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# TODO: Assert no. samples in db = number_of_samples*number_of_replicas
my_campaign.populate_runs_dir()
assert(len(my_campaign.get_campaign_runs_dir()) > 0)
runs_dir = my_campaign.get_campaign_runs_dir()
assert(os.path.exists(runs_dir))
assert(os.path.isdir(runs_dir))
my_campaign.apply_for_each_run_dir(
uq.actions.ExecuteLocal("tests/gauss/gauss_json.py gauss_in.json"))
my_campaign.collate()
# Create a EnsembleBoot analysis element and apply it to the campaign
stats = uq.analysis.EnsembleBoot(groupby=["mu"], qoi_cols=["Value"])
my_campaign.apply_analysis(stats)
print("stats:", my_campaign.get_last_analysis())
def test_lagrange_poly():
assert(uq.analysis.sc_analysis.lagrange_poly(2.0, [8, 4, 9], 0) == -3.5)
assert(uq.analysis.sc_analysis.lagrange_poly(2.0, [8, 4, 9], 1) == 2.0999999999999996)
assert(uq.analysis.sc_analysis.lagrange_poly(2.0, [8, 4, 9], 2) == 2.4000000000000004)
with pytest.raises(IndexError):
uq.analysis.sc_analysis.lagrange_poly(2.0, [8, 4, 9], 3)
number_of_samples=100)
# Associate the sampler with the campaign
my_campaign.set_sampler(my_sampler)
# Will draw all (of the finite set of samples)
my_campaign.draw_samples()
my_campaign.populate_runs_dir()
my_campaign.apply_for_each_run_dir(uq.actions.ExecuteLocal(
"tests/cooling/cooling_model.py cooling_in.json"))
my_campaign.collate()
# Post-processing analysis
my_analysis = uq.analysis.QMCAnalysis(sampler=my_sampler,
qoi_cols=output_columns)
my_campaign.apply_analysis(my_analysis)
results = my_campaign.get_last_analysis()
# Get Descriptive Statistics
stats = results['statistical_moments']['te']
per = results['percentiles']['te']
return stats, per