Handle single Tabular case in combine_distributions

paulromano · Jan 21, 2026 · Jun 15, 2025 · Jun 15, 2025 · Jun 15, 2025 · Jun 15, 2025
commit a9621399ab57b7356957dcec8ad60877001cc0cb
diff --git a/openmc/stats/univariate.py b/openmc/stats/univariate.py
@@ -1921,12 +1921,12 @@ def combine_distributions(
     for i, dist in enumerate(dists):
         cv.check_type(f'dists[{i}]', dist, (Discrete, Tabular))
         cv.check_type(f'probs[{i}]', probs[i], Real)
-        cv.check_greater_than(f'probs[{i}]', probs[i], 0.0)     
+        cv.check_greater_than(f'probs[{i}]', probs[i], 0.0)
 
     # Get list of discrete/continuous distribution indices
     discrete_index = [i for i, d in enumerate(dists) if isinstance(d, Discrete)]
     cont_index = [i for i, d in enumerate(dists) if isinstance(d, Tabular)]
-    
+
     cont_dists = [dists[i] for i in cont_index]
     cont_probs = [probs[i] for i in cont_index]
 
@@ -1936,11 +1936,16 @@ def combine_distributions(
         discrete_probs = [probs[i] for i in discrete_index]
         combined_dist = Discrete.merge(dist_discrete, discrete_probs)
         if cont_index:
-            return Mixture(cont_probs+[1.0], cont_dists+[combined_dist])
+            return Mixture(cont_probs + [1.0], cont_dists + [combined_dist])
         else:
             return combined_dist
     else:
-        return Mixture(cont_probs, cont_dists)
+        if len(cont_dists) == 1:
+            dist = cont_dists[0]
+            return Tabular(dist.x, dist.p * cont_probs[0],
+                           dist.interpolation, bias=dist.bias)
+        else:
+            return Mixture(cont_probs, cont_dists)
 
 
 def check_bias_support(parent: Univariate, bias: Univariate | None):

diff --git a/tests/unit_tests/test_stats.py b/tests/unit_tests/test_stats.py
@@ -633,12 +633,27 @@ def test_combine_distributions():
     assert len(mixed.distribution) == 2
     assert len(mixed.probability) == 2
 
+    # Single tabular returns a tabular distribution with scaled probabilities
+    t_single = openmc.stats.Tabular([0.0, 1.0], [2.0, 0.0])
+    scaled = openmc.stats.combine_distributions([t_single], [0.25])
+    assert isinstance(scaled, openmc.stats.Tabular)
+    assert scaled.p == pytest.approx([0.5, 0.0])
+
+    # Mixture with biased tabular should preserve unbiased mean via weights
+    bias = openmc.stats.Tabular([0.0, 1.0], [2.0, 0.0])
+    t_biased = openmc.stats.Tabular([0.0, 1.0], [1.0, 1.0], bias=bias)
+    d1 = openmc.stats.delta_function(0.0)
+    mixed = openmc.stats.combine_distributions([t_biased, d1], [0.5, 0.5])
+    assert isinstance(mixed, openmc.stats.Mixture)
+    samples, weights = mixed.sample(10_000)
+    assert_sample_mean(samples*weights, 0.25)
+
     # Combine 1 discrete and 2 tabular -- the tabular distributions should
     # combine to produce a uniform distribution with mean 0.5. The combined
     # distribution should have a mean of 0.25.
     t1 = openmc.stats.Tabular([0., 1.], [2.0, 0.0])
     t2 = openmc.stats.Tabular([0., 1.], [0.0, 2.0])
-    d1 = openmc.stats.Discrete([0.0], [1.0])
+    d1 = openmc.stats.delta_function(0.0)
     combined = openmc.stats.combine_distributions([t1, t2, d1], [0.25, 0.25, 0.5])
     assert combined.integral() == pytest.approx(1.0)