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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)