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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: _searching_functions.py

from __future__ import annotations

from ._array_object import Array
from ._dtypes import _result_type, _real_numeric_dtypes

from typing import Optional, Tuple

import numpy as np

def argmax(x: Array, /, *, axis: Optional[int] = None, keepdims: bool = False) -> Array:
    """
    Array API compatible wrapper for :py:func:`np.argmax <numpy.argmax>`.

See its docstring for more information.
    """
    if x.dtype not in _real_numeric_dtypes:
        raise TypeError("Only real numeric dtypes are allowed in argmax")
    return Array._new(np.asarray(np.argmax(x._array, axis=axis, keepdims=keepdims)))

def argmin(x: Array, /, *, axis: Optional[int] = None, keepdims: bool = False) -> Array:
    """
    Array API compatible wrapper for :py:func:`np.argmin <numpy.argmin>`.

See its docstring for more information.
    """
    if x.dtype not in _real_numeric_dtypes:
        raise TypeError("Only real numeric dtypes are allowed in argmin")
    return Array._new(np.asarray(np.argmin(x._array, axis=axis, keepdims=keepdims)))

def nonzero(x: Array, /) -> Tuple[Array, ...]:
    """
    Array API compatible wrapper for :py:func:`np.nonzero <numpy.nonzero>`.

See its docstring for more information.
    """
    return tuple(Array._new(i) for i in np.nonzero(x._array))

def where(condition: Array, x1: Array, x2: Array, /) -> Array:
    """
    Array API compatible wrapper for :py:func:`np.where <numpy.where>`.

See its docstring for more information.
    """
    # Call result type here just to raise on disallowed type combinations
    _result_type(x1.dtype, x2.dtype)
    x1, x2 = Array._normalize_two_args(x1, x2)
    return Array._new(np.where(condition._array, x1._array, x2._array))

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