I am currently using numpy.polyfit(x,y,deg) to fit a polynomial to experimental data. I would however like to fit a polynomial that uses weighting based on the errors of the points.
I have found scipy.curve_fit which makes use of weights and I suppose I could just set the function, 'f', to the form a polynomial of my desired order, and put my weights in 'sigma', which should achieve my goal.
I was wondering is there another, better way of doing this?
Many Thanks.
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