Edit: _sorting_functions.py
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)
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