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position_dlc_position.py

DLCSmoothInterpParams

Bases: SpyglassMixin, Manual

Parameters for extracting the smoothed head position.

Attributes:

Name Type Description
interpolate bool, default True

whether to interpolate over NaN spans
smooth bool, default True

whether to smooth the dataset
smoothing_params dict

smoothing_duration : float, default 0.05 number of frames to smooth over: sampling_rate*smoothing_duration = num_frames
interp_params dict

max_cm_to_interp : int, default 20 maximum distance between high likelihood points on either side of a NaN span to interpolate over
likelihood_thresh float, default 0.95

likelihood below which to NaN and interpolate over
Source code in src/spyglass/position/v1/position_dlc_position.py 
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@schema
class DLCSmoothInterpParams(SpyglassMixin, dj.Manual):
    """
    Parameters for extracting the smoothed head position.

    Attributes
    ----------
    interpolate : bool, default True
        whether to interpolate over NaN spans
    smooth : bool, default True
        whether to smooth the dataset
    smoothing_params : dict
        smoothing_duration : float, default 0.05
            number of frames to smooth over:
            sampling_rate*smoothing_duration = num_frames
    interp_params : dict
        max_cm_to_interp : int, default 20
            maximum distance between high likelihood points on either side of a
            NaN span to interpolate over
    likelihood_thresh : float, default 0.95
        likelihood below which to NaN and interpolate over
    """

    definition = """
    dlc_si_params_name : varchar(80) # name for this set of parameters
    ---
    params: longblob # dictionary of parameters
    """

    @classmethod
    def insert_params(cls, params_name: str, params: dict, **kwargs):
        """Insert parameters for smoothing and interpolation."""
        cls.insert1(
            {"dlc_si_params_name": params_name, "params": params},
            **kwargs,
        )

    @classmethod
    def insert_default(cls, **kwargs):
        """Insert the default set of parameters."""

        default_params = {
            "smooth": True,
            "smoothing_params": {
                "smoothing_duration": 0.05,
                "smooth_method": "moving_avg",
            },
            "interpolate": True,
            "likelihood_thresh": 0.95,
            "interp_params": {"max_cm_to_interp": 15},
            "max_cm_between_pts": 20,
            # This is for use when finding "good spans" and is how many indices
            # to bridge in between good spans see inds_to_span in get_good_spans
            "num_inds_to_span": 20,
        }
        cls.insert1(
            {"dlc_si_params_name": "default", "params": default_params},
            **kwargs,
        )

    @classmethod
    def insert_nan_params(cls, **kwargs) -> None:
        """Insert parameters that only NaN the data."""
        nan_params = {
            "smooth": False,
            "interpolate": False,
            "likelihood_thresh": 0.95,
            "max_cm_between_pts": 20,
            "num_inds_to_span": 20,
        }
        cls.insert1(
            {"dlc_si_params_name": "just_nan", "params": nan_params}, **kwargs
        )

    @classmethod
    def get_default(cls) -> dict:
        """Return the default set of parameters for smoothing calculation."""
        query = cls & {"dlc_si_params_name": "default"}
        if not len(query) > 0:
            cls().insert_default(skip_duplicates=True)
            default = (cls & {"dlc_si_params_name": "default"}).fetch1()
        else:
            default = query.fetch1()
        return default

    @classmethod
    def get_nan_params(cls) -> dict:
        """Return the parameters that NaN the data."""
        query = cls & {"dlc_si_params_name": "just_nan"}
        if not len(query) > 0:
            cls().insert_nan_params(skip_duplicates=True)
            nan_params = (cls & {"dlc_si_params_name": "just_nan"}).fetch1()
        else:
            nan_params = query.fetch1()
        return nan_params

    @staticmethod
    def get_available_methods():
        """Return the available smoothing methods."""
        return _key_to_smooth_func_dict.keys()

    def insert1(self, key, **kwargs):
        """Override insert1 to validate params."""
        params = key.get("params")
        if not isinstance(params, dict):
            raise KeyError("'params' must be a dict in key")

        validate_option(
            option=params.get("max_cm_between_pts"), name="max_cm_between_pts"
        )
        validate_smooth_params(params)

        validate_option(
            params.get("likelihood_thresh"),
            name="likelihood_thresh",
            types=float,
            val_range=(0, 1),
        )

        super().insert1(key, **kwargs)

insert_params(params_name, params, **kwargs) classmethod

Insert parameters for smoothing and interpolation.

