html_url,issue_url,id,node_id,user,created_at,updated_at,author_association,body,reactions,performed_via_github_app,issue
https://github.com/pydata/xarray/issues/6517#issuecomment-1116397246,https://api.github.com/repos/pydata/xarray/issues/6517,1116397246,IC_kwDOAMm_X85Cit6-,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`?","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,1216517115