# -*- 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 itertools
import logging
import numpy
from openquake.risklib import scientific, riskmodels
from openquake.calculators import base
U16 = numpy.uint16
U64 = numpy.uint64
F32 = numpy.float32
F64 = numpy.float64
[docs]def dist_by_asset(data, multi_stat_dt, number):
"""
:param data: array of shape (N, R, L, 2, ...)
:param multi_stat_dt: numpy dtype for statistical outputs
:param number: expected number of units per asset
:returns: array of shape (N, R) with records of type multi_stat_dt
"""
N, R, L = data.shape[:3]
out = numpy.zeros((N, R), multi_stat_dt)
for l, lt in enumerate(multi_stat_dt.names):
out_lt = out[lt]
for n, r in itertools.product(range(N), range(R)):
mean, stddev = data[n, r, l]
out_lt[n, r] = (mean, stddev)
# sanity check on the sum over all damage states
if abs(mean.sum() / number[n] - 1) > 1E-3:
logging.warn(
'Asset #%d, rlz=%d, expected %s, got %s for %s damage',
n, r, number[n], mean.sum(), lt)
return out
[docs]def scenario_damage(riskinputs, riskmodel, param, monitor):
"""
Core function for a damage computation.
:param riskinputs:
:class:`openquake.risklib.riskinput.RiskInput` objects
:param riskmodel:
a :class:`openquake.risklib.riskinput.CompositeRiskModel` instance
:param monitor:
:class:`openquake.baselib.performance.Monitor` instance
:param param:
dictionary of extra parameters
:returns:
a dictionary {'d_asset': [(l, r, a, mean-stddev), ...],
'd_event': damage array of shape R, L, E, D,
'c_asset': [(l, r, a, mean-stddev), ...],
'c_event': damage array of shape R, L, E}
`d_asset` and `d_tag` are related to the damage distributions
whereas `c_asset` and `c_tag` are the consequence distributions.
If there is no consequence model `c_asset` is an empty list and
`c_tag` is a zero-valued array.
"""
c_models = param['consequence_models']
L = len(riskmodel.loss_types)
D = len(riskmodel.damage_states)
E = param['number_of_ground_motion_fields']
R = riskinputs[0].hazard_getter.num_rlzs
result = dict(d_asset=[], d_event=numpy.zeros((E, R, L, D), F64),
c_asset=[], c_event=numpy.zeros((E, R, L), F64))
for ri in riskinputs:
for outputs in riskmodel.gen_outputs(ri, monitor):
r = outputs.rlzi
for l, damages in enumerate(outputs):
loss_type = riskmodel.loss_types[l]
c_model = c_models.get(loss_type)
for a, fraction in enumerate(damages):
asset = outputs.assets[a]
taxo = riskmodel.taxonomy[asset.taxonomy]
damages = fraction * asset.number
result['d_event'][:, r, l] += damages # shape (E, D)
if c_model: # compute consequences
means = [par[0] for par in c_model[taxo].params]
# NB: we add a 0 in front for nodamage state
c_ratio = numpy.dot(fraction, [0] + means)
consequences = c_ratio * asset.value(loss_type)
result['c_asset'].append(
(l, r, asset.ordinal,
scientific.mean_std(consequences)))
result['c_event'][:, r, l] += consequences
# TODO: consequences for the occupants
result['d_asset'].append(
(l, r, asset.ordinal, scientific.mean_std(damages)))
return result
[docs]@base.calculators.add('scenario_damage')
class ScenarioDamageCalculator(base.RiskCalculator):
"""
Scenario damage calculator
"""
core_task = scenario_damage
is_stochastic = True
[docs] def pre_execute(self):
super().pre_execute('scenario')
E = self.oqparam.number_of_ground_motion_fields
self.param['number_of_ground_motion_fields'] = E
self.param['consequence_models'] = riskmodels.get_risk_models(
self.oqparam, 'consequence')
self.riskinputs = self.build_riskinputs('gmf', num_events=E)
self.param['tags'] = list(self.assetcol.tagcol)
[docs] def post_execute(self, result):
"""
Compute stats for the aggregated distributions and save
the results on the datastore.
"""
dstates = self.riskmodel.damage_states
ltypes = self.riskmodel.loss_types
L = len(ltypes)
R = len(self.rlzs_assoc.realizations)
D = len(dstates)
N = len(self.assetcol)
E = self.oqparam.number_of_ground_motion_fields
# damage distributions
dt_list = []
for ltype in ltypes:
dt_list.append((ltype, numpy.dtype([('mean', (F32, D)),
('stddev', (F32, D))])))
multi_stat_dt = numpy.dtype(dt_list)
d_asset = numpy.zeros((N, R, L, 2, D), F32)
for (l, r, a, stat) in result['d_asset']:
d_asset[a, r, l] = stat
self.datastore['dmg_by_asset'] = dist_by_asset(
d_asset, multi_stat_dt, self.assetcol.array['number'])
dmg_dt = [(ds, F32) for ds in self.riskmodel.damage_states]
d_event = numpy.zeros((E, R, L), dmg_dt)
for d, ds in enumerate(self.riskmodel.damage_states):
d_event[ds] = result['d_event'][:, :, :, d]
self.datastore['dmg_by_event'] = d_event
# consequence distributions
if result['c_asset']:
dtlist = [('eid', U64), ('rlzi', U16), ('loss', (F32, L))]
stat_dt = numpy.dtype([('mean', F32), ('stddev', F32)])
c_asset = numpy.zeros((N, R, L), stat_dt)
for (l, r, a, stat) in result['c_asset']:
c_asset[a, r, l] = stat
multi_stat_dt = self.oqparam.loss_dt(stat_dt)
self.datastore['losses_by_asset'] = c_asset
self.datastore['losses_by_event'] = numpy.fromiter(
((eid, rlzi, F32(result['c_event'][eid, rlzi]))
for rlzi in range(R) for eid in range(E)), dtlist)