diff --git a/protdiff/custom_metrics.py b/protdiff/custom_metrics.py
index fb73523..6428d1a 100644
--- a/protdiff/custom_metrics.py
+++ b/protdiff/custom_metrics.py
@@ -4,6 +4,8 @@ Some custom metrics
 import functools
 import multiprocessing
 import logging
+from cmath import rect, phase
+from math import radians, degrees
 
 import numpy as np
 from scipy import stats
@@ -11,6 +13,8 @@ from scipy import stats
 import torch
 from torch.utils.data import Dataset
 
+import utils
+
 
 def kl_from_empirical(u: np.ndarray, v: np.ndarray, nbins: int = 100) -> float:
     """
@@ -75,3 +79,15 @@ def kl_from_dset(dset: Dataset, single_thread: bool = False) -> np.ndarray:
         pool.close()
         pool.join()
     return np.array(kl_values)
+
+
+def wrapped_mean(x: np.ndarray, min_val=-np.pi, max_val=np.pi) -> float:
+    """
+    Wrap the mean function about the given range
+    """
+    # https://rosettacode.org/wiki/Averages/Mean_angle
+    sin_x = np.sin(x)
+    cos_x = np.cos(x)
+
+    retval = np.arctan2(np.mean(sin_x), np.mean(cos_x))
+    return retval
diff --git a/tests/test_metrics.py b/tests/test_metrics.py
index 052eedf..f53c23f 100644
--- a/tests/test_metrics.py
+++ b/tests/test_metrics.py
@@ -44,3 +44,51 @@ class TestKLFromEmpirical(unittest.TestCase):
 
         kl = cm.kl_from_empirical(u, v)
         self.assertEqual(np.inf, kl)
+
+
+class TestWrappedMean(unittest.TestCase):
+    """Test for the wrapped mean function"""
+
+    def setUp(self) -> None:
+        self.rng = np.random.default_rng(seed=6489)
+        self.rad2deg = lambda x: x * 180 / np.pi
+        self.deg2rad = lambda x: x * np.pi / 180
+
+    def test_simple(self):
+        """Test a hand-engineered example"""
+        true_mean = 170
+        x = np.array([true_mean - 30, true_mean + 30])
+        x_rad = self.deg2rad(x)
+        m = cm.wrapped_mean(x_rad)
+        m_deg = self.rad2deg(m)
+        self.assertAlmostEqual(true_mean, m_deg, places=2)
+
+    def test_positive(self):
+        """Simple test"""
+        x = self.rng.normal(loc=3.0, scale=0.25, size=100000)
+        m = cm.wrapped_mean(x)
+        self.assertAlmostEqual(m, 3.0, places=2)
+
+    def test_negative(self):
+        """Test that wrapping a negative mean works"""
+        x = self.rng.normal(loc=-3.0, scale=0.25, size=100000)
+        m = cm.wrapped_mean(x)
+        self.assertAlmostEqual(m, -3.0, places=2)
+
+    def test_zero(self):
+        """Test that a zero mean is still correctly handled"""
+        x = self.rng.normal(loc=0.0, scale=0.25, size=100000)
+        m = cm.wrapped_mean(x)
+        self.assertAlmostEqual(m, 0.0, places=2)
+
+    def test_positive_unwrapped(self):
+        """Test positive values that don't actually require wrapping"""
+        x = self.rng.normal(loc=0.5, scale=0.25, size=100000)
+        m = cm.wrapped_mean(x)
+        self.assertAlmostEqual(m, 0.5, places=2)
+
+    def test_negative_unwrapped(self):
+        """Test negative values don't actually require wrapping"""
+        x = self.rng.normal(loc=-0.5, scale=0.25, size=100000)
+        m = cm.wrapped_mean(x)
+        self.assertAlmostEqual(m, -0.5, places=2)