def compute_stddev_loss(vuln_function, ground_motion_field_set,
epsilon_provider, asset):
"""Compute the standard deviation of the losses for the given asset
using the related ground motion field set and vulnerability function.
:param vuln_function: the vulnerability function used to
compute the loss ratios.
:type vuln_function: :py:class:`openquake.shapes.VulnerabilityFunction`
:param ground_motion_field_set: the set of ground motion
fields used to compute the loss ratios.
:type ground_motion_field_set: :py:class:`dict` with the following
keys:
**IMLs** - tuple of ground motion fields (float)
:param epsilon_provider: service used to get the epsilon when
using the sampled based algorithm.
:type epsilon_provider: object that defines an :py:meth:`epsilon` method
:param asset: the asset used to compute the loss ratios and losses.
:type asset: :py:class:`dict` as provided by
:py:class:`openquake.parser.exposure.ExposurePortfolioFile`
"""
loss_ratios = general.compute_loss_ratios(
vuln_function, ground_motion_field_set, epsilon_provider, asset)
return _stddev_loss_from_loss_ratios(loss_ratios, asset)
def compute_uninsured_losses(vuln_function, gmvs, epsilon_provider, asset):
"""
Compute losses for the given asset using the related set of ground
motion values and vulnerability function.
:param vuln_function: the vulnerability function used to compute the loss
ratios.
:type vuln_function: :py:class:`openquake.shapes.VulnerabilityFunction`
:param gmvs: the set of ground motion values used to compute the loss
ratios.
:type gmvs: :py:class:`dict` with the following keys: **IMLs** - tuple
of ground motion values (float).
:param epsilon_provider: service used to get the epsilon when using the
sampled based algorithm.
:type epsilon_provider: object that defines an :py:meth:`epsilon` method
:param asset: the asset used to compute the loss ratios and losses.
:type asset: an :py:class:`openquake.db.model.ExposureData` instance
"""
loss_ratios = general.compute_loss_ratios(
vuln_function, gmvs, epsilon_provider, asset)
losses = loss_ratios * asset.value
return losses
def get_loss_curve(point, vuln_function, asset):
"Compute loss curve basing on GMF data"
gmf_slice = gmf_slices[point.site]
loss_ratios = general.compute_loss_ratios(
vuln_function, gmf_slice, epsilon_provider, asset)
loss_ratio_curve = general.compute_loss_ratio_curve(
vuln_function, gmf_slice, epsilon_provider, asset,
self.calc_proxy.oq_job_profile.loss_histogram_bins,
loss_ratios=loss_ratios)
return loss_ratio_curve.rescale_abscissae(asset["assetValue"])
def get_loss_curve(point, vuln_function, asset):
"Compute loss curve basing on GMF data"
gmf_slice = gmf_slices[point.site]
loss_ratios = general.compute_loss_ratios(
vuln_function, gmf_slice, epsilon_provider, asset)
loss_ratio_curve = general.compute_loss_ratio_curve(
vuln_function, gmf_slice, epsilon_provider, asset,
self.job_ctxt.oq_job_profile.loss_histogram_bins,
loss_ratios=loss_ratios)
aggregate_curve.append(loss_ratios * asset.value)
return loss_ratio_curve.rescale_abscissae(asset.value)
def compute_loss_ratios(self, asset, gmf_slice):
"""For a given asset and ground motion field, computes
the loss ratios used to obtain the related loss ratio curve
and aggregate loss curve."""
epsilon_provider = general.EpsilonProvider(self.job_ctxt.params)
vuln_function = self.vuln_curves.get(asset.taxonomy, None)
if not vuln_function:
LOGGER.error("Unknown vulnerability function %s for asset %s"
% (asset.taxonomy, asset.asset_ref))
return None
return general.compute_loss_ratios(vuln_function, gmf_slice,
epsilon_provider, asset)
def compute_loss_ratios(self, asset, gmf_slice):
"""For a given asset and ground motion field, computes
the loss ratios used to obtain the related loss ratio curve
and aggregate loss curve."""
epsilon_provider = general.EpsilonProvider(self.job_ctxt.params)
vuln_function = self.vuln_curves.get(asset.taxonomy, None)
if not vuln_function:
LOGGER.error("Unknown vulnerability function %s for asset %s" %
(asset.taxonomy, asset.asset_ref))
return None
return general.compute_loss_ratios(vuln_function, gmf_slice,
epsilon_provider, asset)
def get_loss_curve(point, vuln_function, asset):
"Compute loss curve basing on GMF data"
gmf_slice = gmf_slices[point.site]
loss_ratios = general.compute_loss_ratios(vuln_function, gmf_slice,
epsilon_provider, asset)
loss_ratio_curve = general.compute_loss_ratio_curve(
vuln_function,
gmf_slice,
epsilon_provider,
asset,
self.job_ctxt.oq_job_profile.loss_histogram_bins,
loss_ratios=loss_ratios)
aggregate_curve.append(loss_ratios * asset.value)
return loss_ratio_curve.rescale_abscissae(asset.value)
def _compute_loss_ratios(self, asset, gmf):
"""
Compute the loss ratios for the given asset and associated ground
motion field.
