147 lines
4.4 KiB
Python
147 lines
4.4 KiB
Python
from __future__ import annotations
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from ._array_object import Array
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from ._dtypes import _all_dtypes, _result_type
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from dataclasses import dataclass
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from typing import TYPE_CHECKING, List, Tuple, Union
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if TYPE_CHECKING:
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from ._typing import Dtype
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from collections.abc import Sequence
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import numpy as np
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# Note: astype is a function, not an array method as in NumPy.
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def astype(x: Array, dtype: Dtype, /, *, copy: bool = True) -> Array:
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if not copy and dtype == x.dtype:
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return x
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return Array._new(x._array.astype(dtype=dtype, copy=copy))
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def broadcast_arrays(*arrays: Array) -> List[Array]:
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"""
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Array API compatible wrapper for :py:func:`np.broadcast_arrays <numpy.broadcast_arrays>`.
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See its docstring for more information.
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"""
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from ._array_object import Array
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return [
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Array._new(array) for array in np.broadcast_arrays(*[a._array for a in arrays])
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]
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def broadcast_to(x: Array, /, shape: Tuple[int, ...]) -> Array:
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"""
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Array API compatible wrapper for :py:func:`np.broadcast_to <numpy.broadcast_to>`.
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See its docstring for more information.
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"""
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from ._array_object import Array
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return Array._new(np.broadcast_to(x._array, shape))
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def can_cast(from_: Union[Dtype, Array], to: Dtype, /) -> bool:
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"""
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Array API compatible wrapper for :py:func:`np.can_cast <numpy.can_cast>`.
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See its docstring for more information.
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"""
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if isinstance(from_, Array):
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from_ = from_.dtype
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elif from_ not in _all_dtypes:
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raise TypeError(f"{from_=}, but should be an array_api array or dtype")
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if to not in _all_dtypes:
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raise TypeError(f"{to=}, but should be a dtype")
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# Note: We avoid np.can_cast() as it has discrepancies with the array API,
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# since NumPy allows cross-kind casting (e.g., NumPy allows bool -> int8).
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# See https://github.com/numpy/numpy/issues/20870
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try:
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# We promote `from_` and `to` together. We then check if the promoted
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# dtype is `to`, which indicates if `from_` can (up)cast to `to`.
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dtype = _result_type(from_, to)
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return to == dtype
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except TypeError:
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# _result_type() raises if the dtypes don't promote together
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return False
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# These are internal objects for the return types of finfo and iinfo, since
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# the NumPy versions contain extra data that isn't part of the spec.
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@dataclass
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class finfo_object:
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bits: int
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# Note: The types of the float data here are float, whereas in NumPy they
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# are scalars of the corresponding float dtype.
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eps: float
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max: float
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min: float
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smallest_normal: float
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@dataclass
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class iinfo_object:
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bits: int
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max: int
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min: int
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def finfo(type: Union[Dtype, Array], /) -> finfo_object:
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"""
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Array API compatible wrapper for :py:func:`np.finfo <numpy.finfo>`.
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See its docstring for more information.
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"""
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fi = np.finfo(type)
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# Note: The types of the float data here are float, whereas in NumPy they
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# are scalars of the corresponding float dtype.
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return finfo_object(
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fi.bits,
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float(fi.eps),
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float(fi.max),
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float(fi.min),
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float(fi.smallest_normal),
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)
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def iinfo(type: Union[Dtype, Array], /) -> iinfo_object:
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"""
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Array API compatible wrapper for :py:func:`np.iinfo <numpy.iinfo>`.
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See its docstring for more information.
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"""
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ii = np.iinfo(type)
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return iinfo_object(ii.bits, ii.max, ii.min)
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def result_type(*arrays_and_dtypes: Union[Array, Dtype]) -> Dtype:
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"""
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Array API compatible wrapper for :py:func:`np.result_type <numpy.result_type>`.
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See its docstring for more information.
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"""
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# Note: we use a custom implementation that gives only the type promotions
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# required by the spec rather than using np.result_type. NumPy implements
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# too many extra type promotions like int64 + uint64 -> float64, and does
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# value-based casting on scalar arrays.
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A = []
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for a in arrays_and_dtypes:
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if isinstance(a, Array):
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a = a.dtype
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elif isinstance(a, np.ndarray) or a not in _all_dtypes:
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raise TypeError("result_type() inputs must be array_api arrays or dtypes")
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A.append(a)
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if len(A) == 0:
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raise ValueError("at least one array or dtype is required")
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elif len(A) == 1:
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return A[0]
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else:
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t = A[0]
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for t2 in A[1:]:
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t = _result_type(t, t2)
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return t
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