Keypoint Conversion

frozen_dependencies.txt

@@ -121,6 +121,7 @@ ndx-events==0.2.0
 ndx-grayscalevolume==0.0.2
 ndx-icephys-meta==0.1.0
 ndx-photometry @ git+https://github.com/catalystneuro/ndx-photometry.git@7ea9d755ceac9524125f50ab528b403b135c4530
+-e git+https://github.com/rly/ndx-pose.git@f9dd18a8290897e48bdd6ebeedcc0a7095d86265#egg=ndx_pose
 ndx-spectrum==0.2.2
 nest-asyncio==1.5.7
 networkx==3.1

src/datta_lab_to_nwb/markowitz_gillis_nature_2023/basevideointerface.py

@@ -32,7 +32,7 @@ def __init__(
 
     def add_to_nwbfile(self, nwbfile: NWBFile, metadata: dict) -> None:
         timestamps = pd.read_csv(self.source_data["timestamp_path"]).to_numpy().squeeze()
-        TIMESTAMPS_TO_SECONDS = 1.25e-4
+        TIMESTAMPS_TO_SECONDS = metadata["Constants"]["TIMESTAMPS_TO_SECONDS"]
         timestamps -= timestamps[0]
         timestamps = timestamps * TIMESTAMPS_TO_SECONDS
 

src/datta_lab_to_nwb/markowitz_gillis_nature_2023/fiberphotometryinterface.py

@@ -5,28 +5,13 @@
 
 # NWB Ecosystem
 from pynwb.file import NWBFile
-from pynwb.core import DynamicTableRegion
 from pynwb.ophys import RoiResponseSeries
-from ndx_photometry import (
-    FibersTable,
-    PhotodetectorsTable,
-    ExcitationSourcesTable,
-    DeconvolvedRoiResponseSeries,
-    MultiCommandedVoltage,
-    FiberPhotometry,
-    FluorophoresTable,
-)
-from .basedattainterface import BaseDattaInterface
-from neuroconv.utils import load_dict_from_file
+from .rawfiberphotometryinterface import RawFiberPhotometryInterface
 from neuroconv.tools import nwb_helpers
 from hdmf.backends.hdf5.h5_utils import H5DataIO
 
-# Local
-
-
-class FiberPhotometryInterface(BaseDattaInterface):
-    """Fiber Photometry  interface for markowitz_gillis_nature_2023 conversion"""
 
+class FiberPhotometryInterface(RawFiberPhotometryInterface):
     def __init__(
         self,
         file_path: str,
@@ -57,192 +42,28 @@ def __init__(
             subject_metadata_path=subject_metadata_path,
         )
 
