# -*- coding: utf-8 -*-"""演示二维插值。"""# -*- coding: utf-8 -*-import numpy as npfrom mpl_toolkits.mplot3d import Axes3Dimport matplotlib as mplfrom scipy import interpolateimport matplotlib.cm as cmimport matplotlib.pyplot as plt def func(x, y): return (x + y) * np.exp(-5.0 * (x ** 2 + y ** 2)) # X-Y轴分为20*20的网格x = np.linspace(-1, 1, 20)y = np.linspace(-1, 1, 20)x, y = np.meshgrid(x, y) # 20*20的网格数据 fvals = func(x, y) # 计算每个网格点上的函数值 15*15的值 fig = plt.figure(figsize=(9, 6)) #设置图的大小# Draw sub-graph1ax = plt.subplot(1, 2, 1, projection='3d') #设置图的位置surf = ax.plot_surface(x, y, fvals, rstride=2, cstride=2, cmap=cm.coolwarm, linewidth=0.5, antialiased=True) #第四个第五个参数表示隔多少个取样点画一个小面，第六个表示画图类型，第七个是画图的线宽，第八个表示抗锯齿ax.set_xlabel('x')ax.set_ylabel('y')ax.set_zlabel('f(x, y)') #标签plt.colorbar(surf, shrink=0.5, aspect=5) # 标注 # 二维插值newfunc = interpolate.interp2d(x, y, fvals, kind='cubic') # newfunc为一个函数 # 计算100*100的网格上的插值xnew = np.linspace(-1, 1, 100) # xynew = np.linspace(-1, 1, 100) # yfnew = newfunc(xnew, ynew) # 仅仅是y值 100*100的值 np.shape(fnew) is 100*100xnew, ynew = np.meshgrid(xnew, ynew)ax2 = plt.subplot(1, 2, 2, projection='3d')surf2 = ax2.plot_surface(xnew, ynew, fnew, rstride=2, cstride=2, cmap=cm.coolwarm, linewidth=0.5, antialiased=True)ax2.set_xlabel('xnew')ax2.set_ylabel('ynew')ax2.set_zlabel('fnew(x, y)')plt.colorbar(surf2, shrink=0.5, aspect=5) # 标注plt.show() 
