matplotlib
diff --git a/‎CHANGELOG
Lines changed: 5 additions & 1 deletion b/‎CHANGELOG
Lines changed: 5 additions & 1 deletion
diff --git a/‎lib/matplotlib/axes.py
Lines changed: 8 additions & 2 deletions b/‎lib/matplotlib/axes.py
Lines changed: 8 additions & 2 deletions
diff --git a/‎lib/matplotlib/tests/baseline_images/test_axes/hist_stacked_normed.pdf
6.03 KB b/‎lib/matplotlib/tests/baseline_images/test_axes/hist_stacked_normed.pdf
6.03 KB
diff --git a/‎lib/matplotlib/tests/baseline_images/test_axes/hist_stacked_normed.png
12.9 KB b/‎lib/matplotlib/tests/baseline_images/test_axes/hist_stacked_normed.png
12.9 KB
@@ -1,3 +1,7 @@
+2013-05-08 Changed behavior of hist when given stacked=True and normed=True.
+           Histograms are now stacked first, then the sum is normalized.
+           Previously, each histogram was normalized, then they were stacked.
+
 2013-04-25 Changed all instances of:
 
 	   from matplotlib import MatplotlibDeprecationWarning as mplDeprecation
@@ -13,7 +17,7 @@
 	   margins on auto-scaleing. - TAC
 
 2013-03-19 Added support for passing `linestyle` kwarg to `step` so all `plot`
-	   kwargs are passed to the underlying `plot` call.  -TAC
+      	   kwargs are passed to the underlying `plot` call.  -TAC
 
 2013-02-25 Added classes CubicTriInterpolator, UniformTriRefiner, TriAnalyzer
            to matplotlib.tri module. - GBy
 
@@ -8088,7 +8088,8 @@ def hist(self, x, bins=10, range=None, normed=False, weights=None,
             If `True`, the first element of the return tuple will
             be the counts normalized to form a probability density, i.e.,
             ``n/(len(x)`dbin)``, ie the integral of the histogram will sum to
-            1.
+            1. If *stacked* is also *True*, the sum of the histograms is
+            normalized to 1.
 
         weights : array_like, shape (n, ), optional, default: None
             An array of weights, of the same shape as `x`.  Each value in `x`
@@ -8300,16 +8301,21 @@ def hist(self, x, bins=10, range=None, normed=False, weights=None,
             # this will automatically overwrite bins,
             # so that each histogram uses the same bins
             m, bins = np.histogram(x[i], bins, weights=w[i], **hist_kwargs)
+            m = m.astype(float) # causes problems later if it's an int
             if mlast is None:
                 mlast = np.zeros(len(bins)-1, m.dtype)
-            if normed:
+            if normed and not stacked:
                 db = np.diff(bins)
                 m = (m.astype(float) / db) / m.sum()
             if stacked:
                 m += mlast
                 mlast[:] = m
             n.append(m)
 
+        if stacked and normed:
+            db = np.diff(bins)
+  
7849
          for m in n:
+                m[:] = (m.astype(float) / db) / n[-1].sum()
         if cumulative:
             slc = slice(None)
             if cbook.is_numlike(cumulative) and cumulative < 0: