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fdfitter: Improve inversion performance (Friday afternoon) #558
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JoepVanlier
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fdfitter: Improve inversion performance
fdfitter: Improve inversion performance (Friday afteroon)
Aug 15, 2023
JoepVanlier
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fdfitter: Improve inversion performance (Friday afteroon)
fdfitter: Improve inversion performance (Friday afternoon)
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we didn't include the entire independent range, resulting in an interpolant that covered less of the range than desired
has a fallback chute for when bracketing fails
At f_min = 0, numerical difficulties ensue. Considering that the model isn't valid in this range to begin with, we limit the minimum.
It is no longer needed to explicitly provide finite inversion limits. We will only use the interpolation method in the range where our data lives, and outside we always use explicit optimization.
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Model inversion can be pretty slow and generally, it is cumbersome to have to specify the inversion bounds.
This draft fixes both these issues by introducing a new inversion mode that does simple rootfinding to find the outer extents of the to-be-inverted range. As a fallback method, we fall back to the old method of using
least_squares
to invert the function.Differences with the old method:
Fitter
less general, it these bounds do hold for any sort of F, d model you'd throw at it (positive only).interpolate=True
.