Clean up backend indexing some more

.github/workflows/ci.yaml

@@ -54,6 +54,9 @@ jobs:
           - env: "bare-minimum"
             python-version: "3.10"
             os: ubuntu-latest
+          - env: "bare-min-and-scipy"
+            python-version: "3.10"
+            os: ubuntu-latest
           - env: "min-all-deps"
             python-version: "3.10"
             os: ubuntu-latest

ci/requirements/all-but-dask.yml

@@ -4,7 +4,7 @@ channels:
   - nodefaults
 dependencies:
   - aiobotocore
-  - array-api-strict
+  - array-api-strict<2.4
   - boto3
   - bottleneck
   - cartopy

ci/requirements/all-but-numba.yml

@@ -6,7 +6,7 @@ dependencies:
   # Pin a "very new numpy" (updated Sept 24, 2024)
   - numpy>=2.1.1
   - aiobotocore
-  - array-api-strict
+  - array-api-strict<2.4
   - boto3
   - bottleneck
   - cartopy

ci/requirements/bare-min-and-scipy.yml

@@ -0,0 +1,18 @@
+name: xarray-tests
+channels:
+  - conda-forge
+  - nodefaults
+dependencies:
+  - python=3.10
+  - coveralls
+  - pip
+  - pytest
+  - pytest-cov
+  - pytest-env
+  - pytest-mypy-plugins
+  - pytest-timeout
+  - pytest-xdist
+  - numpy=1.24
+  - packaging=23.1
+  - pandas=2.1
+  - scipy=1.11

ci/requirements/environment-3.14.yml

@@ -4,7 +4,7 @@ channels:
   - nodefaults
 dependencies:
   - aiobotocore
-  - array-api-strict
+  - array-api-strict<2.4
   - boto3
   - bottleneck
   - cartopy

ci/requirements/environment-windows-3.14.yml

@@ -2,7 +2,7 @@ name: xarray-tests
 channels:
   - conda-forge
 dependencies:
-  - array-api-strict
+  - array-api-strict<2.4
   - boto3
   - bottleneck
   - cartopy

ci/requirements/environment-windows.yml

@@ -2,7 +2,7 @@ name: xarray-tests
 channels:
   - conda-forge
 dependencies:
-  - array-api-strict
+  - array-api-strict<2.4
   - boto3
   - bottleneck
   - cartopy

ci/requirements/environment.yml

@@ -4,7 +4,7 @@ channels:
   - nodefaults
 dependencies:
   - aiobotocore
-  - array-api-strict
+  - array-api-strict<2.4
   - boto3
   - bottleneck
   - cartopy

