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ðŸ“ ..
ðŸ“„ __init__.py(10.11 KB)
ðŸ“ __pycache__
ðŸ“„ _array_object.py(42.71 KB)
ðŸ“„ _constants.py(66 B)
ðŸ“„ _creation_functions.py(9.81 KB)
ðŸ“„ _data_type_functions.py(6.14 KB)
ðŸ“„ _dtypes.py(4.71 KB)
ðŸ“„ _elementwise_functions.py(25.38 KB)
ðŸ“„ _indexing_functions.py(601 B)
ðŸ“„ _manipulation_functions.py(3.24 KB)
ðŸ“„ _searching_functions.py(1.67 KB)
ðŸ“„ _set_functions.py(2.88 KB)
ðŸ“„ _sorting_functions.py(1.98 KB)
ðŸ“„ _statistical_functions.py(3.5 KB)
ðŸ“„ _typing.py(1.2 KB)
ðŸ“„ _utility_functions.py(824 B)
ðŸ“„ linalg.py(17.79 KB)
ðŸ“„ setup.py(341 B)
ðŸ“ tests

Editing: _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)

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