semvec/pypi-packages huggingface dataset:
nicegui-tic-tac-toe 0.1.6
TicTacToe on NiceGUI
Requires Python: >=3.12

# NiceGUI TicTacToe

NiceGUI TicTacToe is a TicTacToe that runs on NiceGUI.

## Usage

```
% tic-tac-toe
```

![](https://raw.githubusercontent.com/SaitoTsutomu/nicegui-tic_tac_toe/master/images/main.png)
pyfmm-gui 0.2.3
A simple GUI of PyFMM
Requires Python: >=3.9


<h1 align="center"><b><i>PyFMM-GUI</i></b></h1>


<p align="center">
  <img alt="GitHub code size in bytes" src="https://img.shields.io/github/languages/code-size/Dengda98/PyFMM-GUI">
  <img alt="GitHub License" src="https://img.shields.io/github/license/Dengda98/PyFMM-GUI">
  <img alt="GitHub Actions Workflow Status" src="https://img.shields.io/github/actions/workflow/status/Dengda98/PyFMM-GUI/publish_pypi.yml?label=pypi%20publish">
</p>

基于PyQt5开发的简易图形界面，用于计算任意速度场下的二维全局走时场。计算模块基于我自己开发的C/Python库[***PyFMM***](https://github.com/Dengda98/PyFMM)。  

该GUI本质算是个教学小工具，图像可帮助初学者更好的理解射线追踪，也可更方便、直观地看到不同速度场下射线的扭曲形态。

![图片](./figs/capture1.png)
![图片](./figs/example.gif)

## 安装

1、安装[***PyFMM***](https://github.com/Dengda98/PyFMM).

2、安装PyQt5
```bash 
    conda install pyqt -c conda-forge
```

3、安装本GUI程序，有多种下载方式：
+ **从[PYPI](https://pypi.org/project/pyfmm-kit/)下载（推荐）**  
  **最新更新，已将PyFMM-GUI程序源码同步到[PYPI](https://pypi.org/project/pyfmm-kit/)，可直接运行：**  
  ```bash
  pip install -v pyfmm-gui
  ```  
  进行安装。各操作平台的wheels后续会逐步支持。

+ 从Github下载安装，安装有两个选择：
  + **不提前下载程序包**  

    要求你当前环境中有git工具。这样可直接运行
    ```bash
    # vx.x.x指代Release中的版本号，建议下载最新稳定版本
    pip install -v git+https://github.com/Dengda98/PyFMM-GUI@vx.x.x 
    # 若网速不好可改为gitee镜像
    # pip install -v git+https://gitee.com/Dengda98/PyFMM-GUI@vx.x.x
    ```


  + **提前下载好程序包** 

    注意代码主页更新频繁，**建议在[Release](https://github.com/Dengda98/PyFMM-GUI/releases)中下载最新稳定版本**，不推荐clone，不推荐直接下载主分支。在下载解压后，在程序根目录下运行
    ```bash
    pip install -v .
    ``` 

这样可安装到你的虚拟环境中。 


## 使用
直接运行
```bash 
    pyfmm_gui   # 或者 pyfmm-gui
```  
或者
```bash 
    python -m pyfmm_gui.main
```   


功能按钮很简单，基本“所见即所得”。其中`Update Velocity`下的文本框是个Python脚本窗口，可以自定义FMM网格范围(`xmax, ymax`，最小值均为0)、划分数(`nx, ny`)(组成`xarr, yarr`)，以及2D速度数组`vel2d`。设置好后，点击`Update Velocity`即可计算。


## 其它
如果遇到bug，欢迎联系我(zhudengda@mail.iggcas.ac.cn)，我会完善！
也欢迎提出建议和更多示例！
bareos-restapi-client 0.1.22
A client for Bareos REST API
Requires Python: >=3.10

# Bareos REST API Client

`Bareos REST API` 에 대한 파이썬 클라이언트 모듈입니다.

## 설치

```bash
pip install bareos_restapi_client
```

## 사용법

```py
# e.g.
from bareos_restapi_client import (
    BareosRestApiClient,
    clientResource,
)

client = BareosRestApiClient(base_url='', username='', password='')

client_data = models.clientResource(
    name="testuser",
    address="10.0.0.1",
    password="supersecret",
    connectionfromclienttodirector="yes",
    connectionfromdirectortoclient="no",
    heartbeatinterval="60",
    tlsenable="no",
    tlsrequire="no",
    enabled="yes"
)

response = client.create_client(client_data=client_data)
```
gitbook-scraper 0.1.1
A CLI tool to scrape and structure GitBook documentation
Requires Python: >=3.7

# GitBook Scraper

A command-line tool to scrape and structure GitBook documentation into a single, well-organized markdown file.