-    def get_metadata(self) -> dict:
-        metadata = super().get_metadata()
-        session_metadata = load_dict_from_file(self.source_data["session_metadata_path"])
-        subject_metadata = load_dict_from_file(self.source_data["subject_metadata_path"])
-        tdt_metadata = load_dict_from_file(self.source_data["tdt_metadata_path"])
-        session_metadata = session_metadata[self.source_data["session_uuid"]]
-        subject_metadata = subject_metadata[session_metadata["subject_id"]]
-
-        metadata["FiberPhotometry"]["reference_max"] = session_metadata["reference_max"]
-        metadata["FiberPhotometry"]["signal_max"] = session_metadata["signal_max"]
-        metadata["FiberPhotometry"]["signal_reference_corr"] = session_metadata["signal_reference_corr"]
-        metadata["FiberPhotometry"]["snr"] = session_metadata["snr"]
-        metadata["FiberPhotometry"]["area"] = subject_metadata["photometry_area"]
-        metadata["FiberPhotometry"]["gain"] = float(tdt_metadata["tags"]["OutputGain"])
-        metadata["FiberPhotometry"]["signal_amp"] = tdt_metadata["tags"]["LED1Amp"]
-        metadata["FiberPhotometry"]["reference_amp"] = tdt_metadata["tags"]["LED2Amp"]
-        metadata["FiberPhotometry"]["signal_freq"] = float(tdt_metadata["tags"]["LED1Freq"])
-        metadata["FiberPhotometry"]["reference_freq"] = float(tdt_metadata["tags"]["LED2Freq"])
-        metadata["FiberPhotometry"]["raw_rate"] = tdt_metadata["status"]["sampling_rate"]
-
-        return metadata
-
-    def get_metadata_schema(self) -> dict:
-        metadata_schema = super().get_metadata_schema()
-        metadata_schema["properties"]["FiberPhotometry"] = {
-            "type": "object",
-            "properties": {
-                "area": {"type": "string"},
-                "reference_max": {"type": "number"},
-                "signal_max": {"type": "number"},
-                "signal_reference_corr": {"type": "number"},
-                "snr": {"type": "number"},
-            },
-        }
-        return metadata_schema
-
     def add_to_nwbfile(self, nwbfile: NWBFile, metadata: dict) -> None:
+        super().add_to_nwbfile(nwbfile, metadata)
         session_df = pd.read_parquet(
             self.source_data["file_path"],
             columns=self.source_data["columns"],
             filters=[("uuid", "==", self.source_data["session_uuid"])],
         )
-        photometry_dict = load_tdt_data(self.source_data["tdt_path"], fs=metadata["FiberPhotometry"]["raw_rate"])
-        session_start_index = np.flatnonzero(photometry_dict["sync"])[0]
-        raw_photometry = photometry_dict["pmt00"][session_start_index:]
-        commanded_signal = photometry_dict["pmt00_x"][session_start_index:]
-        commanded_reference = photometry_dict["pmt01_x"][session_start_index:]
-
-        # Commanded Voltage
-        multi_commanded_voltage = MultiCommandedVoltage()
-        commanded_signal_series = multi_commanded_voltage.create_commanded_voltage_series(
-            name="commanded_signal",
-            description=(
-                "A 470nm (blue) LED and a 405nM (UV) LED (Mightex) were sinusoidally modulated at 161Hz and 381Hz, "
-                "respectively (these frequencies were chosen to avoid harmonic cross-talk)."
-            ),
-            data=H5DataIO(commanded_signal, compression=True),
-            frequency=metadata["FiberPhotometry"]["signal_freq"],
-            power=metadata["FiberPhotometry"]["signal_amp"],  # TODO: Fix this in ndx-photometry
-            rate=metadata["FiberPhotometry"]["raw_rate"],
-            unit="volts",
-        )
-        commanded_reference_series = multi_commanded_voltage.create_commanded_voltage_series(
-            name="commanded_reference",
-            description=(
-                "A 470nm (blue) LED and a 405nM (UV) LED (Mightex) were sinusoidally modulated at 161Hz and 381Hz, "
-                "respectively (these frequencies were chosen to avoid harmonic cross-talk)."
-            ),
-            data=H5DataIO(commanded_reference, compression=True),
-            frequency=metadata["FiberPhotometry"]["reference_freq"],
