Fix np.linalg.pinv dimension inference for DataArrays

xarray/core/variable.py

@@ -95,6 +95,60 @@ class MissingDimensionsError(ValueError):
     # TODO: move this to an xarray.exceptions module?
 
 
+def _infer_new_dims(old_dims, old_shape, new_shape):
+    """Infer new dimension names when shape changes in __array_wrap__.
+
+    Handles the case where numpy operations rearrange dimensions (e.g.,
+    np.linalg.pinv swaps the last two axes). When the new shape is a
+    permutation of the old shape, we map dimension names accordingly.
+
+    Parameters
+    ----------
+    old_dims : tuple
+        Original dimension names.
+    old_shape : tuple
+        Original shape.
+    new_shape : tuple
+        New shape after the numpy operation.
+
+    Returns
+    -------
+    tuple
+        New dimension names that match the new shape.
+
+    Raises
+    ------
+    ValueError
+        If the shape change cannot be resolved as a dimension permutation.
+    """
+    if len(old_shape) != len(new_shape):
+        raise ValueError(
+            f"Cannot infer dimensions: shape changed from {old_shape} to {new_shape} "
+            f"(different number of dimensions)"
+        )
+
+    # Try to find a permutation that maps old_shape to new_shape
+    # This handles cases like np.linalg.pinv which swaps last two dims
+    used = [False] * len(old_shape)
+    new_dims = list(old_dims)
+
+    for i, new_size in enumerate(new_shape):
+        found = False
+        for j, old_size in enumerate(old_shape):
+            if not used[j] and old_size == new_size:
+                new_dims[i] = old_dims[j]
+                used[j] = True
+                found = True
+                break
+        if not found:
+            raise ValueError(
+                f"Cannot infer dimensions: shape changed from {old_shape} to {new_shape} "
+                f"(not a permutation of existing dimensions)"
+            )
+
+    return tuple(new_dims)
+
+
 def as_variable(
     obj: T_DuckArray | Any, name=None, auto_convert: bool = True
 ) -> Variable | IndexVariable:
@@ -2432,7 +2486,11 @@ def real(self) -> Variable:
         return self._new(data=self.data.real)
 
     def __array_wrap__(self, obj, context=None, return_scalar=False):
-        return Variable(self.dims, obj)
+        if obj.shape != self.shape:
+            dims = _infer_new_dims(self.dims, self.shape, obj.shape)
+        else:
+            dims = self.dims
+        return Variable(dims, obj)
 
     def _unary_op(self, f, *args, **kwargs):
         keep_attrs = kwargs.pop("keep_attrs", None)

xarray/tests/test_dataarray.py

@@ -7673,3 +7673,16 @@ def test_unstack_index_var() -> None:
         name="x",
     )
     assert_identical(actual, expected)
+
+
+def test_pinv_infers_dims_on_shape_change():
+    # Regression test for GH#11396
+    da = xr.DataArray(
+        np.arange(12).reshape(3, 4),
+        coords={"foo": ["x", "y", "z"], "bar": ["a", "b", "c", "d"]},
+    )
+    result = np.linalg.pinv(da)
+    assert result.shape == (4, 3)
+    assert result.dims == ("bar", "foo")
+    assert list(result.coords["foo"].values) == ["x", "y", "z"]
+    assert list(result.coords["bar"].values) == ["a", "b", "c", "d"]

xarray/tests/test_variable.py

@@ -2947,6 +2947,20 @@ def __init__(self, array):
         orig = Variable(dims=(), data=array2)
         assert isinstance(orig._data.item(), CustomWithValuesAttr)  # type: ignore[union-attr]
 
+    def test_array_wrap_infers_dims_on_shape_change(self):
+        # Regression test for https://github.com/pydata/xarray/issues/11396
+        # np.linalg.pinv swaps last two dims; __array_wrap__ should infer this
+        var = Variable(dims=("x", "y"), data=np.arange(12).reshape(3, 4))
+        result = np.linalg.pinv(var)
+        assert result.shape == (4, 3)
+        assert result.dims == ("y", "x")
+
+    def test_array_wrap_preserves_dims_when_shape_unchanged(self):
+        var = Variable(dims=("x", "y"), data=np.random.rand(3, 3))
+        result = np.linalg.pinv(var)
+        assert result.shape == (3, 3)
+        assert result.dims == ("x", "y")
+
 
 def test_raise_no_warning_for_nan_in_binary_ops():
     with assert_no_warnings():