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from __future__ import annotations
from typing import TYPE_CHECKING
if TYPE_CHECKING:
from ._types import (List, Optional, SupportsDLPack,
SupportsBufferProtocol, Tuple, Union, Array, Device,
Dtype)
from collections.abc import Sequence
from ._dtypes import _all_dtypes
import numpy as np
def asarray(obj: Union[float, NestedSequence[bool|int|float], SupportsDLPack, SupportsBufferProtocol], /, *, dtype: Optional[Dtype] = None, device: Optional[Device] = None, copy: Optional[bool] = None) -> Array:
"""
Array API compatible wrapper for :py:func:`np.asarray <numpy.asarray>`.
See its docstring for more information.
"""
# _array_object imports in this file are inside the functions to avoid
# circular imports
from ._array_object import ndarray
if device is not None:
# Note: Device support is not yet implemented on ndarray
raise NotImplementedError("Device support is not yet implemented")
if copy is not None:
# Note: copy is not yet implemented in np.asarray
raise NotImplementedError("The copy keyword argument to asarray is not yet implemented")
if isinstance(obj, ndarray) and (dtype is None or obj.dtype == dtype):
return obj
if dtype is None and isinstance(obj, int) and (obj > 2**64 or obj < -2**63):
# Give a better error message in this case. NumPy would convert this
# to an object array.
raise OverflowError("Integer out of bounds for array dtypes")
res = np.asarray(obj, dtype=dtype)
if res.dtype not in _all_dtypes:
raise TypeError(f"The array_api namespace does not support the dtype '{res.dtype}'")
return ndarray._new(res)
def arange(start: Union[int, float], /, stop: Optional[Union[int, float]] = None, step: Union[int, float] = 1, *, dtype: Optional[Dtype] = None, device: Optional[Device] = None) -> Array:
"""
Array API compatible wrapper for :py:func:`np.arange <numpy.arange>`.
See its docstring for more information.
"""
from ._array_object import ndarray
if device is not None:
# Note: Device support is not yet implemented on ndarray
raise NotImplementedError("Device support is not yet implemented")
return ndarray._new(np.arange(start, stop=stop, step=step, dtype=dtype))
def empty(shape: Union[int, Tuple[int, ...]], *, dtype: Optional[Dtype] = None, device: Optional[Device] = None) -> Array:
"""
Array API compatible wrapper for :py:func:`np.empty <numpy.empty>`.
See its docstring for more information.
"""
from ._array_object import ndarray
if device is not None:
# Note: Device support is not yet implemented on ndarray
raise NotImplementedError("Device support is not yet implemented")
return ndarray._new(np.empty(shape, dtype=dtype))
def empty_like(x: Array, /, *, dtype: Optional[Dtype] = None, device: Optional[Device] = None) -> Array:
"""
Array API compatible wrapper for :py:func:`np.empty_like <numpy.empty_like>`.
See its docstring for more information.
"""
from ._array_object import ndarray
if device is not None:
# Note: Device support is not yet implemented on ndarray
raise NotImplementedError("Device support is not yet implemented")
return ndarray._new(np.empty_like(x._array, dtype=dtype))
def eye(n_rows: int, n_cols: Optional[int] = None, /, *, k: Optional[int] = 0, dtype: Optional[Dtype] = None, device: Optional[Device] = None) -> Array:
"""
Array API compatible wrapper for :py:func:`np.eye <numpy.eye>`.
See its docstring for more information.
"""
from ._array_object import ndarray
if device is not None:
# Note: Device support is not yet implemented on ndarray
raise NotImplementedError("Device support is not yet implemented")
return ndarray._new(np.eye(n_rows, M=n_cols, k=k, dtype=dtype))
def from_dlpack(x: object, /) -> Array:
# Note: dlpack support is not yet implemented on ndarray
raise NotImplementedError("DLPack support is not yet implemented")
def full(shape: Union[int, Tuple[int, ...]], fill_value: Union[int, float], *, dtype: Optional[Dtype] = None, device: Optional[Device] = None) -> Array:
"""
Array API compatible wrapper for :py:func:`np.full <numpy.full>`.
See its docstring for more information.
