code quality update, removed PoseEstimation handling

environment.yml

@@ -24,7 +24,7 @@ dependencies:
  - pydocstyle
  - toml
  - twine
- - build
+ - python-build
  - pip
  - pip:
  - "--editable=.[dev]"

sleap_io/io/nwb.py

@@ -24,7 +24,6 @@
 except ImportError:
  ArrayLike = np.ndarray
 
-from hdmf.utils import LabelledDict
 from hdmf.build.errors import OrphanContainerBuildError
 
 from pynwb import NWBFile, NWBHDF5IO, ProcessingModule
@@ -72,10 +71,7 @@ def pose_training_to_labels(pose_training: PoseTraining) -> Labels: # type: ign
  training_frames = pose_training.training_frames.training_frames.values()
  for training_frame in training_frames:
  source_video = training_frame.source_video
- if source_video.format == "external" and len(source_video.external_file) == 1:
- video = Video(source_video.external_file[0])
- else:
- raise NotImplementedError("Only single-file external videos are supported.")
+ video = Video(source_video.external_file)
 
  frame_idx = training_frame.source_video_frame_index
  instances = []
@@ -89,7 +85,7 @@ def pose_training_to_labels(pose_training: PoseTraining) -> Labels: # type: ign
  Instance.from_numpy(
  points=instance.node_locations[:], skeleton=skeleton
  )
- ) # `track` field is not stored in `SkeletonInstance` objects
+ )
  labeled_frames.append(
  LabeledFrame(video=video, frame_idx=frame_idx, instances=instances)
  )
@@ -212,8 +208,6 @@ def instance_to_skeleton_instance(
  return SkeletonInstance(
  name=f"skeleton_instance_{id(instance)}",
  id=np.uint64(id(instance)),
- # TODO add a counter in the loop to track the number of instances
- # instead of using id
  node_locations=np_node_locations,
  node_visibility=[point.visible for point in instance.points.values()],
  skeleton=skeleton,
@@ -620,7 +614,7 @@ def append_nwb_data(
  .unique()
  )
 
- for track_index, track_name in enumerate(name_of_tracks_in_video):
+ for track_name in name_of_tracks_in_video:
  pose_estimation_container = build_pose_estimation_container_for_track(
  labels_data_df,
  labels,
@@ -688,52 +682,11 @@ def append_nwb_training(
  pose_training = labels_to_pose_training(labels, skeletons_list, video_info)
  nwb_processing_module.add(pose_training)
 
- confidence_definition = "Softmax output of the deep neural network"
- reference_frame = (
- "The coordinates are in (x, y) relative to the top-left of the image. "
- "Coordinates refer to the midpoint of the pixel. "
- "That is, t the midpoint of the top-left pixel is at (0, 0), whereas "
- "the top-left corner of that same pixel is at (-0.5, -0.5)."
- )
- pose_estimation_series_list = []
- for node in skeletons_list[0].nodes:
- pose_estimation_series = PoseEstimationSeries(
- name=node,
- description=f"Marker placed on {node}",
- data=np.random.rand(100, 2),
- unit="pixels",
- reference_frame=reference_frame,
- timestamps=np.linspace(0, 10, num=100),
- confidence=np.random.rand(100),
- confidence_definition=confidence_definition,
- )
- pose_estimation_series_list.append(pose_estimation_series)
-
- camera = nwbfile.create_device(
+ _ = nwbfile.create_device(
  name=f"camera {i}",
  description=f"Camera used to record video {i}",
  manufacturer="No specified manufacturer",
  )
- try:
- dimensions = np.array([[video.backend.shape[1], video.backend.shape[2]]])
- except AttributeError:
- dimensions = np.array([[400, 400]])
-
- pose_estimation = PoseEstimation(
- name="pose_estimation",
- pose_estimation_series=pose_estimation_series_list,
- description="Estimated positions of the nodes in the video",
- original_videos=[video.filename for video in labels.videos],
- labeled_videos=[video.filename for video in labels.videos],
- dimensions=dimensions,
- devices=[camera],
- scorer="No specified scorer",
- source_software="SLEAP",
- source_software_version=sleap_version,
- skeleton=skeletons_list[0],
- )
- nwb_processing_module.add(pose_estimation)
-
  return nwbfile
 
 
@@ -767,6 +720,7 @@ def append_nwb(
  )
  else:
  nwb_file = append_nwb_data(labels, nwb_file, pose_estimation_metadata)
+
  io.write(nwb_file)