FIX: make colorbars for log normed contour plots have logscale

lib/matplotlib/colorbar.py

@@ -1212,7 +1212,8 @@ def _reset_locator_formatter_scale(self):
         self._minorlocator = None
         self._formatter = None
         self._minorformatter = None
-        if isinstance(self.norm, colors.LogNorm):
+        if (isinstance(self.mappable, contour.ContourSet) and
+                isinstance(self.norm, colors.LogNorm)):
             # if contours have lognorm, give them a log scale...
             self._set_scale('log')
         elif (self.boundaries is not None or

lib/matplotlib/tests/test_contour.py

@@ -7,7 +7,7 @@
 from numpy.testing import assert_array_almost_equal
 import matplotlib as mpl
 from matplotlib.testing.decorators import image_comparison
-from matplotlib import pyplot as plt, rc_context
+from matplotlib import pyplot as plt, rc_context, ticker
 from matplotlib.colors import LogNorm, same_color
 import pytest
 
@@ -154,6 +154,27 @@ def test_given_colors_levels_and_extends():
         plt.colorbar(c, ax=ax)
 
 
+@image_comparison(['contour_log_locator.png'], style='mpl20', remove_text=True)
+def test_log_locator_levels():
+
+    fig, ax = plt.subplots()
+
+    N = 100
+    x = np.linspace(-3.0, 3.0, N)
+    y = np.linspace(-2.0, 2.0, N)
+
+    X, Y = np.meshgrid(x, y)
+
+    Z1 = np.exp(-X**2 - Y**2)
+    Z2 = np.exp(-(X * 10)**2 - (Y * 10)**2)
+    data = Z1 + 50 * Z2
+
+    c = ax.contourf(data, locator=ticker.LogLocator())
+    assert_array_almost_equal(c.levels, np.power(10.0, np.arange(-6, 3)))
+    cb = fig.colorbar(c, ax=ax)
+    assert_array_almost_equal(cb.ax.get_yticks(), c.levels)
+
+
 @image_comparison(['contour_datetime_axis.png'], style='mpl20')
 def test_contour_datetime_axis():
     fig = plt.figure()