The traffic library helps to work with common sources of air traffic data.

Its main purpose is to provide data analysis methods commonly applied to trajectories and airspaces. When a specific function is not provided, the access to the underlying structure is direct, through an attribute pointing to a pandas dataframe.

The library also offers facilities to parse and/or access traffic data from open sources of ADS-B traffic like the OpenSky Network or Eurocontrol DDR files. It is designed to be easily extendable to other sources of data.

Static visualization (images) exports are accessible via Matplotlib/Cartopy. More dynamic visualization frameworks are easily accessible in Jupyter environments with ipyleaflet and altair; or through exports to other formats, including CesiumJS or Google Earth.

Installation

Full installation instructions are to be found in the documentation.

• If you are not familiar/comfortable with your Python environment, please install the latest traffic release in a new, fresh conda environment.

  conda create -n traffic -c conda-forge python=3.10 traffic

• Adjust the Python version you need (>=3.9) and append packages you need for working efficiently, such as Jupyter Lab, xarray, PyTorch or more.

• Then activate the environment every time you need to use the traffic library:

  conda activate traffic

Warning! Dependency resolution may be tricky, esp. if you use an old conda environment where you overwrote conda libraries with pip installs. Please only report installation issues in new, fresh conda environments.

If conda fails to resolve an environment in a reasonable time, consider using a Docker image with a working installation.

For troubleshooting, refer to the appropriate documentation section.

Credits

• Like other researchers before, if you find this project useful for your research and use it in an academic work, you may cite it as:

  @article{olive2019traffic,
    author={Xavier {Olive}},
    journal={Journal of Open Source Software},
    title={traffic, a toolbox for processing and analysing air traffic data},
    year={2019},
    volume={4},
    pages={1518},
    doi={10.21105/joss.01518},
    issn={2475-9066},
  }

• Additionally, you may consider adding a star to the repository. This token of appreciation is often interpreted as positive feedback and improves the visibility of the library.

Documentation

Documentation available at https://traffic-viz.github.io/
Join the Gitter chat for assistance: https://gitter.im/xoolive/traffic

Tests and code quality

Unit and non-regression tests are written in the tests/ directory. You may run pytest from the root directory.

Tests are checked on Github Actions platform upon each commit. Latest status and coverage are displayed with standard badges hereabove.

In addition to unit tests, code is checked against:

• linting and formatting with ruff;
• static typing with mypy

pre-commit hooks are available in the repository.

Feedback and contribution

Any input, feedback, bug report or contribution is welcome.

• Should you encounter any issue, you may want to file it in the issue section of this repository.

• If you intend to contribute to traffic or file a pull request, the best way to ensure continuous integration does not break is to reproduce an environment with the same exact versions of all dependency libraries. Please follow the appropriate section in the documentation.

  Let us know what you want to do just in case we're already working on an implementation of something similar. This way we can avoid any needless duplication of effort. Also, please don't forget to add tests for any new functions.