基于python的数学建模---高阶样条插值

• 为了满足对函数光滑性的需要，我们可以使用一种有弹性的长条（称之为样条），强迫它弯曲通过样本点。

• import numpy as np
  import matplotlib.pylab as pl
  from scipy import interpolate
  
  x = np.linspace(0, 10, 11)
  y = np.sin(x)
  
  pl.figure(figsize=(12, 9))
  pl.plot(x, y, 'ro')
  x_new = np.linspace(0, 10, 101)
  
  for kind in ['nearest', 'zero', 'linear', 'quadratic', 5]:　　#零阶，零阶，一阶，三阶，五阶
      f = interpolate.interp1d(x, y, kind=kind)
      y_new = f(x_new)
      pl.plot(x_new, y_new, label=str(kind))
  pl.xticks(fontsize=20)
  pl.yticks(fontsize=20)

• #标签坐落于右下方
  pl.legend(loc='lower right')
  pl.show()

• 

  分开画图

  import numpy as np
  import matplotlib.pylab as pl
  from scipy import interpolate
  
  x = np.linspace(0, 10, 11)
  y = np.sin(x)
  
  pl.figure(figsize=(12, 9))
  pl.plot(x, y, 'ro')
  x_new = np.linspace(0, 10, 101)
  
  f = interpolate.interp1d(x,y,kind = 5)
  y_new = f(x_new)
  pl.plot(x_new,y_new,label = '5')
  
  pl.xticks(fontsize=20)
  pl.yticks(fontsize=20)
  #标签坐落于右下方
  pl.legend(loc='lower right')
  pl.show()