JezK
Edit File: _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))