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 post_process(self):
# compute aggregate loss curves
for hazard_output in self.considered_hazard_outputs():
loss_curve = models.LossCurve.objects.get(
hazard_output=hazard_output,
aggregate=True, output__oq_job=self.job)
curve_data = loss_curve.aggregatelosscurvedata
tses, time_span = self.hazard_times()
aggregate_loss_curve = scientific.event_based(
curve_data.losses, tses, time_span,
curve_resolution=self.rc.loss_curve_resolution)
curve_data.losses = aggregate_loss_curve.abscissae.tolist()
curve_data.poes = aggregate_loss_curve.ordinates.tolist()
curve_data.save()
event_loss_table_output = models.Output.objects.create_output(
self.job, "Event Loss Table", "event_loss")
for rupture_id, aggregate_loss in self.event_loss_table.items():
models.EventLoss.objects.create(
output=event_loss_table_output,
rupture_id=rupture_id,
aggregate_loss=aggregate_loss)
def test_mean_based_with_partial_correlation(self):
# This is a regression test. Data has not been checked
vf = (
scientific.VulnerabilityFunction(
'PGA',
[0.001, 0.2, 0.3, 0.5, 0.7],
[0.01, 0.1, 0.2, 0.4, 0.8],
[0.01, 0.02, 0.02, 0.01, 0.03]))
gmvs = numpy.array([[10., 20., 30., 40., 50.],
[1., 2., 3., 4., 5.]])
epsilons = scientific.make_epsilons(gmvs, seed=1, correlation=0.5)
loss_matrix = vf.apply_to(gmvs, epsilons)
losses, poes = scientific.event_based(loss_matrix[0], 120, 30, 4)
first_curve_integral = scientific.average_loss(losses, poes)
self.assertAlmostEqual(0.48983614471, first_curve_integral)
wf = workflows.ProbabilisticEventBased(
vulnerability_functions={self.loss_type: vf},
time_span=50,
tses=10000,
loss_curve_resolution=4,
conditional_loss_poes=[0.1, 0.5, 0.9],
insured_losses=False
)
out = wf(self.loss_type, assets, gmvs, epsilons, [1, 2, 3, 4, 5])
self.assert_similar(
out.event_loss_table,
{1: 15.332714802464356,
2: 16.21582466071975,
3: 15.646630129345354,
4: 15.285164778325353,
5: 15.860930792931873,
})
def test_mean_based_with_partial_correlation(self):
# This is a regression test. Data has not been checked
vf = (scientific.VulnerabilityFunction('SOME-TAXONOMY', 'PGA',
[0.001, 0.2, 0.3, 0.5, 0.7],
[0.01, 0.1, 0.2, 0.4, 0.8],
[0.01, 0.02, 0.02, 0.01, 0.03]))
gmvs = numpy.array([[10., 20., 30., 40., 50.], [1., 2., 3., 4., 5.]])
epsilons = scientific.make_epsilons(gmvs, seed=1, correlation=0.5)
loss_matrix = vf.apply_to(gmvs, epsilons)
losses_poes = scientific.event_based(loss_matrix[0], 120, 30, 4)
first_curve_integral = scientific.average_loss(losses_poes)
self.assertAlmostEqual(0.48983614471, first_curve_integral)
wf = workflows.ProbabilisticEventBased(
'PGA',
'SOME-TAXONOMY',
vulnerability_functions={self.loss_type: vf},
investigation_time=50,
risk_investigation_time=50,
ses_per_logic_tree_path=200,
number_of_logic_tree_samples=0,
loss_curve_resolution=4,
conditional_loss_poes=[0.1, 0.5, 0.9],
insured_losses=False)
out = wf(self.loss_type, assets, gmvs, epsilons, [1, 2, 3, 4, 5])
self.assert_similar(
out.event_loss_table, {
1: 15.332714802464356,
2: 16.21582466071975,
3: 15.646630129345354,
4: 15.285164778325353,
5: 15.860930792931873,
})
def test_insured_loss_mean_based(self):
vf = 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")
loss_ratios = scientific.vulnerability_function_applier(
vf, gmf[0:2])
values = [3000, 1000]
insured_limits = [1250., 40.]
deductibles = [40, 13]
insured_average_losses = [
scientific.average_loss(*scientific.event_based(
scientific.insured_losses(
loss_ratios,
deductibles[i] / values[i], insured_limits[i] / values[i]),
50, 50, 20))
for i, loss_ratios in enumerate(loss_ratios)]
numpy.testing.assert_allclose(
[207.86489132 / 3000, 38.07815797 / 1000],
insured_average_losses)
def test_mean_based_with_partial_correlation(self):
# This is a regression test. Data has not been checked
vf = (
scientific.VulnerabilityFunction(
'SOME-TAXONOMY', 'PGA',
[0.001, 0.2, 0.3, 0.5, 0.7],
[0.01, 0.1, 0.2, 0.4, 0.8],
[0.01, 0.02, 0.02, 0.01, 0.03]))
gmvs = numpy.array([[10., 20., 30., 40., 50.],
[1., 2., 3., 4., 5.]])
epsilons = scientific.make_epsilons(gmvs, seed=1, correlation=0.5)
loss_matrix = vf.apply_to(gmvs, epsilons)
losses_poes = scientific.event_based(loss_matrix[0], .25, 4)
first_curve_integral = scientific.average_loss(losses_poes)
self.assertAlmostEqual(0.48983614471, first_curve_integral)
wf = workflows.ProbabilisticEventBased(
'PGA', 'SOME-TAXONOMY',
vulnerability_functions={self.loss_type: vf},
investigation_time=50,
risk_investigation_time=50,
ses_per_logic_tree_path=200,
number_of_logic_tree_samples=0,
loss_curve_resolution=4,
conditional_loss_poes=[0.1, 0.5, 0.9],
insured_losses=False
)
wf.riskmodel = mock.MagicMock()
out = wf(self.loss_type, assets, gmvs, epsilons, [1, 2, 3, 4, 5])
numpy.testing.assert_almost_equal(
out.average_losses, [0.01987912, 0.01929152])
def build_agg_curve(lr_list, insured_losses, ses_ratio, curve_resolution,
monitor):
"""
Build the aggregate loss curve in parallel for each loss type
and realization pair.
