initial refactor for NamedArray

xarray/backends/common.py

@@ -1,6 +1,7 @@
 from __future__ import annotations
 
 import logging
+import math
 import os
 import time
 import traceback
@@ -14,7 +15,7 @@
 from xarray.core import indexing
 from xarray.core.parallelcompat import get_chunked_array_type
 from xarray.core.pycompat import is_chunked_array
-from xarray.core.utils import FrozenDict, NdimSizeLenMixin, is_remote_uri
+from xarray.core.utils import FrozenDict, is_remote_uri
 
 if TYPE_CHECKING:
     from io import BufferedIOBase
@@ -162,9 +163,39 @@ def robust_getitem(array, key, catch=Exception, max_retries=6, initial_delay=500
             time.sleep(1e-3 * next_delay)
 
 
-class BackendArray(NdimSizeLenMixin, indexing.ExplicitlyIndexed):
+class BackendArray(indexing.ExplicitlyIndexed):
     __slots__ = ()
 
+    @property
+    def ndim(self: Any) -> int:
+        """
+        Number of array dimensions.
+
+        See Also
+        --------
+        numpy.ndarray.ndim
+        """
+        return len(self.shape)
+
+    @property
+    def size(self: Any) -> int:
+        """
+        Number of elements in the array.
+
+        Equal to ``np.prod(a.shape)``, i.e., the product of the array’s dimensions.
+
+        See Also
+        --------
+        numpy.ndarray.size
+        """
+        return math.prod(self.shape)
+
+    def __len__(self: Any) -> int:
+        try:
+            return self.shape[0]
+        except IndexError:
+            raise TypeError("len() of unsized object")
+
     def get_duck_array(self, dtype: np.typing.DTypeLike = None):
         key = indexing.BasicIndexer((slice(None),) * self.ndim)
         return self[key]  # type: ignore [index]

xarray/core/alignment.py

@@ -839,7 +839,7 @@ def is_alignable(obj):
         elif raise_on_invalid:
             raise ValueError(
                 "object to align is neither an xarray.Dataset, "
-                "an xarray.DataArray nor a dictionary: {!r}".format(variables)
+                f"an xarray.DataArray nor a dictionary: {variables!r}"
             )
         else:
             out.append(variables)

xarray/core/dataarray.py

@@ -42,10 +42,8 @@
 from xarray.core.merge import PANDAS_TYPES, MergeError
 from xarray.core.options import OPTIONS, _get_keep_attrs
 from xarray.core.utils import (
-    Default,
     HybridMappingProxy,
     ReprObject,
-    _default,
     either_dict_or_kwargs,
     emit_user_level_warning,
 )
@@ -55,6 +53,7 @@
     as_compatible_data,
     as_variable,
 )
+from xarray.namedarray.utils import Default, _default
 from xarray.plot.accessor import DataArrayPlotAccessor
 from xarray.plot.utils import _get_units_from_attrs
 

xarray/core/dataset.py

@@ -91,11 +91,9 @@
 )
 from xarray.core.types import QuantileMethods, T_Dataset
 from xarray.core.utils import (
-    Default,
     Frozen,
     HybridMappingProxy,
     OrderedSet,
-    _default,
     decode_numpy_dict_values,
     drop_dims_from_indexers,
     either_dict_or_kwargs,
@@ -111,6 +109,7 @@
     broadcast_variables,
     calculate_dimensions,
 )
+from xarray.namedarray.utils import Default, _default
 from xarray.plot.accessor import DatasetPlotAccessor
 
 if TYPE_CHECKING:

xarray/core/indexing.py

@@ -2,6 +2,7 @@
 
 import enum
 import functools
+import math
 import operator
 from collections import Counter, defaultdict
 from collections.abc import Hashable, Iterable, Mapping
@@ -26,7 +27,6 @@
 )
 from xarray.core.types import T_Xarray
 from xarray.core.utils import (
-    NDArrayMixin,
     either_dict_or_kwargs,
     get_valid_numpy_dtype,
     is_scalar,
@@ -458,9 +458,53 @@ def get_duck_array(self):
         return self.array
 
 
-class ExplicitlyIndexedNDArrayMixin(NDArrayMixin, ExplicitlyIndexed):
+class ExplicitlyIndexedNDArrayMixin(ExplicitlyIndexed):
     __slots__ = ()
 
+    @property
+    def ndim(self: Any) -> int:
+        """
+        Number of array dimensions.
+
+        See Also
+        --------
+        numpy.ndarray.ndim
+        """
+        return len(self.shape)
+
+    @property
+    def size(self: Any) -> int:
+        """
+        Number of elements in the array.
+
+        Equal to ``np.prod(a.shape)``, i.e., the product of the array’s dimensions.
+
+        See Also
+        --------
+        numpy.ndarray.size
+        """
+        return math.prod(self.shape)
+
+    def __len__(self: Any) -> int:
+        try:
+            return self.shape[0]
+        except IndexError:
+            raise TypeError("len() of unsized object")
+
+    @property
+    def dtype(self: Any) -> np.dtype:
+        return self.array.dtype
+
+    @property
+    def shape(self: Any) -> tuple[int, ...]:
+        return self.array.shape
+
+    def __getitem__(self: Any, key):
+        return self.array[key]
+
+    def __repr__(self: Any) -> str:
+        return f"{type(self).__name__}(array={self.array!r})"
+
     def get_duck_array(self):
         key = BasicIndexer((slice(None),) * self.ndim)
         return self[key]
@@ -471,7 +515,7 @@ def __array__(self, dtype: np.typing.DTypeLike = None) -> np.ndarray:
         return np.asarray(self.get_duck_array(), dtype=dtype)
 
