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

Step 1: Gather data

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

Step 2: 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.