def test_compute_bcr_in_the_classical_psha_calculator(self): self._compute_risk_classical_psha_setup() helpers.delete_profile(self.job) bcr_config = helpers.demo_file('benefit_cost_ratio/config.gem') job_profile, params, sections = engine.import_job_profile( bcr_config, self.job) # We need to adjust a few of the parameters for this test: job_profile.imls = [ 0.005, 0.007, 0.0098, 0.0137, 0.0192, 0.0269, 0.0376, 0.0527, 0.0738, 0.103, 0.145, 0.203, 0.284, 0.397, 0.556, 0.778] params['ASSET_LIFE_EXPECTANCY'] = '50' job_profile.asset_life_expectancy = 50 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) calculator = classical_core.ClassicalRiskCalculator(job_ctxt) [input] = models.inputs4job(self.job.id, input_type="exposure") emdl = input.model() if not emdl: emdl = models.ExposureModel( owner=self.job.owner, input=input, description="c-psha test exposure model", category="c-psha power plants", stco_unit="watt", stco_type="aggregated", reco_unit="joule", reco_type="aggregated") emdl.save() assets = emdl.exposuredata_set.filter(asset_ref="rubcr") if not assets: asset = models.ExposureData(exposure_model=emdl, taxonomy="ID", asset_ref="rubcr", stco=1, reco=123.45, site=GEOSGeometry("POINT(1.0 1.0)")) asset.save() Block.from_kvs(self.job_id, self.block_id) calculator.compute_risk(self.block_id) result_key = kvs.tokens.bcr_block_key(self.job_id, self.block_id) res = kvs.get_value_json_decoded(result_key) expected_result = {'bcr': 0.0, 'eal_original': 0.003032, 'eal_retrofitted': 0.003032} helpers.assertDeepAlmostEqual( self, res, [[[1, 1], [[expected_result, "rubcr"]]]])
def test_compute_bcr(self): cfg_path = helpers.demo_file( 'probabilistic_event_based_risk/config.gem') helpers.delete_profile(self.job) job_profile, params, sections = engine.import_job_profile( cfg_path, self.job) job_profile.calc_mode = 'event_based_bcr' job_profile.interest_rate = 0.05 job_profile.asset_life_expectancy = 50 job_profile.region = GEOSGeometry(shapes.polygon_ewkt_from_coords( '0.0, 0.0, 0.0, 2.0, 2.0, 2.0, 2.0, 0.0')) job_profile.region_grid_spacing = 0.1 job_profile.maximum_distance = 200.0 job_profile.gmf_random_seed = None job_profile.save() params.update(dict(CALCULATION_MODE='Event Based BCR', INTEREST_RATE='0.05', ASSET_LIFE_EXPECTANCY='50', MAXIMUM_DISTANCE='200.0', REGION_VERTEX=('0.0, 0.0, 0.0, 2.0, ' '2.0, 2.0, 2.0, 0.0'), REGION_GRID_SPACING='0.1')) job_ctxt = engine.JobContext( params, self.job_id, sections=sections, oq_job_profile=job_profile) calculator = eb_core.EventBasedRiskCalculator(job_ctxt) self.block_id = 7 SITE = shapes.Site(1.0, 1.0) block = Block(self.job_id, self.block_id, (SITE, )) block.to_kvs() location = GEOSGeometry(SITE.point.to_wkt()) asset = models.ExposureData(exposure_model=self.emdl, taxonomy="ID", asset_ref=22.61, stco=1, reco=123.45, site=location) asset.save() calculator.compute_risk(self.block_id) result_key = kvs.tokens.bcr_block_key(self.job_id, self.block_id) result = kvs.get_value_json_decoded(result_key) expected_result = {'bcr': 0.0, 'eal_original': 0.0, 'eal_retrofitted': 0.0} helpers.assertDeepAlmostEqual( self, [[[1, 1], [[expected_result, "22.61"]]]], result)
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))