issue_comments

I needed this functionality for a project, and piggy-backing off the last couple of comments decided the curve_fit wrapped by apply_ufunc approach works quite well. I put together a PR in #4849. Any feedback welcome!

cheers @TomNicholas , that's helpful. :) I've started messing with the idea in this Gist if you want to have a look.

It's pretty hacky at the moment, but might be helpful as a testbed. (And a way of getting my head around how apply_ufunc would work in this context)

@AndrewWilliams3142 I've tried to extend this to a 3d matrix (timeseries of 2d matrices) using Dask, it seems to work! Have a look here https://gist.github.com/clausmichele/8350e1f7f15e6828f29579914276de71

cheers @TomNicholas , that's helpful. :) I've started messing with the idea in this Gist if you want to have a look.

It's pretty hacky at the moment, but might be helpful as a testbed. (And a way of getting my head around how apply_ufunc would work in this context)

@TomNicholas I'm a bit confused about how the fit_along argument would work actually. If you had 2D data and wanted to fit a 1D function to one of the dimensions, wouldn't you have to either take a mean (or slice?) across the other dimension?

Edit: It's been a hot day here, so apologies if this turns out to be a dumb q haha

This sounds very cool! :) I'm not sure that I have much to add, but given @aulemahal 's good point about the complexity of rewriting curve_fit from scratch, it seems that maybe a good first step would just be to wrap the existing scipy functionality?

Alternatively, given that xr.apply_ufunc can already do this (though it's probably complicated), perhaps it would be good to just have an example in the documentation?

I am also trying to get similar results of scipy curve_fit with xarray and dask. Is there a workaround I can use to fit a sinusoidal function with the current functions/methods? This is the function I use to fit a seasonal trend with scipy: t = 365 def timeseries_function_season (x,a0,a1,a2): return a0+(a1*np.cos(2*np.pi/t*x)+a2*np.sin(2*np.pi/t*x)) timeseries_model_fit,pcov= curve_fit(timeseries_function_season,x,y)

My comments Q.1 : For now xr.apply_ufunc does not accept core dimensions to be chunked along, which would be kinda a sad for curve fitting. However, dask's least square method does, which is part of the reason why I used it in polyfit. On the other hand, scipy's least-squares procedure is not simple. Curve fitting is quite complex and rewriting all the code to use dask might be a project too ambitious, and surely out-of-scope for xarray...

Q.3 : For simple directly declared function, inspect does a good job, but it can get tricky with wrapped functions, which might arise in more complex workflows. Could we have a params arg that takes in a list of names?