# -*- coding: utf-8 -*-
# vim: tabstop=4 shiftwidth=4 softtabstop=4
#
# Copyright (C) 2015-2023 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 time
import os.path
import logging
import operator
from functools import partial
import numpy
import pandas
from scipy import sparse
from openquake.baselib import hdf5, performance, general, python3compat, config
from openquake.hazardlib import stats, InvalidFile
from openquake.commonlib.calc import starmap_from_gmfs, compactify3
from openquake.risklib.scientific import (
total_losses, insurance_losses, MultiEventRNG, LOSSID)
from openquake.calculators import base, event_based
from openquake.calculators.post_risk import (
PostRiskCalculator, post_aggregate, fix_dtypes, fix_investigation_time)
U8 = numpy.uint8
U16 = numpy.uint16
U32 = numpy.uint32
U64 = numpy.uint64
F32 = numpy.float32
F64 = numpy.float64
TWO16 = 2 ** 16
TWO32 = U64(2 ** 32)
get_n_occ = operator.itemgetter(1)
[docs]def fast_agg(keys, values, correl, li, acc):
"""
:param keys: an array of N uint64 numbers encoding (event_id, agg_id)
:param values: an array of (N, D) floats
:param correl: True if there is asset correlation
:param li: loss type index
:param acc: dictionary unique key -> array(L, D)
"""
ukeys, avalues = general.fast_agg2(keys, values)
if correl: # restore the variances
avalues[:, 0] = avalues[:, 0] ** 2
for ukey, avalue in zip(ukeys, avalues):
acc[ukey][li] += avalue
[docs]def average_losses(ln, alt, rlz_id, AR, collect_rlzs):
"""
:returns: a sparse coo matrix with the losses per asset and realization
"""
if collect_rlzs or len(numpy.unique(rlz_id)) == 1:
ldf = pandas.DataFrame(
dict(aid=alt.aid.to_numpy(), loss=alt.loss.to_numpy()))
tot = ldf.groupby('aid').loss.sum()
aids = tot.index.to_numpy()
rlzs = numpy.zeros_like(tot)
return sparse.coo_matrix((tot.to_numpy(), (aids, rlzs)), AR)
else:
ldf = pandas.DataFrame(
dict(aid=alt.aid.to_numpy(), loss=alt.loss.to_numpy(),
rlz=rlz_id[U32(alt.eid)])) # NB: without the U32 here
# the SURA calculation would fail with alt.eid being F64 (?)
tot = ldf.groupby(['aid', 'rlz']).loss.sum()
aids, rlzs = zip(*tot.index)
return sparse.coo_matrix((tot.to_numpy(), (aids, rlzs)), AR)
[docs]def debugprint(ln, asset_loss_table, adf):
"""
Print risk_by_event in a reasonable format. To be used with --nd
"""
if '+' in ln or ln == 'claim':
df = asset_loss_table.set_index('aid').rename(columns={'loss': ln})
df['asset_id'] = python3compat.decode(adf.id[df.index].to_numpy())
del df['variance']
print(df)
[docs]def aggreg(outputs, crmodel, ARK, aggids, rlz_id, ideduc, monitor):
"""
:returns: (avg_losses, agg_loss_table)
"""
mon_agg = monitor('aggregating losses', measuremem=False)
mon_avg = monitor('averaging losses', measuremem=False)
oq = crmodel.oqparam
xtypes = oq.ext_loss_types
if ideduc:
