.. _pylab_examples-griddata_demo: pylab_examples example code: griddata_demo.py ============================================= .. plot:: /home/mdboom/Work/builds/matplotlib/doc/mpl_examples/pylab_examples/griddata_demo.py :: from numpy.random import uniform, seed from matplotlib.mlab import griddata import matplotlib.pyplot as plt import numpy as np # make up data. #npts = int(raw_input('enter # of random points to plot:')) seed(0) npts = 200 x = uniform(-2,2,npts) y = uniform(-2,2,npts) z = x*np.exp(-x**2-y**2) # define grid. xi = np.linspace(-2.1,2.1,100) yi = np.linspace(-2.1,2.1,200) # grid the data. zi = griddata(x,y,z,xi,yi,interp='linear') # contour the gridded data, plotting dots at the nonuniform data points. CS = plt.contour(xi,yi,zi,15,linewidths=0.5,colors='k') CS = plt.contourf(xi,yi,zi,15,cmap=plt.cm.rainbow, vmax=abs(zi).max(), vmin=-abs(zi).max()) plt.colorbar() # draw colorbar # plot data points. plt.scatter(x,y,marker='o',c='b',s=5,zorder=10) plt.xlim(-2,2) plt.ylim(-2,2) plt.title('griddata test (%d points)' % npts) plt.show() # test case that scikits.delaunay fails on, but natgrid passes.. #data = np.array([[-1, -1], [-1, 0], [-1, 1], # [ 0, -1], [ 0, 0], [ 0, 1], # [ 1, -1 - np.finfo(np.float_).eps], [ 1, 0], [ 1, 1], # ]) #x = data[:,0] #y = data[:,1] #z = x*np.exp(-x**2-y**2) ## define grid. #xi = np.linspace(-1.1,1.1,100) #yi = np.linspace(-1.1,1.1,100) ## grid the data. #zi = griddata(x,y,z,xi,yi) Keywords: python, matplotlib, pylab, example, codex (see :ref:`how-to-search-examples`)