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Update Animated Plotter Example
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#!/usr/bin/env python | ||
# coding: utf-8 | ||
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""" | ||
Animated Plotters | ||
================= | ||
This example shows how to animate several 2D state sequences to be plotted in time order. First, | ||
truths, detections, and tracks are created. These are then plotted as animations using two | ||
options that are provided by Stone Soup: Matplotlib-based :class:`~.AnimationPlotter`, and | ||
Plotly-based :class:`~.AnimatedPlotterly`. The two options are then compared with pros and cons | ||
given for both options. | ||
""" | ||
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# %% | ||
# Creating Ground Truths, Detections, and Tracks | ||
# ---------------------------------------------- | ||
# For simplicity, we are going to quickly make a simulation with a basic Kalman Tracker using | ||
# Stone Soup simulators. To see the animations in action, scroll down to "Creating Animations". | ||
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# %% | ||
# All non-generic imports will be given in order of usage. | ||
from datetime import datetime, timedelta | ||
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import numpy as np | ||
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from stonesoup.dataassociator.neighbour import GNNWith2DAssignment | ||
from stonesoup.deleter.error import CovarianceBasedDeleter | ||
from stonesoup.hypothesiser.distance import DistanceHypothesiser | ||
from stonesoup.initiator.simple import MultiMeasurementInitiator | ||
from stonesoup.measures import Mahalanobis | ||
from stonesoup.models.transition.linear import ( | ||
CombinedLinearGaussianTransitionModel, ConstantVelocity) | ||
from stonesoup.models.measurement.linear import LinearGaussian | ||
from stonesoup.predictor.kalman import KalmanPredictor | ||
from stonesoup.simulator.simple import MultiTargetGroundTruthSimulator, SimpleDetectionSimulator | ||
from stonesoup.tracker.simple import MultiTargetTracker | ||
from stonesoup.types.array import StateVector, CovarianceMatrix | ||
from stonesoup.types.state import GaussianState | ||
from stonesoup.updater.kalman import KalmanUpdater | ||
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# %% | ||
# Set up the platform and detection simulators: | ||
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# Models | ||
transition_model = CombinedLinearGaussianTransitionModel( | ||
[ConstantVelocity(1), ConstantVelocity(1)]) | ||
measurement_model = LinearGaussian(4, [0, 2], np.diag([20, 20])) | ||
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start_time = datetime.now().replace(microsecond=0) | ||
timestep = timedelta(seconds=5) | ||
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# Simulators | ||
groundtruth_sim = MultiTargetGroundTruthSimulator( | ||
transition_model=transition_model, | ||
initial_state=GaussianState( | ||
StateVector([[0], [0], [0], [0]]), | ||
CovarianceMatrix(np.diag([1000, 10, 1000, 10])), | ||
timestamp=start_time), | ||
timestep=timestep, | ||
number_steps=60, | ||
birth_rate=0.2, | ||
death_probability=0.05 | ||
) | ||
detection_sim = SimpleDetectionSimulator( | ||
groundtruth=groundtruth_sim, | ||
measurement_model=measurement_model, | ||
meas_range=np.array([[-1, 1], [-1, 1]]) * 2500, # Area to generate clutter | ||
detection_probability=0.9, | ||
clutter_rate=1, | ||
) | ||
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# %% | ||
# Set up the tracker: | ||
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# Filter | ||
predictor = KalmanPredictor(transition_model) | ||
updater = KalmanUpdater(measurement_model) | ||
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# Data Associator | ||
hypothesiser = DistanceHypothesiser(predictor, updater, Mahalanobis(), missed_distance=3) | ||
data_associator = GNNWith2DAssignment(hypothesiser) | ||
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# Initiator & Deleter | ||
deleter = CovarianceBasedDeleter(covar_trace_thresh=1E3) | ||
initiator = MultiMeasurementInitiator( | ||
prior_state=GaussianState(np.array([[0], [0], [0], [0]]), | ||
np.diag([0, 100, 0, 1000]), | ||
timestamp=start_time), | ||
measurement_model=measurement_model, | ||
deleter=deleter, | ||
data_associator=data_associator, | ||
updater=updater, | ||
min_points=3, | ||
) | ||
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# Tracker | ||
tracker = MultiTargetTracker( | ||
initiator=initiator, | ||
deleter=deleter, | ||
detector=detection_sim, | ||
data_associator=data_associator, | ||
updater=updater, | ||
) | ||
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# %% | ||
# Generate the Truths, Detections, and Tracks: | ||
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groundtruth = set() | ||
detections = set() | ||
all_tracks = set() | ||
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for time, tracks in tracker: | ||
groundtruth.update(groundtruth_sim.groundtruth_paths) | ||
detections.update(detection_sim.detections) | ||
all_tracks.update(tracks) | ||
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# %% | ||
# Simulation overview: | ||
from stonesoup.types.detection import Clutter, TrueDetection | ||
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average_life_of_gt = timestep * sum(len(gt) for gt in groundtruth)/len(groundtruth) | ||
n_clutter = sum(isinstance(det, Clutter) for det in detections) | ||
n_true_detections = sum(isinstance(det, TrueDetection) for det in detections) | ||
