matplotlib
diff --git a/‎lib/matplotlib/colorbar.py
Lines changed: 20 additions & 20 deletions b/‎lib/matplotlib/colorbar.py
Lines changed: 20 additions & 20 deletions
diff --git a/‎lib/matplotlib/colors.py
Lines changed: 1 addition & 1 deletion b/‎lib/matplotlib/colors.py
Lines changed: 1 addition & 1 deletion
diff --git a/‎lib/matplotlib/tests/baseline_images/test_contour/contour_log_extension.png
-5 Bytes b/‎lib/matplotlib/tests/baseline_images/test_contour/contour_log_extension.png
-5 Bytes
@@ -18,7 +18,7 @@
 is a thin wrapper over :meth:`~matplotlib.figure.Figure.colorbar`.
 
 '''
-
+import copy
 import logging
 
 import numpy as np
@@ -484,6 +484,7 @@ def draw_all(self):
         # units:
         X, Y = self._mesh()
         C = self._values[:, np.newaxis]
+        # decide minor/major axis
         self.config_axis()
         self._config_axes(X, Y)
         if self.filled:
@@ -560,10 +561,8 @@ def _use_auto_colorbar_locator(self):
         Return if we should use an adjustable tick locator or a fixed
         one.  (check is used twice so factored out here...)
         """
-        return (self.boundaries is None
-                and self.values is None
-                and ((type(self.norm) == colors.Normalize)
-                    or (type(self.norm) == colors.LogNorm)))
+        return ((type(self.norm) == colors.Normalize)
+                    or (type(self.norm) == colors.LogNorm))
 
     def _reset_locator_formatter_scale(self):
         """
@@ -573,13 +572,11 @@ def _reset_locator_formatter_scale(self):
         """
         self.locator = None
         self.formatter = None
-        if (isinstance(self.norm, colors.LogNorm)
-                and self._use_auto_colorbar_locator()):
+        if (isinstance(self.norm, colors.LogNorm)):
             # *both* axes are made log so that determining the
             # mid point is easier.
             self.ax.set_xscale('log')
             self.ax.set_yscale('log')
-
             self.minorticks_on()
         else:
             self.ax.set_xscale('linear')
@@ -1068,26 +1065,29 @@ def _mesh(self):
         These are suitable for a vertical colorbar; swapping and
         transposition for a horizontal colorbar are done outside
         this function.
-        '''
-        # if boundaries and values are None, then we can go ahead and
-        # scale this up for Auto tick location.  Otherwise we
-        # want to keep normalized between 0 and 1 and use manual tick
-        # locations.
 
+        These are scaled between vmin and vmax
+        '''
+        norm = copy.copy(self.norm)
+        norm.vmin = self.vmin
+        norm.vmax = self.vmax
         x = np.array([0.0, 1.0])
         if self.spacing == 'uniform':
             y = self._uniform_y(self._central_N())
         else:
             y = self._proportional_y()
-        if self._use_auto_colorbar_locator():
-            y = self.norm.inverse(y)
-            x = self.norm.inverse(x)
+        xmid = np.array([0.5])
+        try:
+            y = norm.inverse(y)
+            x = norm.inverse(x)
+            xmid = norm.inverse(xmid)
+        except ValueError:
+            dv = self.vmax - self.vmin
+            x = x * dv + self.vmin
+            y = y * dv + self.vmin
+            xmid = xmid * dv + self.vmin
         self._y = y
         X, Y = np.meshgrid(x, y)
-        if self._use_auto_colorbar_locator():
-            xmid = self.norm.inverse(0.5)
-        else:
-            xmid = 0.5
         if self._extend_lower() and not self.extendrect:
             X[0, :] = xmid
         if self._extend_upper() and not self.extendrect:
 
@@ -1253,7 +1253,7 @@ def inverse(self, value):
             BoundaryNorm is not invertible, so calling this method will always
             raise an error
         """
-        return ValueError("BoundaryNorm is not invertible")
+        raise ValueError("BoundaryNorm is not invertible")
 
 
 class NoNorm(Normalize):