-            power=metadata["FiberPhotometry"]["reference_amp"],  # TODO: Fix this in ndx-photometry
-            rate=metadata["FiberPhotometry"]["raw_rate"],
-            unit="volts",
-        )
-        # Excitation Sources Table
-        excitation_sources_table = ExcitationSourcesTable(
-            description=(
-                "A 470nm (blue) LED and a 405nM (UV) LED (Mightex) were sinusoidally modulated at "
-                "161Hz and 381Hz, respectively (these frequencies were chosen to avoid harmonic cross-talk). "
-                "Modulated excitation light was passed through a three-colour fluorescence mini-cube "
-                "(Doric Lenses FMC7_E1(400-410)_F1(420-450)_E2(460-490)_F2(500-540)_E3(550-575)_F3(600-680)_S), "
-                "then through a pigtailed rotary joint "
-                "(Doric Lenses B300-0089, FRJ_1x1_PT_200/220/LWMJ-0.37_1.0m_FCM_0.08m_FCM) and finally into a "
-                "low-autofluorescence fibre-optic patch cord "
-                "(Doric Lenses MFP_200/230/900-0.37_0.75m_FCM-MF1.25_LAF or MFP_200/230/900-0.57_0.75m_FCM-MF1.25_LAF) "
-                "connected to the optical implant in the freely moving mouse."
-            )
-        )
-        excitation_sources_table.add_row(
-            peak_wavelength=470.0,
-            source_type="LED",
-            commanded_voltage=commanded_signal_series,
-        )
-        excitation_sources_table.add_row(
-            peak_wavelength=405.0,
-            source_type="LED",
-            commanded_voltage=commanded_reference_series,
-        )
-
-        # Photodetectors Table
-        photodetectors_table = PhotodetectorsTable(
-            description=(
-                "Emission light was collected through the same patch cord, then passed back through the mini-cube. "
-                "Light on the F2 port was bandpass filtered for green emission (500–540nm) and sent to a silicon "
-                "photomultiplier with an integrated transimpedance amplifier (SensL MiniSM-30035-X08). Voltages from "
-                "the SensL unit were collected through the TDT Active X interface using 24-bit analogue-to-digital "
-                "convertors at >6kHz, and voltage signals driving the UV and blue LEDs were also stored for "
-                "offline analysis."
-            ),
-        )
-        photodetectors_table.add_row(peak_wavelength=527.0, type="PMT", gain=metadata["FiberPhotometry"]["gain"])
-
-        # Fluorophores Table
-        fluorophores_table = FluorophoresTable(
-            description=(
-                "dLight1.1 was selected to visualize dopamine release dynamics in the DLS owing to its rapid rise and "
-                "decay times, comparatively lower dopamine affinity (so as to not saturate binding), as well as its "
-                "responsiveness over much of the physiological range of known DA concentrations in freely moving "
-                "rodents."
-            ),
-        )
-
-        fluorophores_table.add_row(
-            label="dlight1.1",
-            location=metadata["FiberPhotometry"]["area"],
-            coordinates=(0.260, 2.550, -2.40),  # (AP, ML, DV)
-        )
-
-        # Fibers Table
-        fibers_table = FibersTable(
-            description=(
-                "Fiber photometry data with 2 excitation sources (470nm and 405nm), 1 PMT photodetector with "
-                "a peak wavelength of 527nm, and 1 fluorophore (dLight1.1)."
-            )
-        )
-        nwbfile.add_lab_meta_data(
-            FiberPhotometry(
-                fibers=fibers_table,
-                excitation_sources=excitation_sources_table,
-                photodetectors=photodetectors_table,
-                fluorophores=fluorophores_table,
-            )
-        )
-
-        # Important: we add the fibers to the fibers table _after_ adding the metadata
-        # This ensures that we can find this data in their tables of origin
-        fibers_table.add_fiber(
-            excitation_source=0,  # integers indicated rows of excitation sources table
-            photodetector=0,
-            fluorophores=[0],  # potentially multiple fluorophores, so list of indices
