Python/SciPy：如何从 CubicSpline 获取三次样条方程

我正在通过给定的一组数据点生成三次样条曲线图：

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

x = np.array([1, 2, 4, 5])  # sort data points by increasing x value
y = np.array([2, 1, 4, 3])
arr = np.arange(np.amin(x), np.amax(x), 0.01)
s = interpolate.CubicSpline(x, y)
plt.plot(x, y, 'bo', label='Data Point')
plt.plot(arr, s(arr), 'r-', label='Cubic Spline')
plt.legend()
plt.show()

How can I get the spline equations from CubicSpline? I need the equations in the form:

我尝试了各种方法来获取系数，但它们都使用通过数据点以外的不同数据获得的数据。

From 文档:

c(ndarray, shape (4, n-1, ...)) 每段多项式的系数。尾部尺寸与以下尺寸相匹配y, 不包括轴。例如，如果y是 1-d，那么c[k, i]是一个 系数为(x-x[i])**(3-k)在之间的段上x[i] and x[i+1].

So in your example, the coefficients for the first segment [x₁, x₂] would be in column 0:

• y₁ would be s.c[3, 0]
• b₁ would be s.c[2, 0]
• c₁ would be s.c[1, 0]
• d₁ would be s.c[0, 0].

Then for the second segment [x₂, x₃] you would have s.c[3, 1], s.c[2, 1], s.c[1, 1] and s.c[0, 1] for y₂, b₂, c₂, d₂, and so on and so forth.

例如：

x = np.array([1, 2, 4, 5])  # sort data points by increasing x value
y = np.array([2, 1, 4, 3])
arr = np.arange(np.amin(x), np.amax(x), 0.01)
s = interpolate.CubicSpline(x, y)

fig, ax = plt.subplots(1, 1)
ax.hold(True)
ax.plot(x, y, 'bo', label='Data Point')
ax.plot(arr, s(arr), 'k-', label='Cubic Spline', lw=1)

for i in range(x.shape[0] - 1):
    segment_x = np.linspace(x[i], x[i + 1], 100)
    # A (4, 100) array, where the rows contain (x-x[i])**3, (x-x[i])**2 etc.
    exp_x = (segment_x - x[i])[None, :] ** np.arange(4)[::-1, None]
    # Sum over the rows of exp_x weighted by coefficients in the ith column of s.c
    segment_y = s.c[:, i].dot(exp_x)
    ax.plot(segment_x, segment_y, label='Segment {}'.format(i), ls='--', lw=3)

ax.legend()
plt.show()