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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