Merge pull request #96 from catalystneuro/align_keypoints

align keypoints with raw FP

src/datta_lab_to_nwb/markowitz_gillis_nature_2023/preconversion/extract_metadata.py

@@ -457,15 +457,22 @@ def extract_reinforcement_photometry_metadata(data_path: str, example_uuids: str
     return session_metadata, subject_metadata
 
 
-def extract_keypoint_metadata(data_path: str):
+def extract_keypoint_metadata():
     keypoint_subjects = ["dls-dlight-9", "dls-dlight-10", "dls-dlight-11", "dls-dlight-12", "dls-dlight-13"]
+    keypoint_start_times = [
+        "2022-07-14T11:24:31-05:00",
+        "2022-07-13T11:49:49-05:00",
+        "2022-07-13T12:21:37-05:00",
+        "2022-07-13T17:03:55-05:00",
+        "2022-07-13T16:28:19-05:00",
+    ]
     session_metadata, subject_metadata = {}, {}
-    for subject in keypoint_subjects:
+    for subject, session_start_time in zip(keypoint_subjects, keypoint_start_times):
         session_metadata[subject] = dict(
             keypoint=True,
             photometry=True,
             session_description="keypoint session",
-            session_start_time="1901-01-01T00:00:00-05:00",  # TODO: replace with real session start time
+            session_start_time=session_start_time,
             reference_max=np.NaN,
             signal_max=np.NaN,
             signal_reference_corr=np.NaN,
@@ -649,12 +656,11 @@ def get_session_name(session_df):
     (
         reinforcement_photometry_session_metadata,
         reinforcement_photometry_subject_metadata,
-    ) = extract_reinforcement_photometry_metadata(data_path)
-    # velocity_session_metadata, velocity_subject_metadata = extract_velocity_modulation_metadata(
-    #     data_path,
-    # )
-    # keypoint_session_metadata, keypoint_subject_metadata = extract_keypoint_metadata(data_path)
-
+    ) = extract_reinforcement_photometry_metadata(data_path, example_uuids=reinforcement_photometry_examples)
+    velocity_session_metadata, velocity_subject_metadata = extract_velocity_modulation_metadata(
+        data_path,
+    )
+    keypoint_session_metadata, keypoint_subject_metadata = extract_keypoint_metadata()
     path2metadata = {
         # photometry_session_metadata_path: photometry_session_metadata,
         # photometry_subject_metadata_path: photometry_subject_metadata,

src/datta_lab_to_nwb/markowitz_gillis_nature_2023/rawfiberphotometryinterface.py

@@ -287,6 +287,7 @@ def add_to_nwbfile(self, nwbfile: NWBFile, metadata: dict) -> None:
         raw_photometry = RoiResponseSeries(
             name="RawPhotometry",
             description="The raw acquisition with mixed signal from both the blue light excitation (470nm) and UV excitation (405nm).",
+            comments=("Note: Raw photometry data is not temporally aligned for keypoint sessions."),
             data=H5DataIO(raw_photometry[ascending_timestamps_indices], compression=True),
             unit="F",
             timestamps=commanded_signal_series.timestamps,

src/datta_lab_to_nwb/markowitz_gillis_nature_2023_keypoint/convert_session.py

@@ -43,6 +43,7 @@ def session_to_nwb(
         file_path=str(processed_path),
         tdt_path=str(tdt_path),
         tdt_metadata_path=str(tdt_metadata_path),
+        depth_timestamp_path="",
         session_metadata_path=str(session_metadata_path),
         subject_metadata_path=str(subject_metadata_path),
         session_uuid=subject_id,

src/datta_lab_to_nwb/markowitz_gillis_nature_2023_keypoint/fiberphotometryinterface.py

@@ -7,7 +7,7 @@
 # NWB Ecosystem
 from pynwb.file import NWBFile
 from pynwb.ophys import RoiResponseSeries
-from ..markowitz_gillis_nature_2023.rawfiberphotometryinterface import RawFiberPhotometryInterface
+from ..markowitz_gillis_nature_2023.rawfiberphotometryinterface import RawFiberPhotometryInterface, load_tdt_data
 from neuroconv.tools import nwb_helpers
 from hdmf.backends.hdf5.h5_utils import H5DataIO
 
@@ -18,26 +18,47 @@ def __init__(
         file_path: str,
         tdt_path: str,
         tdt_metadata_path: str,
+        depth_timestamp_path: str,
         session_uuid: str,
         session_id: str,
         session_metadata_path: str,
         subject_metadata_path: str,
+        alignment_path: str = None,
     ):
         super().__init__(
             file_path=file_path,
             tdt_path=tdt_path,
             tdt_metadata_path=tdt_metadata_path,
+            depth_timestamp_path=depth_timestamp_path,
             session_uuid=session_uuid,
             session_id=session_id,
             session_metadata_path=session_metadata_path,
             subject_metadata_path=subject_metadata_path,
+            alignment_path=alignment_path,
         )
 
