cab

sleap_io/io/nwb.py

@@ -103,7 +103,7 @@ def labels_to_pose_training(labels: Labels, **kwargs) -> PoseTraining:  # type:
     training_frame_list = []
     for i, labeled_frame in enumerate(labels.labeled_frames):
         training_frame_name = name_generator("training_frame")
-        training_frame_annotator = f"{training_frame_name}{i}"
+        training_frame_annotator = f"{training_frame_name}_{i}"
         skeleton_instances_list = []
         for instance in labeled_frame.instances:
             if isinstance(instance, PredictedInstance):
@@ -202,9 +202,9 @@ def videos_to_source_videos(videos: List[Video]) -> SourceVideos:  # type: ignor
         An NWB SourceVideos object.
     """
     source_videos = []
-    for i, video in enumerate(videos):
+    for video in videos:
         image_series = ImageSeries(
-            name=f"video_{i}",
+            name=name_generator("video"),
             description="Video file",
             unit="NA",
             format="external",
@@ -224,7 +224,8 @@ def sleap_pkg_to_nwb(filename: str, labels: Labels, **kwargs):
         filename: The path to the SLEAP package.
         labels: The SLEAP Labels object.
     """
-    assert filename.endswith(".pkg.slp")
+    if not filename.endswith(".pkg.slp"):
+        raise ValueError("The filename must end with '.pkg.slp'.")
 
     path = filename.split(".slp")[0]
     save_path = Path(path + ".nwb_images")
@@ -502,8 +503,19 @@ def append_nwb_training(
     Returns:
         An in-memory NWB file with the PoseTraining data appended.
     """
-    pose_training = labels_to_pose_training(labels)
+    pose_estimation_metadata = pose_estimation_metadata or dict()
     provenance = labels.provenance
+    default_metadata = dict(scorer=str(provenance))
+    sleap_version = provenance.get("sleap_version", None)
+    default_metadata["source_software_version"] = sleap_version
+    pose_training = labels_to_pose_training(labels)
+
+    for lf in labels.labeled_frames:
+        if lf.has_predicted_instances:
+            labels_data_df = convert_predictions_to_dataframe(labels)
+            break
+        else:
+            labels_data_df = pd.DataFrame()
     raise NotImplementedError