Information Theory of Composite Motifs

Computational simulations in support of our work on the information theory of composite motifs (Mascolo & Erill, 2024)

Installation

1. Clone the repo

   git clone https://github.com/github_username/repo_name.git

2. Install Python requirements

   The dependencies are listed in the itcm_env_minimal.yml file.

   Conda users can create a ready-to-use environment with

   conda env create -f itcm_env_complete.yml

Usage

Run evolutionary simulations

Evolutionary simulations can be run by running the evolve_reg_sys.py script.

The settings can be chosen by editing the settings.json file.

Reproduce the figures in our pre-print

Gather data

The results of all our in silico experiments can be obtained by unzipping the results.zip folder.

Analyze data

The data can be analyzed to regenerate all the Figures in Mascolo & Erill (2024). They will be saved as PNG files.

This can be done in two ways:

1. By running the analyze_results.py script in the src folder.

2. By running the regenerate_figures.ipynb jupyter notebook in the src folder. The notebook allows for step-by-step serial regeneration of all the figures and provides brief descriptions and explanations.