{full,zeros,ones}_like typing

xarray/core/common.py

@@ -13,14 +13,15 @@
     Iterator,
     Mapping,
     TypeVar,
+    Union,
     overload,
 )
 
 import numpy as np
 import pandas as pd
 
 from . import dtypes, duck_array_ops, formatting, formatting_html, ops
-from .npcompat import DTypeLike
+from .npcompat import DTypeLike, DTypeLikeSave
 from .options import OPTIONS, _get_keep_attrs
 from .pycompat import is_duck_dask_array
 from .rolling_exp import RollingExp
@@ -1577,26 +1578,45 @@ def __getitem__(self, value):
         raise NotImplementedError()
 
 
+DTypeMaybeMapping = Union[DTypeLikeSave, Mapping[Any, DTypeLikeSave]]
+
+
 @overload
-def full_like(
-    other: Dataset,
-    fill_value,
-    dtype: DTypeLike | Mapping[Any, DTypeLike] = None,
-) -> Dataset:
+def full_like(other: DataArray, fill_value: Any, dtype: DTypeLikeSave) -> DataArray:
+    ...
+
+
+@overload
+def full_like(other: Dataset, fill_value: Any, dtype: DTypeMaybeMapping) -> Dataset:
     ...
 
 
 @overload
-def full_like(other: DataArray, fill_value, dtype: DTypeLike = None) -> DataArray:
+def full_like(other: Variable, fill_value: Any, dtype: DTypeLikeSave) -> Variable:
     ...
 
 
 @overload
-def full_like(other: Variable, fill_value, dtype: DTypeLike = None) -> Variable:
+def full_like(
+    other: Dataset | DataArray, fill_value: Any, dtype: DTypeMaybeMapping = None
+) -> Dataset | DataArray:
     ...
 
 
-def full_like(other, fill_value, dtype=None):
+@overload
+def full_like(
+    other: Dataset | DataArray | Variable,
+    fill_value: Any,
+    dtype: DTypeMaybeMapping = None,
+) -> Dataset | DataArray | Variable:
+    ...
+
+
+def full_like(
+    other: Dataset | DataArray | Variable,
+    fill_value: Any,
+    dtype: DTypeMaybeMapping = None,
+) -> Dataset | DataArray | Variable:
     """Return a new object with the same shape and type as a given object.
 
     Parameters
@@ -1711,26 +1731,26 @@ def full_like(other, fill_value, dtype=None):
             f"fill_value must be scalar or, for datasets, a dict-like. Received {fill_value} instead."
         )
 
-    if not isinstance(other, Dataset) and isinstance(dtype, Mapping):
-        raise ValueError(
-            "'dtype' cannot be dict-like when passing a DataArray or Variable"
-        )
-
     if isinstance(other, Dataset):
         if not isinstance(fill_value, dict):
             fill_value = {k: fill_value for k in other.data_vars.keys()}
 
+        dtype_: Mapping[Any, DTypeLikeSave]
         if not isinstance(dtype, Mapping):
             dtype_ = {k: dtype for k in other.data_vars.keys()}
         else:
             dtype_ = dtype
 
         data_vars = {
-            k: _full_like_variable(v, fill_value.get(k, dtypes.NA), dtype_.get(k, None))
+            k: _full_like_variable(
+                v.variable, fill_value.get(k, dtypes.NA), dtype_.get(k, None)
+            )
             for k, v in other.data_vars.items()
         }
         return Dataset(data_vars, coords=other.coords, attrs=other.attrs)
     elif isinstance(other, DataArray):
+        if isinstance(dtype, Mapping):
+            raise ValueError("'dtype' cannot be dict-like when passing a DataArray")
         return DataArray(
             _full_like_variable(other.variable, fill_value, dtype),
             dims=other.dims,
@@ -1739,12 +1759,16 @@ def full_like(other, fill_value, dtype=None):
             name=other.name,
         )
     elif isinstance(other, Variable):
+        if isinstance(dtype, Mapping):
+            raise ValueError("'dtype' cannot be dict-like when passing a Variable")
         return _full_like_variable(other, fill_value, dtype)
     else:
         raise TypeError("Expected DataArray, Dataset, or Variable")
 
 
-def _full_like_variable(other, fill_value, dtype: DTypeLike = None):
+def _full_like_variable(
+    other: Variable, fill_value: Any, dtype: DTypeLike = None
+) -> Variable:
     """Inner function of full_like, where other must be a variable"""
     from .variable import Variable
 