xtypes.append('claim')
loss_by_AR = {ln: [] for ln in xtypes}
correl = int(oq.asset_correlation)
(A, R, K), L = ARK, len(xtypes)
acc = general.AccumDict(accum=numpy.zeros((L, 2))) # u8idx->array
value_cols = ['variance', 'loss']
for out in outputs:
for li, ln in enumerate(xtypes):
if ln not in out or len(out[ln]) == 0:
continue
alt = out[ln]
if oq.avg_losses:
with mon_avg:
coo = average_losses(
ln, alt, rlz_id, (A, R), oq.collect_rlzs)
loss_by_AR[ln].append(coo)
with mon_agg:
if correl: # use sigma^2 = (sum sigma_i)^2
alt['variance'] = numpy.sqrt(alt.variance)
eids = alt.eid.to_numpy() * TWO32 # U64
values = numpy.array([alt[col] for col in value_cols]).T
# aggregate all assets
fast_agg(eids + U64(K), values, correl, li, acc)
if len(aggids):
# aggregate assets for each tag combination
aids = alt.aid.to_numpy()
for kids in aggids[:, aids]:
fast_agg(eids + U64(kids), values, correl, li, acc)
lis = range(len(xtypes))
with monitor('building event loss table', measuremem=True):
dic = general.AccumDict(accum=[])
for ukey, arr in acc.items():
eid, kid = divmod(ukey, TWO32)
for li in lis:
if arr[li].any():
dic['event_id'].append(eid)
dic['agg_id'].append(kid)
dic['loss_id'].append(LOSSID[xtypes[li]])
for c, col in enumerate(['variance', 'loss']):
dic[col].append(arr[li, c])
fix_dtypes(dic)
return loss_by_AR, pandas.DataFrame(dic)
[docs]def ebr_from_gmfs(sbe, oqparam, dstore, monitor):
"""
:param slice_by_event: composite array with fields 'start', 'stop'
:param oqparam: OqParam instance
:param dstore: DataStore instance from which to read the GMFs
:param monitor: a Monitor instance
:yields: dictionary of arrays, the output of event_based_risk
"""
if dstore.parent:
dstore.parent.open('r')
gmfcols = oqparam.gmf_data_dt().names
with dstore:
# this is fast compared to reading the GMFs
risk_sids = monitor.read('sids')
s0, s1 = sbe[0]['start'], sbe[-1]['stop']
t0 = time.time()
haz_sids = dstore['gmf_data/sid'][s0:s1]
dt = time.time() - t0
idx, = numpy.where(numpy.isin(haz_sids, risk_sids))
if len(idx) == 0:
return {}
# print('waiting %.1f' % dt)
time.sleep(dt)
with dstore, monitor('reading GMFs', measuremem=True):
start, stop = idx.min(), idx.max() + 1
dic = {}
for col in gmfcols:
if col == 'sid':
dic[col] = haz_sids[idx]
else:
dset = dstore['gmf_data/' + col]
dic[col] = dset[s0+start:s0+stop][idx - start]
df = pandas.DataFrame(dic)
del dic
# if max_gmvs_chunk is too small, there is a huge data transfer in
# avg_losses and the calculation may hang; if too large, run out of memory
slices = performance.split_slices(
df.eid.to_numpy(), oqparam.max_gmvs_chunk)
for s0, s1 in slices:
yield event_based_risk(df[s0:s1], oqparam, monitor)
[docs]def event_based_risk(df, oqparam, monitor):
"""
:param df: a DataFrame of GMFs with fields sid, eid, gmv_X, ...
