python 二维插值并且三维作图

python 二维插值并且三维作图

""" 演示二维插值。 """ # -*- coding: utf-8 -*- import numpy as np from mpl_toolkits.mplot3d import Axes3D import matplotlib as mpl from scipy import interpolate import matplotlib.cm as cm import 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(-3, 3, 20) y = np.linspace(-3, 3, 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-graph1 ax = 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(-3, 3, 100) # x ynew = np.linspace(-3, 3, 100) # y fnew = newfunc(xnew, ynew) # 仅仅是y值 100*100的值 np.shape(fnew) is 100*100 print(fnew) xnew, 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()