-            location=metadata["FiberPhotometry"]["area"],
-        )
-        fibers_table.add_fiber(
-            excitation_source=1,  # integers indicated rows of excitation sources table
-            photodetector=0,
-            fluorophores=[0],  # potentially multiple fluorophores, so list of indices
-            location=metadata["FiberPhotometry"]["area"],
-        )
-
-        # ROI Response Series
-        # Here we set up a list of fibers that our recording came from
-        fibers_ref = DynamicTableRegion(name="rois", data=[0, 1], description="source fibers", table=fibers_table)
-        raw_photometry = RoiResponseSeries(
-            name="RawPhotometry",
-            description="The raw acquisition with mixed signal from both the blue light excitation (470nm) and UV excitation (405nm).",
-            data=H5DataIO(raw_photometry, compression=True),
-            unit="F",
-            starting_time=0.0,
-            rate=metadata["FiberPhotometry"]["raw_rate"],
-            rois=fibers_ref,
-        )
         signal_series = RoiResponseSeries(
             name="SignalDfOverF",
             description="The ΔF/F from the blue light excitation (470nm) corresponding to the dopamine signal.",
             data=H5DataIO(session_df.signal_dff.to_numpy(), compression=True),
             unit="a.u.",
             timestamps=H5DataIO(session_df.timestamp.to_numpy(), compression=True),
-            rois=fibers_ref,
+            rois=self.fibers_ref,
         )
         reference_series = RoiResponseSeries(
             name="ReferenceDfOverF",
             description="The ∆F/F from the isosbestic UV excitation (405nm) corresponding to the reference signal.",
             data=H5DataIO(session_df.reference_dff.to_numpy(), compression=True),
             unit="a.u.",
             timestamps=signal_series.timestamps,
-            rois=fibers_ref,
+            rois=self.fibers_ref,
         )
         reference_fit_series = RoiResponseSeries(
             name="ReferenceDfOverFSmoothed",
@@ -253,7 +74,7 @@ def add_to_nwbfile(self, nwbfile: NWBFile, metadata: dict) -> None:
             data=H5DataIO(session_df.reference_dff_fit.to_numpy(), compression=True),
             unit="a.u.",
             timestamps=signal_series.timestamps,
-            rois=fibers_ref,
+            rois=self.fibers_ref,
         )
         uv_reference_fit_series = RoiResponseSeries(
             name="UVReferenceFSmoothed",
@@ -264,38 +85,10 @@ def add_to_nwbfile(self, nwbfile: NWBFile, metadata: dict) -> None:
             data=H5DataIO(session_df.uv_reference_fit.to_numpy(), compression=True),
             unit="n.a.",
             timestamps=signal_series.timestamps,
-            rois=fibers_ref,
+            rois=self.fibers_ref,
         )
-
-        # Aggregate into OPhys Module and NWBFile
-        nwbfile.add_acquisition(raw_photometry)
         ophys_module = nwb_helpers.get_module(nwbfile, name="ophys", description="Fiber photometry data")
-        ophys_module.add(multi_commanded_voltage)
         ophys_module.add(signal_series)
         ophys_module.add(reference_series)
         ophys_module.add(reference_fit_series)
         ophys_module.add(uv_reference_fit_series)
-
-
-def load_tdt_data(filename, pmt_channels=[0, 3], sync_channel=6, clock_channel=7, nch=8, fs=6103.515625):
-    float_data = np.fromfile(filename, dtype=">f4")
-    int_data = np.fromfile(filename, dtype=">i4")
-
-    photometry_dict = {}
-
-    for i, pmt in enumerate(pmt_channels):
-        photometry_dict["pmt{:02d}".format(i)] = float_data[pmt::nch]
-        photometry_dict["pmt{:02d}_x".format(i)] = float_data[pmt + 1 :: nch]
-        photometry_dict["pmt{:02d}_y".format(i)] = float_data[pmt + 2 :: nch]
-
-    photometry_dict["sync"] = int_data[sync_channel::8]
-    photometry_dict["clock"] = int_data[clock_channel::8]
-
-    if any(np.diff(photometry_dict["clock"]) != 1):
-        raise IOError("Timebase not uniform in TDT file.")
-
-    clock_df = np.diff(photometry_dict["clock"].astype("float64"))
-    clock_df = np.insert(clock_df, 0, 0, axis=0)
-    photometry_dict["tstep"] = np.cumsum(clock_df * 1 / fs)
-
-    return photometry_dict