:param lr_list:
a list of triples `(l, r, data)` where `l` is a loss type string,
`r` as realization index and `data` is an array of pairs
`(rupture_id, loss)` where loss is an array with two values
:param insured_losses:
job.ini configuration parameter
:param ses_ratio:
a ratio obtained from ses_per_logic_tree_path
:param curve_resolution:
the number of discretization steps for the loss curve
:param monitor:
a Monitor instance
:returns:
a dictionary (r, l, i) -> (losses, poes, avg)
"""
result = {}
for l, r, data in lr_list:
if len(data) == 0: # realization with no losses
continue
for i in range(insured_losses + 1): # insured_losses
the_losses = numpy.array(
[loss[i] for _rupid, loss in data], F32)
losses, poes = scientific.event_based(
the_losses, ses_ratio, curve_resolution)
avg = scientific.average_loss((losses, poes))
result[l, r, i] = (losses, poes, avg)
return result
def build_loss_curves(self, elass, loss_type, i):
"""
Build loss curves per asset from a set of losses with length given by
the parameter loss_curve_resolution.
:param elass: a dict (loss_type, asset_id) -> (tag, loss, ins_loss)
:param loss_type: the loss_type
:param i: 1 for loss curves or 2 for insured losses
:returns: an array of loss curves, one for each asset
"""
oq = self.oqparam
C = oq.loss_curve_resolution
lcs = []
for asset in self.assets:
all_losses = [loss[i] for loss in elass[loss_type, asset.id]]
if all_losses:
losses, poes = scientific.event_based(
all_losses, tses=oq.tses,
time_span=oq.risk_investigation_time or
oq.investigation_time, curve_resolution=C)
avg = scientific.average_loss((losses, poes))
else:
losses, poes = numpy.zeros(C), numpy.zeros(C)
avg = 0
lcs.append((losses, poes, avg))
return numpy.array(lcs, self.loss_curve_dt)
def build_agg_curve(lr_data, insured_losses, ses_ratio, curve_resolution, L,
monitor):
"""
Build the aggregate loss curve in parallel for each loss type
and realization pair.
:param lr_data:
a list of triples `(l, r, data)` where `l` is the loss type index,
`r` is the realization index and `data` is an array of kind
`(rupture_id, loss)` or `(rupture_id, loss, loss_ins)`
:param bool insured_losses:
job.ini configuration parameter
:param ses_ratio:
a ratio obtained from ses_per_logic_tree_path
:param curve_resolution:
the number of discretization steps for the loss curve
:param L:
the number of loss types
:param monitor:
a Monitor instance
:returns:
a dictionary (r, l, i) -> (losses, poes, avg)
"""
result = {}
for l, r, data in lr_data:
if len(data) == 0: # realization with no losses
continue
if insured_losses:
gloss = data['loss'][:, 0]
iloss = data['loss'][:, 1]
else:
gloss = data['loss']
losses, poes = scientific.event_based(
gloss, ses_ratio, curve_resolution)
avg = scientific.average_loss((losses, poes))
result[l, r, 'losses'] = losses
result[l, r, 'poes'] = poes
result[l, r, 'avg'] = avg
if insured_losses:
losses_ins, poes_ins = scientific.event_based(
iloss, ses_ratio, curve_resolution)
avg_ins = scientific.average_loss((losses_ins, poes_ins))
result[l, r, 'losses_ins'] = losses_ins
result[l, r, 'poes_ins'] = poes_ins
result[l, r, 'avg_ins'] = avg_ins
return result
def build_agg_curve(lr_data, insured_losses, ses_ratio, curve_resolution, L,
monitor):
"""
Build the aggregate loss curve in parallel for each loss type
and realization pair.
