Merge branch 'v3' into store_tests

src/zarr/array.py

@@ -582,12 +582,12 @@ def store_path(self) -> StorePath:
     def order(self) -> Literal["C", "F"]:
         return self._async_array.order
 
-    def __getitem__(self, selection: Selection) -> npt.NDArray[Any]:
+    def __getitem__(self, selection: Selection) -> NDArrayLike:
         return sync(
             self._async_array.getitem(selection),
         )
 
-    def __setitem__(self, selection: Selection, value: npt.NDArray[Any]) -> None:
+    def __setitem__(self, selection: Selection, value: NDArrayLike) -> None:
         sync(
             self._async_array.setitem(selection, value),
         )

src/zarr/buffer.py

@@ -1,28 +1,94 @@
 from __future__ import annotations
 
 import sys
-from collections.abc import Callable, Iterable
+from collections.abc import Callable, Iterable, Sequence
 from typing import (
     TYPE_CHECKING,
     Any,
     Literal,
     Protocol,
-    TypeAlias,
+    SupportsIndex,
+    runtime_checkable,
 )
 
 import numpy as np
 import numpy.typing as npt
 
+from zarr.common import ChunkCoords
+
 if TYPE_CHECKING:
     from typing_extensions import Self
 
     from zarr.codecs.bytes import Endian
     from zarr.common import BytesLike
 
-# TODO: create a protocol for the attributes we need, for now we alias Numpy's ndarray
-#       both for the array-like and ndarray-like
-ArrayLike: TypeAlias = npt.NDArray[Any]
-NDArrayLike: TypeAlias = npt.NDArray[Any]
+
+@runtime_checkable
+class ArrayLike(Protocol):
+    """Protocol for the array-like type that underlie Buffer"""
+
+    @property
+    def dtype(self) -> np.dtype[Any]: ...
+
+    @property
+    def ndim(self) -> int: ...
+
+    @property
+    def size(self) -> int: ...
+
+    def __getitem__(self, key: slice) -> Self: ...
+
+    def __setitem__(self, key: slice, value: Any) -> None: ...
+
+
+@runtime_checkable
+class NDArrayLike(Protocol):
+    """Protocol for the nd-array-like type that underlie NDBuffer"""
+
+    @property
+    def dtype(self) -> np.dtype[Any]: ...
+
+    @property
+    def ndim(self) -> int: ...
+
+    @property
+    def size(self) -> int: ...
+
+    @property
+    def shape(self) -> ChunkCoords: ...
+
+    def __len__(self) -> int: ...
+
+    def __getitem__(self, key: slice) -> Self: ...
+
+    def __setitem__(self, key: slice, value: Any) -> None: ...
+
+    def reshape(self, shape: ChunkCoords, *, order: Literal["A", "C", "F"] = ...) -> Self: ...
+
+    def view(self, dtype: npt.DTypeLike) -> Self: ...
+
+    def astype(self, dtype: npt.DTypeLike, order: Literal["K", "A", "C", "F"] = ...) -> Self: ...
+
+    def fill(self, value: Any) -> None: ...
+
+    def copy(self) -> Self: ...
+
+    def transpose(self, axes: SupportsIndex | Sequence[SupportsIndex] | None) -> Self: ...
+
+    def ravel(self, order: Literal["K", "A", "C", "F"] = "C") -> Self: ...
+
+    def all(self) -> bool: ...
+
+    def __eq__(self, other: Any) -> Self:  # type: ignore
+        """Element-wise equal
+
+        Notice
+        ------
+        Type checkers such as mypy complains because the return type isn't a bool like
+        its supertype "object", which violates the Liskov substitution principle.
+        This is true, but since NumPy's ndarray is defined as an element-wise equal,
+        our hands are tied.
+        """
 
 
 def check_item_key_is_1d_contiguous(key: Any) -> None:
@@ -124,7 +190,7 @@ def create_zero_length(cls) -> Self:
         return cls(np.array([], dtype="b"))
 
     @classmethod
-    def from_array_like(cls, array_like: NDArrayLike) -> Self:
+    def from_array_like(cls, array_like: ArrayLike) -> Self:
         """Create a new buffer of a array-like object
 
