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  • stefraynaud · 1 ✖

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  • apply_ufunc(dask='parallelized') with multiple outputs · 1 ✖

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  • CONTRIBUTOR · 1 ✖
id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
538993551 https://github.com/pydata/xarray/issues/1815#issuecomment-538993551 https://api.github.com/repos/pydata/xarray/issues/1815 MDEyOklzc3VlQ29tbWVudDUzODk5MzU1MQ== stefraynaud 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();

 
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   apply_ufunc(dask='parallelized') with multiple outputs 287223508

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