@@ -1765,7 +1789,38 @@ def _full_like_variable(other, fill_value, dtype: DTypeLike = None):
     return Variable(dims=other.dims, data=data, attrs=other.attrs)
 
 
-def zeros_like(other, dtype: DTypeLike = None):
+@overload
+def zeros_like(other: DataArray, dtype: DTypeLikeSave) -> DataArray:
+    ...
+
+
+@overload
+def zeros_like(other: Dataset, dtype: DTypeMaybeMapping) -> Dataset:
+    ...
+
+
+@overload
+def zeros_like(other: Variable, dtype: DTypeLikeSave) -> Variable:
+    ...
+
+
+@overload
+def zeros_like(
+    other: Dataset | DataArray, dtype: DTypeMaybeMapping = None
+) -> Dataset | DataArray:
+    ...
+
+
+@overload
+def zeros_like(
+    other: Dataset | DataArray | Variable, dtype: DTypeMaybeMapping = None
+) -> Dataset | DataArray | Variable:
+    ...
+
+
+def zeros_like(
+    other: Dataset | DataArray | Variable, dtype: DTypeMaybeMapping = None
+) -> Dataset | DataArray | Variable:
     """Return a new object of zeros with the same shape and
     type as a given dataarray or dataset.
 
@@ -1821,7 +1876,38 @@ def zeros_like(other, dtype: DTypeLike = None):
     return full_like(other, 0, dtype)
 
 
-def ones_like(other, dtype: DTypeLike = None):
+@overload
+def ones_like(other: DataArray, dtype: DTypeLikeSave) -> DataArray:
+    ...
+
+
+@overload
+def ones_like(other: Dataset, dtype: DTypeMaybeMapping) -> Dataset:
+    ...
+
+
+@overload
+def ones_like(other: Variable, dtype: DTypeLikeSave) -> Variable:
+    ...
+
+
+@overload
+def ones_like(
+    other: Dataset | DataArray, dtype: DTypeMaybeMapping = None
+) -> Dataset | DataArray:
+    ...
+
+
+@overload
+def ones_like(
+    other: Dataset | DataArray | Variable, dtype: DTypeMaybeMapping = None
+) -> Dataset | DataArray | Variable:
+    ...
+
+
+def ones_like(
+    other: Dataset | DataArray | Variable, dtype: DTypeMaybeMapping = None
+) -> Dataset | DataArray | Variable:
     """Return a new object of ones with the same shape and
     type as a given dataarray or dataset.
 

xarray/core/computation.py

@@ -1905,7 +1905,7 @@ def polyval(
     coeffs = coeffs.reindex(
         {degree_dim: np.arange(max_deg + 1)}, fill_value=0, copy=False
     )
-    coord = _ensure_numeric(coord)  # type: ignore # https://github.com/python/mypy/issues/1533 ?
+    coord = _ensure_numeric(coord)
 
     # using Horner's method
     # https://en.wikipedia.org/wiki/Horner%27s_method
@@ -1917,7 +1917,7 @@ def polyval(
     return res
 
 
-def _ensure_numeric(data: T_Xarray) -> T_Xarray:
+def _ensure_numeric(data: Dataset | DataArray) -> Dataset | DataArray:
     """Converts all datetime64 variables to float64
 
     Parameters

xarray/core/missing.py

@@ -1,8 +1,10 @@
+from __future__ import annotations
+
 import datetime as dt
 import warnings
 from functools import partial
 from numbers import Number
-from typing import Any, Callable, Dict, Hashable, Sequence, Union
+from typing import TYPE_CHECKING, Any, Callable, Hashable, Sequence
 
 import numpy as np
 import pandas as pd
@@ -17,8 +19,14 @@
 from .utils import OrderedSet, is_scalar
 from .variable import Variable, broadcast_variables
 
+if TYPE_CHECKING:
+    from .dataarray import DataArray
+    from .dataset import Dataset
+
 
-def _get_nan_block_lengths(obj, dim: Hashable, index: Variable):
+def _get_nan_block_lengths(
+    obj: Dataset | DataArray | Variable, dim: Hashable, index: Variable
+):
     """
     Return an object where each NaN element in 'obj' is replaced by the
     length of the gap the element is in.
@@ -48,8 +56,8 @@ def _get_nan_block_lengths(obj, dim: Hashable, index: Variable):
 class BaseInterpolator:
     """Generic interpolator class for normalizing interpolation methods"""
 
