Jadhav V, Guttal V, Masila DR. 2022 Randomness in the choice of neighbours promotes cohesion in mobile animal groups. R. Soc. Open Sci. 9: 220124, doi: https://doi.org/10.1098/rsos.220124
This repository contains codes to generate data used for analysis in the article, “Randomness in the choice of neighbours promotes cohesion in mobile animal groups”. The codes are tested to run on Matlab version R2018b and newer versions.
You are welcome to use our codes for any of your work, with attribution -- specifically, by citing the above paper.
Generate data of positions, velocities and networks to analyse group cohesion for given group size and parameters.
Use Matlab code /figures/long_sim_data_pw.m to generate all the required data to analyse group cohesion for stochastic pairwise interactions. Positions, velocities of agents are stored in the .mat file named n_pw.mat.
Variables are commented within the code. To reproduce the results, it is advised to run for at least T = 3500 and the number of realisations (no_it) = 15. Interaction rates, neighbourhood range (K) and all the parameters can be changed in long_sim_data_pw.m.
After running the simulation, use the data in n_pw.mat to calculate the cohesion parameter, average number of clusters and group polarisation. To do so, run the Matlab code /figures/clus_ana_pw.m. This file stores all the required data in the cluster_ana_pw.mat file. Similarly, run /figures/alt_clus_ana.m to calculate expanse, an alternate measure of group cohesion.
The folder (figures) also contains avg_connections.m, which is used to construct a network at every time window tw (called conn_time in long_sim_data_pw.m). We also use code DBSCAN.m, written by S. Mostapha Kalami Heris, to quantify group cohesion.
Finally, run the code plot_fig_2_3.m to get figures 2A, B (and inset) of the main text.
To see the collective motion of agents, run the code simulate.m in the folder figures. Make sure that you load n_pw.mat. Variables are defined within the code and can be changed accordingly.
Generate data of positions, velocities and networks to compare group cohesion between stochastic pairwise interaction and Vicsek like averaging model for given group size and parameters.
Use Matlab code /figures/long_sim_data_at.m to generate all the required data to analyse group cohesion for averaging type model. Positions, velocities of agents are stored in the .mat file named n_at.mat.
Similar to codes for figure 2, variables are commented within the code, and it is advised to run for at least T = 3500 and the number of realisations (no_it) = 15 to reproduce the results.
After saving the required data, use the data in n_at.mat to calculate the cohesion parameter, average number of clusters and group polarisation for averaging type interaction. To get these measures run the Matlab code /figures/clus_ana_at.m. This file stores all the required data in the cluster_ana_at.mat file.
Finally, run the code plot_fig_4.m to get figure 4 of the main text.
Note: You also need to run long_sim_data_pw.m and clus_ana_pw.m files for the same N (group size) and K values to generate figure 4.
Similar to as explained above, to see the collective motion of agents for averaging type model, run the code simulate.m in the folder figures. Make sure that you load n_at.mat. Variables are defined within the code and can be changed accordingly.
To simulate the minimal described in Appendix B use the codes in folder toy_model_syn. Use Matlab code /toy_model_syn/long_sim_data_pw.m to generate positions, velocities and networks to analyse group cohesion in minimal model with pairwise interaction. Positions, velocities of agents are stored in the .mat file named n_pw.mat.
Similar to above sections, variables are commented within the code, and it is advised to run for at least T = 3500 and the number of realisations (no_it) = 15 to reproduce the results.
Use data in n_pw.mat to calculate the cohesion parameter, average number of clusters and group polarisation for minimal model with pairwise interaction. To get these measures run the Matlab code /toy_model_syn/clus_ana_pw.m. This file stores all the required data in the cluster_ana_pw.mat file. Similarly, run /toy_model_syn/alt_clus_ana.m to calculate expanse, an alternate measure of group cohesion for minimal model with pairwise interaction.
Finally, run the code plot_coh_para.m to get cohesion parameter (C) as a function of K.
To see the collective motion of agents for minimal model, run the code simulate.m in the folder toy_model_syn. Make sure that you load n_pw.mat. Variables are defined within the code and can be changed accordingly.
You are welcome to use our codes for any of your work, with attribution -- specifically, by citing the paper:
Jadhav V, Guttal V, Masila DR. 2022 Randomness in the choice of neighbours promotes cohesion in mobile animal groups. R. Soc. Open Sci. 9: 220124, doi: https://doi.org/10.1098/rsos.220124