Consistent handling of 0-dimensional XArrays for dtype=object #44
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Numpy unpacks 0-dimensional ararys when indexed. We should do the same.
| @@ -23,9 +23,6 @@ def _as_compatible_data(data): | |||
| required = ['dtype', 'shape', 'size', 'ndim'] | |||
| if not all(hasattr(data, attr) for attr in required): | |||
| data = np.asarray(data) | |||
| if data.ndim == 0: | |||
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Note: this (and the lines in src/xray/common.py) are the residuals of an old fix I did which turned out to be incomplete. I think it's safer to always guarantee that _data satisfies the nd-array API and then unpack the array if/when the data array is accessed.
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@akleeman Do these unit tests make more sense? Now everything is explicitly compared against what the ndarray does. |
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Yes much clearer, thanks. As soon as build passes I'll merge it in. |
akleeman
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Mar 4, 2014
Consistent handling of 0-dimensional XArrays for dtype=object
keewis
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Jan 17, 2024
* swapped original init for a .from_dict method * updated tests * corrected options for creating datatree objects
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Numpy unpacks 0-dimensional ararys when indexed. We should do the same.