def test_calc_sensitivity_morris_pass(self):
"""Test compute sensitivity default"""
exp_unc, impf_unc, _ = make_input_vars()
haz = haz_dem()
unc_calc = CalcImpact(exp_unc, impf_unc, haz)
unc_data = unc_calc.make_sample(N=4, sampling_method='latin')
unc_data = unc_calc.uncertainty(unc_data,
calc_eai_exp=True,
calc_at_event=True)
unc_data = unc_calc.sensitivity(unc_data, sensitivity_method='morris')
self.assertEqual(unc_data.sensitivity_method, 'morris')
self.assertTupleEqual(unc_data.sensitivity_kwargs, tuple({}.items()))
for name, attr in unc_data.__dict__.items():
if 'sens_df' in name:
np.testing.assert_array_equal(
attr.param.unique(), np.array(['x_exp', 'x_paa', 'x_mdd']))
np.testing.assert_array_equal(
attr.si.unique(),
np.array(['mu', 'mu_star', 'sigma', 'mu_star_conf']))
if 'eai' in name:
self.assertEqual(
attr.size,
len(unc_data.param_labels) * 4 *
(len(exp_unc.evaluate().gdf) + 3))
elif 'at_event' in name:
self.assertEqual(
attr.size,
len(unc_data.param_labels) * 4 * (haz.size + 3))
else:
self.assertEqual(len(attr), len(unc_data.param_labels) * 4)
def test_calc_uncertainty_pool_pass(self):
"""Test parallel compute the uncertainty distribution for an impact"""
exp_unc, impf_unc, _ = make_input_vars()
haz = haz_dem()
unc_calc = CalcImpact(exp_unc, impf_unc, haz)
unc_data = unc_calc.make_sample(N=2)
pool = Pool(nodes=2)
try:
unc_data = unc_calc.uncertainty(unc_data,
calc_eai_exp=False,
calc_at_event=False,
pool=pool)
finally:
pool.close()
pool.join()
pool.clear()
self.assertEqual(unc_data.unit, exp_dem().value_unit)
self.assertListEqual(unc_calc.rp, [5, 10, 20, 50, 100, 250])
self.assertEqual(unc_calc.calc_eai_exp, False)
self.assertEqual(unc_calc.calc_at_event, False)
self.assertEqual(unc_data.aai_agg_unc_df.size, unc_data.n_samples)
self.assertEqual(unc_data.tot_value_unc_df.size, unc_data.n_samples)
self.assertEqual(unc_data.freq_curve_unc_df.size,
unc_data.n_samples * len(unc_calc.rp))
self.assertTrue(unc_data.eai_exp_unc_df.empty)
self.assertTrue(unc_data.at_event_unc_df.empty)
def test_make_sample_pass(self):
"""Test generate sample"""
exp_unc, _, haz_unc = make_input_vars()
impf = impf_dem()
unc_calc = CalcImpact(exp_unc, impf, haz_unc)
#default sampling saltelli
unc_data = unc_calc.make_sample(
N=2, sampling_kwargs={'calc_second_order': True})
self.assertEqual(unc_data.n_samples,
2 * (2 * 2 + 2)) # N * (2 * D + 2)
self.assertTrue(isinstance(unc_data.samples_df, pd.DataFrame))
np.testing.assert_array_equal(unc_data.samples_df.columns.values,
np.array(['x_exp', 'x_haz']))
# #latin sampling
unc_data = unc_calc.make_sample(N=1,
sampling_method='latin',
sampling_kwargs={'seed': 11245})
self.assertEqual(unc_data.n_samples, 1)
self.assertTrue(isinstance(unc_data.samples_df, pd.DataFrame))
np.testing.assert_array_equal(unc_data.samples_df.columns.values,
np.array(['x_exp', 'x_haz']))
def test_save_load_pass(self):
"""Test save and load output data"""
exp_unc, impf_unc, _ = make_input_vars()
haz = haz_dem()
unc_calc = CalcImpact(exp_unc, impf_unc, haz)
unc_data_save = unc_calc.make_sample(
N=2, sampling_kwargs={'calc_second_order': True})
filename = unc_data_save.to_hdf5()
unc_data_load = UncOutput.from_hdf5(filename)
for attr_save, val_save in unc_data_save.__dict__.items():
if isinstance(val_save, pd.DataFrame):
df_load = getattr(unc_data_load, attr_save)
self.assertTrue(df_load.equals(val_save))
