test_stochastic_assessment.m

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function OK = test_stochastic_assessment
  % consistency check for the StochasticAssessment helper classes
  %
  % Test cases:
  %   - 1. compute errors for fixed (M,N) combinations
  %   - 2. compute error estimators for fixed (M,N) combinations
  %   - 3. same as 2, but compute operator conditions as well.
  %
  % Finally all three outputs are merged
  %  TODO: compare the result with some expected values

  OK = 1;

  matfile = fullfile(rbmatlabhome, 'test', 'test_rb_richards_fv_data');
  load(matfile);

  rbdd = get_by_description(detailed_data.datatree, 'rb', rbmodel);

  disp('Test 1');
  rbmodel.enable_error_estimator = false;
  rbmodel.Mstrich                = 0;
  assessment1 = Postprocess.StochasticAssessment.Assessment(matfile, rbmodel, 3);
  assessment1.M_by_N_ratio       = 0;
  assessment1.compute_errors     = true;
  assessment1.samples            = {[0.1 0.5 1]};
  try
    evalc('output1 = assessment1.compute();');
  catch exception
    OK = false;
    disp('> failed');
    disp(getReport(exception));
  end
  if OK
    disp('> passed');
  end
  
  disp('Test 2');
  assessment2 = copy(assessment1);
  assessment2.compute_estimates             = true;
  assessment2.rmodel.enable_error_estimator = true;
  assessment2.rmodel.Mstrich                = 1;
  
  try
    evalc('output2 = assessment2.compute();');
  catch exception
    OK = false;
    disp('> failed');
    disp(getReport(exception));
  end
  if OK
    disp('> passed');
  end

  disp('Test 3');
  assessment3 = copy(assessment2);
  assessment3.compute_conditions = true;
  try
    evalc('output = assessment3.compute();');
  catch exception
    OK = false;
    disp('> failed');
    disp(getReport(exception));
  end
  if OK
    disp('> passed');
  end

  output1.merge(output2);
  output.merge(output1);

end