issue_comments

It's usually expand_dims.

We should add an FAQ for little things like this.

I ran into the same issue as @bradyrx with writable arrays and apply_ufunc. An addition to the docs FAQ or apply_ufunc docs would help clarify that you can't write to the array inputs in the ufunc. I also want to +1 @bradyrx 's idea of making an arg to apply_ufunc that copies the input arrays to handle this common use case.

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,) ```

Cheers @djhoese

Yes, let's leave this open. Two follow-ups from https://github.com/pydata/xarray/issues/2891#issuecomment-482880911; we would welcome PRs:

• Mention the work-around of using .copy() in the error message xarray prints when an array is read-only.

• Add a copy argument to expand_dims, so users can write copy=True if they want a writeable result.

@max-sixty That's exactly it. What's really weird for this is that the original code in Satpy is using a dask array and not a numpy array. It seemed very strange to both copy the DataArray (.copy()), convert the dask array to a numpy array (np.asarray), and then still get a read-only array.

I can understand how xarray would treat numpy arrays and dask arrays the same when it comes to this, but coming from outside the project it is very surprising that a dask array would be marked as read-only when it was used to just create a "new" numpy array.

Feel free to close this or use it as a marker to clarify some documentation or error messages as mentioned in #2891.

https://github.com/pydata/xarray/issues/2891

Thanks for the report @djhoese . Confirmed in 0.15.0 too.

Do you know which of those steps causes the array to become non-writeable?