+    def get_original_timestamps(self, metadata) -> np.ndarray:
+        processed_photometry = joblib.load(self.source_data["file_path"])
+        timestamps = np.arange(processed_photometry["dlight"].shape[0]) / metadata["Constants"]["VIDEO_SAMPLING_RATE"]
+        return timestamps
+
+    def align_processed_timestamps(
+        self, metadata: dict
+    ) -> np.ndarray:  # TODO: align timestamps if we get alignment_df.parquet
+        timestamps = self.get_original_timestamps(metadata=metadata)
+        self.set_aligned_timestamps(aligned_timestamps=timestamps)
+        return self.aligned_timestamps
+
+    def align_raw_timestamps(self, metadata: dict) -> np.ndarray:  # TODO: remove if we get alignment_df.parquet
+        photometry_dict = load_tdt_data(self.source_data["tdt_path"], fs=metadata["FiberPhotometry"]["raw_rate"])
+        timestamps = photometry_dict["tstep"]
+        self.set_aligned_timestamps(aligned_timestamps=timestamps)
+        return self.aligned_timestamps
+
     def add_to_nwbfile(self, nwbfile: NWBFile, metadata: dict) -> None:
-        SAMPLING_RATE = 30
         super().add_to_nwbfile(nwbfile, metadata)
         processed_photometry = joblib.load(self.source_data["file_path"])
-        timestamps = np.arange(processed_photometry["dlight"].shape[0]) / SAMPLING_RATE
+        timestamps = self.align_processed_timestamps(metadata)
         signal_series = RoiResponseSeries(
             name="SignalF",
             description=(

src/datta_lab_to_nwb/markowitz_gillis_nature_2023_keypoint/irvideointerface.py

@@ -17,15 +17,20 @@ def __init__(
         session_id: str,
         session_metadata_path: str,
         subject_metadata_path: str,
+        alignment_path: str = None,
     ):
         super().__init__(
             data_path=Path(data_path),
             session_uuid=session_uuid,
             session_id=session_id,
             session_metadata_path=session_metadata_path,
             subject_metadata_path=subject_metadata_path,
+            alignment_path=alignment_path,
         )
 
+    def get_original_timestamps(self, metadata) -> np.ndarray:
+        raise NotImplementedError  # TODO: align timestamps if we get alignment_df.parquet
+
     def add_to_nwbfile(self, nwbfile: NWBFile, metadata: dict) -> None:
         SAMPLING_RATE = metadata["Constants"]["VIDEO_SAMPLING_RATE"]
         matched_timestamp_path = (

src/datta_lab_to_nwb/markowitz_gillis_nature_2023_keypoint/keypointinterface.py

@@ -21,6 +21,7 @@ def __init__(
         session_metadata_path: str,
         subject_metadata_path: str,
         summary_image_path: str,
+        alignment_path: str = None,
     ):
         super().__init__(
             file_path=file_path,
@@ -29,6 +30,7 @@ def __init__(
             session_id=session_id,
             session_metadata_path=session_metadata_path,
             subject_metadata_path=subject_metadata_path,
+            alignment_path=alignment_path,
         )
 
     def get_metadata_schema(self) -> dict:
@@ -41,14 +43,17 @@ def get_metadata_schema(self) -> dict:
         }
         return metadata_schema
 
+    def get_original_timestamps(self, metadata) -> np.ndarray:
+        raise NotImplementedError  # TODO: align timestamps if we get alignment_df.parquet
+
     def add_to_nwbfile(self, nwbfile: NWBFile, metadata: dict) -> None:
         SAMPLING_RATE = metadata["Constants"]["VIDEO_SAMPLING_RATE"]
         keypoint_dict = joblib.load(self.source_data["file_path"])
         raw_keypoints = keypoint_dict["positions_median"]
         timestamps = H5DataIO(np.arange(raw_keypoints.shape[0]) / SAMPLING_RATE, compression=True)
 
         index_to_name = metadata["Keypoint"]["index_to_name"]
-        camera_names = ["bottom", "side1", "side2", "side3", "side4", "top"]
+        camera_names = ["bottom", "side1", "side2", "side3", "side4", "top"]  # as confirmed by email with authors
         keypoints = []
         for camera in camera_names:
             nwbfile.create_device(

src/datta_lab_to_nwb/markowitz_gillis_nature_2023_keypoint/post_conversion/reproduce_figS3.py

@@ -125,7 +125,7 @@ def reproduce_figS3(nwbfile_paths, config_path, metadata):
             timestamps = (
                 nwbfile.processing["behavior"]
                 .data_interfaces["keypoints"]
-                .pose_estimation_series["rostral spine"]
+                .pose_estimation_series["rostral_spine"]
                 .timestamps[:]
             )
             positions_median = np.zeros((len(timestamps), 15, 3))