Updated violinplot. Removed pdf and svg test images.

Author: solvents

lib/matplotlib/axes/_axes.py

@@ -6898,14 +6898,7 @@ def violin(self, vpstats, positions=None, vert=True, widths=0.5,
         medians = []
 
         # Collections to be returned
-        artists = {
-            'bodies': [],
-            'cmeans': None,
-            'cmaxes': None,
-            'cmins': None,
-            'cbars': None,
-            'cmedians': None
-        }
+        artists = {}
 
         N = len(vpstats)
         datashape_message = ("List of violinplot statistics and `{0}` "
@@ -6927,53 +6920,50 @@ def violin(self, vpstats, positions=None, vert=True, widths=0.5,
         pmins = -0.25 * np.array(widths) + positions
         pmaxes = 0.25 * np.array(widths) + positions
 
-        # Check hold status
-        if not self._hold:
-            self.cla()
-        hold_status = self._hold
-
         # Check whether we are rendering vertically or horizontally
         if vert:
             fill = self.fill_betweenx
-            rlines = self.hlines
-            blines = self.vlines
+            perp_lines = self.hlines
+            par_lines = self.vlines
         else:
             fill = self.fill_between
-            rlines = self.vlines
-            blines = self.hlines
+            perp_lines = self.vlines
+            par_lines = self.hlines
 
         # Render violins
+        bodies = []
         for stats, pos, width in zip(vpstats, positions, widths):
             # The 0.5 factor reflects the fact that we plot from v-p to
             # v+p
             vals = np.array(stats['vals'])
             vals = 0.5 * width * vals / vals.max()
-            artists['bodies'] += [fill(stats['coords'],
-                                       -vals + pos,
-                                       vals + pos,
-                                       facecolor='y',
-                                       alpha=0.3)]
+            bodies += [fill(stats['coords'],
+                            -vals + pos,
+                            vals + pos,
+                            facecolor='y',
+                            alpha=0.3)]
             means.append(stats['mean'])
             mins.append(stats['min'])
             maxes.append(stats['max'])
             medians.append(stats['median'])
+        artists['bodies'] = bodies
 
         # Render means
         if showmeans:
-            artists['cmeans'] = rlines(means, pmins, pmaxes, colors='r')
+            artists['cmeans'] = perp_lines(means, pmins, pmaxes, colors='r')
 
         # Render extrema
         if showextrema:
-            artists['cmaxes'] = rlines(maxes, pmins, pmaxes, colors='r')
-            artists['cmins'] = rlines(mins, pmins, pmaxes, colors='r')
-            artists['cbars'] = blines(positions, mins, maxes, colors='r')
+            artists['cmaxes'] = perp_lines(maxes, pmins, pmaxes, colors='r')
+            artists['cmins'] = perp_lines(mins, pmins, pmaxes, colors='r')
+            artists['cbars'] = par_lines(positions, mins, maxes, colors='r')
 
         # Render medians
         if showmedians:
-            artists['cmedians'] = rlines(medians, pmins, pmaxes, colors='r')
-
-        # Reset hold
-        self.hold(hold_status)
+            artists['cmedians'] = perp_lines(medians,
+                                             pmins,
+                                             pmaxes,
+                                             colors='r')
 
         return artists
 

lib/matplotlib/cbook.py

@@ -2137,6 +2137,14 @@ def is_math_text(s):
 
 
 def _reshape_2D(X):
+    """
+    Converts a non-empty list or an ndarray of two or fewer dimensions
+    into a list of iterable objects so that in
+
+        for v in _reshape_2D(X):
+
+    v is iterable and can be used to instantiate a 1D array.
+    """
     if hasattr(X, 'shape'):
         # one item
         if len(X.shape) == 1: