max_I = np.max(states['I']['result'])
final_R = states['R']['result'][-1]
final_S = states['S']['result'][-1]
print("max_I", max_I)
print("final_R", final_R)
print("final_S", final_S)
mI_actual = 0.25920385864652234
fR_actual = 0.96616595
fS_actual = 0.03365363
tol = 1e-2
assert (abs(max_I - mI_actual) / mI_actual) < tol
assert (abs(final_R - fR_actual) / fR_actual) < tol
assert (abs(final_S - fS_actual) / fS_actual) < tol
return states, params
if __name__ == '__main__':
import matplotlib.pyplot as plt
test_model_partials()
states, params = test_baseline_run()
fig = make_plots(states, params)
fig.suptitle('Baseline SEIR')
plt.show()
t_initial_bounds,
t_duration_bounds,
fix_initial=True,
fix_duration=True)
p.driver = om.pyOptSparseDriver()
p.driver.options['optimizer'] = 'IPOPT'
p.driver.options['print_results'] = False
p.driver.opt_settings['hessian_approximation'] = 'limited-memory'
# p.driver.opt_settings['mu_init'] = 1.0E-2
p.driver.opt_settings['nlp_scaling_method'] = 'gradient-based'
p.driver.opt_settings['print_level'] = 5
p.driver.opt_settings['linear_solver'] = 'mumps'
p.driver.opt_settings['max_iter'] = 500
phase0.add_boundary_constraint('I', loc='final', upper=0.01, scaler=1.0)
#phase0.add_control('sigma', targets=['sigma'], lower=0.0, upper=beta, ref=beta)
#phase0.add_objective('max_I', scaler=1e5)
phase0.add_objective('time', loc='final', scaler=1.0)
phase0.add_timeseries_output('theta')
setup_and_run_phase(states, p, phase0, traj, 200.0)
print(states['I']['result'][-1])
make_plots(states, params)
plt.show()
