ENH: Disable fuzzing on histogram boundaries

Previously a fuzzy rounded comparison was used for the rightmost bin of histogramdd.

It's not clear why this was done, and it resulted in surprising behavior.

This also removes the restriction that bin edges must be floats, and allows integer arrays to be passed (and returned)

Fixes gh-10864

1 files changed, 8 insertions, 21 deletions

diff --git a/numpy/lib/histograms.py b/numpy/lib/histograms.py
index 90e19769e..bd3c68306 100644
--- a/numpy/lib/histograms.py
+++ b/numpy/lib/histograms.py
@@ -877,12 +877,6 @@ def histogramdd(sample, bins=10, range=None, normed=False, weights=None):
         # bins is an integer
         bins = D*[bins]
 
-    # avoid rounding issues for comparisons when dealing with inexact types
-    if np.issubdtype(sample.dtype, np.inexact):
-        edge_dt = sample.dtype
-    else:
-        edge_dt = float
-
     # normalize the range argument
     if range is None:
         range = (None,) * D
@@ -896,13 +890,12 @@ def histogramdd(sample, bins=10, range=None, normed=False, weights=None):
                 raise ValueError(
                     '`bins[{}]` must be positive, when an integer'.format(i))
             smin, smax = _get_outer_edges(sample[:,i], range[i])
-            edges[i] = np.linspace(smin, smax, bins[i] + 1, dtype=edge_dt)
+            edges[i] = np.linspace(smin, smax, bins[i] + 1)
         elif np.ndim(bins[i]) == 1:
-            edges[i] = np.asarray(bins[i], edge_dt)
-            # not just monotonic, due to the use of mindiff below
-            if np.any(edges[i][:-1] >= edges[i][1:]):
+            edges[i] = np.asarray(bins[i])
+            if np.any(edges[i][:-1] > edges[i][1:]):
                 raise ValueError(
-                    '`bins[{}]` must be strictly increasing, when an array'
+                    '`bins[{}]` must be monotonically increasing, when an array'
                     .format(i))
         else:
             raise ValueError(
@@ -921,16 +914,10 @@ def histogramdd(sample, bins=10, range=None, normed=False, weights=None):
     # For the rightmost bin, we want values equal to the right edge to be
     # counted in the last bin, and not as an outlier.
     for i in _range(D):
-        # Rounding precision
-        mindiff = dedges[i].min()
-        if not np.isinf(mindiff):
-            decimal = int(-np.log10(mindiff)) + 6
-            # Find which points are on the rightmost edge.
-            not_smaller_than_edge = (sample[:, i] >= edges[i][-1])
-            on_edge = (np.around(sample[:, i], decimal) ==
-                       np.around(edges[i][-1], decimal))
-            # Shift these points one bin to the left.
-            Ncount[i][on_edge & not_smaller_than_edge] -= 1
+        # Find which points are on the rightmost edge.
+        on_edge = (sample[:, i] == edges[i][-1])
+        # Shift these points one bin to the left.
+        Ncount[i][on_edge] -= 1
 
     # Compute the sample indices in the flattened histogram matrix.
     # This raises an error if the array is too large.