Simplify transpose

movement/io/load_poses.py

@@ -463,7 +463,7 @@ def _ds_from_sleap_analysis_file(
 
     with h5py.File(file.path, "r") as f:
         # transpose to shape: (n_frames, n_space, n_keypoints, n_tracks)
-        tracks = f["tracks"][:].transpose((3, 1, 2, 0))
+        tracks = f["tracks"][:].transpose(3, 1, 2, 0)
         # Create an array of NaNs for the confidence scores
         scores = np.full(tracks.shape[:1] + tracks.shape[2:], np.nan)
         individual_names = [n.decode() for n in f["track_names"][:]] or None

movement/io/save_poses.py

@@ -35,14 +35,14 @@ def _ds_to_dlc_style_df(
 
     """
     # Concatenate the pose tracks and confidence scores into one array
-    # and reverse the order of the dimensions except for the time dimension
     tracks_with_scores = np.concatenate(
         (
             ds.position.data,
             ds.confidence.data[:, np.newaxis, ...],
         ),
         axis=1,
     )
+    # Reverse the order of the dimensions except for the time dimension
     transpose_order = [0] + list(range(tracks_with_scores.ndim - 1, 0, -1))
     tracks_with_scores = tracks_with_scores.transpose(transpose_order)
 
@@ -324,8 +324,8 @@ def to_sleap_analysis_file(ds: xr.Dataset, file_path: str | Path) -> None:
     pos_x = ds.position.sel(space="x").values
     # Mask denoting which individuals are present in each frame
     track_occupancy = (~np.all(np.isnan(pos_x), axis=1)).astype(int)
-    tracks = np.transpose(ds.position.data, (3, 1, 2, 0))
-    point_scores = np.transpose(ds.confidence.data, (2, 1, 0))
+    tracks = ds.position.data.transpose(3, 1, 2, 0)
+    point_scores = ds.confidence.data.T
     instance_scores = np.full((n_individuals, n_frames), np.nan, dtype=float)
     tracking_scores = np.full((n_individuals, n_frames), np.nan, dtype=float)
     labels_path = (