vprobe electrodes locations

requirements.txt

@@ -1,5 +1,5 @@
-neuroconv==0.4.4
-spikeinterface==0.98.2
+neuroconv==0.4.7
+spikeinterface==0.99.1
 nwbwidgets
 nwbinspector
 pre-commit

setup.py

@@ -1,6 +1,7 @@
 # -*- coding: utf-8 -*-
 from pathlib import Path
-from setuptools import setup, find_packages
+
+from setuptools import find_packages, setup
 
 requirements_file_path = Path(__file__).parent / "requirements.txt"
 with open(requirements_file_path) as file:

src/jazayeri_lab_to_nwb/watters/__init__.py

@@ -1,4 +0,0 @@
-from .wattersbehaviorinterface import WattersEyePositionInterface, WattersPupilSizeInterface
-from .watterstrialsinterface import WattersTrialsInterface
-from .wattersrecordinginterface import WattersDatRecordingInterface
-from .wattersnwbconverter import WattersNWBConverter

src/jazayeri_lab_to_nwb/watters/watters_convert_session.py

@@ -1,16 +1,16 @@
 """Primary script to run to convert an entire session for of data using the NWBConverter."""
-import os
 import datetime
 import glob
 import json
 import logging
+import os
 from pathlib import Path
 from typing import Union
 from uuid import uuid4
 from zoneinfo import ZoneInfo
 
 from neuroconv.tools.data_transfers import automatic_dandi_upload
-from neuroconv.utils import load_dict_from_file, dict_deep_update
+from neuroconv.utils import dict_deep_update, load_dict_from_file
 
 from jazayeri_lab_to_nwb.watters import WattersNWBConverter
 
@@ -64,6 +64,7 @@ def session_to_nwb(
     """
     if dandiset_id is not None:
         import dandi  # check importability
+
         assert os.getenv("DANDI_API_KEY"), (
             "Unable to find environment variable 'DANDI_API_KEY'. "
             "Please retrieve your token from DANDI and set this environment variable."
@@ -249,5 +250,5 @@ def session_to_nwb(
         output_dir_path=output_dir_path,
         stub_test=stub_test,
         overwrite=overwrite,
-        # dandiset_id = "000620", 
+        # dandiset_id = "000620",
     )

src/jazayeri_lab_to_nwb/watters/wattersbehaviorinterface.py

@@ -1,13 +1,13 @@
 """Primary classes for converting experiment-specific behavior."""
-import numpy as np
 from pathlib import Path
-from pynwb import NWBFile, TimeSeries
-from pynwb.behavior import SpatialSeries
-from hdmf.backends.hdf5 import H5DataIO
 
+import numpy as np
+from hdmf.backends.hdf5 import H5DataIO
 from neuroconv.basetemporalalignmentinterface import BaseTemporalAlignmentInterface
-from neuroconv.utils import DeepDict, FolderPathType, FilePathType
 from neuroconv.tools.nwb_helpers import get_module
+from neuroconv.utils import DeepDict, FilePathType, FolderPathType
+from pynwb import NWBFile, TimeSeries
+from pynwb.behavior import SpatialSeries
 
 
 class NumpyTemporalAlignmentMixin:

src/jazayeri_lab_to_nwb/watters/wattersnwbconverter.py

@@ -1,30 +1,30 @@
 """Primary NWBConverter class for this dataset."""
 import json
 import logging
-import numpy as np
-from typing import Optional
 from pathlib import Path
+from typing import Optional
 
+import numpy as np
 from neuroconv import NWBConverter
-from neuroconv.utils import FolderPathType
+from neuroconv.basetemporalalignmentinterface import BaseTemporalAlignmentInterface
 from neuroconv.datainterfaces import (
-    SpikeGLXRecordingInterface,
     KiloSortSortingInterface,
+    SpikeGLXRecordingInterface,
+)
+from neuroconv.datainterfaces.ecephys.basesortingextractorinterface import (
+    BaseSortingExtractorInterface,
 )
-from neuroconv.datainterfaces.ecephys.baserecordingextractorinterface import BaseRecordingExtractorInterface
-from neuroconv.datainterfaces.ecephys.basesortingextractorinterface import BaseSortingExtractorInterface
-from neuroconv.basetemporalalignmentinterface import BaseTemporalAlignmentInterface
 from neuroconv.datainterfaces.text.timeintervalsinterface import TimeIntervalsInterface
-
+from neuroconv.utils import FolderPathType
 from spikeinterface.core.waveform_tools import has_exceeding_spikes
 from spikeinterface.curation import remove_excess_spikes
 
