DIms_type

Author: crusaderky

xarray/core/computation.py

@@ -24,7 +24,7 @@
 from xarray.core.parallelcompat import get_chunked_array_type
 from xarray.core.pycompat import is_chunked_array, is_duck_dask_array
 from xarray.core.types import Dims, T_DataArray
-from xarray.core.utils import is_dict_like, is_scalar
+from xarray.core.utils import is_dict_like, is_scalar, parse_dims
 from xarray.core.variable import Variable
 from xarray.util.deprecation_helpers import deprecate_dims
 
@@ -1775,7 +1775,7 @@ def dot(
     ----------
     *arrays : DataArray or Variable
         Arrays to compute.
-    dim : str, iterable of hashable, "..." or None, optional
+    dim : Hashable, iterable of Hashable, ..., or None, optional
         Which dimensions to sum over. Ellipsis ('...') sums over all dimensions.
         If not specified, then all the common dimensions are summed over.
     **kwargs : dict
@@ -1875,16 +1875,14 @@ def dot(
     einsum_axes = "abcdefghijklmnopqrstuvwxyz"
     dim_map = {d: einsum_axes[i] for i, d in enumerate(all_dims)}
 
-    if dim is ...:
-        dim = all_dims
-    elif isinstance(dim, str):
-        dim = (dim,)
-    elif dim is None:
-        # find dimensions that occur more than one times
+    if dim is None:
+        # find dimensions that occur more than once
         dim_counts: Counter = Counter()
         for arr in arrays:
             dim_counts.update(arr.dims)
         dim = tuple(d for d, c in dim_counts.items() if c > 1)
+    else:
+        dim = parse_dims(dim, all_dims=tuple(all_dims))
 
     dot_dims: set[Hashable] = set(dim)
 

xarray/core/types.py

@@ -2,7 +2,7 @@
 
 import datetime
 import sys
-from collections.abc import Hashable, Iterable, Iterator, Mapping, Sequence
+from collections.abc import Collection, Hashable, Iterator, Mapping, Sequence
 from typing import (
     TYPE_CHECKING,
     Any,
@@ -182,8 +182,7 @@ def copy(
 DsCompatible = Union["Dataset", "DaCompatible"]
 GroupByCompatible = Union["Dataset", "DataArray"]
 
-Dims = Union[str, Iterable[Hashable], "ellipsis", None]
-OrderedDims = Union[str, Sequence[Union[Hashable, "ellipsis"]], "ellipsis", None]
+Dims = Union[Hashable, Collection[Hashable], None]  # Note: Hashable include ellipsis
 
 # FYI in some cases we don't allow `None`, which this doesn't take account of.
 T_ChunkDim: TypeAlias = Union[int, Literal["auto"], None, tuple[int, ...]]

xarray/core/utils.py

@@ -76,7 +76,7 @@
 import pandas as pd
 
 if TYPE_CHECKING:
-    from xarray.core.types import Dims, ErrorOptionsWithWarn, OrderedDims, T_DuckArray
+    from xarray.core.types import Dims, ErrorOptionsWithWarn, T_DuckArray
 
 K = TypeVar("K")
 V = TypeVar("V")
@@ -988,7 +988,7 @@ def drop_missing_dims(
 
 @overload
 def parse_dims(
-    dim: str | Iterable[Hashable] | T_None,
+    dim: Dims,
     all_dims: tuple[Hashable, ...],
     *,
     check_exists: bool = True,
@@ -999,12 +999,12 @@ def parse_dims(
 
 @overload
 def parse_dims(
-    dim: str | Iterable[Hashable] | T_None,
+    dim: Dims,
     all_dims: tuple[Hashable, ...],
     *,
     check_exists: bool = True,
     replace_none: Literal[False],
-) -> tuple[Hashable, ...] | T_None:
+) -> tuple[Hashable, ...] | None | ellipsis:
     ...
 
 
@@ -1042,16 +1042,19 @@ def parse_dims(
         if replace_none:
             return all_dims
         return dim
-    if isinstance(dim, str):
+    if isinstance(dim, Collection) and not isinstance(dim, str):
+        dim = tuple(dim)
+    else:
         dim = (dim,)
+
     if check_exists:
         _check_dims(set(dim), set(all_dims))
-    return tuple(dim)
+    return dim
 
 
 @overload
 def parse_ordered_dims(
-    dim: str | Sequence[Hashable | ellipsis] | T_None,
+    dim: Dims,
     all_dims: tuple[Hashable, ...],
     *,
     check_exists: bool = True,
@@ -1062,7 +1065,7 @@ def parse_ordered_dims(
 
 @overload
 def parse_ordered_dims(
-    dim: str | Sequence[Hashable | ellipsis] | T_None,
+    dim: Dims,
     all_dims: tuple[Hashable, ...],
     *,
     check_exists: bool = True,
@@ -1072,24 +1075,22 @@ def parse_ordered_dims(
 
 
 def parse_ordered_dims(
-    dim: OrderedDims,
+    dim: Dims,
     all_dims: tuple[Hashable, ...],
     *,
     check_exists: bool = True,
     replace_none: bool = True,
 ) -> tuple[Hashable, ...] | None | ellipsis:
     """Parse one or more dimensions.
 
