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Merge pull request #816 from dstl/animated_sm_tutorials
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Plot FOV on Sensor Management tutorials
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@sdhiscocks
sdhiscocks committed Jun 19, 2023
2 parents b5e1981 + d53044a commit 6028965
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Showing 3 changed files with 183 additions and 13 deletions.
63 changes: 59 additions & 4 deletions docs/tutorials/sensormanagement/01_SingleSensorManagement.py
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Original file line number Diff line number Diff line change
Expand Up @@ -154,7 +154,7 @@
ydirection *= -1

# %%
# Plot the ground truths. This is done using the :class:`~.Plotterly` class from Stone Soup.
# Plot the ground truths. This is done using the :class:`~.AnimatedPlotterly` class from Stone Soup.

from stonesoup.plotter import AnimatedPlotterly

Expand Down Expand Up @@ -340,6 +340,9 @@
# Here the chosen target for observation is selected randomly using the method :meth:`choose_actions()` from the class
# :class:`~.RandomSensorManager`.

import copy

sensor_history_A = dict()
for timestep in timesteps[1:]:

# Generate chosen configuration
Expand All @@ -355,6 +358,9 @@

sensorA.act(timestep)

# Store sensor history for plotting
sensor_history_A[timestep] = copy.copy(sensorA)

# Observe this ground truth
# i.e. {z}k
measurementsA |= sensorA.measure(OrderedSet(truth[timestep] for truth in truths), noise=True)
Expand All @@ -371,12 +377,56 @@
track.append(hypothesis.prediction)

# %%
# Plot ground truths, tracks and uncertainty ellipses for each target.
# Plot ground truths, tracks and uncertainty ellipses for each target. This uses the Stone Soup
# :class:`~.AnimatedPlotterly`, with added code to plot the field of view of the sensor.

import plotly.graph_objects as go
from stonesoup.functions import pol2cart

plotterA = AnimatedPlotterly(timesteps, tail_length=1)
plotterA = AnimatedPlotterly(timesteps, tail_length=1, sim_duration=10)
plotterA.plot_sensors(sensorA)
plotterA.plot_ground_truths(truths, [0, 2])
plotterA.plot_tracks(tracksA, [0, 2], uncertainty=True, plot_history=False)


def plot_sensor_fov(fig, sensor_history):
# Plot sensor field of view
trace_base = len(fig.data)
fig.add_trace(go.Scatter(mode='lines',
line=go.scatter.Line(color='black',
dash='dash')))

for frame in fig.frames:
traces_ = list(frame.traces)
data_ = list(frame.data)
x = [0, 0]
y = [0, 0]
timestring = frame.name
timestamp = datetime.strptime(timestring, "%Y-%m-%d %H:%M:%S")

if timestamp in sensor_history:
sensor = sensor_history[timestamp]
for i, fov_side in enumerate((-1, 1)):
range_ = min(getattr(sensor, 'max_range', np.inf), 100)
x[i], y[i] = pol2cart(range_,
sensor.dwell_centre[0, 0]
+ sensor.fov_angle / 2 * fov_side) \
+ sensor.position[[0, 1], 0]
else:
continue

data_.append(go.Scatter(x=[x[0], sensor.position[0], x[1]],
y=[y[0], sensor.position[1], y[1]],
mode="lines",
line=go.scatter.Line(color='black',
dash='dash'),
showlegend=False))
traces_.append(trace_base)
frame.traces = traces_
frame.data = data_


plot_sensor_fov(plotterA.fig, sensor_history_A)
plotterA.fig

# %%
Expand Down Expand Up @@ -405,6 +455,7 @@
# The chosen action is given to the sensor, measurements are made and the tracks updated based on these measurements.
# Predictions are made for tracks which have not been observed by the sensor.

sensor_history_B = dict()
for timestep in timesteps[1:]:

# Generate chosen configuration
Expand All @@ -420,6 +471,9 @@

sensorB.act(timestep)

# Store sensor history for plotting
sensor_history_B[timestep] = copy.copy(sensorB)

# Observe this ground truth
# i.e. {z}k
measurementsB |= sensorB.measure(OrderedSet(truth[timestep] for truth in truths), noise=True)
Expand All @@ -437,10 +491,11 @@
# %%
# Plot ground truths, tracks and uncertainty ellipses for each target.

plotterB = AnimatedPlotterly(timesteps, tail_length=1)
plotterB = AnimatedPlotterly(timesteps, tail_length=1, sim_duration=10)
plotterB.plot_sensors(sensorB)
plotterB.plot_ground_truths(truths, [0, 2])
plotterB.plot_tracks(tracksB, [0, 2], uncertainty=True, plot_history=False)
plot_sensor_fov(plotterB.fig, sensor_history_B)
plotterB.fig

# %%
Expand Down
64 changes: 60 additions & 4 deletions docs/tutorials/sensormanagement/02_MultiSensorManagement.py
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Original file line number Diff line number Diff line change
Expand Up @@ -104,7 +104,7 @@
ydirection *= -1