"""
epsilon_provider = general.EpsilonProvider(self.job_ctxt.params)
vulnerability_function = self.vulnerability_curves.get(
asset.taxonomy, None)
if not vulnerability_function:
LOGGER.error("Unknown vulnerability function %s for asset %s"
% (asset.taxonomy, asset.asset_ref))
return None
return general.compute_loss_ratios(vulnerability_function, gmf,
epsilon_provider, asset)
def _compute_loss_ratios(self, asset, gmf):
"""
Compute the loss ratios for the given asset and associated ground
motion field.
"""
epsilon_provider = general.EpsilonProvider(self.job_ctxt.params)
vulnerability_function = self.vulnerability_curves.get(
asset.taxonomy, None)
if not vulnerability_function:
LOGGER.error("Unknown vulnerability function %s for asset %s" %
(asset.taxonomy, asset.asset_ref))
return None
return general.compute_loss_ratios(vulnerability_function, gmf,
epsilon_provider, asset)
def get_loss_curve(site, vuln_function, asset):
"Compute loss curve basing on GMF data"
gmvs = self._get_gmvs_at(general.hazard_input_site(
self.job_ctxt, site))
gmf_slice = {"IMLs": gmvs, "TSES": self._tses(),
"TimeSpan": self._time_span()}
loss_ratios = general.compute_loss_ratios(
vuln_function, gmf_slice, epsilon_provider, asset)
loss_ratio_curve = general.compute_loss_ratio_curve(
vuln_function, gmf_slice, epsilon_provider, asset,
self.job_ctxt.oq_job_profile.loss_histogram_bins,
loss_ratios=loss_ratios)
aggregate_curve.append(loss_ratios * asset.value)
return loss_ratio_curve.rescale_abscissae(asset.value)
def compute_stddev_loss(vuln_function, gmf_set, epsilon_provider, asset):
"""Compute the standard deviation of the losses for the given asset
using the related ground motion field set and vulnerability function.
:param vuln_function: the vulnerability function used to
compute the loss ratios.
:type vuln_function: :py:class:`openquake.shapes.VulnerabilityFunction`
:param gmf_set: the ground motion fields used to compute the loss ratios
:type gmf_set: :py:class:`dict` with the following
keys:
**IMLs** - tuple of ground motion fields (float)
:param epsilon_provider: service used to get the epsilon when
using the sampled based algorithm.
:type epsilon_provider: object that defines an :py:meth:`epsilon` method
:param asset: the asset used to compute the loss ratios and losses.
:type asset: an :py:class:`openquake.db.model.ExposureData` instance
"""
loss_ratios = general.compute_loss_ratios(vuln_function, gmf_set,
epsilon_provider, asset)
return _stddev_loss_from_loss_ratios(loss_ratios, asset)
def get_loss_curve(site, vuln_function, asset):
"Compute loss curve basing on GMF data"
gmvs = self._get_gmvs_at(
general.hazard_input_site(self.job_ctxt, site))
gmf_slice = {
"IMLs": gmvs,
"TSES": self._tses(),
"TimeSpan": self._time_span()
}
loss_ratios = general.compute_loss_ratios(vuln_function, gmf_slice,
epsilon_provider, asset)
loss_ratio_curve = general.compute_loss_ratio_curve(
vuln_function,
gmf_slice,
epsilon_provider,
asset,
self.job_ctxt.oq_job_profile.loss_histogram_bins,
loss_ratios=loss_ratios)
aggregate_curve.append(loss_ratios * asset.value)
return loss_ratio_curve.rescale_abscissae(asset.value)
def compute_risk(self, block_id, **kwargs):
"""
This method will perform two distinct (but similar) computations and
return a result for each computation. The computations are as follows:
First:
For a given block of sites, compute loss values for all assets in the
block. This computation will yield a single loss value per realization
for the region block.
Second:
For each asset in the given block of sites, we need compute loss
(where loss = loss_ratio * asset_value) for each realization. This
gives 1 loss value _per_ asset _per_ realization. We then need to take
the mean & standard deviation.
Other info:
The GMF data for each realization is stored in the KVS by the preceding
scenario hazard job.
:param block_id: id of the region block data we need to pull from the
KVS
:type block_id: str
:keyword vuln_model:
dict of :py:class:`openquake.shapes.VulnerabilityFunction` objects,
keyed by the vulnerability function name as a string
:keyword epsilon_provider:
:py:class:`openquake.risk.job.EpsilonProvider` object
:returns: 2-tuple of the following data:
* 1-dimensional :py:class:`numpy.ndarray` of loss values for this
region block (again, 1 value per realization)
* list of 2-tuples containing site, loss, and asset
information.
