# This is the 20% increase from the original parameter values - not needed in real cases
factor = 1.2
builder_est = TemplateBuilder()
builder_est.add_complementary_state_variable('T', 293.15)
builder_est.add_complementary_state_variable('Tc', 293.15)
builder_est.set_odes_rule(rule_odes)
for com in components:
builder_est.add_mixture_component(com.name, com.init)
for param in parameters:
builder_est.add_parameter(param.name,
bounds=param.bounds,
init=param.init * factor)
# New method to add state variance to the model builder
builder_est.add_state_variance(noise)
# New method to add times like everything else
builder_est.set_model_times(times)
est_param = EstimationPotential(builder_est,
exp_data,
simulation_data=results,
verbose=True)
est_param.estimate()
est_param.plot_results()
data_filename = 'dow_data.csv'
dataDirectory = Path(Path(__file__).resolve().parent, data_directory)
filename = dataDirectory.joinpath(data_filename)
Z_data = read_concentration_data(filename)
factor = 1.2
builder_est = TemplateBuilder()
builder_est.set_odes_rule(rule_odes)
noise = {c.name: c.sigma * 1 for c in components}
for c in components:
Z_data[c.name] = add_noise_to_signal(Z_data[c.name],
np.sqrt(noise[c.name]))
builder_est.add_state_variance({k: np.sqrt(v) for k, v in noise.items()})
for com in components:
builder_est.add_mixture_component(com.name, com.init)
for param in kinetic_parameters:
builder_est.add_parameter(param.name,
bounds=param.bounds,
init=param.init * factor)
# New method to add state variance to the model builder
builder_est.add_state_variance(noise)
# New method to add times like everything else
builder_est.set_model_times(times)
est_param = EstimationPotential(builder_est,