def test_baseline_run():
from pandemic.models.SEIRDS import SEIRDS, states, params, s_params, ns, t_duration_bounds, t_initial_bounds
# test baseline model
p, phase0, traj = generate_phase(SEIRDS,
ns,
states,
params,
s_params,
t_initial_bounds,
t_duration_bounds,
fix_initial=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_objective('time', loc='final', scaler=1.0)
phase0.add_timeseries_output('theta')
setup_and_run_phase(states, p, phase0, traj, t_duration_bounds[0])
max_I = np.max(states['I']['result'])
final_R = states['R']['result'][-1]
final_S = states['S']['result'][-1]
final_D = states['D']['result'][-1]
print("max_I", max_I)
print("final_R", final_R)
print("final_S", final_S)
print("final_D", final_D)
mI_actual = 0.26364939220443845
fR_actual = 0.66815968
fS_actual = 0.26927833
fD_actual = 0.05378587
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
assert (abs(final_D - fD_actual) / fD_actual) < tol
return states, params
def test_baseline_run():
ns = 35
t_initial_bounds = [0.0, 1.0]
t_duration_bounds = [200.0, 301.00]
from pandemic.models.SIR import SIR, states, params, s_params
p, phase0, traj = generate_phase(SIR,
ns,
states,
params,
s_params,
t_initial_bounds,
t_duration_bounds,
fix_initial=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=0.05, scaler=1.0)
phase0.add_objective('time', loc='final', scaler=1.0)
phase0.add_timeseries_output('theta')
setup_and_run_phase(states, p, phase0, traj, t_duration_bounds[0])
max_I = np.max(states['I']['result'])
final_S = states['S']['result'][-1]
print("max_I", max_I)
print("final_S", final_S)
err = abs(max_I - 0.3592) / 0.3592
assert (err < 1e4)
return states, params
# set up model states
for state in states:
states[state]['ds'] = ds
params['beta']['val'] = beta
params['alpha']['val'] = alpha
params['gamma']['val'] = gamma
params['epsilon']['val'] = epsilon
params['mu']['val'] = mu
t_initial_bounds = [0.0, 1.0]
t_duration_bounds = [200.0, 301.00]
p, phase0, traj = generate_phase(SEIRDS, ns, states, params, s_params,
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)
