Matlab_spline

1 x=[1.3889, 2.7778, 4.1667, 5.5556, 6.9444, 8.3333, 9.7222, 11.1111,12.500];
2 y=[1.25, 2.40, 3.25, 4.00, 4.50, 5.50, 6.50, 7.00, 8.00];
3 x_new=0:0.1:15;
4 y_1=spline(x,y,x_new);
5 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

 1 import numpy as np
 2 from scipy import interpolate
 3 import pylab as pl
 4 
 5 x = np.array([
 6     1.3889, 2.7778, 4.1667, 5.5556, 6.9444, 8.3333, 9.7222, 11.1111,12.5000
 7     ])
 8 y = np.array([
 9     1.25, 2.40, 3.25, 4.00, 4.50, 5.50, 6.50, 7.00, 8.00
10     ])
11 
12 xnew = np.linspace(0, 15, 101)
13 pl.plot(x, y, "ro")
14 
15 for kind in ["nearest", "zero", "spline", "quadratic", "cubic"]:  # 插值方式
16     # "nearest","zero"为阶梯插值
17     # "spline" 样条插值
18     # "quadratic","cubic" 为2阶、3阶B样条曲线插值
19     f = interpolate.interp1d(x, y, kind=kind, fill_value = 'extrapolate')
20     ynew = f(xnew)
21     pl.plot(xnew, ynew, label=str(kind))
22 pl.legend(loc="lower right")
23 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)