from __future__ import annotations from ._array_object import Array from ._dtypes import _result_type, _real_numeric_dtypes 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 `. See its docstring for more information. """ if x.dtype not in _real_numeric_dtypes: raise TypeError("Only real numeric dtypes are allowed in argmax") 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 `. See its docstring for more information. """ if x.dtype not in _real_numeric_dtypes: raise TypeError("Only real numeric dtypes are allowed in argmin") 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 `. See its docstring for more information. """ return tuple(Array._new(i) for i in np.nonzero(x._array)) def where(condition: Array, x1: Array, x2: Array, /) -> Array: """ Array API compatible wrapper for :py:func:`np.where `. See its docstring for more information. """ # Call result type here just to raise on disallowed type combinations _result_type(x1.dtype, x2.dtype) x1, x2 = Array._normalize_two_args(x1, x2) return Array._new(np.where(condition._array, x1._array, x2._array))