def test_assets_for_cell_with_one(self):
# A single asset in the risk cell is found.
site = shapes.Site(10.15, 46.15)
self.job_ctxt.oq_job_profile.region_grid_spacing = 0.3
self.job_ctxt.oq_job_profile.save()
[asset] = BaseRiskCalculator.assets_for_cell(self.job.id, site)
self.assertEqual(self.sites[1], self._to_site(asset.site))
def test_assets_for_cell_with_one(self):
# A single asset in the risk cell is found.
site = shapes.Site(10.15, 46.15)
self.job_ctxt.oq_job_profile.region_grid_spacing = 0.3
self.job_ctxt.oq_job_profile.save()
[asset] = BaseRiskCalculator.assets_for_cell(self.job.id, site)
self.assertEqual(self.sites[1], self._to_site(asset.site))
def test_multiple_assets_per_site(self):
site = shapes.Site(1.0, 2.0)
assets = BaseRiskCalculator.assets_at(self.job.id, site)
self.assertEqual(2, len(assets))
self.assertEqual("ASSET_1", assets[0].asset_ref)
self.assertEqual("ASSET_2", assets[1].asset_ref)
def _compute_loss(self, block_id):
"""
Calculate and store in the kvs the loss data.
"""
block = Block.from_kvs(self.job_ctxt.job_id, block_id)
vuln_curves = vulnerability.load_vuln_model_from_kvs(self.job_ctxt.job_id)
lrem_steps = self.job_ctxt.oq_job_profile.lrem_steps_per_interval
loss_poes = conditional_loss_poes(self.job_ctxt.params)
assets_getter = lambda site: BaseRiskCalculator.assets_at(self.job_ctxt.job_id, site)
hazard_getter = lambda site: (
self.job_ctxt.region.grid.point_at(site),
self._get_db_curve(hazard_input_site(self.job_ctxt, site)),
)
def on_asset_complete(asset, point, loss_ratio_curve, loss_curve, loss_conditionals):
loss_key = kvs.tokens.loss_curve_key(self.job_ctxt.job_id, point.row, point.column, asset.asset_ref)
kvs.get_client().set(loss_key, loss_curve.to_json())
for poe, loss in loss_conditionals.items():
key = kvs.tokens.loss_key(self.job_ctxt.job_id, point.row, point.column, asset.asset_ref, poe)
kvs.get_client().set(key, loss)
loss_ratio_key = kvs.tokens.loss_ratio_key(self.job_ctxt.job_id, point.row, point.column, asset.asset_ref)
kvs.get_client().set(loss_ratio_key, loss_ratio_curve.to_json())
classical.compute(
block.sites, assets_getter, vuln_curves, hazard_getter, lrem_steps, loss_poes, on_asset_complete
)
def test_multiple_assets_per_site(self):
site = shapes.Site(1.0, 2.0)
assets = BaseRiskCalculator.assets_at(self.job.id, site)
self.assertEqual(2, len(assets))
self.assertEqual("ASSET_1", assets[0].asset_ref)
self.assertEqual("ASSET_2", assets[1].asset_ref)
def test_assets_for_cell_with_no_assets_matching(self):
# An empty list is returned when no assets exist for a given
# risk cell.
site = shapes.Site(99.15000, 15.16667)
self.job_ctxt.oq_job_profile.region_grid_spacing = 0.05
self.job_ctxt.oq_job_profile.save()
self.assertEqual([],
BaseRiskCalculator.assets_for_cell(self.job.id, site))
def test_assets_for_cell_with_no_assets_matching(self):
# An empty list is returned when no assets exist for a given
# risk cell.
site = shapes.Site(99.15000, 15.16667)
self.job_ctxt.oq_job_profile.region_grid_spacing = 0.05
self.job_ctxt.oq_job_profile.save()
self.assertEqual([],
BaseRiskCalculator.assets_for_cell(self.job.id, site))
def test_assets_for_cell_with_more_than_one(self):
# All assets in the risk cell are found.
site = shapes.Site(10.3, 46.3)
self.job_ctxt.oq_job_profile.region_grid_spacing = 0.6
self.job_ctxt.oq_job_profile.save()
assets = BaseRiskCalculator.assets_for_cell(self.job.id, site)
self.assertEqual(3, len(assets))
