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/1815#issuecomment-538993551,https://api.github.com/repos/pydata/xarray/issues/1815,538993551,MDEyOklzc3VlQ29tbWVudDUzODk5MzU1MQ==,1941408,2019-10-07T12:48:01Z,2019-10-07T12:48:01Z,CONTRIBUTOR,"@andersy005 here is a very little demo of linear regression using lstsq (not linregress) in which only slope and intercept are kept. It is here applied to an array of sea surface temperature.
I hope it can help.

```python
ds = xr.open_dataset('sst_2D.nc', chunks={'X': 30, 'Y': 30})
def ulinregress(x, y): # the universal function
    ny, nx, nt = y.shape ; y = np.moveaxis(y, -1, 0).reshape((nt, -1)) # nt, ny*nx
    return np.linalg.lstsq(np.vstack([x, np.ones(nt)]).T, y)[0].T.reshape(ny, nx, 2)
time = (ds['time'] - np.datetime64(""1950-01-01"")) / np.timedelta64(1, 'D')
ab = xr.apply_ufunc(ulinregress, time, ds['sst'], dask='parallelized', 
                    input_core_dims=[['time'], ['time']], 
                    output_dtypes=['d'], output_sizes={'coef': 2, }, output_core_dims=[['coef']])
series = ds['sst'][:, 0, 0].load()
line = series.copy() ; line[:] = ab[0, 0, 0] * time + ab[0, 0, 1]
series.plot(label='Original') ; line.plot(label='Linear regression') ; plt.legend();
```","{""total_count"": 1, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 1, ""rocket"": 0, ""eyes"": 0}",,287223508