Geosimulation using fields and agents
This repository holds a Jupyter notebook demonstrating the Daisyworld model implementation in Campo, a YAML file to create the Python environment required to run the model, and necessary scripts for pre- and postprocessing.
You can run the Jupyter notebook either online using Google Colab, or locally on your own computer.
More information will be given in the Agile 2023 workshop.
Running online in Google Colab
You need a Google account to run the notebook.
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Open the notebook using the following link: Colab notebook
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Create a copy of the notebook in your own Google Drive by using the Copy to Drive button.
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After copying you can run the notebook. The first code cell will install the required software and may take a few minutes to complete.
Running on your local machine
How to install
A few steps are required to run the Jupyter notebook. General information on Jupyter notebooks and manuals can be found here. The user guide and short reference on Conda can be found here.
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You will need a working Python environment, we recommend to install Miniconda. Follow their instructions given at:
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Open a terminal (Linux/macOS) or Miniconda command prompt (Windows) and browse to a location where you want to store the course contents.
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Clone this repository, or download and uncompress the zip file. Afterwards change to the
agile2023folder. -
Create the required Python environment:
Linux/macOS:
conda env create -f environment/environment.yamlWindows:
conda env create -f environment\environment.yaml
The environment file will create a environment named agile2023 using Python 3.11. In case you prefer a different name or Python version you need to edit the environment file.
How to run
Activate the environment in the command prompt:
conda activate agile2023
Then change to the notebook folder.
You can now start the Jupyter notebook from the command prompt. The notebook will open in your browser:
jupyter lab course.ipynb
Further reading
Background on DaisyWorld:
https://en.wikipedia.org/wiki/Daisyworld
Scientific literature about Campo and LUE:
M.P. de Bakker, K. de Jong, O. Schmitz, D. Karssenberg (2017). Design and demonstration of a data model to integrate agent-based and field-based modelling. Environmental Modelling & Software, 89, 172-189, DOI: 10.1016/j.envsoft.2016.11.016.
K. de Jong, D. Karssenberg (2019). A physical data model for spatio-temporal objects. Environmental Modelling & Software, 122, 104553, DOI: 10.1016/j.envsoft.2019.104553.
K. de Jong, D. Panja, M. van Kreveld, D. Karssenberg (2021). An environmental modelling framework based on asynchronous many-tasks: Scalability and usability. Environmental Modelling & Software, 139, 104998, DOI: 10.1016/j.envsoft.2021.104998.