## Features

- 📚 Scrapes any GitBook documentation site
- 🌳 Maintains original document hierarchy and structure
- 📝 Generates a single, well-formatted markdown file
- ⚡ Fast and polite scraping with rate limiting
- 🛠️ Configurable output format and structure
- 🔄 Automatic retry on failed requests
- 📋 Table of contents generation
- 🎯 Selective TOC item extraction

## Installation

```bash
pip install gitbook-scraper
```

## Quick Start

```bash
# Basic usage
gitbook-scraper https://your-gitbook-url.io

# Specify output file
gitbook-scraper https://your-gitbook-url.io -o documentation.md

# With table of contents
gitbook-scraper https://your-gitbook-url.io --toc

# Custom rate limiting
gitbook-scraper https://your-gitbook-url.io --delay 1.0

# Extract specific TOC items
gitbook-scraper https://your-gitbook-url.io -t "Getting Started" -t "Advanced Topics"
```

## Advanced Usage

### Command Line Options

```bash
Options:
  -o, --output TEXT     Output file path [default: documentation.md]
  --toc                 Generate table of contents [default: False]
  --delay FLOAT        Delay between requests in seconds [default: 0.5]
  --retries INTEGER    Number of retries for failed requests [default: 3]
  --timeout INTEGER    Request timeout in seconds [default: 10]
  --debug             Enable debug logging [default: False]
  --no-cleanup        Keep intermediate files [default: False]
  -t, --toc-items TEXT  Specific TOC items to extract (can be specified multiple times)
  --help             Show this message and exit
```

### Python API

```python
from gitbook_scraper import GitbookScraper

# Basic usage
scraper = GitbookScraper(
    base_url="https://your-gitbook-url.io",
    output_file="documentation.md",
    generate_toc=True,
    delay=0.5
)

# Extract specific TOC items
scraper = GitbookScraper(
    base_url="https://your-gitbook-url.io",
    output_file="documentation.md",
    generate_toc=True,
    toc_items=["Getting Started", "Advanced Topics"]
)

scraper.scrape()
```

## Configuration

The tool can be configured using environment variables:

```bash
# Set default output directory
export GITBOOK_SCRAPER_OUTPUT_DIR="./docs"

# Set custom user agent
export GITBOOK_SCRAPER_USER_AGENT="Custom User Agent"

# Set default delay
export GITBOOK_SCRAPER_DELAY=1.0
```

## Error Handling

The scraper implements automatic retries with exponential backoff for failed requests. Common issues and solutions:

- Rate limiting: Increase the delay between requests
- Timeout errors: Increase the timeout value
- Navigation extraction fails: Try different selectors with `--selector-file`

## Contributing

1. Fork the repository
2. Create your feature branch (`git checkout -b feature/feature`)
3. Commit your changes (`git commit -m 'Add feature'`)
4. Push to the branch (`git push origin feature/feature`)
5. Open a Pull Request

## License

Distributed under the MIT License. See `LICENSE` for more information.
dAngr 0.0.13
Debugging with Angr package
Requires Python: >=3.10

# dAngr - A Debugger for Angr

dAngr offers a streamlined approach to leverage [angr's](https://angr.io) powerful symbolic execution capabilities within a user-friendly command-line interface (CLI) environment, eliminating the necessity for extensive programming knowledge usually required to use angr. 
Providing powerful capabilities for analyzing and debugging binaries.

## Features

- Perform symbolic execution on binaries for debugging purposes.
- Command-line interface for easy interaction.
- Easy way to call a function with chosen arguments without the hassle of memory management and argument parsing.
- Platform independent execution

## Installation


To use dAngr, follow these steps:

0. Preferable create a virtual environment, for instance using [venv](https://docs.python.org/3/library/venv.html).

1. Install the required dependencies by running the following command:

```bash
pip install .
```
Note: on MacOS you may need to run the following for now:
```bash
pip install --pre --no-binary capstone capstone==5.0.0.post1
```

2. Run dAngr:

```bash
dAngr
```

### Docker

For simplicity dAngr is also available using a Dockerfile.

Build Dockerfile to create an clean environment for dAngr.
```bash
docker build -t dangr .
```

Run the Docker image and get the dAngr command prompt.
```bash
docker run -it dangr
```

If you wish to run the dAngr Docker on your own binaries, you may need use shared volumes.
```bash
docker run -it dAngr -v <loal-binary-dir>:/home/ubuntu/dAngr/<binaryfile>
```

## Help
1. In the debugger prompt, you can find help as follows:
```bash
(dAngr)> help
```
## Documentation

Further documentation on the commands that may be used can be found [here](./docs/documentation.md).

## Example Usage

A basic example, demonstrating the capabilities of dAngr, can be found [here](./examples/basic_example/)

A more practical example, a simplified version of the vulnerability found in the Eufy ecosystem, can be found [here](./examples/aes_example/)

## Advanced Symobic Use Case
Without specifying any concrete inputs, dAngr will execute the target binary using symbolic inputs.  

## Contributing

Contributions to dAngr are welcome! If you find any bugs or have suggestions for new features, please open an issue or submit a pull request on GitHub.

## Research
Check out our other research [here](https://distrinet.cs.kuleuven.be/research/publications) 


## Contact
Have questions or feedback? Don't hesitate to reach out to us! [open an issue](https://github.com/angr-debugging/dAngr/issues) on our repository or feel free to contact one of the authors.


## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.


## Notes:
When installing pygraphviz, you may need to first install graphviz with dev extensions:
apt-get install graphviz graphviz-dev
tira-measure 0.0.11
Measuring what really matters.
Requires Python: >=3.8

# The Measure Python API

Install via:

```
pip3 install tira-measure
```

Usage:

```py
from tira_measure import Environment

environment = Environment(["git", "system"], ..., polling_ms=1000)  # defaults would be all measures, and some reasonable polling interval.

# Then measure with a context manager...
with environment.measure():
  # do something

# ...or a function decorator...
@environment.measure
def do_something():
  # do something
do_something()

# ...or manually...
environment.start_measuring()
# do something
environment.stop_measuring()

print("start git hash", environment.measurements[0]["git"])
```



Run tests via:
```
pytest
```