# %%
# Plot the ground truths. This is done using the :class:`~.Plotterly` class from Stone Soup.
# Plot the ground truths. This is done using the :class:`~.AnimatedPlotterly` class from Stone Soup.

from stonesoup.plotter import AnimatedPlotterly

Expand Down Expand Up @@ -307,7 +307,10 @@
# :class:`~.ChangeDwellAction`, selected at random.

from ordered_set import OrderedSet
from collections import defaultdict
import copy

sensor_history_A = defaultdict(dict)
for timestep in timesteps[1:]:

# Generate chosen configuration
Expand All @@ -322,6 +325,7 @@

for sensor in sensor_setA:
sensor.act(timestep)
sensor_history_A[timestep][sensor] = copy.copy(sensor)

# Observe this ground truth
measurementsA |= sensor.measure(OrderedSet(truth[timestep] for truth in truths), noise=True)
Expand All @@ -339,12 +343,61 @@

# %%
# Plot ground truths, tracks and uncertainty ellipses for each target. The positions of the sensors are indicated
# by black x markers.
# by black x markers. This uses the Stone Soup
# :class:`~.AnimatedPlotterly`, with added code to plot the field of view of the sensor.

plotterA = AnimatedPlotterly(timesteps, tail_length=1)
import plotly.graph_objects as go
from stonesoup.functions import pol2cart

plotterA = AnimatedPlotterly(timesteps, tail_length=1, sim_duration=10)
plotterA.plot_sensors(sensor_setA)
plotterA.plot_ground_truths(truths, [0, 2])
plotterA.plot_tracks(tracksA, [0, 2], uncertainty=True, plot_history=False)


def plot_sensor_fov(fig, sensor_set, sensor_history):
# Plot sensor field of view
trace_base = len(fig.data)
for _ in sensor_set:
fig.add_trace(go.Scatter(mode='lines',
line=go.scatter.Line(color='black',
dash='dash')))

for frame in fig.frames:
traces_ = list(frame.traces)
data_ = list(frame.data)

timestring = frame.name
timestamp = datetime.strptime(timestring, "%Y-%m-%d %H:%M:%S")

for n, sensor_ in enumerate(sensor_set):
x = [0, 0]
y = [0, 0]

if timestamp in sensor_history:
sensor = sensor_history[timestamp][sensor_]
for i, fov_side in enumerate((-1, 1)):
range_ = min(getattr(sensor, 'max_range', np.inf), 100)
x[i], y[i] = pol2cart(range_,
sensor.dwell_centre[0, 0]
+ sensor.fov_angle / 2 * fov_side) \
+ sensor.position[[0, 1], 0]
else:
continue

data_.append(go.Scatter(x=[x[0], sensor.position[0], x[1]],
y=[y[0], sensor.position[1], y[1]],
mode="lines",
line=go.scatter.Line(color='black',
dash='dash'),
showlegend=False))
traces_.append(trace_base + n)

frame.traces = traces_
frame.data = data_


plot_sensor_fov(plotterA.fig, sensor_setA, sensor_history_A)
plotterA.fig

# %%
Expand Down Expand Up @@ -378,6 +431,7 @@
# the tracks updated based on these measurements. Predictions are made for tracks
# which have not been observed by the sensors.

sensor_history_B = defaultdict(dict)
for timestep in timesteps[1:]:

# Generate chosen configuration
Expand All @@ -392,6 +446,7 @@

for sensor in sensor_setB:
sensor.act(timestep)
sensor_history_B[timestep][sensor] = copy.copy(sensor)

# Observe this ground truth
measurementsB |= sensor.measure(OrderedSet(truth[timestep] for truth in truths), noise=True)
Expand All @@ -410,10 +465,11 @@
# %%
# Plot ground truths, tracks and uncertainty ellipses for each target.

plotterB = AnimatedPlotterly(timesteps, tail_length=1)
plotterB = AnimatedPlotterly(timesteps, tail_length=1, sim_duration=10)
plotterB.plot_sensors(sensor_setB)
plotterB.plot_ground_truths(truths, [0, 2])
plotterB.plot_tracks(tracksB, [0, 2], uncertainty=True, plot_history=False)
plot_sensor_fov(plotterB.fig, sensor_setB, sensor_history_B)
plotterB.fig

# %%
Expand Down
69 changes: 64 additions & 5 deletions docs/tutorials/sensormanagement/03_OptimisedSensorManagement.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -97,7 +97,7 @@
ydirection *= -1

# %%
# Plot the ground truths. This is done using the :class:`~.Plotterly` class from Stone Soup.
# Plot the ground truths. This is done using the :class:`~.AnimatedPlotterly` class from Stone Soup.

from stonesoup.plotter import AnimatedPlotterly

Expand Down Expand Up @@ -308,11 +308,14 @@
# Each sensor manager is run in the same way as in the previous tutorials.

from ordered_set import OrderedSet
from collections import defaultdict
import time
import copy

