Estime.parametersUncertainty(uncertaintyMethod='Geral',
objectiveFunctionMapping=True,
lower_bound=[7.2e-3, 26400],
upper_bound=[7.7e-3, 28600],
parametersReport=True,
iterations=200)
#%% Evaluating model predictions
# export_y: Exports the calculated data of y, its uncertainty, and degrees of freedom in a txt with comma separation (True or False);
# export_y_xls: Exports the calculated data of y, its uncertainty, and degrees of freedom in a xls (True or False);
# export_cov_y: Exports the covariance matrix of y (True or False);
# export_x: Exports the calculated data of x, its uncertainty, and degrees of freedom in a txt with comma separation(True or False);
# export_cov_x: Exports the covariance matrix of x (True or False).
Estime.prediction(export_y=True,
export_y_xls=True,
export_cov_y=True,
export_x=True,
export_cov_x=True)
#%% Evaluating residuals and quality index
Estime.residualAnalysis(report=True)
#%% Plotting the main results
Estime.plots()
# =================================================================================
# OPTIONAL: VALIDATION
# =================================================================================
u"""
If the user wishes, it is possible to do the same analysis as before with the prediction data.
The procedure to be followed is similar to the one previously carried out. The only difference is in the argument inserted in the setConjunto method.
#%% Setting the observed data set # Independent variables Estime.setDados(0,(time,uxtime),(temperature,uxtemperature)) # Dependent variable Estime.setDados(1,(y,uy)) # Defining the previous data set to be used to parameter estimation Estime.setConjunto() #%% Optimization - estimating the parameters # initial_estimate: list containing initial estimate for optimization algorithm Estime.optimize(initial_estimative=[0.5,25000]) #%% Evaluating the parameters uncertainty and coverage region # using solely default options Estime.parametersUncertainty() #%% Evaluating model predictions # using solely default options Estime.prediction() #%% Evaluating residuals and quality index # using solely default options Estime.residualAnalysis() #%% Plotting the main results # using solely default options Estime.plots() #%% Reference of this case study # SCHWAAB, M.M.;PINTO, J.C. Análise de Dados Experimentais I: Fundamentos da Estátistica e Estimação de Parâmetros. Rio de Janeiro: e-papers, 2007.