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While thinking about #93 i got the idea, that it might be usefull to add support for measurement uncertainties. I'm not sure if lmfit / scipy.optimize can use this information ootb, but pint (#91) can and we could combine it with #53.
The text was updated successfully, but these errors were encountered:
I think lmfit does support calculating confidence intervals for a fit instead of the error estimates but not really using an uncertainty of the measurement data. I don't know if this can be somehow injected in the minimize function lmfit is using, but other than that we could also do three fits on the mean and edge case data and calculate the uncertainty from this (but well.... not my favourite idea :D). scipy however has a parameter to insert a std dev, at least I used it some time ago.
I think it's a nice feature, because afaik is the measurement uncertainty included in files generated from Woollam instruments
While thinking about #93 i got the idea, that it might be usefull to add support for measurement uncertainties. I'm not sure if lmfit / scipy.optimize can use this information ootb, but pint (#91) can and we could combine it with #53.
The text was updated successfully, but these errors were encountered: