TYP: explicit DTypeLike | None (pydata#10738)

* TYP: explicit `DTypeLike | None`

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

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

---------

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

Author: jorenham

xarray/backends/common.py

@@ -309,11 +309,11 @@ class BackendArray(NdimSizeLenMixin, indexing.ExplicitlyIndexed):
     async def async_getitem(self, key: indexing.ExplicitIndexer) -> np.typing.ArrayLike:
         raise NotImplementedError("Backend does not support asynchronous loading")
 
-    def get_duck_array(self, dtype: np.typing.DTypeLike = None):
+    def get_duck_array(self, dtype: np.typing.DTypeLike | None = None):
         key = indexing.BasicIndexer((slice(None),) * self.ndim)
         return self[key]  # type: ignore[index]
 
-    async def async_get_duck_array(self, dtype: np.typing.DTypeLike = None):
+    async def async_get_duck_array(self, dtype: np.typing.DTypeLike | None = None):
         key = indexing.BasicIndexer((slice(None),) * self.ndim)
         return await self.async_getitem(key)
 
@@ -644,7 +644,7 @@ def _infer_dtype(array, name=None):
     )
 
 
-def _copy_with_dtype(data, dtype: np.typing.DTypeLike):
+def _copy_with_dtype(data, dtype: np.typing.DTypeLike | None):
     """Create a copy of an array with the given dtype.
 
     We use this instead of np.array() to ensure that custom object dtypes end

xarray/coding/common.py

@@ -53,7 +53,7 @@ class _ElementwiseFunctionArray(indexing.ExplicitlyIndexedNDArrayMixin):
     Values are computed upon indexing or coercion to a NumPy array.
     """
 
-    def __init__(self, array, func: Callable, dtype: np.typing.DTypeLike):
+    def __init__(self, array, func: Callable, dtype: np.typing.DTypeLike | None):
         assert not is_chunked_array(array)
         self.array = indexing.as_indexable(array)
         self.func = func
@@ -86,7 +86,7 @@ def __repr__(self) -> str:
         return f"{type(self).__name__}({self.array!r}, func={self.func!r}, dtype={self.dtype!r})"
 
 
-def lazy_elemwise_func(array, func: Callable, dtype: np.typing.DTypeLike):
+def lazy_elemwise_func(array, func: Callable, dtype: np.typing.DTypeLike | None):
     """Lazily apply an element-wise function to an array.
     Parameters
     ----------

xarray/coding/variables.py

@@ -122,7 +122,7 @@ def _apply_mask(
     data: np.ndarray,
     encoded_fill_values: list,
     decoded_fill_value: Any,
-    dtype: np.typing.DTypeLike,
+    dtype: np.typing.DTypeLike | None,
 ) -> np.ndarray:
     """Mask all matching values in a NumPy arrays."""
     data = np.asarray(data, dtype=dtype)
@@ -426,7 +426,9 @@ def decode(self, variable: Variable, name: T_Name = None):
         return Variable(dims, data, attrs, encoding, fastpath=True)
 
 
-def _scale_offset_decoding(data, scale_factor, add_offset, dtype: np.typing.DTypeLike):
+def _scale_offset_decoding(
+    data, scale_factor, add_offset, dtype: np.typing.DTypeLike | None
+):
     data = data.astype(dtype=dtype, copy=True)
     if scale_factor is not None:
         data *= scale_factor

xarray/core/accessor_dt.py

@@ -244,7 +244,7 @@ class TimeAccessor(Generic[T_DataArray]):
     def __init__(self, obj: T_DataArray) -> None:
         self._obj = obj
 
-    def _date_field(self, name: str, dtype: DTypeLike) -> T_DataArray:
+    def _date_field(self, name: str, dtype: DTypeLike | None) -> T_DataArray:
         if dtype is None:
             dtype = self._obj.dtype
         result = _get_date_field(_index_or_data(self._obj), name, dtype)

xarray/core/accessor_str.py

@@ -112,7 +112,7 @@ def _apply_str_ufunc(
     *,
     func: Callable,
     obj: Any,
-    dtype: DTypeLike = None,
+    dtype: DTypeLike | None = None,
     output_core_dims: list | tuple = ((),),
     output_sizes: Mapping[Any, int] | None = None,
     func_args: tuple = (),
@@ -224,7 +224,7 @@ def _apply(
         self,
         *,
         func: Callable,
-        dtype: DTypeLike = None,
+        dtype: DTypeLike | None = None,
         output_core_dims: list | tuple = ((),),
         output_sizes: Mapping[Any, int] | None = None,
         func_args: tuple = (),

xarray/core/common.py

@@ -164,7 +164,7 @@ def __complex__(self: Any) -> complex:
         return complex(self.values)
 
     def __array__(
-        self: Any, dtype: np.typing.DTypeLike = None, /, *, copy: bool | None = None
+        self: Any, dtype: DTypeLike | None = None, /, *, copy: bool | None = None
     ) -> np.ndarray:
         if not copy:
             if np.lib.NumpyVersion(np.__version__) >= "2.0.0":
@@ -2073,12 +2073,12 @@ def get_chunksizes(
     return Frozen(chunks)
 
 
-def is_np_datetime_like(dtype: DTypeLike) -> bool:
+def is_np_datetime_like(dtype: DTypeLike | None) -> bool:
     """Check if a dtype is a subclass of the numpy datetime types"""
     return np.issubdtype(dtype, np.datetime64) or np.issubdtype(dtype, np.timedelta64)
 
 
-def is_np_timedelta_like(dtype: DTypeLike) -> bool:
+def is_np_timedelta_like(dtype: DTypeLike | None) -> bool:
     """Check whether dtype is of the timedelta64 dtype."""
     return np.issubdtype(dtype, np.timedelta64)
 

xarray/core/datatree.py

@@ -818,7 +818,7 @@ def __iter__(self) -> Iterator[str]:
         return itertools.chain(self._data_variables, self._children)  # type: ignore[arg-type]
 
     def __array__(
-        self, dtype: np.typing.DTypeLike = None, /, *, copy: bool | None = None
+        self, dtype: np.typing.DTypeLike | None = None, /, *, copy: bool | None = None
     ) -> np.ndarray:
         raise TypeError(
             "cannot directly convert a DataTree into a "

xarray/core/dtypes.py

@@ -238,7 +238,7 @@ def preprocess_types(t):
 
 
 def result_type(
-    *arrays_and_dtypes: np.typing.ArrayLike | np.typing.DTypeLike,
+    *arrays_and_dtypes: np.typing.ArrayLike | np.typing.DTypeLike | None,
     xp=None,
 ) -> np.dtype:
     """Like np.result_type, but with type promotion rules matching pandas.

xarray/core/extension_array.py

@@ -160,7 +160,7 @@ def ndim(self) -> int:
         return 1
 
     def __array__(
-        self, dtype: np.typing.DTypeLike = None, /, *, copy: bool | None = None
+        self, dtype: np.typing.DTypeLike | None = None, /, *, copy: bool | None = None
     ) -> np.ndarray:
         if Version(np.__version__) >= Version("2.0.0"):
             return np.asarray(self.array, dtype=dtype, copy=copy)

xarray/core/groupby.py

@@ -211,7 +211,7 @@ def data(self) -> np.ndarray:
         return np.arange(self.size, dtype=int)
 
     def __array__(
-        self, dtype: np.typing.DTypeLike = None, /, *, copy: bool | None = None
+        self, dtype: np.typing.DTypeLike | None = None, /, *, copy: bool | None = None
     ) -> np.ndarray:
         if copy is False:
             raise NotImplementedError(f"An array copy is necessary, got {copy = }.")