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Marine Modelling in Python

A Python course given at the Royal Netherlands Institute for Sea Research (NIOZ) - Yerseke

This repository represent the materials used and develop at the PhD course "Marine modelling in Python" which starts at September 17 2018 in Yerseke, the Netherlands.

Participants and contributors were (in aplhabetical order): Camilla Bertolini, Celine van Bijsterveldt, Chiu Cheng, Tom Cox, Greg Fivash, Evert de Froe, Marieke Hagg, Rosanna van Hespen, Anna van der Kaaden, Marc Rovira, Tri Tri Juliansyah Muharam Sambas, Koen Siteur, Jaco de Smit, Tanja Stratmann, Roeland van de Vijsel, Daphne van der Wal, Lauren Wiesebron, Eva-Maria Zetsche, and Heyue Zhang.

The repository will contain the lecture sheets, the example Jupyter notebooks, and the notebooks developed by the students during the course, about the challenges that are posed to the student and worked on in groups.

Table of contents

Folder: Lectures

Python_Course_lecture_1.ipynb

Explains how to use Python, including basic operations and what development environment to use.

Python_Course_lecture_2.ipynb

How to making a basic, non-spatial model is Python, and plotting the results.

Python_Course_lecture_3.ipynb

Spatial modelling, using the mussel bed self-organisation model as an example.

Python_Course_lecture_4.ipynb

Hydrodynamics: modelling the effects of a plant tussock to flowing water

Python_Course_lecture_5.ipynb

Parallel computing, using a Fractal and Klausmeier's arid vegetation model as an example.

Python_Course_lecture_6.ipynb

Individual-based model, using mussel aggregative patterning as an example.

Folder: Examples

The following document titles are self-explanatory

1.1 - Logistic growth.ipynb

1.2 - Natural resource modelling.ipynb

1.3 - Chaos.ipynb

2.1 - Predator-Prey dynamics.ipynb

2.2 - Lotka-Volterra Competition.ipynb

3.1 - Spatial modelling of patterned Mussel beds.ipynb

3.2 - Arid vegetation patterns - Klausmeier.ipynb

3.3 - Predator-Prey Spiral Waves.ipynb

4.1 - Shallow water equations with a Tussock.ipynb

5.1 - Using Numba to accelerate the mussel model.ipynb

5.2 - Using PyOpenCL to accelerate the mussel model.ipynb

6.1 - IBM - Mussel aggregation patterns.ipynb

Folder: Accelerating

The following document titles are self-explanatory

Accelerating Arid vegetation patterns - 1 Standard model.ipynb

Accelerating Arid vegetation patterns - 2 Numba.ipynb

Accelerating Arid vegetation patterns - 3 cpp.ipynb

Accelerating Arid vegetation patterns - 4 PyOpenCL.ipynb

Accelerating the Mandelbrot fractal - 1 Numba.ipynb

Accelerating the Mandelbrot fractal - 2 Cython.ipynb

Accelerating the Mandelbrot fractal - 3 cpp.ipynb

Folder: Groupwork

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