From 4ef1faf766818a7c01569386d27451774406d542 Mon Sep 17 00:00:00 2001
From: Paul Romano <paul.k.romano@gmail.com>
Date: Fri, 16 Aug 2024 11:33:59 -0500
Subject: [PATCH] Add delta_function convenience function (#3090)

---
 docs/source/pythonapi/stats.rst |  1 +
 openmc/stats/univariate.py      | 21 +++++++++++++++++++++
 tests/unit_tests/test_stats.py  |  7 +++++++
 3 files changed, 29 insertions(+)

diff --git a/docs/source/pythonapi/stats.rst b/docs/source/pythonapi/stats.rst
index ffb4fcda2ba..b72896c1860 100644
--- a/docs/source/pythonapi/stats.rst
+++ b/docs/source/pythonapi/stats.rst
@@ -28,6 +28,7 @@ Univariate Probability Distributions
    :nosignatures:
    :template: myfunction.rst
 
+   openmc.stats.delta_function
    openmc.stats.muir
 
 Angular Distributions
diff --git a/openmc/stats/univariate.py b/openmc/stats/univariate.py
index a47f4f32030..10822c06fa7 100644
--- a/openmc/stats/univariate.py
+++ b/openmc/stats/univariate.py
@@ -312,6 +312,27 @@ def clip(self, tolerance: float = 1e-6, inplace: bool = False) -> Discrete:
             return type(self)(new_x, new_p)
 
 
+def delta_function(value: float, intensity: float = 1.0) -> Discrete:
+    """Return a discrete distribution with a single point.
+
+    .. versionadded:: 0.15.1
+
+    Parameters
+    ----------
+    value : float
+        Value of the random variable.
+    intensity : float, optional
+        When used for an energy distribution, this can be used to assign an
+        intensity.
+
+    Returns
+    -------
+    Discrete distribution with a single point
+
+    """
+    return Discrete([value], [intensity])
+
+
 class Uniform(Univariate):
     """Distribution with constant probability over a finite interval [a,b]
 
diff --git a/tests/unit_tests/test_stats.py b/tests/unit_tests/test_stats.py
index 5451c0a2cac..0414fc22559 100644
--- a/tests/unit_tests/test_stats.py
+++ b/tests/unit_tests/test_stats.py
@@ -50,6 +50,13 @@ def test_discrete():
     assert_sample_mean(samples, exp_mean)
 
 
+def test_delta_function():
+    d = openmc.stats.delta_function(14.1e6)
+    assert isinstance(d, openmc.stats.Discrete)
+    np.testing.assert_array_equal(d.x, [14.1e6])
+    np.testing.assert_array_equal(d.p, [1.0])
+
+
 def test_merge_discrete():
     x1 = [0.0, 1.0, 10.0]
     p1 = [0.3, 0.2, 0.5]