def compute_loss_ratio_curve(self, col, row, asset, gmf_slice):
"""Compute the loss ratio curve for a single site."""
# fail if the asset has an unknown vulnerability code
vuln_function = self.vuln_curves.get(
asset["vulnerabilityFunctionReference"], None)
if not vuln_function:
LOGGER.error(
"Unknown vulnerability function %s for asset %s"
% (asset["vulnerabilityFunctionReference"], asset["assetID"]))
return None
epsilon_provider = risk_job.EpsilonProvider(self.params)
loss_ratio_curve = probabilistic_event_based.compute_loss_ratio_curve(
vuln_function, gmf_slice, epsilon_provider, asset,
self._get_number_of_samples())
# NOTE(JMC): Early exit if the loss ratio is all zeros
if not False in (loss_ratio_curve.ordinates == 0.0):
return None
key = kvs.tokens.loss_ratio_key(self.id, row, col, asset["assetID"])
kvs.set(key, loss_ratio_curve.to_json())
LOGGER.warn("RESULT: loss ratio curve is %s, write to key %s" % (
loss_ratio_curve, key))
return loss_ratio_curve
def compute_loss_ratio_curve(self, point, asset, hazard_curve):
""" Computes the loss ratio curve and stores in kvs
the curve itself """
# we get the vulnerability function related to the asset
vuln_function = self.vuln_curves.get(
asset["vulnerabilityFunctionReference"], None)
if not vuln_function:
LOGGER.error(
"Unknown vulnerability function %s for asset %s"
% (asset["vulnerabilityFunctionReference"],
asset["assetID"]))
return None
loss_ratio_curve = cpsha_based.compute_loss_ratio_curve(
vuln_function, hazard_curve)
loss_key = kvs.tokens.loss_ratio_key(
self.job_id, point.row, point.column, asset['assetID'])
kvs.set(loss_key, loss_ratio_curve.to_json())
return loss_ratio_curve
def compute_loss_ratio_curve(self, col, row, asset, gmf_slice, loss_ratios):
"""Compute the loss ratio curve for a single asset."""
vuln_function = self.vuln_curves.get(asset["vulnerabilityFunctionReference"], None)
if not vuln_function:
LOGGER.error(
"Unknown vulnerability function %s for asset %s"
% (asset["vulnerabilityFunctionReference"], asset["assetID"])
)
return None
epsilon_provider = general.EpsilonProvider(self.params)
loss_ratio_curve = prob.compute_loss_ratio_curve(
vuln_function, gmf_slice, epsilon_provider, asset, self._get_number_of_samples(), loss_ratios=loss_ratios
)
# NOTE (jmc): Early exit if the loss ratio is all zeros
if not False in (loss_ratio_curve.ordinates == 0.0):
return None
key = kvs.tokens.loss_ratio_key(self.job_id, row, col, asset["assetID"])
kvs.set(key, loss_ratio_curve.to_json())
LOGGER.debug("Loss ratio curve is %s, write to key %s" % (loss_ratio_curve, key))
return loss_ratio_curve
def compute_loss_ratio_curve(self, col, row, asset, gmf_slice):
"""Compute the loss ratio curve for a single site."""
# fail if the asset has an unknown vulnerability code
vuln_function = self.vuln_curves.get(
asset["vulnerabilityFunctionReference"], None)
if not vuln_function:
LOGGER.error(
"Unknown vulnerability function %s for asset %s" %
(asset["vulnerabilityFunctionReference"], asset["assetID"]))
return None
epsilon_provider = risk_job.EpsilonProvider(self.params)
loss_ratio_curve = probabilistic_event_based.compute_loss_ratio_curve(
vuln_function, gmf_slice, epsilon_provider, asset,
self._get_number_of_samples())
# NOTE(JMC): Early exit if the loss ratio is all zeros
if not False in (loss_ratio_curve.ordinates == 0.0):
return None
key = kvs.tokens.loss_ratio_key(self.id, row, col, asset["assetID"])
kvs.set(key, loss_ratio_curve.to_json())
LOGGER.warn("RESULT: loss ratio curve is %s, write to key %s" %
(loss_ratio_curve, key))
return loss_ratio_curve
def compute_conditional_loss(self, col, row, loss_curve, asset, loss_poe):
""" Compute the conditional loss for a loss curve and probability of
exceedance """
loss_conditional = common.compute_conditional_loss(loss_curve, loss_poe)
key = kvs.tokens.loss_key(self.id, row, col, asset["AssetID"], loss_poe)
LOGGER.debug("RESULT: conditional loss is %s, write to key %s" % (
loss_conditional, key))
kvs.set(key, loss_conditional)
def compute_loss_curve(self, column, row, loss_ratio_curve, asset):
"""Compute the loss curve for a single site."""
if asset is None:
return None
loss_curve = loss_ratio_curve.rescale_abscissae(asset["assetValue"])
key = kvs.tokens.loss_curve_key(self.id, row, column, asset["assetID"])
LOGGER.warn("RESULT: loss curve is %s, write to key %s" %
(loss_curve, key))
kvs.set(key, loss_curve.to_json())
return loss_curve
def compute_loss_curve(self, column, row, loss_ratio_curve, asset):
"""Compute the loss curve for a single site."""
