feat: HasX attributes

pyproject.toml

@@ -126,6 +126,8 @@ ignore = [
   "FIX", # flake8-fixme
   "ISC001", # Conflicts with formatter
   "PYI041", # Use `float` instead of `int | float`
+  "TD002", # Missing author in TODO
+  "TD003", # Missing issue link for this TODO
 ]
 
 [tool.ruff.lint.pydocstyle]

src/array_api_typing/__init__.py

@@ -4,9 +4,25 @@
     "Array",
     "HasArrayNamespace",
     "HasDType",
+    "HasDevice",
+    "HasMatrixTranspose",
+    "HasNDim",
+    "HasShape",
+    "HasSize",
+    "HasTranspose",
     "__version__",
     "__version_tuple__",
 )
 
-from ._array import Array, HasArrayNamespace, HasDType
+from ._array import (
+    Array,
+    HasArrayNamespace,
+    HasDevice,
+    HasDType,
+    HasMatrixTranspose,
+    HasNDim,
+    HasShape,
+    HasSize,
+    HasTranspose,
+)
 from ._version import version as __version__, version_tuple as __version_tuple__

src/array_api_typing/_array.py

@@ -1,10 +1,17 @@
 __all__ = (
     "Array",
     "HasArrayNamespace",
+    "HasDType",
+    "HasDevice",
+    "HasMatrixTranspose",
+    "HasNDim",
+    "HasShape",
+    "HasSize",
+    "HasTranspose",
 )
 
 from types import ModuleType
-from typing import Literal, Protocol
+from typing import Literal, Protocol, Self
 from typing_extensions import TypeVar
 
 NamespaceT_co = TypeVar("NamespaceT_co", covariant=True, default=ModuleType)
@@ -67,10 +74,131 @@ def dtype(self, /) -> DTypeT_co:
         ...
 
 
+class HasDevice(Protocol):
+    """Protocol for array classes that have a device attribute."""
+
+    @property
+    def device(self) -> object:  # TODO: more specific type
+        """Hardware device the array data resides on."""
+        ...
+
+
+class HasMatrixTranspose(Protocol):
+    """Protocol for array classes that have a matrix transpose attribute."""
+
+    @property
+    def mT(self) -> Self:  # noqa: N802
+        """Transpose of a matrix (or a stack of matrices).
+
+        If an array instance has fewer than two dimensions, an error should be
+        raised.
+
+        Returns:
+            Self: array whose last two dimensions (axes) are permuted in reverse
+                order relative to original array (i.e., for an array instance
+                having shape `(..., M, N)`, the returned array must have shape
+                `(..., N, M))`.  The returned array must have the same data type
+                as the original array.
+
+        """
+        ...
+
+
+class HasNDim(Protocol):
+    """Protocol for array classes that have a number of dimensions attribute."""
+
+    @property
+    def ndim(self) -> int:
+        """Number of array dimensions (axes).
+
+        Returns:
+            int: number of array dimensions (axes).
+
+        """
+        ...
+
+
+class HasShape(Protocol):
+    """Protocol for array classes that have a shape attribute."""
+
+    @property
+    def shape(self) -> tuple[int | None, ...]:
+        """Shape of the array.
+
+        Returns:
+            tuple[int | None, ...]: array dimensions. An array dimension must be None
+                if and only if a dimension is unknown.
+
+        Notes:
+            For array libraries having graph-based computational models, array
+            dimensions may be unknown due to data-dependent operations (e.g.,
+            boolean indexing; `A[:, B > 0]`) and thus cannot be statically
+            resolved without knowing array contents.
+
+        """
+        ...
+
+
+class HasSize(Protocol):
+    """Protocol for array classes that have a size attribute."""
+
+    @property
+    def size(self) -> int | None:
+        """Number of elements in an array.
+
+        Returns:
+            int | None: number of elements in an array. The returned value must
+                be `None` if and only if one or more array dimensions are
+                unknown.
+
+        Notes:
+            This must equal the product of the array's dimensions.
+
+        """
+        ...
+
+
+class HasTranspose(Protocol):
+    """Protocol for array classes that support the transpose operation."""
+
+    @property
+    def T(self) -> Self:  # noqa: N802
+        """Transpose of the array.
+
+        The array instance must be two-dimensional. If the array instance is not
+        two-dimensional, an error should be raised.
+
+        Returns:
+            Self: two-dimensional array whose first and last dimensions (axes)
+                are permuted in reverse order relative to original array. The
+                returned array must have the same data type as the original
+                array.
+
+        Notes:
+            Limiting the transpose to two-dimensional arrays (matrices) deviates
+            from the NumPy et al practice of reversing all axes for arrays
+            having more than two-dimensions. This is intentional, as reversing
+            all axes was found to be problematic (e.g., conflicting with the
+            mathematical definition of a transpose which is limited to matrices;
+            not operating on batches of matrices; et cetera). In order to
+            reverse all axes, one is recommended to use the functional
+            `PermuteDims` interface found in this specification.
+
+        """
+        ...
+
+
 class Array(
-    HasArrayNamespace[NamespaceT_co],
     # ------ Attributes -------
     HasDType[DTypeT_co],
+    HasDevice,
+    HasMatrixTranspose,
+    HasNDim,
+    HasShape,
+    HasSize,
+    HasTranspose,
+    # ------- Methods ---------
+    HasArrayNamespace[NamespaceT_co],
     # -------------------------
     Protocol[DTypeT_co, NamespaceT_co],
 ):

