Consolidate TypeVars in a single place

xarray/core/_typed_ops.pyi

@@ -9,24 +9,23 @@ from .dataarray import DataArray
 from .dataset import Dataset
 from .groupby import DataArrayGroupBy, DatasetGroupBy, GroupBy
 from .npcompat import ArrayLike
+from .types import (
+    DaCompatible,
+    DsCompatible,
+    GroupByIncompatible,
+    ScalarOrArray,
+    T_DataArray,
+    T_Dataset,
+    T_Variable,
+    VarCompatible,
+)
 from .variable import Variable
 
 try:
     from dask.array import Array as DaskArray
 except ImportError:
     DaskArray = np.ndarray
 
-# DatasetOpsMixin etc. are parent classes of Dataset etc.
-T_Dataset = TypeVar("T_Dataset", bound="DatasetOpsMixin")
-T_DataArray = TypeVar("T_DataArray", bound="DataArrayOpsMixin")
-T_Variable = TypeVar("T_Variable", bound="VariableOpsMixin")
-
-ScalarOrArray = Union[ArrayLike, np.generic, np.ndarray, DaskArray]
-DsCompatible = Union[Dataset, DataArray, Variable, GroupBy, ScalarOrArray]
-DaCompatible = Union[DataArray, Variable, DataArrayGroupBy, ScalarOrArray]
-VarCompatible = Union[Variable, ScalarOrArray]
-GroupByIncompatible = Union[Variable, GroupBy]
-
 class DatasetOpsMixin:
     __slots__ = ()
     def _binary_op(self, other, f, reflexive=...): ...

xarray/core/common.py

@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 import warnings
 from contextlib import suppress
 from html import escape
@@ -36,10 +38,10 @@
 if TYPE_CHECKING:
     from .dataarray import DataArray
     from .dataset import Dataset
+    from .types import T_DataWithCoords, T_Xarray
     from .variable import Variable
     from .weighted import Weighted
 
-T_DataWithCoords = TypeVar("T_DataWithCoords", bound="DataWithCoords")
 
 C = TypeVar("C")
 T = TypeVar("T")
@@ -795,9 +797,7 @@ def groupby_bins(
             },
         )
 
-    def weighted(
-        self: T_DataWithCoords, weights: "DataArray"
-    ) -> "Weighted[T_DataWithCoords]":
+    def weighted(self: T_DataWithCoords, weights: "DataArray") -> Weighted[T_Xarray]:
         """
         Weighted operations.
 

xarray/core/computation.py

@@ -21,7 +21,6 @@
     Optional,
     Sequence,
     Tuple,
-    TypeVar,
     Union,
 )
 
@@ -36,11 +35,9 @@
 from .variable import Variable
 
 if TYPE_CHECKING:
-    from .coordinates import Coordinates  # noqa
-    from .dataarray import DataArray
+    from .coordinates import Coordinates
     from .dataset import Dataset
-
-    T_DSorDA = TypeVar("T_DSorDA", DataArray, Dataset)
+    from .types import T_Xarray
 
 _NO_FILL_VALUE = utils.ReprObject("<no-fill-value>")
 _DEFAULT_NAME = utils.ReprObject("<default-name>")
@@ -199,7 +196,7 @@ def result_name(objects: list) -> Any:
     return name
 
 
-def _get_coords_list(args) -> List["Coordinates"]:
+def _get_coords_list(args) -> List[Coordinates]:
     coords_list = []
     for arg in args:
         try:
@@ -400,8 +397,8 @@ def apply_dict_of_variables_vfunc(
 
 
 def _fast_dataset(
-    variables: Dict[Hashable, Variable], coord_variables: Mapping[Any, Variable]
-) -> "Dataset":
+    variables: Dict[Hashable, Variable], coord_variables: Mapping[Hashable, Variable]
+) -> Dataset:
     """Create a dataset as quickly as possible.
 