:param oqparam: parameters coming from the job.ini
:param monitor: a Monitor instance
:returns: a dictionary of arrays
"""
with monitor('reading crmodel', measuremem=True):
crmodel = monitor.read('crmodel')
ideduc = monitor.read('assets/ideductible')
aggids = monitor.read('aggids')
rlz_id = monitor.read('rlz_id')
weights = [1] if oqparam.collect_rlzs else monitor.read('weights')
ARK = (oqparam.A, len(weights), oqparam.K)
if oqparam.ignore_master_seed or oqparam.ignore_covs:
rng = None
else:
rng = MultiEventRNG(oqparam.master_seed, df.eid.unique(),
int(oqparam.asset_correlation))
outs = gen_outputs(df, crmodel, rng, monitor)
avg, alt = aggreg(outs, crmodel, ARK, aggids, rlz_id, ideduc.any(),
monitor)
return dict(avg=avg, alt=alt, gmf_bytes=df.memory_usage().sum())
[docs]def gen_outputs(df, crmodel, rng, monitor):
"""
:param df: GMF dataframe (a slice of events)
:param crmodel: CompositeRiskModel instance
:param rng: random number generator
:param monitor: Monitor instance
:yields: one output per taxonomy and slice of events
"""
mon_risk = monitor('computing risk', measuremem=False)
fil_mon = monitor('filtering GMFs', measuremem=False)
ass_mon = monitor('reading assets', measuremem=False)
sids = df.sid.to_numpy()
for s0, s1 in monitor.read('start-stop'):
with ass_mon:
assets = monitor.read('assets', slice(s0, s1)).set_index('ordinal')
for taxo in assets.taxonomy.unique():
adf = assets[assets.taxonomy == taxo]
with fil_mon:
# *crucial* for the performance of the next step
gmf_df = df[numpy.isin(sids, adf.site_id.unique())]
if len(gmf_df) == 0: # common enough
continue
with mon_risk:
out = crmodel.get_output(
adf, gmf_df, crmodel.oqparam._sec_losses, rng)
yield out
[docs]def set_oqparam(oq, assetcol, dstore):
"""
Set the attributes .M, .K, .A, .ideduc, ._sec_losses
"""
try:
K = len(dstore['agg_keys'])
except KeyError:
K = 0
sec_losses = [] # one insured loss for each loss type with a policy
try:
policy_df = dstore.read_df('policy')
except KeyError:
pass
else:
if 'reinsurance' not in oq.inputs:
sec_losses.append(
partial(insurance_losses, policy_df=policy_df))
ideduc = assetcol['ideductible'].any()
if oq.total_losses:
sec_losses.append(
partial(total_losses, kind=oq.total_losses, ideduc=ideduc))
elif ideduc:
# subtract the insurance deductible for a single loss_type
[lt] = oq.loss_types
sec_losses.append(partial(total_losses, kind=lt, ideduc=ideduc))
oq._sec_losses = sec_losses
oq.ideduc = int(ideduc)
oq.M = len(oq.all_imts())
oq.K = K
oq.A = assetcol['ordinal'].max() + 1
[docs]def ebrisk(proxies, cmaker, stations, dstore, monitor):
"""
:param proxies: list of RuptureProxies with the same trt_smr
:param cmaker: ContextMaker instance associated to the trt_smr
:param stations: empty pair or (station_data, station_sitecol)
:param monitor: a Monitor instance
:returns: a dictionary of arrays
"""
cmaker.oq.ground_motion_fields = True
for block in general.block_splitter(
proxies, 20_000, event_based.rup_weight):
dic = event_based.event_based(block, cmaker, stations, dstore, monitor)
if len(dic['gmfdata']):
gmf_df = pandas.DataFrame(dic['gmfdata'])
yield event_based_risk(gmf_df, cmaker.oq, monitor)
[docs]@base.calculators.add('ebrisk', 'scenario_risk', 'event_based_risk')
class EventBasedRiskCalculator(event_based.EventBasedCalculator):
"""
Event based risk calculator generating event loss tables
"""
core_task = ebrisk
is_stochastic = True
precalc = 'event_based'
accept_precalc = ['scenario', 'event_based', 'event_based_risk', 'ebrisk']
[docs] def save_tmp(self, monitor):
"""
Save some useful data in the file calc_XXX_tmp.hdf5
"""
