Some minor typos etc
Option to train per-band emulators for images
Moving towards a fast implementation that runs the GP emulators per band, doing all pixels for a particular band in a single go
(it,s quite fast already). However, it hasn,t been tested, and might actually be wrong. Need some example data to throw into
it and see what happens.
So I can now do spatial (single res only so far, bear with me!) multiparameter inversions using the emulators. Apart from going into gp_emulator and making that more efficient, it is pretty much at the limit of speed right now.
Prior thingy gives starting optimisation value
Added the first_guess method to the image GP. Not tested. Needs to have something in the state to call this method2
Mostly working. Havent tested the optimisation yet, but everything is flattened and in the right co-ordinates now
So now we can do initialisation using the inverse GPs. For constant parameters, we just revert to the prior. It apparently works fairly well
 A simple spatial test using the PROBA data
So time series *WITH* uncertainty now works. And there was much rejoicing. only uncetainty missing is the spatial one now...
cleaned up the operators a bit
Added eoldas_utils, a repository of useful functions to declutter the class definitions
Adding more doc strings to classes
More cleaning up, docs...
Some extra cleaning up
create emulators is not part of eoldas_ng
notebooks moved out of main eoldas_ng package
ignore ipython autosaves
commented out some stuff
Mainly bugfixes. One was that the partial derivatives of the observation operator cost function were not updated if the parameter was set as CONSTANT. There was a summation over all possible sites to be performed that was missing. Other stuff includes some generalisation of the hessians.