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  • General curve fitting method · 2 ✖

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672987876 https://github.com/pydata/xarray/issues/4300#issuecomment-672987876 https://api.github.com/repos/pydata/xarray/issues/4300 MDEyOklzc3VlQ29tbWVudDY3Mjk4Nzg3Ng== TomNicholas 35968931 2020-08-12T16:45:23Z 2020-08-12T16:45:23Z MEMBER

@AndrewWilliams3142 fair question: what I was envisaging was taking slices along that dimension(s), performing the curve fitting once for each slice (which should parallelize through apply_ufunc), then returning the optimised fitting parameters as a DataArray/Dataset which varied along that dimension. For example:

```python

2D dataarray of surface height with x & t dependence

height_data

def pulse_shape(x, peak_height, peak_location, FWHM): return peak_height * np.exp(-((x-peak_location)/FWHM)^2.0)

returned fit_params has t dependence

fit_params = height_data.fit(pulse_shape, fit_along='x')

Plot a graph of change in peak height over t

fit_params['peak_height'].plot(x='t') ```

 
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   General curve fitting method 671609109
671475798 https://github.com/pydata/xarray/issues/4300#issuecomment-671475798 https://api.github.com/repos/pydata/xarray/issues/4300 MDEyOklzc3VlQ29tbWVudDY3MTQ3NTc5OA== shoyer 1217238 2020-08-10T17:06:11Z 2020-08-10T17:06:11Z MEMBER

+1 for just wrapping the existing functionality in SciPy for now. If we want a version of curve_fitthat supports dask, I would suggest implementingcurve_fit` with dask first, and then using that from xarray.

I am OK with using inspect from the standard library for determining default parameter names. inspect.signature is reasonably robust. But there should definitely be an optional argument for setting parameter names explicitly.

 
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   General curve fitting method 671609109

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