The ever-growing data complexity, size, and speed at which it is created in experiment renders humanbased analysis not only impractical, but also largely limits the discovery of latent features, which often equip a material with a certain functionality . This has stimulated the development of automated computer-based and machine-learning analysis algorithms to harvest the rich information contained in the data and to turn the data into interpretable physical quantities . For example, principle component analysis and clustering were employed to automatically separate different phases in a bismuth ferrite sample at atomic resolution obtained from a multi-gigabyte 4D-STEM data set . The development of open-source-based data-analysis tools has been paramount for treating and interpreting multidimensional and large-scale data sets from different microscope manufacturers in an efficient manner and provide flexible platforms towards on-the-fly data analysis even of big data sets .