:param lr_data:
a list of triples `(l, r, data)` where `l` is the loss type index,
`r` is the realization index and `data` is an array of kind
`(rupture_id, loss)` or `(rupture_id, loss, loss_ins)`
:param bool insured_losses:
job.ini configuration parameter
:param ses_ratio:
a ratio obtained from ses_per_logic_tree_path
:param curve_resolution:
the number of discretization steps for the loss curve
:param L:
the number of loss types
:param monitor:
a Monitor instance
:returns:
a dictionary (r, l, i) -> (losses, poes, avg)
"""
result = {}
for l, r, data in lr_data:
if len(data) == 0: # realization with no losses
continue
if insured_losses:
gloss = data['loss'][:, 0]
iloss = data['loss'][:, 1]
else:
gloss = data['loss']
losses, poes = scientific.event_based(gloss, ses_ratio,
curve_resolution)
avg = scientific.average_loss((losses, poes))
result[l, r, 'losses'] = losses
result[l, r, 'poes'] = poes
result[l, r, 'avg'] = avg
if insured_losses:
losses_ins, poes_ins = scientific.event_based(
iloss, ses_ratio, curve_resolution)
avg_ins = scientific.average_loss((losses_ins, poes_ins))
result[l, r, 'losses_ins'] = losses_ins
result[l, r, 'poes_ins'] = poes_ins
result[l, r, 'avg_ins'] = avg_ins
return result
def test_mean_based(self):
epsilons = scientific.make_epsilons([gmf[0]], seed=1, correlation=0)
vulnerability_function_rm = (
scientific.VulnerabilityFunction(
'RM', 'PGA',
[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]))
vulnerability_function_rc = (
scientific.VulnerabilityFunction(
'RC', 'PGA',
[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]))
cr = 50 # curve resolution
curve_rm_1 = scientific.event_based(
vulnerability_function_rm.apply_to(
[gmf[0]], epsilons)[0], 50, 50, cr)
curve_rm_2 = scientific.event_based(
vulnerability_function_rm.apply_to(
[gmf[1]], epsilons)[0], 50, 50, cr)
curve_rc = scientific.event_based(
vulnerability_function_rc.apply_to(
[gmf[2]], epsilons)[0], 50, 50, cr)
for i, curve_rm in enumerate([curve_rm_1, curve_rm_2]):
conditional_loss = scientific.conditional_loss_ratio(
curve_rm[0], curve_rm[1], 0.8)
self.assertAlmostEqual([0.0490311, 0.0428061][i], conditional_loss)
self.assertAlmostEqual(
[0.070219108, 0.04549904][i],
scientific.average_loss(curve_rm))
conditional_loss = scientific.conditional_loss_ratio(
curve_rc[0], curve_rc[1], 0.8)
self.assertAlmostEqual(0.0152273, conditional_loss)
self.assertAlmostEqual(
0.0152393, scientific.average_loss(curve_rc))
def test_mean_based(self):
epsilons = scientific.make_epsilons([gmf[0]], seed=1, correlation=0)
vulnerability_function_rm = (
scientific.VulnerabilityFunction(
'RM', 'PGA',
[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]))
vulnerability_function_rc = (
scientific.VulnerabilityFunction(
'RC', 'PGA',
[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]))
cr = 50 # curve resolution
curve_rm_1 = scientific.event_based(
vulnerability_function_rm.apply_to(
[gmf[0]], epsilons)[0], 1, cr)
curve_rm_2 = scientific.event_based(
vulnerability_function_rm.apply_to(
[gmf[1]], epsilons)[0], 1, cr)
curve_rc = scientific.event_based(
vulnerability_function_rc.apply_to(
[gmf[2]], epsilons)[0], 1, cr)
for i, curve_rm in enumerate([curve_rm_1, curve_rm_2]):
conditional_loss = scientific.conditional_loss_ratio(
curve_rm[0], curve_rm[1], 0.8)
self.assertAlmostEqual([0.0490311, 0.0428061][i], conditional_loss)
self.assertAlmostEqual(
[0.070219108, 0.04549904][i],
scientific.average_loss(curve_rm))
conditional_loss = scientific.conditional_loss_ratio(
curve_rc[0], curve_rc[1], 0.8)
self.assertAlmostEqual(0.0152273, conditional_loss)
self.assertAlmostEqual(
0.0152393, scientific.average_loss(curve_rc))
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"))
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"))
curve_rm_1 = scientific.event_based(
scientific.vulnerability_function_applier(
vulnerability_function_rm, [gmf[0]])[0], 50, 50)
curve_rm_2 = scientific.event_based(
scientific.vulnerability_function_applier(
vulnerability_function_rm, [gmf[1]])[0], 50, 50)
curve_rc = scientific.event_based(
scientific.vulnerability_function_applier(
vulnerability_function_rc, [gmf[2]])[0], 50, 50)
for i, curve_rm in enumerate([curve_rm_1, curve_rm_2]):
conditional_loss = scientific.conditional_loss_ratio(
curve_rm[0], curve_rm[1], 0.8)
self.assertAlmostEqual([0.0490311, 0.0428061][i], conditional_loss)
self.assertAlmostEqual(
[0.070219108, 0.04549904][i],
scientific.average_loss(curve_rm[0], curve_rm[1]))
conditional_loss = scientific.conditional_loss_ratio(
curve_rc[0], curve_rc[1], 0.8)
self.assertAlmostEqual(0.0152273, conditional_loss)
self.assertAlmostEqual(
0.0152393,
scientific.average_loss(curve_rc[0], curve_rc[1]))
def insured_losses(loss_type, unit, assets, loss_ratio_matrix):
for asset, losses in zip(assets, loss_ratio_matrix):
asset_insured_losses, poes = scientific.event_based(
scientific.insured_losses(
losses,
asset.value(loss_type),
asset.deductible(loss_type),
asset.insurance_limit(loss_type)),
tses=unit.calc.tses,
time_span=unit.calc.time_span)
# FIXME(lp). Insured losses are still computed as absolute
# values.
yield asset_insured_losses / asset.value(loss_type), poes
def test_mean_based_with_partial_correlation(self):
# This is a regression test. Data has not been checked
vf = (
scientific.VulnerabilityFunction(
[0.001, 0.2, 0.3, 0.5, 0.7], [0.01, 0.1, 0.2, 0.4, 0.8],
[0.01, 0.02, 0.02, 0.01, 0.03], "LN"))
gmvs = numpy.array([[10., 20., 30., 40., 50.],
[1., 2., 3., 4., 5.]])
