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Numerical solutions to spatial competition in sessile organisms

Author: Eddie Lee, edlee@santafe.edu

This is the Github code repository for the manuscript "Growth, death, and competition in sessile organisms" by Edward D. Lee, Christopher P. Kempes, and Geoffrey B. West. The preprint is located here.

The code is distributed with an MIT license.

Installation

You can use Anaconda to set up your Python 3 environment to reproduce the automaton simulation results. First, git clone the repo and create the appropriate environment. Note that the code below installs some custom modules to run.

$ git clone https://github.com/eltrompetero/forests.git
$ git clone https://github.com/eltrompetero/workspace.git
$ git clone https://github.com/eltrompetero/misc.git
$ conda env create -f forests/forests.yml
$ conda activate forests
$ mkdir cache
$ mkdir plotting

This will create and activate the appropriate Anaconda environment named forests. This environment is optimized for an AMD processor and an Intel-based machine may require a different set of compiled packages.

Reproduction

The code and parameter settings for simulations shown in the figures are in pyutils/pipeline.py. The figures are in plotting (pnas).ipynb.

Simulation results are shown in the Jupyter notebook plotting (pnas).ipynb. To run the notebook, the reader might run (after following the installation instructions above)

$ jupyter notebook

The code in the notebook relies on pickles generated from the pipeline functions that cache simulation output.

Mathematica code for running the mean-field solutions is in the mathematica directory.

Further simulation and extensions

We suggest that those interested in running our simulations for particular systems modify the parameter values detailed in pipeline.py appropriately. The source code there also provides examples of how to run our automaton simulations.

Technical specs

The code must be run on a multi-threaded machine with ample RAM (we suggest at least 32GB available) and sufficient hard drive space (~50GB). Some of the simulations may take many hours to run.

We used an Ubuntu system running on a system with an AMD Ryzen 7 1700 Eight-Core Processor (with 16 threads) at 3.0GHz, 1.5TB of SSD space, 32GB of RAM, and 256GB of PCIe drive swap space, which was much more than ample to finish each individual simulation call inside pipeline.py within hours.

Troubleshooting

Please open an issue on GitHub for any questions or issues. This way anybody else with a similar problem will be able to follow the exchange in the future.