# Make sure the assets associated with the first 3 added sites were
# selected.
for s, a in zip(self.sites, sorted(assets, key=lambda a: a.site.x)):
self.assertEqual(s, self._to_site(a.site))
def test_asset_bcr_per_site(self):
self._make_job({})
self._prepare_bcr_result()
job = BaseRiskCalculator(self.job)
bcr_per_site = job.asset_bcr_per_site()
self.assertEqual(bcr_per_site, [
(shapes.Site(-1.1, 19.0), [
[{u'bcr': 35.1, 'eal_original': 12.34, 'eal_retrofitted': 4},
u'assetID-191'],
[{u'bcr': 35.2, 'eal_original': 2.5, 'eal_retrofitted': 2.2},
u'assetID-192']
]),
(shapes.Site(2.3, 20.0), [
[{u'bcr': 35.1, 'eal_original': 1.23, 'eal_retrofitted': 0.3},
u'assetID-201'],
[{u'bcr': 35.2, 'eal_original': 4, 'eal_retrofitted': 0.4},
u'assetID-202']
])
])
def test_assets_for_cell_with_more_than_one(self):
# All assets in the risk cell are found.
site = shapes.Site(10.3, 46.3)
self.job_ctxt.oq_job_profile.region_grid_spacing = 0.6
self.job_ctxt.oq_job_profile.save()
assets = BaseRiskCalculator.assets_for_cell(self.job.id, site)
self.assertEqual(3, len(assets))
# Make sure the assets associated with the first 3 added sites were
# selected.
for s, a in zip(self.sites, sorted(assets, key=lambda a: a.site.x)):
self.assertEqual(s, self._to_site(a.site))
def _compute_bcr(self, block_id):
"""
Calculate and store in the kvs the benefit-cost ratio data for block.
A value is stored with key :func:`openquake.kvs.tokens.bcr_block_key`.
See :func:`openquake.risk.job.general.compute_bcr_for_block` for result
data structure spec.
"""
result = defaultdict(list)
block = Block.from_kvs(self.job_ctxt.job_id, block_id)
vulnerability_model_original = vulnerability.load_vuln_model_from_kvs(
self.job_ctxt.job_id)
vulnerability_model_retrofitted = (
vulnerability.load_vuln_model_from_kvs(
self.job_ctxt.job_id, retrofitted=True))
steps = self.job_ctxt.oq_job_profile.lrem_steps_per_interval
assets_getter = lambda site: BaseRiskCalculator.assets_at(
self.job_ctxt.job_id, site)
hazard_getter = lambda site: (
self._get_db_curve(hazard_input_site(self.job_ctxt, site)))
bcr = api.bcr(api.classical(vulnerability_model_original, steps=steps),
api.classical(vulnerability_model_retrofitted, steps=steps),
float(self.job_ctxt.params["INTEREST_RATE"]),
float(self.job_ctxt.params["ASSET_LIFE_EXPECTANCY"]))
for asset_output in api.compute_on_sites(
block.sites, assets_getter, hazard_getter, bcr):
asset = asset_output.asset
result[(asset.site.x, asset.site.y)].append(({
"bcr": asset_output.bcr,
"eal_original": asset_output.eal_original,
"eal_retrofitted": asset_output.eal_retrofitted},
asset.asset_ref))
bcr = result.items()
bcr_block_key = kvs.tokens.bcr_block_key(
self.job_ctxt.job_id, block_id)
kvs.set_value_json_encoded(bcr_block_key, bcr)
LOGGER.debug("bcr result for block %s: %r", block_id, bcr)
return True
def _compute_loss(self, block_id):
"""
Calculate and store in the kvs the loss data.
