Static visualisation tools are accessible with Matplotlib through the cartes library, which leverages access to more projections and to data from OpenStreetMap. More dynamic visualisations in Jupyter Lab are accessible thanks to the altair and ipyleaflet libraries; other exports to various formats (including CesiumJS or Google Earth) are also available.
We recommend creating a fresh conda environment for a first installation:
# Recommended options if not set up yet conda config --set channel_priority strict conda config --add channels conda-forge # Installation conda create -n traffic -c conda-forge python=3.9 traffic conda activate traffic
Adjust the Python version (>=3.7) and append packages you may need for future works (e.g.
Then activate the environment each time you need to use the
conda activate traffic
Please only report installation issues in fresh conda environments.
Consider using mamba for a faster Conda experience.
# -n option is followed by the name of the environment conda update -n traffic -c conda-forge traffic
You may also install or update
traffic in an existing environment with pip:
pip install --upgrade traffic
For the most recent development version, clone the Github repository:
git clone https://github.com/xoolive/traffic cd traffic/ pip install .[dev]
If you intend to file a pull request, please activate