     Beware: the `variables` dict is modified INPLACE.
@@ -1729,7 +1726,7 @@ def _calc_idxminmax(
     return res
 
 
-def unify_chunks(*objects: T_DSorDA) -> Tuple[T_DSorDA, ...]:
+def unify_chunks(*objects: T_Xarray) -> Tuple[T_Xarray, ...]:
     """
     Given any number of Dataset and/or DataArray objects, returns
     new objects with unified chunk size along all chunked dimensions.

xarray/core/dataarray.py

@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 import datetime
 import warnings
 from typing import (
@@ -12,7 +14,6 @@
     Optional,
     Sequence,
     Tuple,
-    TypeVar,
     Union,
     cast,
 )
@@ -70,8 +71,6 @@
     assert_unique_multiindex_level_names,
 )
 
-T_DataArray = TypeVar("T_DataArray", bound="DataArray")
-T_DSorDA = TypeVar("T_DSorDA", "DataArray", Dataset)
 if TYPE_CHECKING:
     try:
         from dask.delayed import Delayed
@@ -86,6 +85,8 @@
     except ImportError:
         iris_Cube = None
 
+    from .types import T_DataArray, T_Xarray
+
 
 def _infer_coords_and_dims(
     shape, coords, dims
@@ -3698,11 +3699,11 @@ def unify_chunks(self) -> "DataArray":
 
     def map_blocks(
         self,
-        func: Callable[..., T_DSorDA],
+        func: Callable[..., T_Xarray],
         args: Sequence[Any] = (),
         kwargs: Mapping[str, Any] = None,
         template: Union["DataArray", "Dataset"] = None,
-    ) -> T_DSorDA:
+    ) -> T_Xarray:
         """
         Apply a function to each block of this DataArray.
 

xarray/core/dataset.py

@@ -25,7 +25,6 @@
     Sequence,
     Set,
     Tuple,
-    TypeVar,
     Union,
     cast,
     overload,
@@ -109,8 +108,7 @@
     from ..backends import AbstractDataStore, ZarrStore
     from .dataarray import DataArray
     from .merge import CoercibleMapping
-
-    T_DSorDA = TypeVar("T_DSorDA", DataArray, "Dataset")
+    from .types import T_Xarray
 
     try:
         from dask.delayed import Delayed
@@ -6630,11 +6628,11 @@ def unify_chunks(self) -> "Dataset":
 
     def map_blocks(
         self,
-        func: "Callable[..., T_DSorDA]",
+        func: "Callable[..., T_Xarray]",
         args: Sequence[Any] = (),
         kwargs: Mapping[str, Any] = None,
         template: Union["DataArray", "Dataset"] = None,
-    ) -> "T_DSorDA":
+    ) -> "T_Xarray":
         """
         Apply a function to each block of this Dataset.
 

xarray/core/parallel.py

@@ -1,7 +1,10 @@
+from __future__ import annotations
+
 import collections
 import itertools
 import operator
 from typing import (
+    TYPE_CHECKING,
     Any,
     Callable,
     DefaultDict,
@@ -12,7 +15,6 @@
     Mapping,
     Sequence,
     Tuple,
-    TypeVar,
     Union,
 )
 
@@ -32,7 +34,8 @@
     pass
 
 
-T_DSorDA = TypeVar("T_DSorDA", DataArray, Dataset)
+if TYPE_CHECKING:
+    from .types import T_Xarray
 
 
 def unzip(iterable):
@@ -122,8 +125,8 @@ def make_meta(obj):
 
 
 def infer_template(
-    func: Callable[..., T_DSorDA], obj: Union[DataArray, Dataset], *args, **kwargs
-) -> T_DSorDA:
+    func: Callable[..., T_Xarray], obj: Union[DataArray, Dataset], *args, **kwargs
+) -> T_Xarray:
     """Infer return object by running the function on meta objects."""
     meta_args = [make_meta(arg) for arg in (obj,) + args]
 
@@ -162,12 +165,12 @@ def _get_chunk_slicer(dim: Hashable, chunk_index: Mapping, chunk_bounds: Mapping
 
 
 def map_blocks(
-    func: Callable[..., T_DSorDA],
+    func: Callable[..., T_Xarray],
     obj: Union[DataArray, Dataset],
     args: Sequence[Any] = (),
     kwargs: Mapping[str, Any] = None,
     template: Union[DataArray, Dataset] = None,
-) -> T_DSorDA:
+) -> T_Xarray:
     """Apply a function to each block of a DataArray or Dataset.
 
