Fix typing errors using mypy 1.2 (#7752)

* test newest mypy

* Update ci-additional.yaml

* remove ignores

* add typing

* Use ClassVar

* Generalize data_vars typing concat.

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* Use a normal method to retrieve a type of Variable

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* ignore plotfunc error

* force reinstall

* remove outdated comments

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

Author: Illviljan

.github/workflows/ci-additional.yaml

@@ -119,7 +119,7 @@ jobs:
           python xarray/util/print_versions.py
       - name: Install mypy
         run: |
-          python -m pip install 'mypy<0.990'
+          python -m pip install mypy --force-reinstall
 
       - name: Run mypy
         run: |
@@ -173,7 +173,7 @@ jobs:
           python xarray/util/print_versions.py
       - name: Install mypy
         run: |
-          python -m pip install 'mypy<0.990'
+          python -m pip install mypy --force-reinstall
 
       - name: Run mypy
         run: |

xarray/core/combine.py

@@ -369,9 +369,8 @@ def _nested_combine(
     return combined
 
 
-# Define type for arbitrarily-nested list of lists recursively
-# Currently mypy cannot handle this but other linters can (https://stackoverflow.com/a/53845083/3154101)
-DATASET_HYPERCUBE = Union[Dataset, Iterable["DATASET_HYPERCUBE"]]  # type: ignore[misc]
+# Define type for arbitrarily-nested list of lists recursively:
+DATASET_HYPERCUBE = Union[Dataset, Iterable["DATASET_HYPERCUBE"]]
 
 
 def combine_nested(

xarray/core/concat.py

@@ -1,7 +1,7 @@
 from __future__ import annotations
 
 from collections.abc import Hashable, Iterable
-from typing import TYPE_CHECKING, Any, cast, overload
+from typing import TYPE_CHECKING, Any, Union, cast, overload
 
 import pandas as pd
 
@@ -27,12 +27,14 @@
         JoinOptions,
     )
 
+    T_DataVars = Union[ConcatOptions, Iterable[Hashable]]
+
 
 @overload
 def concat(
     objs: Iterable[T_Dataset],
     dim: Hashable | T_DataArray | pd.Index,
-    data_vars: ConcatOptions | list[Hashable] = "all",
+    data_vars: T_DataVars = "all",
     coords: ConcatOptions | list[Hashable] = "different",
     compat: CompatOptions = "equals",
     positions: Iterable[Iterable[int]] | None = None,
@@ -47,7 +49,7 @@ def concat(
 def concat(
     objs: Iterable[T_DataArray],
     dim: Hashable | T_DataArray | pd.Index,
-    data_vars: ConcatOptions | list[Hashable] = "all",
+    data_vars: T_DataVars = "all",
     coords: ConcatOptions | list[Hashable] = "different",
     compat: CompatOptions = "equals",
     positions: Iterable[Iterable[int]] | None = None,
@@ -61,7 +63,7 @@ def concat(
 def concat(
     objs,
     dim,
-    data_vars="all",
+    data_vars: T_DataVars = "all",
     coords="different",
     compat: CompatOptions = "equals",
     positions=None,
@@ -291,7 +293,7 @@ def _calc_concat_dim_index(
     return dim, index
 
 
-def _calc_concat_over(datasets, dim, dim_names, data_vars, coords, compat):
+def _calc_concat_over(datasets, dim, dim_names, data_vars: T_DataVars, coords, compat):
     """
     Determine which dataset variables need to be concatenated in the result,
     """
@@ -445,7 +447,7 @@ def _parse_datasets(
 def _dataset_concat(
     datasets: list[T_Dataset],
     dim: str | T_DataArray | pd.Index,
-    data_vars: str | list[str],
+    data_vars: T_DataVars,
     coords: str | list[str],
     compat: CompatOptions,
     positions: Iterable[Iterable[int]] | None,
@@ -665,7 +667,7 @@ def get_indexes(name):
 def _dataarray_concat(
     arrays: Iterable[T_DataArray],
     dim: str | T_DataArray | pd.Index,
-    data_vars: str | list[str],
+    data_vars: T_DataVars,
     coords: str | list[str],
     compat: CompatOptions,
     positions: Iterable[Iterable[int]] | None,

xarray/core/rolling.py

@@ -376,7 +376,7 @@ def _construct(
             window_dim = {d: window_dim_kwargs[str(d)] for d in self.dim}
 
         window_dims = self._mapping_to_list(
-            window_dim, allow_default=False, allow_allsame=False  # type: ignore[arg-type]  # https://github.com/python/mypy/issues/12506
+            window_dim, allow_default=False, allow_allsame=False
         )
         strides = self._mapping_to_list(stride, default=1)
 
