Merged revisions 8679 via svnmerge from

https://matplotlib.svn.sourceforge.net/svnroot/matplotlib/branches/v1_0_maint ........ r8679 | efiring | 2010-09-04 14:38:45 -1000 (Sat, 04 Sep 2010) | 2 lines [3024007] Fix ancient bug in hist inherited from numpy, fixed in numpy 1.5 ........ svn path=/trunk/matplotlib/; revision=8680
2840392e9e20142e836e4d1c902cdff6c69d1a39
commit 2840392e9e20142e836e4d1c902cdff6c69d1a39
diff --git a/lib/matplotlib/axes.py b/lib/matplotlib/axes.py
@@ -7466,6 +7466,12 @@ def hist(x, bins=10, range=None, normed=False, weights=None,
               pdf, bins, patches = ax.hist(...)
               print np.sum(pdf * np.diff(bins))
 
+            .. Note:: Until numpy release 1.5, the underlying numpy
+                      histogram function was incorrect with *normed*=*True*
+                      if bin sizes were unequal.  MPL inherited that
+                      error.  It is now corrected within MPL when using
+                      earlier numpy versions
+
           *weights*
             An array of weights, of the same shape as *x*.  Each value in
             *x* only contributes its associated weight towards the bin
@@ -7647,7 +7653,10 @@ def hist(x, bins=10, range=None, normed=False, weights=None,
                 xmax = max(xmax, xi.max())
             range = (xmin, xmax)
 
-        hist_kwargs = dict(range=range, normed=bool(normed))
+        #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=range)
         if np.__version__ < "1.3": # version 1.1 and 1.2
             hist_kwargs['new'] = True
 
@@ -7656,7 +7665,20 @@ def hist(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)
+            if normed:
+                db = np.diff(bins)
+                m = (m.astype(float) / db) / m.sum()
             n.append(m)
+        if normed and db.std() > 0.01 * db.mean():
+            warnings.warn("""
+            This release fixes a normalization bug in the NumPy histogram
+            function prior to version 1.5, occuring with non-uniform
+            bin widths. The returned and plotted value is now a density:
+                n / (N * bin width),
+            where n is the bin count and N the total number of points.
+            """)
+
+
 
         if cumulative:
             slc = slice(None)