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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)
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