# Start timer for cell execution time
cell_start_time1 = time.time()

sensor_history_A = defaultdict(dict)
for timestep in timesteps[1:]:

# Generate chosen configuration
Expand All @@ -327,6 +330,7 @@

for sensor in sensor_setA:
sensor.act(timestep)
sensor_history_A[timestep][sensor] = copy.copy(sensor)

# Observe this ground truth
measurementsA |= sensor.measure(OrderedSet(truth[timestep] for truth in truths), noise=True)
Expand All @@ -346,12 +350,61 @@

# %%
# Plot ground truths, tracks and uncertainty ellipses for each target. The positions of the sensors are indicated
# by black x markers.
# by black x markers.This uses the Stone Soup
# :class:`~.AnimatedPlotterly`, with added code to plot the field of view of the sensor.

plotterA = AnimatedPlotterly(timesteps, tail_length=1)
import plotly.graph_objects as go
from stonesoup.functions import pol2cart

plotterA = AnimatedPlotterly(timesteps, tail_length=1, sim_duration=10)
plotterA.plot_sensors(sensor_setA)
plotterA.plot_ground_truths(truths, [0, 2])
plotterA.plot_tracks(set(tracksA), [0, 2], uncertainty=True, plot_history=False)


def plot_sensor_fov(fig, sensor_set, sensor_history):
# Plot sensor field of view
trace_base = len(fig.data)
for _ in sensor_set:
fig.add_trace(go.Scatter(mode='lines',
line=go.scatter.Line(color='black',
dash='dash')))

for frame in fig.frames:
traces_ = list(frame.traces)
data_ = list(frame.data)

timestring = frame.name
timestamp = datetime.strptime(timestring, "%Y-%m-%d %H:%M:%S")

for n, sensor_ in enumerate(sensor_set):
x = [0, 0]
y = [0, 0]

if timestamp in sensor_history:
sensor = sensor_history[timestamp][sensor_]
for i, fov_side in enumerate((-1, 1)):
range_ = min(getattr(sensor, 'max_range', np.inf), 100)
x[i], y[i] = pol2cart(range_,
sensor.dwell_centre[0, 0]
+ sensor.fov_angle / 2 * fov_side) \
+ sensor.position[[0, 1], 0]
else:
continue

data_.append(go.Scatter(x=[x[0], sensor.position[0], x[1]],
y=[y[0], sensor.position[1], y[1]],
mode="lines",
line=go.scatter.Line(color='black',
dash='dash'),
showlegend=False))
traces_.append(trace_base + n)

frame.traces = traces_
frame.data = data_


plot_sensor_fov(plotterA.fig, sensor_setA, sensor_history_A)
plotterA.fig

# %%
Expand All @@ -364,6 +417,7 @@
# Start timer for cell execution time
cell_start_time2 = time.time()

sensor_history_B = defaultdict(dict)
for timestep in timesteps[1:]:

# Generate chosen configuration
Expand All @@ -378,6 +432,7 @@

for sensor in sensor_setB:
sensor.act(timestep)
sensor_history_B[timestep][sensor] = copy.copy(sensor)

# Observe this ground truth
measurementsB |= sensor.measure(OrderedSet(truth[timestep] for truth in truths), noise=True)
Expand All @@ -398,10 +453,11 @@
# %%
# Plot ground truths, tracks and uncertainty ellipses for each target.

plotterB = AnimatedPlotterly(timesteps, tail_length=1)
plotterB = AnimatedPlotterly(timesteps, tail_length=1, sim_duration=10)
plotterB.plot_sensors(sensor_setB)
plotterB.plot_ground_truths(truths, [0, 2])
plotterB.plot_tracks(tracksB, [0, 2], uncertainty=True, plot_history=False)
plot_sensor_fov(plotterB.fig, sensor_setB, sensor_history_B)
plotterB.fig

# %%
Expand All @@ -411,6 +467,7 @@
# Start timer for cell execution time
cell_start_time3 = time.time()

sensor_history_C = defaultdict(dict)
for timestep in timesteps[1:]:

# Generate chosen configuration
Expand All @@ -425,6 +482,7 @@

for sensor in sensor_setC:
sensor.act(timestep)
sensor_history_C[timestep][sensor] = copy.copy(sensor)

# Observe this ground truth
measurementsC |= sensor.measure(OrderedSet(truth[timestep] for truth in truths), noise=True)
Expand All @@ -445,10 +503,11 @@
# %%
# Plot ground truths, tracks and uncertainty ellipses for each target.

plotterC = AnimatedPlotterly(timesteps, tail_length=1)
plotterC = AnimatedPlotterly(timesteps, tail_length=1, sim_duration=10)
plotterC.plot_sensors(sensor_setC)
plotterC.plot_ground_truths(truths, [0, 2])
plotterC.plot_tracks(tracksC, [0, 2], uncertainty=True, plot_history=False)
plot_sensor_fov(plotterC.fig, sensor_setC, sensor_history_C)
plotterC.fig

# %%
Expand Down

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