matlab插值函数的优缺点,Python和Matlab插值函数的不同结果

我正在将代码从Matlab转换为Python2.7，在转换interp1函数时遇到问题。我看过已经贴出来的类似问题，但还没有解决。问题是新生成的值(yn)的向量的第一个值不同，而其余的几乎相同。当使用不同的插值方法时，我得到的值略有不同，但是同样的问题。目前我真的不知道为什么会这样。有没有人对此有任何了解或看到我可能犯的错误？谢谢。在

变量：x = [5.5 , 5.46678 , 5.408315, 5.33929 , 5.261025, 5.17605 ,

5.08684 , 4.995375, 4.902755, 4.80942 , 4.7157 , 4.621815,

4.52782 , 4.433715, 4.339555, 4.245395, 4.151235, 4.05713 ,

3.962915, 3.868645, 3.77432 , 3.680105, 3.585945, 3.491895,

3.397845, 3.303905, 3.21002 , 3.11619 , 3.02247 , 2.928805,

2.835195, 2.741695, 2.64836 , 2.55519 , 2.462295, 2.36951 ,

2.27689 , 2.184435, 2.092255, 2.00035 , 1.908775, 1.817475,

1.726505, 1.63592 , 1.54583 , 1.4564 , 1.36752 , 1.279245,

1.19163 , 1.10539 , 1.02135 , 0.94006 , 0.86163 , 0.786115,

0.713515, 0.64394 , 0.577555, 0.514635, 0.45562 , 0.400785,

0.35035 , 0.304425, 0.26301 , 0.22627 , 0.193875, 0.16544 ,

0.14058 , 0.118745, 0.09944 , 0.082335, 0.067265, 0.05401 ,

0.042405, 0.03234 , 0.023595, 0.01628 , 0.010175, 0.00528 ,

0.001485, -0.00121 , -0.002805, -0.003355, -0.00275 , -0.000935]

y = [0.19896, 0.18722, 0.155 , 0.13133, 0.11168, 0.09543,

0.0815 , 0.06556, 0.04191, 0.0117 , 0.00513, 0.00123,

-0.0036 , -0.00885, -0.01429, -0.01985, -0.02532, -0.03065,

-0.03574, -0.04082, -0.04594, -0.05104, -0.05596, -0.06091,

-0.06561, -0.07023, -0.07482, -0.07913, -0.08341, -0.08749,

-0.09155, -0.09551, -0.09952, -0.10334, -0.10694, -0.11011,

-0.11319, -0.11587, -0.11856, -0.12092, -0.12277, -0.12428,

-0.12506, -0.12567, -0.12567, -0.12497, -0.12369, -0.12135,

-0.11944, -0.1191 , -0.11983, -0.11819, -0.11197, -0.10004,

-0.08016, -0.05285, -0.01569, 0.03055, 0.08527, 0.1492 ,

0.21971, 0.29507, 0.37453, 0.45682, 0.53766, 0.61562,

0.6916 , 0.763 , 0.82907, 0.88665, 0.9367 , 0.97418,

0.99617, 0.99807, 0.97457, 0.91708, 0.81796, 0.66987,

0.46359, 0.19778, -0.13378, -0.54232, -1.0126 , -1.5297 ]

xn = [ 0., 0.61111111, 1.22222222, 1.83333333, 2.44444444,

3.05555556, 3.66666667, 4.27777778, 4.88888889, 5.5 ]

Matlab代码：

^{pr2}$

Python代码：from scipy.interpolate import InterpolatedUnivariateSpline

yn_f1 = InterpolatedUnivariateSpline(x[::-1], y[::-1])

yn_py1 = yn_f1(xn)

from scipy.interpolate import interp1d

yn_f2 = interp1d(x[::-1], y[::-1])

yn_py2 = yn_f2(xn)

import numpy as np

yn_py3 = np.interp(xn, x[::-1], y[::-1])

结果：yn_mat = [-0.7596, -0.0345, -0.1201, -0.1240, -0.1075,

-0.0819, -0.0517, -0.0179, 0.0374, 0.1990 ]

yn_py1 = [-0.23310355, -0.03594415, -0.11996893, -0.12406894, -0.10757466,

-0.08191329, -0.05174936, -0.01793778, 0.0371338 , 0.19896 ]

yn_py2 = [ 0.31712327, -0.03447354, -0.12010691, -0.12401772, -0.10754986,

-0.08189905, -0.05174217, -0.01793785, 0.03742192, 0.19896 ]

yn_py3 = [ 0.31712327, -0.03447354, -0.12010691, -0.12401772, -0.10754986,

-0.08189905, -0.05174217, -0.01793785, 0.03742192, 0.19896 ]