def test_make_sample_pass(self):
"""Test generate sample"""
exp_unc, _, haz_unc = make_imp_uncs()
unc = Uncertainty({'exp': exp_unc, 'haz': haz_unc})
#default sampling saltelli
unc.make_sample(N=1, sampling_kwargs={'calc_second_order': True})
self.assertEqual(unc.n_samples, 1 * (2 * 2 + 2)) # N * (2 * D + 2)
self.assertTrue(isinstance(unc.samples_df, pd.DataFrame))
self.assertTrue(
np.allclose(unc.samples_df['x_exp'],
np.array([
1.239453, 1.837109, 1.239453, 1.239453, 1.837109,
1.837109
]),
rtol=1e-05))
self.assertListEqual(list(unc.samples_df['x_haz']),
[0.0, 0.0, 1.0, 1.0, 0.0, 1.0])
#latin sampling
unc.make_sample(N=1,
sampling_method='latin',
sampling_kwargs={'seed': 11245})
self.assertEqual(unc.n_samples, 1)
self.assertTrue(isinstance(unc.samples_df, pd.DataFrame))
self.assertTrue(
np.allclose(unc.samples_df['x_exp'],
np.array([2.58309]),
rtol=1e-05))
self.assertListEqual(list(unc.samples_df['x_haz']), [2.0])
def test_plot_sample_pass(self):
exp_unc, _, haz_unc = make_imp_uncs()
unc = Uncertainty({'exp': exp_unc, 'haz': haz_unc})
unc.make_sample(N=1)
unc.plot_sample()
plt.close()
def test_save_pass(self):
"""Test save samples"""
exp_unc, impf_unc, haz_unc = make_imp_uncs()
unc = Uncertainty({'exp': exp_unc, 'impf': impf_unc, 'haz': haz_unc})
unc.make_sample(1)
filename = unc.save_samples_df()
unc_imp = UncImpact(exp_unc, impf_unc, haz_unc)
unc_imp.load_samples_df(filename)
unc_imp.calc_distribution()
unc_imp.calc_sensitivity()
def test_est_comp_time_pass(self):
exp_unc, _, haz_unc = make_imp_uncs()
unc = Uncertainty({'exp': exp_unc, 'haz': haz_unc})
unc.make_sample(N=1, sampling_kwargs={'calc_second_order': False})
est = unc.est_comp_time(0.12345)
self.assertEqual(est, 1 * (2 + 2) * 0.123) # N * (D + 2)
pool = Pool(nodes=4)
est = unc.est_comp_time(0.12345, pool)
self.assertEqual(est, 1 * (2 + 2) * 0.123 / 4) # N * (D + 2)
pool.close()
pool.join()
pool.clear()
