晋太元中,武陵人捕鱼为业。缘溪行,忘路之远近。忽逢桃花林,夹岸数百步,中无杂树,芳草鲜美,落英缤纷。渔人甚异之,复前行,欲穷其林。 林尽水源,便得一山,山有小口,仿佛若有光。便舍船,从口入。初极狭,才通人。复行数十步,豁然开朗。土地平旷,屋舍俨然,有良田、美池、桑竹之属。阡陌交通,鸡犬相闻。其中往来种作,男女衣着,悉如外人。黄发垂髫,并怡然自乐。 见渔人,乃大惊,问所从来。具答之。便要还家,设酒杀鸡作食。村中闻有此人,咸来问讯。自云先世避秦时乱,率妻子邑人来此绝境,不复出焉,遂与外人间隔。问今是何世,乃不知有汉,无论魏晋。此人一一为具言所闻,皆叹惋。余人各复延至其家,皆出酒食。停数日,辞去。此中人语云:“不足为外人道也。”(间隔 一作:隔绝) 既出,得其船,便扶向路,处处志之。及郡下,诣太守,说如此。太守即遣人随其往,寻向所志,遂迷,不复得路。 南阳刘子骥,高尚士也,闻之,欣然规往。未果,寻病终。后遂无问津者。
| DIR:/opt/cloudlinux/venv/lib64/python3.11/site-packages/numpy/typing/tests/data/pass/ |
| Current File : //opt/cloudlinux/venv/lib64/python3.11/site-packages/numpy/typing/tests/data/pass/numeric.py |
"""
Tests for :mod:`numpy.core.numeric`.
Does not include tests which fall under ``array_constructors``.
"""
from __future__ import annotations
import numpy as np
class SubClass(np.ndarray):
...
i8 = np.int64(1)
A = np.arange(27).reshape(3, 3, 3)
B: list[list[list[int]]] = A.tolist()
C = np.empty((27, 27)).view(SubClass)
np.count_nonzero(i8)
np.count_nonzero(A)
np.count_nonzero(B)
np.count_nonzero(A, keepdims=True)
np.count_nonzero(A, axis=0)
np.isfortran(i8)
np.isfortran(A)
np.argwhere(i8)
np.argwhere(A)
np.flatnonzero(i8)
np.flatnonzero(A)
np.correlate(B[0][0], A.ravel(), mode="valid")
np.correlate(A.ravel(), A.ravel(), mode="same")
np.convolve(B[0][0], A.ravel(), mode="valid")
np.convolve(A.ravel(), A.ravel(), mode="same")
np.outer(i8, A)
np.outer(B, A)
np.outer(A, A)
np.outer(A, A, out=C)
np.tensordot(B, A)
np.tensordot(A, A)
np.tensordot(A, A, axes=0)
np.tensordot(A, A, axes=(0, 1))
np.isscalar(i8)
np.isscalar(A)
np.isscalar(B)
np.roll(A, 1)
np.roll(A, (1, 2))
np.roll(B, 1)
np.rollaxis(A, 0, 1)
np.moveaxis(A, 0, 1)
np.moveaxis(A, (0, 1), (1, 2))
np.cross(B, A)
np.cross(A, A)
np.indices([0, 1, 2])
np.indices([0, 1, 2], sparse=False)
np.indices([0, 1, 2], sparse=True)
np.binary_repr(1)
np.base_repr(1)
np.allclose(i8, A)
np.allclose(B, A)
np.allclose(A, A)
np.isclose(i8, A)
np.isclose(B, A)
np.isclose(A, A)
np.array_equal(i8, A)
np.array_equal(B, A)
np.array_equal(A, A)
np.array_equiv(i8, A)
np.array_equiv(B, A)
np.array_equiv(A, A)
|