name : _set_functions.py from __future__ import annotations from ._array_object import Array from typing import NamedTuple import numpy as np # Note: np.unique() is split into four functions in the array API: # unique_all, unique_counts, unique_inverse, and unique_values (this is done # to remove polymorphic return types). # Note: The various unique() functions are supposed to return multiple NaNs. # This does not match the NumPy behavior, however, this is currently left as a # TODO in this implementation as this behavior may be reverted in np.unique(). # See https://github.com/numpy/numpy/issues/20326. # Note: The functions here return a namedtuple (np.unique() returns a normal # tuple). class UniqueAllResult(NamedTuple): values: Array indices: Array inverse_indices: Array counts: Array class UniqueCountsResult(NamedTuple): values: Array counts: Array class UniqueInverseResult(NamedTuple): values: Array inverse_indices: Array def unique_all(x: Array, /) -> UniqueAllResult: """ Array API compatible wrapper for :py:func:`np.unique <numpy.unique>`. See its docstring for more information. """ values, indices, inverse_indices, counts = np.unique( x._array, return_counts=True, return_index=True, return_inverse=True, equal_nan=False, ) # np.unique() flattens inverse indices, but they need to share x's shape # See https://github.com/numpy/numpy/issues/20638 inverse_indices = inverse_indices.reshape(x.shape) return UniqueAllResult( Array._new(values), Array._new(indices), Array._new(inverse_indices), Array._new(counts), ) def unique_counts(x: Array, /) -> UniqueCountsResult: res = np.unique( x._array, return_counts=True, return_index=False, return_inverse=False, equal_nan=False, ) return UniqueCountsResult(*[Array._new(i) for i in res]) def unique_inverse(x: Array, /) -> UniqueInverseResult: """ Array API compatible wrapper for :py:func:`np.unique <numpy.unique>`. See its docstring for more information. """ values, inverse_indices = np.unique( x._array, return_counts=False, return_index=False, return_inverse=True, equal_nan=False, ) # np.unique() flattens inverse indices, but they need to share x's shape # See https://github.com/numpy/numpy/issues/20638 inverse_indices = inverse_indices.reshape(x.shape) return UniqueInverseResult(Array._new(values), Array._new(inverse_indices)) def unique_values(x: Array, /) -> Array: """ Array API compatible wrapper for :py:func:`np.unique <numpy.unique>`. See its docstring for more information. """ res = np.unique( x._array, return_counts=False, return_index=False, return_inverse=False, equal_nan=False, ) return Array._new(res)