Source code in src/spyglass/position/v1/position_dlc_position.py 
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@classmethod
def insert_params(cls, params_name: str, params: dict, **kwargs):
    """Insert parameters for smoothing and interpolation."""
    cls.insert1(
        {"dlc_si_params_name": params_name, "params": params},
        **kwargs,
    )

insert_default(**kwargs) classmethod

Insert the default set of parameters.

Source code in src/spyglass/position/v1/position_dlc_position.py 
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@classmethod
def insert_default(cls, **kwargs):
    """Insert the default set of parameters."""

    default_params = {
        "smooth": True,
        "smoothing_params": {
            "smoothing_duration": 0.05,
            "smooth_method": "moving_avg",
        },
        "interpolate": True,
        "likelihood_thresh": 0.95,
        "interp_params": {"max_cm_to_interp": 15},
        "max_cm_between_pts": 20,
        # This is for use when finding "good spans" and is how many indices
        # to bridge in between good spans see inds_to_span in get_good_spans
        "num_inds_to_span": 20,
    }
    cls.insert1(
        {"dlc_si_params_name": "default", "params": default_params},
        **kwargs,
    )

insert_nan_params(**kwargs) classmethod

Insert parameters that only NaN the data.

Source code in src/spyglass/position/v1/position_dlc_position.py 
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@classmethod
def insert_nan_params(cls, **kwargs) -> None:
    """Insert parameters that only NaN the data."""
    nan_params = {
        "smooth": False,
        "interpolate": False,
        "likelihood_thresh": 0.95,
        "max_cm_between_pts": 20,
        "num_inds_to_span": 20,
    }
    cls.insert1(
        {"dlc_si_params_name": "just_nan", "params": nan_params}, **kwargs
    )

get_default() classmethod

Return the default set of parameters for smoothing calculation.

Source code in src/spyglass/position/v1/position_dlc_position.py 
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@classmethod
def get_default(cls) -> dict:
    """Return the default set of parameters for smoothing calculation."""
    query = cls & {"dlc_si_params_name": "default"}
    if not len(query) > 0:
        cls().insert_default(skip_duplicates=True)
        default = (cls & {"dlc_si_params_name": "default"}).fetch1()
    else:
        default = query.fetch1()
    return default

get_nan_params() classmethod

Return the parameters that NaN the data.

Source code in src/spyglass/position/v1/position_dlc_position.py 
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@classmethod
def get_nan_params(cls) -> dict:
    """Return the parameters that NaN the data."""
    query = cls & {"dlc_si_params_name": "just_nan"}
    if not len(query) > 0:
        cls().insert_nan_params(skip_duplicates=True)
        nan_params = (cls & {"dlc_si_params_name": "just_nan"}).fetch1()
    else:
        nan_params = query.fetch1()
    return nan_params

get_available_methods() staticmethod

Return the available smoothing methods.

Source code in src/spyglass/position/v1/position_dlc_position.py 
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@staticmethod
def get_available_methods():
    """Return the available smoothing methods."""
    return _key_to_smooth_func_dict.keys()

insert1(key, **kwargs)

Override insert1 to validate params.

Source code in src/spyglass/position/v1/position_dlc_position.py 
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def insert1(self, key, **kwargs):
    """Override insert1 to validate params."""
    params = key.get("params")
    if not isinstance(params, dict):
        raise KeyError("'params' must be a dict in key")

    validate_option(
        option=params.get("max_cm_between_pts"), name="max_cm_between_pts"
    )
    validate_smooth_params(params)

    validate_option(
        params.get("likelihood_thresh"),
        name="likelihood_thresh",
        types=float,
        val_range=(0, 1),
    )

    super().insert1(key, **kwargs)

DLCSmoothInterp

Bases: SpyglassMixin, Computed

Interpolates across low likelihood periods and smooths the position Can take a few minutes.

