Code and simulation data for model of ideal chemotactic agent combining spatial and temporal gradient-sensing
收藏资源简介：
This repository contains python code and simulation data files for the paper “Information theory of chemotactic agents using both spatial and temporal gradient-sensing” by Julian Rode, Maja Novak, Benjamin M. Friedrich. The code in this repository was developed by Julian Rode based on an earlier version by Andrea Auconi in the research group of Benjamin M. Friedrich at TU Dresden, Dresden, Germany. The python package ‘pyegotaxis’ contained in ’pyegotaxis-0.2.zip’ is a snapshot (tag 0.2) of the git repository used for all simulations and data analysis in the paper, available also here:https://github.com/rodjul42/pyegotaxis The file ‘readme.MD’ in ‘pyegotaxis-0.2.zip’ includes instructions for installation and a minimal example. The jupyter notebooks contained in notebooks.zip recreate the figures 2-5 of the paper, as well as the supplementary figures S1-S10 and movies, using the data files. Data files needed to recreate figures: maxLD0_sum.fea Needed for Fig 2 and S1 Maximum likelihood no motility noise infoD0_sum.fea Needed for Fig 2 and S1 Infotaxis no motility noise maxLDrot_sum.fea Needed for Fig 3 and S2 Maximum likelihood motility noise infoDrot_sum.fea Needed for Fig 3 and S2 Infotaxis motility noise infoINFO_small_sum.fea Needed for Fig 4 and Fig 5 Infotaxis SC vs TC maxLINFO_small_sum.fea Needed for Fig 4 and Fig 5 Maximum likelihood SC vs TC expdata.hdf Needed for Fig 5 SC vs TC data from experiments expdata_trajectories.npz Needed for Fig 5 Trajectories from experiments expdata.hdf Needed for Fig 5 SC vs TC data from experiments 10008_0.0.npz Needed for Fig S3 example for SC vs TC infotaxis 2222_0.0.npz Needed for Fig S3 example for SC vs TC maximum likelihood Drotlam_kernel_large_sum2.fea Needed for Fig S4 Infotaxis motility noise larger domain spaceD_lT_sum.fea Needed for Fig S5 infotaxis smaller space discretization and normal target size spaceD_O_sum.fea Needed for Fig S5 infotaxis normal space discretization and normal target size spaceD_sum.fea Needed for Fig S5 infotaxis smaller space discretization and smaller target size infomaxlEXP_sum.fea Needed for Fig S6 Maximum likelihood SC vs TC for exponentialconcentration field infoInfoEXP_20_sum.fea Needed for Fig S6 Infotaxis SC vs TC for exponentialconcentration field infoRoblam_small_sum.fea Needed for Fig S7 Infotaxis robustness sensing noise maxLRoblam_small_sum.fea Needed for Fig S7 Maximum likelihood