TBarrier Notebook contains a series of jupyter notebooks that guide you through methods to extract advective, diffusive, stochastic and active transport barriers from discrete velocity data. It implements algorithms discussed in more detail in the following forthcoming book:
G. Haller, Transport Barriers and Coherent Structures – Advective, diffusive, stochastic and methods (with the assistance of A. Encinas-Bartos). Cambridge University Press, to appear (2022)
- Run the code using the Jupyter notebooks available in this repository's TBarrier directory.
The notebooks were written and tested with Python 3.7.
Familiarity with Python and its core libraries NumPy, scipy, Matplotlib, Scikit-Learn is assumed.
The jupyter notebooks were tested with Python 3.7.
The libraries used to run this book are listed in requirements.txt.
For a complete Installation-guideline we refer to the 'Installation.md'in this repository.
You can read more about using conda environments in the Managing Environments section of the conda documentation.
The notebooks will be continuously update so please always check out the latest version.
The code in this repository, including all code samples in the notebooks listed above, is released under the GNU license. Read more at the Open Source Initiative.
The text content of the notebook is released under the CC-BY-NC-ND license. Read more at Creative Commons.
When using this code, please cite the following source for the underlying theory:
G. Haller, Transport Barriers and Coherent Structures – Advective, diffusive, stochastic and methods (with the assistance of A. Encinas-Bartos). Cambridge University Press, to appear (2022)
Please report any issues/bugs to Alex Pablo Encinas Bartos (enalex@ethz.ch)