Spectroscopy Data Analysis in Python Using HyperSpy
Tutorial for the eBEAM2024 school on nano-optics with free electrons
Aussois, September 1-13, 2024
Dear attendants of the eBEAM summer school,
we are happy to introduce you to data analysis using HyperSpy and its extensions LumiSpy and exSPy at the eBEAM 2024.
To follow the interactive tutorials and make maximum use of the limited time available, we kindly ask you to bring your laptop and already install the HyperSpy-bundle matching your system before coming to the school. The HyperSpy bundle ships a python environment including all relevant packages.
Edit: The slides from the HyperSpy lecture at the summer school can now also be found in this repository: eBEAM24_HyperSpy-LumiSpy-eXSpy.pdf (use the rightclick menu to download separately from the rest of the repository).
Follow the installation guide for the HyperSpy bundle. The demo notebooks have been tested to run on the HyperSpy bundle version 2024.07.15. Some of the examples might not run with older HyperSpy versions (<2.0.0 / bundle releases up to 2023.11.20) and we do not guarantee for operation with newer HyperSpy versions, though the syntax/API should not change until the next major release (v3.0.0) will be released.
If you already have python installed on your system and prefer not installing the bundle, we recommend creating a new environment for the tutorial and installing at least the following packages using pip or conda:
hyperspy, exspy, lumispy, hyperspy-gui-ipywidgets, jupyter-lab, ipympl, scikit-learn, numba
Note that you should have at least version 1.26.4 of numpy installed.
Otherwise, have a look at the full list of packages included in the HyperSpy-bundle.
(if you run into problems, a number of experienced colleagues can help you in the breaks of the summer school)
The tutorials are based on Jupyter Notebooks.
Download the tutorial notebooks and demo data as zip file from this repository and unpack in a local directory.
The tutorial is split in three jupyter notebooks to cater both for participants with ot without precious experience using HyperSpy:
1_Intro_HyperSpy-LumiSpy-eXSpy.ipynb- A basic introduction to HyperSpy to get started with core functionalitie2_AdvancedExamples_HyperSpy-LumiSpy-eXSpy.ipynb- Some more advanced usages examples for users with previous experience3_MachineLearning_Plasmonic_EELS_BlindSourceSeparation.ipynb- A dedicated file introducing the machine learning features for denoising and decomposition of spectral maps
The relevant datasets are provided in the subfolder data.
If possible, try to already launch jupyter-lab on your computer and open the first tutorial notebooks to make sure that we can directly dive into the HyperSpy usage during the tutorials.
There are multiple ways to start jupyter-lab, see for example the JupyterLab User Guide or the Ansys JupyterLab Quick Start Guide.
If you are new to programming or programming with python, we recommend the W3 schools Python Tutorial.
(Non-interactive) Visualizing the tutorial notebooks online:
(Interactive) Running the tutorial notebooks online (may be slow):
