Call _transform_vmin_vmax during SymLogNorm.__init__

lib/matplotlib/colors.py

@@ -1054,6 +1054,8 @@ def __init__(self,  linthresh, linscale=1.0,
         Normalize.__init__(self, vmin, vmax, clip)
         self.linthresh = float(linthresh)
         self._linscale_adj = (linscale / (1.0 - np.e ** -1))
+        if vmin is not None and vmax is not None:
+            self._transform_vmin_vmax()
 
     def __call__(self, value, clip=None):
         if clip is None:

lib/matplotlib/tests/test_colors.py

@@ -22,6 +22,7 @@
 import matplotlib
 import matplotlib.colors as mcolors
 import matplotlib.cm as cm
+import matplotlib.colorbar as mcolorbar
 import matplotlib.cbook as cbook
 import matplotlib.pyplot as plt
 from matplotlib.testing.decorators import (image_comparison,
@@ -224,6 +225,17 @@ def test_SymLogNorm():
     assert_array_almost_equal(normed_vals, expected)
 
 
+@cleanup
+def test_SymLogNorm_colorbar():
+    """
+    Test un-called SymLogNorm in a colorbar.
+    """
+    norm = mcolors.SymLogNorm(0.1, vmin=-1, vmax=1, linscale=1)
+    fig = plt.figure()
+    cbar = mcolorbar.ColorbarBase(fig.add_subplot(111), norm=norm)
+    plt.close(fig)
+
+
 def _inverse_tester(norm_instance, vals):
     """
     Checks if the inverse of the given normalization is working.