dewi-kadita: A Python Library for Idealized Fish Schooling Simulation with Entropy-Based Diagnostics

TL;DR

dewi-kadita is an open-source Python library that simulates fish schooling behavior using a 3D model and introduces entropy-based metrics to analyze organizational complexity, validated across canonical configurations.

Contribution

The library implements a 3D Couzin zone model with novel entropy diagnostics and a unified Oceanic Schooling Index, enhancing analysis of collective behavior in marine systems.

Findings

Successfully reproduces known phase behaviors in canonical configurations

Entropy metrics discriminate organizational mechanisms beyond classical order parameters

Achieves 10-100x acceleration with JIT compilation for large-scale simulations

Abstract

Collective motion in fish schools exemplifies emergent self-organization in active matter systems, yet computational tools for simulating and analyzing these dynamics remain fragmented across research groups. We present dewi-kadita, an open-source Python library implementing the three-dimensional Couzin zone-based model with comprehensive entropy diagnostics tailored for marine collective behavior research. The library introduces seven information-theoretic metrics -- school cohesion entropy, polarization entropy, depth stratification entropy, angular momentum entropy, nearest-neighbor entropy, velocity correlation entropy, and school shape entropy -- that characterize distinct organizational features inaccessible to classical order parameters. These metrics combine into an Oceanic Schooling Index (OSI) providing a single scalar measure of collective disorder. Validation across four canonical configurations (swarm, torus, dynamic parallel, highly parallel) confirms correct reproduction of known phase behaviors: the swarm maintains disorder with polarization $P < 0.1$ and OSI $\approx 0.71$, while the highly parallel state achieves $P = 0.998$ with OSI $= 0.24$ and velocity correlation entropy vanishing to zero. The entropy framework successfully discriminates the torus and dynamic parallel configurations that exhibit comparable order parameter magnitudes through different organizational mechanisms. Numba just-in-time (JIT) compilation accelerates pairwise interaction calculations by $10$--$100\times$, enabling simulations of $150$--$250$ agents over $1000$--$2000$ time steps within five minutes on standard workstation hardware. NetCDF4 output ensures interoperability with oceanographic analysis tools. The library addresses the need for standardized, reproducible infrastructure in collective behavior modeling analogous to established molecular dynamics codes.