def test_constant(self):
expected = [10] * 100
actual = scientific.event_based(expected, 50, 50, 11)
numpy.testing.assert_allclose([10] * 11, actual.abscissae)
numpy.testing.assert_allclose(
numpy.arange(0, 1.1, 0.1), actual.ordinates)
def test_sample_based_beta(self):
vulnerability_function_rm = (
scientific.VulnerabilityFunction(
[0.001, 0.2, 0.3, 0.5, 0.7], [0.01, 0.1, 0.2, 0.4, 0.8],
[0.0001, 0.0001, 0.0001, 0.0001, 0.0001], "BT", "RC"))
vulnerability_function_rc = (
scientific.VulnerabilityFunction(
[0.001, 0.2, 0.3, 0.5, 0.7], [0.0035, 0.07, 0.14, 0.28, 0.56],
[0.0001, 0.0001, 0.0001, 0.0001, 0.0001], "BT", "RC"))
vulnerability_model = {"RM": vulnerability_function_rm,
"RC": vulnerability_function_rc}
peb_calculator = api.ProbabilisticEventBased(
vulnerability_model, None, None)
peb_conditional_losses = api.ConditionalLosses([0.99], peb_calculator)
for i in range(3):
asset_output = peb_conditional_losses(
sb.input_models_asset[i],
{"IMLs": gmf_bd[i], "TSES": 2500, "TimeSpan": 50})
self.assert_allclose(
sb.expected_poes[i],
asset_output.insured_losses.ordinates)
self.assert_allclose(
sb.expected_losses[i],
asset_output.insured_losses.abscissae)
aggregate_curve = scientific.event_based(
peb_calculator.aggregate_losses, 2500, 50, 10)
expected_aggregate_poes = [1.0, 0.732864698034, 0.228948414196,
0.147856211034, 0.0768836536134,
0.0768836536134, 0.0198013266932,
0.0198013266932, 0.0198013266932]
expected_aggregate_losses = [
102.669407618, 308.008222854, 513.347038089,
718.685853325, 924.024668561, 1129.3634838,
1334.70229903, 1540.04111427, 1745.3799295]
self.assert_allclose(
expected_aggregate_poes, aggregate_curve.ordinates)
self.assert_allclose(
expected_aggregate_losses, aggregate_curve.abscissae)
def __call__(self, asset, hazard):
taxonomies = self.vulnerability_model.keys()
vulnerability_function = self.vulnerability_model[asset.taxonomy]
if self._aggregate_losses is None:
self._aggregate_losses = numpy.zeros(len(hazard))
vulnerability_function.seed(
self.seed, self.correlation_type, taxonomies)
self.loss_ratios = vulnerability_function(hazard)
loss_ratio_curve = scientific.event_based(
self.loss_ratios,
tses=self.tses, time_span=self.time_span,
curve_resolution=self.curve_resolution)
losses = self.loss_ratios * asset.value
loss_curve = loss_ratio_curve.rescale_abscissae(asset.value)
self._aggregate_losses += losses
return scientific.ProbabilisticEventBasedOutput(
asset, losses, loss_ratio_curve, loss_curve,
None, None, None, None)
def test_mean_based(self):
vulnerability_function_rm = (
scientific.VulnerabilityFunction(
[0.001, 0.2, 0.3, 0.5, 0.7], [0.01, 0.1, 0.2, 0.4, 0.8],
[0.0, 0.0, 0.0, 0.0, 0.0], "LN", "RM"))
vulnerability_function_rc = (
scientific.VulnerabilityFunction(
[0.001, 0.2, 0.3, 0.5, 0.7], [0.0035, 0.07, 0.14, 0.28, 0.56],
[0.0, 0.0, 0.0, 0.0, 0.0], "LN", "RC"))
vulnerability_model = {"RM": vulnerability_function_rm,
"RC": vulnerability_function_rc}
peb_calculator = api.ProbabilisticEventBased(
vulnerability_model, 50, 50)
peb_conditional_losses = api.ConditionalLosses(
[CONDITIONAL_LOSS_POES], peb_calculator)
for i in range(3):
asset_output = peb_conditional_losses(
mb.input_models_asset[i], gmf[i])
self.assertAlmostEqual(
mb.expected_loss_map[i],
asset_output.conditional_losses[CONDITIONAL_LOSS_POES],
delta=0.05 * mb.expected_loss_map[i])
self.assert_allclose(mb.expected_poes,
asset_output.loss_ratio_curve.ordinates)
self.assert_allclose(mb.expected_poes,
asset_output.loss_curve.ordinates)
self.assert_allclose(mb.expected_losses[i],
asset_output.loss_curve.abscissae)
self.assert_allclose(
mb.expected_losses[i] / mb.input_models_asset[i].value,
asset_output.loss_ratio_curve.abscissae)
expected_aggregate_poes = [0, 0.020408606, 0.04081678, 0.061224955,
0.08163313, 0.102041305, 0.12244948,
0.142857655, 0.163265829, 0.183674004,
0.204082179, 0.224490354, 0.244898529,
0.265306704, 0.285714879, 0.306123053,
0.326531228, 0.346939403, 0.367347578,
0.387755753, 0.408163928, 0.428572102,
0.448980277, 0.469388452, 0.489796627,
0.510204802, 0.530612977, 0.551021151,
0.571429326, 0.591837501, 0.612245676,
0.632653851, 0.653062026, 0.673470201,
0.693878375, 0.71428655, 0.734694725,
0.7551029, 0.775511075, 0.79591925,
0.816327424, 0.836735599, 0.857143774,
0.877551949, 0.897960124, 0.918368299,
0.938776473, 0.959184648, 0.979592823,
1][::-1]
expected_aggregate_losses = [330.0596974, 326.5857542, 323.111811,
319.6378677, 316.1639245, 312.6899813,
309.2160381, 305.7420949, 302.2681517,
298.7942084, 295.3202652, 291.846322,
288.3723788, 284.8984356, 281.4244924,
277.9505491, 274.4766059, 271.0026627,
267.5287195, 264.0547763, 260.5808331,
257.1068899, 253.6329466, 250.1590034,
246.6850602, 243.211117, 239.7371738,
236.2632306, 232.7892873, 229.3153441,
225.8414009, 222.3213759, 217.0814518,
211.8415276, 206.6016035, 201.3616793,
196.1217552, 190.881831, 185.6419069,
180.4019828, 175.1620586, 169.9221345,
164.6822103, 155.7023863, 144.5398623,
133.3773383, 122.2148143, 115.92256,
115.8386574, 55.3134][::-1]
aggregate_curve = scientific.event_based(
peb_calculator.aggregate_losses, 50, 50)
self.assert_allclose(
expected_aggregate_losses, aggregate_curve.abscissae)
self.assert_allclose(
expected_aggregate_poes, aggregate_curve.ordinates)