loss_matrix = scientific.vulnerability_function_applier(
vf, gmvs, seed=1, asset_correlation=0.5)
losses, poes = scientific.event_based(loss_matrix[0], 120, 30, 4)
first_curve_integral = scientific.average_loss(losses, poes)
self.assertAlmostEqual(0.48983614471, first_curve_integral)
def post_process(self):
# compute aggregate loss curves
for hazard_output in self.considered_hazard_outputs():
loss_curve = models.LossCurve.objects.get(
hazard_output=hazard_output,
aggregate=True, output__oq_job=self.job)
curve_data = loss_curve.aggregatelosscurvedata
tses, time_span = self.hazard_times()
aggregate_loss_curve = scientific.event_based(
curve_data.losses, tses, time_span,
curve_resolution=self.rc.loss_curve_resolution)
curve_data.losses = aggregate_loss_curve.abscissae.tolist()
curve_data.poes = aggregate_loss_curve.ordinates.tolist()
curve_data.save()
def test_insured_loss_mean_based(self):
vf = scientific.VulnerabilityFunction(
'VF', 'PGA',
[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])
epsilons = scientific.make_epsilons(gmf[0:2], seed=1, correlation=0)
loss_ratios = vf.apply_to(gmf[0:2], epsilons)
values = [3000, 1000]
insured_limits = [1250., 40.]
deductibles = [40, 13]
insured_average_losses = [
scientific.average_loss(scientific.event_based(
scientific.insured_losses(
lrs,
deductibles[i] / values[i], insured_limits[i] / values[i]),
50, 50, 20))
for i, lrs in enumerate(loss_ratios)]
numpy.testing.assert_allclose(
[207.86489132 / 3000, 38.07815797 / 1000],
insured_average_losses)
wf = workflows.ProbabilisticEventBased(
'PGA', 'SOME-TAXONOMY',
vulnerability_functions={self.loss_type: vf},
risk_investigation_time=50,
hazard_investigation_time=50,
ses_per_logic_tree_path=200,
number_of_logic_tree_samples=0,
loss_curve_resolution=4,
conditional_loss_poes=[0.1, 0.5, 0.9],
insured_losses=True
)
out = wf(self.loss_type, assets, gmf[0:2], epsilons, [1, 2, 3, 4, 5])
self.assert_similar(
out.event_loss_table,
{1: 0.20314761658291458,
2: 0,
3: 0,
4: 0,
5: 0,
})
def build_agg_loss_curve_and_map(self, losses):
"""
Build a loss curve from a set of losses with length given by
the parameter loss_curve_resolution.
:param losses: a sequence of losses
:returns: a quartet (losses, poes, avg, loss_map)
"""
oq = self.oqparam
clp = oq.conditional_loss_poes
losses_poes = scientific.event_based(
losses, tses=oq.tses, time_span=oq.risk_investigation_time or
oq.investigation_time, curve_resolution=oq.loss_curve_resolution)
loss_map = scientific.loss_map_matrix(
clp, [losses_poes]).reshape(len(clp)) if clp else None
return (losses_poes[0], losses_poes[1],
scientific.average_loss(losses_poes), loss_map)
def __call__(self, ground_motion_fields):
if not len(ground_motion_fields):
return numpy.array([[]]), []
self.vulnerability_function.init_distribution(
len(ground_motion_fields), len(ground_motion_fields[0]),
self.seed, self.correlation)
loss_ratios = [
self.vulnerability_function(ground_motion_field)
for ground_motion_field in ground_motion_fields]
return (loss_ratios,
[scientific.event_based(
asset_loss_ratios,
tses=self.tses, time_span=self.time_span,
curve_resolution=self.curve_resolution)
for asset_loss_ratios in loss_ratios])
def test_insured_loss_mean_based(self):
vf = scientific.VulnerabilityFunction('VF', 'PGA',
[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])
epsilons = scientific.make_epsilons(gmf[0:2], seed=1, correlation=0)
loss_ratios = vf.apply_to(gmf[0:2], epsilons)
values = [3000., 1000.]
insured_limits = [1250., 40.]
deductibles = [40., 13.]
insured_average_losses = [
scientific.average_loss(
scientific.event_based(
scientific.insured_losses(lrs, deductibles[i] / values[i],
insured_limits[i] / values[i]),
50, 50, 20)) for i, lrs in enumerate(loss_ratios)
]
numpy.testing.assert_allclose([0.05667045, 0.02542965],
insured_average_losses)
wf = workflows.ProbabilisticEventBased(
'PGA',
'SOME-TAXONOMY',
vulnerability_functions={self.loss_type: vf},
investigation_time=50,
risk_investigation_time=50,
ses_per_logic_tree_path=200,
number_of_logic_tree_samples=0,
loss_curve_resolution=4,
conditional_loss_poes=[0.1, 0.5, 0.9],
insured_losses=True)
out = wf(self.loss_type, assets, gmf[0:2], epsilons, [1, 2, 3, 4, 5])
self.assert_similar(out.event_loss_table, {
1: 0.20314761658291458,
2: 0,
3: 0,
4: 0,
5: 0,
})
def test_insured_loss_mean_based(self):
vf = scientific.VulnerabilityFunction(
'VF', 'PGA',
[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])
epsilons = scientific.make_epsilons(gmf[0:2], seed=1, correlation=0)
loss_ratios = vf.apply_to(gmf[0:2], epsilons)
values = [3000., 1000.]
insured_limits = [1250., 40.]
deductibles = [40., 13.]
