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  • shoyer · 1 ✖

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  • Loading from NetCDF creates unnecessary numpy.ndarray-views that clears the OWNDATA-flag · 1 ✖

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  • MEMBER · 1 ✖
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1116397246 https://github.com/pydata/xarray/issues/6517#issuecomment-1116397246 https://api.github.com/repos/pydata/xarray/issues/6517 IC_kwDOAMm_X85Cit6- shoyer 1217238 2022-05-03T18:09:42Z 2022-05-03T18:09:42Z MEMBER

I'm a little skeptical that it makes sense to add special case logic into Xarray in an attempt to keep NumPy's "OWNDATA" flag up to date. There are lots of places where we create views of data from existing arrays inside Xarray operations.

There are definitely cases where Xarray's internal operations do memory copies followed by views, which would also result in datasets with misleading "OWNDATA" flags if you look only at resulting datasets, e.g., DataArray.interp() which definitely does internal memory copies: ```

y = xarray.DataArray([1, 2, 3], dims='x', coords={'x': [0, 1, 2]}) y.interp(x=0.5).data.flags C_CONTIGUOUS : True F_CONTIGUOUS : True OWNDATA : False WRITEABLE : True ALIGNED : True WRITEBACKIFCOPY : False UPDATEIFCOPY : False ```

Overall, I just don't think this is a reliable way to trace memory allocation with NumPy. Maybe you could do better by also tracing back to source arrays with .base?

 
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   Loading from NetCDF creates unnecessary numpy.ndarray-views that clears the OWNDATA-flag 1216517115

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