TintX is an adaptation of the tint tracking algorithm. Tint and tintX are easy-to-use storm cell tracking packages. While Tint is meant to be applied to radar data using the py-ART toolkit tintX can be applied with any data - for example output from numerical weather prediction models. The original tracking algorithm has been developed by a team of researchers at Monash University Raut et al. 2020.

The tintX package can be installed using the conda-froge conda channel:

conda install -c conda-forge tintx

Alternatively the package can be installed with pip:

python -m pip install tintx

Documentation can be found on the official documentation page of this library.

If you just want to try the usage and play with tracking data you can follow this link to launch and familiarise yourself with the tracking by executing one of the example notebooks.

Any contributions to improve this software in any way are welcome. Below is a check list that makes sure your contributions can be added as fast as possible to tintX:

• Create a fork of this repository and clone this fork (not the original code)
• Create a new branch in the forked repository git checkout -b my-new-branch
• Add your changes
• Make sure all tests are sill running. To do so run the following commands
  • make lint
  • make test
  • make docs
• Create a new pull request to the main branch of the original repository.

You can add more examples to the docs/source documentation folder. Because notebooks are executed automatically by the unit tests GitHub workflow, you should make sure that any additional dependencies imported in the notebook are added to the docs section in the setup.py. All notebooks should be run with a kernel called tintx to install a new kernel named tintx run the following command in the root directory of the cloned repository:

python -m ipykernel install --name tintx --display-name "tintX kernel"\
    --env DATA_FIELS $PWD/docs/source/_static/data --user

Make also sure to add additional link(s) to the notebook readme file.

This work is the adaptation of tracking code in R created by Bhupendra Raut who was working at Monash University, Australia in the Australian Research Council's Centre of Excellence for Climate System Science led by Christian Jakob. This work was supported by the Department of Energy, Atmospheric Systems Research (ASR) under Grant DE-SC0014063, “The vertical structure of convective mass-flux derived from modern radar systems - Data analysis in support of cumulus parametrization”

The development of this software was funded by the Australian Research Council's Centre of Excellence for Climate Extremes under the funding number CE170100023.