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?

FYI: Since h5netcdf recently moved to version 1.0, I've checked with latest xarray (2022.3.0) and latest h5netcdf (1.0.0). The OP example with the OP fix reproduces nicely as well with the updated fix.

I don't know this well — maybe others can comment — but the example checks out.

Would we take this as a PR? Is there a simpler way to express that logic?

For others, here's the diff:

```diff diff --git "indexing.original.py" "indexing.patched.py" --- "indexing.original.py" +++ "indexing.patched.py" @@ -709,8 +709,12 @@ def explicit_indexing_adapter( """ raw_key, numpy_indices = decompose_indexer(key, shape, indexing_support) result = raw_indexing_method(raw_key.tuple) - if numpy_indices.tuple: - result = NumpyIndexingAdapter(np.asarray(result))[numpy_indices] + if numpy_indices.tuple and (not isinstance(result, np.ndarray) + or not all(i == slice(None, None, None) for i in numpy_indices.tuple)): + # The conditions within parentehses are to avoid unnecessary array slice/view-creation + # that would set flags['OWNDATA'] to False for no reason. + # Index the loaded np.ndarray. + result = NumpyIndexingAdapter(np.asarray(result))[numpy_indices] return result

@@ -1156,6 +1160,11 @@ class NumpyIndexingAdapter(ExplicitlyIndexedNDArrayMixin):

 def __getitem__(self, key):
     array, key = self._indexing_array_and_key(key)

• if ((len(key) == len(array.shape) or (len(key) == len(array.shape) + 1 and key[-1] is ...))
• and all(isinstance(i, slice) and i == slice(None, None, None) for i in key[:len(array.shape)])
• and isinstance(array, np.ndarray)): # (The isinstance-check is because nputils.NumpyVIndexAdapter() has not been tested.)

• Avoid unnecessary array slice/view-creation that would set flags['OWNDATA'] to False for no reason.

• return array return array[key]

  def setitem(self, key, value):

```