vic1309 / tbarrier

Methods to extract advective, diffusive, stochastic and active transport barriers from 2D and 3D data.

TBarrier Notebook

TBarrier notebooks contains a series of jupyter notebooks that guide you through methods used to extract advective, diffusive, stochastic and active transport barriers from discrete velocity data

How to Use this Repository

• Run the code using the Jupyter notebooks available in this repository's TBarrier directory.

About

The handbook was written and tested with Python 3.7.

Familiarity with Python and its core libraries NumPy, scipy, Matplotlib, Scikit-Learn is assumed.

Authors

Alex Pablo Encinas Bartos, Balint Kaszas and George Haller

Software

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.

License

Code

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.

Text

The text content of the book is released under the CC-BY-NC-ND license. Read more at Creative Commons.

Video tutorials:

Video tutorials for the individual jupyter notebooks can be found on Youtube:

TBarrier tutorial

References

When using this code, please cite the following source for the underlying theory:

G. Haller, Transport Barriers and Coherent Structures in Flow Data– Advective, diffusive, stochastic and methods (with the assistance of A. Encinas-Bartos). Cambridge University Press, to appear (2022)