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position_dlc_selection.py

DLCPosSelection ΒΆ

Bases: SpyglassMixin, Manual

Specify collection of upstream DLCCentroid and DLCOrientation entries to combine into a set of position information

Source code in src/spyglass/position/v1/position_dlc_selection.py 
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@schema
class DLCPosSelection(SpyglassMixin, dj.Manual):
    """
    Specify collection of upstream DLCCentroid and DLCOrientation entries
    to combine into a set of position information
    """

    definition = """
    -> DLCCentroid.proj(dlc_si_cohort_centroid='dlc_si_cohort_selection_name', centroid_analysis_file_name='analysis_file_name')
    -> DLCOrientation.proj(dlc_si_cohort_orientation='dlc_si_cohort_selection_name', orientation_analysis_file_name='analysis_file_name')
    """

DLCPosV1 ΒΆ

Bases: SpyglassMixin, Computed

Combines upstream DLCCentroid and DLCOrientation entries into a single entry with a single Analysis NWB file

Source code in src/spyglass/position/v1/position_dlc_selection.py 
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@schema
class DLCPosV1(SpyglassMixin, dj.Computed):
    """
    Combines upstream DLCCentroid and DLCOrientation
    entries into a single entry with a single Analysis NWB file
    """

    definition = """
    -> DLCPosSelection
    ---
    -> AnalysisNwbfile
    position_object_id      : varchar(80)
    orientation_object_id   : varchar(80)
    velocity_object_id      : varchar(80)
    pose_eval_result        : longblob
    """

    def make(self, key):
        orig_key = copy.deepcopy(key)
        key["pose_eval_result"] = self.evaluate_pose_estimation(key)

        pos_nwb = (DLCCentroid & key).fetch_nwb()[0]
        ori_nwb = (DLCOrientation & key).fetch_nwb()[0]

        pos_obj = pos_nwb["dlc_position"].spatial_series["position"]
        vel_obj = pos_nwb["dlc_velocity"].time_series["velocity"]
        vid_frame_obj = pos_nwb["dlc_velocity"].time_series["video_frame_ind"]
        ori_obj = ori_nwb["dlc_orientation"].spatial_series["orientation"]

        position = pynwb.behavior.Position()
        orientation = pynwb.behavior.CompassDirection()
        velocity = pynwb.behavior.BehavioralTimeSeries()

        position.create_spatial_series(
            name=pos_obj.name,
            timestamps=np.asarray(pos_obj.timestamps),
            conversion=pos_obj.conversion,
            data=np.asarray(pos_obj.data),
            reference_frame=pos_obj.reference_frame,
            comments=pos_obj.comments,
            description=pos_obj.description,
        )

        orientation.create_spatial_series(
            name=ori_obj.name,
            timestamps=np.asarray(ori_obj.timestamps),
            conversion=ori_obj.conversion,
            data=np.asarray(ori_obj.data),
            reference_frame=ori_obj.reference_frame,
            comments=ori_obj.comments,
            description=ori_obj.description,
        )

        velocity.create_timeseries(
            name=vel_obj.name,
            timestamps=np.asarray(vel_obj.timestamps),
            conversion=vel_obj.conversion,
            unit=vel_obj.unit,
            data=np.asarray(vel_obj.data),
            comments=vel_obj.comments,
            description=vel_obj.description,
        )

        velocity.create_timeseries(
            name=vid_frame_obj.name,
            unit=vid_frame_obj.unit,
            timestamps=np.asarray(vid_frame_obj.timestamps),
            data=np.asarray(vid_frame_obj.data),
            description=vid_frame_obj.description,
            comments=vid_frame_obj.comments,
        )

        # Add to Analysis NWB file
        key["analysis_file_name"] = AnalysisNwbfile().create(
            key["nwb_file_name"]
        )
        nwb_analysis_file = AnalysisNwbfile()
        key["orientation_object_id"] = nwb_analysis_file.add_nwb_object(
            key["analysis_file_name"], orientation
        )
        key["position_object_id"] = nwb_analysis_file.add_nwb_object(
            key["analysis_file_name"], position
        )
        key["velocity_object_id"] = nwb_analysis_file.add_nwb_object(
            key["analysis_file_name"], velocity
        )

        nwb_analysis_file.add(
            nwb_file_name=key["nwb_file_name"],
            analysis_file_name=key["analysis_file_name"],
        )
        self.insert1(key)

        from ..position_merge import PositionOutput

        part_name = to_camel_case(self.table_name.split("__")[-1])
        # TODO: The next line belongs in a merge table function
        PositionOutput._merge_insert(
            [orig_key], part_name=part_name, skip_duplicates=True
        )

