.. _images_contours_and_fields-pcolormesh_levels: images_contours_and_fields example code: pcolormesh_levels.py ============================================================= .. plot:: /home/tcaswell/source/p/matplotlib/doc/mpl_examples/images_contours_and_fields/pcolormesh_levels.py :: """ Shows how to combine Normalization and Colormap instances to draw "levels" in pcolor, pcolormesh and imshow type plots in a similar way to the levels keyword argument to contour/contourf. """ import matplotlib.pyplot as plt from matplotlib.colors import BoundaryNorm from matplotlib.ticker import MaxNLocator import numpy as np # make these smaller to increase the resolution dx, dy = 0.05, 0.05 # generate 2 2d grids for the x & y bounds y, x = np.mgrid[slice(1, 5 + dy, dy), slice(1, 5 + dx, dx)] z = np.sin(x)**10 + np.cos(10 + y*x) * np.cos(x) # x and y are bounds, so z should be the value *inside* those bounds. # Therefore, remove the last value from the z array. z = z[:-1, :-1] levels = MaxNLocator(nbins=15).tick_values(z.min(), z.max()) # pick the desired colormap, sensible levels, and define a normalization # instance which takes data values and translates those into levels. cmap = plt.get_cmap('PiYG') norm = BoundaryNorm(levels, ncolors=cmap.N, clip=True) fig, (ax0, ax1) = plt.subplots(nrows=2) im = ax0.pcolormesh(x, y, z, cmap=cmap, norm=norm) fig.colorbar(im, ax=ax0) ax0.set_title('pcolormesh with levels') # contours are *point* based plots, so convert our bound into point # centers cf = ax1.contourf(x[:-1, :-1] + dx/2., y[:-1, :-1] + dy/2., z, levels=levels, cmap=cmap) fig.colorbar(cf, ax=ax1) ax1.set_title('contourf with levels') # adjust spacing between subplots so `ax1` title and `ax0` tick labels # don't overlap fig.tight_layout() plt.show() Keywords: python, matplotlib, pylab, example, codex (see :ref:`how-to-search-examples`)