Add week long session

src/cai_lab_to_nwb/zaki_2024/interfaces/__init__.py

@@ -1,5 +1,5 @@
 from .eztrack_interface import EzTrackFreezingBehaviorInterface
-from .zaki_2024_edf_interface import Zaki2024EDFInterface
+from .zaki_2024_edf_interface import Zaki2024EDFInterface, Zaki2024MultiEDFInterface
 from .minian_interface import MinianSegmentationInterface, MinianMotionCorrectionInterface
 from .zaki_2024_sleep_classification_interface import Zaki2024SleepClassificationInterface
 from .miniscope_imaging_interface import MiniscopeImagingInterface

src/cai_lab_to_nwb/zaki_2024/interfaces/zaki_2024_edf_interface.py

@@ -1,7 +1,12 @@
+from typing import Union
 from pathlib import Path
-from neuroconv.basedatainterface import BaseDataInterface
+
 from pynwb import NWBFile, TimeSeries
 from pynwb.device import Device
+
+from neuroconv.basedatainterface import BaseDataInterface
+from neuroconv.utils import DeepDict
+
 from mne.io import read_raw_edf
 from datetime import datetime, timedelta
 import numpy as np
@@ -10,7 +15,7 @@
 class Zaki2024EDFInterface(BaseDataInterface):
     def __init__(
         self,
-        file_path: Path,
+        file_path: Union[Path, str],
         verbose: bool = False,
     ):
         self.file_path = Path(file_path)
@@ -117,3 +122,98 @@ def add_to_nwbfile(
         nwbfile.add_device(device)
 
         return nwbfile
+
+
+class Zaki2024MultiEDFInterface(BaseDataInterface):
+    def __init__(
+        self,
+        file_paths: list[Path],
+        verbose: bool = False,
+    ):
+        self.file_paths = file_paths
+        self.verbose = verbose
+        super().__init__(file_paths=file_paths)
+
+    def add_to_nwbfile(
+        self,
+        nwbfile: NWBFile,
+        stub_test: bool = False,
+        stub_frames: int = 100,
+        **conversion_options,
+    ) -> NWBFile:
+        """
+        Adds data from EEG, EMG, temperature, and activity channels to an NWBFile.
+
+        Parameters
+        ----------
+        nwbfile : NWBFile
+            The NWBFile object to which data will be added.
+        stub_test : bool, optional
+            If True, loads only a subset of frames (controlled by `stub_frames` parameter)
+            to facilitate testing and faster execution. Default is False.
+        stub_frames : int, optional
+            The number of frames to load if `stub_test` is True. Default is 100.
+
+        Returns
+        -------
+        NWBFile
+            The NWBFile object with added data and metadata from the specified channels.
+        """
+        channels_dict = {
+            "Temp": {
+                "name": "TemperatureSignal",
+                "description": "Temperature signal recorder with HD-X02 wireless telemetry probe",
+                "unit": "celsius",
+            },
+            "EEG": {
+                "name": "EEGSignal",
+                "description": "EEG signal recorder with HD-X02 wireless telemetry probe",
+                "unit": "volts",
+            },
+            "EMG": {
+                "name": "EMGSignal",
+                "description": "EMG signal recorder with HD-X02 wireless telemetry probe",
+                "unit": "volts",
+            },
+            # TODO: Figure out if the units of activity are correct, the raw format marks Volts
+            # TODO: My own reading of the header indicates that the physical units is counts
+            "Activity": {
+                "name": "ActivitySignal",
+                "description": "Activity signal recorder with HD-X02 wireless telemetry probe. It refers to the motion of the probe relative to the receiver and it can be used as a proxy for locomotion.",
+                "unit": "n.a.",
+            },
+        }
+        concatenated_data = None
+        concatenated_times = []
+
+        # Loop over each EDF file and concatenate data and timestamps
+        for file_path in self.file_paths:
+            edf_reader = read_raw_edf(input_fname=file_path, verbose=self.verbose)
+            data, times = edf_reader.get_data(picks=list(channels_dict.keys()), return_times=True)
+            data = data.astype(np.float32)
+            # Slice the data and timestamps within the time range
+            if stub_test:
+                concatenated_data = data[:, :stub_frames]
+                break
+            # Concatenate along the time axis
+            if concatenated_data is None:
+                concatenated_data = data
+            else:
+                concatenated_data = np.concatenate((concatenated_data, data), axis=1)
+            concatenated_times.extend(times)
+
+        for channel_index, channel_name in enumerate(channels_dict.keys()):
+            time_series_kwargs = channels_dict[channel_name].copy()
+            time_series_kwargs.update(
+                data=concatenated_data[channel_index], starting_time=0.0, rate=edf_reader.info["sfreq"]
+            )
+            time_series = TimeSeries(**time_series_kwargs)
+            nwbfile.add_acquisition(time_series)
+
+        # Add device
+        description = "Wireless telemetry probe used to record EEG, EMG, temperature, and activity data"
+        name = "HD-X02, Data Science International"
+        device = Device(name=name, description=description)
+        nwbfile.add_device(device)
+
+        return nwbfile

