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')