Added ArbitaryNorm and RootNorm classes in colors, as well as examples of

alvarosg · alvarosg · commit 2d90c5aa40f3 · 2016-10-23T23:51:50.000+01:00
use.
diff --git a/doc/users/plotting/examples/colormap_normalizations_arbitrarynorm.py b/doc/users/plotting/examples/colormap_normalizations_arbitrarynorm.py
@@ -0,0 +1,62 @@
+"""
+Demonstration of using the ArbitraryNorm classes for normalization.
+"""
+
+import numpy as np
+import matplotlib.colors as colors
+import matplotlib.pyplot as plt
+import matplotlib.cm as cm
+
+x=np.linspace(0,16*np.pi,1024)
+y=np.linspace(-1,1,512)
+X,Y=np.meshgrid(x,y)
+
+data=np.zeros(X.shape)
+
+data[Y>0]=np.cos(X[Y>0])*Y[Y>0]**2
+
+for i,val in enumerate(np.arange(-1,1.1,0.2)):
+    if val<0:data[(X>(i*(50./11)))*(Y<0)]=val
+    if val>0:data[(X>(i*(50./11)))*(Y<0)]=val*2
+            
+figsize=(16,10)
+cmap=cm.gist_rainbow
+
+plt.figure(figsize=figsize)
+plt.pcolormesh(x,y,data,cmap=cmap)
+plt.title('Linear Scale')
+plt.colorbar(format='%.3g')
+plt.xlim(0,16*np.pi)
+plt.ylim(-1,1)
+
+plt.figure(figsize=figsize)
+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)
+plt.pcolormesh(x,y,data,cmap=cmap,norm=norm)
+plt.title('Arbitrary norm')
+plt.colorbar(ticks=norm.ticks(),format='%.3g')
+plt.xlim(0,16*np.pi)
+plt.ylim(-1,1)
+
+plt.figure(figsize=figsize)
+norm=colors.PositiveArbitraryNorm(vmin=0,
+                                  fpos=(lambda x: x**0.5),
+                                  fposinv=(lambda x: x**2))
+plt.pcolormesh(x,y,data,cmap=cmap,norm=norm)
+plt.title('Positive arbitrary norm')
+plt.colorbar(ticks=norm.ticks(),format='%.3g')
+plt.xlim(0,16*np.pi)
+plt.ylim(-1,1)
+
+plt.figure(figsize=figsize)
+norm=colors.NegativeArbitraryNorm(vmax=0,
+                                  fneg=(lambda x: x**0.5),
+                                  fneginv=(lambda x: x**2))
+plt.pcolormesh(x,y,data,cmap=cmap,norm=norm)
+plt.title('Negative arbitrary norm')
+plt.colorbar(ticks=norm.ticks(),format='%.3g')
+plt.xlim(0,16*np.pi)
+plt.ylim(-1,1)
diff --git a/doc/users/plotting/examples/colormap_normalizations_rootnorm.py b/doc/users/plotting/examples/colormap_normalizations_rootnorm.py
@@ -0,0 +1,54 @@
+"""
+Demonstration of using the RootNorm classes for normalization.
+"""
+
+import numpy as np
+import matplotlib.colors as colors
+import matplotlib.pyplot as plt
+import matplotlib.cm as cm
+
+x=np.linspace(0,16*np.pi,1024)
+y=np.linspace(-1,1,512)
+X,Y=np.meshgrid(x,y)
+
+data=np.zeros(X.shape)
+
+data[Y>0]=np.cos(X[Y>0])*Y[Y>0]**2
+
+for i,val in enumerate(np.arange(-1,1.1,0.2)):
+    if val<0:data[(X>(i*(50./11)))*(Y<0)]=val
+    if val>0:data[(X>(i*(50./11)))*(Y<0)]=val*2
+            
+figsize=(16,10)
+cmap=cm.gist_rainbow
+
+plt.figure(figsize=figsize)
+plt.pcolormesh(x,y,data,cmap=cmap)
+plt.title('Linear Scale')
+plt.colorbar(format='%.3g')
+plt.xlim(0,16*np.pi)
+plt.ylim(-1,1)
+
+plt.figure(figsize=figsize)
+norm=colors.SymRootNorm(orderpos=7,orderneg=2,center=0.3)
+plt.pcolormesh(x,y,data,cmap=cmap,norm=norm)
+plt.title('Symmetric root norm')
+plt.colorbar(ticks=norm.ticks(),format='%.3g')
+plt.xlim(0,16*np.pi)
+plt.ylim(-1,1)
+
+plt.figure(figsize=figsize)
+norm=colors.PositiveRootNorm(vmin=0,orderpos=5)
+plt.pcolormesh(x,y,data,cmap=cmap,norm=norm)
+plt.title('Positive root norm')
+plt.colorbar(ticks=norm.ticks(),format='%.3g')
+plt.xlim(0,16*np.pi)
+plt.ylim(-1,1)
+
+plt.figure(figsize=figsize)
+norm=colors.NegativeRootNorm(vmax=0,orderneg=5)
+plt.pcolormesh(x,y,data,cmap=cmap,norm=norm)
+plt.title('Negative root norm')
+plt.colorbar(ticks=norm.ticks(),format='%.3g')
+plt.xlim(0,16*np.pi)
+plt.ylim(-1,1)
diff --git a/lib/matplotlib/colors.py b/lib/matplotlib/colors.py