    def fetch1_dataframe(self):
        nwb_data = self.fetch_nwb()[0]
        index = pd.Index(
            np.asarray(nwb_data["position"].get_spatial_series().timestamps),
            name="time",
        )
        COLUMNS = [
            "video_frame_ind",
            "position_x",
            "position_y",
            "orientation",
            "velocity_x",
            "velocity_y",
            "speed",
        ]
        return pd.DataFrame(
            np.concatenate(
                (
                    np.asarray(
                        nwb_data["velocity"]
                        .time_series["video_frame_ind"]
                        .data,
                        dtype=int,
                    )[:, np.newaxis],
                    np.asarray(nwb_data["position"].get_spatial_series().data),
                    np.asarray(
                        nwb_data["orientation"].get_spatial_series().data
                    )[:, np.newaxis],
                    np.asarray(
                        nwb_data["velocity"].time_series["velocity"].data
                    ),
                ),
                axis=1,
            ),
            columns=COLUMNS,
            index=index,
        )

    @classmethod
    def evaluate_pose_estimation(cls, key):
        likelihood_thresh = []
        valid_fields = (
            DLCSmoothInterpCohort.BodyPart().fetch().dtype.fields.keys()
        )
        centroid_key = {k: val for k, val in key.items() if k in valid_fields}
        centroid_key["dlc_si_cohort_selection_name"] = key[
            "dlc_si_cohort_centroid"
        ]
        orientation_key = centroid_key.copy()
        orientation_key["dlc_si_cohort_selection_name"] = key[
            "dlc_si_cohort_orientation"
        ]
        centroid_bodyparts, centroid_si_params = (
            DLCSmoothInterpCohort.BodyPart & centroid_key
        ).fetch("bodypart", "dlc_si_params_name")
        orientation_bodyparts, orientation_si_params = (
            DLCSmoothInterpCohort.BodyPart & orientation_key
        ).fetch("bodypart", "dlc_si_params_name")
        for param in np.unique(
            np.concatenate((centroid_si_params, orientation_si_params))
        ):
            likelihood_thresh.append(
                (
                    DLCSmoothInterpParams() & {"dlc_si_params_name": param}
                ).fetch1("params")["likelihood_thresh"]
            )

        if len(np.unique(likelihood_thresh)) > 1:
            raise ValueError("more than one likelihood threshold used")
        like_thresh = likelihood_thresh[0]
        bodyparts = np.unique([*centroid_bodyparts, *orientation_bodyparts])
        fields = list(DLCPoseEstimation.BodyPart.fetch().dtype.fields.keys())
        pose_estimation_key = {k: v for k, v in key.items() if k in fields}
        pose_estimation_df = pd.concat(
            {
                bodypart: (
                    DLCPoseEstimation.BodyPart()
                    & {**pose_estimation_key, **{"bodypart": bodypart}}
                ).fetch1_dataframe()
                for bodypart in bodyparts.tolist()
            },
            axis=1,
        )
        df_filter = {
            bodypart: pose_estimation_df[bodypart]["likelihood"] < like_thresh
            for bodypart in bodyparts
            if bodypart in pose_estimation_df.columns
        }
        sub_thresh_percent_dict = {
            bodypart: (
                len(
                    np.where(
                        ~np.isnan(
                            pose_estimation_df[bodypart]["likelihood"].where(
                                df_filter[bodypart]
                            )
                        )
                    )[0]
                )
                / len(pose_estimation_df)
            )
            * 100
            for bodypart in bodyparts
        }
        return sub_thresh_percent_dict

DLCPosVideo ΒΆ

Bases: SpyglassMixin, Computed

Creates a video of the computed head position and orientation as well as the original LED positions overlaid on the video of the animal.

Use for debugging the effect of position extraction parameters.

Source code in src/spyglass/position/v1/position_dlc_selection.py 
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@schema
class DLCPosVideo(SpyglassMixin, dj.Computed):
    """Creates a video of the computed head position and orientation as well as
    the original LED positions overlaid on the video of the animal.

