.. _pylab_examples-contour_demo: pylab_examples example code: contour_demo.py ============================================ .. plot:: /home/tcaswell/other_source/matplotlib/doc/mpl_examples/pylab_examples/contour_demo.py :: #!/usr/bin/env python """ Illustrate simple contour plotting, contours on an image with a colorbar for the contours, and labelled contours. See also contour_image.py. """ import matplotlib import numpy as np import matplotlib.cm as cm import matplotlib.mlab as mlab import matplotlib.pyplot as plt matplotlib.rcParams['xtick.direction'] = 'out' matplotlib.rcParams['ytick.direction'] = 'out' delta = 0.025 x = np.arange(-3.0, 3.0, delta) y = np.arange(-2.0, 2.0, delta) X, Y = np.meshgrid(x, y) Z1 = mlab.bivariate_normal(X, Y, 1.0, 1.0, 0.0, 0.0) Z2 = mlab.bivariate_normal(X, Y, 1.5, 0.5, 1, 1) # difference of Gaussians Z = 10.0 * (Z2 - Z1) # Create a simple contour plot with labels using default colors. The # inline argument to clabel will control whether the labels are draw # over the line segments of the contour, removing the lines beneath # the label plt.figure() CS = plt.contour(X, Y, Z) plt.clabel(CS, inline=1, fontsize=10) plt.title('Simplest default with labels') # contour labels can be placed manually by providing list of positions # (in data coordinate). See ginput_manual_clabel.py for interactive # placement. plt.figure() CS = plt.contour(X, Y, Z) manual_locations = [(-1, -1.4), (-0.62, -0.7), (-2, 0.5), (1.7, 1.2), (2.0, 1.4), (2.4, 1.7)] plt.clabel(CS, inline=1, fontsize=10, manual=manual_locations) plt.title('labels at selected locations') # You can force all the contours to be the same color. plt.figure() CS = plt.contour(X, Y, Z, 6, colors='k', # negative contours will be dashed by default ) plt.clabel(CS, fontsize=9, inline=1) plt.title('Single color - negative contours dashed') # You can set negative contours to be solid instead of dashed: matplotlib.rcParams['contour.negative_linestyle'] = 'solid' plt.figure() CS = plt.contour(X, Y, Z, 6, colors='k', # negative contours will be dashed by default ) plt.clabel(CS, fontsize=9, inline=1) plt.title('Single color - negative contours solid') # And you can manually specify the colors of the contour plt.figure() CS = plt.contour(X, Y, Z, 6, linewidths=np.arange(.5, 4, .5), colors=('r', 'green', 'blue', (1,1,0), '#afeeee', '0.5') ) plt.clabel(CS, fontsize=9, inline=1) plt.title('Crazy lines') # Or you can use a colormap to specify the colors; the default # colormap will be used for the contour lines plt.figure() im = plt.imshow(Z, interpolation='bilinear', origin='lower', cmap=cm.gray, extent=(-3,3,-2,2)) levels = np.arange(-1.2, 1.6, 0.2) CS = plt.contour(Z, levels, origin='lower', linewidths=2, extent=(-3,3,-2,2)) #Thicken the zero contour. zc = CS.collections[6] plt.setp(zc, linewidth=4) plt.clabel(CS, levels[1::2], # label every second level inline=1, fmt='%1.1f', fontsize=14) # make a colorbar for the contour lines CB = plt.colorbar(CS, shrink=0.8, extend='both') plt.title('Lines with colorbar') #plt.hot() # Now change the colormap for the contour lines and colorbar plt.flag() # We can still add a colorbar for the image, too. CBI = plt.colorbar(im, orientation='horizontal', shrink=0.8) # This makes the original colorbar look a bit out of place, # so let's improve its position. l,b,w,h = plt.gca().get_position().bounds ll,bb,ww,hh = CB.ax.get_position().bounds CB.ax.set_position([ll, b+0.1*h, ww, h*0.8]) plt.show() Keywords: python, matplotlib, pylab, example, codex (see :ref:`how-to-search-examples`)