Update behavior

src/schneider_lab_to_nwb/schneider_2024/schneider_2024_behaviorinterface.py

@@ -2,7 +2,7 @@
 from pynwb.file import NWBFile
 from pydantic import FilePath
 import numpy as np
-from h5py import File
+from pymatreader import read_mat
 from hdmf.common.table import DynamicTableRegion
 from pynwb.behavior import BehavioralTimeSeries, TimeSeries
 from pynwb.device import Device
@@ -77,35 +77,52 @@ def get_metadata_schema(self) -> dict:
                 },
             },
         }
+        metadata_schema["properties"]["Behavior"]["properties"]["Trials"] = {
+            "type": "array",
+            "items": {
+                "type": "object",
+                "properties": {
+                    "name": {"type": "string"},
+                    "description": {"type": "string"},
+                },
+            },
+        }
         return metadata_schema
 
     def add_to_nwbfile(self, nwbfile: NWBFile, metadata: dict):
         # Read Data
         file_path = self.source_data["file_path"]
-        with File(file_path, "r") as file:
-            behavioral_time_series, name_to_times, name_to_values = [], dict(), dict()
-            for time_series_dict in metadata["Behavior"]["TimeSeries"]:
-                name = time_series_dict["name"]
-                timestamps = np.array(file["continuous"][name]["time"]).squeeze()
-                data = np.array(file["continuous"][name]["value"]).squeeze()
-                time_series = TimeSeries(
-                    name=name,
-                    timestamps=timestamps,
-                    data=data,
-                    unit="a.u.",
-                    description=time_series_dict["description"],
-                )
-                behavioral_time_series.append(time_series)
-            for event_dict in metadata["Behavior"]["Events"]:
-                name = event_dict["name"]
-                times = np.array(file["events"][name]["time"]).squeeze()
-                name_to_times[name] = times
-            for event_dict in metadata["Behavior"]["ValuedEvents"]:
-                name = event_dict["name"]
-                times = np.array(file["events"][name]["time"]).squeeze()
-                values = np.array(file["events"][name]["value"]).squeeze()
-                name_to_times[name] = times
-                name_to_values[name] = values
+        file = read_mat(file_path)
+        behavioral_time_series, name_to_times, name_to_values, name_to_trial_array = [], dict(), dict(), dict()
+        for time_series_dict in metadata["Behavior"]["TimeSeries"]:
+            name = time_series_dict["name"]
+            timestamps = np.array(file["continuous"][name]["time"]).squeeze()
+            data = np.array(file["continuous"][name]["value"]).squeeze()
+            time_series = TimeSeries(
+                name=name,
+                timestamps=timestamps,
+                data=data,
+                unit="a.u.",
+                description=time_series_dict["description"],
+            )
+            behavioral_time_series.append(time_series)
+        for event_dict in metadata["Behavior"]["Events"]:
+            name = event_dict["name"]
+            times = np.array(file["events"][name]["time"]).squeeze()
+            name_to_times[name] = times
+        for event_dict in metadata["Behavior"]["ValuedEvents"]:
+            name = event_dict["name"]
+            times = np.array(file["events"][name]["time"]).squeeze()
+            values = np.array(file["events"][name]["value"]).squeeze()
+            name_to_times[name] = times
+            name_to_values[name] = values
+
+        trial_start_times = np.array(file["events"]["push"]["time"]).squeeze()
+        trial_stop_times = np.array(file["events"]["push"]["time_end"]).squeeze()
+        for trials_dict in metadata["Behavior"]["Trials"]:
+            name = trials_dict["name"]
+            trial_array = np.array(file["events"]["push"][name]).squeeze()
+            name_to_trial_array[name] = trial_array
 
         # Add Data to NWBFile
         behavior_module = nwb_helpers.get_module(
@@ -167,6 +184,14 @@ def add_to_nwbfile(self, nwbfile: NWBFile, metadata: dict):
         task = Task(event_types=event_types_table)
         nwbfile.add_lab_meta_data(task)
 