insured_average_losses = [
scientific.average_loss(scientific.event_based(
scientific.insured_losses(
lrs,
deductibles[i] / values[i], insured_limits[i] / values[i]),
1, 20))
for i, lrs in enumerate(loss_ratios)]
numpy.testing.assert_allclose([0.05667045, 0.02542965],
insured_average_losses)
wf = workflows.ProbabilisticEventBased(
'PGA', 'SOME-TAXONOMY',
vulnerability_functions={self.loss_type: vf},
investigation_time=50,
risk_investigation_time=50,
ses_per_logic_tree_path=200,
number_of_logic_tree_samples=0,
loss_curve_resolution=4,
conditional_loss_poes=[0.1, 0.5, 0.9],
insured_losses=True
)
wf.riskmodel = mock.MagicMock()
out = wf(self.loss_type, assets, gmf[0:2], epsilons, [1, 2, 3, 4, 5])
numpy.testing.assert_almost_equal(
out.average_losses, [0.00473820568, 0.0047437959417])
numpy.testing.assert_almost_equal(
out.average_insured_losses, [0, 0])
def test_mean_based_with_no_correlation(self):
# This is a regression test. Data has not been checked
vf = (
scientific.VulnerabilityFunction(
'SOME-TAXONOMY', 'PGA',
[0.001, 0.2, 0.3, 0.5, 0.7],
[0.01, 0.1, 0.2, 0.4, 0.8],
[0.01, 0.02, 0.02, 0.01, 0.03]))
gmvs = numpy.array([[10., 20., 30., 40., 50.],
[1., 2., 3., 4., 5.]])
epsilons = scientific.make_epsilons(gmvs, seed=1, correlation=0)
loss_matrix = vf.apply_to(gmvs, epsilons)
losses_poes = scientific.event_based(
loss_matrix[0], 120, 30, curve_resolution=4)
first_curve_integral = scientific.average_loss(losses_poes)
self.assertAlmostEqual(0.500993631, first_curve_integral)
wf = workflows.ProbabilisticEventBased(
'PGA', 'SOME-TAXONOMY',
vulnerability_functions={self.loss_type: vf},
risk_investigation_time=50,
hazard_investigation_time=50,
ses_per_logic_tree_path=200,
number_of_logic_tree_samples=0,
loss_curve_resolution=4,
conditional_loss_poes=[0.1, 0.5, 0.9],
insured_losses=False
)
out = wf(self.loss_type, assets, gmvs, epsilons, [1, 2, 3, 4, 5])
self.assert_similar(
out.event_loss_table,
{1: 16.246646231503398,
2: 15.613885199116158,
3: 15.669704465134854,
4: 16.241922530992454,
5: 16.010104452203464,
})
def test_mean_based_with_perfect_correlation(self):
# This is a regression test. Data has not been checked
vf = (
scientific.VulnerabilityFunction(
'SOME-TAXONOMY', 'PGA',
[0.001, 0.2, 0.3, 0.5, 0.7],
[0.01, 0.1, 0.2, 0.4, 0.8],
[0.01, 0.02, 0.02, 0.01, 0.03]))
gmvs = [[10., 20., 30., 40., 50.],
[1., 2., 3., 4., 5.]]
epsilons = scientific.make_epsilons(gmvs, seed=1, correlation=1)
loss_matrix = vf.apply_to(gmvs, epsilons)
losses_poes = scientific.event_based(loss_matrix[0], 120, 30, 4)
first_curve_integral = scientific.average_loss(losses_poes)
self.assertAlmostEqual(0.483041416, first_curve_integral)
wf = workflows.ProbabilisticEventBased(
'PGA', 'SOME-TAXONOMY',
vulnerability_functions={self.loss_type: vf},
risk_investigation_time=50,
hazard_investigation_time=50,
ses_per_logic_tree_path=200,
number_of_logic_tree_samples=0,
loss_curve_resolution=4,
conditional_loss_poes=[0.1, 0.5, 0.9],
insured_losses=False
)
out = wf(self.loss_type, assets, gmvs, epsilons, [1, 2, 3, 4, 5])
self.assert_similar(
out.event_loss_table,
{1: 15.232320555463319,
2: 16.248173683693864,
3: 15.583030510462981,
4: 15.177382760499968,
5: 15.840499250058254,
})
def test_mean_based_with_no_correlation(self):
# This is a regression test. Data has not been checked
vf = (scientific.VulnerabilityFunction('SOME-TAXONOMY', 'PGA',
[0.001, 0.2, 0.3, 0.5, 0.7],
[0.01, 0.1, 0.2, 0.4, 0.8],
[0.01, 0.02, 0.02, 0.01, 0.03]))
gmvs = numpy.array([[10., 20., 30., 40., 50.], [1., 2., 3., 4., 5.]])
epsilons = scientific.make_epsilons(gmvs, seed=1, correlation=0)
loss_matrix = vf.apply_to(gmvs, epsilons)
losses_poes = scientific.event_based(loss_matrix[0],
120,
30,
curve_resolution=4)
first_curve_integral = scientific.average_loss(losses_poes)
self.assertAlmostEqual(0.500993631, first_curve_integral)
wf = workflows.ProbabilisticEventBased(
'PGA',
'SOME-TAXONOMY',
vulnerability_functions={self.loss_type: vf},
investigation_time=50,
risk_investigation_time=50,
ses_per_logic_tree_path=200,
number_of_logic_tree_samples=0,
loss_curve_resolution=4,
conditional_loss_poes=[0.1, 0.5, 0.9],
insured_losses=False)
out = wf(self.loss_type, assets, gmvs, epsilons, [1, 2, 3, 4, 5])
self.assert_similar(
out.event_loss_table, {
1: 16.246646231503398,
2: 15.613885199116158,
3: 15.669704465134854,
4: 16.241922530992454,
5: 16.010104452203464,
})
def test_insured_loss_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"))
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"))
calculator_rm = api.ProbabilisticEventBased(
vulnerability_function_rm, time_span=50, tses=50,
curve_resolution=20)
calculator_rc = api.ProbabilisticEventBased(
vulnerability_function_rc, time_span=50, tses=50,
curve_resolution=20)
loss_ratios_rm, _curves_rm = calculator_rm(gmf[0:2])
loss_ratios_rc, [_curve_rc] = calculator_rc([gmf[2]])
values = [3000, 1000, 2000]
insured_limits = [1250., 40., 500.]