src/datta_lab_to_nwb/markowitz_gillis_nature_2023/markowitz_gillis_nature_2023_metadata.yaml

@@ -30,6 +30,9 @@ Subject:
   species: Mus musculus
   age: P6W/P15W
   description: This study utilized wild-type DAT-IRES-Cre (The Jackson Laboratory 006660) and Ai32 (The Jackson Laboratory 012569) mice, both male and female, between 6-15 weeks of age.
+Constants:
+  VIDEO_SAMPLING_RATE: 30.0
+  TIMESTAMPS_TO_SECONDS: 1.25e-4
 Behavior:
   Position:
     reference_frame: TBD

src/datta_lab_to_nwb/markowitz_gillis_nature_2023/moseqextractinterface.py

@@ -42,7 +42,7 @@ def add_to_nwbfile(self, nwbfile: NWBFile, metadata: dict) -> None:
             loglikelihood_video = np.array(file["frames_mask"])
 
             # Timestamps
-            TIMESTAMPS_TO_SECONDS = 1.25e-4
+            TIMESTAMPS_TO_SECONDS = metadata["Constants"]["TIMESTAMPS_TO_SECONDS"]
             timestamps = np.array(file["timestamps"]) * TIMESTAMPS_TO_SECONDS
             timestamps -= timestamps[0]
 

src/datta_lab_to_nwb/markowitz_gillis_nature_2023/preconversion/extract_metadata.py

@@ -289,6 +289,30 @@ def extract_reinforcement_photometry_metadata(
     return session_metadata, subject_metadata
 
 
+def extract_keypoint_metadata(data_path: str):
+    keypoint_subjects = ["dls-dlight-9", "dls-dlight-10", "dls-dlight-11", "dls-dlight-12", "dls-dlight-13"]
+    session_metadata, subject_metadata = {}, {}
+    for subject in keypoint_subjects:
+        session_metadata[subject] = dict(
+            keypoint=True,
+            photometry=True,
+            session_description="keypoint session",
+            session_start_time="1901-01-01T00:00:00-05:00",  # TODO: replace with real session start time
+            reference_max=np.NaN,
+            signal_max=np.NaN,
+            signal_reference_corr=np.NaN,
+            snr=np.NaN,
+            subject_id=subject,
+        )
+        subject_metadata[subject] = dict(
+            genotype="dls-dlight",
+            opsin="n/a",
+            photometry_area="dls",
+            sex="U",
+        )
+    return session_metadata, subject_metadata
+
+
 def resolve_duplicates(photometry_metadata, photometry_ids, reinforcement_metadata, reinforcement_ids):
     resolved_metadata = {}
     _resolve_duplicates(
@@ -416,6 +440,8 @@ def get_session_name(session_df):
     reinforcement_photometry_subject_metadata_path = metadata_path / "reinforcement-photometry-subject-metadata.yaml"
     velocity_session_metadata_path = metadata_path / "velocity-modulation-session-metadata.yaml"
     velocity_subject_metadata_path = metadata_path / "velocity-modulation-subject-metadata.yaml"
+    keypoint_session_metadata_path = metadata_path / "keypoint-session-metadata.yaml"
+    keypoint_subject_metadata_path = metadata_path / "keypoint-subject-metadata.yaml"
 
     # Example UUIDs
     dls_dlight_1_example = "18dc5ad5-13f0-4297-8b21-75d434770e57"
@@ -451,6 +477,7 @@ def get_session_name(session_df):
     velocity_session_metadata, velocity_subject_metadata = extract_velocity_modulation_metadata(
         data_path, example_uuids=velocity_modulation_examples
     )
+    keypoint_session_metadata, keypoint_subject_metadata = extract_keypoint_metadata(data_path)
 
     path2metadata = {
         photometry_session_metadata_path: photometry_session_metadata,
@@ -461,6 +488,8 @@ def get_session_name(session_df):
         reinforcement_photometry_subject_metadata_path: reinforcement_photometry_subject_metadata,
         velocity_session_metadata_path: velocity_session_metadata,
         velocity_subject_metadata_path: velocity_subject_metadata,
+        keypoint_session_metadata_path: keypoint_session_metadata,
+        keypoint_subject_metadata_path: keypoint_subject_metadata,
     }
     for path, resolved_dict in path2metadata.items():
         with open(path, "w") as f: