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.
Follow the installation guide for the HyperSpy bundle.
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, numba
Otherwise, have a look at the full list of packages included in the HyperSpy-bundle.
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 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):