robustness sensing noise mDrotinfo_sum.fea Needed for Fig S8 infotaxis robustness motility noise mDrotmaxL_sum.fea Needed for Fig S8 Maximum likelihood robustness motility noise gamma.fea Needed for Fig S9 infotaxis robustness non-radial concentration field, x(t0)=(-R0,0) gamma2.fea Needed for Fig S9 infotaxis robustness non-radial concentration field, x(t0)=(0,-R0) vdrift.fea Needed for Fig S10 infotaxis robustness concentration field distorted by drift, x(t0)=(0,-R0) vdrift2.fea Needed for Fig S10 infotaxis robustness concentration field distorted by drift, x(t0)=(-R0,0) vdrift3.fea Needed for Fig S10 infotaxis robustness concentration field distorted by drift, x(t0)=(R0,0)
本仓库包含Julian Rode、Maja Novak与Benjamin M. Friedrich合著论文《基于空间与时间梯度感知的趋化智能体信息理论》配套的Python代码与仿真数据文件。 本仓库中的代码由Julian Rode基于Andrea Auconi开发的早期版本修改而来，该早期版本出自德国德累斯顿工业大学（TU Dresden）Benjamin M. Friedrich研究团队。 压缩包`pyegotaxis-0.2.zip`内包含的Python包`pyegotaxis`是本论文所有仿真与数据分析所用Git仓库的快照（版本标签为0.2），其公开仓库地址为：https://github.com/rodjul42/pyegotaxis。 该压缩包内的`readme.MD`文件包含安装指南与极简使用示例。 `notebooks.zip`中的Jupyter笔记本可借助上述数据文件复现论文中的图2至图5，以及补充图S1至S10与配套动画。 复现图表所需的数据文件如下： maxLD0_sum.fea：用于复现图2与补充图S1，对应无运动噪声场景下的最大似然分析 infoD0_sum.fea：用于复现图2与补充图S1，对应无运动噪声场景下的信息趋化（Infotaxis）分析 maxLDrot_sum.fea：用于复现图3与补充图S2，对应带运动噪声场景下的最大似然分析 infoDrot_sum.fea：用于复现图3与补充图S2，对应带运动噪声场景下的信息趋化分析 infoINFO_small_sum.fea：用于复现图4与图5，对应信息趋化的空间趋化（Spatial Chemotaxis，SC）与时间趋化（Temporal Chemotaxis，TC）对比分析 maxLINFO_small_sum.fea：用于复现图4与图5，对应最大似然法的空间趋化与时间趋化对比分析 expdata.hdf：用于复现图5，对应实验获取的空间趋化与时间趋化实测数据 expdata_trajectories.npz：用于复现图5，对应实验获取的运动轨迹数据 expdata.hdf：用于复现图5，对应实验获取的空间趋化与时间趋化实测数据 10008_0.0.npz：用于复现补充图S3，对应空间趋化与时间趋化的信息趋化分析示例 2222_0.0.npz：用于复现补充图S3，对应空间趋化与时间趋化的最大似然分析示例 Drotlam_kernel_large_sum2.fea：用于复现补充图S4，对应大计算域下带运动噪声的信息趋化分析 spaceD_lT_sum.fea：用于复现补充图S5，对应更小空间离散化与标准靶标尺寸的信息趋化分析 spaceD_O_sum.fea：用于复现补充图S5，对应标准空间离散化与标准靶标尺寸的信息趋化分析 spaceD_sum.fea：用于复现补充图S5，对应更小空间离散化与更小靶标尺寸的信息趋化分析 infomaxlEXP_sum.fea：用于复现补充图S6，对应指数浓度场下的最大似然法空间趋化与时间趋化对比分析 infoInfoEXP_20_sum.fea：用于复现补充图S6，对应指数浓度场下的信息趋化空间趋化与时间趋化对比分析 infoRoblam_small_sum.fea：用于复现补充图S7，对应信息趋化的感知噪声鲁棒性分析 maxLRoblam_small_sum.fea：用于复现补充图S7，对应最大似然法的感知噪声鲁棒性分析 mDrotinfo_sum.fea：用于复现补充图S8，对应信息趋化的运动噪声鲁棒性分析 mDrotmaxL_sum.fea：用于复现补充图S8，对应最大似然法的运动噪声鲁棒性分析 gamma.fea：用于复现补充图S9，对应非径向浓度场下的信息趋化鲁棒性分析，初始位置x(t0)=(-R0,0) gamma2.fea：用于复现补充图S9，对应非径向浓度场下的信息趋化鲁棒性分析，初始位置x(t0)=(0,-R0) vdrift.fea：用于复现补充图S10，对应带漂移畸变浓度场的信息趋化鲁棒性分析，初始位置x(t0)=(0,-R0) vdrift2.fea：用于复现补充图S10，对应带漂移畸变浓度场的信息趋化鲁棒性分析，初始位置x(t0)=(-R0,0) vdrift3.fea：用于复现补充图S10，对应带漂移畸变浓度场的信息趋化鲁棒性分析，初始位置x(t0)=(R0,0)
