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| from __future__ import annotations
| from typing import Any
| import numpy as np
|
|
| class Object:
| def __ceil__(self) -> Object:
| return self
|
| def __floor__(self) -> Object:
| return self
|
| def __ge__(self, value: object) -> bool:
| return True
|
| def __array__(self) -> np.ndarray[Any, np.dtype[np.object_]]:
| ret = np.empty((), dtype=object)
| ret[()] = self
| return ret
|
|
| AR_LIKE_b = [True, True, False]
| AR_LIKE_u = [np.uint32(1), np.uint32(2), np.uint32(3)]
| AR_LIKE_i = [1, 2, 3]
| AR_LIKE_f = [1.0, 2.0, 3.0]
| AR_LIKE_O = [Object(), Object(), Object()]
| AR_U: np.ndarray[Any, np.dtype[np.str_]] = np.zeros(3, dtype="U5")
|
| np.fix(AR_LIKE_b)
| np.fix(AR_LIKE_u)
| np.fix(AR_LIKE_i)
| np.fix(AR_LIKE_f)
| np.fix(AR_LIKE_O)
| np.fix(AR_LIKE_f, out=AR_U)
|
| np.isposinf(AR_LIKE_b)
| np.isposinf(AR_LIKE_u)
| np.isposinf(AR_LIKE_i)
| np.isposinf(AR_LIKE_f)
| np.isposinf(AR_LIKE_f, out=AR_U)
|
| np.isneginf(AR_LIKE_b)
| np.isneginf(AR_LIKE_u)
| np.isneginf(AR_LIKE_i)
| np.isneginf(AR_LIKE_f)
| np.isneginf(AR_LIKE_f, out=AR_U)
|
|
