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  • bradyrx · 1 ✖

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  • Xarray operations produce read-only array · 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
655142333 https://github.com/pydata/xarray/issues/3813#issuecomment-655142333 https://api.github.com/repos/pydata/xarray/issues/3813 MDEyOklzc3VlQ29tbWVudDY1NTE0MjMzMw== bradyrx 8881170 2020-07-07T21:22:30Z 2020-07-07T21:22:30Z CONTRIBUTOR

FYI, this is also seen on xr.apply_ufunc, but only when vectorize=True. It seems like ndarrays write switch are turned off when vectorize=True. This is also solved by .copy(), which is good anways to avoid mutating the original ndarrays. Perhaps also a copy=bool could be added to apply_ufunc to create copies of the ndarrays? I'd be happy to lead that PR if it makes sense.

Example:

``python def match_nans(a, b): """Pairwise matching of nans between two time series.""" # Try with and without.copy` commands. # a = a.copy() # b = b.copy() if np.isnan(a).any() or np.isnan(b).any(): idx = np.logical_or(np.isnan(a), np.isnan(b)) a[idx], b[idx] = np.nan, np.nan return a, b

A = xr.DataArray(np.random.rand(10, 5), dims=['time', 'space']) B = xr.DataArray(np.random.rand(10, 5), dims=['time', 'space']) A[0, 1] = np.nan B[5, 0] = np.nan

xr.apply_ufunc(match_nans, A, B, input_core_dims=[['time'], ['time']], output_core_dims=[['time'], ['time']], # Try with and without vectorize. vectorize=True,) ```

 
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   Xarray operations produce read-only array 573031381

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