os.path.join(
os.path.dirname(
os.path.abspath(inspect.getfile(inspect.currentframe()))),
'..', '..', 'data_sets'))
filename = os.path.join(dataDirectory, 'Dij.txt')
D_frame = read_spectral_data_from_txt(filename)
######################################
builder = TemplateBuilder()
components = {'A': 1e-3, 'B': 0, 'C': 0}
builder.add_mixture_component(components)
# note the parameter is not fixed
builder.add_parameter('k1', bounds=(0.0, 5.0))
builder.add_parameter('k2', bounds=(0.0, 1.0))
builder.add_spectral_data(D_frame)
# define explicit system of ODEs
def rule_odes(m, t):
exprs = dict()
exprs['A'] = -m.P['k1'] * m.Z[t, 'A']
exprs['B'] = m.P['k1'] * m.Z[t, 'A'] - m.P['k2'] * m.Z[t, 'B']
exprs['C'] = m.P['k2'] * m.Z[t, 'B']
return exprs
builder.set_odes_rule(rule_odes)
pyomo_model = builder.create_pyomo_model(0.0, 10.0)
optimizer = ParameterEstimator(pyomo_model)
optimizer.apply_discretization('dae.collocation',
tee=True,
variances=sigmas,
seed=123453256)
if with_plots:
results_sim.C.plot.line()
results_sim.S.plot.line()
plt.show()
#################################################################################
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)