         Parameters
@@ -153,7 +219,7 @@ def from_bytes(cls, bytes_like: BytesLike) -> Self:
         """
         return cls.from_array_like(np.frombuffer(bytes_like, dtype="b"))
 
-    def as_array_like(self) -> NDArrayLike:
+    def as_array_like(self) -> ArrayLike:
         """Return the underlying array (host or device memory) of this buffer
 
         This will never copy data.
@@ -164,22 +230,6 @@ def as_array_like(self) -> NDArrayLike:
         """
         return self._data
 
-    def as_nd_buffer(self, *, dtype: npt.DTypeLike) -> NDBuffer:
-        """Create a new NDBuffer from this one.
-
-        This will never copy data.
-
-        Parameters
-        ----------
-        dtype
-           The datatype of the returned buffer (reinterpretation of the bytes)
-
-        Return
-        ------
-            New NDbuffer representing `self.as_array_like()`
-        """
-        return NDBuffer.from_ndarray_like(self._data.view(dtype=dtype))
-
     def as_numpy_array(self) -> npt.NDArray[Any]:
         """Return the buffer as a NumPy array (host memory).
 
@@ -223,17 +273,8 @@ def __add__(self, other: Buffer) -> Self:
 
         other_array = other.as_array_like()
         assert other_array.dtype == np.dtype("b")
-        return self.__class__(np.concatenate((self._data, other_array)))
-
-    def __eq__(self, other: Any) -> bool:
-        if isinstance(other, bytes | bytearray):
-            # Many of the tests compares `Buffer` with `bytes` so we
-            # convert the bytes to a Buffer and try again
-            return self == self.from_bytes(other)
-        if isinstance(other, Buffer):
-            return (self._data == other.as_array_like()).all()
-        raise ValueError(
-            f"equal operator not supported between {self.__class__} and {other.__class__}"
+        return self.__class__(
+            np.concatenate((np.asanyarray(self._data), np.asanyarray(other_array)))
         )
 
 
@@ -345,22 +386,6 @@ def as_ndarray_like(self) -> NDArrayLike:
         """
         return self._data
 
-    def as_buffer(self) -> Buffer:
-        """Create a new Buffer from this one.
-
-        Warning
-        -------
-        Copies data if the buffer is non-contiguous.
-
-        Return
-        ------
-            The new buffer (might be data copy)
-        """
-        data = self._data
-        if not self._data.flags.contiguous:
-            data = np.ascontiguousarray(self._data)
-        return Buffer(data.reshape(-1).view(dtype="b"))  # Flatten the array without copy
-
     def as_numpy_array(self) -> npt.NDArray[Any]:
         """Return the buffer as a NumPy array (host memory).
 
@@ -393,8 +418,8 @@ def byteorder(self) -> Endian:
         else:
             return Endian(sys.byteorder)
 
-    def reshape(self, newshape: Iterable[int]) -> Self:
-        return self.__class__(self._data.reshape(tuple(newshape)))
+    def reshape(self, newshape: ChunkCoords) -> Self:
+        return self.__class__(self._data.reshape(newshape))
 
     def astype(self, dtype: npt.DTypeLike, order: Literal["K", "A", "C", "F"] = "K") -> Self:
         return self.__class__(self._data.astype(dtype=dtype, order=order))
@@ -419,8 +444,8 @@ def fill(self, value: Any) -> None:
     def copy(self) -> Self:
         return self.__class__(self._data.copy())
 
-    def transpose(self, *axes: np.SupportsIndex) -> Self:  # type: ignore[name-defined]
-        return self.__class__(self._data.transpose(*axes))
+    def transpose(self, axes: SupportsIndex | Sequence[SupportsIndex] | None) -> Self:
+        return self.__class__(self._data.transpose(axes))
 
 
 def as_numpy_array_wrapper(func: Callable[[npt.NDArray[Any]], bytes], buf: Buffer) -> Buffer:

src/zarr/codecs/bytes.py

@@ -8,7 +8,7 @@
 import numpy as np
 
 from zarr.abc.codec import ArrayBytesCodec
-from zarr.buffer import Buffer, NDBuffer
+from zarr.buffer import Buffer, NDArrayLike, NDBuffer
 from zarr.codecs.registry import register_codec
 from zarr.common import parse_enum, parse_named_configuration
 