oq = self.oqparam
monitor.save('sids', self.sitecol.sids)
adf = self.assetcol.to_dframe().sort_values(['taxonomy', 'ordinal'])
# NB: this is subtle! without the second ordering by 'ordinal'
# the asset dataframe will be ordered differently on AMD machines
# with respect to Intel machines, depending on the machine, thus
# causing different losses
del adf['id']
monitor.save('assets', adf)
tss = performance.idx_start_stop(adf.taxonomy.to_numpy())
# storing start-stop indices in a smart way, so that the assets are
# read from the workers in chunks of at most 1 million elements
monitor.save('start-stop', compactify3(tss))
monitor.save('crmodel', self.crmodel)
monitor.save('rlz_id', self.rlzs)
monitor.save('weights', self.datastore['weights'][:])
if oq.K:
aggids, _ = self.assetcol.build_aggids(
oq.aggregate_by, oq.max_aggregations)
else:
aggids = ()
monitor.save('aggids', aggids)
[docs] def pre_execute(self):
oq = self.oqparam
if oq.calculation_mode == 'ebrisk':
oq.ground_motion_fields = False
parent = self.datastore.parent
if parent:
self.datastore['full_lt'] = parent['full_lt']
self.parent_events = ne = len(parent['events'])
logging.info('There are %d ruptures and %d events',
len(parent['ruptures']), ne)
else:
self.parent_events = None
super().pre_execute()
parentdir = (os.path.dirname(self.datastore.ppath)
if self.datastore.ppath else None)
oq.hdf5path = self.datastore.filename
oq.parentdir = parentdir
logging.info(
'There are {:_d} ruptures and {:_d} events'.format(
len(self.datastore['ruptures']),
len(self.datastore['events'])))
self.events_per_sid = numpy.zeros(self.N, U32)
self.datastore.swmr_on()
set_oqparam(oq, self.assetcol, self.datastore)
ct = oq.concurrent_tasks or 1
oq.maxweight = int(oq.ebrisk_maxsize / ct)
self.A = A = len(self.assetcol)
self.L = L = len(oq.loss_types)
ELT = len(oq.ext_loss_types)
if oq.calculation_mode == 'event_based_risk' and oq.avg_losses:
R = 1 if oq.collect_rlzs else self.R
logging.info('Transfering %s per core in avg_losses',
general.humansize(A * ELT * 8 * R))
if A * ELT * 8 > int(config.memory.avg_losses_max):
raise ValueError('For large exposures you must set '
'avg_losses=false')
elif A * ELT * self.R * 8 > int(config.memory.avg_losses_max):
raise ValueError('For large exposures you must set '
'collect_rlzs = true')
if (oq.aggregate_by and self.E * A > oq.max_potential_gmfs and
all(val == 0 for val in oq.minimum_asset_loss.values())):
logging.warning('The calculation is really big; consider setting '
'minimum_asset_loss')
base.create_risk_by_event(self)
self.rlzs = self.datastore['events']['rlz_id']
self.num_events = numpy.bincount(self.rlzs, minlength=self.R)
self.xtypes = oq.ext_loss_types
if self.assetcol['ideductible'].any():
self.xtypes.append('claim')
if oq.avg_losses:
self.create_avg_losses()
alt_nbytes = 4 * self.E * L
if alt_nbytes / (oq.concurrent_tasks or 1) > TWO32:
raise RuntimeError('The risk_by_event is too big to be transfer'
'ed with %d tasks' % oq.concurrent_tasks)
[docs] def create_avg_losses(self):
oq = self.oqparam
ws = self.datastore['weights']
R = 1 if oq.collect_rlzs else len(ws)
fix_investigation_time(oq, self.datastore)
if oq.collect_rlzs:
if oq.investigation_time: # event_based
self.avg_ratio = numpy.array([oq.time_ratio / len(ws)])
else: # scenario
self.avg_ratio = numpy.array([1. / self.num_events.sum()])
else:
if oq.investigation_time: # event_based
self.avg_ratio = numpy.array([oq.time_ratio] * len(ws))
else: # scenario
self.avg_ratio = 1. / self.num_events
self.avg_losses = {}
for lt in self.xtypes:
self.avg_losses[lt] = numpy.zeros((self.A, R), F32)
