pyBarSim 0.0.2

Python package to simulate wave-dominated shallow-marine environments

pyBarSim

pyBarSim is a Python package to simulate wave-dominated shallow-marine environments using Storms (2003)'s BarSim.

Installation

You can directly install pyBarSim from pip:

pip install pybarsim

Or from GitHub using pip:

pip install git+https://github.com/grongier/pybarsim.git

Usage

Basic use:

import numpy as np
from pybarsim import BarSim2D
import matplotlib.pyplot as plt

# Set the parameters
run_time = 10000.
barsim = BarSim2D(np.linspace(1000., 900., 200),
                  np.array([(0., 950.), (run_time, 998.)]),
                  np.array([(0., 25.), (run_time, 5.)]),
                  spacing=100.)
# Run the simulation
barsim.run(run_time=10000., dt_fair_weather=15., dt_storm=1.)
# Interpolate the outputs into a regular grid
barsim.regrid(900., 1000., 0.5)
# Compute the mean grain size
barsim.summarize()
# Plot the median grid size in the regular grid
barsim.record_['Mean grain size'].plot(figsize=(12, 4))
plt.show()

For a more complete example, see the Jupyter notebook using_pybarsim.ipynb or the Binder link above.

Citation

If you use pyBarSim in your research, please cite the original article:

Storms, J.E.A. (2003). Event-based stratigraphic simulation of wave-dominated shallow-marine environments. Marine Geology, 199(1), 83-100. doi:10.1016/S0025-3227(03)00144-0

Here is the corresponding BibTex entry if you use LaTex:

@Article{Storms2003,
	author="Storms, Joep E.A.",
	title="Event-based stratigraphic simulation of wave-dominated shallow-marine environments",
	journal="Marine Geology",
	year="2003",
	volume="199",
	number="1",
	pages="83--100",
	issn="0025-3227",
	doi="https://doi.org/10.1016/S0025-3227(03)00144-0",
}

Credits

This software was written by:

Guillaume Rongier	Joep Storms	Andrea Cuesta Cano

License

Copyright notice: Technische Universiteit Delft hereby disclaims all copyright interest in the program pyBarSim written by the Author(s). Prof. Dr. Ir. J.D. Jansen, Dean of the Faculty of Civil Engineering and Geosciences

© 2023, G. Rongier, J.E.A. Storms, A. Cuesta Cano

This work is licensed under a MIT OSS licence.