-    cons_kwargs: Dict[str, Any]
-    call_kwargs: Dict[str, Any]
+    cons_kwargs: dict[str, Any]
+    call_kwargs: dict[str, Any]
     f: Callable
     method: str
 
@@ -213,7 +221,7 @@ def _apply_over_vars_with_dim(func, self, dim=None, **kwargs):
 
 
 def get_clean_interp_index(
-    arr, dim: Hashable, use_coordinate: Union[str, bool] = True, strict: bool = True
+    arr, dim: Hashable, use_coordinate: str | bool = True, strict: bool = True
 ):
     """Return index to use for x values in interpolation or curve fitting.
 
@@ -300,10 +308,10 @@ def get_clean_interp_index(
 def interp_na(
     self,
     dim: Hashable = None,
-    use_coordinate: Union[bool, str] = True,
+    use_coordinate: bool | str = True,
     method: str = "linear",
     limit: int = None,
-    max_gap: Union[int, float, str, pd.Timedelta, np.timedelta64, dt.timedelta] = None,
+    max_gap: int | float | str | pd.Timedelta | np.timedelta64 | dt.timedelta = None,
     keep_attrs: bool = None,
     **kwargs,
 ):

xarray/core/npcompat.py

@@ -28,14 +28,49 @@
 # THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
 # (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
 # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-from typing import TYPE_CHECKING, Any, Literal, Sequence, TypeVar, Union
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    List,
+    Literal,
+    Sequence,
+    Tuple,
+    Type,
+    TypeVar,
+    Union,
+)
 
 import numpy as np
 from packaging.version import Version
 
 # Type annotations stubs
 try:
     from numpy.typing import ArrayLike, DTypeLike
+    from numpy.typing._dtype_like import _DTypeLikeNested, _ShapeLike, _SupportsDType
+
+    # Xarray requires a Mapping[Hashable, dtype] in many places which
+    # conflics with numpys own DTypeLike (with dtypes for fields).
+    # https://numpy.org/devdocs/reference/typing.html#numpy.typing.DTypeLike
+    # This is a copy of this DTypeLike that allows only non-Mapping dtypes.
+    DTypeLikeSave = Union[
+        np.dtype,
+        # default data type (float64)
+        None,
+        # array-scalar types and generic types
+        Type[Any],
+        # character codes, type strings or comma-separated fields, e.g., 'float64'
+        str,
+        # (flexible_dtype, itemsize)
+        Tuple[_DTypeLikeNested, int],
+        # (fixed_dtype, shape)
+        Tuple[_DTypeLikeNested, _ShapeLike],
+        # (base_dtype, new_dtype)
+        Tuple[_DTypeLikeNested, _DTypeLikeNested],
+        # because numpy does the same?
+        List[Any],
+        # anything with a dtype attribute
+        _SupportsDType[np.dtype],
+    ]
 except ImportError:
     # fall back for numpy < 1.20, ArrayLike adapted from numpy.typing._array_like
     from typing import Protocol
@@ -46,8 +81,14 @@ class _SupportsArray(Protocol):
             def __array__(self) -> np.ndarray:
                 ...
 
+        class _SupportsDTypeFallback(Protocol):
+            @property
+            def dtype(self) -> np.dtype:
+                ...
+
     else:
         _SupportsArray = Any
+        _SupportsDTypeFallback = Any
 
     _T = TypeVar("_T")
     _NestedSequence = Union[
@@ -72,7 +113,16 @@ def __array__(self) -> np.ndarray:
     # with the same name (ArrayLike and DTypeLike from the try block)
     ArrayLike = _ArrayLikeFallback  # type: ignore
     # fall back for numpy < 1.20
-    DTypeLike = Union[np.dtype, str]  # type: ignore[misc]
+    DTypeLikeSave = Union[  # type: ignore[misc]
+        np.dtype,
+        str,
+        None,
+        Type[Any],
+        Tuple[Any, Any],
+        List[Any],
+        _SupportsDTypeFallback,
+    ]
+    DTypeLike = DTypeLikeSave  # type: ignore[misc]
 
 
 if Version(np.__version__) >= Version("1.20.0"):