tests/integration/test_numpy1p0.pyi

@@ -49,5 +49,33 @@ a_ns: xpt.Array[Any, ModuleType] = nparr
 # Note that `np.array_api` uses dtype objects, not dtype classes, so we can't
 # type annotate specific dtypes like `np.float32` or `np.int32`.
 _: xpt.Array[dtype[Any]] = nparr
-_: xpt.Array[dtype[Any]] = nparr_i32
-_: xpt.Array[dtype[Any]] = nparr_f32
+x_f32: xpt.Array[dtype[Any]] = nparr_f32
+x_i32: xpt.Array[dtype[Any]] = nparr_i32
+
+# Check Attribute `.dtype`
+_: dtype[Any] = x_f32.dtype
+_: dtype[Any] = x_i32.dtype
+
+# Check Attribute `.device`
+_: object = x_f32.device
+_: object = x_i32.device
+
+# Check Attribute `.mT`
+_: xpt.Array[dtype[Any]] = x_f32.mT
+_: xpt.Array[dtype[Any]] = x_i32.mT
+
+# Check Attribute `.ndim`
+_: int = x_f32.ndim
+_: int = x_i32.ndim
+
+# Check Attribute `.shape`
+_: tuple[int | None, ...] = x_f32.shape
+_: tuple[int | None, ...] = x_i32.shape
+
+# Check Attribute `.size`
+_: int | None = x_f32.size
+_: int | None = x_i32.size
+
+# Check Attribute `.T`
+_: xpt.Array[dtype[Any]] = x_f32.T
+_: xpt.Array[dtype[Any]] = x_i32.T

tests/integration/test_numpy2p0.pyi

@@ -11,12 +11,13 @@ import array_api_typing as xpt
 # DType aliases
 F32: TypeAlias = np.float32
 I32: TypeAlias = np.int32
+B: TypeAlias = np.bool_
 
 # Define NDArrays against which we can test the protocols
 nparr: npt.NDArray[Any]
 nparr_i32: npt.NDArray[I32]
 nparr_f32: npt.NDArray[F32]
-nparr_b: npt.NDArray[np.bool_]
+nparr_b: npt.NDArray[B]
 
 # =========================================================
 # `xpt.HasArrayNamespace`
@@ -42,7 +43,7 @@ _: xpt.HasArrayNamespace[dict[str, int]] = nparr  # not caught
 _: xpt.HasDType[Any] = nparr
 _: xpt.HasDType[np.dtype[I32]] = nparr_i32
 _: xpt.HasDType[np.dtype[F32]] = nparr_f32
-_: xpt.HasDType[np.dtype[np.bool_]] = nparr_b
+_: xpt.HasDType[np.dtype[B]] = nparr_b
 
 # =========================================================
 # `xpt.Array`
@@ -52,6 +53,41 @@ a_ns: xpt.Array[Any, ModuleType] = nparr
 
 # Check DTypeT_co assignment
 _: xpt.Array[Any] = nparr
-_: xpt.Array[np.dtype[I32]] = nparr_i32
-_: xpt.Array[np.dtype[F32]] = nparr_f32
-_: xpt.Array[np.dtype[np.bool_]] = nparr_b
+x_f32: xpt.Array[np.dtype[F32]] = nparr_f32
+x_i32: xpt.Array[np.dtype[I32]] = nparr_i32
+x_b: xpt.Array[np.dtype[B]] = nparr_b
+
+# Check Attribute `.dtype`
+_: np.dtype[F32] = x_f32.dtype
+_: np.dtype[I32] = x_i32.dtype
+_: np.dtype[B] = x_b.dtype
+
+# Check Attribute `.device`
+_: object = x_f32.device
+_: object = x_i32.device
+_: object = x_b.device
+
+# Check Attribute `.mT`
+_: xpt.Array[np.dtype[F32]] = x_f32.mT
+_: xpt.Array[np.dtype[I32]] = x_i32.mT
+_: xpt.Array[np.dtype[B]] = x_b.mT
+
+# Check Attribute `.ndim`
+_: int = x_f32.ndim
+_: int = x_i32.ndim
+_: int = x_b.ndim
+
+# Check Attribute `.shape`
+_: tuple[int | None, ...] = x_f32.shape
+_: tuple[int | None, ...] = x_i32.shape
+_: tuple[int | None, ...] = x_b.shape
+
+# Check Attribute `.size`
+_: int | None = x_f32.size
+_: int | None = x_i32.size
+_: int | None = x_b.size
+
+# Check Attribute `.T`
+_: xpt.Array[np.dtype[F32]] = x_f32.T
+_: xpt.Array[np.dtype[I32]] = x_i32.T
+_: xpt.Array[np.dtype[B]] = x_b.T