If you’re looking to analyse your hyperspectral images (spectrum-images, image-images or n-dimensional- n-dimensional datasets), I can highly recommend hyperspy [1].
One of the brilliant ideas hyperspy incorporates is that we consider datasets to have a navigation dimension and a signal dimension (think, you measure a spectrum at each point on an image), and you can easily transpose between them. This means that you can «move around» on the image and see what the spectrum looks like, or transpose and see what the image looks like as a function of the spectrum.
In particular I think the model building, where you can fit components to your dataset, is really useful.
It works best with the Jedi LSP - pyright doesn’t support the way we added lazy loading / extensions to the base hyperspy package.