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