Processes data and code for Dynamics of Lagrangian Sensor Particles

This repository entails the data and Pythoncode for the publication "Dynamics of Lagrangian Sensor Particles: The Effect of Non-Homogeneous Mass Distribution" in the journal "Processes". In the following a brief introduction and guide based on the folders in the repository is laid out. More code specific instructions can be found in the respective codes. 01 --> The tracking always begins with the same 01_milti[...] folder in which the python code with OpenCV algorithm is located. For tracking the tracking to work certain directories are required in which the raw images are to be stored (separate from anything else) as well as a directory in which the results are to be save (not the same directory as the raw data). After tracking is completed for all respective experiments and the results directories are adequately labelled and stored any of the other code files can be used for respective analyses. The order of folders beyond the first 01 directory has no relevance to the order of evaluation however can ease the understanding of evaluated data if followed. 02 --> Evaluation of amount of circulations and respective circulation time in experimental vat. (code can be extended to calculate the circulation time based on the various plains that are artificially set) 03 --> Code for the calculation of the amount of contacts with the vat floor. Code requires certain visual evaluations based on the LP trajectories, as the plain/barrier for the contact evaluation has to be manually set. 04 --> Contains two codes that can be applied to results data to combine individual results into larger more processable arrays within python 05 --> Contains the code to plot the trajectory of single experiments of Lagrangian particles based on their positional results and velocity at respective position, highlighting the trajectory over the experiment. 06 --> Condes to create 1D histograms based on the probability density distribution and velocity distributions in cumulative experiments. 07 --> Codes for plotting the 2D probability density distribution (2D Histograms) of Lagrangian Particles based on the cumulative experiments. Code provides values for the 2D grid, plotting is conducted in Origin Lab or similar graphing tools, graphing can also be conducted in python whereby the seaborn (matplotlib) library is suggested. 08 --> Contain the code for the dimensionless evaluation of the results based on the respective Stokes number approaches and weighted averages. 2D histograms are also vital to this evaluation, whereby the plotting is again conducted in Origin Lab as values are only calculated in code. 09 --> Directory does not contain any python codes but instead contains the respective Origin Lab files for the graphing, plotting and evaluation of results calculated via python is given. Respective tables, histograms and heat maps are hereby given to be used as templates if necessary. The project used the Origin 2023 (64-bit) version, if no Origin license is available then Origin Lab provides a free Origin Viewer with which the projects can be opened and viewed. (https://www.originlab.com/viewer/)

本存储库包含了期刊《Processes》中发表文章《拉格朗日传感器粒子的动力学：非均匀质量分布的影响》所涉及的数据及Python代码。以下基于存储库中的文件夹，概述了简要的介绍与指南。更具体的代码使用说明可在相应代码中查阅。01 --> 追踪过程始终从包含基于OpenCV算法的Python代码的01_milti[...]文件夹开始。为了使追踪过程能够正常工作，需要特定的目录来存储原始图像（应与其它内容分离），以及一个用于保存结果的目录（不应与原始数据目录相同）。完成所有相应实验的追踪后，并对结果目录进行适当的标记和存储，便可以使用其他代码文件进行相应的分析。除首个01目录之外的其他文件夹的顺序对评估顺序没有影响，但遵循顺序有助于加深对评估数据的理解。02 --> 对实验槽中循环次数及相应循环时间的评估。（代码可扩展以基于人为设定的各种平面计算循环时间）03 --> 计算与槽底接触次数的代码。该代码需要基于LP轨迹的某些视觉评估，因为接触评估的平面/障碍物需要手动设置。04 --> 包含两个代码，可用于将结果数据合并成更大的、更易于处理的Python数组。05 --> 包含基于实验中拉格朗日粒子的位置结果和相应位置的速度，绘制单个实验轨迹的代码，并突出显示实验过程中的轨迹。06 --> 创建基于累积实验的概率密度分布和速度分布的1D直方图的代码。07 --> 基于累积实验绘制拉格朗日粒子的2D概率密度分布（2D直方图）的代码。该代码提供2D网格的值，绘图在Origin Lab或类似绘图工具中进行，也可以在Python中进行绘图，建议使用seaborn（matplotlib）库。08 --> 包含基于各自斯托克斯数方法和加权平均的无量纲结果评估代码。2D直方图对于此评估也至关重要，绘图再次在Origin Lab中进行，因为值仅在代码中计算。09 --> 目录不包含任何Python代码，而是包含用于通过Python计算结果绘图、绘图和评估的相应Origin Lab文件。提供相应的表格、直方图和热图作为模板，如有必要。本项目使用Origin 2023（64位）版本，如无Origin许可证，Origin Lab提供免费的Origin Viewer，可用于打开和查看项目。（https://www.originlab.com/viewer/）