    Use for debugging the effect of position extraction parameters."""

    definition = """
    -> DLCPosVideoSelection
    ---
    """

    def make(self, key):
        from tqdm import tqdm as tqdm

        params = (DLCPosVideoParams & key).fetch1("params")
        if "video_params" not in params:
            params["video_params"] = {}
        M_TO_CM = 100
        interval_list_name = (
            convert_epoch_interval_name_to_position_interval_name(
                {
                    "nwb_file_name": key["nwb_file_name"],
                    "epoch": key["epoch"],
                },
                populate_missing=False,
            )
        )
        key["interval_list_name"] = interval_list_name
        epoch = (
            int(
                key["interval_list_name"]
                .replace("pos ", "")
                .replace(" valid times", "")
            )
            + 1
        )
        pose_estimation_key = {
            "nwb_file_name": key["nwb_file_name"],
            "epoch": epoch,
            "dlc_model_name": key["dlc_model_name"],
            "dlc_model_params_name": key["dlc_model_params_name"],
        }
        pose_estimation_params, video_filename, output_dir = (
            DLCPoseEstimationSelection() & pose_estimation_key
        ).fetch1(
            "pose_estimation_params", "video_path", "pose_estimation_output_dir"
        )
        print(f"video filename: {video_filename}")
        meters_per_pixel = (DLCPoseEstimation() & pose_estimation_key).fetch1(
            "meters_per_pixel"
        )
        crop = None
        if "cropping" in pose_estimation_params:
            crop = pose_estimation_params["cropping"]
        print("Loading position data...")
        position_info_df = (
            DLCPosV1()
            & {
                "nwb_file_name": key["nwb_file_name"],
                "epoch": epoch,
                "dlc_si_cohort_centroid": key["dlc_si_cohort_centroid"],
                "dlc_centroid_params_name": key["dlc_centroid_params_name"],
                "dlc_si_cohort_orientation": key["dlc_si_cohort_orientation"],
                "dlc_orientation_params_name": key[
                    "dlc_orientation_params_name"
                ],
            }
        ).fetch1_dataframe()
        pose_estimation_df = pd.concat(
            {
                bodypart: (
                    DLCPoseEstimation.BodyPart()
                    & {**pose_estimation_key, **{"bodypart": bodypart}}
                ).fetch1_dataframe()
                for bodypart in (
                    DLCSmoothInterpCohort.BodyPart & pose_estimation_key
                )
                .fetch("bodypart")
                .tolist()
            },
            axis=1,
        )
        assert len(pose_estimation_df) == len(position_info_df), (
            f"length of pose_estimation_df: {len(pose_estimation_df)} "
            f"does not match the length of position_info_df: {len(position_info_df)}."
        )

        nwb_base_filename = key["nwb_file_name"].replace(".nwb", "")
        if Path(output_dir).exists():
            output_video_filename = (
                f"{Path(output_dir).as_posix()}/"
                f"{nwb_base_filename}_{epoch:02d}_"
                f'{key["dlc_si_cohort_centroid"]}_'
                f'{key["dlc_centroid_params_name"]}'
                f'{key["dlc_orientation_params_name"]}.mp4'
            )
        else:
            output_video_filename = (
                f"{nwb_base_filename}_{epoch:02d}_"
                f'{key["dlc_si_cohort_centroid"]}_'
                f'{key["dlc_centroid_params_name"]}'
                f'{key["dlc_orientation_params_name"]}.mp4'
            )
        idx = pd.IndexSlice
        video_frame_inds = (
            position_info_df["video_frame_ind"].astype(int).to_numpy()
        )
        centroids = {
            bodypart: pose_estimation_df.loc[
                :, idx[bodypart, ("x", "y")]
            ].to_numpy()
            for bodypart in pose_estimation_df.columns.levels[0]
        }
        if params.get("incl_likelihood", None):
            likelihoods = {
                bodypart: pose_estimation_df.loc[
                    :, idx[bodypart, ("likelihood")]
                ].to_numpy()
                for bodypart in pose_estimation_df.columns.levels[0]
            }
        else:
            likelihoods = None
        position_mean = {
            "DLC": np.asarray(position_info_df[["position_x", "position_y"]])
        }
        orientation_mean = {
            "DLC": np.asarray(position_info_df[["orientation"]])
        }
        position_time = np.asarray(position_info_df.index)
        cm_per_pixel = meters_per_pixel * M_TO_CM
        percent_frames = params.get("percent_frames", None)
        frames = params.get("frames", None)
        if frames is not None:
            frames_arr = np.arange(frames[0], frames[1])
        else:
            frames_arr = frames

        print("Making video...")
        make_video(
            video_filename=video_filename,
            video_frame_inds=video_frame_inds,
            position_mean=position_mean,
            orientation_mean=orientation_mean,
            centroids=centroids,
            likelihoods=likelihoods,
            position_time=position_time,
            video_time=None,
            processor=params.get("processor", "matplotlib"),
            frames=frames_arr,
            percent_frames=percent_frames,
            output_video_filename=output_video_filename,
            cm_to_pixels=cm_per_pixel,
            disable_progressbar=False,
            crop=crop,
            **params["video_params"],
        )