在python中进行样条插值的更快方法

如何解决在python中进行样条插值的更快方法

我正尝试用3度样条插值30点，如下所示：

from sympy import interpolating_spline
domain_points = [0.01,0.01888888888888889,0.027777777777777776,0.03666666666666667,0.04555555555555556,0.05444444444444445,0.06333333333333332,0.07222222222222222,0.0811111111111111,0.09,0.09888888888888889,0.10777777777777778,0.11666666666666665,0.12555555555555556,0.13444444444444445,0.14333333333333334,0.15222222222222223,0.16111111111111112,0.17,0.30833333333333335,0.44666666666666666,0.5850000000000001,0.7233333333333334,0.8616666666666667,1.0,2.5,4.0,5.5,7.0,8.5]
range_points = list(map(lambda x: x-1/x,domain_points))

interpolating_spline(3,x,domain_points,range_points)

这在我的机器上花费了大约1分钟42秒。我在做错什么使它变得如此缓慢吗？有没有一种方法可以加快速度，或者有一个更快的样条插值库，这样我就可以插很多点了？

解决方法

如果只希望数字插值，请使用scipy

https://docs.scipy.org/doc/scipy/reference/tutorial/interpolate.html#spline-interpolation

Sympy用于代数运算。

,

使用scipy.interpolate.splrep()。默认值为k=3，但最多支持k=5。

编辑，针对下面的评论，您也可以使用interpolate.InterpolatedUnivariateSpline()，它实际上会为您提供相同的结果。代码和图形已更新以显示此内容。

import numpy as np
import matplotlib.pyplot as plt
from scipy import interpolate

domain_points = [0.01,0.01888888888888889,0.027777777777777776,0.03666666666666667,0.04555555555555556,0.05444444444444445,0.06333333333333332,0.07222222222222222,0.0811111111111111,0.09,0.09888888888888889,0.10777777777777778,0.11666666666666665,0.12555555555555556,0.13444444444444445,0.14333333333333334,0.15222222222222223,0.16111111111111112,0.17,0.30833333333333335,0.44666666666666666,0.5850000000000001,0.7233333333333334,0.8616666666666667,1.0,2.5,4.0,5.5,7.0,8.5]
range_points  = list(map(lambda x: x-1/x,domain_points))

# using splrep
tck           = interpolate.splrep(domain_points,range_points,s=0)
splrep_points = interpolate.splev(domain_points,tck)

tock = interpolate.InterpolatedUnivariateSpline(domain_points,range_points)
xs   = np.linspace(np.min(domain_points),np.max(domain_points),1000)

plt.figure()
plt.plot(domain_points,'x',domain_points,splrep_points,'b',xs,tock(xs),'r')
plt.legend(['Data','splrep','InterpolatedUnivariateSpline'])
plt.show()

哪个给...