+        # Add Trials Table
+        for start_time, stop_time in zip(trial_start_times, trial_stop_times):
+            nwbfile.add_trial(start_time=start_time, stop_time=stop_time)
+        for trials_dict in metadata["Behavior"]["Trials"]:
+            name = trials_dict["name"]
+            trial_array = name_to_trial_array[name]
+            nwbfile.add_trial_column(name=name, description=trials_dict["description"], data=trial_array)
+
         # Add Devices
         for device_kwargs in metadata["Behavior"]["Devices"]:
             device = Device(**device_kwargs)

src/schneider_lab_to_nwb/schneider_2024/schneider_2024_convert_session.py

@@ -12,14 +12,19 @@
 from schneider_lab_to_nwb.schneider_2024 import Schneider2024NWBConverter
 
 
-def session_to_nwb(data_dir_path: Union[str, Path], output_dir_path: Union[str, Path], stub_test: bool = False):
-
-    data_dir_path = Path(data_dir_path)
+def session_to_nwb(
+    recording_folder_path: Union[str, Path],
+    sorting_folder_path: Union[str, Path],
+    behavior_file_path: Union[str, Path],
+    video_folder_path: Union[str, Path],
+    output_dir_path: Union[str, Path],
+    stub_test: bool = False,
+):
+    recording_folder_path = Path(recording_folder_path)
+    sorting_folder_path = Path(sorting_folder_path)
+    behavior_file_path = Path(behavior_file_path)
+    video_folder_path = Path(video_folder_path)
     output_dir_path = Path(output_dir_path)
-    recording_folder_path = data_dir_path / "Raw Ephys" / "m69_2023-10-31_17-24-15_Day1_A1"
-    sorting_folder_path = data_dir_path / "Processed Ephys" / "m69_2023-10-31_17-24-15_Day1_A1"
-    behavior_file_path = data_dir_path / "Behavior" / "m69_231031" / "raw_m69_231031_001.mat"
-    video_folder_path = data_dir_path / "Video" / "m69_231031"
     video_file_paths = [
         file_path for file_path in video_folder_path.glob("*.mp4") if not file_path.name.startswith("._")
     ]
@@ -96,14 +101,25 @@ def session_to_nwb(data_dir_path: Union[str, Path], output_dir_path: Union[str,
 
 def main():
     # Parameters for conversion
-    data_dir_path = Path("/Volumes/T7/CatalystNeuro/Schneider/Schneider sample Data")
+    data_dir_path = Path("/Volumes/T7/CatalystNeuro/Schneider")
     output_dir_path = Path("/Volumes/T7/CatalystNeuro/Schneider/conversion_nwb")
     stub_test = True
 
     if output_dir_path.exists():
         shutil.rmtree(output_dir_path, ignore_errors=True)
+
+    # Example Session w/ old ephys + new behavior
+    recording_folder_path = data_dir_path / "Schneider sample Data" / "Raw Ephys" / "m69_2023-10-31_17-24-15_Day1_A1"
+    sorting_folder_path = (
+        data_dir_path / "Schneider sample Data" / "Processed Ephys" / "m69_2023-10-31_17-24-15_Day1_A1"
+    )
+    behavior_file_path = data_dir_path / "NWB_Share" / "Sample behavior data" / "m74_ephysSample.mat"
+    video_folder_path = data_dir_path / "Schneider sample Data" / "Video" / "m69_231031"
     session_to_nwb(
-        data_dir_path=data_dir_path,
+        recording_folder_path=recording_folder_path,
+        sorting_folder_path=sorting_folder_path,
+        behavior_file_path=behavior_file_path,
+        video_folder_path=video_folder_path,
         output_dir_path=output_dir_path,
         stub_test=stub_test,
     )

