""" Module that defines the TrackingData class that wraps the position data for a given node/bodypart that has been tracked. """ import numpy as np class TrackingData(object): """Class that represents tracking data, i.e. positions of an agent tracked in an environment. These data are the x, and y-positions, the time at which the agent was detected, and the quality associated with the position estimation. TrackingData contains these data and offers a few functions to work with it. Using the 'quality_threshold', 'temporal_limits', or the 'position_limits' data can be filtered (see filter_tracks function). The 'interpolate' function allows to fill up the gaps that may result from filtering with linearly interpolated data points. More may follow... """ def __init__(self, x, y, time, quality, node="", fps=None, quality_threshold=None, temporal_limits=None, position_limits=None) -> None: """ Initialize a TrackingData object. Parameters ---------- x : float The x-coordinates of the tracking data. y : float The y-coordinates of the tracking data. time : float The time vector associated with the x-, and y-coordinates. quality : float The quality score associated with the position estimates. node : str, optional The node name associated with the data. Default is an empty string. fps : float, optional The frames per second of the tracking data. Default is None. quality_threshold : float, optional The quality threshold for the tracking data. Default is None. temporal_limits : tuple, optional The temporal limits for the tracking data. Default is None. position_limits : tuple, optional The position limits for the tracking data. Default is None. """ self._orgx = x self._orgy = y self._orgtime = time self._orgquality = quality self._x = x self._y = y self._time = time self._quality = quality self._node = node self._threshold = quality_threshold self._position_limits = position_limits self._time_limits = temporal_limits self._fps = fps @property def original_positions(self): return self._orgx, self._orgy @property def original_quality(self): return self._orgquality def interpolate(self, start_time=None, end_time=None, min_count=5): if len(self._x) < min_count: print( f"{self._node} data has less than {min_count} data points with sufficient quality ({len(self._x)})!" ) return None, None, None start = self._time[0] if start_time is None else start_time end = self._time[-1] if end_time is None else end_time time = np.arange(start, end, 1.0 / self._fps) x = np.interp(time, self._time, self._x) y = np.interp(time, self._time, self._y) return x, y, time @property def quality_threshold(self): """Property that holds the quality filter setting. Returns ------- float : the quality threshold """ return self._threshold @quality_threshold.setter def quality_threshold(self, new_threshold): """Setter of the quality threshold that should be applied when filtering the data. Setting this to None removes the quality filter. Data points that have a quality score below the given threshold are discarded. Parameters ---------- new_threshold : float """ self._threshold = new_threshold @property def position_limits(self): """ Get the position limits of the tracking data. Returns: tuple: A 4-tuple containing the start x, and y positions, width and height limits. """ return self._position_limits @position_limits.setter def position_limits(self, new_limits): """Sets the limits for the position filter. 'new_limits' must be a 4-tuple of the form (x0, y0, width, height). If None, the limits will be removed. Data points outside the position limits are discarded. Parameters ---------- new_limits: 4-tuple tuple of x-position, y-position, the width and the height. Passing None removes the filter Raises ------ ValueError, if new_value is not a 4-tuple """ if new_limits is not None and not ( isinstance(new_limits, (tuple, list)) and len(new_limits) == 4 ): raise ValueError( f"The new_limits vector must be a 4-tuple of the form (x, y, width, height)" ) self._position_limits = new_limits @property def temporal_limits(self): """ Get the temporal limits of the tracking data. Returns: tuple: A tuple containing the start and end time of the tracking data. """ return self._time_limits @temporal_limits.setter def temporal_limits(self, new_limits): """Limits for temporal filter. The limits must be a 2-tuple of start and end time. Setting this to None removes the filter. Data points the are associated with times outside the limits are discarded. Parameters ---------- new_limits : 2-tuple The new limits in the form (start, end) given in seconds. Returns ------- None Raises ------ ValueError if the limits are not valid. """ if new_limits is not None and not ( isinstance(new_limits, (tuple, list)) and len(new_limits) == 2 ): raise ValueError( f"The new_limits vector must be a 2-tuple of the form (start, end). " ) self._time_limits = new_limits def filter_tracks(self, align_time=True): """Applies the filters to the tracking data. All filters will be applied sequentially, i.e. an AND connection. To change the filter settings use the setters for 'quality_threshold', 'temporal_limits', 'position_limits'. Setting them to None disables the respective filter discarding the setting. Parameters ---------- align_time: bool Controls whether the time vector is aligned to the first time point at which the agent is within the positional_limits. Default = True """ self._x = self._orgx.copy() self._y = self._orgy.copy() self._time = self._orgtime.copy() self._quality = self.original_quality.copy() if self.position_limits is not None: x_max = self.position_limits[0] + self.position_limits[2] y_max = self.position_limits[1] + self.position_limits[3] indices = np.where( (self._x >= self.position_limits[0]) & (self._x < x_max) & (self._y >= self.position_limits[1]) & (self._y < y_max) ) self._x = self._x[indices] self._y = self._y[indices] self._time = self._time[indices] - self._time[0] if align_time else 0.0 self._quality = self._quality[indices] if self.temporal_limits is not None: indices = np.where( (self._time >= self.temporal_limits[0]) & (self._time < self.temporal_limits[1]) ) self._x = self._x[indices] self._y = self._y[indices] self._time = self._time[indices] self._quality = self._quality[indices] if self.quality_threshold is not None: indices = np.where((self._quality >= self.quality_threshold)) self._x = self._x[indices] self._y = self._y[indices] self._time = self._time[indices] self._quality = self._quality[indices] def positions(self): """Returns the filtered data (if filters have been applied, otherwise the original data). Returns ------- np.ndarray The x-positions np.ndarray The y-positions np.ndarray The time vector np.ndarray The detection quality """ return self._x, self._y, self._time, self._quality def speed(self, x=None, y=None, t=None): """ Returns the agent's speed as a function of time and position. The speed estimation is associated to the time/position between two sample points. If any of the arguments is not provided, the function will use the x,y coordinates that are stored within the object, otherwise, if all are provided, the user-provided values will be used. Since the velocities are estimated from the difference between two sample points the returned velocities and positions are assigned to positions and times between the respective sampled positions/times. Parameters ---------- x: np.ndarray The x-coordinates, defaults to None y: np.ndarray The y-coordinates, defaults to None t: np.ndarray The time vector, defaults to None Returns ------- np.ndarray: The time vector. np.ndarray: The speed. tuple of np.ndarray The position """ if x is None or y is None or t is None: x = self._x.copy() y = self._y.copy() t = self._time.copy() dt = np.diff(t) speed = np.sqrt(np.diff(x)**2 + np.diff(y)**2) / dt t = t[:-1] + dt / 2 x = x[:-1] + np.diff(x) / 2 y = y[:-1] + np.diff(y) / 2 return t, speed, (x, y) def __repr__(self) -> str: s = f"Tracking data of node '{self._node}'!" return s