dewi-kadita: 一种用于理想化鱼群行为模拟的Python库，基于熵的诊断
dewi-kadita: A Python Library for Idealized Fish Schooling Simulation with Entropy-Based Diagnostics
AI总结：
dewi-kadita是一种基于熵的Python库，用于模拟理想化鱼群行为并提供熵诊断，通过七个信息论度量标准表征集体无序，支持高效模拟和海洋学分析工具的互操作性。
摘要：
鱼群中的集体运动体现了主动物质系统中涌现的自组织现象，但用于模拟和分析这些动态的计算工具在不同研究组之间仍然碎片化。我们介绍了dewi-kadita，一个开源的Python库，实现了三维Couzin区域模型，并提供了全面的熵诊断，专门针对海洋集体行为研究。该库引入了七个信息论度量标准——鱼群凝聚力熵、极化熵、深度分层熵、角动量熵、最近邻熵、速度相关熵和鱼群形状熵——这些度量标准可以表征不同的组织特征，这些特征无法通过经典顺序参数来描述。这些度量标准结合成一个海洋鱼群指数（OSI），提供了一个集体无序的单一标量度量。在四个典型配置（群集、环形、动态并行、高度并行）上的验证确认了正确再现已知相行为：群集保持无序，极化P < 0.1且OSI≈0.71，而高度并行状态达到P=0.998，OSI=0.24且速度相关熵趋近于零。熵框架成功区分了环形和动态并行配置，这两种配置表现出相似的顺序参数幅度，但通过不同的组织机制。Numba即时编译加速了成对相互作用的计算，速度提升10-100倍，使在标准工作站硬件上能够在五分钟内完成150-250个代理在1000-2000个时间步的模拟。NetCDF4输出确保了与海洋学分析工具的互操作性。该库解决了集体行为建模中对标准化、可重复基础设施的需求，类似于已建立的分子动力学代码。
英文摘要：
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.
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