-from . import (
-    WattersDatRecordingInterface,
+from .wattersbehaviorinterface import (
     WattersEyePositionInterface,
     WattersPupilSizeInterface,
-    WattersTrialsInterface,
 )
+from .wattersrecordinginterface import WattersDatRecordingInterface
+from .watterstrialsinterface import WattersTrialsInterface
 
 
 class WattersNWBConverter(NWBConverter):

src/jazayeri_lab_to_nwb/watters/wattersrecordinginterface.py

@@ -1,82 +1,22 @@
 """Primary class for Watters Plexon probe data."""
-import os
 import json
-import numpy as np
-from pynwb import NWBFile
+import os
 from pathlib import Path
 from typing import Optional, Union
 
-from neuroconv.datainterfaces.ecephys.baserecordingextractorinterface import BaseRecordingExtractorInterface
+import numpy as np
+import probeinterface as pi
+from neuroconv.datainterfaces.ecephys.baserecordingextractorinterface import (
+    BaseRecordingExtractorInterface,
+)
 from neuroconv.utils import FilePathType
+from pynwb import NWBFile
 from spikeinterface import BaseRecording
 
 
-def add_electrode_locations(
-    recording_extractor: BaseRecording,
-    probe_metadata_file: FilePathType,
-    probe_name: str,
-    probe_key: str,
-) -> list[dict]:
-    with open(probe_metadata_file, "r") as f:
-        all_probe_metadata = json.load(f)
-    probe_metadata = None
-    for entry in all_probe_metadata:
-        if entry["label"] == probe_key:
-            probe_metadata = entry
-
-    if probe_metadata is None:
-        return []
-
-    probe_coord_system = probe_metadata["coordinate_system"]
-    coord_names = probe_coord_system.split("[")[1].split("]")[0].split(",")
-    electrode_metadata = [
-        {
-            "name": "x",
-            "description": f"{coord_names[0].strip()} coordinate. {probe_coord_system}",
-        },
-        {
-            "name": "y",
-            "description": f"{coord_names[1].strip()} coordinate. {probe_coord_system}",
-        },
-    ]
-    if len(coord_names) == 3:
-        electrode_metadata.append(
-            {
-                "name": "z",
-                "description": f"{coord_names[2].strip()} coordinate. {probe_coord_system}",
-            },
-        )
-
-    channel_ids = recording_extractor.get_channel_ids()
-    recording_extractor.set_property(
-        key="group_name",
-        ids=channel_ids,
-        values=[probe_name] * len(channel_ids),
-    )
-    coordinates = probe_metadata["coordinates"]
-    recording_extractor.set_property(
-        key="x",
-        values=[coordinates["first_channel"][0], coordinates["last_channel"][0]],
-        ids=channel_ids[[0, -1]],
-    )
-    recording_extractor.set_property(
-        key="y",
-        values=[coordinates["first_channel"][1], coordinates["last_channel"][1]],
-        ids=channel_ids[[0, -1]],
-    )
-    if len(coord_names) == 3:
-        recording_extractor.set_property(
-            key="z",
-            values=[coordinates["first_channel"][2], coordinates["last_channel"][2]],
-            ids=channel_ids[[0, -1]],
-        )
-
-    return electrode_metadata
-
-
 class WattersDatRecordingInterface(BaseRecordingExtractorInterface):
 
