#pyomo_model2.P['k1'].value = 0.006655
#pyomo_model2.P['k1'].fixed = True
v_estimator = VarianceEstimator(pyomo_model2)
v_estimator.apply_discretization('dae.collocation',
nfe=60,
ncp=3,
scheme='LAGRANGE-RADAU')
# Provide good initial guess
p_guess = {'k1': 0.006655}
raw_results = v_estimator.run_lsq_given_P('ipopt', p_guess, tee=False)
v_estimator.initialize_from_trajectory('Z', raw_results.Z)
v_estimator.initialize_from_trajectory('S', raw_results.S)
v_estimator.initialize_from_trajectory('dZdt', raw_results.dZdt)
v_estimator.initialize_from_trajectory('C', raw_results.C)
options = dict()
A_set = [
l for i, l in enumerate(pyomo_model2.meas_lambdas) if (i % 4 == 0)
]
results_variances = v_estimator.run_opt('ipopt',
tee=True,
solver_options=options,
tolerance=1e-4,
max_iter=40,
subset_lambdas=A_set)
#################################################################################
builder = TemplateBuilder()
builder.add_mixture_component(concentrations)
builder.add_parameter('k', bounds=(0.0, 1.0))
builder.add_spectral_data(results_sim.D)
builder.set_odes_rule(rule_odes)
opt_model = builder.create_pyomo_model(0.0, 200.0)
v_estimator = VarianceEstimator(opt_model)
v_estimator.apply_discretization('dae.collocation',
nfe=4,
ncp=1,
scheme='LAGRANGE-RADAU')
v_estimator.initialize_from_trajectory('Z', results_sim.Z)
v_estimator.initialize_from_trajectory('S', results_sim.S)
v_estimator.scale_variables_from_trajectory('Z', results_sim.Z)
v_estimator.scale_variables_from_trajectory('S', results_sim.S)
options = {} #{'mu_init': 1e-6, 'bound_push': 1e-6}
results_variances = v_estimator.run_opt('ipopt',
tee=True,
solver_options=options,
tolerance=1e-6)
print("\nThe estimated variances are:\n")
for k, v in six.iteritems(results_variances.sigma_sq):
print(k, v)
sigmas = results_variances.sigma_sq #results_variances.sigma_sq