Source code in src/spyglass/position/v1/position_dlc_position.py 
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@schema
class DLCSmoothInterp(SpyglassMixin, dj.Computed):
    """
    Interpolates across low likelihood periods and smooths the position
    Can take a few minutes.
    """

    definition = """
    -> DLCSmoothInterpSelection
    ---
    -> AnalysisNwbfile
    dlc_smooth_interp_position_object_id : varchar(80)
    dlc_smooth_interp_info_object_id : varchar(80)
    """
    log_path = None

    def make(self, key):
        """Populate the DLCSmoothInterp table.

        Uses a decorator to log the output to a file.

        1. Fetches the DLC output dataframe from DLCPoseEstimation
        2. NaNs low likelihood points and interpolates across them
        3. Optionally smooths and interpolates the data
        4. Create position and video frame index NWB objects
        5. Add NWB objects to AnalysisNwbfile
        6. Insert the key into DLCSmoothInterp.
        """
        self.log_path = (
            Path(infer_output_dir(key=key, makedir=False)) / "log.log"
        )
        self._logged_make(key)
        logger.info("inserted entry into DLCSmoothInterp")

    @file_log(logger, console=False)
    def _logged_make(self, key):
        METERS_PER_CM = 0.01

        logger.info("-----------------------")
        idx = pd.IndexSlice
        # Get labels to smooth from Parameters table
        params = (DLCSmoothInterpParams() & key).fetch1("params")
        # Get DLC output dataframe
        logger.info("fetching Pose Estimation Dataframe")

        bp_key = key.copy()
        if test_mode:  # during testing, analysis_file not in BodyPart table
            bp_key.pop("analysis_file_name", None)

        dlc_df = (DLCPoseEstimation.BodyPart() & bp_key).fetch1_dataframe()
        dt = np.median(np.diff(dlc_df.index.to_numpy()))
        logger.info("Identifying indices to NaN")
        df_w_nans, bad_inds = nan_inds(
            dlc_df.copy(),
            max_dist_between=params["max_cm_between_pts"],
            likelihood_thresh=params.pop("likelihood_thresh"),
            inds_to_span=params["num_inds_to_span"],
        )

        nan_spans = get_span_start_stop(np.where(bad_inds)[0])

        if interp_params := params.get("interpolate"):
            logger.info("interpolating across low likelihood times")
            interp_df = interp_pos(df_w_nans.copy(), nan_spans, **interp_params)
        else:
            interp_df = df_w_nans.copy()
            logger.info("skipping interpolation")

        if params.get("smooth"):
            smooth_params = params.get("smoothing_params")
            smooth_method = smooth_params.get("smooth_method")
            smooth_func = _key_to_smooth_func_dict[smooth_method]

            dt = np.median(np.diff(dlc_df.index.to_numpy()))
            logger.info(f"Smoothing using method: {smooth_method}")
            smooth_df = smooth_func(
                interp_df,
                smoothing_duration=smooth_params.get("smoothing_duration"),
                sampling_rate=1 / dt,
                **params["smoothing_params"],
            )
        else:
            smooth_df = interp_df.copy()
            logger.info("skipping smoothing")

        final_df = smooth_df.drop(["likelihood"], axis=1)
        final_df = final_df.rename_axis("time").reset_index()
        position_nwb_data = (
            (DLCPoseEstimation.BodyPart() & bp_key)
            .fetch_nwb()[0]["dlc_pose_estimation_position"]
            .get_spatial_series()
        )
        key["analysis_file_name"] = AnalysisNwbfile().create(
            key["nwb_file_name"]
        )

