Hyperspectral fitting using hyperspy

This set of Jupyter notebooks are used in the analysis of hyperspectral datasets with the hyperspy library.

The file "clmap_type-IIB_HPHT_diamond-spikes_removed.hspy" is the example dataset.

Summary of the points covered in the different notebooks:

• 01 - Data Cleaning : covers how to fit and remove background, correct imaging shift using cross correlation
• 02 and 03 - Noise Analysis and eV conversion: covers how to measure and model heteroscedastic noise in the dataset using Principal Component Analysis. Conversion from nm to eV spectral representation with jacobian conversion, setting of a variance model in the hyperspy object
• 04 - Model Fitting: Defining and fitting a model on the data. Saving the results
• 04bis - Fit results plot formatting: Formatting plots with all fit results as well as \(\chi^2 \) statistical goodness-of-fit indicators

At the time of initial upload (30.11.2021), hyperspy is in v 1.6.5 which does not support non-uniform axes for data treatment in eV space. The file "Readme - Installing hyperspy from source" contains instructions on how to install the development version.