unpin numpy (pydata#8061)

* unpin `numpy`

* dispatch to `np.empty_like` if there's no missing value

* make sure there's no code path without `create_template`

* declare `create_template` as callable

* ignore the intentionally wrong typing

* mark the ignore as intentional, even if it might not be used

this important because the fix in `numpy` that now means we *don't*
need it anymore has been around for less than 3 months (requires a
sufficiently new version of `mypy`).

* also directly check that `Variable.unstack` does not raise warnings

* fix the unstack test

ci/requirements/all-but-dask.yml

@@ -23,7 +23,7 @@ dependencies:
   - netcdf4
   - numba
   - numbagg
-  - numpy<1.24
+  - numpy
   - packaging
   - pandas
   - pint<0.21

ci/requirements/doc.yml

@@ -19,7 +19,7 @@ dependencies:
   - nbsphinx
   - netcdf4>=1.5
   - numba
-  - numpy>=1.21,<1.24
+  - numpy>=1.21
   - packaging>=21.3
   - pandas>=1.4
   - pooch

ci/requirements/environment-windows.yml

@@ -22,7 +22,7 @@ dependencies:
   - netcdf4
   - numba
   - numbagg
-  - numpy<1.24
+  - numpy
   - packaging
   - pandas
   - pint<0.21

ci/requirements/environment.yml

@@ -25,7 +25,7 @@ dependencies:
   - numba
   - numbagg
   - numexpr
-  - numpy<1.24
+  - numpy
   - packaging
   - pandas
   - pint<0.21

xarray/core/variable.py

@@ -7,6 +7,7 @@
 import warnings
 from collections.abc import Hashable, Iterable, Mapping, Sequence
 from datetime import timedelta
+from functools import partial
 from typing import TYPE_CHECKING, Any, Callable, Literal, NoReturn
 
 import numpy as np
@@ -1836,15 +1837,20 @@ def _unstack_once(
         new_shape = tuple(list(reordered.shape[: len(other_dims)]) + new_dim_sizes)
         new_dims = reordered.dims[: len(other_dims)] + new_dim_names
 
+        create_template: Callable
         if fill_value is dtypes.NA:
             is_missing_values = math.prod(new_shape) > math.prod(self.shape)
             if is_missing_values:
                 dtype, fill_value = dtypes.maybe_promote(self.dtype)
+
+                create_template = partial(np.full_like, fill_value=fill_value)
             else:
                 dtype = self.dtype
                 fill_value = dtypes.get_fill_value(dtype)
+                create_template = np.empty_like
         else:
             dtype = self.dtype
+            create_template = partial(np.full_like, fill_value=fill_value)
 
         if sparse:
             # unstacking a dense multitindexed array to a sparse array
@@ -1867,12 +1873,7 @@ def _unstack_once(
             )
 
         else:
-            data = np.full_like(
-                self.data,
-                fill_value=fill_value,
-                shape=new_shape,
-                dtype=dtype,
-            )
+            data = create_template(self.data, shape=new_shape, dtype=dtype)
 
             # Indexer is a list of lists of locations. Each list is the locations
             # on the new dimension. This is robust to the data being sparse; in that

xarray/tests/test_coding_strings.py

@@ -33,7 +33,7 @@ def test_vlen_dtype() -> None:
     assert strings.check_vlen_dtype(dtype) is bytes
 
     # check h5py variant ("vlen")
-    dtype = np.dtype("O", metadata={"vlen": str})  # type: ignore[call-overload]
+    dtype = np.dtype("O", metadata={"vlen": str})  # type: ignore[call-overload,unused-ignore]
     assert strings.check_vlen_dtype(dtype) is str
 
     assert strings.check_vlen_dtype(np.dtype(object)) is None

xarray/tests/test_dataarray.py

@@ -2490,6 +2490,19 @@ def test_unstack_pandas_consistency(self) -> None:
         actual = DataArray(s, dims="z").unstack("z")
         assert_identical(expected, actual)
 
+    @pytest.mark.filterwarnings("error")
+    def test_unstack_roundtrip_integer_array(self) -> None:
+        arr = xr.DataArray(
+            np.arange(6).reshape(2, 3),
+            coords={"x": ["a", "b"], "y": [0, 1, 2]},
+            dims=["x", "y"],
+        )
+
+        stacked = arr.stack(z=["x", "y"])
+        roundtripped = stacked.unstack()
+
+        assert_identical(arr, roundtripped)
+
     def test_stack_nonunique_consistency(self, da) -> None:
         da = da.isel(time=0, drop=True)  # 2D
         actual = da.stack(z=["a", "x"])

xarray/tests/test_groupby.py

@@ -728,7 +728,7 @@ def test_groupby_dataset_iter() -> None:
 def test_groupby_dataset_errors() -> None:
     data = create_test_data()
     with pytest.raises(TypeError, match=r"`group` must be"):
-        data.groupby(np.arange(10))
+        data.groupby(np.arange(10))  # type: ignore
     with pytest.raises(ValueError, match=r"length does not match"):
         data.groupby(data["dim1"][:3])
     with pytest.raises(TypeError, match=r"`group` must be"):

xarray/tests/test_variable.py

@@ -1707,6 +1707,15 @@ def test_stack_unstack_consistency(self):
         actual = v.stack(z=("x", "y")).unstack(z={"x": 2, "y": 2})
         assert_identical(actual, v)
 
+    @pytest.mark.filterwarnings("error::RuntimeWarning")
+    def test_unstack_without_missing(self):
+        v = Variable(["z"], [0, 1, 2, 3])
+        expected = Variable(["x", "y"], [[0, 1], [2, 3]])
+
+        actual = v.unstack(z={"x": 2, "y": 2})
+
+        assert_identical(actual, expected)
+
     def test_broadcasting_math(self):
         x = np.random.randn(2, 3)
         v = Variable(["a", "b"], x)