self.assertEqual(unc_data_load.sampling_method,
unc_data_save.sampling_method)
self.assertEqual(unc_data_load.sampling_kwargs,
unc_data_save.sampling_kwargs)
filename.unlink()
unc_data_save = unc_calc.uncertainty(unc_data_save,
calc_eai_exp=True,
calc_at_event=False)
filename = unc_data_save.to_hdf5()
unc_data_load = UncOutput.from_hdf5(filename)
for attr_save, val_save in unc_data_save.__dict__.items():
if isinstance(val_save, pd.DataFrame):
df_load = getattr(unc_data_load, attr_save)
self.assertTrue(df_load.equals(val_save))
self.assertEqual(unc_data_load.sampling_method,
unc_data_save.sampling_method)
self.assertEqual(unc_data_load.sampling_kwargs,
unc_data_save.sampling_kwargs)
filename.unlink()
unc_data_save = unc_calc.sensitivity(
unc_data_save, sensitivity_kwargs={'calc_second_order': True})
filename = unc_data_save.to_hdf5()
unc_data_load = UncOutput.from_hdf5(filename)
for attr_save, val_save in unc_data_save.__dict__.items():
if isinstance(val_save, pd.DataFrame):
df_load = getattr(unc_data_load, attr_save)
self.assertTrue(df_load.equals(val_save))
self.assertEqual(unc_data_load.sampling_method,
unc_data_save.sampling_method)
self.assertEqual(unc_data_load.sampling_kwargs,
unc_data_save.sampling_kwargs)
self.assertEqual(unc_data_load.sensitivity_method,
unc_data_save.sensitivity_method)
self.assertEqual(unc_data_load.sensitivity_kwargs,
unc_data_save.sensitivity_kwargs)
filename.unlink()
def test_calc_sensitivity_pass(self):
"""Test compute sensitivity default"""
exp_unc, impf_unc, _ = make_input_vars()
haz = haz_dem()
unc_calc = CalcImpact(exp_unc, impf_unc, haz)
unc_data = unc_calc.make_sample(
N=4, sampling_kwargs={'calc_second_order': True})
unc_data = unc_calc.uncertainty(unc_data,
calc_eai_exp=False,
calc_at_event=False)
unc_data = unc_calc.sensitivity(
unc_data, sensitivity_kwargs={'calc_second_order': True})
self.assertEqual(unc_data.sensitivity_method, 'sobol')
self.assertTupleEqual(unc_data.sensitivity_kwargs,
tuple({'calc_second_order': 'True'}.items()))
for name, attr in unc_data.__dict__.items():
if 'sens_df' in name:
if 'eai' in name:
self.assertTrue(attr.empty)
elif 'at_event' in name:
self.assertTrue(attr.empty)
else:
np.testing.assert_array_equal(
attr.param.unique(),
np.array(['x_exp', 'x_paa', 'x_mdd']))
np.testing.assert_array_equal(
attr.si.unique(),
np.array([
'S1', 'S1_conf', 'ST', 'ST_conf', 'S2', 'S2_conf'
]))
self.assertEqual(len(attr),
len(unc_data.param_labels) * (4 + 3 + 3))
def test_init_pass(self):
"""Test initiliazation uncertainty"""
exp_iv, impf_iv, haz_iv = make_input_vars()
unc_calc = CalcImpact(exp_iv, impf_iv, haz_iv)
self.assertTupleEqual(
unc_calc.input_var_names,
('exp_input_var', 'impf_input_var', 'haz_input_var'))
self.assertTupleEqual(
unc_calc.metric_names,
('aai_agg', 'freq_curve', 'at_event', 'eai_exp', 'tot_value'))
self.assertEqual(unc_calc.value_unit, exp_iv.evaluate().value_unit)
self.assertTrue(
unc_calc.exp_input_var.evaluate(x_exp=1).gdf.equals(
exp_dem(1).gdf))
impf1 = unc_calc.impf_input_var.evaluate(x_paa=1,
x_mdd=1).get_func()['TC'][1]
impf2 = impf_dem(1, 1).get_func()['TC'][1]
np.testing.assert_array_almost_equal(impf1.calc_mdr(impf1.intensity),
impf2.calc_mdr(impf2.intensity))
haz1 = unc_calc.haz_input_var.evaluate(x_haz=1)
haz2 = haz_dem(1)
self.assertListEqual(haz1.event_name, haz2.event_name)