Rename the array class in the array API namespace from ndarray to Array

The actual class name doesn't matter because it isn't part of the namespace
API (arrays should be constructed with the array creation functions like
asarray()). However, it is better to use a name that is different from the
existing NumPy array object to avoid ambiguity.

1 files changed, 16 insertions, 16 deletions

diff --git a/numpy/_array_api/_array_object.py b/numpy/_array_api/_array_object.py
index 89ec3ba1a..9ea0eef18 100644
--- a/numpy/_array_api/_array_object.py
+++ b/numpy/_array_api/_array_object.py
@@ -22,13 +22,13 @@ from ._dtypes import _boolean_dtypes, _integer_dtypes, _floating_dtypes
 
 from typing import TYPE_CHECKING
 if TYPE_CHECKING:
-    from ._types import Any, Optional, PyCapsule, Tuple, Union, Array
+    from ._types import Any, Optional, PyCapsule, Tuple, Union, Device, Dtype
 
 import numpy as np
 
-class ndarray:
+class Array:
     """
-    ndarray object for the array API namespace.
+    n-d array object for the array API namespace.
 
     See the docstring of :py:obj:`np.ndarray <numpy.ndarray>` for more
     information.
@@ -46,7 +46,7 @@ class ndarray:
     @classmethod
     def _new(cls, x, /):
         """
-        This is a private method for initializing the array API ndarray
+        This is a private method for initializing the array API Array
         object.
 
         Functions outside of the array_api submodule should not use this
@@ -64,9 +64,9 @@ class ndarray:
         obj._array = x
         return obj
 
-    # Prevent ndarray() from working
+    # Prevent Array() from working
     def __new__(cls, *args, **kwargs):
-        raise TypeError("The array_api ndarray object should not be instantiated directly. Use an array creation function, such as asarray(), instead.")
+        raise TypeError("The array_api Array object should not be instantiated directly. Use an array creation function, such as asarray(), instead.")
 
     # These functions are not required by the spec, but are implemented for
     # the sake of usability.
@@ -75,13 +75,13 @@ class ndarray:
         """
         Performs the operation __str__.
         """
-        return self._array.__str__().replace('array', 'ndarray')
+        return self._array.__str__().replace('array', 'Array')
 
     def __repr__(self: Array, /) -> str:
         """
         Performs the operation __repr__.
         """
-        return self._array.__repr__().replace('array', 'ndarray')
+        return self._array.__repr__().replace('array', 'Array')
 
     # Helper function to match the type promotion rules in the spec
     def _promote_scalar(self, scalar):
@@ -109,7 +109,7 @@ class ndarray:
         # behavior for integers within the bounds of the integer dtype.
         # Outside of those bounds we use the default NumPy behavior (either
         # cast or raise OverflowError).
-        return ndarray._new(np.array(scalar, self.dtype))
+        return Array._new(np.array(scalar, self.dtype))
 
     @staticmethod
     def _normalize_two_args(x1, x2):
@@ -135,9 +135,9 @@ class ndarray:
             # performant. broadcast_to(x1._array, x2.shape) is much slower. We
             # could also manually type promote x2, but that is more complicated
             # and about the same performance as this.
-            x1 = ndarray._new(x1._array[None])
+            x1 = Array._new(x1._array[None])
         elif x2.shape == () and x1.shape != ():
-            x2 = ndarray._new(x2._array[None])
+            x2 = Array._new(x2._array[None])
         return (x1, x2)
 
     # Everything below this line is required by the spec.
@@ -284,7 +284,7 @@ class ndarray:
         Additionally, it should be noted that indices that would return a
         scalar in NumPy will return a shape () array. Array scalars are not allowed
         in the specification, only shape () arrays. This is done in the
-        ``ndarray._new`` constructor, not this function.
+        ``Array._new`` constructor, not this function.
 
         """
         if isinstance(key, slice):
@@ -313,7 +313,7 @@ class ndarray:
             return key
 
         elif isinstance(key, tuple):
-            key = tuple(ndarray._validate_index(idx, None) for idx in key)
+            key = tuple(Array._validate_index(idx, None) for idx in key)
 
             for idx in key:
                 if isinstance(idx, np.ndarray) and idx.dtype in _boolean_dtypes or isinstance(idx, (bool, np.bool_)):
@@ -329,11 +329,11 @@ class ndarray:
             ellipsis_i = key.index(...) if n_ellipsis else len(key)
 
             for idx, size in list(zip(key[:ellipsis_i], shape)) + list(zip(key[:ellipsis_i:-1], shape[:ellipsis_i:-1])):
-                ndarray._validate_index(idx, (size,))
+                Array._validate_index(idx, (size,))
             return key
         elif isinstance(key, bool):
             return key
-        elif isinstance(key, ndarray):
+        elif isinstance(key, Array):
             if key.dtype in _integer_dtypes:
                 if key.shape != ():
                     raise IndexError("Integer array indices with shape != () are not allowed in the array API namespace")
@@ -346,7 +346,7 @@ class ndarray:
             return operator.index(key)
         except TypeError:
             # Note: This also omits boolean arrays that are not already in
-            # ndarray() form, like a list of booleans.
+            # Array() form, like a list of booleans.
             raise IndexError("Only integers, slices (`:`), ellipsis (`...`), and boolean arrays are valid indices in the array API namespace")
 
     def __getitem__(self: Array, key: Union[int, slice, ellipsis, Tuple[Union[int, slice, ellipsis], ...], Array], /) -> Array: