semvec/pypi-packages huggingface dataset:
air-gui 0.0.2
Gtk 3 Python gui for Aircrack
Requires Python: >=3.6

# Aircrack-Gui

Aircrack-Gui is a Python gui for Aicrack written with help of Gtk 3.0.

First priority was to make every step intuitive and to remove any need in typing with your hands)

## Requirements

PyGoObject version: 3.36.0

## Installation

Right now the only way to use this gui is to download and run.
I am working on adding installation script and pip support.

## Usage
Run air-ng.py with Python 3.

```bash
python3 air-ng.py
```

## Todo
1. Proper installation.
2. Extra labels.
3. Add screenshots.
4. Comment out everything.

## Contributing
Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

Please make sure to update tests as appropriate.

## License
[GNU GPLv3](https://choosealicense.com/licenses/gpl-3.0//)
CANdas 2.0.2
Manage CAN Data elegantly.
Requires Python: >=3.5

# CANdas - The dataframe for CAN* bus data

> Manage and analyze CAN bus data elegantly and efficiently, locally or in the cloud.

![CANdas Jupyter Lab example](http://img.lionsracing.de/1O1PIJXVSJ4ZCLSM.png)

*[Controller Area Network](https://en.wikipedia.org/wiki/CAN_bus)

## Features

- Common format for dealing with CAN data
- Enrich plots of the logging data with data from the dbc files automatically
- Versatile and extensible plotting functions for all kinds of signals
- Easily export CAN data to a pandas dataframe
- Data can be pushed to a SQL database
- Download logging file from SQL database instead of having all of them on disk
- Method chaining philosophy to create powerful and minimalistic pipelines
- Feature demo can be found [here](https://gist.github.com/JulianWgs/2fa20fabfa87d709a0b6c394eba9266c)

## Documentation

Extensive documentation can be found here: https://lionsracing.gitlab.io/candas/index.html

## Installation

- For the database features you need a SQL-Driver. Please install it beforehand.

```bash
pip install candas
```

## Contributing

1. Fork the repository.

2. Install prerequisites.

   ```
   pip install -r requirements.txt
   ```

3. Implement the new feature or bug fix.

4. Implement test case(s) to ensure that future changes do not break legacy.

5. Run the tests.

6. Create a pull request.