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from __future__ import annotations
from ._array_object import Array
from ._dtypes import _real_numeric_dtypes
import numpy as np
# Note: the descending keyword argument is new in this function
def argsort(
x: Array, /, *, axis: int = -1, descending: bool = False, stable: bool = True
) -> Array:
"""
Array API compatible wrapper for :py:func:`np.argsort <numpy.argsort>`.
See its docstring for more information.
"""
if x.dtype not in _real_numeric_dtypes:
raise TypeError("Only real numeric dtypes are allowed in argsort")
# Note: this keyword argument is different, and the default is different.
kind = "stable" if stable else "quicksort"
if not descending:
res = np.argsort(x._array, axis=axis, kind=kind)
else:
# As NumPy has no native descending sort, we imitate it here. Note that
# simply flipping the results of np.argsort(x._array, ...) would not
# respect the relative order like it would in native descending sorts.
res = np.flip(
np.argsort(np.flip(x._array, axis=axis), axis=axis, kind=kind),
axis=axis,
)
# Rely on flip()/argsort() to validate axis
normalised_axis = axis if axis >= 0 else x.ndim + axis
max_i = x.shape[normalised_axis] - 1
res = max_i - res
return Array._new(res)
# Note: the descending keyword argument is new in this function
def sort(
x: Array, /, *, axis: int = -1, descending: bool = False, stable: bool = True
) -> Array:
"""
Array API compatible wrapper for :py:func:`np.sort <numpy.sort>`.
See its docstring for more information.
"""
if x.dtype not in _real_numeric_dtypes:
raise TypeError("Only real numeric dtypes are allowed in sort")
# Note: this keyword argument is different, and the default is different.
kind = "stable" if stable else "quicksort"
res = np.sort(x._array, axis=axis, kind=kind)
if descending:
res = np.flip(res, axis=axis)
return Array._new(res)