This repository contains the code to showcase the methods and algorithms presented in the paper T.A. de Jong et al., Quantitative analysis of spectroscopic Low Energy Electron Microscopy data: High-dynamic range imaging, drift correction and cluster analysis, Ultramicroscopy, Volume 213, 2020, DOI: 10.1016/j.ultramic.2019.112913.

In addition it contains the code to stitch LEEM images using a similar algorithm.

It is organized as a set of notebooks, reproducing the different techniques and algorithms as presented in the paper, as well as the Figures. The notebooks are in some cases supported by a separate Python file with library functions. For human readable diffs, each notebook is shadowed by a Python file using jupytext.

The code makes extensive use of dask for lazy and parallel computation, the N-D labeled arrays and datasets library xarray, as well as the usual components of the scipy stack such as numpy, matplotlib and skimage.

• Git clone or download this repository.
• pip install . Consider the -e flag for an editable install.
• (Alternatively) Create a Python environment with the necessary packages, either from requirements.txt or (for conda users) from environment.yml.
• Activate the environment and start a Jupyter notebook and have a look at the notebooks

The data is available separately at http://doi.org/10.4121/uuid:7f672638-66f6-4ec3-a16c-34181cc45202 (via https://researchdata.4tu.nl/). The zeroth notebook facilitates easy download of all (or parts of) related data. The data of 6 - Stitching is not yet available.

This work was financially supported by the Netherlands Organisation for Scientific Research (NWO/OCW) as part of the Frontiers of Nanoscience (NanoFront) program.