if asset is None:
return None
loss_curve = loss_ratio_curve.rescale_abscissae(asset["assetValue"])
key = kvs.tokens.loss_curve_key(self.id, row, column, asset["assetID"])
LOGGER.warn("RESULT: loss curve is %s, write to key %s" % (
loss_curve, key))
kvs.set(key, loss_curve.to_json())
return loss_curve
def compute_conditional_loss(self, col, row, loss_curve, asset, loss_poe):
""" Compute the conditional loss for a loss curve and probability of
exceedance """
loss_conditional = common.compute_conditional_loss(
loss_curve, loss_poe)
key = kvs.tokens.loss_key(self.id, row, col, asset["assetID"],
loss_poe)
LOGGER.debug("RESULT: conditional loss is %s, write to key %s" %
(loss_conditional, key))
kvs.set(key, loss_conditional)
def compute_conditional_loss(job_id, col, row, loss_curve, asset, loss_poe):
"""Compute the conditional loss for a loss curve and Probability of
Exceedance (PoE)."""
loss_conditional = common.compute_conditional_loss(
loss_curve, loss_poe)
key = kvs.tokens.loss_key(
job_id, row, col, asset["assetID"], loss_poe)
LOG.debug("Conditional loss is %s, write to key %s" %
(loss_conditional, key))
kvs.set(key, loss_conditional)
def setUp(self):
self.job_id = 1234
self.params = {}
self.params["OUTPUT_DIR"] = test.OUTPUT_DIR
# parameters needed to compute tses and time span
self.params["NUMBER_OF_SEISMICITY_HISTORIES"] = 10
self.params["NUMBER_OF_LOGIC_TREE_SAMPLES"] = 2
self.params["INVESTIGATION_TIME"] = 50.0
self.params["AGGREGATE_LOSS_CURVE"] = 1
self.engine = job.Job(self.params, self.job_id, ".")
# adding the curves to kvs
kvs.set(kvs.tokens.loss_curve_key(self.job_id, 1, 1, 5),
shapes.Curve([(1.0, 0.1), (2.0, 0.1)]).to_json())
kvs.set(kvs.tokens.loss_curve_key(self.job_id, 1, 2, 5),
shapes.Curve([(3.0, 0.1), (4.0, 0.1)]).to_json())
kvs.set(kvs.tokens.loss_curve_key(self.job_id, 1, 3, 5),
shapes.Curve([(5.0, 0.1), (6.0, 0.1)]).to_json())
# deleting old file
self._delete_test_file()
def compute_loss_curve(self, point, loss_ratio_curve, asset):
"""
Computes the loss ratio and store it in kvs to provide
data to the @output decorator which does the serialization
in the RiskJobMixin, more details inside
openquake.risk.job.RiskJobMixin -- for details see
RiskJobMixin._write_output_for_block and the output decorator
:param point: the point of the grid we want to compute
:type point: :py:class:`openquake.shapes.GridPoint`
:param loss_ratio_curve: the loss ratio curve
:type loss_ratio_curve: :py:class `openquake.shapes.Curve`
:param asset: the asset for which to compute the loss curve
:type asset: :py:class:`dict` as provided by
:py:class:`openquake.parser.exposure.ExposurePortfolioFile`
"""
loss_curve = compute_loss_curve(loss_ratio_curve, asset['assetValue'])
loss_key = kvs.tokens.loss_curve_key(self.job_id, point.row,
point.column, asset['assetID'])
kvs.set(loss_key, loss_curve.to_json())
def compute_loss_curve(self, point, loss_ratio_curve, asset):
"""
Computes the loss ratio and store it in kvs to provide
data to the @output decorator which does the serialization
in the RiskJobMixin, more details inside
openquake.risk.job.RiskJobMixin -- for details see
RiskJobMixin._write_output_for_block and the output decorator
:param point: the point of the grid we want to compute
:type point: :py:class:`openquake.shapes.GridPoint`
:param loss_ratio_curve: the loss ratio curve
:type loss_ratio_curve: :py:class `openquake.shapes.Curve`
:param asset: the asset for which to compute the loss curve
:type asset: :py:class:`dict` as provided by
:py:class:`openquake.parser.exposure.ExposurePortfolioFile`
"""
loss_curve = compute_loss_curve(loss_ratio_curve, asset['assetValue'])
loss_key = kvs.tokens.loss_curve_key(self.job_id, point.row,
point.column, asset['assetID'])
kvs.set(loss_key, loss_curve.to_json())
def compute_loss_ratio_curve(self, col, row, asset, gmf_slice ): # site_id
"""Compute the loss ratio curve for a single site."""
