# 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.
Instead of "type = estimacao" it becomes "type = predicao". It is necessary to enter at least 4 data for each prediction variable.
"""
#%% Setting the validation data set
#inputs
# input 1
time = [
#%% Evaluating the parameters uncertainty and coverage region
# uncertaintyMethod: method for calculating the covariance matrix of the parameters: 2InvHessian, Geral, SensibilidadeModelo
# Geral obtains the parameters uncertainty matrix without approximations (most accurate), while 2InvHessian and SensibilidadeModelo involves
# some approximations.
# objectiveFunctionMapping: Deals with mapping the objective function (True or False);
# parametersReport: Informs whether the parameters report should be created (True or False).
# iterations: Number of iterations to perform the mapping of the objective function. The higher the better mapping, but it
# increases the execution time
Estimation.parametersUncertainty(uncertaintyMethod='Geral',
objectiveFunctionMapping=True,
iterations=5000,
parametersReport=False)
#%% 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);
Estimation.prediction(export_y=True,
export_y_xls=True,
export_cov_y=True,
export_x=True)
#%% Evaluating residuals and quality index
# using solely default options
Estimation.residualAnalysis()
#%% Plotting the main results
# using solely default options
Estimation.plots()