src/schneider_lab_to_nwb/schneider_2024/schneider_2024_metadata.yaml

@@ -41,19 +41,41 @@ Behavior:
     - name: lick
       description: Samples values for entire duration of experiment for voltage signal readout from a custom infrared/capacitive lickometer sensor (Schneider Lab).  Digital signals for licking and lever movement were collected by a data acquisition card (National Instruments) connected to a computer and logged by custom Matlab software (Mathworks, PsychToolBox) and sampled at 2kHz.
   Events:
-    - name: target
-      description: Time at which the target zone is entered during a press.
+    - name: toneIN
+      description: Time at which target zone is entered and target entry tone is played.
     - name: targetOUT
       description: Time at which the target zone is overshot during a press.
-    - name: toneIN
-      description: Time at which target entry tone is played.
     - name: toneOUT
-      description: Time at which target exit tone is played.
+      description: Time at which target exit tone is played (this is delayed 50ms relative to targetOUT so that entry and exit tones don't bleed into each other.
     - name: valve
       description: Times at which solenoid valve opens to deliver water after a correct trial.
   ValuedEvents:
     - name: tuningTones
-      description: Times at which tuning tones are played to an animal after a behavioral experiment during ephys recording sessions.
+      description: Times at which tuning tones are played to an animal after a behavioral experiment during ephys recording sessions. Integer values correspond to the frequency of the tuning tone played i.e. 2 for 2kHz, 4 for 4kHz, etc.
+  Devices:
+    - name: rotary_encoder
+      description: H5 BALL BEARING OPTICAL SHAFT ENCODER
+      manufacturer: US Digital
+    - name: lickometer
+      description: The lickometer comprised a custom-mounted (3D printed using Formlabs Form2) IR-beam emitter and receiver. IR signal was titrated and pre-processed using a custom printed circuit board (designed by Melissa Caras and Dan Sanes) to generate a binary TTL signal with IR sensitivity controlled by a potentiometer.
+      manufacturer: Schneider Lab
+  Trials:
+    - name: rewarded
+      description: Indicates if trial was rewarded (NaN = trial not rewarded, 1 = trial rewarded).
+    - name: time_reward_s
+      description: Time of reward if rewarded, otherwise NaN.
+    - name: opto_trial
+      description: Indicates if trial was an optogenetic stimulation trial (NaN = non opto trial, 1 = opto trial).
+    - name: opto_time
+      description: Time of optogenetic stimulation if opto trial, otherwise NaN.
+    - name: opto_time_end
+      description: Time of start of optogenetic stimulation if it occurs, otherwise NaN.
+    - name: ITI_respect
+      description: Whether or not trial start obeyed inter trial interval wait time (300ms).
+    - name: ThresholdVector
+      description: Position of start of the target zone on a given trial (in raw encoder values corresponding to value read out by quadrature encoder).
+    - name: endZone_ThresholdVector
+      description: Position of ending/exit position of target zone on a given trial (in raw encoder values corresponding to value read out by quadrature encoder).
   VideoCamera1:
     - name: video_camera_1
       description: Two IR video cameras (AAK CA20 600TVL 2.8MM) are used to monitor the experiments from different angles of interest, allowing for offline analysis of body movements, pupillometry, and other behavioral data if necessary. Camera 1 is a side angle view of the mouse.
@@ -62,13 +84,6 @@ Behavior:
     - name: video_camera_2
       description: Two IR video cameras (AAK CA20 600TVL 2.8MM) are used to monitor the experiments from different angles of interest, allowing for offline analysis of body movements, pupillometry, and other behavioral data if necessary. Camera 2 is a zoomed-in view of the pupil of the mouse.
       unit: Frames
-  Devices:
-    - name: rotary_encoder
-      description: H5 BALL BEARING OPTICAL SHAFT ENCODER
-      manufacturer: US Digital
-    - name: lickometer
-      description: The lickometer comprised a custom-mounted (3D printed using Formlabs Form2) IR-beam emitter and receiver. IR signal was titrated and pre-processed using a custom printed circuit board (designed by Melissa Caras and Dan Sanes) to generate a binary TTL signal with IR sensitivity controlled by a potentiometer.
-      manufacturer: Schneider Lab
 
 Sorting:
   units_description: Neural spikes will be sorted offline using Kilosort 2.5 and Phy2 software and manually curated to ensure precise spike time acquisition.

src/schneider_lab_to_nwb/schneider_2024/schneider_2024_notes.md

@@ -1,9 +1,6 @@
 # Notes concerning the schneider_2024 conversion
 
 ## Behavior
-- opto events is just [0, 1] like empty variables (lick events), and opto_time in 'push' is all NaNs.  Where is the actual opto stim times recorded?
-- TuningTones has a values field with integers, what does this correspond to?
-- 'push' is basically some kind of trials table, but need descriptions for variables ex. ITI_respect?
 
 ## Video