-    ExtractorName = "NumpyRecording"
+    ExtractorName = "BinaryRecordingExtractor"
 
     def __init__(
         self,
@@ -88,37 +28,56 @@ def __init__(
         t_start: float = 0.0,
         sampling_frequency: float = 30000.0,
         channel_ids: Optional[list] = None,
-        gain_to_uv: list = [1.0],
+        gain_to_uv: list = 1.0,
+        offset_to_uv: list = 0.0,
         probe_metadata_file: Optional[FilePathType] = None,
         probe_name: str = "vprobe",
         probe_key: Optional[str] = None,
     ):
-        traces = np.memmap(file_path, dtype=dtype, mode="r").reshape(-1, channel_count)
         source_data = {
-            "traces_list": [traces],
+            "file_paths": [file_path],
             "sampling_frequency": sampling_frequency,
+            "num_channels": channel_count,
             "t_starts": [t_start],
             "channel_ids": channel_ids,
+            "gain_to_uV": gain_to_uv,
+            "offset_to_uV": offset_to_uv,
+            "dtype": dtype,
         }
         super().__init__(verbose=verbose, es_key=es_key, **source_data)
-        if gain_to_uv is not None:
-            if len(gain_to_uv) == 1:
-                gain_to_uv = np.full((channel_count,), gain_to_uv[0], dtype=float)
-            else:
-                assert len(gain_to_uv) == channel_count, (
-                    f"There are {channel_count} channels " f"but `gain_to_uv` has length {len(gain_to_uv)}"
-                )
-                gain_to_uv = np.array(gain_to_uv, dtype=float)
-            self.recording_extractor.set_property("gain_to_uV", gain_to_uv)
-        self.probe_metadata_file = probe_metadata_file
+
+        # this is used for metadata naming
         self.probe_name = probe_name
-        self.probe_key = probe_key
 
-        self.electrode_metadata = None
-        if self.probe_metadata_file is not None and self.probe_key is not None:
-            self.electrode_metadata = add_electrode_locations(
-                self.recording_extractor, self.probe_metadata_file, self.probe_name, self.probe_key
-            )
+        # add probe information
+        probe_metadata = None
+        if probe_metadata_file is not None and probe_key is not None:
+            with open(probe_metadata_file, "r") as f:
+                all_probe_metadata = json.load(f)
+            for entry in all_probe_metadata:
+                if entry["label"] == probe_key:
+                    probe_metadata = entry
+
+        if probe_metadata is not None and "electrodes_locations" in probe_metadata:
+            # Grab electrode position from metadata
+            locations_array = np.array(probe_metadata["electrodes_locations"])
+            ndim = locations_array.shape[1]
+            probe = pi.Probe(ndim=ndim)
+            probe.set_contacts(locations_array)
+        else:
+            # Generate V-probe geometry: 64 channels arranged vertically with 50 um spacing
+            probe = pi.generate_linear_probe(num_elec=channel_count, ypitch=50)
+        probe.set_device_channel_indices(np.arange(channel_count))
+        probe.name = probe_name
+
+        # set probe to interface recording
+        self.set_probe(probe, group_mode="by_probe")
+
+        # set group_name property to match electrode group name in metadata
+        self.recording_extractor.set_property(
+            key="group_name",
+            values=[probe_name] * len(self.recording_extractor.channel_ids),
+        )
 
     def get_metadata(self) -> dict:
         metadata = super().get_metadata()
@@ -139,8 +98,4 @@ def get_metadata(self) -> dict:
         ]
         metadata["Ecephys"]["ElectrodeGroup"] = electrode_groups
 
-        if self.electrode_metadata is None:
-            return metadata
-
-        metadata["Ecephys"]["Electrodes"] = self.electrode_metadata
         return metadata

src/jazayeri_lab_to_nwb/watters/watterstrialsinterface.py

@@ -1,14 +1,14 @@
 """Primary class for converting experiment-specific behavior."""
 import json
-import numpy as np
-import pandas as pd
 import warnings
 from pathlib import Path
-from pynwb import NWBFile
 from typing import Optional
 
+import numpy as np
+import pandas as pd
 from neuroconv.datainterfaces.text.timeintervalsinterface import TimeIntervalsInterface
-from neuroconv.utils import DeepDict, FolderPathType, FilePathType
+from neuroconv.utils import DeepDict, FilePathType, FolderPathType
+from pynwb import NWBFile
 
 
 class WattersTrialsInterface(TimeIntervalsInterface):