def setup_complex_pi(cls): PI = PredictionIntervals() results = gf.setup_pseudo_results() results['s2chain'] = None DS = gf.basic_data_structure() PI.setup_prediction_interval_calculation(results=results, data=DS, modelfunction=gf.predmodelfun) PI.generate_prediction_intervals(sstype=0, nsample=500, calc_pred_int=False, waitbar=False) return PI
def test_generate_credible_intervals(self): PI = PredictionIntervals() results = gf.setup_pseudo_results() results['s2chain'] = None DS = gf.basic_data_structure() PI.setup_prediction_interval_calculation(results=results, data=DS, modelfunction=gf.predmodelfun) PI.generate_prediction_intervals(sstype=None, nsample=500, calc_pred_int=False, waitbar=False) cint = PI.intervals['credible_intervals'] pint = PI.intervals['prediction_intervals'] self.common_set_1(cint, pint)
def test_generate_credible_intervals_with_waitbar(self): PI = PredictionIntervals() results = gf.setup_pseudo_results() results['s2chain'] = None DS = gf.basic_data_structure() PI.setup_prediction_interval_calculation(results=results, data=DS, modelfunction=gf.predmodelfun) PI.generate_prediction_intervals(sstype=None, nsample=500, calc_pred_int=False, waitbar=True) cint = PI.intervals['credible_intervals'] pint = PI.intervals['prediction_intervals'] self.common_set_1(cint, pint) self.assertEqual(PI._PredictionIntervals__wbarstatus.percentage(3), 3.0, msg='Expect 3.0')