Merge remote-tracking branch 'upstream/v1.2.x'

mdboom · mdboom · commit 7574ae701555 · 2013-05-16T12:08:30.000-04:00
Conflicts:
	lib/matplotlib/axes.py
diff --git a/lib/matplotlib/axes.py b/lib/matplotlib/axes.py
@@ -2016,8 +2016,11 @@ def draw(self, renderer=None, inframe=False):
         if self.axison and self._frameon:
             artists.extend(self.spines.itervalues())
 
-        dsu = [(a.zorder, a) for a in artists
-               if not a.get_animated()]
+        if self.figure.canvas.is_saving():
+            dsu = [(a.zorder, a) for a in artists]
+        else:
+            dsu = [(a.zorder, a) for a in artists
+                   if not a.get_animated()]
 
         # add images to dsu if the backend support compositing.
         # otherwise, does the manaul compositing  without adding images to dsu.
@@ -6031,15 +6034,15 @@ def computeConfInterval(data, med, iq, bootstrap):
                 wisk_hi = q3
             else:
                 wisk_hi = max(wisk_hi)
-            
+
             # get low extreme
             lo_val = q1 - whis * iq
             wisk_lo = np.compress(d >= lo_val, d)
             if len(wisk_lo) == 0 or np.min(wisk_lo) > q1:
                 wisk_lo = q1
             else:
                 wisk_lo = min(wisk_lo)
-            
+
             # get fliers - if we are showing them
             flier_hi = []
             flier_lo = []
diff --git a/lib/matplotlib/backend_bases.py b/lib/matplotlib/backend_bases.py
@@ -1571,13 +1571,21 @@ def __init__(self, figure):
         self.scroll_pick_id = self.mpl_connect('scroll_event', self.pick)
         self.mouse_grabber = None  # the axes currently grabbing mouse
         self.toolbar = None  # NavigationToolbar2 will set me
+        self._is_saving = False
         if False:
             ## highlight the artists that are hit
             self.mpl_connect('motion_notify_event', self.onHilite)
             ## delete the artists that are clicked on
             #self.mpl_disconnect(self.button_pick_id)
             #self.mpl_connect('button_press_event',self.onRemove)
 
+    def is_saving(self):
+        """
+        Returns `True` when the renderer is in the process of saving
+        to a file, rather than rendering for an on-screen buffer.
+        """
+        return self._is_saving
+
     def onRemove(self, ev):
         """
         Mouse event processor which removes the top artist
@@ -2205,6 +2213,7 @@ def print_figure(self, filename, dpi=None, facecolor='w', edgecolor='w',
         else:
             _bbox_inches_restore = None
 
+        self._is_saving = True
         try:
             #result = getattr(self, method_name)(
             result = print_method(
@@ -2223,6 +2232,7 @@ def print_figure(self, filename, dpi=None, facecolor='w', edgecolor='w',
             self.figure.set_facecolor(origfacecolor)
             self.figure.set_edgecolor(origedgecolor)
             self.figure.set_canvas(self)
+            self._is_saving = False
             #self.figure.canvas.draw() ## seems superfluous
         return result
 
@@ -2269,6 +2279,7 @@ def switch_backends(self, FigureCanvasClass):
         figure size or line props), will be reflected in the other
         """
         newCanvas = FigureCanvasClass(self.figure)
+        newCanvas._is_saving = self._is_saving
         return newCanvas
 
     def mpl_connect(self, s, func):
diff --git a/lib/matplotlib/backends/backend_ps.py b/lib/matplotlib/backends/backend_ps.py
@@ -824,14 +824,13 @@ def draw_gouraud_triangles(self, gc, points, colors, trans):
         assert colors.shape[1] == 3
         assert colors.shape[2] == 4
 
-        points = trans.transform(points)
-
         shape = points.shape
         flat_points = points.reshape((shape[0] * shape[1], 2))
+        flat_points = trans.transform(flat_points)
         flat_colors = colors.reshape((shape[0] * shape[1], 4))
-        points_min = np.min(flat_points, axis=0) - (1 << 8)
-        points_max = np.max(flat_points, axis=0) + (1 << 8)
-        factor = float(0xffffffff) / (points_max - points_min)
+        points_min = np.min(flat_points, axis=0) - (1 << 12)
+        points_max = np.max(flat_points, axis=0) + (1 << 12)
+        factor = np.ceil(float(2 ** 32 - 1) / (points_max - points_min))
 
         xmin, ymin = points_min
         xmax, ymax = points_max