@@ -753,7 +753,7 @@ def construct(
             window_dim = {d: window_dim_kwargs[str(d)] for d in self.dim}
 
         window_dims = self._mapping_to_list(
-            window_dim, allow_default=False, allow_allsame=False  # type: ignore[arg-type]  # https://github.com/python/mypy/issues/12506
+            window_dim, allow_default=False, allow_allsame=False
         )
         strides = self._mapping_to_list(stride, default=1)
 

xarray/core/utils.py

@@ -113,7 +113,7 @@ def get_valid_numpy_dtype(array: np.ndarray | pd.Index):
         dtype = np.dtype("O")
     elif hasattr(array, "categories"):
         # category isn't a real numpy dtype
-        dtype = array.categories.dtype  # type: ignore[union-attr]
+        dtype = array.categories.dtype
     elif not is_valid_numpy_dtype(array.dtype):
         dtype = np.dtype("O")
     else:

xarray/tests/test_concat.py

@@ -538,8 +538,7 @@ def test_concat_data_vars_typing(self) -> None:
         actual = concat(objs, dim="x", data_vars="minimal")
         assert_identical(data, actual)
 
-    def test_concat_data_vars(self):
-        # TODO: annotating this func fails
+    def test_concat_data_vars(self) -> None:
         data = Dataset({"foo": ("x", np.random.randn(10))})
         objs: list[Dataset] = [data.isel(x=slice(5)), data.isel(x=slice(5, None))]
         for data_vars in ["minimal", "different", "all", [], ["foo"]]:

xarray/tests/test_plot.py

@@ -2042,7 +2042,7 @@ def test_seaborn_palette_as_cmap(self) -> None:
     def test_convenient_facetgrid(self) -> None:
         a = easy_array((10, 15, 4))
         d = DataArray(a, dims=["y", "x", "z"])
-        g = self.plotfunc(d, x="x", y="y", col="z", col_wrap=2)
+        g = self.plotfunc(d, x="x", y="y", col="z", col_wrap=2)  # type: ignore[arg-type] # https://github.com/python/mypy/issues/15015
 
         assert_array_equal(g.axs.shape, [2, 2])
         for (y, x), ax in np.ndenumerate(g.axs):
@@ -2051,7 +2051,7 @@ def test_convenient_facetgrid(self) -> None:
             assert "x" == ax.get_xlabel()
 
         # Inferring labels
-        g = self.plotfunc(d, col="z", col_wrap=2)
+        g = self.plotfunc(d, col="z", col_wrap=2)  # type: ignore[arg-type] # https://github.com/python/mypy/issues/15015
         assert_array_equal(g.axs.shape, [2, 2])
         for (y, x), ax in np.ndenumerate(g.axs):
             assert ax.has_data()

xarray/tests/test_variable.py

@@ -1,6 +1,7 @@
 from __future__ import annotations
 
 import warnings
+from abc import ABC, abstractmethod
 from copy import copy, deepcopy
 from datetime import datetime, timedelta
 from textwrap import dedent
@@ -61,8 +62,10 @@ def var():
     return Variable(dims=list("xyz"), data=np.random.rand(3, 4, 5))
 
 
-class VariableSubclassobjects:
-    cls: staticmethod[Variable]
+class VariableSubclassobjects(ABC):
+    @abstractmethod
+    def cls(self, *args, **kwargs) -> Variable:
+        raise NotImplementedError
 
     def test_properties(self):
         data = 0.5 * np.arange(10)
@@ -1056,7 +1059,8 @@ def test_rolling_window_errors(self, dim, window, window_dim, center):
 
 
 class TestVariable(VariableSubclassobjects):
-    cls = staticmethod(Variable)
+    def cls(self, *args, **kwargs) -> Variable:
+        return Variable(*args, **kwargs)
 
     @pytest.fixture(autouse=True)
     def setup(self):
@@ -2228,13 +2232,10 @@ def test_coarsen_keep_attrs(self, operation="mean"):
         assert new.attrs == _attrs
 
 
-def _init_dask_variable(*args, **kwargs):
-    return Variable(*args, **kwargs).chunk()
-
-
 @requires_dask
 class TestVariableWithDask(VariableSubclassobjects):
-    cls = staticmethod(_init_dask_variable)
+    def cls(self, *args, **kwargs) -> Variable:
+        return Variable(*args, **kwargs).chunk()
 
     def test_chunk(self):
         unblocked = Variable(["dim_0", "dim_1"], np.ones((3, 4)))
@@ -2346,7 +2347,8 @@ def test_as_sparse(self):
 
 
 class TestIndexVariable(VariableSubclassobjects):
-    cls = staticmethod(IndexVariable)
+    def cls(self, *args, **kwargs) -> IndexVariable:
+        return IndexVariable(*args, **kwargs)
 
     def test_init(self):
         with pytest.raises(ValueError, match=r"must be 1-dimensional"):