pybind11
  - numpy

上述是plcoding库的基本运行环境，如果想要顺利执行手册中的代码，则可能还需要安装matplotlib、tqdm、jupyter、torch等常用Python库，这里不再赘述。

**从源码**：下载源码到本地、配好上述运行环境之后，进入CMakeLists.txt同级目录，执行pip的本地安装指令：

```bash
pip install .
```

**从PyPI**：（推荐）

```bash
pip install plcoding
```

## 开发进度

- 已实现：
  - C++核心功能模块，包括快速极化模块、通用的SC译码器模块（2025.05.25）
  - plcoding模块下的部分基本函数（2025.05.27）
  - 配套手册的基本内容已完成（2025.06.03）
  - plcoding.channel模块下的channel类和modulator类（2025.06.04）
  - plcoding.channel模块下的coder类（2025.07.01）
  - plcoding.source模块、以及从整数到比特流的映射函数（2025.07.15）
  - plcoding.research.monotone模块，可用于研究单调链极化码（2025.08.24）
  - 发不到pypi，包名为plcoding（2025.09.07）

- 待实现
  - 各类构造法（启发式、密度演化等）
  - 各类快速译码算法（对数似然比、最小和近似）
  - 各类极化核矩阵的编译码
  - ...

### License

MIT License

## 开发者

欢迎在github提交issue，或者私下交流：

- 📧 Email: [rzc1937986979@163.com](mailto:rzc1937986979@163.com)
- 💬 QQ: 1937986979
ScratchAgent 0.1.0
A Python agent framework for building intelligent automation tools
License: MIT
Requires Python: >=3.9
Keywords: agent, automation, ai, framework

# ScratchAgent

A Python agent framework for building intelligent automation tools.

## Description

ScratchAgent is a lightweight and flexible framework designed to help developers create intelligent agents for automation tasks. Whether you're building chatbots, automation scripts, or AI-powered tools, ScratchAgent provides the foundation you need.

## Features

- Simple and intuitive API
- Extensible architecture
- Easy integration with existing Python projects
- Lightweight with minimal dependencies

## Installation

You can install ScratchAgent from PyPI:

```bash
pip install ScratchAgent
```

Or install the development version:

```bash
pip install ScratchAgent[dev]
```

## Quick Start

```python
from agent import greet

# Basic usage
message = greet("World")
print(message)  # Output: Hello, World!
```

## Command Line Usage

After installation, you can use the command line interface:

```bash
scratchagent
```

## Development

To set up for development:

1. Clone the repository:
```bash
git clone https://github.com/AbQaadir/ScratchAgent.git
cd ScratchAgent
```

2. Install in development mode:
```bash
pip install -e .[dev]
```

3. Run tests:
```bash
pytest
```

## Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

## License

This project is licensed under the MIT License - see the LICENSE file for details.

## Author

- **Qaadir** - [AbQaadir](https://github.com/AbQaadir)

## Changelog

### Version 0.1.0
- Initial release
- Basic greeting functionality
- Command line interface
jsslib 1.0.21
JSON Structure Search library
License: MIT
Requires Python: >=3.6

# jsslib

JSON Structure Search library

Example
```
from jsslib import JSS

jss = JSS()

jss.LoadTable('./lang/table/table.lst')

print(jss.RunSql("SELECT TOP 10 id, Zi FROM zi WHERE PinYin = 'ding1' and id > 2;"))
```

Output
```
[{'Zi': '丁', 'id': 4}, {'Zi': '灯', 'id': 2077}, {'Zi': '钉', 'id': 3123}, {'Zi': '仃', 'id': 3426}, {'Zi': '叮', 'id': 3725}, {'Zi': '町', 'id': 5646}, {'Zi': '盯', 'id': 5756}, {'Zi': '酊', 'id': 7027}, {'Zi': '玎', 'id': 7917}]
```