目录

  1.算法流程步骤

  2.算法核心代码

  3.算法效果展示

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1.算法流程步骤

#算法的核心就是利用scipy中的interpolate来完成工作
#一共是5种一维插值算法形式:
#插值方法：1.阶梯插值 2.线性插值 3.2阶样条插值 4.3阶样条插值
#"nearest"==阶梯插值
#"zero"==阶梯插值
#"slinear"=线性插值
#"quadratic"==2阶样条插值
#"cubic"==3阶样条插值
#最后写成列表形式:style=["nearest","zero","slinear","quadratic","cubic"]

  2.算法核心代码

#导入库和数据
import numpy as np
from scipy import interpolate
import pylab as plb

x0=np.linspace(0,10,11)
y=(np.cos(x0)+np.sin(x0))*np.exp(x0*0.1)
plb.plot(x0,y,'ro')
x1=np.linspace(0,10,101)

#插值方法：1.阶梯插值 2.线性插值 3.2阶样条插值 4.3阶样条插值
#"nearest"和"zero"==阶梯插值
#"slinear"=线性插值
#"quadratic"==2阶样条插值
#"cubic"==3阶样条插值
style=["nearest","zero","slinear","quadratic","cubic"]
for i in style:
    function=interpolate.interp1d(x0,y,kind=i)
    y_new=function(x1)
    plb.plot(x1,y_new,label=str(i))
plb.legend(loc=3, borderaxespad=0., bbox_to_anchor=(0, 0))
plb.title('Multiple interpolation methods for one-dimensional interpolation',color='black')
plb.xlabel('x value range',color='r')
plb.ylabel('y value range',color='r')
plb.savefig('C:/Users/Zeng Zhong Yan/Desktop/一维插值法.png', dpi=200, bbox_inches='tight')
plb.show()

  3.算法效果展示