src/cai_lab_to_nwb/zaki_2024/zaki_2024_convert_week_session.py

@@ -0,0 +1,98 @@
+"""Primary script to run to convert an entire session for of data using the NWBConverter."""
+
+import time
+from natsort import natsorted
+from pathlib import Path
+from typing import Union
+from datetime import datetime, timedelta
+import pandas as pd
+import json
+from neuroconv.utils import load_dict_from_file, dict_deep_update
+
+from zaki_2024_nwbconverter import Zaki2024NWBConverter
+
+
+def session_to_nwb(
+    data_dir_path: Union[str, Path],
+    output_dir_path: Union[str, Path],
+    subject_id: str,
+    stub_test: bool = False,
+    verbose: bool = True,
+):
+
+    if verbose:
+        print(f"Converting week-long session")
+        start = time.time()
+
+    data_dir_path = Path(data_dir_path)
+    output_dir_path = Path(output_dir_path)
+    if stub_test:
+        output_dir_path = output_dir_path / "nwb_stub"
+    output_dir_path.mkdir(parents=True, exist_ok=True)
+
+    nwbfile_path = output_dir_path / f"{subject_id}_week_session.nwb"
+
+    source_data = dict()
+    conversion_options = dict()
+
+    # Add EEG, EMG, Temperature and Activity signals
+    edf_folder_path = data_dir_path / "Ca_EEG_EDF" / (subject_id + "_EDF")
+    edf_file_paths = natsorted(edf_folder_path.glob("*.edf"))
+    assert edf_file_paths, f"No .edf files found in {edf_folder_path}"
+
+    source_data.update(
+        dict(
+            MultiEDFSignals=dict(
+                file_paths=edf_file_paths,
+            )
+        )
+    )
+    conversion_options.update(dict(MultiEDFSignals=dict(stub_test=stub_test)))
+
+    converter = Zaki2024NWBConverter(source_data=source_data)
+
+    # Add datetime to conversion
+    metadata = converter.get_metadata()
+
+    from mne.io import read_raw_edf
+
+    edf_reader = read_raw_edf(input_fname=edf_file_paths[0], verbose=verbose)
+    session_start_time = edf_reader.info["meas_date"]
+    metadata["NWBFile"]["session_start_time"] = session_start_time
+
+    # Update default metadata with the editable in the corresponding yaml file
+    editable_metadata_path = Path(__file__).parent / "zaki_2024_metadata.yaml"
+    editable_metadata = load_dict_from_file(editable_metadata_path)
+    metadata = dict_deep_update(metadata, editable_metadata)
+
+    metadata["Subject"]["subject_id"] = subject_id
+
+    # Run conversion
+    converter.run_conversion(
+        metadata=metadata, nwbfile_path=nwbfile_path, conversion_options=conversion_options, overwrite=True
+    )
+
+    if verbose:
+        stop_time = time.time()
+        conversion_time_seconds = stop_time - start
+        if conversion_time_seconds <= 60 * 3:
+            print(f"Conversion took {conversion_time_seconds:.2f} seconds")
+        elif conversion_time_seconds <= 60 * 60:
+            print(f"Conversion took {conversion_time_seconds / 60:.2f} minutes")
+        else:
+            print(f"Conversion took {conversion_time_seconds / 60 / 60:.2f} hours")
+
+
+if __name__ == "__main__":
+
+    # Parameters for conversion
+    data_dir_path = Path("D:/")
+    output_dir_path = Path("D:/cai_lab_conversion_nwb/")
+    subject_id = "Ca_EEG3-4"
+    stub_test = False
+    session_to_nwb(
+        data_dir_path=data_dir_path,
+        output_dir_path=output_dir_path,
+        stub_test=stub_test,
+        subject_id=subject_id,
+    )

src/cai_lab_to_nwb/zaki_2024/zaki_2024_nwbconverter.py

@@ -10,6 +10,7 @@
 from interfaces import (
     MinianSegmentationInterface,
     Zaki2024EDFInterface,
+    Zaki2024MultiEDFInterface,
     EzTrackFreezingBehaviorInterface,
     Zaki2024SleepClassificationInterface,
     MiniscopeImagingInterface,
@@ -27,6 +28,7 @@ class Zaki2024NWBConverter(NWBConverter):
         MinianMotionCorrection=MinianMotionCorrectionInterface,
         SleepClassification=Zaki2024SleepClassificationInterface,
         EDFSignals=Zaki2024EDFInterface,
+        MultiEDFSignals=Zaki2024MultiEDFInterface,
         FreezingBehavior=EzTrackFreezingBehaviorInterface,
         Video=VideoInterface,
         ShockStimuli=Zaki2024ShockStimuliInterface,