def plot_sensitivity_second_order(self):
"""Test plot sensitivity indices 2nd order"""
exp_unc, impf_unc, _ = make_imp_uncs()
haz = haz_dem()
unc = UncImpact(exp_unc, impf_unc, haz)
unc.make_sample(N=1)
unc.calc_distribution()
unc.calc_sensitivity()
unc.plot_sensitivity2d()
plt.close()
def test_plot_distribution_pass(self):
exp_unc, impf_unc, _ = make_imp_uncs()
haz = haz_dem()
unc = UncImpact(exp_unc, impf_unc, haz)
unc.make_sample(N=1)
unc.calc_distribution()
unc.plot_rp_distribution()
plt.close()
def test_plot_distribution_pass(self):
"""Test plot the distribution (uncertainty) of the impact metrics)"""
exp_unc, impf_unc, _ = make_imp_uncs()
haz = haz_dem()
unc = UncImpact(exp_unc, impf_unc, haz)
unc.make_sample(N=1)
unc.calc_distribution()
unc.plot_rp_distribution()
plt.close()
def test_init_pass(self):
exp_unc, impf_unc, _ = make_imp_uncs()
haz = haz_dem()
unc = UncImpact(exp_unc, impf_unc, haz)
self.assertSetEqual(set(unc.metrics.keys()),
{'aai_agg', 'freq_curve', 'eai_exp', 'at_event'})
self.assertSetEqual(set(unc.unc_vars.keys()), {'exp', 'impf', 'haz'})
def test_calc_distribution_pass(self):
"""Test compute the uncertainty distribution for an impact"""
exp_unc, impf_unc, _ = make_imp_uncs()
haz = haz_dem()
unc = UncImpact(exp_unc, impf_unc, haz)
unc.make_sample(N=1)
unc.calc_distribution(calc_eai_exp=False, calc_at_event=False)
self.assertListEqual(unc.rp, [5, 10, 20, 50, 100, 250])
self.assertEqual(unc.calc_eai_exp, False)
self.assertEqual(unc.calc_at_event, False)
self.assertTrue(
np.allclose(
unc.metrics['aai_agg'].aai_agg,
np.array([
9.600984e+07, 1.668144e+08, 8.068803e+08, 1.274945e+08,
1.071482e+09, 2.215182e+08, 1.401932e+09, 1.861671e+09
])))
self.assertTrue(np.allclose(unc.metrics['freq_curve'].rp5,
np.zeros(8)))
self.assertTrue(
np.allclose(
unc.metrics['freq_curve'].rp250,
np.array([
2.880990e+09, 5.005640e+09, 2.421225e+10, 3.825758e+09,
3.215222e+10, 6.647149e+09, 4.206811e+10, 5.586359e+10
])))
self.assertTrue(unc.metrics['eai_exp'].empty)
self.assertTrue(unc.metrics['at_event'].empty)
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_plot_sensitivity_map_pass(self):
"""Test plot the map of the largest sensitivity index for eai_exp"""
exp_unc, impf_unc, _ = make_imp_uncs()
haz = haz_dem()
#Default parameters
unc = UncImpact(exp_unc, impf_unc, haz)
unc.make_sample(N=1)
unc.calc_distribution(calc_eai_exp=True)
unc.calc_sensitivity()
unc.plot_sensitivity_map(exp_unc.uncvar_func(x_exp=1))
plt.close()
#Non-default parameters
unc = UncImpact(exp_unc, impf_unc, haz)
unc.make_sample(N=1, sampling_method='morris')
unc.calc_distribution(calc_eai_exp=True)
unc.calc_sensitivity(salib_method='morris')
unc.plot_sensitivity_map(exp_unc.uncvar_func(x_exp=1), salib_si='mu')
plt.close()