# If the asset has a vuln function code we don't have loaded, return
# fail
vuln_function = self.vuln_curves.get(
asset["VulnerabilityFunction"], None)
if not vuln_function:
LOGGER.error("Unknown vulnerability function %s for asset %s"
% (asset["VulnerabilityFunction"], asset["AssetID"]))
return None
loss_ratio_curve = probabilistic_event_based.compute_loss_ratio_curve(
vuln_function, gmf_slice)
# NOTE(JMC): Early exit if the loss ratio is all zeros
if not False in (loss_ratio_curve.ordinates == 0.0):
return None
key = kvs.tokens.loss_ratio_key(self.id, row, col, asset["AssetID"])
LOGGER.warn("RESULT: loss ratio curve is %s, write to key %s" % (
loss_ratio_curve, key))
kvs.set(key, loss_ratio_curve.to_json())
return loss_ratio_curve
def compute_loss_ratio_curve(self, point, asset, hazard_curve):
""" Computes the loss ratio curve and stores in kvs
the curve itself
:param point: the point of the grid we want to compute
:type point: :py:class:`openquake.shapes.GridPoint`
:param asset: the asset used to compute the loss curve
:type asset: :py:class:`dict` as provided by
:py:class:`openquake.parser.exposure.ExposurePortfolioFile`
:param hazard_curve: the hazard curve used to compute the
loss ratio curve
:type hazard_curve: :py:class:`openquake.shapes.Curve`
"""
# we get the vulnerability function related to the asset
vuln_function_reference = asset["vulnerabilityFunctionReference"]
vuln_function = self.vuln_curves.get(
vuln_function_reference, None)
if not vuln_function:
LOGGER.error(
"Unknown vulnerability function %s for asset %s"
% (asset["vulnerabilityFunctionReference"],
asset["assetID"]))
return None
loss_ratio_curve = cpsha_based.compute_loss_ratio_curve(
vuln_function, hazard_curve)
loss_ratio_key = kvs.tokens.loss_ratio_key(
self.job_id, point.row, point.column, asset['assetID'])
kvs.set(loss_ratio_key, loss_ratio_curve.to_json())
return loss_ratio_curve
def compute_loss_ratio_curve(self, point, asset, hazard_curve):
""" Computes the loss ratio curve and stores in kvs
the curve itself
:param point: the point of the grid we want to compute
:type point: :py:class:`openquake.shapes.GridPoint`
:param asset: the asset used to compute the loss curve
:type asset: :py:class:`dict` as provided by
:py:class:`openquake.parser.exposure.ExposurePortfolioFile`
:param hazard_curve: the hazard curve used to compute the
loss ratio curve
:type hazard_curve: :py:class:`openquake.shapes.Curve`
"""
# we get the vulnerability function related to the asset
vuln_function_reference = asset["vulnerabilityFunctionReference"]
vuln_function = self.vuln_curves.get(vuln_function_reference, None)
if not vuln_function:
LOGGER.error(
"Unknown vulnerability function %s for asset %s" %
(asset["vulnerabilityFunctionReference"], asset["assetID"]))
return None
loss_ratio_curve = cpsha_based.compute_loss_ratio_curve(
vuln_function, hazard_curve)
loss_ratio_key = kvs.tokens.loss_ratio_key(self.job_id, point.row,
point.column,
asset['assetID'])
kvs.set(loss_ratio_key, loss_ratio_curve.to_json())
return loss_ratio_curve
def compute_loss_ratio_curve(self, point, asset, hazard_curve):
""" Computes the loss ratio curve and stores in kvs
the curve itself """
# we get the vulnerability function related to the asset
vuln_function = self.vuln_curves.get(
asset["vulnerabilityFunctionReference"], None)
if not vuln_function:
LOGGER.error(
"Unknown vulnerability function %s for asset %s" %
(asset["vulnerabilityFunctionReference"], asset["assetID"]))
return None
loss_ratio_curve = cpsha_based.compute_loss_ratio_curve(
vuln_function, hazard_curve)
loss_key = kvs.tokens.loss_ratio_key(self.job_id, point.row,
point.column, asset['assetID'])
kvs.set(loss_key, loss_ratio_curve.to_json())
return loss_ratio_curve
def test_can_build_an_aggregate_curve_from_kvs(self):
curve_1 = shapes.Curve([(1.0, 0.0), (2.0, 0.0),
(3.0, 0.0), (4.0, 0.0)])
curve_2 = shapes.Curve([(5.0, 0.0), (6.0, 0.0),
(7.0, 0.0), (8.0, 0.0)])
curve_3 = shapes.Curve([(9.0, 0.0), (10.0, 0.0),
(11.0, 0.0), (12.0, 0.0)])
job_id = 1234
kvs.set(kvs.tokens.loss_curve_key(job_id, 1, 1, 5), curve_1.to_json())
kvs.set(kvs.tokens.loss_curve_key(job_id, 1, 2, 5), curve_2.to_json())
kvs.set(kvs.tokens.loss_curve_key(job_id, 1, 3, 5), curve_3.to_json())
aggregate_curve = prob.AggregateLossCurve.from_kvs(job_id)
expected_losses = numpy.array((15.0, 18.0, 21.0, 24.0))
self.assertTrue(numpy.allclose(expected_losses, aggregate_curve.losses))
