Still, we'd need to add the new bright and shiny algorithms to what is now scipy.optimize.curve_fit. It seems to me it only depends on the least_squares interface, and the latter is not very likely to change substantially. Hence there's no harm in making a branch from the NLSQ PR, adding the curve_fit (one commit, or two), and submitting that.
For starters, I'd keep the loss functions out --- just add bounds and method kwargs.
I know you're not a fan :-).
Still, we'd need to add the new bright and shiny algorithms to what is now
scipy.optimize.curve_fit
. It seems to me it only depends on the least_squares interface, and the latter is not very likely to change substantially. Hence there's no harm in making a branch from the NLSQ PR, adding the curve_fit (one commit, or two), and submitting that. For starters, I'd keep the loss functions out --- just addbounds
andmethod
kwargs.