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  • clausmichele · 2 ✖

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  • General curve fitting method · 2 ✖

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id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
677666962 https://github.com/pydata/xarray/issues/4300#issuecomment-677666962 https://api.github.com/repos/pydata/xarray/issues/4300 MDEyOklzc3VlQ29tbWVudDY3NzY2Njk2Mg== clausmichele 31700619 2020-08-20T13:32:33Z 2020-08-20T13:40:07Z CONTRIBUTOR

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

 
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   General curve fitting method 671609109
669065910 https://github.com/pydata/xarray/issues/4300#issuecomment-669065910 https://api.github.com/repos/pydata/xarray/issues/4300 MDEyOklzc3VlQ29tbWVudDY2OTA2NTkxMA== clausmichele 31700619 2020-08-05T08:44:06Z 2020-08-05T08:44:06Z CONTRIBUTOR

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)

 
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   General curve fitting method 671609109

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