@@ -75,7 +75,13 @@ async def _decode_single(
             dtype = np.dtype(f"{prefix}{chunk_spec.dtype.str[1:]}")
         else:
             dtype = np.dtype(f"|{chunk_spec.dtype.str[1:]}")
-        chunk_array = chunk_bytes.as_nd_buffer(dtype=dtype)
+
+        as_array_like = chunk_bytes.as_array_like()
+        if isinstance(as_array_like, NDArrayLike):
+            as_nd_array_like = as_array_like
+        else:
+            as_nd_array_like = np.asanyarray(as_array_like)
+        chunk_array = NDBuffer.from_ndarray_like(as_nd_array_like.view(dtype=dtype))
 
         # ensure correct chunk shape
         if chunk_array.shape != chunk_spec.shape:
@@ -96,7 +102,11 @@ async def _encode_single(
                 # see https://github.com/numpy/numpy/issues/26473
                 new_dtype = chunk_array.dtype.newbyteorder(self.endian.name)  # type: ignore[arg-type]
                 chunk_array = chunk_array.astype(new_dtype)
-        return chunk_array.as_buffer()
+
+        as_nd_array_like = chunk_array.as_ndarray_like()
+        # Flatten the nd-array (only copy if needed)
+        as_nd_array_like = as_nd_array_like.ravel().view(dtype="b")
+        return Buffer.from_array_like(as_nd_array_like)
 
     def compute_encoded_size(self, input_byte_length: int, _chunk_spec: ArraySpec) -> int:
         return input_byte_length

src/zarr/testing/store.py

@@ -4,6 +4,7 @@
 
 from zarr.abc.store import Store
 from zarr.buffer import Buffer
+from zarr.testing.utils import assert_bytes_equal
 
 
 def _normalize_byte_range(

src/zarr/testing/utils.py

@@ -0,0 +1,18 @@
+from __future__ import annotations
+
+from zarr.buffer import Buffer
+from zarr.common import BytesLike
+
+
+def assert_bytes_equal(b1: Buffer | BytesLike | None, b2: Buffer | BytesLike | None) -> None:
+    """Help function to assert if two bytes-like or Buffers are equal
+
+    Warning
+    -------
+    Always copies data, only use for testing and debugging
+    """
+    if isinstance(b1, Buffer):
+        b1 = b1.to_bytes()
+    if isinstance(b2, Buffer):
+        b2 = b2.to_bytes()
+    assert b1 == b2

tests/v3/conftest.py

@@ -1,5 +1,7 @@
 from __future__ import annotations
 
+from collections.abc import Iterator
+from types import ModuleType
 from typing import TYPE_CHECKING
 
 from zarr.common import ZarrFormat
@@ -81,3 +83,10 @@ async def async_group(request: pytest.FixtureRequest, tmpdir) -> AsyncGroup:
         exists_ok=False,
     )
     return agroup
+
+
+@pytest.fixture(params=["numpy", "cupy"])
+def xp(request: pytest.FixtureRequest) -> Iterator[ModuleType]:
+    """Fixture to parametrize over numpy-like libraries"""
+
+    yield pytest.importorskip(request.param)

tests/v3/test_buffer.py

@@ -8,9 +8,7 @@
 import pytest
 
 from zarr.array import AsyncArray
-from zarr.buffer import NDBuffer
-from zarr.store.core import StorePath
-from zarr.store.memory import MemoryStore
+from zarr.buffer import ArrayLike, NDArrayLike, NDBuffer
 
 if TYPE_CHECKING:
     from typing_extensions import Self
@@ -41,12 +39,17 @@ def create(
         return ret
 
 
+def test_nd_array_like(xp):
+    ary = xp.arange(10)
+    assert isinstance(ary, ArrayLike)
+    assert isinstance(ary, NDArrayLike)
+
+
 @pytest.mark.asyncio
-async def test_async_array_factory():
-    store = StorePath(MemoryStore())
+async def test_async_array_factory(store_path):
     expect = np.zeros((9, 9), dtype="uint16", order="F")
     a = await AsyncArray.create(
-        store / "test_async_array",
+        store_path,
         shape=expect.shape,
         chunk_shape=(5, 5),
         dtype=expect.dtype,