doc/whats-new.rst

@@ -26,6 +26,8 @@ Bug fixes
 ~~~~~~~~~
 - Fix Pydap test_cmp_local_file for numpy 2.3.0 changes, 1. do always return arrays for all versions and 2. skip astype(str) for numpy >= 2.3.0 for expected data. (:pull:`10421`)
   By `Kai Mühlbauer <https://github.com/kmuehlbauer>`_.
+- Fix the SciPy backend for netCDF3 files . (:issue:`8909`, :pull:`10376`)
+  By `Deepak Cherian <https://github.com/dcherian>`_.
 
 
 Documentation

xarray/backends/memory.py

@@ -5,6 +5,7 @@
 import numpy as np
 
 from xarray.backends.common import AbstractWritableDataStore
+from xarray.core import indexing
 from xarray.core.variable import Variable
 
 
@@ -24,7 +25,12 @@ def get_attrs(self):
         return self._attributes
 
     def get_variables(self):
-        return self._variables
+        res = {}
+        for k, v in self._variables.items():
+            v = v.copy(deep=True)
+            res[k] = v
+            v._data = indexing.LazilyIndexedArray(v._data)
+        return res
 
     def get_dimensions(self):
         return {d: s for v in self._variables.values() for d, s in v.dims.items()}

xarray/backends/scipy_.py

@@ -190,7 +190,7 @@ def ds(self):
     def open_store_variable(self, name, var):
         return Variable(
             var.dimensions,
-            ScipyArrayWrapper(name, self),
+            indexing.LazilyIndexedArray(ScipyArrayWrapper(name, self)),
             _decode_attrs(var._attributes),
         )
 

xarray/coding/common.py

@@ -63,6 +63,10 @@ def __init__(self, array, func: Callable, dtype: np.typing.DTypeLike):
     def dtype(self) -> np.dtype:
         return np.dtype(self._dtype)
 
+    def transpose(self, order):
+        # For elementwise functions, we can compose transpose and function application
+        return type(self)(self.array.transpose(order), self.func, self.dtype)
+
     def _oindex_get(self, key):
         return type(self)(self.array.oindex[key], self.func, self.dtype)
 

xarray/coding/strings.py

@@ -221,7 +221,7 @@ class StackedBytesArray(indexing.ExplicitlyIndexedNDArrayMixin):
     values, when accessed, are automatically stacked along the last dimension.
 
     >>> indexer = indexing.BasicIndexer((slice(None),))
-    >>> StackedBytesArray(np.array(["a", "b", "c"], dtype="S1"))[indexer]
+    >>> np.array(StackedBytesArray(np.array(["a", "b", "c"], dtype="S1"))[indexer])
     array(b'abc', dtype='|S3')
     """
 
@@ -250,14 +250,17 @@ def __repr__(self):
         return f"{type(self).__name__}({self.array!r})"
 
     def _vindex_get(self, key):
-        return _numpy_char_to_bytes(self.array.vindex[key])
+        return type(self)(self.array.vindex[key])
 
     def _oindex_get(self, key):
-        return _numpy_char_to_bytes(self.array.oindex[key])
+        return type(self)(self.array.oindex[key])
 
     def __getitem__(self, key):
         # require slicing the last dimension completely
         key = type(key)(indexing.expanded_indexer(key.tuple, self.array.ndim))
         if key.tuple[-1] != slice(None):
             raise IndexError("too many indices")
-        return _numpy_char_to_bytes(self.array[key])
+        return type(self)(self.array[key])
+
+    def get_duck_array(self):
+        return _numpy_char_to_bytes(self.array.get_duck_array())

xarray/coding/variables.py

@@ -21,6 +21,7 @@
 )
 from xarray.coding.times import CFDatetimeCoder, CFTimedeltaCoder
 from xarray.core import dtypes, duck_array_ops, indexing
+from xarray.core.types import Self
 from xarray.core.variable import Variable
 
 if TYPE_CHECKING:
@@ -58,13 +59,16 @@
         return np.dtype(self.array.dtype.kind + str(self.array.dtype.itemsize))
 
     def _oindex_get(self, key):
-        return np.asarray(self.array.oindex[key], dtype=self.dtype)
+        return type(self)(self.array.oindex[key])
 
     def _vindex_get(self, key):
-        return np.asarray(self.array.vindex[key], dtype=self.dtype)
+        return type(self)(self.array.vindex[key])
 
-    def __getitem__(self, key) -> np.ndarray:
-        return np.asarray(self.array[key], dtype=self.dtype)
+    def __getitem__(self, key) -> Self:
+        return type(self)(self.array[key])
+
+    def get_duck_array(self):
+        return duck_array_ops.astype(self.array.get_duck_array(), dtype=self.dtype)
 
 
 class BoolTypeArray(indexing.ExplicitlyIndexedNDArrayMixin):
@@ -96,13 +100,16 @@
         return np.dtype("bool")
 
     def _oindex_get(self, key):
-        return np.asarray(self.array.oindex[key], dtype=self.dtype)
+        return type(self)(self.array.oindex[key])
 
     def _vindex_get(self, key):
-        return np.asarray(self.array.vindex[key], dtype=self.dtype)
+        return type(self)(self.array.vindex[key])
+
+    def __getitem__(self, key) -> Self:
+        return type(self)(self.array[key])
 
-    def __getitem__(self, key) -> np.ndarray:
-        return np.asarray(self.array[key], dtype=self.dtype)
+    def get_duck_array(self):
+        return duck_array_ops.astype(self.array.get_duck_array(), dtype=self.dtype)
 
 
 def _apply_mask(
@@ -234,7 +241,7 @@
             # otherwise numpy unsigned ints will silently cast to the signed counterpart
             fill_value = fill_value.item()
         # passes if provided fill value fits in encoded on-disk type
         new_fill = encoded_dtype.type(fill_value)
     except OverflowError:
         encoded_kind_str = "signed" if encoded_dtype.kind == "i" else "unsigned"
         warnings.warn(

xarray/conventions.py

@@ -18,7 +18,7 @@
 )
 from xarray.core.utils import emit_user_level_warning
 from xarray.core.variable import IndexVariable, Variable
-from xarray.namedarray.utils import is_duck_dask_array
+from xarray.namedarray.utils import is_duck_array
 
 CF_RELATED_DATA = (
     "bounds",
@@ -248,7 +248,15 @@ def decode_cf_variable(
 
     encoding.setdefault("dtype", original_dtype)
 
-    if not is_duck_dask_array(data):
+    if (
+        # we don't need to lazily index duck arrays
+        not is_duck_array(data)
+        # These arrays already support lazy indexing
+        # OR for IndexingAdapters, it makes no sense to wrap them
+        and not isinstance(data, indexing.ExplicitlyIndexedNDArrayMixin)
+    ):
+        # this path applies to bare BackendArray objects.
+        # It is not hit for any internal Xarray backend
         data = indexing.LazilyIndexedArray(data)
 
     return Variable(dimensions, data, attributes, encoding=encoding, fastpath=True)

xarray/core/indexes.py

@@ -717,7 +717,7 @@ def from_variables(
 
         # preserve wrapped pd.Index (if any)
         # accessing `.data` can load data from disk, so we only access if needed
-        data = var._data.array if hasattr(var._data, "array") else var.data
+        data = var._data if isinstance(var._data, PandasIndexingAdapter) else var.data  # type: ignore[redundant-expr]
         # multi-index level variable: get level index
         if isinstance(var._data, PandasMultiIndexingAdapter):
             level = var._data.level

xarray/tests/test_backends.py

@@ -1427,6 +1427,25 @@ def test_string_object_warning(self) -> None:
             with self.roundtrip(original) as actual:
                 assert_identical(original, actual)
 
+    @pytest.mark.parametrize(
+        "indexer",
+        (
+            {"y": [1]},
+            {"y": slice(2)},
+            {"y": 1},
+            {"x": [1], "y": [1]},
+            {"x": ("x0", [0, 1]), "y": ("x0", [0, 1])},
+        ),
+    )
+    def test_indexing_roundtrip(self, indexer) -> None:
+        # regression test for GH8909
+        ds = xr.Dataset()
+        ds["A"] = xr.DataArray([[1, "a"], [2, "b"]], dims=["x", "y"])
+        with self.roundtrip(ds) as ds2:
+            expected = ds2.sel(indexer)
+            with self.roundtrip(expected) as actual:
+                assert_identical(actual, expected)
+
 
 class NetCDFBase(CFEncodedBase):
     """Tests for all netCDF3 and netCDF4 backends."""