Source code for stonesoup.metricgenerator.uncertaintymetric

# -*- coding: utf-8 -*-

import numpy as np

from .base import MetricGenerator
from ..types.state import State, StateMutableSequence
from ..types.metric import SingleTimeMetric, TimeRangeMetric
from ..types.time import TimeRange

[docs]class SumofCovarianceNormsMetric(MetricGenerator): """ Computes the sum of the covariance matrix norms of each state at a time step. The matrix norm calculated is the Frobenius norm. The metric generator will return this value at each time step in the track(s) as a measure of the uncertainty. """
[docs] def compute_metric(self, manager): """Computes the metric using the data in the metric manager Parameters ---------- manager : :class:`~.MetricManager` Contains the data to be used to create the metric Returns ------- metric : list :class:`~.Metric` Containing the metric information. The value of the metric is a list of the metric at each timestamp """ return self.compute_over_time(self.extract_states(manager.tracks))
[docs] @staticmethod def extract_states(object_with_states): """ Extracts a list of states from a list of (or single) objects containing states. This method is defined to handle :class:`~.StateMutableSequence` and :class:`~.State` types. Parameters ---------- object_with_states: object containing a list of states Method of state extraction depends on the type of the object Returns ------- : list of :class:`~.State` """ state_list = StateMutableSequence() for element in list(object_with_states): if isinstance(element, StateMutableSequence): state_list.extend(element.states) elif isinstance(element, State): state_list.append(element) else: raise ValueError( "{!r} has no state extraction method".format(element)) return state_list
[docs] def compute_over_time(self, track_states): """Compute the metric using the data in the metric manager Parameters ---------- track_states : list of :class:`~.State` List of states created by a filter Returns ---------- metric : TimeRangeMetric Covering the duration that states exist for in the parameters. Metric.value contains a list of the sums of covariance matrix norms at each timestamp """ # Make a sorted list of all the unique timestamps used timestamps = sorted({state.timestamp for state in track_states}) covnorm_sums = [] for timestamp in timestamps: track_points = [state for state in track_states if state.timestamp == timestamp] covnorm_sums.append(self.compute_sum_covariancenorms(track_points)) return TimeRangeMetric( title='Sum of Covariance Norms Metric', value=covnorm_sums, time_range=TimeRange(min(timestamps), max(timestamps)), generator=self)
[docs] def compute_sum_covariancenorms(self, track_states): """ Computes the sum of covariance norms metric for a single time step. Parameters ---------- track_states: list of :class:`~.State` List of states created by a filter Returns ------- metric: SingleTimeMetric The sum of covariance matrix norms metric at a single time step """ timestamps = {state.timestamp for state in track_states} if len(timestamps) > 1: raise ValueError( 'All states must be from the same time to compute total uncertainty') covnorms_sum = 0 for state in track_states: covnorm = np.linalg.norm(state.covar) covnorms_sum += covnorm return SingleTimeMetric(title='Covariance Matrix Norm Sum', value=covnorms_sum, timestamp=timestamps.pop(), generator=self)