Setting a variance model in the lumispy object after Jacobian transform about lumispy HOT 4 CLOSED

Dear @jordiferrero,

thank for putting a comment on this. Indeed, it would be great to implement this! I think it is much of a problem, the noise properties of hyperspy admit a signal object of the same dimensions as the data object in the sig.metadata.Signal.Noise_properties.variance properties of the object. There's even a method dedicated to setting it up: data_ev.estimate_poissonian_noise_variance(sev_varmap)

Based on my workflow, I would do something like this:

variance_map_nm = data_nm.get_variance_map() #Assuming that a variance map already exists
data_eV = data_nm.to_eV() #Considering that the jacobian conversion is performed

EnergyAxis = data_eV.axes_manager.signal_axes[0] #To get axis points of the energy axis

variance_map_eV = sev._deepcopy_with_new_data(\
            varmap.isig[::-1].data*(hc/EnergyAxis**2)**2) #Jacobian-square renormalised energy map

data_eV.estimate_poissonian_noise_variance(variance_map_eV )

What do you think?

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Thanks for the initiative.

In principle, I would add a function var2eV similar to

It would differ only by the additional squaring (your 4th line). Then in

We would have to do the same for to_invcm.

I could draft a PR proposal later today, unless you want to do it yourself.

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Just to document this discussion for the future.
@jlaehne has set as default the s.estimate_poissonian_noise_variance() for estimating the noise in any general noise signal.
However, if you pass in your own noise signal, as @LMSC-NTappy suggests, then the Jacobian transformation also works.

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