# -*- coding: utf-8 -*-
# vim: tabstop=4 shiftwidth=4 softtabstop=4
#
# Copyright (C) 2014-2021 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 import hdf5
from openquake.baselib.general import AccumDict, humansize
from openquake.hazardlib.stats import set_rlzs_stats, avg_std
from openquake.calculators import base, views
U16 = numpy.uint16
U32 = numpy.uint32
F32 = numpy.float32
F64 = numpy.float64
[docs]def floats_in(numbers):
"""
:param numbers: an array of numbers
:returns: number of non-uint32 number
"""
return (U32(numbers) != numbers).sum()
[docs]def bin_ddd(fractions, n, seed):
"""
Converting fractions into discrete damage distributions using bincount
and numpy.random.choice
"""
n = int(n)
D = fractions.shape[1] # shape (E, D)
ddd = numpy.zeros(fractions.shape, U32)
numpy.random.seed(seed)
for e, frac in enumerate(fractions):
ddd[e] = numpy.bincount(
numpy.random.choice(D, n, p=frac/frac.sum()), minlength=D)
return ddd
[docs]def run_sec_sims(damages, haz, sec_sims, seed):
"""
:param damages: array of shape (E, D) for a given asset
:param haz: dataframe of size E with a probability field
:param sec_sims: pair (probability field, number of simulations)
:param seed: random seed to use
Run secondary simulations and update the array damages
"""
[(prob_field, num_sims)] = sec_sims
numpy.random.seed(seed)
probs = haz[prob_field].to_numpy() # LiqProb
affected = numpy.random.random((num_sims, 1)) < probs # (N, E)
for d, buildings in enumerate(damages.T[1:], 1):
# doing the mean on the secondary simulations for each event
damages[:, d] = numpy.mean(affected * buildings, axis=0) # shape E
[docs]def scenario_damage(riskinputs, param, monitor):
"""
Core function for a damage computation.
:param riskinputs:
:class:`openquake.risklib.riskinput.RiskInput` objects
:param monitor:
:class:`openquake.baselib.performance.Monitor` instance
:param param:
dictionary of extra parameters
:returns:
a dictionary of arrays
"""
crmodel = monitor.read('crmodel')
L = len(crmodel.loss_types)
D = len(crmodel.damage_states)
consequences = crmodel.get_consequences()
# algorithm used to compute the discrete damage distributions
float_dmg_dist = param['float_dmg_dist']
z = numpy.zeros((L, D - 1), F32 if float_dmg_dist else U32)
d_event = AccumDict(accum=z)
res = {'d_event': d_event, 'd_asset': []}
for name in consequences:
res['avg_' + name] = []
res[name + '_by_event'] = AccumDict(accum=numpy.zeros(L, F64))
# using F64 here is necessary: with F32 the non-commutativity
# of addition would hurt too much with multiple tasks
seed = param['master_seed']
num_events = param['num_events'] # per realization
acc = [] # (aid, eid, lid, ds...)
sec_sims = param['secondary_simulations'].items()
for ri in riskinputs:
# here instead F32 floats are ok
for out in ri.gen_outputs(crmodel, monitor):
r = out.rlzi
ne = num_events[r] # total number of events
for lti, loss_type in enumerate(crmodel.loss_types):
for asset, fractions in zip(ri.assets, out[loss_type]):
aid = asset['ordinal']
if float_dmg_dist:
damages = fractions * asset['number']
if sec_sims:
run_sec_sims(
damages, out.haz, sec_sims, seed + aid)
else:
damages = bin_ddd(
fractions, asset['number'], seed + aid)
# damages has shape E', D with E' == len(out.eids)
for e, ddd in enumerate(damages):
dmg = ddd[1:]
if dmg.sum():
eid = out.eids[e] # (aid, eid, l) is unique
acc.append((aid, eid, lti) + tuple(dmg))
d_event[eid][lti] += ddd[1:]
tot = damages.sum(axis=0) # (E', D) -> D
nodamage = asset['number'] * (ne - len(damages))
tot[0] += nodamage
res['d_asset'].append((lti, r, aid, tot))
# TODO: use the ddd, not the fractions in compute_csq
csq = crmodel.compute_csq(asset, fractions, loss_type)
for name, values in csq.items():
res['avg_%s' % name].append(
(lti, r, asset['ordinal'], values.sum(axis=0)))
by_event = res[name + '_by_event']
for eid, value in zip(out.eids, values):
by_event[eid][lti] += value
res['aed'] = numpy.array(acc, param['asset_damage_dt'])
return res
[docs]@base.calculators.add('scenario_damage', 'event_based_damage')
class ScenarioDamageCalculator(base.RiskCalculator):
"""
Damage calculator
"""
core_task = scenario_damage
is_stochastic = True
precalc = 'event_based'
accept_precalc = ['scenario', 'event_based', 'event_based_risk']
[docs] def pre_execute(self):
oq = self.oqparam
super().pre_execute()
num_floats = floats_in(self.assetcol['number'])
if num_floats:
logging.warning(
'The exposure contains %d non-integer asset numbers: '
'using floating point damage distributions', num_floats)
bad = self.assetcol['number'] > 2**32 - 1
for ass in self.assetcol[bad]:
aref = self.assetcol.tagcol.id[ass['id']]
logging.error("The asset %s has number=%s > 2^32-1!",
aref, ass['number'])
self.param['secondary_simulations'] = oq.secondary_simulations
self.param['float_dmg_dist'] = oq.float_dmg_dist or num_floats
self.param['asset_damage_dt'] = self.crmodel.asset_damage_dt(
oq.float_dmg_dist or num_floats)
self.param['master_seed'] = oq.master_seed
self.param['num_events'] = ne = numpy.bincount( # events by rlz
self.datastore['events']['rlz_id'], minlength=self.R)
if (ne == 0).any():
logging.warning('There are realizations with zero events')
self.datastore.create_dframe(
'dd_data', self.param['asset_damage_dt'], 'gzip')
self.riskinputs = self.build_riskinputs('gmf')
[docs] def combine(self, acc, res):
"""
Combine the results and grows dd_data
"""
if res is None:
raise MemoryError('You ran out of memory!')