        # Add dataframe to AnalysisNwbfile
        nwb_analysis_file = AnalysisNwbfile()
        position = pynwb.behavior.Position()
        video_frame_ind = pynwb.behavior.BehavioralTimeSeries()
        logger.info("Creating NWB objects")
        position.create_spatial_series(
            name="position",
            timestamps=final_df.time.to_numpy(),
            conversion=METERS_PER_CM,
            data=final_df.loc[:, idx[("x", "y")]].to_numpy(),
            reference_frame=position_nwb_data.reference_frame,
            comments=position_nwb_data.comments,
            description="x_position, y_position",
        )
        video_frame_ind.create_timeseries(
            name="video_frame_ind",
            timestamps=final_df.time.to_numpy(),
            data=final_df.loc[:, idx["video_frame_ind"]].to_numpy(),
            unit="index",
            comments="no comments",
            description="video_frame_ind",
        )
        key["dlc_smooth_interp_position_object_id"] = (
            nwb_analysis_file.add_nwb_object(
                analysis_file_name=key["analysis_file_name"],
                nwb_object=position,
            )
        )
        key["dlc_smooth_interp_info_object_id"] = (
            nwb_analysis_file.add_nwb_object(
                analysis_file_name=key["analysis_file_name"],
                nwb_object=video_frame_ind,
            )
        )
        nwb_analysis_file.add(
            nwb_file_name=key["nwb_file_name"],
            analysis_file_name=key["analysis_file_name"],
        )
        self.insert1(key)
        AnalysisNwbfile().log(key, table=self.full_table_name)

    def fetch1_dataframe(self) -> pd.DataFrame:
        """Fetch a single dataframe."""
        nwb_data = self.fetch_nwb()[0]
        index = pd.Index(
            np.asarray(
                nwb_data["dlc_smooth_interp_position"]
                .get_spatial_series()
                .timestamps
            ),
            name="time",
        )
        COLUMNS = [
            "video_frame_ind",
            "x",
            "y",
        ]
        return pd.DataFrame(
            np.concatenate(
                (
                    np.asarray(
                        nwb_data["dlc_smooth_interp_info"]
                        .time_series["video_frame_ind"]
                        .data,
                        dtype=int,
                    )[:, np.newaxis],
                    np.asarray(
                        nwb_data["dlc_smooth_interp_position"]
                        .get_spatial_series()
                        .data
                    ),
                ),
                axis=1,
            ),
            columns=COLUMNS,
            index=index,
        )

make(key)

Populate the DLCSmoothInterp table.

Uses a decorator to log the output to a file.

  1. Fetches the DLC output dataframe from DLCPoseEstimation
  2. NaNs low likelihood points and interpolates across them
  3. Optionally smooths and interpolates the data
  4. Create position and video frame index NWB objects
  5. Add NWB objects to AnalysisNwbfile
  6. Insert the key into DLCSmoothInterp.
Source code in src/spyglass/position/v1/position_dlc_position.py 
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def make(self, key):
    """Populate the DLCSmoothInterp table.

    Uses a decorator to log the output to a file.

    1. Fetches the DLC output dataframe from DLCPoseEstimation
    2. NaNs low likelihood points and interpolates across them
    3. Optionally smooths and interpolates the data
    4. Create position and video frame index NWB objects
    5. Add NWB objects to AnalysisNwbfile
    6. Insert the key into DLCSmoothInterp.
    """
    self.log_path = (
        Path(infer_output_dir(key=key, makedir=False)) / "log.log"
    )
    self._logged_make(key)
    logger.info("inserted entry into DLCSmoothInterp")

fetch1_dataframe()

Fetch a single dataframe.

Source code in src/spyglass/position/v1/position_dlc_position.py 
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def fetch1_dataframe(self) -> pd.DataFrame:
    """Fetch a single dataframe."""
    nwb_data = self.fetch_nwb()[0]
    index = pd.Index(
        np.asarray(
            nwb_data["dlc_smooth_interp_position"]
            .get_spatial_series()
            .timestamps
        ),
        name="time",
    )
    COLUMNS = [
        "video_frame_ind",
        "x",
        "y",
    ]
    return pd.DataFrame(
        np.concatenate(
            (
                np.asarray(
                    nwb_data["dlc_smooth_interp_info"]
                    .time_series["video_frame_ind"]
                    .data,
                    dtype=int,
                )[:, np.newaxis],
                np.asarray(
                    nwb_data["dlc_smooth_interp_position"]
                    .get_spatial_series()
                    .data
                ),
            ),
            axis=1,
        ),
        columns=COLUMNS,
        index=index,
    )

nan_inds(dlc_df, max_dist_between, likelihood_thresh, inds_to_span)

Replace low likelihood points with NaNs and interpolate over them.

