added a warning concerning the buggy normalization in histogram with non-uniform bin widths

2 files changed, 15 insertions, 1 deletions

diff --git a/numpy/lib/function_base.py b/numpy/lib/function_base.py
index 0ef75b02a..292dbe41d 100644
--- a/numpy/lib/function_base.py
+++ b/numpy/lib/function_base.py
@@ -155,8 +155,10 @@ def histogram(a, bins=10, range=None, normed=False, weights=None):
             mn -= 0.5
             mx += 0.5
         bins = linspace(mn, mx, bins+1, endpoint=True)
+        uniform = True
     else:
         bins = asarray(bins)
+        uniform = False
         if (np.diff(bins) < 0).any():
             raise AttributeError(
                     'bins must increase monotonically.')
@@ -191,7 +193,16 @@ def histogram(a, bins=10, range=None, normed=False, weights=None):
 
     if normed:
         db = array(np.diff(bins), float)
+        if not uniform:
+            warnings.warn("""
+            This release of NumPy fixes a normalization bug in histogram
+            function occuring with non-uniform bin widths. The returned 
+            value is now a density: n / (N * bin width), where n is the 
+            bin count and N the total number of points. 
+            """)
         return n/db/n.sum(), bins
+        
+        
     else:
         return n, bins
 
diff --git a/numpy/lib/tests/test_function_base.py b/numpy/lib/tests/test_function_base.py
index 9507d191c..8e2de7ace 100644
--- a/numpy/lib/tests/test_function_base.py
+++ b/numpy/lib/tests/test_function_base.py
@@ -565,6 +565,7 @@ class TestHistogram(TestCase):
         area = sum(a * diff(b))
         assert_almost_equal(area, 1)
 
+        warnings.simplefilter('ignore', Warning)
         # Check with non-constant bin widths
         v = np.arange(10)
         bins = [0,1,3,6,10]
@@ -583,6 +584,7 @@ class TestHistogram(TestCase):
         # mailing list Aug. 6, 2010. 
         counts, dmy = np.histogram([1,2,3,4], [0.5,1.5,np.inf], normed=True)
         assert_equal(counts, [.25, 0])
+        warnings.resetwarnings()
         
     def test_outliers(self):
         # Check that outliers are not tallied
@@ -645,11 +647,12 @@ class TestHistogram(TestCase):
         wa, wb = histogram([1, 2, 2, 4], bins=4, weights=[4, 3, 2, 1], normed=True)
         assert_array_almost_equal(wa, array([4, 5, 0, 1]) / 10. / 3. * 4)
 
+        warnings.simplefilter('ignore', Warning)
         # Check weights with non-uniform bin widths
         a,b = histogram(np.arange(9), [0,1,3,6,10], \
                         weights=[2,1,1,1,1,1,1,1,1], normed=True)
         assert_almost_equal(a, [.2, .1, .1, .075])
-
+        warnings.resetwarnings()
 
 class TestHistogramdd(TestCase):
     def test_simple(self):