示例#1
0
文件: core.py 项目: arbeit/oq-engine
    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
示例#2
0
文件: core.py 项目: arbeit/oq-engine
    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)