Some minor debugging

examples/sample_entropy.py

@@ -141,7 +141,7 @@ def main() -> None:
     fractions = []
     for _, delta_t in enumerate(delta_t_list):
         reshaped_data = dynsight.onion.helpers.reshape_from_nt(data, delta_t)
-        state_list, labels = dynsight.onion.onion_uni(reshaped_data)
+        _, labels = dynsight.onion.onion_uni(reshaped_data)
 
         tmp_list = []
         tmp_frac = []

examples/video_to_trajectory.py

@@ -30,7 +30,7 @@ def plot_results(
 
     n_detections = [len(result) for result in instance.prediction_results]
 
-    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 4))
+    _, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 4))
 
     ax1.plot(n_detections, marker="o")
     ax1.set_title("N° Detections in Time")

pyproject.toml

@@ -91,8 +91,10 @@ convention = "google"
     "ANN001",
     "PLR0912",
     "PLR0915",
+    "RUF059",
 ]
 "docs/source/conf.py" = ["D100", "INP001"]
+"docs/source/_static/recipes/*" = ["RUF059"]
 
 [tool.mypy]
 show_error_codes = true

src/dynsight/_internal/analysis/entropy.py

@@ -216,7 +216,7 @@ def compute_shannon_multi(
     if data.size == 0:
         msg = "data is empty"
         raise ValueError(msg)
-    n_points, n_dims = data.shape
+    _, n_dims = data.shape
     if n_dims != len(data_ranges) or n_dims != len(n_bins):
         msg = "Mismatch between data dimensions, data_ranges, and n_bins"
         raise ValueError(msg)

src/dynsight/_internal/descriptors/misc.py

@@ -97,7 +97,7 @@ def orientational_order_param(
 
 def _compute_aver_align(
     neigh_list_t: list[AtomGroup],
-    frame_vel: NDArray[np.float64],
+    frame_vel: NDArray[np.float64],  # shape (n_atoms, n_dim)
 ) -> NDArray[np.float64]:
     """Computes the average alignment for all the atoms in a frame."""
     phi_t = np.zeros(len(frame_vel))
@@ -114,7 +114,10 @@ def _compute_aver_align(
             continue  # no self-counting, no neighbors with v = 0.0
 
         alignments = np.array(
-            [1 - cosine(atom_i, frame_vel[j]) for j in valid_neighbors]
+            [
+                1 - cosine(np.array(atom_i), frame_vel[j])
+                for j in valid_neighbors
+            ]
         )
         phi_t[i] = np.mean(alignments)
     return phi_t

src/dynsight/_internal/lens/lens.py

@@ -108,13 +108,17 @@ def neighbour_change_in_time(
     Returns:
         tuple:
             - lens_array: The calculated LENS parameter.
-                It's a numpy.array of shape (n_particles, n_frames - 1)
+                It's a np.array of shape (n_particles, n_frames - delay + 1)
             - number_of_neighs: The count of neighbors per frame.
-                It's a numpy.array of shape (n_particles, n_frames)
+                It's a np.array of shape (n_particles, n_frames)
             - lens_numerators: The numerators used for calculating LENS.
-                It's a numpy.array of shape (n_particles, n_frames - 1)
+                It's a np.array of shape (n_particles, n_frames - delay + 1)
             - lens_denominators: The denominators used for calculating LENS.
-                It's a numpy.array of shape (n_particles, n_frames - 1)
+                It's a np.array of shape (n_particles, n_frames - delay + 1)
+
+    Note:
+        The first frame of the output array is identically zero. This is due
+        to compatibility with older code.
 
     Example:
 
@@ -151,10 +155,10 @@ def neighbour_change_in_time(
     n_atoms = np.asarray(neigh_list_per_frame, dtype=object).shape[1]
     n_frames = np.asarray(neigh_list_per_frame, dtype=object).shape[0]
 
-    lens_array = np.zeros((n_atoms, n_frames))  # The LENS values
-    number_of_neighs = np.zeros((n_atoms, n_frames), dtype=int)  # The NN
-    lens_numerators = np.zeros((n_atoms, n_frames))  # LENS numerator
-    lens_denominators = np.zeros((n_atoms, n_frames))  # LENS denominator
+    lens_array = np.zeros((n_atoms, n_frames - delay + 1))
+    number_of_neighs = np.zeros((n_atoms, n_frames), dtype=int)
+    lens_numerators = np.zeros((n_atoms, n_frames - delay + 1))
+    lens_denominators = np.zeros((n_atoms, n_frames - delay + 1))
 
     # each nnlist contains also the atom that generates them,
     # so 0 nn is a 1 element list

src/dynsight/_internal/trajectory/cluster_insight.py

@@ -446,7 +446,7 @@ def dump_colored_trj(self, trj: Trj, file_path: Path) -> None:
         if self.labels.shape != (n_atoms, n_frames):
             msg = (
                 f"Shape mismatch: Trj should have {self.labels.shape[0]} "
-                f"atoms, {self.labels.shape[0]} frames, but has {n_atoms} "
+                f"atoms, {self.labels.shape[1]} frames, but has {n_atoms} "
                 f"atoms, {n_frames} frames."
             )
             logger.log(msg)

tests/analysis/test_sample_entropy.py

@@ -40,7 +40,7 @@ def test_too_short(
 
 def test_too_small_rfact(random_data: NDArray[np.float64]) -> None:
     """Test that a too small r_factor raises a RuntimeError."""
-    with pytest.raises(RuntimeError, match="No matching sequences found."):
+    with pytest.raises(RuntimeError, match=r"No matching sequences found."):
         dynsight.analysis.sample_entropy(random_data, r_factor=0.0)
 
 

tests/trajectory/test_trj.py

@@ -161,19 +161,19 @@ def test_onion_analysis(universe: MDAnalysis.Universe) -> None:
 def test_insight_load_errors(file_paths: dict[str, Path]) -> None:
     """Test insight load errors."""
     with pytest.raises(
-        ValueError, match="'dataset_file' key not found in JSON file."
+        ValueError, match=r"'dataset_file' key not found in JSON file."
     ):
         _ = Insight.load_from_json(file_paths["files_dir"] / "empty.json")
 
     with pytest.raises(
-        ValueError, match="'labels_file' key not found in JSON file."
+        ValueError, match=r"'labels_file' key not found in JSON file."
     ):
         _ = ClusterInsight.load_from_json(
             file_paths["files_dir"] / "ins_1_test.json"
         )
 
     with pytest.raises(
-        ValueError, match="'reshaped_data_file' key not found in JSON file."
+        ValueError, match=r"'reshaped_data_file' key not found in JSON file."
     ):
         _ = OnionInsight.load_from_json(
             file_paths["files_dir"] / "cl_ins_test.json"