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