python 一个figure上显示多个图像的实例

import matplotlib.pyplot as plt  #创建新的figurefig = plt.figure() #必须通过add_subplot()创建一个或多个绘图#ax = fig.add_subplot(221) #绘制2x2两行两列共四个图，编号从1开始ax1 = fig.add_subplot(221)ax2 = fig.add_subplot(222)ax3 = fig.add_subplot(223)ax4 = fig.add_subplot(224) #图片的显示plt.show()

#coding=utf-8import matplotlib.pyplot as plt import numpy as np points = np.arange(-5,5,0.01) xs,ys = np.meshgrid(points,points) z = np.sqrt(xs**2 + ys**2) #创建新的figurefig = plt.figure() #绘制2x2两行两列共四个图，编号从1开始ax = fig.add_subplot(221)ax.imshow(z) ax = fig.add_subplot(222)#使用自定义的colormap(灰度图)ax.imshow(z,cmap=plt.cm.gray) ax = fig.add_subplot(223)#使用自定义的colormapax.imshow(z,cmap=plt.cm.cool) ax = fig.add_subplot(224)#使用自定义的colormapax.imshow(z,cmap=plt.cm.hot) #图片的显示plt.show()

  # -*- coding: utf-8 -*-  """  演示二维插值。  """  import numpy as np  from scipy import interpolate  import pylab as pl  import matplotlib as mpl    def func(x, y):   return (x+y)*np.exp(-5.0*(x**2 + y**2))    # X-Y轴分为15*15的网格  y,x= np.mgrid[-1:1:15j, -1:1:15j]    fvals = func(x,y) # 计算每个网格点上的函数值 15*15的值  print len(fvals[0])    #三次样条二维插值  newfunc = interpolate.interp2d(x, y, fvals, kind='cubic')    # 计算100*100的网格上的插值  xnew = np.linspace(-1,1,100)#x  ynew = np.linspace(-1,1,100)#y  fnew = newfunc(xnew, ynew)#仅仅是y值 100*100的值    # 绘图  # 为了更明显地比较插值前后的区别，使用关键字参数interpolation='nearest'  # 关闭imshow()内置的插值运算。  pl.subplot(121)  im1=pl.imshow(fvals, extent=[-1,1,-1,1], cmap=mpl.cm.hot, interpolation='nearest', origin="lower")#pl.cm.jet  #extent=[-1,1,-1,1]为x,y范围 favals为  pl.colorbar(im1)    pl.subplot(122)  im2=pl.imshow(fnew, extent=[-1,1,-1,1], cmap=mpl.cm.hot, interpolation='nearest', origin="lower")  pl.colorbar(im2)    pl.show()