 
-class ImplicitToExplicitIndexingAdapter(NDArrayMixin):
+class ImplicitToExplicitIndexingAdapter:
     """Wrap an array, converting tuples into the indicated explicit indexer."""
 
     __slots__ = ("array", "indexer_cls")
@@ -483,6 +527,47 @@ def __init__(self, array, indexer_cls=BasicIndexer):
     def __array__(self, dtype: np.typing.DTypeLike = None) -> np.ndarray:
         return np.asarray(self.get_duck_array(), dtype=dtype)
 
+    @property
+    def ndim(self: Any) -> int:
+        """
+        Number of array dimensions.
+
+        See Also
+        --------
+        numpy.ndarray.ndim
+        """
+        return len(self.shape)
+
+    @property
+    def size(self: Any) -> int:
+        """
+        Number of elements in the array.
+
+        Equal to ``np.prod(a.shape)``, i.e., the product of the array’s dimensions.
+
+        See Also
+        --------
+        numpy.ndarray.size
+        """
+        return math.prod(self.shape)
+
+    def __len__(self: Any) -> int:
+        try:
+            return self.shape[0]
+        except IndexError:
+            raise TypeError("len() of unsized object")
+
+    @property
+    def dtype(self: Any) -> np.dtype:
+        return self.array.dtype
+
+    @property
+    def shape(self: Any) -> tuple[int, ...]:
+        return self.array.shape
+
+    def __repr__(self: Any) -> str:
+        return f"{type(self).__name__}(array={self.array!r})"
+
     def get_duck_array(self):
         return self.array.get_duck_array()
 
@@ -1303,7 +1388,7 @@ def __init__(self, array):
         if not isinstance(array, np.ndarray):
             raise TypeError(
                 "NumpyIndexingAdapter only wraps np.ndarray. "
-                "Trying to wrap {}".format(type(array))
+                f"Trying to wrap {type(array)}"
             )
         self.array = array
 

xarray/core/utils.py

@@ -41,7 +41,6 @@
 import inspect
 import io
 import itertools
-import math
 import os
 import re
 import sys
@@ -57,7 +56,6 @@
     MutableSet,
     Sequence,
 )
-from enum import Enum
 from typing import (
     TYPE_CHECKING,
     Any,
@@ -542,69 +540,6 @@ def __repr__(self) -> str:
         return f"{type(self).__name__}({list(self)!r})"
 
 
-class NdimSizeLenMixin:
-    """Mixin class that extends a class that defines a ``shape`` property to
-    one that also defines ``ndim``, ``size`` and ``__len__``.
-    """
-
-    __slots__ = ()
-
-    @property
-    def ndim(self: Any) -> int:
-        """
-        Number of array dimensions.
-
-        See Also
-        --------
-        numpy.ndarray.ndim
-        """
-        return len(self.shape)
-
-    @property
-    def size(self: Any) -> int:
-        """
-        Number of elements in the array.
-
-        Equal to ``np.prod(a.shape)``, i.e., the product of the array’s dimensions.
-
-        See Also
-        --------
-        numpy.ndarray.size
-        """
-        return math.prod(self.shape)
-
-    def __len__(self: Any) -> int:
-        try:
-            return self.shape[0]
-        except IndexError:
-            raise TypeError("len() of unsized object")
-
-
-class NDArrayMixin(NdimSizeLenMixin):
-    """Mixin class for making wrappers of N-dimensional arrays that conform to
-    the ndarray interface required for the data argument to Variable objects.
-
-    A subclass should set the `array` property and override one or more of
-    `dtype`, `shape` and `__getitem__`.
-    """
-
-    __slots__ = ()
-
-    @property
-    def dtype(self: Any) -> np.dtype:
-        return self.array.dtype
-
-    @property
-    def shape(self: Any) -> tuple[int, ...]:
-        return self.array.shape
-
-    def __getitem__(self: Any, key):
-        return self.array[key]
-
-    def __repr__(self: Any) -> str:
-        return f"{type(self).__name__}(array={self.array!r})"
-
-
 class ReprObject:
     """Object that prints as the given value, for use with sentinel values."""
 
@@ -1108,14 +1043,6 @@ def __get__(self, obj: None | object, cls) -> type[_Accessor] | _Accessor:
         return self._accessor(obj)  # type: ignore  # assume it is a valid accessor!
 
 
-# Singleton type, as per https://github.com/python/typing/pull/240
-class Default(Enum):
-    token = 0
-
-
-_default = Default.token
-
-
 def iterate_nested(nested_list):
     for item in nested_list:
         if isinstance(item, list):