F stats and other associated methods for obtaining confidence intervals are far superior to a simple estimation of te co variance matrix for non-linear models (and others). The primary reason for this is the lack of assumptions about the Gaussian nature of error when using these methods. For non-linear systems,...

Because I don't have your data, I can't test it, but it looks like you're almost there. Your "gausfit2" should be a ModelFit object (http://cars9.uchicago.edu/software/python/lmfit/model.html#model.ModelFit). Therefore, all you would need to do to generate a report is the following: print gausfit2.fit_report #will print you a fit report from your model...

python,scipy,curve-fitting,lmfit

Hm, should that be out = mod1.fit(spectra_beBG[:,1], pars, x=spectra_beBG[:,0]) That is, you want to fit "y", and pass in the "pars" and "x" array to help calculate the model with those parameters and independent variables....

I'm pretty sure the Model Class is new to lmfit-py release 0.8.1 so you may have to update lmfit. (I have the offline documentation for 0.7.2 which does not include a section on the Model Class, but the documentation for version 0.8.1 has it)...