if args.mode == "test1":
inputs = all_inputs[:,:,args.case]
simdata = DiscreteTimeSystemSimulation(sys, est, x_init, P_init, 2*len(beacon_x), inputs, nruns)
simdata.analysis()
# Chi2 Overlay Plots
chi2_overlay(simdata.NEES_est[0,:], simdata.state_dim,
r"Overlay with EKF $\rho_k$", plot_nbins=args.plot_nbins, plt_chi2_pdf=False)
chi2_overlay(simdata.NEES_true[0,:], simdata.state_dim,
r"Overlay with EKF Monte-Carlo $\rho_k$", plot_nbins=args.plot_nbins)
# MC Confidence Bounds plot
alpha = 0.95
NEES_est_sum = np.sum(simdata.NEES_est, axis=0)
NEES_true_sum = np.sum(simdata.NEES_true, axis=0)
interval_bounds_plot(NEES_est_sum, simdata.state_dim, alpha,
"{} interval for EKF Estimated NEES".format(alpha), simdata.nruns)
interval_bounds_plot(NEES_true_sum, simdata.state_dim, alpha,
"{} interval for EKF True NEES".format(alpha), simdata.nruns)
# Timeseries plot
simdata.plt_timeseries(stepsize=2)
# White noise plot
simdata.plt_inn(1/dt)
plt.show()
# Save one run from all of them
elif args.mode == "saveall":
num_test_cases = 1
print("Scale: {}".format(scale.value))
# Apply scalar mapping to all estimated covariances
s = scale.value
for simdata in simdata_test:
simdata.Pest = s * simdata.Pest
# Evaluate covariance on test data
simdata = simdata_test[0]
# Calculate NEES and divergence
simdata.calc_tot_NEES()
simdata.calc_divergence(nbins=nbins)
simdata.compute_sigma_percentages()
# Chi2 Overlay Plot
chi2_overlay(simdata.NEES_est[0, :],
simdata.state_dim,
"",
plot_nbins=40,
plt_chi2_pdf=True)
# Confidence bounds plot
NEES_est_sum = np.sum(simdata.NEES_est, axis=0)
alpha = 0.95
interval_bounds_plot(NEES_est_sum, simdata.state_dim, alpha,
"{} interval for EKF Scalar-Adjusted NEES".format(alpha),
simdata.nruns)
plt.show()