Major update: complete refactorization of code. A much more powerful version of ArbitraryNorm introduced. All the other classes now derive from this one. Included support for certain named functions as strings. Added automatic normalization of the provided functions. A smarter automatic way of adding ticks to the colorbar implemented. Examples changed accordingly.

alvarosg · alvarosg · commit 1d22b90be9ad · 2016-10-23T23:51:58.000+01:00
diff --git a/doc/users/plotting/examples/colormap_normalizations_arbitrarynorm.py b/doc/users/plotting/examples/colormap_normalizations_arbitrarynorm.py
@@ -45,24 +45,8 @@ def gauss2d(x, y, a0, x0, y0, wx, wy):
 
 cmap = cm.gist_rainbow
 
-norms = [('Linear Scale', None),
-         ('Arbitrary norm',
-          colors.ArbitraryNorm(fpos=(lambda x: x**0.2),
-                               fposinv=(lambda x: x**5),
-                               fneg=(lambda x: x**0.5),
-                               fneginv=(lambda x: x**2),
-                               center=0.4)),
-         ('Positive arbitrary norm',
-          colors.PositiveArbitraryNorm(vmin=0,
-                                       fpos=(lambda x: x**0.5),
-                                       fposinv=(lambda x: x**2))),
-         ('Negative arbitrary norm',
-          colors.NegativeArbitraryNorm(vmax=0,
-                                       fneg=(lambda x: x**0.5),
-                                       fneginv=(lambda x: x**2)))]
-
-
-for label, norm in norms:
+
+def makePlot(norm, label):
     fig, ax = plt.subplots()
     cax = ax.pcolormesh(x, y, data, cmap=cmap, norm=norm)
     ax.set_title(label)
@@ -74,4 +58,29 @@ def gauss2d(x, y, a0, x0, y0, wx, wy):
         ticks = None
     cbar = fig.colorbar(cax, format='%.3g', ticks=ticks)
 
+
+makePlot(None, 'Regular linear scale')
+
+
+norm = colors.SingleArbitraryNorm(vmin=0, f='sqrt')
+makePlot(norm, 'Single arbitrary norm')
+
+norm = colors.SingleArbitraryNorm(vmin=0,
+                                  f=(lambda x: x**0.25),
+                                  finv=(lambda x: x**4))
+makePlot(norm, 'Simple arbitrary norm, custom function')
+
+
+norm = colors.MirrorArbitraryNorm(fpos='crt',
+                                  fneg='linear',
+                                  center_cm=0.3,
+                                  center_data=0.0)
+makePlot(norm, 'Mirror arbitrary norm')
+
+
+norm = colors.ArbitraryNorm(flist=['linear', 'quadratic', 'cubic'],
+                            refpoints_cm=[0.5, 0.75],
+                            refpoints_data=[0., 1.])
+makePlot(norm, 'Arbitrary norm')
+
 plt.show()
diff --git a/doc/users/plotting/examples/colormap_normalizations_rootnorm.py b/doc/users/plotting/examples/colormap_normalizations_rootnorm.py
@@ -3,7 +3,7 @@
 Examples of colormap root normalization
 ============================================
 
-Here I plot an image array with data spanning for a large dynamic range, 
+Here I plot an image array with data spanning for a large dynamic range,
 using different normalizations. Look at how each of them enhances
 different features.
 
@@ -44,16 +44,8 @@ def gauss2d(x, y, a0, x0, y0, wx, wy):
 
 cmap = cm.gist_rainbow
 
-norms = [('Linear Scale', None),
-         ('Symmetric root norm',
-          colors.SymRootNorm(orderpos=7, orderneg=2, center=0.3)),
-         ('Positive root norm',
-          colors.PositiveRootNorm(vmin=0, orderpos=5)),
-         ('Negative root norm',
-          colors.NegativeRootNorm(vmax=0, orderneg=5))]
 
-
-for label, norm in norms:
+def makePlot(norm, label):
     fig, ax = plt.subplots()
     cax = ax.pcolormesh(x, y, data, cmap=cmap, norm=norm)
     ax.set_title(label)
@@ -65,4 +57,14 @@ def gauss2d(x, y, a0, x0, y0, wx, wy):
         ticks = None
     cbar = fig.colorbar(cax, format='%.3g', ticks=ticks)
 
+
+makePlot(None, 'Regular linear scale')
+
+norm = colors.RootNorm(vmin=0, orderpos=5)
+makePlot(norm, 'Root norm')
+
+norm = colors.MirrorRootNorm(
+    orderpos=7, orderneg=2, center_cm=0.3, center_data=0.)
+makePlot(norm, 'Mirror root norm')
+
 plt.show()
diff --git a/lib/matplotlib/colors.py b/lib/matplotlib/colors.py
