Images of simulated glitches in Numpy format

These images refer to the dataset of simulated glitches used in the Razzano & Cuoco 2018 paper (Classical and Quantum Gravity, 25, 9, see reference below).The data set is divided in different directories containing the training, testing and validation datasets for the seven glitch classes:NOISEGAUSSSGRDCHIRPLIKEWHISTLELIKESCATTEREDLIKE In order to build the directory tree, download all the files into a directory and then unzip all the files.This will create 3 subdirectories (train,test,val), with subdirectories for each class.In order to read the data, you can use the load command of Numpy, e.g.:import numpy as npf = np.load("train/GAUSS/4063_GAUSS_spec_data.npy")print(f.shape)The output will be (513, 241)