scipy - fit data(measurements) with numerical datasets -


i have data have set of numerically determined model curves. find 1 least square deviation, need vary 1 parameter, amplitude of these model curves.

i used fitting analytic functions, did not find way handle such problem.

is there solution?

thanks lot!

one of optimize functions should trick. can read section on optimization in manual. without specifics on data or model wish match, it's hard recommend more specific. example, if cost function has many maxima , minima or not differentiable, you'll have choose of more expensive routines.


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