The traffic library helps working 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 visualisation (images) exports are accessible via Matplotlib/Cartopy. More dynamic visualisation frameworks are easily accessible in Jupyter environments with ipyleaflet and altair; or through exports to other formats, including CesiumJS or Google Earth.
Full installation instructions are in the documentation.
If you are not familiar/comfortable with your Python environment, please install traffic latest release in a new, fresh conda environment.
conda create -n traffic -c conda-forge python=3.9 trafficAdjust the Python version you need (>=3.7) 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 trafficWarning!
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.
For troubleshooting, refer to the appropriate documentation section.
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 a positive feedback and improves the visibility of the library.
Documentation available at https://traffic-viz.github.io/
Join the Gitter chat: https://gitter.im/xoolive/traffic
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, code is checked against static typing with mypy (pre-commit hooks are available in the repository) and extra quality checks performed by Codacy.
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. Please first activate the DEBUG messages recorded using Python
logging mechanism with the following snippet:
import logging
logging.basicConfig(level=logging.DEBUG)Bug fixes and improvements in the library are also always helpful.
If you share a fix together with the issue, I can include it in the code for you. But since you did the job, pull requests (PR) let you keep the authorship on your additions. For details on creating a PR see GitHub documentation Creating a pull request. You can add more details about your example in the PR such as motivation for the example or why you thought it would be a good addition. You will get feedback in the PR discussion if anything needs to be changed. To make changes continue to push commits made in your local example branch to origin and they will be automatically shown in the PR.
You may find the process troublesome but please keep in mind it is actually easier that way to keep track of corrections and to remember why things are the way they are.