.. _pylab_examples-image_masked: pylab_examples example code: image_masked.py ============================================ .. plot:: /home/tcaswell/src/p/matplotlib/doc/mpl_examples/pylab_examples/image_masked.py :: """ imshow with masked array input and out-of-range colors. The second subplot illustrates the use of BoundaryNorm to get a filled contour effect. """ from copy import copy import numpy as np import matplotlib.pyplot as plt import matplotlib.colors as colors import matplotlib.mlab as mlab # compute some interesting data x0, x1 = -5, 5 y0, y1 = -3, 3 x = np.linspace(x0, x1, 500) y = np.linspace(y0, y1, 500) 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) Z = 10*(Z2 - Z1) # difference of Gaussians # Set up a colormap: # use copy so that we do not mutate the global colormap instance palette = copy(plt.cm.gray) palette.set_over('r', 1.0) palette.set_under('g', 1.0) palette.set_bad('b', 1.0) # Alternatively, we could use # palette.set_bad(alpha = 0.0) # to make the bad region transparent. This is the default. # If you comment out all the palette.set* lines, you will see # all the defaults; under and over will be colored with the # first and last colors in the palette, respectively. Zm = np.ma.masked_where(Z > 1.2, Z) # By setting vmin and vmax in the norm, we establish the # range to which the regular palette color scale is applied. # Anything above that range is colored based on palette.set_over, etc. # set up the axes fig, (ax1, ax2) = plt.subplots(nrows=2, figsize=(6, 5.4)) # plot using 'continuous' color map im = ax1.imshow(Zm, interpolation='bilinear', cmap=palette, norm=colors.Normalize(vmin=-1.0, vmax=1.0), aspect='auto', origin='lower', extent=[x0, x1, y0, y1]) ax1.set_title('Green=low, Red=high, Blue=masked') cbar = fig.colorbar(im, extend='both', shrink=0.9, ax=ax1) cbar.set_label('uniform') for ticklabel in ax1.xaxis.get_ticklabels(): ticklabel.set_visible(False) # Plot using a small number of colors, with unevenly spaced boundaries. im = ax2.imshow(Zm, interpolation='nearest', cmap=palette, norm=colors.BoundaryNorm([-1, -0.5, -0.2, 0, 0.2, 0.5, 1], ncolors=palette.N), aspect='auto', origin='lower', extent=[x0, x1, y0, y1]) ax2.set_title('With BoundaryNorm') cbar = fig.colorbar(im, extend='both', spacing='proportional', shrink=0.9, ax=ax2) cbar.set_label('proportional') fig.suptitle('imshow, with out-of-range and masked data') plt.show() Keywords: python, matplotlib, pylab, example, codex (see :ref:`how-to-search-examples`)