dewi-kadita：基于熵诊断的理想化鱼群模拟Python库

摘要

鱼类集群运动展现了活性物质系统中涌现的自组织现象，然而用于模拟和分析这些动力学的计算工具仍分散于不同研究团队。我们推出dewi-kadita这一开源Python库，该库实现了基于库津区域的三维模型，并配备了专为海洋集体行为研究定制的综合熵诊断工具。该库引入七种信息论度量指标——集群内聚熵、极化熵、深度分层熵、角动量熵、最近邻熵、速度关联熵和集群形态熵——这些指标可表征经典序参数无法捕捉的独特组织特征。这些指标综合形成海洋集群指数（OSI），为集体无序度提供单一标量度量。在四种典型构型（无序群、环状群、动态平行群、高度平行群）中的验证证实了已知相行为的正确复现：无序群保持极化度P < 0.1且OSI约等于0.71的紊乱状态，而高度平行态实现P = 0.998且OSI = 0.24，速度关联熵趋近于零。该熵框架成功区分了通过不同组织机制呈现相似序参数量级的环状群和动态平行群构型。Numba即时编译技术将成对相互作用计算速度提升10-100倍，使标准工作站能在五分钟内完成150-250个智能体超过1000-2000个时间步长的模拟。NetCDF4输出格式确保了与海洋学分析工具的互操作性。该库满足了集体行为建模领域对标准化、可复现基础设施的需求，其作用类似于成熟的分子动力学代码。

English

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--100times, 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.

dewi-kadita：基于熵诊断的理想化鱼群模拟Python库

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

摘要

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