Update __array__ signatures with copy

pyproject.toml

@@ -323,7 +323,6 @@ filterwarnings = [
   "default:Using a non-tuple sequence for multidimensional indexing is deprecated:FutureWarning",
   "default:Duplicate dimension names present:UserWarning:xarray.namedarray.core",
   "default:::xarray.tests.test_strategies", # TODO: remove once we know how to deal with a changed signature in protocols
-  "ignore:__array__ implementation doesn't accept a copy keyword, so passing copy=False failed.",
 ]
 
 log_cli_level = "INFO"

xarray/core/common.py

@@ -163,7 +163,7 @@ def __complex__(self: Any) -> complex:
         return complex(self.values)
 
     def __array__(
-        self: Any, dtype: DTypeLike | None = None, copy: bool | None = None
+        self: Any, dtype: np.typing.DTypeLike = None, /, *, copy: bool | None = None
     ) -> np.ndarray:
         if not copy:
             if np.lib.NumpyVersion(np.__version__) >= "2.0.0":

xarray/core/datatree.py

@@ -55,6 +55,7 @@
     from xarray.core.dataset import calculate_dimensions
 
 if TYPE_CHECKING:
+    import numpy as np
     import pandas as pd
 
     from xarray.core.datatree_io import T_DataTreeNetcdfEngine, T_DataTreeNetcdfTypes
@@ -737,7 +738,9 @@ def __bool__(self) -> bool:
     def __iter__(self) -> Iterator[str]:
         return itertools.chain(self._data_variables, self._children)  # type: ignore[arg-type]
 
-    def __array__(self, dtype=None, copy=None):
+    def __array__(
+        self, dtype: np.typing.DTypeLike = None, /, *, copy: bool | None = None
+    ) -> np.ndarray:
         raise TypeError(
             "cannot directly convert a DataTree into a "
             "numpy array. Instead, create an xarray.DataArray "

xarray/core/groupby.py

@@ -193,7 +193,11 @@ def values(self) -> range:
     def data(self) -> range:
         return range(self.size)
 
-    def __array__(self) -> np.ndarray:
+    def __array__(
+        self, dtype: np.typing.DTypeLike = None, /, *, copy: bool | None = None
+    ) -> np.ndarray:
+        if copy is False:
+            raise NotImplementedError(f"An array copy is necessary, got {copy = }.")
         return np.arange(self.size)
 
     @property

xarray/core/indexing.py

@@ -13,6 +13,7 @@
 
 import numpy as np
 import pandas as pd
+from packaging.version import Version
 
 from xarray.core import duck_array_ops
 from xarray.core.nputils import NumpyVIndexAdapter
@@ -505,9 +506,14 @@ class ExplicitlyIndexed:
 
     __slots__ = ()
 
-    def __array__(self, dtype: np.typing.DTypeLike = None) -> np.ndarray:
+    def __array__(
+        self, dtype: np.typing.DTypeLike = None, /, *, copy: bool | None = None
+    ) -> np.ndarray:
         # Leave casting to an array up to the underlying array type.
-        return np.asarray(self.get_duck_array(), dtype=dtype)
+        if Version(np.__version__) >= Version("2.0.0"):
+            return np.asarray(self.get_duck_array(), dtype=dtype, copy=copy)
+        else:
+            return np.asarray(self.get_duck_array(), dtype=dtype)
 
     def get_duck_array(self):
         return self.array
@@ -520,11 +526,6 @@ def get_duck_array(self):
         key = BasicIndexer((slice(None),) * self.ndim)
         return self[key]
 
-    def __array__(self, dtype: np.typing.DTypeLike = None) -> np.ndarray:
-        # This is necessary because we apply the indexing key in self.get_duck_array()
-        # Note this is the base class for all lazy indexing classes
-        return np.asarray(self.get_duck_array(), dtype=dtype)
-
     def _oindex_get(self, indexer: OuterIndexer):
         raise NotImplementedError(
             f"{self.__class__.__name__}._oindex_get method should be overridden"
@@ -570,8 +571,13 @@ def __init__(self, array, indexer_cls: type[ExplicitIndexer] = BasicIndexer):
         self.array = as_indexable(array)
         self.indexer_cls = indexer_cls
 
-    def __array__(self, dtype: np.typing.DTypeLike = None) -> np.ndarray:
-        return np.asarray(self.get_duck_array(), dtype=dtype)
+    def __array__(
+        self, dtype: np.typing.DTypeLike = None, /, *, copy: bool | None = None
+    ) -> np.ndarray:
+        if Version(np.__version__) >= Version("2.0.0"):
+            return np.asarray(self.get_duck_array(), dtype=dtype, copy=copy)
+        else:
+            return np.asarray(self.get_duck_array(), dtype=dtype)
 
     def get_duck_array(self):
         return self.array.get_duck_array()
@@ -830,9 +836,6 @@ def __init__(self, array):
     def _ensure_cached(self):
         self.array = as_indexable(self.array.get_duck_array())
 
-    def __array__(self, dtype: np.typing.DTypeLike = None) -> np.ndarray:
-        return np.asarray(self.get_duck_array(), dtype=dtype)
-
     def get_duck_array(self):
         self._ensure_cached()
         return self.array.get_duck_array()
@@ -1674,15 +1677,21 @@ def __init__(self, array: pd.Index, dtype: DTypeLike = None):
     def dtype(self) -> np.dtype:
         return self._dtype
 
