Remove warning

Author: dcherian

xarray/core/variable.py

@@ -158,17 +158,9 @@ def as_variable(obj, name=None) -> Variable | IndexVariable:
             f"explicit list of dimensions: {obj!r}"
         )
 
-    if name is not None and name in obj.dims:
-        # convert the Variable into an Index
-        if obj.ndim != 1:
-            warnings.warn(
-                f"{name!r} has more than 1-dimension and the same name as one of its "
-                f"dimensions {obj.dims!r}. Xarray will not automatically "
-                "create an Index object that would allow label-based selection.",
-                RuntimeWarning,
-            )
-        else:
-            obj = obj.to_index_variable()
+    if name is not None and name in obj.dims and obj.ndim == 1:
+        # automatically convert the Variable into an Index
+        obj = obj.to_index_variable()
 
     return obj
 

xarray/tests/test_dataset.py

@@ -472,9 +472,9 @@ def test_constructor(self) -> None:
         with pytest.raises(ValueError, match=r"already exists as a scalar"):
             Dataset({"x": 0, "y": ("x", [1, 2, 3])})
 
-        with pytest.warns(RuntimeWarning, match=r"will not automatically"):
-            actual = Dataset({"a": x1, "x": z})
-            assert "x" not in actual.xindexes
+        # nD coordinate variable "x" sharing name with dimension
+        actual = Dataset({"a": x1, "x": z})
+        assert "x" not in actual.xindexes
 
         # verify handling of DataArrays
         expected = Dataset({"x": x1, "z": z})

xarray/tests/test_variable.py

@@ -1247,9 +1247,10 @@ def test_as_variable(self):
         expected = Variable(("x", "y"), data)
         with pytest.raises(ValueError, match=r"without explicit dimension names"):
             as_variable(data, name="x")
-        with pytest.warns(RuntimeWarning, match=r"has more than 1-dimension"):
-            actual = as_variable(expected, name="x")
-            assert_identical(expected, actual)
+
+        # name of nD variable matches dimension name
+        actual = as_variable(expected, name="x")
+        assert_identical(expected, actual)
 
         # test datetime, timedelta conversion
         dt = np.array([datetime(1999, 1, 1) + timedelta(days=x) for x in range(10)])