Don't index into __builtins__ (not supported by PyPy).

anntzer · anntzer · commit dd45300e7f2f · 2017-03-18T13:34:18.000-07:00
diff --git a/lib/matplotlib/axes/_axes.py b/lib/matplotlib/axes/_axes.py
@@ -6147,13 +6147,6 @@ def _normalize_input(inp, ename='input'):
         if bins is None:
             bins = rcParams['hist.bins']
 
-        # xrange becomes range after 2to3
-        bin_range = range
-        range = __builtins__["range"]
-
-        # NOTE: the range keyword overwrites the built-in func range !!!
-        #       needs to be fixed in numpy                           !!!
-
         # Validate string inputs here so we don't have to clutter
         # subsequent code.
         if histtype not in ['bar', 'barstacked', 'step', 'stepfilled']:
@@ -6172,11 +6165,11 @@ def _normalize_input(inp, ename='input'):
         # process the unit information
         self._process_unit_info(xdata=x, kwargs=kwargs)
         x = self.convert_xunits(x)
-        if bin_range is not None:
-            bin_range = self.convert_xunits(bin_range)
+        if range is not None:
+            range = self.convert_xunits(range)
 
         # Check whether bins or range are given explicitly.
-        binsgiven = (cbook.iterable(bins) or bin_range is not None)
+        binsgiven = (cbook.iterable(bins) or range is not None)
 
         # basic input validation
         flat = np.ravel(x)
@@ -6205,7 +6198,8 @@ def _normalize_input(inp, ename='input'):
                     'weights should have the same shape as x')
 
         if color is None:
-            color = [self._get_lines.get_next_color() for i in xrange(nx)]
+            color = [
+                self._get_lines.get_next_color() for i in xrange(nx)]
         else:
             color = mcolors.to_rgba_array(color)
             if len(color) != nx:
@@ -6224,12 +6218,12 @@ def _normalize_input(inp, ename='input'):
                 if len(xi) > 0:
                     xmin = min(xmin, xi.min())
                     xmax = max(xmax, xi.max())
-            bin_range = (xmin, xmax)
+            range = (xmin, xmax)
 
         # hist_kwargs = dict(range=range, normed=bool(normed))
         # We will handle the normed kwarg within mpl until we
         # get to the point of requiring numpy >= 1.5.
-        hist_kwargs = dict(range=bin_range)
+        hist_kwargs = dict(range=range)
 
         n = []
         mlast = None
@@ -6514,10 +6508,7 @@ def hist2d(self, x, y, bins=10, range=None, normed=False, weights=None,
         .. plot:: mpl_examples/statistics/plot_hist.py
         """
 
-        # xrange becomes range after 2to3
-        bin_range = range
-        range = __builtins__["range"]
-        h, xedges, yedges = np.histogram2d(x, y, bins=bins, range=bin_range,
+        h, xedges, yedges = np.histogram2d(x, y, bins=bins, range=range,
                                            normed=normed, weights=weights)
 
         if cmin is not None:
