html_url,issue_url,id,node_id,user,created_at,updated_at,author_association,body,reactions,performed_via_github_app,issue
https://github.com/pydata/xarray/issues/4300#issuecomment-672987876,https://api.github.com/repos/pydata/xarray/issues/4300,672987876,MDEyOklzc3VlQ29tbWVudDY3Mjk4Nzg3Ng==,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')
```","{""total_count"": 1, ""+1"": 1, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,671609109