Analyzing EELS data with HyperSpy

Timestamps:
1:36 - Using Jupyter notebooks
3:35 - Loading, signal basics and visualizing data
7:56 - Removing background and summing Ti-L23 edge
11:36 - Documentatinon for functions: docstrings
14:14 - Curve fitting quantification: model based approach
27:41 - Analysing the fine structure
29:46 - Thickness measurement with low loss signal
34:47 - Deconvolution
35:53 - Analysing the oxygen-K edge fine structure with the model based approach
38:24 - Making, adding and fitting components
45:05 - Visualizing the results
50:26 - Other notebooks (HyperSpy demos), documentation and related Python libraries

HyperSpy is a scientific Python library used to analyse a large variety of multidimensional data, with a focus on transmission electron microscopy. In this webinar, we'll be going through functionalities related to analysing electron energy loss spectra. From the more standard background subtraction and simple integration of intensities, to the more advanced model based approach of Oxygen K-edge energy loss near edge structure.

HyperSpy website: https://hyperspy.org
HyperSpy demos: https://github.com/hyperspy/hyperspy-demos/
HyperSpy demos binder: https://mybinder.org/v2/gh/hyperspy/hyperspy-demos/master
HyperSpy gitter: https://gitter.im/hyperspy/hyperspy

Atomap: https://atomap.org
pixStem: https://pixstem.org
pyXem: http://www.pyxem.org

Journal article on fine structure fitting in Ultramicroscopy: https://doi.org/10.1016/j.ultramic.2016.07.004
Preprint of this article on arXiv: https://arxiv.org/abs/1608.07814

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 838001 - MAGIMOX and No 823717 - ESTEEM3 (https://www.esteem3.eu/).

Written, recorded and edited by Dr. Magnus Nord, Electron microscopy for materials science (EMAT), University of Antwerp, Belgium.