| Server IP : 104.21.93.192 / Your IP : 216.73.216.73 Web Server : LiteSpeed System : Linux premium900.web-hosting.com 4.18.0-553.22.1.lve.1.el8.x86_64 #1 SMP Tue Oct 8 15:52:54 UTC 2024 x86_64 User : redwjova ( 1790) PHP Version : 8.1.32 Disable Function : NONE MySQL : OFF | cURL : ON | WGET : ON | Perl : ON | Python : ON | Sudo : OFF | Pkexec : OFF Directory : /opt/cloudlinux/venv/lib/python3.11/site-packages/numpy/typing/tests/data/pass/ |
Upload File : |
from __future__ import annotations
from typing import Any
import numpy as np
from numpy._typing import ArrayLike, _SupportsArray
x1: ArrayLike = True
x2: ArrayLike = 5
x3: ArrayLike = 1.0
x4: ArrayLike = 1 + 1j
x5: ArrayLike = np.int8(1)
x6: ArrayLike = np.float64(1)
x7: ArrayLike = np.complex128(1)
x8: ArrayLike = np.array([1, 2, 3])
x9: ArrayLike = [1, 2, 3]
x10: ArrayLike = (1, 2, 3)
x11: ArrayLike = "foo"
x12: ArrayLike = memoryview(b'foo')
class A:
def __array__(self, dtype: None | np.dtype[Any] = None) -> np.ndarray:
return np.array([1, 2, 3])
x13: ArrayLike = A()
scalar: _SupportsArray = np.int64(1)
scalar.__array__()
array: _SupportsArray = np.array(1)
array.__array__()
a: _SupportsArray = A()
a.__array__()
a.__array__()
# Escape hatch for when you mean to make something like an object
# array.
object_array_scalar: Any = (i for i in range(10))
np.array(object_array_scalar)