def __init__(self, exp_input_var, impf_input_var, haz_input_var):
"""Initialize UncCalcImpact
Sets the uncertainty input variables, the impact metric_names, and the
units.
Parameters
----------
exp_input_var : climada.engine.uncertainty.input_var.InputVar or climada.entity.Exposure
Exposure uncertainty variable or Exposure
impf_input_var : climada.engine.uncertainty.input_var.InputVar or climada.entity.ImpactFuncSet
Impact function set uncertainty variable or Impact function set
haz_input_var : climada.engine.uncertainty.input_var.InputVar or climada.hazard.Hazard
Hazard uncertainty variable or Hazard
"""
Calc.__init__(self)
self.input_var_names = ('exp_input_var', 'impf_input_var',
'haz_input_var')
self.exp_input_var = InputVar.var_to_inputvar(exp_input_var)
self.impf_input_var = InputVar.var_to_inputvar(impf_input_var)
self.haz_input_var = InputVar.var_to_inputvar(haz_input_var)
self.metric_names = ('aai_agg', 'freq_curve', 'at_event', 'eai_exp',
'tot_value')
self.value_unit = self.exp_input_var.evaluate().value_unit
self.check_distr()
def test_plot_pass(self):
impf = impf_dem()
distr_dict = {
"x_paa": sp.stats.beta(0.5, 1),
"x_mdd": sp.stats.uniform(0.8, 1.2),
"x_lit": sp.stats.randint(0, 10)
}
impf_iv = InputVar(impf, distr_dict)
self.assertIsNotNone(impf_iv.plot())
plt.close()
def test_var_to_inputvar(self):
exp = exp_dem()
distr_dict = {"x_exp": sp.stats.uniform(0.8, 1.2)}
var = InputVar.var_to_inputvar(exp)
self.assertDictEqual(var.distr_dict, {})
self.assertTrue(isinstance(var.func(), Exposures))
iv_var = InputVar.var_to_inputvar(InputVar(exp, distr_dict))
self.assertDictEqual(iv_var.distr_dict, distr_dict)
self.assertTrue(isinstance(iv_var, InputVar))
def test_evaluate_pass(self):
impf = impf_dem
distr_dict = {
"x_paa": sp.stats.beta(0.5, 1),
"x_mdd": sp.stats.uniform(0.8, 0.4)
}
impf_iv = InputVar(impf, distr_dict)
#Direct function evaluate
impf_eval = impf_iv.func(**{'x_paa': 0.8, 'x_mdd': 1.1})
impf_true = impf_dem(x_paa=0.8, x_mdd=1.1)
self.assertEqual(impf_eval.size(), impf_true.size())
impf_func1 = impf_eval.get_func()['TC'][1]
impf_func2 = impf_true.get_func()['TC'][1]
np.testing.assert_array_equal(impf_func1.intensity,
impf_func2.intensity)
np.testing.assert_array_equal(impf_func1.mdd, impf_func2.mdd)
np.testing.assert_array_equal(impf_func1.paa, impf_func2.paa)
self.assertEqual(impf_func1.id, impf_func2.id)
self.assertEqual(impf_func1.haz_type, impf_func2.haz_type)
#Specific evaluate
impf_eval = impf_iv.evaluate(x_paa=0.8, x_mdd=1.1)
impf_true = impf_dem(x_paa=0.8, x_mdd=1.1)
self.assertEqual(impf_eval.size(), impf_true.size())
impf_func1 = impf_eval.get_func()['TC'][1]
impf_func2 = impf_true.get_func()['TC'][1]
np.testing.assert_array_equal(impf_func1.intensity,
impf_func2.intensity)
np.testing.assert_array_equal(impf_func1.mdd, impf_func2.mdd)
np.testing.assert_array_equal(impf_func1.paa, impf_func2.paa)
self.assertEqual(impf_func1.id, impf_func2.id)
self.assertEqual(impf_func1.haz_type, impf_func2.haz_type)
#Average evaluate (default)
impf_eval = impf_iv.evaluate()
impf_true = impf_dem(x_paa=0.3333333333333333, x_mdd=1.0)
self.assertEqual(impf_eval.size(), impf_true.size())
impf_func1 = impf_eval.get_func()['TC'][1]
impf_func2 = impf_true.get_func()['TC'][1]
np.testing.assert_array_almost_equal(impf_func1.intensity,
impf_func2.intensity)
np.testing.assert_array_almost_equal(impf_func1.mdd, impf_func2.mdd)
np.testing.assert_array_almost_equal(impf_func1.paa, impf_func2.paa)
self.assertEqual(impf_func1.id, impf_func2.id)
self.assertEqual(impf_func1.haz_type, impf_func2.haz_type)
def test_init_pass(self):
impf = impf_dem
distr_dict = {
"x_paa": sp.stats.beta(0.5, 1),
"x_mdd": sp.stats.uniform(0.8, 1.2)
}
impf_iv = InputVar(impf, distr_dict)
self.assertListEqual(impf_iv.labels, ['x_paa', 'x_mdd'])
self.assertTrue(isinstance(impf_iv.distr_dict, dict))
def __init__(self, haz_input_var, ent_input_var,
haz_fut_input_var=None, ent_fut_input_var=None):
"""Initialize UncCalcCostBenefit
Sets the uncertainty input variables, the cost benefit metric_names,
and the units.
