diff --git a/foldingdiff/angles_and_coords.py b/foldingdiff/angles_and_coords.py
index 7a49c9c..62e68c9 100644
--- a/foldingdiff/angles_and_coords.py
+++ b/foldingdiff/angles_and_coords.py
@@ -60,25 +60,19 @@ def canonical_distances_and_dihedrals(
     for a in non_dihedral_angles:
         if a == "tau" or a == "N:CA:C":
             # tau = N - CA - C internal angles
-            idx = np.array(
-                [list(range(i, i + 3)) for i in range(3, len(backbone_atoms), 3)]
-                + [(0, 0, 0)]
-            )
+            r = np.arange(3, len(backbone_atoms), 3)
+            idx = np.hstack([np.vstack([r, r + 1, r + 2]), np.zeros((3, 1))]).T
         elif a == "CA:C:1N":  # Same as C-N angle in nerf
             # This measures an angle between two residues. Due to the way we build
             # proteins out later, we do not need to meas
-            idx = np.array(
-                [(i + 1, i + 2, i + 3) for i in range(0, len(backbone_atoms) - 3, 3)]
-                + [(0, 0, 0)]
-            )
+            r = np.arange(0, len(backbone_atoms) - 3, 3)
+            idx = np.hstack([np.vstack([r + 1, r + 2, r + 3]), np.zeros((3, 1))]).T
         elif a == "C:1N:1CA":
-            idx = np.array(
-                [(i + 2, i + 3, i + 4) for i in range(0, len(backbone_atoms) - 3, 3)]
-                + [(0, 0, 0)]
-            )
+            r = np.arange(0, len(backbone_atoms) - 3, 3)
+            idx = np.hstack([np.vstack([r + 2, r + 3, r + 4]), np.zeros((3, 1))]).T
         else:
             raise ValueError(f"Unrecognized angle: {a}")
-        calc_angles[a] = struc.index_angle(backbone_atoms, indices=idx)
+        calc_angles[a] = struc.index_angle(backbone_atoms, indices=idx.astype(int))
 
     # At this point we've only looked at dihedral and angles; check value range
     for k, v in calc_angles.items():
@@ -92,29 +86,25 @@ def canonical_distances_and_dihedrals(
             # Since this is measuring the distance between pairs of residues, there
             # is one fewer such measurement than the total number of residues like
             # for dihedrals. Therefore, we pad this with a null 0 value at the end.
-            idx = np.array(
-                [(i + 2, i + 3) for i in range(0, len(backbone_atoms) - 3, 3)]
-                + [(0, 0)]
-            )
+            r = np.arange(0, len(backbone_atoms) - 3, 3)
+            idx = np.hstack([np.vstack([r + 2, r + 3]), np.zeros((2, 1))]).T
         elif d == "N:CA":
             # We start resconstructing with a fixed initial residue so we do not need
             # to predict or record the initial distance. Additionally we pad with a
             # null value at the end
-            idx = np.array(
-                [(i, i + 1) for i in range(3, len(backbone_atoms), 3)] + [(0, 0)]
-            )
+            r = np.arange(3, len(backbone_atoms), 3)
+            idx = np.hstack([np.vstack([r, r + 1]), np.zeros((2, 1))]).T
             assert len(idx) == len(calc_angles["phi"])
         elif d == "CA:C":
             # We start reconstructing with a fixed initial residue so we do not need
             # to predict or record the initial distance. Additionally, we pad with a
             # null value at the end.
-            idx = np.array(
-                [(i + 1, i + 2) for i in range(3, len(backbone_atoms), 3)] + [(0, 0)]
-            )
+            r = np.arange(3, len(backbone_atoms), 3)
+            idx = np.hstack([np.vstack([r + 1, r + 2]), np.zeros((2, 1))]).T
             assert len(idx) == len(calc_angles["phi"])
         else:
             raise ValueError(f"Unrecognized distance: {d}")
-        calc_angles[d] = struc.index_distance(backbone_atoms, indices=idx)
+        calc_angles[d] = struc.index_distance(backbone_atoms, indices=idx.astype(int))
 
     return pd.DataFrame({k: calc_angles[k].squeeze() for k in distances + angles})
 
@@ -368,18 +358,20 @@ def build_aa_sidechain_dict(
     that specify how to build out that sidechain's atoms from the backbone
     """
     if not reference_pdbs:
-        glob.glob(
+        reference_pdbs = glob.glob(
             os.path.join(os.path.dirname(os.path.dirname(__file__)), "data/*.pdb")
         )
 
+    ref_file_counter = 0
     retval = {}
     for pdb in reference_pdbs:
         try:
             sidechain_angles = collect_aa_sidechain_angles(pdb)
             retval.update(sidechain_angles)  # Overwrites any existing key/value pairs
+            ref_file_counter += 1
         except ValueError:
             continue
-    logging.info(f"Built sidechain dictionary with {len(retval)} amino acids")
+    logging.info(f"Built sidechain dictionary with {len(retval)} amino acids from {ref_file_counter} files")
     return retval
 
 
diff --git a/tests/test_data.py b/tests/test_data.py
index f2e9096..f3bcc4d 100644
--- a/tests/test_data.py
+++ b/tests/test_data.py
@@ -164,4 +164,5 @@ class TestNoisedDataset(unittest.TestCase):
             "sqrt_alphas_cumprod_t"
         ]
         recovered = utils.modulo_with_wrapped_range(recovered, -np.pi, np.pi)
-        self.assertTrue(torch.allclose(recovered, orig_angles, atol=1e-5))
+        delta = recovered - orig_angles
+        self.assertTrue(torch.allclose(delta, torch.zeros_like(delta), atol=1e-4), f"Got non-zero delta on de-noise: {delta}")
diff --git a/tests/test_losses.py b/tests/test_losses.py
index 8ba8ffc..5f82846 100644
--- a/tests/test_losses.py
+++ b/tests/test_losses.py
@@ -11,7 +11,7 @@ class TestRadianSmoothL1Loss(unittest.TestCase):
         """
         Easy test of basic wrapping functionality
         """
-        l = losses.radian_smooth_l1_loss(torch.tensor(0.1), 2 * torch.pi, beta=1.0)
+        l = losses.radian_smooth_l1_loss(torch.tensor(0.1), torch.tensor(2 * torch.pi), beta=1.0)
         self.assertAlmostEqual(0.0050, l.item())
 
     def test_rounding(self):