average_life_of_track = timestep * sum(len(track) for track in all_tracks)/len(all_tracks) | ||
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print("The simulation produced:\n", | ||
len(groundtruth), "Ground truth paths with an average life of", average_life_of_gt, "\n", | ||
n_clutter, "Clutter Detections\n", | ||
n_true_detections, "True Detections\n", | ||
len(all_tracks), "Tracks with an average life of", average_life_of_track, "\n", ) | ||
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# %% | ||
# Creating Animations | ||
# ------------------- | ||
# We now create animations using both plotters and compare them. | ||
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# %% | ||
# AnimationPlotter | ||
# ^^^^^^^^^^^^^^^^ | ||
# :class:`~.AnimationPlotter` is built on Matplotlib. Here we show off some of its functionality, | ||
# and save the output. First, we create the plotter object and add an argument for the legend. One | ||
# drawback with this plotter is that the user cannot currently set a custom title. | ||
from stonesoup.plotter import AnimationPlotter | ||
plotter = AnimationPlotter(legend_kwargs=dict(loc='upper left')) | ||
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# %% | ||
# Plot the truths, detections, and tracks, and provide the mapping from state space to Cartesian: | ||
plotter.plot_ground_truths(groundtruth, mapping=[0, 2]) | ||
plotter.plot_measurements(detections, mapping=[0, 2]) | ||
plotter.plot_tracks(all_tracks, mapping=[0, 2]) | ||
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# %% | ||
# The following command ensures the animation is playable via the interactive player in | ||
# Jupyter Notebooks: | ||
import matplotlib | ||
matplotlib.rcParams['animation.html'] = 'jshtml' | ||
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# %% | ||
# Run the animation. To prevent a cluttered plot, include an argument that deletes | ||
# information older than 60 seconds: | ||
plotter.run(plot_item_expiry=timedelta(seconds=60)) | ||
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# %% | ||
# Finally, save the animation: | ||
plotter.save('example_animation.gif') | ||
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# %% | ||
# AnimatedPlotterly | ||
# ^^^^^^^^^^^^^^^^^ | ||
# We now create the Plotly-based :class:`~.AnimatedPlotterly`. We must provide the plotter with a | ||
# list of equally space simulation timesteps for it to initialise. There are also optional | ||
# arguments that are explained in the docs. | ||
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from stonesoup.plotter import AnimatedPlotterly | ||
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timesteps = [start_time + timedelta(seconds=5*k) for k in range(60)] | ||
fig = AnimatedPlotterly(timesteps, tail_length=0.2, title="Plotterly Animation") | ||
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# %% | ||
# Plot the data and show the animation: | ||
fig.plot_ground_truths(groundtruth, mapping=[0, 2]) | ||
fig.plot_measurements(detections, mapping=[0, 2]) | ||
fig.plot_tracks(all_tracks, mapping=[0, 2]) | ||
fig.show() | ||
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# %% | ||
# Comparing Plotters | ||
# ------------------ | ||
# | ||
# It is fairly apparent that :class:`~.AnimatedPlotterly` offers more functionality and interaction | ||
# than :class:`~.AnimationPlotter`. The user can view information on each data point by hovering | ||
# over it, zoom into a specific area, and turn on and off specific traces. This is especially | ||
# useful for this example because the ground truth is impossible to view if the tracks are plotted | ||
# over it. I.e. in :class:`~.AnimationPlotter`, | ||
# to view ground truth, you would need to make a separate plotter that doesn't plot the tracks. | ||
# | ||
# However, there are a couple of drawbacks to :class:`~.AnimatedPlotterly`. If you can't extract | ||
# a list of equally-spaced timesteps from your simulation, :class:`~.AnimatedPlotterly` won't work. | ||
# It also more computationally expensive than :class:`~.AnimationPlotter`, so struggles to load | ||
# and render large volumes of data. This can be seen by forcing the animation in Tutorial 4 to | ||
# display every particle - set the plotter's `tail_length` to 1. Then set both `particle` and | ||
# `plot_history` to True when plotting tracks. In addition, there is no current functionality | ||
# to save the animation without using a screen-capturing tool. | ||
# | ||
# Finally, :class:`~.AnimatedPlotterly` has a more complex framework than | ||
# :class:`~.AnimationPlotter`, hence adding custom data is | ||
# harder. An example is somewhat shown in the sensor management tutorials, where a sensor's field | ||
# of view is manually added, but a dedicated example may be written in the future. | ||
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# %% | ||
# Conclusion | ||
# ^^^^^^^^^^ | ||
# In conclusion, the Plotterly-derived :class:`~.AnimatedPlotterly` provides a more detailed and | ||
# interactive user experience than the Matplotlib-derived :class:`~.AnimationPlotter`, so is | ||
# recommended for most use cases. However, :class:`~.AnimatedPlotterly` may be a better choice if: | ||
# | ||
# 1. the simulation timesteps are non-linear, or cannot be easily extracted | ||
# 2. many data points are being displayed at once | ||
# 3. the user desires to save the animation without using screen-capturing tools | ||
# 4. the user desires to add custom data quickly. |
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