Add offline sessions to the conversion script

src/cai_lab_to_nwb/zaki_2024/zaki_2024_convert_all_sessions.py

@@ -7,7 +7,7 @@
 import traceback
 from tqdm import tqdm
 
-from zaki_2024_convert_conditioning_session import session_to_nwb
+from zaki_2024_convert_session import session_to_nwb
 
 
 def dataset_to_nwb(
@@ -16,6 +16,7 @@ def dataset_to_nwb(
     output_dir_path: Union[str, Path],
     max_workers: int = 1,
     verbose: bool = True,
+    stub_test: bool = False,
 ):
     """Convert the entire dataset to NWB.
 
@@ -41,6 +42,7 @@ def dataset_to_nwb(
         for session_to_nwb_kwargs in session_to_nwb_kwargs_per_session:
             session_to_nwb_kwargs["output_dir_path"] = output_dir_path
             session_to_nwb_kwargs["verbose"] = verbose
+            session_to_nwb_kwargs["stub_test"] = stub_test
             exception_file_path = data_dir_path / f"ERROR_<nwbfile_name>.txt"  # Add error file path here
             futures.append(
                 executor.submit(
@@ -101,8 +103,7 @@ def get_session_to_nwb_kwargs_per_session(
         session_times_file_path = data_dir_path / "Ca_EEG_Experiment" / subject_id / (subject_id + "_SessionTimes.csv")
         if session_times_file_path.is_file():
             session_times_df = pd.read_csv(session_times_file_path)
-            tasks = [task for task in session_times_df["Session"] if "Offline" not in task]
-            for task in tasks:
+            for task in session_times_df["Session"]:
                 session_id = subject_id + "_" + task
                 session_row = session_times_df[session_times_df["Session"] == task].iloc[0]
                 date_str = session_row["Date"]
@@ -112,7 +113,6 @@ def get_session_to_nwb_kwargs_per_session(
                         data_dir_path=data_dir_path,
                         subject_id=subject_id,
                         session_id=session_id,
-                        stub_test=True,
                         date_str=date_str,
                         time_str=time_str,
                     )
@@ -130,10 +130,11 @@ def get_session_to_nwb_kwargs_per_session(
     output_dir_path = Path("D:/cai_lab_conversion_nwb/")
     max_workers = 1
     verbose = True
-
+    stub_test = True
     dataset_to_nwb(
         data_dir_path=data_dir_path,
         output_dir_path=output_dir_path,
         max_workers=max_workers,
         verbose=verbose,
+        stub_test=stub_test,
     )

src/cai_lab_to_nwb/zaki_2024/zaki_2024_convert_conditioning_session.py → src/cai_lab_to_nwb/zaki_2024/zaki_2024_convert_session.py

@@ -51,10 +51,14 @@ def session_to_nwb(
     source_data = dict()
     conversion_options = dict()
 
-    experiment_dir_path = data_dir_path / "Ca_EEG_Experiment" / subject_id / (subject_id + "_Sessions")
+    if "Offline" in session_id:
+        offline_day = session_id.split("Session")[0]
+        experiment_dir_path = data_dir_path / "Ca_EEG_Experiment" / subject_id / (subject_id + "_Offline") / offline_day
+    else:
+        experiment_dir_path = data_dir_path / "Ca_EEG_Experiment" / subject_id / (subject_id + "_Sessions") / session_id
 
     # Add Imaging
-    folder_path = experiment_dir_path / session_id / date_str / time_str
+    folder_path = experiment_dir_path / date_str / time_str
     miniscope_folder_path = get_miniscope_folder_path(folder_path)
     if miniscope_folder_path is not None:
         source_data.update(dict(MiniscopeImaging=dict(folder_path=miniscope_folder_path)))
@@ -79,15 +83,15 @@ def session_to_nwb(
         print("No motion corrected data found at {}".format(motion_corrected_video))
 
     # Add Behavioral Video
-    video_file_path = experiment_dir_path / session_id / (session_id + ".wmv")
+    video_file_path = experiment_dir_path / (session_id + ".wmv")
     if video_file_path.is_file():
         source_data.update(dict(Video=dict(file_paths=[video_file_path])))
         conversion_options.update(dict(Video=dict(stub_test=stub_test)))
     elif verbose:
         print("No behavioral video found at {}".format(video_file_path))
 
     # Add Freezing Analysis output
-    freezing_output_file_path = experiment_dir_path / session_id / (session_id + "_FreezingOutput.csv")
+    freezing_output_file_path = experiment_dir_path / (session_id + "_FreezingOutput.csv")
     if freezing_output_file_path.is_file():
         source_data.update(
             dict(FreezingBehavior=dict(file_path=freezing_output_file_path, video_sampling_frequency=30.0))
@@ -106,6 +110,17 @@ def session_to_nwb(
     elif verbose:
         print("No .edf file found at {}".format(edf_file_path))
 
+    # Add Sleep Classification output
+    sleep_classification_file_path = (
+        data_dir_path / "Ca_EEG_Sleep" / subject_id / "AlignedSleep" / (session_id + "_AlignedSleep.csv")
+    )
+    if sleep_classification_file_path.is_file():
+        source_data.update(
+            dict(SleepClassification=dict(file_path=sleep_classification_file_path, video_sampling_frequency=30.0))
+        )
+    elif verbose:
+        print("No sleep classification output csv file found at {}".format(sleep_classification_file_path))
+
     converter = Zaki2024NWBConverter(source_data=source_data)
 
     # Add datetime to conversion
@@ -144,7 +159,7 @@ def session_to_nwb(
     # Parameters for conversion
     data_dir_path = Path("D:/")
     subject_id = "Ca_EEG3-4"
-    task = "NeutralExposure"
+    task = "OfflineDay1Session1"
     session_id = subject_id + "_" + task
     output_dir_path = Path("D:/cai_lab_conversion_nwb/")
     stub_test = True