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✏️ Editing: _manipulation_functions.py

from __future__ import annotations

from ._array_object import Array
from ._data_type_functions import result_type

from typing import List, Optional, Tuple, Union

import numpy as np

# Note: the function name is different here
def concat(
    arrays: Union[Tuple[Array, ...], List[Array]], /, *, axis: Optional[int] = 0
) -> Array:
    """
    Array API compatible wrapper for :py:func:`np.concatenate <numpy.concatenate>`.

See its docstring for more information.
    """
    # Note: Casting rules here are different from the np.concatenate default
    # (no for scalars with axis=None, no cross-kind casting)
    dtype = result_type(*arrays)
    arrays = tuple(a._array for a in arrays)
    return Array._new(np.concatenate(arrays, axis=axis, dtype=dtype))

def expand_dims(x: Array, /, *, axis: int) -> Array:
    """
    Array API compatible wrapper for :py:func:`np.expand_dims <numpy.expand_dims>`.

See its docstring for more information.
    """
    return Array._new(np.expand_dims(x._array, axis))

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

See its docstring for more information.
    """
    return Array._new(np.flip(x._array, axis=axis))

# Note: The function name is different here (see also matrix_transpose).
# Unlike transpose(), the axes argument is required.
def permute_dims(x: Array, /, axes: Tuple[int, ...]) -> Array:
    """
    Array API compatible wrapper for :py:func:`np.transpose <numpy.transpose>`.

See its docstring for more information.
    """
    return Array._new(np.transpose(x._array, axes))

# Note: the optional argument is called 'shape', not 'newshape'
def reshape(x: Array, 
            /, 
            shape: Tuple[int, ...],
            *,
            copy: Optional[Bool] = None) -> Array:
    """
    Array API compatible wrapper for :py:func:`np.reshape <numpy.reshape>`.

See its docstring for more information.
    """

data = x._array
    if copy:
        data = np.copy(data)

reshaped = np.reshape(data, shape)

if copy is False and not np.shares_memory(data, reshaped):
        raise AttributeError("Incompatible shape for in-place modification.")

return Array._new(reshaped)

def roll(
    x: Array,
    /,
    shift: Union[int, Tuple[int, ...]],
    *,
    axis: Optional[Union[int, Tuple[int, ...]]] = None,
) -> Array:
    """
    Array API compatible wrapper for :py:func:`np.roll <numpy.roll>`.

See its docstring for more information.
    """
    return Array._new(np.roll(x._array, shift, axis=axis))

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

See its docstring for more information.
    """
    return Array._new(np.squeeze(x._array, axis=axis))

def stack(arrays: Union[Tuple[Array, ...], List[Array]], /, *, axis: int = 0) -> Array:
    """
    Array API compatible wrapper for :py:func:`np.stack <numpy.stack>`.

See its docstring for more information.
    """
    # Call result type here just to raise on disallowed type combinations
    result_type(*arrays)
    arrays = tuple(a._array for a in arrays)
    return Array._new(np.stack(arrays, axis=axis))

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