GRITCLEAN Code and Files -- Part 4

This dataset contains a compressed folder of the data and MATLAB scripts used produce relevant figures and candidates for GRITCLEAN: A glitch veto scheme for Gravitational wave data as presented in https://arxiv.org/abs/2401.15237 The codes in this dataset include: A PSO-based matched filtering search pipeline which can be run on either the positive or the negative chirp time space. A standalone MATLAB script called GRITCLEAN.m which can run the GRITCLEAN hierarchical vetoes on a set of positive and negative chirp time space estimated parameters. A plotting script plot_candidates.m to generate Figures 4, 6, 7, 9, 10, 11 and 12. A Figures directory that contains scripts to generate Figures 1, 2 (including inset spectrogram) and 3. The files in this dataset include: GVSsegPSDtrainidxs.mat, a binary MATLAB file containing training indices for all segments from which the Power Spectral Densities (PSDs) are estimated, this is done via the scripts provided, namely, getsegPSD.m and createPSD.m. A sample HDF5 file used (H-H1_GWOSC_O3a_4KHZ_R1-1243394048-4096.hdf5) JSON files containing information about the data segments and the strain data files from which they originate from. Text files containing the parameters estimated by the PSO-based pipeline across the positive and negative chirp time space runs. Detailed instructions on dependencies, downloading the dataset and running the codes are given in a README.txt file included with this dataset. The user is recommended to go through this file first. The scripts enclosed have dependencies on JSONLAB , the Parallel Computing Toolbox and Signal Processing Toolbox for MATLAB, along with additional scripts provided in GitHub repositories Accelerated-Network-Analysis and SDMBIGDAT19 . Instructions on installing these dependencies are provided in README.txt. All codes have been developed and tested on MATLAB R2022 and R2023.本数据集包含一份压缩文件夹，内含用于生成GRITCLEAN相关图表与候选结果的数据及MATLAB脚本。GRITCLEAN是一种面向引力波数据的脉冲干扰否决方案，相关研究成果已发表于https://arxiv.org/abs/2401.15237。 本数据集包含的代码如下： - 一款基于粒子群优化（Particle Swarm Optimization, PSO）的匹配滤波搜索流水线，可在正或负啁啾时间域上运行。 - 一款独立的MATLAB脚本`GRITCLEAN.m`，可针对一组正、负啁啾时间域的估计参数运行GRITCLEAN层级否决流程。 - 绘图脚本`plot_candidates.m`，用于生成图4、图6、图7、图9、图10、图11及图12。 - 一个绘图脚本目录，内含用于生成图1、图2（含内嵌频谱图）及图3的相关脚本。 本数据集包含的文件如下： - `GVSsegPSDtrainidxs.mat`：二进制MATLAB文件，存储了所有用于估计功率谱密度（Power Spectral Density, PSD）的片段的训练索引，该流程可通过配套脚本`getsegPSD.m`与`createPSD.m`实现。 - 一份示例HDF5文件`H-H1_GWOSC_O3a_4KHZ_R1-1243394048-4096.hdf5`。 - 若干JSON文件，存储了数据片段及其来源的应变数据文件的相关信息。 - 若干文本文件，存储了基于PSO的流水线在正、负啁啾时间域运行时所估计的参数。 本数据集附带的`README.txt`文件中详细说明了依赖项配置、数据集下载方式以及代码运行方法，建议用户优先阅读该文档。 本数据集内含的脚本依赖MATLAB的JSONLAB库、并行计算工具箱（Parallel Computing Toolbox）与信号处理工具箱（Signal Processing Toolbox），同时还依赖GitHub仓库`Accelerated-Network-Analysis`与`SDMBIGDAT19`中提供的额外脚本。相关依赖的安装说明已收录于`README.txt`中。 所有代码均已在MATLAB R2022与R2023版本上完成开发与测试。