removed OffsetNorm.inverse method

phobson · OceanWolf · commit 04ea1075107c · 2015-09-14T00:42:59.000+02:00
This will allow the ticks of colors to be spaced as desired. Also simplified the math per the brilliant @joferkington http://stackoverflow.com/a/20146989/1552748
diff --git a/lib/matplotlib/colors.py b/lib/matplotlib/colors.py
@@ -1033,46 +1033,14 @@ def __call__(self, value, clip=None):
                 result = ma.array(np.clip(result.filled(vmax), vmin, vmax),
                                   mask=mask)
 
-            # ma division is very slow; we can take a shortcut
-            resdat = result.data
-
-            #First scale to -1 to 1 range, than to from 0 to 1.
-            resdat -= vcenter
-            resdat[resdat > 0] /= abs(vmax - vcenter)
-            resdat[resdat < 0] /= abs(vmin - vcenter)
-
-            resdat /= 2.
-            resdat += 0.5
-            result = np.ma.array(resdat, mask=result.mask, copy=False)
+            x, y = [vmin, vcenter, vmax], [0, 0.5, 1]
+            result = np.ma.masked_array(np.interp(value, x, y))
 
         if is_scalar:
             result = result[0]
 
         return result
 
-    def inverse(self, value):
-        if not self.scaled():
-            raise ValueError("Not invertible until scaled")
-
-        vmin, vcenter, vmax = self.vmin, self.vcenter, self.vmax
-        vmin = float(self.vmin)
-        vcenter = float(self.vcenter)
-        vmax = float(self.vmax)
-
-        if cbook.iterable(value):
-            val = ma.asarray(value)
-            val = 2 * (val - 0.5)
-            val[val > 0] *= abs(vmax - vcenter)
-            val[val < 0] *= abs(vmin - vcenter)
-            val += vcenter
-            return val
-        else:
-            val = 2 * (val - 0.5)
-            if val < 0:
-                return val * abs(vmin - vcenter) + vcenter
-            else:
-                return val * abs(vmax - vcenter) + vcenter
-
     def autoscale_None(self, A):
         ' autoscale only None-valued vmin or vmax'
         if self.vmin is None and np.size(A) > 0:
