.. _pylab_examples-griddata_demo:

pylab_examples example code: griddata_demo.py
=============================================



.. plot:: /home/mandrake/rpm/BUILD/matplotlib-1.1.1/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.jet)
    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`)