Rename numpy._array_api to numpy.array_api

Instead of the leading underscore, the experimentalness of the module will be
indicated by omitting a warning on import. That we, we do not have to change
the API from underscore to no underscore when the module is no longer
experimental.

1 files changed, 44 insertions, 0 deletions

diff --git a/numpy/array_api/_searching_functions.py b/numpy/array_api/_searching_functions.py
new file mode 100644
index 000000000..d80720850
--- /dev/null
+++ b/numpy/array_api/_searching_functions.py
@@ -0,0 +1,44 @@
+from __future__ import annotations
+
+from ._array_object import Array
+from ._dtypes import _result_type
+
+from typing import Optional, Tuple
+
+import numpy as np
+
+def argmax(x: Array, /, *, axis: Optional[int] = None, keepdims: bool = False) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.argmax <numpy.argmax>`.
+
+    See its docstring for more information.
+    """
+    # Note: this currently fails as np.argmax does not implement keepdims
+    return Array._new(np.asarray(np.argmax(x._array, axis=axis, keepdims=keepdims)))
+
+def argmin(x: Array, /, *, axis: Optional[int] = None, keepdims: bool = False) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.argmin <numpy.argmin>`.
+
+    See its docstring for more information.
+    """
+    # Note: this currently fails as np.argmin does not implement keepdims
+    return Array._new(np.asarray(np.argmin(x._array, axis=axis, keepdims=keepdims)))
+
+def nonzero(x: Array, /) -> Tuple[Array, ...]:
+    """
+    Array API compatible wrapper for :py:func:`np.nonzero <numpy.nonzero>`.
+
+    See its docstring for more information.
+    """
+    return Array._new(np.nonzero(x._array))
+
+def where(condition: Array, x1: Array, x2: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
+
+    See its docstring for more information.
+    """
+    # Call result type here just to raise on disallowed type combinations
+    _result_type(x1.dtype, x2.dtype)
+    return Array._new(np.where(condition._array, x1._array, x2._array))