def TEST1():
R = 0.3**2 * np.eye(2) # Measurement noise
P = 0.2**2 * np.eye(4) # Process noise
boundaries = [-1, 15, 0, 10, 10,
10] # [x_min, x_max, y_min, y_max, vx, vy]
balls = [
init_ball([0, 3, 2, -6], R, P, 'g', boundaries,
True), # Initialization of all balls
init_ball([8, 5, -1, -1], R, P, 'b', boundaries, True),
init_ball([0, 7, 4, -2], R, P, 'r', boundaries, True)
]
R_sim = 0.3**2 * np.eye(2) # Simulation measurement noise
P_D_SIM = 0.9 # Probability producing a ball measurement
P_FP_SIM = 0.2 # Probability of producing a outlier
meas_obj = MeasurementObject(R_sim, boundaries[:4], P_D_SIM, P_FP_SIM)
delta_t = .05 # Simulation time step
time = 5 # Total simulation time
target_map = {
0: 2,
1: 0,
2: 1
} # Optional map to pair estimated balls (keys) to simulated balls (values)
monte_carlo_jpdaf_simulation(sim_time=time, \
delta_t=delta_t, \
all_balls=balls, \
meas_obj=meas_obj, \
plot_boundaries=meas_obj.boundaries, \
noplot=False, \
target_map=target_map)
def TEST4():
R = 0.2**2 * np.eye(2)
P = 0.15**2 * np.eye(4)
boundaries = [-1, 4, 0, 4, 10, 12]
balls = [
init_ball([0, 1, 0.5, -8], R, P, 'g', boundaries, 0.5, 1.5),
init_ball([0, 1.5, 0.7, -6], R, P, 'm', boundaries, 0, 1)
]
R = 0.1**2 * np.eye(2)
meas_obj = MeasurementObject(R, boundaries[:4], 0.9)
delta_t = 1 / 40.
time = 7
monte_carlo_jpdaf_simulation(sim_time=time, \
delta_t=delta_t, \
all_balls=balls, \
meas_obj=meas_obj, \
plot_boundaries=meas_obj.boundaries, \
noplot=False)
def TEST2():
R = 0.2**2 * np.eye(2)
P = 0.2**2 * np.eye(4)
boundaries = [-1, 10, 0, 15, 10, 10]
balls = [
init_ball([0, 10, 2, -3], R, P, 'g', boundaries, False),
init_ball([0, 10, 2, -3], R, P, 'b', boundaries, True)
]
R = 0.1**2 * np.eye(2)
meas_obj = MeasurementObject(R, boundaries[:4])
delta_t = .05
time = 5
monte_carlo_jpdaf_simulation(sim_time=time, \
delta_t=delta_t, \
all_balls=balls, \
meas_obj=meas_obj, \
plot_boundaries=meas_obj.boundaries, \
noplot=False)
def TEST7():
R = 0.2**2 * np.eye(2)
P = 0.25**2 * np.eye(4)
boundaries = [-1, 4, 0, 4, 10, 10]
balls = [
init_ball([4, 2, -3, -8], R, P, 'g', boundaries, 0.25),
init_ball([3.2, 2, -0.2, -8], R, P, 'b', boundaries, 0, 0.3)
]
R = 0.03**2 * np.eye(2)
meas_obj = MeasurementObject(R, boundaries[:4], 0.95)
delta_t = 1 / 60
time = 5
monte_carlo_jpdaf_simulation(sim_time=time, \
delta_t=delta_t, \
all_balls=balls, \
meas_obj=meas_obj, \
plot_boundaries=meas_obj.boundaries, \
noplot=False)
def TEST6():
R = 0.2**2 * np.eye(2)
P = 0.2**2 * np.eye(4)
boundaries = [-1, 10, 0, 10, 10, 10]
balls = [
init_ball([0, 10, 2, -1], R, P, 'g', boundaries),
init_ball([5, 10, -2, -1], R, P, 'b', boundaries),
init_ball([7, 0, -2, 10], R, P, 'm', boundaries)
]
R = 0.1**2 * np.eye(2)
meas_obj = MeasurementObject(R, boundaries[:4], 0.9, 0.1)
delta_t = .05
time = 1
monte_carlo_jpdaf_simulation(sim_time=time, \
delta_t=delta_t, \
all_balls=balls, \
meas_obj=meas_obj, \
plot_boundaries=meas_obj.boundaries, \
noplot=False)
def TEST1():
R = 0.2**2 * np.eye(2)
P = 0.2**2 * np.eye(4)
boundaries = [-1, 10, 0, 10, 10,
10] # [x_min, x_max, y_min, y_max, vx, vy]
balls = [
init_ball([0, 3, 2, -6], R, P, 'g', boundaries, True),
init_ball([8, 5, -1, -1], R, P, 'b', boundaries, True),
init_ball([0, 7, 4, -2], R, P, 'y', boundaries, True)
]
R = 0.3**2 * np.eye(2)
meas_obj = MeasurementObject(R, boundaries[:4], 1, 0.2)
delta_t = .05
time = 5
monte_carlo_jpdaf_simulation(sim_time=time, \
delta_t=delta_t, \
all_balls=balls, \
meas_obj=meas_obj, \
plot_boundaries=meas_obj.boundaries, \
noplot=False)
def TEST5():
R = 0.2**2 * np.eye(2)
P = 0.2**2 * np.eye(4)
boundaries = [-1, 10, 0, 10, 10, 10]
balls = [
init_ball([0, 10, 2, -7], R, P, 'g', boundaries),
init_ball([8, 5, -1, -3], R, P, 'b', boundaries)
]
R = 0.1**2 * np.eye(2)
meas_obj = MeasurementObject(R, boundaries[:4])
delta_t = 0.05
time = 5
target_map = {0: 1, 1: 0}
monte_carlo_jpdaf_simulation(sim_time=time, \
delta_t=delta_t, \
all_balls=balls, \
meas_obj=meas_obj, \
plot_boundaries=meas_obj.boundaries, \
noplot=False,
target_map=target_map)