self.datastore.create_dset(
'avg_losses-rlzs/' + lt, F32, (self.A, R))
self.datastore.set_shape_descr(
'avg_losses-rlzs/' + lt, asset_id=self.assetcol['id'], rlz=R)
[docs] def execute(self):
"""
Compute risk from GMFs or ruptures depending on what is stored
"""
oq = self.oqparam
self.gmf_bytes = 0
if oq.calculation_mode == 'ebrisk' or 'gmf_data' not in self.datastore:
# start from ruptures
if (oq.ground_motion_fields and
'gsim_logic_tree' not in oq.inputs and
oq.gsim == '[FromFile]'):
raise InvalidFile('%s: missing gsim or gsim_logic_tree_file'
% oq.inputs['job_ini'])
elif not hasattr(oq, 'maximum_distance'):
raise InvalidFile('Missing maximum_distance in %s'
% oq.inputs['job_ini'])
full_lt = self.datastore['full_lt']
smap = event_based.starmap_from_rups(
ebrisk, oq, full_lt, self.sitecol, self.datastore,
self.save_tmp)
smap.reduce(self.agg_dicts)
if self.gmf_bytes == 0:
raise RuntimeError(
'No GMFs were generated, perhaps they were '
'all below the minimum_intensity threshold')
logging.info(
'Produced %s of GMFs', general.humansize(self.gmf_bytes))
else: # start from GMFs
smap = starmap_from_gmfs(ebr_from_gmfs, oq, self.datastore,
self._monitor)
self.save_tmp(smap.monitor)
smap.reduce(self.agg_dicts)
if self.parent_events:
assert self.parent_events == len(self.datastore['events'])
return 1
[docs] def log_info(self, eids):
"""
Printing some information about the risk calculation
"""
logging.info('Processing {:_d} rows of gmf_data'.format(len(eids)))
E = len(numpy.unique(eids))
K = self.oqparam.K
logging.info('Risk parameters (rel_E={:_d}, K={:_d}, L={})'.
format(E, K, self.L))
[docs] def agg_dicts(self, dummy, dic):
"""
:param dummy: unused parameter
:param dic: dictionary with keys "avg", "alt"
"""
if not dic:
return
self.gmf_bytes += dic.pop('gmf_bytes')
self.oqparam.ground_motion_fields = False # hack
with self.monitor('saving risk_by_event'):
alt = dic.pop('alt')
if alt is not None:
for name in alt.columns:
dset = self.datastore['risk_by_event/' + name]
hdf5.extend(dset, alt[name].to_numpy())
with self.monitor('saving avg_losses'):
for ln, ls in dic.pop('avg').items():
for coo in ls:
self.avg_losses[ln][coo.row, coo.col] += coo.data
[docs] def post_execute(self, dummy):
"""
Compute and store average losses from the risk_by_event dataset,
and then loss curves and maps.
"""
oq = self.oqparam
K = self.datastore['risk_by_event'].attrs.get('K', 0)
upper_limit = self.E * (K + 1) * len(self.xtypes)
if upper_limit < 1E7:
# sanity check on risk_by_event if not too large
alt = self.datastore.read_df('risk_by_event')
size = len(alt)
assert size <= upper_limit, (size, upper_limit)
# sanity check on uniqueness by (agg_id, loss_id, event_id)
arr = alt[['agg_id', 'loss_id', 'event_id']].to_numpy()
uni, cnt = numpy.unique(arr, axis=0, return_counts=True)
if len(uni) < len(arr):
dupl = uni[cnt > 1] # (agg_id, loss_id, event_id)
raise RuntimeError(
'risk_by_event contains %d duplicates for event %s' %
(len(arr) - len(uni), dupl[0, 2]))
if oq.avg_losses:
logging.info('Storing avg_losses-rlzs')
for lt in self.xtypes:
al = self.avg_losses[lt]
for r in range(self.R):
al[:, r] *= self.avg_ratio[r]
name = 'avg_losses-rlzs/' + lt
self.datastore[name][:] = al
stats.set_rlzs_stats(self.datastore, name,
asset_id=self.assetcol['id'])
self.build_aggcurves()
if oq.reaggregate_by:
post_aggregate(self.datastore.calc_id,
','.join(oq.reaggregate_by))
[docs] def build_aggcurves(self):
prc = PostRiskCalculator(self.oqparam, self.datastore.calc_id)
prc.assetcol = self.assetcol
if hasattr(self, 'exported'):
prc.exported = self.exported
prc.run(exports='')
