Source code for openquake.calculators.scenario

# -*- coding: utf-8 -*-
# vim: tabstop=4 shiftwidth=4 softtabstop=4
# Copyright (C) 2014-2017 GEM Foundation
# OpenQuake is free software: you can redistribute it and/or modify it
# under the terms of the GNU Affero General Public License as published
# by the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
# OpenQuake is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# GNU Affero General Public License for more details.
# You should have received a copy of the GNU Affero General Public License
# along with OpenQuake. If not, see <>.
import collections
import numpy

from openquake.hazardlib.calc.gmf import GmfComputer
from openquake.hazardlib.gsim.base import ContextMaker
from openquake.commonlib import readinput, source, calc
from openquake.calculators import base

[docs]@base.calculators.add('scenario') class ScenarioCalculator(base.HazardCalculator): """ Scenario hazard calculator """ is_stochastic = True
[docs] def pre_execute(self): """ Read the site collection and initialize GmfComputer and seeds """ super(ScenarioCalculator, self).pre_execute() oq = self.oqparam trunc_level = oq.truncation_level correl_model = oq.get_correl_model() ebr, self.sitecol = readinput.get_rupture_sitecol(oq, self.sitecol) self.gsims = readinput.get_gsims(oq) self.datastore['events'] = rupser = calc.RuptureSerializer(self.datastore)[ebr]) rupser.close() = GmfComputer( ebr, self.sitecol, oq.imtls, ContextMaker(self.gsims), trunc_level, correl_model) gsim_lt = readinput.get_gsim_lt(oq) cinfo = source.CompositionInfo.fake(gsim_lt) self.datastore['csm_info'] = cinfo self.rlzs_assoc = cinfo.get_rlzs_assoc()
[docs] def init(self): pass
[docs] def execute(self): """ Compute the GMFs and return a dictionary gsim -> array(N, E, I) """ self.gmfa = collections.OrderedDict() with self.monitor('computing gmfs', autoflush=True): n = self.oqparam.number_of_ground_motion_fields for gsim in self.gsims: gmfa =, n) # shape (I, N, E) self.gmfa[gsim] = gmfa.transpose(1, 2, 0) # shape (N, E, I) return self.gmfa
[docs] def post_execute(self, dummy): with self.monitor('saving gmfs', autoflush=True): base.save_gmf_data( self.datastore, self.sitecol, numpy.array(list(self.gmfa.values())))