with self.monitor('saving dd_data', measuremem=True):
aed = res.pop('aed', ())
if len(aed) == 0:
return acc + res
for name in aed.dtype.names:
hdf5.extend(self.datastore['dd_data/' + name], aed[name])
return acc + res
[docs] def post_execute(self, result):
"""
Compute stats for the aggregated distributions and save
the results on the datastore.
"""
if not result:
self.collapsed()
return
dstates = self.crmodel.damage_states
ltypes = self.crmodel.loss_types
L = self.L = len(ltypes)
R = self.R
D = len(dstates)
A = len(self.assetcol)
E = len(self.datastore['events'])
# reduction factor
matrixsize = A * E * L * 4
realsize = self.datastore.getsize('dd_data')
logging.info('Saving %s in dd_data (instead of %s)',
humansize(realsize), humansize(matrixsize))
oq = self.oqparam
# damage by asset
d_asset = numpy.zeros((A, R, L, D), F32)
for (l, r, a, tot) in result['d_asset']:
d_asset[a, r, l] = tot / self.param['num_events'][r]
self.datastore['damages-rlzs'] = d_asset
set_rlzs_stats(self.datastore,
'damages',
asset_id=self.assetcol['id'],
loss_type=oq.loss_names,
dmg_state=dstates)
# damage by event: make sure the sum of the buildings is consistent
rlz = self.datastore['events']['rlz_id']
weights = self.datastore['weights'][:][rlz]
tot = self.assetcol['number'].sum()
dt = F32 if self.param['float_dmg_dist'] else U32
dbe = numpy.zeros((self.E, L, D), dt) # shape E, L, D
dbe[:, :, 0] = tot
for e, dmg_by_lt in result['d_event'].items():
for li, dmg in enumerate(dmg_by_lt):
dbe[e, li, 0] = tot - dmg.sum()
dbe[e, li, 1:] = dmg
self.datastore['dmg_by_event'] = dbe
self.datastore['avg_portfolio_damage'] = avg_std(
dbe.astype(float), weights)
self.datastore.set_shape_descr(
'avg_portfolio_damage',
kind=['avg', 'std'], loss_type=ltypes, dmg_state=dstates)
self.sanity_check()
# consequence distributions
del result['d_asset']
del result['d_event']
dtlist = [('event_id', U32), ('rlz_id', U16), ('loss', (F32, (L,)))]
ne = self.param['num_events']
for name, csq in result.items():
if name.startswith('avg_'):
c_asset = numpy.zeros((A, R, L), F32)
for (l, r, a, stat) in result[name]:
if oq.investigation_time: # event_based_damage
c_asset[a, r, l] = stat * oq.ses_ratio
else: # scenario_damage
c_asset[a, r, l] = stat / ne[r]
self.datastore[name + '-rlzs'] = c_asset
set_rlzs_stats(self.datastore, name,
asset_id=self.assetcol['id'],
loss_type=oq.loss_names)
elif name.endswith('_by_event'):
arr = numpy.zeros(len(csq), dtlist)
for i, (eid, loss) in enumerate(csq.items()):
arr[i] = (eid, rlz[eid], loss)
self.datastore[name] = arr
[docs] def sanity_check(self):
"""
Sanity check on the total number of buildings
"""
E0 = self.param['num_events'][0]
avg0 = self.datastore['damages-rlzs'][:, 0].sum(axis=0) # (L, D)
if not len(self.datastore['dd_data/aid']):
logging.warning('There is no damage at all!')
elif 'avg_portfolio_damage' in self.datastore:
df = views.portfolio_damage_error(
'avg_portfolio_damage', self.datastore)
rst = views.rst_table(df)
logging.info('Portfolio damage\n%s' % rst)
num_buildings = avg0.sum(axis=1)
if self.oqparam.investigation_time: # event_based_damage
# N = avg * T / E
num_buildings /= self.oqparam.ses_ratio * E0
expected = self.assetcol['number'].sum()
nums = set(num_buildings) | {expected}
if len(nums) > 1:
numdic = dict(expected=expected)
for lt, num in zip(self.oqparam.loss_names, num_buildings):
numdic[lt] = num
logging.info(
'Due to rounding errors inherent in floating-point arithmetic,'
' the total number of buildings is not exact: %s', numdic)