deductibles = [40, 13, 15]
insured_losses = [scientific.event_based(
scientific.insured_losses(
loss_ratios, values[i], deductibles[i], insured_limits[i]),
50, 50, 20)
for i, loss_ratios in enumerate(loss_ratios_rm + loss_ratios_rc)]
for i, insured_loss_curve in enumerate(insured_losses):
numpy.testing.assert_allclose(
il.expected_poes[i], insured_loss_curve.ordinates, rtol=10E-5)
numpy.testing.assert_allclose(
il.expected_losses[i],
insured_loss_curve.abscissae, rtol=10E-5)
def test_mean_based_with_perfect_correlation(self):
# This is a regression test. Data has not been checked
vf = (scientific.VulnerabilityFunction('SOME-TAXONOMY', 'PGA',
[0.001, 0.2, 0.3, 0.5, 0.7],
[0.01, 0.1, 0.2, 0.4, 0.8],
[0.01, 0.02, 0.02, 0.01, 0.03]))
gmvs = [[10., 20., 30., 40., 50.], [1., 2., 3., 4., 5.]]
epsilons = scientific.make_epsilons(gmvs, seed=1, correlation=1)
loss_matrix = vf.apply_to(gmvs, epsilons)
losses_poes = scientific.event_based(loss_matrix[0], 120, 30, 4)
first_curve_integral = scientific.average_loss(losses_poes)
self.assertAlmostEqual(0.483041416, first_curve_integral)
wf = workflows.ProbabilisticEventBased(
'PGA',
'SOME-TAXONOMY',
vulnerability_functions={self.loss_type: vf},
investigation_time=50,
risk_investigation_time=50,
ses_per_logic_tree_path=200,
number_of_logic_tree_samples=0,
loss_curve_resolution=4,
conditional_loss_poes=[0.1, 0.5, 0.9],
insured_losses=False)
out = wf(self.loss_type, assets, gmvs, epsilons, [1, 2, 3, 4, 5])
self.assert_similar(
out.event_loss_table, {
1: 15.232320555463319,
2: 16.248173683693864,
3: 15.583030510462981,
4: 15.177382760499968,
5: 15.840499250058254,
})
def test_zero_curve(self):
expected = [0.] * 100
losses, poes = scientific.event_based(expected, 50, 50, 11)
numpy.testing.assert_allclose([0.] * 11, losses)
numpy.testing.assert_allclose([0.] * 11, poes, atol=1E-10)
def post_process(self):
"""
Compute aggregate loss curves and event loss tables
"""
oq = self.oqparam
tses = oq.investigation_time * oq.ses_per_logic_tree_path
with self.monitor('post processing', autoflush=True):
inserter = writer.CacheInserter(models.EventLossData,
max_cache_size=10000)
for (loss_type, out_id), event_loss_table in self.acc.items():
if out_id: # values for individual realizations
hazard_output = models.Output.objects.get(pk=out_id)
event_loss = models.EventLoss.objects.get(
output__oq_job=self.job,
output__output_type='event_loss',
loss_type=loss_type, hazard_output=hazard_output)
if isinstance(hazard_output.output_container,
models.SESCollection):
ses_coll = hazard_output.output_container
rupture_ids = ses_coll.get_ruptures().values_list(
'id', flat=True)
else: # extract the SES collection from the Gmf
rupture_ids = models.SESRupture.objects.filter(
rupture__ses_collection__trt_model__lt_model=
hazard_output.output_container.
lt_realization.lt_model).values_list(
'id', flat=True)
for rupture_id in rupture_ids:
if rupture_id in event_loss_table:
inserter.add(
models.EventLossData(
event_loss_id=event_loss.id,
rupture_id=rupture_id,
aggregate_loss=event_loss_table[
rupture_id]))
inserter.flush()
aggregate_losses = [
event_loss_table[rupture_id]
for rupture_id in rupture_ids
if rupture_id in event_loss_table]
if aggregate_losses:
aggregate_loss = scientific.event_based(
aggregate_losses, tses=tses,
time_span=oq.investigation_time,
curve_resolution=oq.loss_curve_resolution)
models.AggregateLossCurveData.objects.create(
loss_curve=models.LossCurve.objects.create(
aggregate=True, insured=False,
hazard_output=hazard_output,
loss_type=loss_type,
output=models.Output.objects.create_output(
self.job,
"aggregate loss curves. "
"loss_type=%s hazard=%s" % (
loss_type, hazard_output),
"agg_loss_curve")),
losses=aggregate_loss[0],
poes=aggregate_loss[1],
average_loss=scientific.average_loss(
aggregate_loss),
stddev_loss=numpy.std(aggregate_losses))
def post_process(self):
"""
Compute aggregate loss curves and event loss tables
"""
with EnginePerformanceMonitor('post processing', self.job.id):
time_span, tses = self.hazard_times()
for loss_type, event_loss_table in self.event_loss_tables.items():
for hazard_output in self.rc.hazard_outputs():
event_loss = models.EventLoss.objects.create(
output=models.Output.objects.create_output(
self.job,
"Event Loss Table. type=%s, hazard=%s" % (
loss_type, hazard_output.id),
"event_loss"),
loss_type=loss_type,
hazard_output=hazard_output)
inserter = writer.CacheInserter(models.EventLossData, 9999)
ruptures = models.SESRupture.objects.filter(
ses__ses_collection__lt_realization=
hazard_output.output_container.lt_realization)
for rupture in ruptures:
if rupture.id in event_loss_table:
inserter.add(
models.EventLossData(
event_loss_id=event_loss.id,
rupture_id=rupture.id,
aggregate_loss=event_loss_table[
rupture.id]))
inserter.flush()
aggregate_losses = [
event_loss_table[rupture.id]
for rupture in ruptures
if rupture.id in event_loss_table]
if aggregate_losses:
aggregate_loss_losses, aggregate_loss_poes = (
scientific.event_based(
aggregate_losses, tses=tses,
time_span=time_span,
curve_resolution=self.rc.loss_curve_resolution
))
models.AggregateLossCurveData.objects.create(
loss_curve=models.LossCurve.objects.create(
aggregate=True, insured=False,
hazard_output=hazard_output,
loss_type=loss_type,
output=models.Output.objects.create_output(
self.job,
"aggregate loss curves. "
"loss_type=%s hazard=%s" % (
loss_type, hazard_output),
"agg_loss_curve")),
losses=aggregate_loss_losses,
poes=aggregate_loss_poes,
average_loss=scientific.average_loss(
aggregate_loss_losses, aggregate_loss_poes),
stddev_loss=numpy.std(aggregate_losses))
def post_process(self):
"""
Compute aggregate loss curves and event loss tables
"""
with self.monitor('post processing'):
inserter = writer.CacheInserter(models.EventLossData,
max_cache_size=10000)
time_span, tses = self.hazard_times()
for (loss_type, out_id), event_loss_table in self.acc.items():
if out_id: # values for individual realizations
hazard_output = models.Output.objects.get(pk=out_id)
event_loss = models.EventLoss.objects.get(
output__oq_job=self.job,
output__output_type='event_loss',
loss_type=loss_type, hazard_output=hazard_output)
if isinstance(hazard_output.output_container,
models.SESCollection):
ses_coll = hazard_output.output_container
rupture_ids = ses_coll.get_ruptures().values_list(
'id', flat=True)
else: # extract the SES collection from the Gmf
rupture_ids = models.SESRupture.objects.filter(
rupture__ses_collection__trt_model__lt_model=
hazard_output.output_container.
lt_realization.lt_model).values_list(
'id', flat=True)
for rupture_id in rupture_ids:
if rupture_id in event_loss_table:
inserter.add(
models.EventLossData(
event_loss_id=event_loss.id,
rupture_id=rupture_id,
aggregate_loss=event_loss_table[
rupture_id]))
inserter.flush()
aggregate_losses = [
event_loss_table[rupture_id]
for rupture_id in rupture_ids
if rupture_id in event_loss_table]
if aggregate_losses:
aggregate_loss_losses, aggregate_loss_poes = (
scientific.event_based(
aggregate_losses, tses=tses,
time_span=time_span,
curve_resolution=self.rc.loss_curve_resolution
))
models.AggregateLossCurveData.objects.create(
loss_curve=models.LossCurve.objects.create(
aggregate=True, insured=False,
hazard_output=hazard_output,
loss_type=loss_type,
output=models.Output.objects.create_output(
self.job,
"aggregate loss curves. "
"loss_type=%s hazard=%s" % (
loss_type, hazard_output),
"agg_loss_curve")),
losses=aggregate_loss_losses,
poes=aggregate_loss_poes,
average_loss=scientific.average_loss(
aggregate_loss_losses, aggregate_loss_poes),
stddev_loss=numpy.std(aggregate_losses))
def post_process(self):
"""
Compute aggregate loss curves and event loss tables
"""
with EnginePerformanceMonitor('post processing', self.job.id):
time_span, tses = self.hazard_times()
for loss_type, event_loss_table in self.event_loss_tables.items():
for hazard_output in self.rc.hazard_outputs():
event_loss = models.EventLoss.objects.create(
output=models.Output.objects.create_output(
self.job, "Event Loss Table. type=%s, hazard=%s" %
(loss_type, hazard_output.id), "event_loss"),
loss_type=loss_type,
hazard_output=hazard_output)
inserter = writer.CacheInserter(models.EventLossData, 9999)
rupture_ids = models.SESRupture.objects.filter(
ses__ses_collection__lt_realization=hazard_output.
output_container.lt_realization).values_list('id',
flat=True)
for rupture_id in rupture_ids:
if rupture_id in event_loss_table:
inserter.add(
models.EventLossData(
event_loss_id=event_loss.id,
rupture_id=rupture_id,
aggregate_loss=event_loss_table[rupture_id]
))
inserter.flush()
aggregate_losses = [
event_loss_table[rupture_id]
for rupture_id in rupture_ids
if rupture_id in event_loss_table
]
if aggregate_losses:
aggregate_loss_losses, aggregate_loss_poes = (
scientific.event_based(
aggregate_losses,
tses=tses,
time_span=time_span,
curve_resolution=self.rc.loss_curve_resolution)
)
models.AggregateLossCurveData.objects.create(
loss_curve=models.LossCurve.objects.create(
aggregate=True,
insured=False,
hazard_output=hazard_output,
loss_type=loss_type,
output=models.Output.objects.create_output(
self.job, "aggregate loss curves. "
"loss_type=%s hazard=%s" %
(loss_type, hazard_output),
"agg_loss_curve")),
losses=aggregate_loss_losses,
poes=aggregate_loss_poes,
average_loss=scientific.average_loss(
aggregate_loss_losses, aggregate_loss_poes),
stddev_loss=numpy.std(aggregate_losses))
def test_zero_curve(self):
expected = [0.] * 100
losses, poes = scientific.event_based(expected, 1, 11)
numpy.testing.assert_allclose([0.] * 11, losses)
numpy.testing.assert_allclose([0.] * 11, poes, atol=1E-10)
def event_based(job_id, hazard,
seed, vulnerability_function,
output_containers,
conditional_loss_poes, insured_losses,
time_span, tses,
loss_curve_resolution, asset_correlation,
hazard_montecarlo_p):
"""
Celery task for the event based risk calculator.
