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_DSorDA
     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_DSorDA]:
         """
         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_DSorDA
 
 _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:
@@ -401,7 +398,7 @@ def apply_dict_of_variables_vfunc(
 
 def _fast_dataset(
     variables: Dict[Hashable, Variable], coord_variables: Mapping[Hashable, Variable]
-) -> "Dataset":
+) -> Dataset:
     """Create a dataset as quickly as possible.
 
     Beware: the `variables` dict is modified INPLACE.

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,
 )
@@ -76,8 +77,6 @@
     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
@@ -92,6 +91,8 @@
     except ImportError:
         iris_Cube = None
 
+    from .types import T_DSorDA
+
 
 def _infer_coords_and_dims(
     shape, coords, dims

xarray/core/dataset.py

@@ -25,7 +25,6 @@
     Sequence,
     Set,
     Tuple,
-    TypeVar,
     Union,
     cast,
     overload,
@@ -110,8 +109,7 @@
     from ..backends import AbstractDataStore, ZarrStore
     from .dataarray import DataArray
     from .merge import CoercibleMapping
-
-    T_DSorDA = TypeVar("T_DSorDA", DataArray, "Dataset")
+    from .types import T_DSorDA
 
     try:
         from dask.delayed import Delayed

xarray/core/parallel.py

@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 try:
     import dask
     import dask.array
@@ -11,6 +13,7 @@
 import itertools
 import operator
 from typing import (
+    TYPE_CHECKING,
     Any,
     Callable,
     DefaultDict,
@@ -21,7 +24,6 @@
     Mapping,
     Sequence,
     Tuple,
-    TypeVar,
     Union,
 )
 
@@ -33,7 +35,8 @@
 from .dataarray import DataArray
 from .dataset import Dataset
 
-T_DSorDA = TypeVar("T_DSorDA", DataArray, Dataset)
+if TYPE_CHECKING:
+    from .types import T_DSorDA
 
 
 def unzip(iterable):

xarray/core/rolling_exp.py

@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 from distutils.version import LooseVersion
 from typing import TYPE_CHECKING, Generic, Hashable, Mapping, TypeVar, Union
 
@@ -7,12 +9,6 @@
 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")
-
 
 def _get_alpha(com=None, span=None, halflife=None, alpha=None):
     # pandas defines in terms of com (converting to alpha in the algo)
@@ -79,6 +75,17 @@ def _get_center_of_mass(comass, span, halflife, alpha):
     return float(comass)
 
 
+# We seem to need to redefine T_DSorDA here, rather than importing `core.types`, because
+# a) it needs to be defined (can't be a string) b) it can't be behind an `if
+# TYPE_CHECKING` branch and c) we have import errors if we import it without at the
+# module level like: from .types import T_DSorDA
+
+if TYPE_CHECKING:
+    from .dataarray import DataArray
+    from .dataset import Dataset
+T_DSorDA = TypeVar("T_DSorDA", "DataArray", "Dataset")
+
+
 class RollingExp(Generic[T_DSorDA]):
     """
     Exponentially-weighted moving window object.

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_DSorDA = TypeVar("T_DSorDA", "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]

xarray/core/variable.py

@@ -1,10 +1,13 @@
+from __future__ import annotations
+
 import copy
 import itertools
 import numbers
 import warnings
 from collections import defaultdict
 from datetime import timedelta
 from typing import (
+    TYPE_CHECKING,
     Any,
     Dict,
     Hashable,
@@ -13,7 +16,6 @@
     Optional,
     Sequence,
     Tuple,
-    TypeVar,
     Union,
 )
 
@@ -58,17 +60,8 @@
 # https://github.com/python/mypy/issues/224
 BASIC_INDEXING_TYPES = integer_types + (slice,)
 
-VariableType = TypeVar("VariableType", bound="Variable")
-"""Type annotation to be used when methods of Variable return self or a copy of self.
-When called from an instance of a subclass, e.g. IndexVariable, mypy identifies the
-output as an instance of the subclass.
-
-Usage::
-
-   class Variable:
-       def f(self: VariableType, ...) -> VariableType:
-           ...
-"""
+if TYPE_CHECKING:
+    from .types import T_Variable
 
 
 class MissingDimensionsError(ValueError):
@@ -357,15 +350,15 @@ def data(self, data):
         self._data = data
 
     def astype(
-        self: VariableType,
+        self: T_Variable,
         dtype,
         *,
         order=None,
         casting=None,
         subok=None,
         copy=None,
         keep_attrs=True,
-    ) -> VariableType:
+    ) -> T_Variable:
         """
         Copy of the Variable object, with data cast to a specified type.
 
@@ -763,7 +756,7 @@ def _broadcast_indexes_vectorized(self, key):
 
         return out_dims, VectorizedIndexer(tuple(out_key)), new_order
 
-    def __getitem__(self: VariableType, key) -> VariableType:
+    def __getitem__(self: T_Variable, key) -> T_Variable:
         """Return a new Variable object whose contents are consistent with
         getting the provided key from the underlying data.
 
@@ -782,7 +775,7 @@ def __getitem__(self: VariableType, key) -> VariableType:
             data = np.moveaxis(data, range(len(new_order)), new_order)
         return self._finalize_indexing_result(dims, data)
 
-    def _finalize_indexing_result(self: VariableType, dims, data) -> VariableType:
+    def _finalize_indexing_result(self: T_Variable, dims, data) -> T_Variable:
         """Used by IndexVariable to return IndexVariable objects when possible."""
         return self._replace(dims=dims, data=data)
 
@@ -962,12 +955,12 @@ def copy(self, deep=True, data=None):
         return self._replace(data=data)
 
     def _replace(
-        self: VariableType,
+        self: T_Variable,
         dims=_default,
         data=_default,
         attrs=_default,
         encoding=_default,
-    ) -> VariableType:
+    ) -> T_Variable:
         if dims is _default:
             dims = copy.copy(self._dims)
         if data is _default:
@@ -1100,11 +1093,11 @@ def _to_dense(self):
         return self.copy(deep=False)
 
     def isel(
-        self: VariableType,
+        self: T_Variable,
         indexers: Mapping[Hashable, Any] = None,
         missing_dims: str = "raise",
         **indexers_kwargs: Any,
-    ) -> VariableType:
+    ) -> T_Variable:
         """Return a new array indexed along the specified dimension(s).
 
         Parameters