BUGS: in colorbar: divide-by-zero, and undesired masked array

efiring · efiring · commit 85dfccd0f069 · 2017-06-27T11:06:19.000-04:00
Closes #8534 (divide-by-zero warning). Also fixes a bug that became evident with #8801, which causes pcolormesh to fail if X or Y is a masked array. Although masked arrays are not supported by pcolormesh, previously it would ignore the mask. Locators always return masked arrays, so the present commit explicitly converts to an ndarray the Locator output used by proportional colorbars.
diff --git a/lib/matplotlib/colorbar.py b/lib/matplotlib/colorbar.py
@@ -801,6 +801,7 @@ def _proportional_y(self):
             y = y / (self._boundaries[-1] - self._boundaries[0])
         else:
             y = self.norm(self._boundaries.copy())
+            y = np.ma.filled(y, np.nan)
         if self.extend == 'min':
             # Exclude leftmost interval of y.
             clen = y[-1] - y[1]
@@ -811,21 +812,22 @@ def _proportional_y(self):
             clen = y[-2] - y[0]
             automin = (y[1] - y[0]) / clen
             automax = (y[-2] - y[-3]) / clen
-        else:
+        elif self.extend == 'both':
             # Exclude leftmost and rightmost intervals in y.
             clen = y[-2] - y[1]
             automin = (y[2] - y[1]) / clen
             automax = (y[-2] - y[-3]) / clen
-        extendlength = self._get_extension_lengths(self.extendfrac,
-                                                   automin, automax,
-                                                   default=0.05)
+        if self.extend in ('both', 'min', 'max'):
+            extendlength = self._get_extension_lengths(self.extendfrac,
+                                                       automin, automax,
+                                                       default=0.05)
         if self.extend in ('both', 'min'):
             y[0] = 0. - extendlength[0]
         if self.extend in ('both', 'max'):
             y[-1] = 1. + extendlength[1]
         yi = y[self._inside]
         norm = colors.Normalize(yi[0], yi[-1])
-        y[self._inside] = norm(yi)
+        y[self._inside] = np.ma.filled(norm(yi), np.nan)
         return y
 
     def _mesh(self):
