.. _sphx_glr_gallery_images_contours_and_fields_griddata_demo.py: ============= Griddata Demo ============= .. image:: /gallery/images_contours_and_fields/images/sphx_glr_griddata_demo_001.png :align: center .. code-block:: python from matplotlib.mlab import griddata import matplotlib.pyplot as plt import numpy as np # make up data. random_state = np.random.RandomState(19680801) npts = 200 x = random_state.uniform(-2, 2, npts) y = random_state.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, vmax=abs(zi).max(), vmin=-abs(zi).max()) plt.colorbar() # draw colorbar # plot data points. plt.scatter(x, y, marker='o', s=5, zorder=10) plt.xlim(-2, 2) plt.ylim(-2, 2) plt.title('griddata test (%d points)' % npts) plt.show() **Total running time of the script:** ( 0 minutes 0.064 seconds) .. only :: html .. container:: sphx-glr-footer .. container:: sphx-glr-download :download:`Download Python source code: griddata_demo.py ` .. container:: sphx-glr-download :download:`Download Jupyter notebook: griddata_demo.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_