Parameters
----------
haz_input_var : climada.engine.uncertainty.input_var.InputVar
or climada.hazard.Hazard
Hazard uncertainty variable or Hazard for the present Hazard
in climada.engine.CostBenefit.calc
ent_input_var : climada.engine.uncertainty.input_var.InputVar
or climada.entity.Entity
Entity uncertainty variable or Entity for the present Entity
in climada.engine.CostBenefit.calc
haz_fut_input_var: climada.engine.uncertainty.input_var.InputVar
or climada.hazard.Hazard, optional
Hazard uncertainty variable or Hazard for the future Hazard
The Default is None.
ent_fut_input_var : climada.engine.uncertainty.input_var.InputVar
or climada.entity.Entity, optional
Entity uncertainty variable or Entity for the future Entity
in climada.engine.CostBenefit.calc
"""
Calc.__init__(self)
self.input_var_names = ('haz_input_var', 'ent_input_var',
'haz_fut_input_var', 'ent_fut_input_var')
self.haz_input_var = InputVar.var_to_inputvar(haz_input_var)
self.ent_input_var = InputVar.var_to_inputvar(ent_input_var)
self.haz_fut_input_var = InputVar.var_to_inputvar(haz_fut_input_var)
self.ent_fut_input_var = InputVar.var_to_inputvar(ent_fut_input_var)
self.metric_names = ('tot_climate_risk', 'benefit',
'cost_ben_ratio',
'imp_meas_present', 'imp_meas_future')
self.value_unit = self.ent_input_var.evaluate().exposures.value_unit
self.check_distr()
def make_input_vars():
exp = exp_dem
exp_distr = {
"x_exp": sp.stats.uniform(0.8, 2),
}
exp_unc = InputVar(exp, exp_distr)
impf = impf_dem
impf_distr = {
"x_paa": sp.stats.beta(0.5, 1),
"x_mdd": sp.stats.uniform(0.8, 1.2)
}
impf_unc = InputVar(impf, impf_distr)
haz = haz_dem
haz_distr = {
"x_haz": sp.stats.alpha(a=2, loc=1, scale=1),
}
haz_unc = InputVar(haz, haz_distr)
return exp_unc, impf_unc, haz_unc
def make_costben_iv():
entdem = ent_dem()
ent_iv = InputVar.ent(impf_set=entdem.impact_funcs,
disc_rate=entdem.disc_rates,
exp_list=[entdem.exposures],
meas_set=entdem.measures,
bounds_noise=[0.3, 1.9],
bounds_cost=[0.5, 1.5],
bounds_impfi=[-2, 5],
haz_id_dict={'TC': [1]})
entfutdem = ent_fut_dem()
entfut_iv = InputVar.entfut(impf_set=entfutdem.impact_funcs,
exp_list=[entfutdem.exposures],
meas_set=entfutdem.measures,
bounds_eg=[0.8, 1.5],
bounds_mdd=[0.7, 0.9],
bounds_paa=[1.3, 2],
haz_id_dict={'TC': [1]})
return ent_iv, entfut_iv