Merge pull request #12108 from AetherUnbound/bugfix/12107

BUG: Allow boolean subtract in histogram

2 files changed, 25 insertions, 0 deletions

diff --git a/numpy/lib/histograms.py b/numpy/lib/histograms.py
index f03f30fb0..6a66e4a73 100644
--- a/numpy/lib/histograms.py
+++ b/numpy/lib/histograms.py
@@ -220,6 +220,14 @@ _hist_bin_selectors = {'auto': _hist_bin_auto,
 def _ravel_and_check_weights(a, weights):
     """ Check a and weights have matching shapes, and ravel both """
     a = np.asarray(a)
+
+    # Ensure that the array is a "subtractable" dtype
+    if a.dtype == np.bool_:
+        warnings.warn("Converting input from {} to {} for compatibility."
+                      .format(a.dtype, np.uint8),
+                      RuntimeWarning, stacklevel=2)
+        a = a.astype(np.uint8)
+
     if weights is not None:
         weights = np.asarray(weights)
         if weights.shape != a.shape:
diff --git a/numpy/lib/tests/test_histograms.py b/numpy/lib/tests/test_histograms.py
index 561f5f938..a71060a46 100644
--- a/numpy/lib/tests/test_histograms.py
+++ b/numpy/lib/tests/test_histograms.py
@@ -141,6 +141,23 @@ class TestHistogram(object):
         counts_hist, xedges, yedges = np.histogram2d(x, y, bins=100)
         assert_equal(counts_hist.sum(), 3.)
 
+    def test_bool_conversion(self):
+        # gh-12107
+        # Reference integer histogram
+        a = np.array([1, 1, 0], dtype=np.uint8)
+        int_hist, int_edges = np.histogram(a)
+
+        # Should raise an warning on booleans
+        # Ensure that the histograms are equivalent, need to suppress
+        # the warnings to get the actual outputs
+        with suppress_warnings() as sup:
+            rec = sup.record(RuntimeWarning, 'Converting input from .*')
+            hist, edges = np.histogram([True, True, False])
+            # A warning should be issued
+            assert_equal(len(rec), 1)
+            assert_array_equal(hist, int_hist)
+            assert_array_equal(edges, int_edges)
+
     def test_weights(self):
         v = np.random.rand(100)
         w = np.ones(100) * 5