The first element of each 2-tuple shall be a
:py:class:`openquake.shapes.Site` object, which represents the
geographical location of the asset loss.
The second element shall be a list of
2-tuples of dicts representing the loss and asset data (in that
order).
Example::
[(<Site(-117.0, 38.0)>, [
({'mean_loss': 200.0, 'stddev_loss': 100},
{'assetID': 'a171'}),
({'mean_loss': 200.0, 'stddev_loss': 100},
{'assetID': 'a187'})
]),
(<Site(-118.0, 39.0)>, [
({'mean_loss': 50, 'stddev_loss': 50.0},
{'assetID': 'a192'})
])]
"""
vuln_model = kwargs["vuln_model"]
insured_losses = kwargs["insured_losses"]
epsilon_provider = general.EpsilonProvider(self.job_ctxt.params)
block = general.Block.from_kvs(self.job_ctxt.job_id, block_id)
block_losses = []
loss_map_data = {}
for site in block.sites:
gmvs = {"IMLs": general.load_gmvs_at(
self.job_ctxt.job_id, general.hazard_input_site(
self.job_ctxt, site))}
assets = general.BaseRiskCalculator.assets_at(
self.job_ctxt.job_id, site)
for asset in assets:
vuln_function = vuln_model[asset.taxonomy]
loss_ratios = general.compute_loss_ratios(
vuln_function, gmvs, epsilon_provider, asset)
losses = loss_ratios * asset.value
if insured_losses:
losses = general.compute_insured_losses(asset, losses)
asset_site = shapes.Site(asset.site.x, asset.site.y)
loss = ({
"mean_loss": numpy.mean(losses),
"stddev_loss": numpy.std(losses, ddof=1)}, {
"assetID": asset.asset_ref
})
block_losses.append(losses)
collect_block_data(loss_map_data, asset_site, loss)
sum_block_losses = reduce(lambda x, y: x + y, block_losses)
return sum_block_losses, loss_map_data
def compute_risk(self, block_id, **kwargs):
"""
This method will perform two distinct (but similar) computations and
return a result for each computation. The computations are as follows:
First:
For a given block of sites, compute loss values for all assets in the
block. This computation will yield a single loss value per realization
for the region block.
Second:
For each asset in the given block of sites, we need compute loss
(where loss = loss_ratio * asset_value) for each realization. This
gives 1 loss value _per_ asset _per_ realization. We then need to take
the mean & standard deviation.
Other info:
The GMF data for each realization is stored in the KVS by the preceding
scenario hazard job.
:param block_id: id of the region block data we need to pull from the
KVS
:type block_id: str
:keyword vuln_model:
dict of :py:class:`openquake.shapes.VulnerabilityFunction` objects,
keyed by the vulnerability function name as a string
:keyword epsilon_provider:
:py:class:`openquake.risk.job.EpsilonProvider` object
:returns: 2-tuple of the following data:
* 1-dimensional :py:class:`numpy.ndarray` of loss values for this
region block (again, 1 value per realization)
* list of 2-tuples containing site, loss, and asset
information.
The first element of each 2-tuple shall be a
:py:class:`openquake.shapes.Site` object, which represents the
geographical location of the asset loss.
The second element shall be a list of
2-tuples of dicts representing the loss and asset data (in that
order).
Example::
[(<Site(-117.0, 38.0)>, [
({'mean_loss': 200.0, 'stddev_loss': 100},
{'assetID': 'a171'}),
({'mean_loss': 200.0, 'stddev_loss': 100},
{'assetID': 'a187'})
]),
(<Site(-118.0, 39.0)>, [
({'mean_loss': 50, 'stddev_loss': 50.0},
{'assetID': 'a192'})
])]
"""
vuln_model = kwargs["vuln_model"]
insured_losses = kwargs["insured_losses"]
epsilon_provider = general.EpsilonProvider(self.job_ctxt.params)
block = general.Block.from_kvs(self.job_ctxt.job_id, block_id)
block_losses = []
loss_map_data = {}
for site in block.sites:
gmvs = {
"IMLs":
general.load_gmvs_at(
self.job_ctxt.job_id,
general.hazard_input_site(self.job_ctxt, site))
}
assets = general.BaseRiskCalculator.assets_at(
self.job_ctxt.job_id, site)
for asset in assets:
vuln_function = vuln_model[asset.taxonomy]
loss_ratios = general.compute_loss_ratios(
vuln_function, gmvs, epsilon_provider, asset)
losses = loss_ratios * asset.value
if insured_losses:
losses = general.compute_insured_losses(asset, losses)
asset_site = shapes.Site(asset.site.x, asset.site.y)
loss = ({
"mean_loss": numpy.mean(losses),
"stddev_loss": numpy.std(losses, ddof=1)
}, {
"assetID": asset.asset_ref
})
block_losses.append(losses)
collect_block_data(loss_map_data, asset_site, loss)
sum_block_losses = reduce(lambda x, y: x + y, block_losses)
return sum_block_losses, loss_map_data