Source code in src/spyglass/position/v1/position_dlc_position.py 
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def nan_inds(
    dlc_df: pd.DataFrame,
    max_dist_between,
    likelihood_thresh: float,
    inds_to_span: int,
):
    """Replace low likelihood points with NaNs and interpolate over them."""
    idx = pd.IndexSlice

    # Could either NaN sub-likelihood threshold inds here and then not consider
    # in jumping... OR just keep in back pocket when checking jumps against
    # last good point

    subthresh_inds = get_subthresh_inds(
        dlc_df, likelihood_thresh=likelihood_thresh
    )
    df_subthresh_indices = dlc_df.index[subthresh_inds]
    dlc_df.loc[idx[df_subthresh_indices], idx[("x", "y")]] = np.nan

    # To further determine which indices are the original point and which are
    # jump points. There could be a more efficient method of doing this screen
    # inds for jumps to baseline

    subthresh_inds_mask = np.zeros(len(dlc_df), dtype=bool)
    subthresh_inds_mask[subthresh_inds] = True
    jump_inds_mask = np.zeros(len(dlc_df), dtype=bool)
    _, good_spans = get_good_spans(
        subthresh_inds_mask, inds_to_span=inds_to_span
    )

    if len(good_spans) == 0:
        # Prevents ref before assignment error of mask on return
        # TODO: instead of raise, insert empty dataframe
        raise ValueError("No good spans found in the data")

    for span in good_spans[::-1]:
        if np.sum(np.isnan(dlc_df.iloc[span[0] : span[-1]].x)) > 0:
            nan_mask = np.isnan(dlc_df.iloc[span[0] : span[-1]].x)
            good_start = np.arange(span[0], span[1])[~nan_mask]
            start_point = good_start[int(len(good_start) // 2)]
        else:
            start_point = span[0] + int(span_length(span) // 2)

        for ind in range(start_point, span[0], -1):
            if subthresh_inds_mask[ind]:
                continue
            previous_good_inds = np.where(
                np.logical_and(
                    ~np.isnan(dlc_df.iloc[ind + 1 : start_point].x),
                    ~jump_inds_mask[ind + 1 : start_point],
                    ~subthresh_inds_mask[ind + 1 : start_point],
                )
            )[0]
            last_good_ind = (
                ind + 1 + np.min(previous_good_inds)
                if len(previous_good_inds) > 0
                else start_point
            )
            good_x, good_y = dlc_df.loc[
                idx[dlc_df.index[last_good_ind]], ["x", "y"]
            ]
            if (
                (dlc_df.y.iloc[ind] < int(good_y - max_dist_between))
                | (dlc_df.y.iloc[ind] > int(good_y + max_dist_between))
            ) | (
                (dlc_df.x.iloc[ind] < int(good_x - max_dist_between))
                | (dlc_df.x.iloc[ind] > int(good_x + max_dist_between))
            ):
                jump_inds_mask[ind] = True
        for ind in range(start_point, span[-1], 1):
            if subthresh_inds_mask[ind]:
                continue
            previous_good_inds = np.where(
                np.logical_and(
                    ~np.isnan(dlc_df.iloc[start_point:ind].x),
                    ~jump_inds_mask[start_point:ind],
                    ~subthresh_inds_mask[start_point:ind],
                )
            )[0]
            if len(previous_good_inds) >= 1:
                last_good_ind = start_point + np.max(previous_good_inds)
            else:
                last_good_ind = start_point
            good_x, good_y = dlc_df.loc[
                idx[dlc_df.index[last_good_ind]], ["x", "y"]
            ]
            if (
                (dlc_df.y.iloc[ind] < int(good_y - max_dist_between))
                | (dlc_df.y.iloc[ind] > int(good_y + max_dist_between))
            ) | (
                (dlc_df.x.iloc[ind] < int(good_x - max_dist_between))
                | (dlc_df.x.iloc[ind] > int(good_x + max_dist_between))
            ):
                jump_inds_mask[ind] = True
        bad_inds_mask = np.logical_or(jump_inds_mask, subthresh_inds_mask)
        dlc_df.loc[bad_inds_mask, idx[("x", "y")]] = np.nan
    return dlc_df, bad_inds_mask

get_good_spans(bad_inds_mask, inds_to_span=50)

This function takes in a boolean mask of good and bad indices and determines spans of consecutive good indices. It combines two neighboring spans with a separation of less than inds_to_span and treats them as a single good span.

