Merge branch 'master' of https://github.com/numpy/numpy into einsum_c_buglet

1 files changed, 15 insertions, 5 deletions

diff --git a/numpy/lib/histograms.py b/numpy/lib/histograms.py
index 8474bd5d3..03c365ab6 100644
--- a/numpy/lib/histograms.py
+++ b/numpy/lib/histograms.py
@@ -22,6 +22,16 @@ array_function_dispatch = functools.partial(
 _range = range
 
 
+def _ptp(x):
+    """Peak-to-peak value of x.
+
+    This implementation avoids the problem of signed integer arrays having a
+    peak-to-peak value that cannot be represented with the array's data type.
+    This function returns an unsigned value for signed integer arrays.
+    """
+    return _unsigned_subtract(x.max(), x.min())
+
+
 def _hist_bin_sqrt(x, range):
     """
     Square root histogram bin estimator.
@@ -40,7 +50,7 @@ def _hist_bin_sqrt(x, range):
     h : An estimate of the optimal bin width for the given data.
     """
     del range  # unused
-    return x.ptp() / np.sqrt(x.size)
+    return _ptp(x) / np.sqrt(x.size)
 
 
 def _hist_bin_sturges(x, range):
@@ -63,7 +73,7 @@ def _hist_bin_sturges(x, range):
     h : An estimate of the optimal bin width for the given data.
     """
     del range  # unused
-    return x.ptp() / (np.log2(x.size) + 1.0)
+    return _ptp(x) / (np.log2(x.size) + 1.0)
 
 
 def _hist_bin_rice(x, range):
@@ -87,7 +97,7 @@ def _hist_bin_rice(x, range):
     h : An estimate of the optimal bin width for the given data.
     """
     del range  # unused
-    return x.ptp() / (2.0 * x.size ** (1.0 / 3))
+    return _ptp(x) / (2.0 * x.size ** (1.0 / 3))
 
 
 def _hist_bin_scott(x, range):
@@ -137,7 +147,7 @@ def _hist_bin_stone(x, range):
     """
 
     n = x.size
-    ptp_x = np.ptp(x)
+    ptp_x = _ptp(x)
     if n <= 1 or ptp_x == 0:
         return 0
 
@@ -184,7 +194,7 @@ def _hist_bin_doane(x, range):
             np.true_divide(temp, sigma, temp)
             np.power(temp, 3, temp)
             g1 = np.mean(temp)
-            return x.ptp() / (1.0 + np.log2(x.size) +
+            return _ptp(x) / (1.0 + np.log2(x.size) +
                                     np.log2(1.0 + np.absolute(g1) / sg1))
     return 0.0