"""
block = Block.from_kvs(self.job_ctxt.job_id, block_id)
vulnerability_model = vulnerability.load_vuln_model_from_kvs(
self.job_ctxt.job_id)
steps = self.job_ctxt.oq_job_profile.lrem_steps_per_interval
assets_getter = lambda site: BaseRiskCalculator.assets_at(
self.job_ctxt.job_id, site)
hazard_getter = lambda site: (
self._get_db_curve(hazard_input_site(self.job_ctxt, site)))
calculator = api.conditional_losses(
conditional_loss_poes(self.job_ctxt.params),
api.classical(vulnerability_model, steps=steps))
for asset_output in api.compute_on_sites(block.sites,
assets_getter, hazard_getter, calculator):
location = asset_output.asset.site
point = self.job_ctxt.region.grid.point_at(
shapes.Site(location.x, location.y))
loss_key = kvs.tokens.loss_curve_key(
self.job_ctxt.job_id, point.row,
point.column, asset_output.asset.asset_ref)
kvs.get_client().set(loss_key, asset_output.loss_curve.to_json())
loss_ratio_key = kvs.tokens.loss_ratio_key(self.job_ctxt.job_id,
point.row, point.column, asset_output.asset.asset_ref)
kvs.get_client().set(loss_ratio_key,
asset_output.loss_ratio_curve.to_json())
for poe, loss in asset_output.conditional_losses.items():
key = kvs.tokens.loss_key(
self.job_ctxt.job_id, point.row, point.column,
asset_output.asset.asset_ref, poe)
kvs.get_client().set(key, loss)
def test_compute_risk_in_the_classical_psha_calculator(self):
"""
tests ClassicalRiskCalculator.compute_risk by retrieving
all the loss curves in the kvs and checks their presence
"""
helpers.delete_profile(self.job)
cls_risk_cfg = helpers.demo_file(
'classical_psha_based_risk/config.gem')
job_profile, params, sections = engine.import_job_profile(
cls_risk_cfg, self.job)
# We need to adjust a few of the parameters for this test:
params['REGION_VERTEX'] = '0.0, 0.0, 0.0, 2.0, 2.0, 2.0, 2.0, 0.0'
job_profile.region = GEOSGeometry(shapes.polygon_ewkt_from_coords(
params['REGION_VERTEX']))
job_profile.save()
job_ctxt = engine.JobContext(
params, self.job_id, sections=sections, oq_job_profile=job_profile)
self._compute_risk_classical_psha_setup()
calculator = classical_core.ClassicalRiskCalculator(job_ctxt)
calculator.vuln_curves = {"ID": self.vuln_function}
block = Block.from_kvs(self.job_id, self.block_id)
# computes the loss curves and puts them in kvs
calculator.compute_risk(self.block_id)
for point in block.grid(job_ctxt.region):
assets = BaseRiskCalculator.assets_for_cell(
self.job_id, point.site)
for asset in assets:
loss_ratio_key = kvs.tokens.loss_ratio_key(
self.job_id, point.row, point.column, asset.asset_ref)
self.assertTrue(kvs.get_client().get(loss_ratio_key))
loss_key = kvs.tokens.loss_curve_key(
self.job_id, point.row, point.column, asset.asset_ref)
self.assertTrue(kvs.get_client().get(loss_key))
def _compute_bcr(self, block_id):
"""
Calculate and store in the kvs the benefit-cost ratio data for block.
A value is stored with key :func:`openquake.kvs.tokens.bcr_block_key`.
See :func:`openquake.risk.job.general.compute_bcr_for_block` for result
data structure spec.
"""
job_ctxt = self.job_ctxt
job_id = job_ctxt.job_id
block = Block.from_kvs(job_id, block_id)
result = defaultdict(list)
def on_asset_complete(asset, bcr, eal_original, eal_retrofitted):
result[(asset.site.x, asset.site.y)].append(
({"bcr": bcr, "eal_original": eal_original, "eal_retrofitted": eal_retrofitted}, asset.asset_ref)
)
benefit_cost_ratio.compute(
block.sites,
lambda site: BaseRiskCalculator.assets_at(job_id, site),
vulnerability.load_vuln_model_from_kvs(job_id),
vulnerability.load_vuln_model_from_kvs(job_id, retrofitted=True),
lambda site: self._get_db_curve(hazard_input_site(self.job_ctxt, site)),
self.job_ctxt.oq_job_profile.lrem_steps_per_interval,
float(job_ctxt.params["INTEREST_RATE"]),
float(job_ctxt.params["ASSET_LIFE_EXPECTANCY"]),
on_asset_complete,
)
bcr = result.items()
bcr_block_key = kvs.tokens.bcr_block_key(job_ctxt.job_id, block_id)
kvs.set_value_json_encoded(bcr_block_key, bcr)
LOGGER.debug("bcr result for block %s: %r", block_id, bcr)
return True
def test_one_asset_per_site(self):
site = shapes.Site(2.0, 2.0)
assets = BaseRiskCalculator.assets_at(self.job.id, site)
self.assertEqual(1, len(assets))
self.assertEqual("ASSET_3", assets[0].asset_ref)
def test_one_asset_per_site(self):
site = shapes.Site(2.0, 2.0)
assets = BaseRiskCalculator.assets_at(self.job.id, site)
self.assertEqual(1, len(assets))
self.assertEqual("ASSET_3", assets[0].asset_ref)
def test_no_assets_at_site(self):
# nothing is stored at this location
site = shapes.Site(10.0, 10.0)
self.assertEqual([], BaseRiskCalculator.assets_at(self.job.id, site))
def test_no_assets_at_site(self):
# nothing is stored at this location
site = shapes.Site(10.0, 10.0)
self.assertEqual([], BaseRiskCalculator.assets_at(self.job.id, site))