"""
from ._array_object import ndarray
if device is not None:
# Note: Device support is not yet implemented on ndarray
raise NotImplementedError("Device support is not yet implemented")
if isinstance(fill_value, ndarray) and fill_value.ndim == 0:
fill_value = fill_value._array[...]
res = np.full(shape, fill_value, dtype=dtype)
if res.dtype not in _all_dtypes:
# This will happen if the fill value is not something that NumPy
# coerces to one of the acceptable dtypes.
raise TypeError("Invalid input to full")
return ndarray._new(res)
def full_like(x: Array, /, fill_value: Union[int, float], *, dtype: Optional[Dtype] = None, device: Optional[Device] = None) -> Array:
"""
Array API compatible wrapper for :py:func:`np.full_like <numpy.full_like>`.
See its docstring for more information.
"""
from ._array_object import ndarray
if device is not None:
# Note: Device support is not yet implemented on ndarray
raise NotImplementedError("Device support is not yet implemented")
res = np.full_like(x._array, fill_value, dtype=dtype)
if res.dtype not in _all_dtypes:
# This will happen if the fill value is not something that NumPy
# coerces to one of the acceptable dtypes.
raise TypeError("Invalid input to full_like")
return ndarray._new(res)
def linspace(start: Union[int, float], stop: Union[int, float], /, num: int, *, dtype: Optional[Dtype] = None, device: Optional[Device] = None, endpoint: bool = True) -> Array:
"""
Array API compatible wrapper for :py:func:`np.linspace <numpy.linspace>`.
See its docstring for more information.
"""
from ._array_object import ndarray
if device is not None:
# Note: Device support is not yet implemented on ndarray
raise NotImplementedError("Device support is not yet implemented")
return ndarray._new(np.linspace(start, stop, num, dtype=dtype, endpoint=endpoint))
def meshgrid(*arrays: Sequence[Array], indexing: str = 'xy') -> List[Array, ...]:
"""
Array API compatible wrapper for :py:func:`np.meshgrid <numpy.meshgrid>`.
See its docstring for more information.
"""
from ._array_object import ndarray
return [ndarray._new(array) for array in np.meshgrid(*[a._array for a in arrays], indexing=indexing)]
def ones(shape: Union[int, Tuple[int, ...]], *, dtype: Optional[Dtype] = None, device: Optional[Device] = None) -> Array:
"""
Array API compatible wrapper for :py:func:`np.ones <numpy.ones>`.
See its docstring for more information.
"""
from ._array_object import ndarray
if device is not None:
# Note: Device support is not yet implemented on ndarray
raise NotImplementedError("Device support is not yet implemented")
return ndarray._new(np.ones(shape, dtype=dtype))
def ones_like(x: Array, /, *, dtype: Optional[Dtype] = None, device: Optional[Device] = None) -> Array:
"""
Array API compatible wrapper for :py:func:`np.ones_like <numpy.ones_like>`.
See its docstring for more information.
"""
from ._array_object import ndarray
if device is not None:
# Note: Device support is not yet implemented on ndarray
raise NotImplementedError("Device support is not yet implemented")
return ndarray._new(np.ones_like(x._array, dtype=dtype))
def zeros(shape: Union[int, Tuple[int, ...]], *, dtype: Optional[Dtype] = None, device: Optional[Device] = None) -> Array:
"""
Array API compatible wrapper for :py:func:`np.zeros <numpy.zeros>`.
See its docstring for more information.
"""
from ._array_object import ndarray
if device is not None:
# Note: Device support is not yet implemented on ndarray
raise NotImplementedError("Device support is not yet implemented")
return ndarray._new(np.zeros(shape, dtype=dtype))
def zeros_like(x: Array, /, *, dtype: Optional[Dtype] = None, device: Optional[Device] = None) -> Array:
"""
Array API compatible wrapper for :py:func:`np.zeros_like <numpy.zeros_like>`.
See its docstring for more information.
"""
from ._array_object import ndarray
if device is not None:
# Note: Device support is not yet implemented on ndarray
raise NotImplementedError("Device support is not yet implemented")
return ndarray._new(np.zeros_like(x._array, dtype=dtype))
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