Source code for neuroconv.datainterfaces.behavior.sleap.sleapdatainterface

from pathlib import Path

import numpy as np
from pydantic import FilePath, validate_call
from pynwb.file import NWBFile

from .sleap_utils import extract_timestamps
from ....basetemporalalignmentinterface import BaseTemporalAlignmentInterface




class SLEAPInterface(BaseTemporalAlignmentInterface):
    """Data interface for SLEAP datasets."""

    display_name = "SLEAP"
    keywords = ("pose estimation", "tracking", "video")
    associated_suffixes = (".slp", ".mp4")
    info = "Interface for SLEAP pose estimation datasets."



    @classmethod
    def get_source_schema(cls) -> dict:
        source_schema = super().get_source_schema()
        source_schema["properties"]["file_path"]["description"] = "Path to the .slp file (the output of sleap)"
        source_schema["properties"]["video_file_path"][
            "description"
        ] = "Path of the video for extracting timestamps (optional)."
        return source_schema


    @validate_call
    def __init__(
        self,
        file_path: FilePath,
        video_file_path: FilePath | None = None,
        verbose: bool = False,
        frames_per_second: float | None = None,
    ):
        """
        Interface for writing sleap .slp files to nwb using the sleap-io library.

        Parameters
        ----------
        file_path : FilePath
            Path to the .slp file (the output of sleap)
        verbose : bool, default: False
            controls verbosity. ``True`` by default.
        video_file_path : FilePath, optional
            The file path of the video for extracting timestamps.
        frames_per_second : float, optional
            The frames per second (fps) or sampling rate of the video.
        """

        # This import is to assure that the ndx_pose is in the global namespace when an pynwb.io object is created
        # For more detail, see https://github.com/rly/ndx-pose/issues/36
        import ndx_pose  # noqa: F401

        self.file_path = Path(file_path)
        self.video_file_path = video_file_path
        self.video_sample_rate = frames_per_second
        self.verbose = verbose
        self._timestamps = None
        super().__init__(file_path=file_path)



    def get_original_timestamps(self) -> np.ndarray:
        if self.video_file_path is None:
            raise ValueError(
                "Unable to fetch the original timestamps from the video! "
                "Please specify 'video_file_path' when initializing the interface."
            )
        return np.array(extract_timestamps(self.video_file_path))




    def get_timestamps(self) -> np.ndarray:
        timestamps = self._timestamps if self._timestamps is not None else self.get_original_timestamps()
        return timestamps




    def set_aligned_timestamps(self, aligned_timestamps: np.ndarray):
        self._timestamps = aligned_timestamps




    def add_to_nwbfile(
        self,
        nwbfile: NWBFile,
        metadata: dict | None = None,
    ):
        """
        Conversion from DLC output files to nwb. Derived from sleap-io library.

        Parameters
        ----------
        nwbfile: NWBFile
            nwb file to which the recording information is to be added
        metadata: dict
            metadata info for constructing the nwb file (optional).
        """

        pose_estimation_metadata = dict()
        if self.video_file_path or self._timestamps:
            video_timestamps = self.get_timestamps()
            pose_estimation_metadata.update(video_timestamps=video_timestamps)

        if self.video_sample_rate:
            pose_estimation_metadata.update(video_sample_rate=self.video_sample_rate)

        from sleap_io import load_slp
        from sleap_io.io.nwb_predictions import append_nwb_data

        labels = load_slp(self.file_path)
        append_nwb_data(labels=labels, nwbfile=nwbfile, pose_estimation_metadata=pose_estimation_metadata)