Quantitative Data Analysis for spectroscopic LEEM.

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.

Implementation

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.

Getting started

• 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

Data

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.

Acknowledgements

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