Images of simulated glitches in Numpy format

These images refer to the dataset of simulated glitches used in the Razzano &amp; Cuoco 2018 paper (Classical and Quantum Gravity, 25, 9, see reference below).<br>The data set is divided in different directories containing the training, testing and validation datasets for the seven glitch classes:<br>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.<br>In order to read the data, you can use the load command of Numpy, e.g.:<br>import numpy as npf = np.load("train/GAUSS/4063_GAUSS_spec_data.npy")print(f.shape)The output will be (513, 241)<br>