-    def __array__(self, dtype: DTypeLike = None) -> np.ndarray:
+    def __array__(
+        self, dtype: np.typing.DTypeLike = None, /, *, copy: bool | None = None
+    ) -> np.ndarray:
         if dtype is None:
             dtype = self.dtype
         array = self.array
         if isinstance(array, pd.PeriodIndex):
             with suppress(AttributeError):
                 # this might not be public API
                 array = array.astype("object")
-        return np.asarray(array.values, dtype=dtype)
+
+        if Version(np.__version__) >= Version("2.0.0"):
+            return np.asarray(array.values, dtype=dtype, copy=copy)
+        else:
+            return np.asarray(array.values, dtype=dtype)
 
     def get_duck_array(self) -> np.ndarray:
         return np.asarray(self)
@@ -1831,15 +1840,17 @@ def __init__(
         super().__init__(array, dtype)
         self.level = level
 
-    def __array__(self, dtype: DTypeLike = None) -> np.ndarray:
+    def __array__(
+        self, dtype: np.typing.DTypeLike = None, /, *, copy: bool | None = None
+    ) -> np.ndarray:
         if dtype is None:
             dtype = self.dtype
         if self.level is not None:
             return np.asarray(
                 self.array.get_level_values(self.level).values, dtype=dtype
             )
         else:
-            return super().__array__(dtype)
+            return super().__array__(dtype, copy=copy)
 
     def _convert_scalar(self, item):
         if isinstance(item, tuple) and self.level is not None:

xarray/namedarray/_typing.py

@@ -153,15 +153,15 @@ def __getitem__(
 
     @overload
     def __array__(
-        self, dtype: None = ..., /, *, copy: None | bool = ...
+        self, dtype: None = ..., /, *, copy: bool | None = ...
     ) -> np.ndarray[Any, _DType_co]: ...
     @overload
     def __array__(
-        self, dtype: _DType, /, *, copy: None | bool = ...
+        self, dtype: _DType, /, *, copy: bool | None = ...
     ) -> np.ndarray[Any, _DType]: ...
 
     def __array__(
-        self, dtype: _DType | None = ..., /, *, copy: None | bool = ...
+        self, dtype: _DType | None = ..., /, *, copy: bool | None = ...
     ) -> np.ndarray[Any, _DType] | np.ndarray[Any, _DType_co]: ...
 
     # TODO: Should return the same subclass but with a new dtype generic.

xarray/tests/arrays.py

@@ -24,7 +24,9 @@ def __init__(self, array):
     def get_duck_array(self):
         raise UnexpectedDataAccess("Tried accessing data")
 
-    def __array__(self, dtype: np.typing.DTypeLike = None):
+    def __array__(
+        self, dtype: np.typing.DTypeLike = None, /, *, copy: bool | None = None
+    ) -> np.ndarray:
         raise UnexpectedDataAccess("Tried accessing data")
 
     def __getitem__(self, key):
@@ -49,7 +51,9 @@ def __init__(self, array: np.ndarray):
     def __getitem__(self, key):
         return type(self)(self.array[key])
 
-    def __array__(self, dtype: np.typing.DTypeLike = None):
+    def __array__(
+        self, dtype: np.typing.DTypeLike = None, /, *, copy: bool | None = None
+    ) -> np.ndarray:
         raise UnexpectedDataAccess("Tried accessing data")
 
     def __array_namespace__(self):
@@ -140,7 +144,9 @@ def __repr__(self: Any) -> str:
     def get_duck_array(self):
         raise UnexpectedDataAccess("Tried accessing data")
 
-    def __array__(self, dtype: np.typing.DTypeLike = None):
+    def __array__(
+        self, dtype: np.typing.DTypeLike = None, /, *, copy: bool | None = None
+    ) -> np.ndarray:
         raise UnexpectedDataAccess("Tried accessing data")
 
     def __getitem__(self, key) -> "ConcatenatableArray":

xarray/tests/test_assertions.py

@@ -173,9 +173,11 @@ def dims(self):
             warnings.warn("warning in test", stacklevel=2)
             return super().dims
 
-        def __array__(self, dtype=None, copy=None):
+        def __array__(
+            self, dtype: np.typing.DTypeLike = None, /, *, copy: bool | None = None
+        ) -> np.ndarray:
             warnings.warn("warning in test", stacklevel=2)
-            return super().__array__()
+            return super().__array__(dtype, copy=copy)
 
     a = WarningVariable("x", [1])
     b = WarningVariable("x", [2])

xarray/tests/test_formatting.py

@@ -942,7 +942,9 @@ def test_lazy_array_wont_compute() -> None:
     from xarray.core.indexing import LazilyIndexedArray
 
     class LazilyIndexedArrayNotComputable(LazilyIndexedArray):
-        def __array__(self, dtype=None, copy=None):
+        def __array__(
+            self, dtype: np.typing.DTypeLike = None, /, *, copy: bool | None = None
+        ) -> np.ndarray:
             raise NotImplementedError("Computing this array is not possible.")
 
     arr = LazilyIndexedArrayNotComputable(np.array([1, 2]))

xarray/tests/test_namedarray.py

@@ -8,6 +8,7 @@
 
 import numpy as np
 import pytest
+from packaging.version import Version
 
 from xarray.core.indexing import ExplicitlyIndexed
 from xarray.namedarray._typing import (
@@ -53,8 +54,14 @@ def shape(self) -> _Shape:
 class CustomArray(
     CustomArrayBase[_ShapeType_co, _DType_co], Generic[_ShapeType_co, _DType_co]
 ):
-    def __array__(self) -> np.ndarray[Any, np.dtype[np.generic]]:
-        return np.array(self.array)
+    def __array__(
+        self, dtype: np.typing.DTypeLike = None, /, *, copy: bool | None = None
+    ) -> np.ndarray[Any, np.dtype[np.generic]]:
+
+        if Version(np.__version__) >= Version("2.0.0"):
+            return np.asarray(self.array, dtype=dtype, copy=copy)
+        else:
+            return np.asarray(self.array, dtype=dtype)
 
 
 class CustomArrayIndexable(