-    A single dimension must be always a str, multiple dimensions
-    can be Hashables. This supports e.g. using a tuple as a dimension.
     An ellipsis ("...") in a sequence of dimensions will be
     replaced with all remaining dimensions. This only makes sense when
     the input is a sequence and not e.g. a set.
 
     Parameters
     ----------
-    dim : str, Sequence of Hashable or "...", "..." or None
-        Dimension(s) to parse. If "..." appears in a Sequence
+    dim : Hashable or ... (or Sequence thereof), or None
+        Dimension(s) to parse. If ... appears in a Sequence
         it always gets replaced with all remaining dims
     all_dims : tuple of Hashable
         All possible dimensions.
@@ -1103,7 +1104,7 @@ def parse_ordered_dims(
     parsed_dims : tuple of Hashable
         Input dimensions as a tuple.
     """
-    if dim is not None and dim is not ... and not isinstance(dim, str) and ... in dim:
+    if isinstance(dim, Sequence) and not isinstance(dim, str) and ... in dim:
         dims_set: set[Hashable | ellipsis] = set(dim)
         all_dims_set = set(all_dims)
         if check_exists:
@@ -1126,9 +1127,9 @@ def parse_ordered_dims(
         )
 
 
-def _check_dims(dim: set[Hashable | ellipsis], all_dims: set[Hashable]) -> None:
-    wrong_dims = dim - all_dims
-    if wrong_dims and wrong_dims != {...}:
+def _check_dims(dim: set[Hashable], all_dims: set[Hashable]) -> None:
+    wrong_dims = (dim - all_dims) - {...}
+    if wrong_dims:
         wrong_dims_str = ", ".join(f"'{d!s}'" for d in wrong_dims)
         raise ValueError(
             f"Dimension(s) {wrong_dims_str} do not exist. Expected one or more of {all_dims}"

xarray/tests/test_interp.py

@@ -838,8 +838,8 @@ def test_interpolate_chunk_1d(
                         if chunked:
                             dest[dim] = xr.DataArray(data=dest[dim], dims=[dim])
                             dest[dim] = dest[dim].chunk(2)
-                actual = da.interp(method=method, **dest, kwargs=kwargs)  # type: ignore
-                expected = da.compute().interp(method=method, **dest, kwargs=kwargs)  # type: ignore
+                actual = da.interp(method=method, **dest, kwargs=kwargs)
+                expected = da.compute().interp(method=method, **dest, kwargs=kwargs)
 
                 assert_identical(actual, expected)
 

xarray/tests/test_utils.py

@@ -1,6 +1,6 @@
 from __future__ import annotations
 
-from collections.abc import Hashable, Iterable, Sequence
+from collections.abc import Hashable
 
 import numpy as np
 import pandas as pd
@@ -255,19 +255,21 @@ def test_infix_dims_errors(supplied, all_):
     ["dim", "expected"],
     [
         pytest.param("a", ("a",), id="str"),
+        pytest.param(1, (1,), id="non_str_hashable"),
         pytest.param(["a", "b"], ("a", "b"), id="list_of_str"),
         pytest.param(["a", 1], ("a", 1), id="list_mixed"),
+        pytest.param(["a", ...], ("a", ...), id="list_with_ellipsis"),
         pytest.param(("a", "b"), ("a", "b"), id="tuple_of_str"),
         pytest.param(["a", ("b", "c")], ("a", ("b", "c")), id="list_with_tuple"),
         pytest.param((("b", "c"),), (("b", "c"),), id="tuple_of_tuple"),
+        pytest.param({"a", 1}, tuple({"a", 1}), id="non_sequence_collection"),
+        pytest.param((), (), id="empty_tuple"),
+        pytest.param(set(), (), id="empty_collection"),
         pytest.param(None, None, id="None"),
         pytest.param(..., ..., id="ellipsis"),
     ],
 )
-def test_parse_dims(
-    dim: str | Iterable[Hashable] | None,
-    expected: tuple[Hashable, ...],
-) -> None:
+def test_parse_dims(dim, expected):
     all_dims = ("a", "b", 1, ("b", "c"))  # selection of different Hashables
     actual = utils.parse_dims(dim, all_dims, replace_none=False)
     assert actual == expected
@@ -297,7 +299,7 @@ def test_parse_dims_replace_none(dim: None | ellipsis) -> None:
         pytest.param(["x", 2], id="list_missing_all"),
     ],
 )
-def test_parse_dims_raises(dim: str | Iterable[Hashable]) -> None:
+def test_parse_dims_raises(dim):
     all_dims = ("a", "b", 1, ("b", "c"))  # selection of different Hashables
     with pytest.raises(ValueError, match="'x'"):
         utils.parse_dims(dim, all_dims, check_exists=True)
@@ -313,10 +315,7 @@ def test_parse_dims_raises(dim: str | Iterable[Hashable]) -> None:
         pytest.param(["a", ..., "b"], ("a", "c", "b"), id="list_with_middle_ellipsis"),
     ],
 )
-def test_parse_ordered_dims(
-    dim: str | Sequence[Hashable | ellipsis],
-    expected: tuple[Hashable, ...],
-) -> None:
+def test_parse_ordered_dims(dim, expected):
     all_dims = ("a", "b", "c")
     actual = utils.parse_ordered_dims(dim, all_dims)
     assert actual == expected