Dual Simulated SEND and EDX dataset

A simulated SEND + EDX dataset along with the code used to produce it. code SEND_ground_Truth_Segment_Model-AB.ipynb (ipynb which outlines the code for end to end data production, however some of the actual SEND data production is done through the gen_data.py and add_noise.py files due to system memory requirements making a cluster job more convenient) gen_data.py (python file for creating an intermediate simulated SEND dataset) add_noise.py (python file that takes the intermediate SEND dataset and samples it to produce pseudo-experimental data) phase_maps Contains pairs of jpg/npy files that show/quantify the proportional of each phase at each pixel location as constructed in the atomic model data SEND.hspy (the simulated SEND dataset as per the atomic model) EDS.hspy (the simulated EDS dataset) EDS-varied-dose.zip (EDS simulations at different electron doses) atomic_model.xyz (ASE atomic model for the simulated data) labelled_voxels.npy (the phase labels for the 3d array of volumetric-pixels used to produce the atomic model) Added in newer version: the VAE processing of the SEND data has been included data RadialData data_radial.hspy (A radial transformation of the simulated SEND dataset used for VAE testing) data_radial_training_data.hspy (The data_radial.hspy dataset but with pattern populations reweighted to better represent high variance regions) navigation_axis_variance.npy (The mean variance within the 2D diffraction signal at each pixel probe position) signal_axis_variance.npy (The variance of each signal pixel in the 2D diffraction pattern, averaged for each pixel probe position) RadialModel best_model.hdf5 (The trained weights for the VAE) PCA_comps_mse N (The number of PCA components used to estimate the centroids of the clustering) latspacedata.npy (The coordinates of the simulated SEND data in the 2d latent space) mapdata.npy (The assigned cluster label to each of the patterns in the simulated SEND data) Regions i.jpg (the region and radial pattern of each of the clusters ML_clusters_mse encode_data.npy (The coordinates of the simulated SEND data in the 2d latent space) enc_mask.npy (The encoded data transformed into a density based fixed image) ml_cluster_map.npy (The ML predictions of the centroid locations) ML_clusters N (This folder is then the same as the PCA equivalent)