Matlab_spline

x=[1.3889, 2.7778, 4.1667, 5.5556, 6.9444, 8.3333, 9.7222, 11.1111,12.500];
y=[1.25, 2.40, 3.25, 4.00, 4.50, 5.50, 6.50, 7.00, 8.00];
x_new=0:0.1:15;
y_1=spline(x,y,x_new);
plot(x,y,'o',x_new,y_1);title('spline fit');

样本插值

除了样本插值，还有nearest, zero,quadratic,cubic

都可以用numpy进行实现

用numpy实现的图像与matlab差不多，但是视觉上感觉numpy好看一些，而且用起来顺手

import numpy as np
from scipy import interpolate
import pylab as pl

x = np.array([
    1.3889, 2.7778, 4.1667, 5.5556, 6.9444, 8.3333, 9.7222, 11.1111,12.5000
    ])
y = np.array([
    1.25, 2.40, 3.25, 4.00, 4.50, 5.50, 6.50, 7.00, 8.00
    ])

xnew = np.linspace(0, 15, 101)
pl.plot(x, y, "ro")

for kind in ["nearest", "zero", "spline", "quadratic", "cubic"]:  # 插值方式
    # "nearest","zero"为阶梯插值
    # "spline" 样条插值
    # "quadratic","cubic" 为2阶、3阶B样条曲线插值
    f = interpolate.interp1d(x, y, kind=kind, fill_value = 'extrapolate')
    ynew = f(xnew)
    pl.plot(xnew, ynew, label=str(kind))
pl.legend(loc="lower right")
pl.show()

但是用numpy实现插值存在一个问题，无法采样！！！

所以在最后还是只好用matlab进行插值！

command windows:

>>pp = spline(x,y)