How to use the stonesoup.types.metric.TimeRangeMetric function in stonesoup

for timestamp in timestamps:
            meas_points = [state
                           for state in measured_states
                           if state.timestamp == timestamp]
            truth_points = [state
                            for state in truth_states
                            if state.timestamp == timestamp]
            metric, truth_to_measured_assignment = self.compute_gospa_metric(
                    meas_points, truth_points)
            gospa_metrics.append(metric)

        # If only one timestamp is present then return a SingleTimeMetric
        if len(timestamps) == 1:
            return gospa_metrics[0]
        else:
            return TimeRangeMetric(
                title='GOSPA Metrics',
                value=gospa_metrics,
                time_range=TimeRange(min(timestamps), max(timestamps)),
                generator=self)

----------
        manager: MetricManager
            Containing the data to be used to create the metric

        Returns
        -------
        TimeRangeMetric
            Contains the metric information
        """
        numerator = sum(self._tl_j(manager, truth).total_seconds()
                        for truth in manager.groundtruth_paths)
        denominator = sum(self._t_j(truth).total_seconds()
                          for truth in manager.groundtruth_paths)

        timestamps = manager.list_timestamps()
        return TimeRangeMetric(
            title="SIAP LS",
            value=numerator / denominator,
            time_range=TimeRange(min(timestamps), max(timestamps)),
            generator=self)

Containing the data to be used to create the metric

        Returns
        -------
        TimeRangeMetric
            Contains the metric information
        """

        r = self._r(manager)
        if r == 0:
            value = np.inf
        else:
            value = 1 / r

        timestamps = manager.list_timestamps()
        return TimeRangeMetric(
            title="SIAP LT",
            value=value,
            time_range=TimeRange(min(timestamps), max(timestamps)),
            generator=self)

Parameters
        ----------
        manager : MetricManager
            Containing the data to be used to create the metric(s)

        Returns
        -------
        TimeRangeMetric
            Contains the metric information

        """

        timestamps = manager.list_timestamps()
        C = self._jt_sum(manager, timestamps) / self._j_sum(
            manager, timestamps)
        return TimeRangeMetric(
            title="SIAP C",
            value=C,
            time_range=TimeRange(min(timestamps), max(timestamps)),
            generator=self)

----------
        manager: MetricManager
            Containing the data to be used to create the metric

        Returns
        -------
        TimeRangeMetric
            Contains the metric information
        """
        timestamps = manager.list_timestamps()
        numerator = sum(
            self._n_t(manager, timestamp) - self._na_t(manager, timestamp)
            for timestamp in timestamps)

        S = numerator / self._n_sum(manager, timestamps)
        return TimeRangeMetric(
            title="SIAP S",
            value=S,
            time_range=TimeRange(min(timestamps), max(timestamps)),
            generator=self)

for timestamp in timestamps:
            meas_points = [state
                           for state in measured_states
                           if state.timestamp == timestamp]
            truth_points = [state
                            for state in truth_states
                            if state.timestamp == timestamp]
            ospa_distances.append(
                self.compute_OSPA_distance(meas_points, truth_points))

        # If only one timestamp is present then return a SingleTimeMetric
        if len(timestamps) == 1:
            return ospa_distances[0]
        else:
            return TimeRangeMetric(
                title='OSPA distances',
                value=ospa_distances,
                time_range=TimeRange(min(timestamps), max(timestamps)),
                generator=self)

Parameters
        ----------
        manager: MetricManager
            Containing the data to be used to create the metric

        Returns
        -------
        TimeRangeMetric
            Contains the metric information
        """

        timestamps = manager.list_timestamps()
        A = self._na_sum(manager, timestamps) / self._jt_sum(
            manager, timestamps)
        return TimeRangeMetric(
            title="SIAP A",
            value=A,
            time_range=TimeRange(min(timestamps), max(timestamps)),
            generator=self)

# Make a list of all the unique timestamps used
        timestamps = {state.timestamp for state in manager.tracks}
        timestamps |= {state.timestamp
                       for path in manager.groundtruth_paths
                       for state in path}

        # Number of tracks
        metrics.append(TimeRangeMetric(
            title='Number of targets',
            value=len(manager.groundtruth_paths),
            time_range=TimeRange(
                start_timestamp=min(timestamps),
                end_timestamp=max(timestamps)),
            generator=self))

        metrics.append(TimeRangeMetric(
            title='Number of tracks',
            value=len(manager.tracks),
            time_range=TimeRange(
                start_timestamp=min(timestamps),
                end_timestamp=max(timestamps)),
            generator=self))

        metrics.append(TimeRangeMetric(
            title='Track-to-target ratio',
            value=len(manager.tracks) / len(manager.groundtruth_paths),
            time_range=TimeRange(
                start_timestamp=min(timestamps),
                end_timestamp=max(timestamps)),
            generator=self))

        return metrics