Remove __array__

This makes it raise an exception, since it isn't supported by the standard (if
we just leave it unimplemented, then np.asarray() returns an object dtype
array, which is not good).

There is one issue here from the test suite, which is that this breaks the
logic in asarray() for converting lists of array_api_strict 0-D arrays. I'm
not yet sure what to do about that.

Fixes #67.

Author: asmeurer

array_api_strict/_array_object.py

@@ -125,28 +125,17 @@ def __repr__(self: Array, /) -> str:
             mid = np.array2string(self._array, separator=', ', prefix=prefix, suffix=suffix)
         return prefix + mid + suffix
 
-    # This function is not required by the spec, but we implement it here for
-    # convenience so that np.asarray(array_api_strict.Array) will work.
+    # Disallow __array__, meaning calling `np.func()` on an array_api_strict
+    # array will give an error. If we don't explicitly disallow it, NumPy
+    # defaults to creating an object dtype array, which would lead to
+    # confusing error messages at best and surprising bugs at worst.
+    #
+    # The alternative of course is to just support __array__, which is what we
+    # used to do. But this isn't actually supported by the standard, so it can
+    # lead to code assuming np.asarray(other_array) would always work in the
+    # standard.
     def __array__(self, dtype: None | np.dtype[Any] = None, copy: None | bool = None) -> npt.NDArray[Any]:
-        """
-        Warning: this method is NOT part of the array API spec. Implementers
-        of other libraries need not include it, and users should not assume it
-        will be present in other implementations.
-
-        """
-        # copy keyword is new in 2.0.0; for older versions don't use it
-        # retry without that keyword.
-        if np.__version__[0] < '2':
-            return np.asarray(self._array, dtype=dtype)
-        elif np.__version__.startswith('2.0.0-dev0'):
-            # Handle dev version for which we can't know based on version
-            # number whether or not the copy keyword is supported.
-            try:
-                return np.asarray(self._array, dtype=dtype, copy=copy)
-            except TypeError:
-                return np.asarray(self._array, dtype=dtype)
-        else:
-            return np.asarray(self._array, dtype=dtype, copy=copy)
+        raise ValueError("Conversion from an array_api_strict array to a NumPy ndarray is not supported")
 
     # These are various helper functions to make the array behavior match the
     # spec in places where it either deviates from or is more strict than

array_api_strict/tests/test_array_object.py

@@ -342,13 +342,6 @@ def test_array_properties():
     assert isinstance(b.mT, Array)
     assert b.mT.shape == (3, 2)
 
-def test___array__():
-    a = ones((2, 3), dtype=int16)
-    assert np.asarray(a) is a._array
-    b = np.asarray(a, dtype=np.float64)
-    assert np.all(np.equal(b, np.ones((2, 3), dtype=np.float64)))
-    assert b.dtype == np.float64
-
 def test_allow_newaxis():
     a = ones(5)
     indexed_a = a[None, :]