fix video alignment for opto sessions

pyproject.toml

@@ -17,7 +17,7 @@ classifiers = [
 dependencies = [
   "neuroconv==0.6.6",
   "ndx-events==0.2.0",
-  "pymatreader==1.0.0"
+  "pymatreader==1.0.0",
   "spikeinterface==0.101.2"
 ]
 

src/schneider_lab_to_nwb/zempolich_2024/zempolich_2024_behaviorinterface.py

@@ -89,7 +89,6 @@ def get_metadata_schema(self) -> dict:
         }
         return metadata_schema
 
-
     def add_to_nwbfile(
         self, nwbfile: NWBFile, metadata: dict, normalize_timestamps: bool = False, verbose: bool = False
     ):
@@ -110,7 +109,7 @@ def add_to_nwbfile(
         file_path = self.source_data["file_path"]
         file = read_mat(file_path)
         behavioral_time_series, name_to_times, name_to_values, name_to_trial_array = [], dict(), dict(), dict()
-        starting_timestamp = file["continuous"][metadata["Behavior"]["TimeSeries"][0]["name"]]["time"][0]
+        starting_timestamp = get_starting_timestamp(file)
         for time_series_dict in metadata["Behavior"]["TimeSeries"]:
             name = time_series_dict["name"]
             timestamps = np.array(file["continuous"][name]["time"]).squeeze()
@@ -232,3 +231,15 @@ def add_to_nwbfile(
         for device_kwargs in metadata["Behavior"]["Devices"]:
             device = Device(**device_kwargs)
             nwbfile.add_device(device)
+
+
+def get_starting_timestamp(mat_file: dict):
+    starting_timestamp = np.min(
+        [
+            mat_file["continuous"]["encoder"]["time"][0],
+            mat_file["continuous"]["lick"]["time"][0],
+            mat_file["continuous"]["cam"]["time"][0][0],
+            mat_file["continuous"]["cam"]["time"][1][0],
+        ]
+    )
+    return starting_timestamp

src/schneider_lab_to_nwb/zempolich_2024/zempolich_2024_nwbconverter.py

@@ -13,6 +13,7 @@
     Zempolich2024OptogeneticInterface,
     Zempolich2024IntrinsicSignalOpticalImagingInterface,
 )
+from schneider_lab_to_nwb.zempolich_2024.zempolich_2024_behaviorinterface import get_starting_timestamp
 
 
 class Zempolich2024NWBConverter(NWBConverter):
@@ -28,7 +29,7 @@ class Zempolich2024NWBConverter(NWBConverter):
         ISOI=Zempolich2024IntrinsicSignalOpticalImagingInterface,
     )
 
-    def temporally_align_data_interfaces(self):
+    def temporally_align_data_interfaces(self) -> None:
         """Align timestamps between data interfaces.
 
         It is called by run_conversion() after the data interfaces have been initialized but before the data is added
@@ -39,5 +40,16 @@ def temporally_align_data_interfaces(self):
         behavior_file_path = Path(behavior_interface.source_data["file_path"])
         file = read_mat(behavior_file_path)
         cam1_timestamps, cam2_timestamps = file["continuous"]["cam"]["time"]
+        if self.conversion_options["Behavior"].get("normalize_timestamps", False):
+            starting_timestamp = get_starting_timestamp(mat_file=file)
+            cam1_timestamps -= starting_timestamp
+            cam2_timestamps -= starting_timestamp
         self.data_interface_objects["VideoCamera1"].set_aligned_timestamps([cam1_timestamps])
         self.data_interface_objects["VideoCamera2"].set_aligned_timestamps([cam2_timestamps])
+
+    # NOTE: passing in conversion_options as an attribute is a temporary solution until the neuroconv library is updated
+    #  to allow for easier customization of the conversion process
+    # (see https://github.com/catalystneuro/neuroconv/pull/1162).
+    def run_conversion(self, **kwargs):
+        self.conversion_options = kwargs["conversion_options"]
+        super().run_conversion(**kwargs)

src/schneider_lab_to_nwb/zempolich_2024/zempolich_2024_optogeneticinterface.py

@@ -9,6 +9,8 @@
 
 from neuroconv.basedatainterface import BaseDataInterface
 
+from .zempolich_2024_behaviorinterface import get_starting_timestamp
+
 
 class Zempolich2024OptogeneticInterface(BaseDataInterface):
     """Optogenetic interface for schneider_2024 conversion"""
@@ -57,7 +59,7 @@ def add_to_nwbfile(
             np.logical_not(np.isnan(offset_times))
         ), "Some of the offset times are nan when onset times are not nan."
         power = metadata["Optogenetics"]["OptogeneticSeries"]["power"]
-        starting_timestamp = file["continuous"][metadata["Behavior"]["TimeSeries"][0]["name"]]["time"][0]
+        starting_timestamp = get_starting_timestamp(mat_file=file)
         if normalize_timestamps:
             onset_times = onset_times - starting_timestamp
             offset_times = offset_times - starting_timestamp