:param job_id: the id of the current
:class:`openquake.engine.db.models.OqJob`
:param dict hazard:
A dictionary mapping IDs of
:class:`openquake.engine.db.models.Output` (with output_type set
to 'gmf_collection') to a tuple where the first element is an
instance of
:class:`..hazard_getters.GroundMotionValuesGetter`,
and the second element is the corresponding weight.
:param seed:
the seed used to initialize the rng
:param dict output_containers: a dictionary mapping hazard Output
ID to a list (a, b, c, d) where a is the ID of the
:class:`openquake.engine.db.models.LossCurve` output container used to
store the computed loss curves; b is the dictionary poe->ID of
the :class:`openquake.engine.db.models.LossMap` output container used
to store the computed loss maps; c is the same as a but for
insured losses; d is the ID of the
:class:`openquake.engine.db.models.AggregateLossCurve` output container
used to store the computed loss curves
:param conditional_loss_poes:
The poes taken into accout to compute the loss maps
:param bool insured_losses: True if insured losses should be computed
:param time_span: the time span considered
:param tses: time of the stochastic event set
:param loss_curve_resolution: the curve resolution, i.e. the
number of points which defines the loss curves
:param float asset_correlation: a number ranging from 0 to 1
representing the correlation between the generated loss ratios
"""
loss_ratio_curves = OrderedDict()
event_loss_table = dict()
for hazard_output_id, hazard_data in hazard.items():
hazard_getter, _ = hazard_data
(loss_curve_id, loss_map_ids,
mean_loss_curve_id, quantile_loss_curve_ids,
insured_curve_id, aggregate_loss_curve_id) = (
output_containers[hazard_output_id])
# FIXME(lp). We should not pass the exact same seed for
# different hazard
calculator = api.ProbabilisticEventBased(
vulnerability_function,
curve_resolution=loss_curve_resolution,
time_span=time_span,
tses=tses,
seed=seed,
correlation=asset_correlation)
with logs.tracing('getting input data from db'):
assets, gmvs_ruptures, missings = hazard_getter()
if len(assets):
ground_motion_values = numpy.array(gmvs_ruptures)[:, 0]
rupture_id_matrix = numpy.array(gmvs_ruptures)[:, 1]
else:
# we are relying on the fact that if all the hazard_getter
# in this task will either return some results or they all
# return an empty result set.
logs.LOG.info("Exit from task as no asset could be processed")
base.signal_task_complete(
job_id=job_id,
event_loss_table=dict(),
num_items=len(missings))
return
with logs.tracing('computing risk'):
loss_ratio_matrix, loss_ratio_curves[hazard_output_id] = (
calculator(ground_motion_values))
with logs.tracing('writing results'):
with db.transaction.commit_on_success(using='reslt_writer'):
for i, loss_ratio_curve in enumerate(
loss_ratio_curves[hazard_output_id]):
asset = assets[i]
# loss curves
general.write_loss_curve(
loss_curve_id, asset, loss_ratio_curve)
# loss maps
for poe in conditional_loss_poes:
general.write_loss_map_data(
loss_map_ids[poe], asset,
scientific.conditional_loss_ratio(
loss_ratio_curve, poe))
# insured losses
if insured_losses:
insured_loss_curve = scientific.event_based(
scientific.insured_losses(
loss_ratio_matrix[i],
asset.value,
asset.deductible,
asset.ins_limit),
tses,
time_span,
loss_curve_resolution)
insured_loss_curve.abscissae = (
insured_loss_curve.abscissae / asset.value)
general.write_loss_curve(
insured_curve_id, asset, insured_loss_curve)
# update the event loss table of this task
for i, asset in enumerate(assets):
for j, rupture_id in enumerate(rupture_id_matrix[i]):
loss = loss_ratio_matrix[i][j] * asset.value
event_loss_table[rupture_id] = (
event_loss_table.get(rupture_id, 0) + loss)
# update the aggregate losses
aggregate_losses = sum(
loss_ratio_matrix[i] * asset.value
for i, asset in enumerate(assets))
general.update_aggregate_losses(
aggregate_loss_curve_id, aggregate_losses)
# compute mean and quantile loss curves if multiple hazard
# realizations are computed
if len(hazard) > 1 and (mean_loss_curve_id or quantile_loss_curve_ids):
weights = [data[1] for _, data in hazard.items()]
with logs.tracing('writing curve statistics'):
with db.transaction.commit_on_success(using='reslt_writer'):
loss_ratio_curve_matrix = loss_ratio_curves.values()
# here we are relying on the fact that assets do not
# change across different logic tree realizations (as
# the hazard grid does not change, so the hazard
# getters always returns the same assets)
for i, asset in enumerate(assets):
general.curve_statistics(
asset,
loss_ratio_curve_matrix[i],
weights,
mean_loss_curve_id,
quantile_loss_curve_ids,
hazard_montecarlo_p,
assume_equal="image")
base.signal_task_complete(job_id=job_id,
num_items=len(assets) + len(missings),
event_loss_table=event_loss_table)
