matplotlib
diff --git a/‎tutorials/colors/colormapnorms.py
Lines changed: 28 additions & 14 deletions b/‎tutorials/colors/colormapnorms.py
Lines changed: 28 additions & 14 deletions
@@ -145,7 +145,9 @@
 # Another normalization that comes with Matplotlib is `.colors.BoundaryNorm`.
 # In addition to *vmin* and *vmax*, this takes as arguments boundaries between
 # which data is to be mapped.  The colors are then linearly distributed between
-# these "bounds".  For instance:
+# these "bounds".  It can also take an *extend* argument to add upper and/or
+# lower out-of-bounds values to the range over which the colors are
+# distributed For instance:
 #
 # .. ipython::
 #
@@ -161,30 +163,42 @@
 # Note: Unlike the other norms, this norm returns values from 0 to *ncolors*-1.
 
 N = 100
-X, Y = np.mgrid[-3:3:complex(0, N), -2:2:complex(0, N)]
+X, Y = np.meshgrid(np.linspace(-3, 3, N), np.linspace(-2, 2, N))
 Z1 = np.exp(-X**2 - Y**2)
 Z2 = np.exp(-(X - 1)**2 - (Y - 1)**2)
-Z = (Z1 - Z2) * 2
+Z = ((Z1 - Z2) * 2)[:-1, :-1]
 
-fig, ax = plt.subplots(3, 1, figsize=(8, 8))
+fig, ax = plt.subplots(2, 2, figsize=(8, 6), constrained_layout=True)
 ax = ax.flatten()
-# even bounds gives a contour-like effect
-bounds = np.linspace(-1, 1, 10)
-norm = colors.BoundaryNorm(boundaries=bounds, ncolors=256)
-pcm = ax[0].pcolormesh(X, Y, Z, norm=norm, cmap='RdBu_r', shading='auto')
-fig.colorbar(pcm, ax=ax[0], extend='both', orientation='vertical')
 
-# uneven bounds changes the colormapping:
-bounds = np.array([-0.25, -0.125, 0, 0.5, 1])
+# Default norm:
+pcm = ax[0].pcolormesh(X, Y, Z, cmap='RdBu_r')
+fig.colorbar(pcm, ax=ax[0], orientation='vertical')
+ax[0].set_title('Default norm')
+
+# Even bounds give a contour-like effect:
+bounds = np.linspace(-1.5, 1.5, 7)
 norm = colors.BoundaryNorm(boundaries=bounds, ncolors=256)
-pcm = ax[1].pcolormesh(X, Y, Z, norm=norm, cmap='RdBu_r', shading='auto')
+pcm = ax[1].pcolormesh(X, Y, Z, norm=norm, cmap='RdBu_r')
 fig.colorbar(pcm, ax=ax[1], extend='both', orientation='vertical')
+ax[1].set_title('BoundaryNorm: 7 boundaries')
 
-pcm = ax[2].pcolormesh(X, Y, Z, cmap='RdBu_r', vmin=-np.max(Z), shading='auto')
+# Bounds may be unevenly spaced:
+bounds = np.array([-0.2, -0.1, 0, 0.5, 1])
+norm = colors.BoundaryNorm(boundaries=bounds, ncolors=256)
+pcm = ax[2].pcolormesh(X, Y, Z, norm=norm, cmap='RdBu_r')
 fig.colorbar(pcm, ax=ax[2], extend='both', orientation='vertical')
+ax[2].set_title('BoundaryNorm: nonuniform')
+
+# With out-of-bounds colors:
+bounds = np.linspace(-1.5, 1.5, 7)
+norm = colors.BoundaryNorm(boundaries=bounds, ncolors=256, extend='both')
+pcm = ax[3].pcolormesh(X, Y, Z, norm=norm, cmap='RdBu_r')
+# The colorbar inherits the "extend" argument from BoundaryNorm.
+fig.colorbar(pcm, ax=ax[3], orientation='vertical')
+ax[3].set_title('BoundaryNorm: extend="both"')
 plt.show()
 
-
 ###############################################################################
 # TwoSlopeNorm: Different mapping on either side of a center
 # ----------------------------------------------------------