Fix type hinting and simplify implementation of combine_distributions (#3445)

Co-authored-by: Paul Romano <paul.k.romano@gmail.com>

Author: GuySten

openmc/stats/univariate.py

@@ -397,7 +397,7 @@ def from_xml_element(cls, elem: ET.Element):
     def merge(
         cls,
         dists: Sequence[Discrete],
-        probs: Sequence[int]
+        probs: Sequence[float]
     ):
         """Merge multiple discrete distributions into a single distribution
 
@@ -1897,7 +1897,7 @@ def clip(self, tolerance: float = 1e-6, inplace: bool = False) -> Mixture:
 
 
 def combine_distributions(
-    dists: Sequence[Univariate],
+    dists: Sequence[Discrete | Tabular],
     probs: Sequence[float]
 ):
     """Combine distributions with specified probabilities
@@ -1912,41 +1912,40 @@ def combine_distributions(
 
     Parameters
     ----------
-    dists : iterable of openmc.stats.Univariate
+    dists : sequence of openmc.stats.Discrete or openmc.stats.Tabular
         Distributions to combine
-    probs : iterable of float
+    probs : sequence of float
         Probability (or intensity) of each distribution
 
     """
-    # Get copy of distribution list so as not to modify the argument
-    dist_list = deepcopy(dists)
+    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)
 
     # Get list of discrete/continuous distribution indices
-    discrete_index = [i for i, d in enumerate(dist_list) if isinstance(d, Discrete)]
-    cont_index = [i for i, d in enumerate(dist_list) if isinstance(d, Tabular)]
+    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)]
 
-    # Apply probabilites to continuous distributions
-    for i in cont_index:
-        dist = dist_list[i]
-        dist._p *= probs[i]
+    cont_dists = [dists[i] for i in cont_index]
+    cont_probs = [probs[i] for i in cont_index]
 
     if discrete_index:
         # Create combined discrete distribution
-        dist_discrete = [dist_list[i] for i in discrete_index]
+        dist_discrete = [dists[i] for i in discrete_index]
         discrete_probs = [probs[i] for i in discrete_index]
         combined_dist = Discrete.merge(dist_discrete, discrete_probs)
-
-        # Replace multiple discrete distributions with merged
-        for idx in reversed(discrete_index):
-            dist_list.pop(idx)
-        dist_list.append(combined_dist)
-
-    # Combine discrete and continuous if present
-    if len(dist_list) > 1:
-        probs = [1.0]*len(dist_list)
-        dist_list[:] = [Mixture(probs, dist_list.copy())]
-
-    return dist_list[0]
+        if cont_index:
+            return Mixture(cont_probs + [1.0], cont_dists + [combined_dist])
+        else:
+            return combined_dist
+    else:
+        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):

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)