     .. warning::

xarray/core/rolling_exp.py

@@ -1,17 +1,14 @@
+from __future__ import annotations
+
 from distutils.version import LooseVersion
-from typing import TYPE_CHECKING, Generic, Hashable, Mapping, TypeVar, Union
+from typing import Generic, Hashable, Mapping, Union
 
 import numpy as np
 
 from .options import _get_keep_attrs
 from .pdcompat import count_not_none
 from .pycompat import is_duck_dask_array
-
-if TYPE_CHECKING:
-    from .dataarray import DataArray  # noqa: F401
-    from .dataset import Dataset  # noqa: F401
-
-T_DSorDA = TypeVar("T_DSorDA", "DataArray", "Dataset")
+from .types import T_Xarray
 
 
 def _get_alpha(com=None, span=None, halflife=None, alpha=None):
@@ -79,7 +76,7 @@ def _get_center_of_mass(comass, span, halflife, alpha):
     return float(comass)
 
 
-class RollingExp(Generic[T_DSorDA]):
+class RollingExp(Generic[T_Xarray]):
     """
     Exponentially-weighted moving window object.
     Similar to EWM in pandas
@@ -103,16 +100,16 @@ class RollingExp(Generic[T_DSorDA]):
 
     def __init__(
         self,
-        obj: T_DSorDA,
+        obj: T_Xarray,
         windows: Mapping[Hashable, Union[int, float]],
         window_type: str = "span",
     ):
-        self.obj: T_DSorDA = obj
+        self.obj: T_Xarray = obj
         dim, window = next(iter(windows.items()))
         self.dim = dim
         self.alpha = _get_alpha(**{window_type: window})
 
-    def mean(self, keep_attrs: bool = None) -> T_DSorDA:
+    def mean(self, keep_attrs: bool = None) -> T_Xarray:
         """
         Exponentially weighted moving average.
 
@@ -139,7 +136,7 @@ def mean(self, keep_attrs: bool = None) -> T_DSorDA:
             move_exp_nanmean, dim=self.dim, alpha=self.alpha, keep_attrs=keep_attrs
         )
 
-    def sum(self, keep_attrs: bool = None) -> T_DSorDA:
+    def sum(self, keep_attrs: bool = None) -> T_Xarray:
         """
         Exponentially weighted moving sum.
 

xarray/core/types.py

@@ -0,0 +1,31 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING, TypeVar, Union
+
+import numpy as np
+
+if TYPE_CHECKING:
+    from .common import DataWithCoords
+    from .dataarray import DataArray
+    from .dataset import Dataset
+    from .groupby import DataArrayGroupBy, GroupBy
+    from .npcompat import ArrayLike
+    from .variable import Variable
+
+    try:
+        from dask.array import Array as DaskArray
+    except ImportError:
+        DaskArray = np.ndarray
+
+T_Dataset = TypeVar("T_Dataset", bound="Dataset")
+T_DataArray = TypeVar("T_DataArray", bound="DataArray")
+T_Variable = TypeVar("T_Variable", bound="Variable")
+# Maybe we rename this to T_Data or something less Fortran-y?
+T_Xarray = TypeVar("T_Xarray", "DataArray", "Dataset")
+T_DataWithCoords = TypeVar("T_DataWithCoords", bound="DataWithCoords")
+
+ScalarOrArray = Union["ArrayLike", np.generic, np.ndarray, "DaskArray"]
+DsCompatible = Union["Dataset", "DataArray", "Variable", "GroupBy", "ScalarOrArray"]
+DaCompatible = Union["DataArray", "Variable", "DataArrayGroupBy", "ScalarOrArray"]
+VarCompatible = Union["Variable", "ScalarOrArray"]
+GroupByIncompatible = Union["Variable", "GroupBy"]