Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Finite Difference Sensitivity #3

Open
lheagy opened this issue Apr 7, 2018 · 4 comments
Open

Finite Difference Sensitivity #3

lheagy opened this issue Apr 7, 2018 · 4 comments

Comments

@lheagy
Copy link
Member

lheagy commented Apr 7, 2018

Over in opengeophysics/scratchpad, @prisae and I have been working on using SimPEG to invert empymod simulations. One piece we wrote to facilitate this is a util to approximate the sensitivity of a function using a finite difference approximation. This might fit in matrixutils? I would appreciate thoughts on scope here. cc @leouieda, @prisae, @rowanc1

@prisae
Copy link
Member

prisae commented Apr 7, 2018

Not sure. It wouldn't be my first thought to search a gradient function in a package called matrixutils. But then, the Jacobian usually is a matrix. I guess it would fit.

@prisae
Copy link
Member

prisae commented Apr 7, 2018

Or would it fit better into https://github.com/opengeophysics/deeplook? @leouieda

@rowanc1
Copy link
Member

rowanc1 commented Apr 10, 2018

Any chance we can just rely on this:
https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.optimize.approx_fprime.html

I sort of agree with @prisae that this is weird to be in the matrixutils repo, seems like it fits with inversion or optimization much more.

@leouieda
Copy link

I agree that this could be probably fit in better with the inversion packages. I think PyGIMLi has something like this as well.

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Labels
None yet
Projects
None yet
Development

No branches or pull requests

4 participants