Rename function as suggested in PR review

src/kbmod/trajectory_utils.py

@@ -266,7 +266,7 @@ def evaluate_trajectory_mse(trj, x_vals, y_vals, zeroed_times, centered=True):
     return np.mean(sq_err)
 
 
-def ave_trajectory_distance(trjA, trjB, times=[0.0]):
+def avg_trajectory_distance(trjA, trjB, times=[0.0]):
     """Evaluate the average distance between two trajectories (in pixels)
     across different times.
 
@@ -347,7 +347,7 @@ def match_trajectory_sets(traj_query, traj_base, threshold, times=[0.0]):
     dists = np.zeros((num_query, num_base))
     for q_idx in range(num_query):
         for b_idx in range(num_base):
-            dists[q_idx][b_idx] = ave_trajectory_distance(traj_query[q_idx], traj_base[b_idx], times)
+            dists[q_idx][b_idx] = avg_trajectory_distance(traj_query[q_idx], traj_base[b_idx], times)
 
     # Use scipy to solve the optimal bipartite matching problem.
     row_inds, col_inds = linear_sum_assignment(dists)

tests/test_trajectory_utils.py

@@ -156,34 +156,34 @@ def test_evaluate_trajectory_mse(self):
 
         self.assertRaises(ValueError, evaluate_trajectory_mse, trj, [], [], [])
 
-    def test_ave_trajectory_distance(self):
+    def test_avg_trajectory_distance(self):
         times_0 = np.array([0.0])
         times_1 = np.array([0.0, 1.0])
         times_5 = np.array([0.0, 1.0, 2.0, 3.0, 4.0])
 
         # A trajectory is always zero pixels from itself.
         trjA = Trajectory(x=1, y=2, vx=1.0, vy=-1.0)
-        self.assertAlmostEqual(ave_trajectory_distance(trjA, trjA, times_0), 0.0)
-        self.assertAlmostEqual(ave_trajectory_distance(trjA, trjA, times_1), 0.0)
-        self.assertAlmostEqual(ave_trajectory_distance(trjA, trjA, times_5), 0.0)
+        self.assertAlmostEqual(avg_trajectory_distance(trjA, trjA, times_0), 0.0)
+        self.assertAlmostEqual(avg_trajectory_distance(trjA, trjA, times_1), 0.0)
+        self.assertAlmostEqual(avg_trajectory_distance(trjA, trjA, times_5), 0.0)
 
         # Create a trajectory with a constant 1 pixel offset in the y direction.
         trjB = Trajectory(x=1, y=1, vx=1.0, vy=-1.0)
-        self.assertAlmostEqual(ave_trajectory_distance(trjA, trjB, times_0), 1.0)
-        self.assertAlmostEqual(ave_trajectory_distance(trjA, trjB, times_1), 1.0)
-        self.assertAlmostEqual(ave_trajectory_distance(trjA, trjB, times_5), 1.0)
+        self.assertAlmostEqual(avg_trajectory_distance(trjA, trjB, times_0), 1.0)
+        self.assertAlmostEqual(avg_trajectory_distance(trjA, trjB, times_1), 1.0)
+        self.assertAlmostEqual(avg_trajectory_distance(trjA, trjB, times_5), 1.0)
 
         # Create a trajectory with an increasing offset in the x direction.
         trjC = Trajectory(x=1, y=2, vx=2.0, vy=-1.0)
-        self.assertAlmostEqual(ave_trajectory_distance(trjA, trjC, times_0), 0.0)
-        self.assertAlmostEqual(ave_trajectory_distance(trjA, trjC, times_1), 0.5)
-        self.assertAlmostEqual(ave_trajectory_distance(trjA, trjC, times_5), 2.0)
+        self.assertAlmostEqual(avg_trajectory_distance(trjA, trjC, times_0), 0.0)
+        self.assertAlmostEqual(avg_trajectory_distance(trjA, trjC, times_1), 0.5)
+        self.assertAlmostEqual(avg_trajectory_distance(trjA, trjC, times_5), 2.0)
 
         # Create a trajectory with an increasing offset in the y direction.
         trjC = Trajectory(x=1, y=2, vx=1.0, vy=1.0)
-        self.assertAlmostEqual(ave_trajectory_distance(trjA, trjC, times_0), 0.0)
-        self.assertAlmostEqual(ave_trajectory_distance(trjA, trjC, times_1), 1.0)
-        self.assertAlmostEqual(ave_trajectory_distance(trjA, trjC, times_5), 4.0)
+        self.assertAlmostEqual(avg_trajectory_distance(trjA, trjC, times_0), 0.0)
+        self.assertAlmostEqual(avg_trajectory_distance(trjA, trjC, times_1), 1.0)
+        self.assertAlmostEqual(avg_trajectory_distance(trjA, trjC, times_5), 4.0)
 
         # A list of empty times is invalid.
         with self.assertRaises(ValueError):