# -*- coding: utf-8 -*-
# vim: tabstop=4 shiftwidth=4 softtabstop=4
#
# Copyright (C) 2014-2018 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
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# 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 <http://www.gnu.org/licenses/>.
import logging
import numpy
from openquake.baselib.python3compat import zip, encode
from openquake.baselib.general import AccumDict
from openquake.risklib import scientific, riskinput
from openquake.calculators import base
U16 = numpy.uint16
U64 = numpy.uint64
F32 = numpy.float32
F64 = numpy.float64 # higher precision to avoid task order dependency
stat_dt = numpy.dtype([('mean', F32), ('stddev', F32)])
[docs]def scenario_risk(riskinputs, riskmodel, param, monitor):
"""
Core function for a scenario computation.
:param riskinput:
a of :class:`openquake.risklib.riskinput.RiskInput` object
:param riskmodel:
a :class:`openquake.risklib.riskinput.CompositeRiskModel` instance
:param param:
dictionary of extra parameters
:param monitor:
:class:`openquake.baselib.performance.Monitor` instance
:returns:
a dictionary {
'agg': array of shape (E, L, R, 2),
'avg': list of tuples (lt_idx, rlz_idx, asset_ordinal, statistics)
}
where E is the number of simulated events, L the number of loss types,
R the number of realizations and statistics is an array of shape
(n, R, 4), with n the number of assets in the current riskinput object
"""
E = param['number_of_ground_motion_fields']
L = len(riskmodel.loss_types)
I = param['insured_losses'] + 1
R = riskinputs[0].hazard_getter.num_rlzs
result = dict(agg=numpy.zeros((E, R, L * I), F32), avg=[],
all_losses=AccumDict(accum={}))
for ri in riskinputs:
for outputs in riskmodel.gen_outputs(ri, monitor):
r = outputs.rlzi
assets = outputs.assets
for l, losses in enumerate(outputs):
if losses is None: # this may happen
continue
stats = numpy.zeros((len(assets), I), stat_dt) # mean, stddev
for a, asset in enumerate(assets):
stats['mean'][a] = losses[a].mean()
stats['stddev'][a] = losses[a].std(ddof=1)
result['avg'].append((l, r, asset.ordinal, stats[a]))
agglosses = losses.sum(axis=0) # shape E, I
for i in range(I):
result['agg'][:, r, l + L * i] += agglosses[:, i]
if param['asset_loss_table']:
aids = [asset.ordinal for asset in outputs.assets]
result['all_losses'][l, r] += AccumDict(zip(aids, losses))
return result
[docs]@base.calculators.add('scenario_risk')
class ScenarioRiskCalculator(base.RiskCalculator):
"""
Run a scenario risk calculation
"""
core_task = scenario_risk
is_stochastic = True
[docs] def pre_execute(self):
"""
Compute the GMFs, build the epsilons, the riskinputs, and a dictionary
with the unit of measure, used in the export phase.
"""
oq = self.oqparam
super().pre_execute('scenario')
self.assetcol = self.datastore['assetcol']
A = len(self.assetcol)
E = oq.number_of_ground_motion_fields
if oq.ignore_covs:
# all zeros; the data transfer is not so big in scenario
eps = numpy.zeros((A, E), numpy.float32)
else:
logging.info('Building the epsilons')
eps = riskinput.make_eps(
self.assetcol, E, oq.master_seed, oq.asset_correlation)
self.riskinputs = self.build_riskinputs('gmf', eps, E)
self.param['number_of_ground_motion_fields'] = E
self.param['insured_losses'] = self.oqparam.insured_losses
self.param['asset_loss_table'] = self.oqparam.asset_loss_table
[docs] def post_execute(self, result):
"""
Compute stats for the aggregated distributions and save
the results on the datastore.
"""
loss_dt = self.oqparam.loss_dt()
LI = len(loss_dt.names)
dtlist = [('eid', U64), ('rlzi', U16), ('loss', (F32, LI))]
I = self.oqparam.insured_losses + 1
with self.monitor('saving outputs', autoflush=True):
A = len(self.assetcol)
# agg losses
res = result['agg']
E, R, LI = res.shape
L = LI // I
mean, std = scientific.mean_std(res) # shape (R, LI)
agglosses = numpy.zeros((R, LI), stat_dt)
agglosses['mean'] = F32(mean)
agglosses['stddev'] = F32(std)
# losses by asset
losses_by_asset = numpy.zeros((A, R, LI), stat_dt)
for (l, r, aid, stat) in result['avg']:
for i in range(I):
losses_by_asset[aid, r, l + L * i] = stat[i]
self.datastore['losses_by_asset'] = losses_by_asset
self.datastore['agglosses-rlzs'] = agglosses
# losses by event
lbe = numpy.fromiter(
((eid, rlzi, res[eid, rlzi])
for rlzi in range(R) for eid in range(E)), dtlist)
self.datastore['losses_by_event'] = lbe
loss_types = ' '.join(self.oqparam.loss_dt().names)
self.datastore.set_attrs('losses_by_event', loss_types=loss_types)
# all losses
if self.oqparam.asset_loss_table:
array = numpy.zeros((A, E, R), loss_dt)
for (l, r), losses_by_aid in result['all_losses'].items():
for aid in losses_by_aid:
lba = losses_by_aid[aid] # (E, I)
for i in range(I):
lt = loss_dt.names[l + L * i]
array[lt][aid, :, r] = lba[:, i]
self.datastore['all_losses-rlzs'] = array
tags = [encode(tag) for tag in self.assetcol.tagcol]
self.datastore.set_attrs('all_losses-rlzs', tags=tags)