Parameters:

Name Type Description Default
bad_inds_mask boolean mask

A boolean mask where True is a bad index and False is a good index.

 required
inds_to_span int

This indicates how many indices between two good spans should be bridged to form a single good span. For instance if span A is (1500, 2350) and span B is (2370, 3700), then span A and span B would be combined into span A (1500, 3700) since one would want to identify potential jumps in the space in between the original A and B.

 50

Returns:

Name Type Description
good_spans list

List of spans of good indices, unmodified.
modified_spans list

spans that are amended to bridge up to inds_to_span consecutive bad indices
Source code in src/spyglass/position/v1/position_dlc_position.py 
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def get_good_spans(bad_inds_mask, inds_to_span: int = 50):
    """
    This function takes in a boolean mask of good and bad indices and
    determines spans of consecutive good indices. It combines two neighboring
    spans with a separation of less than inds_to_span and treats them as a
    single good span.

    Parameters
    ----------
    bad_inds_mask : boolean mask
        A boolean mask where True is a bad index and False is a good index.
    inds_to_span : int, default 50
        This indicates how many indices between two good spans should
        be bridged to form a single good span.
        For instance if span A is (1500, 2350) and span B is (2370, 3700),
        then span A and span B would be combined into span A (1500, 3700)
        since one would want to identify potential jumps in the space in between
        the original A and B.

    Returns
    -------
    good_spans : list
        List of spans of good indices, unmodified.
    modified_spans : list
        spans that are amended to bridge up to inds_to_span consecutive bad indices
    """
    good = get_span_start_stop(np.arange(len(bad_inds_mask))[~bad_inds_mask])

    if len(good) < 1:
        return None, good
    elif len(good) == 1:  # if all good, no need to modify
        return good, good

    modified_spans = []
    for (start1, stop1), (start2, stop2) in zip(good[:-1], good[1:]):
        check_existing = [
            entry
            for entry in modified_spans
            if start1 in range(entry[0] - inds_to_span, entry[1] + inds_to_span)
        ]
        if len(check_existing) > 0:
            modify_ind = modified_spans.index(check_existing[0])
            if (start2 - stop1) <= inds_to_span:
                modified_spans[modify_ind] = (check_existing[0][0], stop2)
            else:
                modified_spans[modify_ind] = (check_existing[0][0], stop1)
                modified_spans.append((start2, stop2))
            continue
        if (start2 - stop1) <= inds_to_span:
            modified_spans.append((start1, stop2))
        else:
            modified_spans.append((start1, stop1))
            modified_spans.append((start2, stop2))
    return good, modified_spans

span_length(x)

Return the length of a span.

Source code in src/spyglass/position/v1/position_dlc_position.py 
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def span_length(x):
    """Return the length of a span."""
    return x[-1] - x[0]

get_subthresh_inds(dlc_df, likelihood_thresh)

Return indices of subthresh points.

Source code in src/spyglass/position/v1/position_dlc_position.py 
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def get_subthresh_inds(dlc_df: pd.DataFrame, likelihood_thresh: float):
    """Return indices of subthresh points."""
    df_filter = dlc_df["likelihood"] < likelihood_thresh
    sub_thresh_inds = np.where(
        ~np.isnan(dlc_df["likelihood"].where(df_filter))
    )[0]
    nand_inds = np.where(np.isnan(dlc_df["x"]))[0]
    all_nan_inds = list(set(sub_thresh_inds).union(set(nand_inds)))
    all_nan_inds.sort()
    # TODO: add option to return sub_thresh_percent
    # sub_thresh_percent = (len(sub_thresh_inds) / len(dlc_df)) * 100
    return all_nan_inds