# -*- coding: utf-8 -*-
# vim: tabstop=4 shiftwidth=4 softtabstop=4
#
# Copyright (C) 2015-2019 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 os.path
import logging
import collections
import operator
import numpy
from openquake.baselib import hdf5
from openquake.baselib.python3compat import zip
from openquake.baselib.general import AccumDict, cached_property, get_indices
from openquake.hazardlib.probability_map import ProbabilityMap
from openquake.hazardlib.stats import compute_pmap_stats
from openquake.hazardlib.calc.stochastic import sample_ruptures
from openquake.hazardlib import InvalidFile
from openquake.hazardlib.source import rupture
from openquake.risklib.riskinput import str2rsi
from openquake.baselib import parallel
from openquake.commonlib import calc, util, logs
from openquake.calculators import base, extract
from openquake.calculators.getters import (
GmfGetter, RuptureGetter, gen_rupture_getters)
from openquake.calculators.classical import ClassicalCalculator
from openquake.engine import engine
U8 = numpy.uint8
U16 = numpy.uint16
U32 = numpy.uint32
U64 = numpy.uint64
F32 = numpy.float32
F64 = numpy.float64
TWO32 = U64(2 ** 32)
by_grp = operator.attrgetter('src_group_id')
# ######################## GMF calculator ############################ #
[docs]def update_nbytes(dstore, key, array):
nbytes = dstore.get_attr(key, 'nbytes', 0)
dstore.set_attrs(key, nbytes=nbytes + array.nbytes)
[docs]def get_mean_curves(dstore):
"""
Extract the mean hazard curves from the datastore, as a composite
array of length nsites.
"""
return dict(extract.extract(dstore, 'hcurves?kind=mean'))['mean']
# ########################################################################## #
[docs]def compute_gmfs(rupgetter, srcfilter, param, monitor):
"""
Compute GMFs and optionally hazard curves
"""
getter = GmfGetter(rupgetter, srcfilter, param['oqparam'])
with monitor('getting ruptures'):
getter.init()
return getter.compute_gmfs_curves(monitor)
[docs]@base.calculators.add('event_based')
class EventBasedCalculator(base.HazardCalculator):
"""
Event based PSHA calculator generating the ground motion fields and
the hazard curves from the ruptures, depending on the configuration
parameters.
"""
core_task = compute_gmfs
is_stochastic = True
accept_precalc = ['event_based', 'event_based_risk', 'ucerf_hazard']
build_ruptures = sample_ruptures
@cached_property
def csm_info(self):
"""
:returns: a cached CompositionInfo object
"""
try:
return self.csm.info
except AttributeError:
return self.datastore.parent['csm_info']
[docs] def init(self):
if hasattr(self, 'csm'):
self.check_floating_spinning()
self.rupser = calc.RuptureSerializer(self.datastore)
[docs] def init_logic_tree(self, csm_info):
self.trt_by_grp = csm_info.grp_by("trt")
self.rlzs_assoc = csm_info.get_rlzs_assoc()
self.rlzs_by_gsim_grp = csm_info.get_rlzs_by_gsim_grp()
self.samples_by_grp = csm_info.get_samples_by_grp()
self.num_rlzs_by_grp = {
grp_id:
sum(len(rlzs) for rlzs in self.rlzs_by_gsim_grp[grp_id].values())
for grp_id in self.rlzs_by_gsim_grp}
[docs] def acc0(self):
"""
Initial accumulator, a dictionary (grp_id, gsim) -> curves
"""
self.L = len(self.oqparam.imtls.array)
zd = {r: ProbabilityMap(self.L) for r in range(self.R)}
return zd
[docs] def build_events_from_sources(self):
"""
Prefilter the composite source model and store the source_info
"""
oq = self.oqparam
gsims_by_trt = self.csm.gsim_lt.values
logging.info('Building ruptures')
smap = parallel.Starmap(
self.build_ruptures.__func__, monitor=self.monitor())
eff_ruptures = AccumDict(accum=0) # grp_id => potential ruptures
calc_times = AccumDict(accum=numpy.zeros(2, F32))
ses_idx = 0
for sm_id, sm in enumerate(self.csm.source_models):
logging.info('Sending %s', sm)
for sg in sm.src_groups:
if not sg.sources:
continue
par = self.param.copy()
par['gsims'] = gsims_by_trt[sg.trt]
if sg.atomic: # do not split the group
smap.submit(sg, self.src_filter, par)
else: # traditional groups
for block in self.block_splitter(sg.sources, key=by_grp):
if 'ucerf' in oq.calculation_mode:
for i in range(oq.ses_per_logic_tree_path):
par['ses_seeds'] = [
(ses_idx, oq.ses_seed + i + 1)]
smap.submit(block, self.src_filter, par)
ses_idx += 1
else:
smap.submit(block, self.src_filter, par)
mon = self.monitor('saving ruptures')
for dic in smap:
if dic['calc_times']:
calc_times += dic['calc_times']
if dic['eff_ruptures']:
eff_ruptures += dic['eff_ruptures']
if dic['rup_array']:
with mon:
self.rupser.save(dic['rup_array'])
self.rupser.close()
if not self.rupser.nruptures:
raise RuntimeError('No ruptures were generated, perhaps the '
'investigation time is too short')
# logic tree reduction, must be called before storing the events
self.store_rlz_info(eff_ruptures)
self.init_logic_tree(self.csm.info)
with self.monitor('store source_info', autoflush=True):
self.store_source_info(calc_times)
logging.info('Reordering the ruptures and storing the events')
attrs = self.datastore.getitem('ruptures').attrs
sorted_ruptures = self.datastore.getitem('ruptures')[()]
# order the ruptures by serial
sorted_ruptures.sort(order='serial')
ngroups = len(self.csm.info.trt_by_grp)
grp_indices = numpy.zeros((ngroups, 2), U32)
grp_ids = sorted_ruptures['grp_id']
for grp_id, [startstop] in get_indices(grp_ids).items():
grp_indices[grp_id] = startstop
self.datastore['ruptures'] = sorted_ruptures
self.datastore.set_attrs('ruptures', grp_indices=grp_indices, **attrs)
self.save_events(sorted_ruptures)
[docs] def gen_rupture_getters(self):
"""
:returns: a list of RuptureGetters
"""
dstore = (self.datastore.parent if self.datastore.parent
else self.datastore)
hdf5cache = dstore.hdf5cache()
mode = 'r+' if os.path.exists(hdf5cache) else 'w'
with hdf5.File(hdf5cache, mode) as cache:
if 'ruptures' not in cache:
dstore.hdf5.copy('ruptures', cache)
if 'rupgeoms' not in cache:
dstore.hdf5.copy('rupgeoms', cache)
yield from gen_rupture_getters(
dstore, concurrent_tasks=self.oqparam.concurrent_tasks or 1,
hdf5cache=hdf5cache)
if self.datastore.parent:
self.datastore.parent.close()
@cached_property
def eid2idx(self):
"""
:returns: a dict eid -> index in the events table
"""
return dict(zip(self.datastore['events']['id'], range(self.E)))
[docs] def agg_dicts(self, acc, result):
"""
:param acc: accumulator dictionary
:param result: an AccumDict with events, ruptures, gmfs and hcurves
"""
sav_mon = self.monitor('saving gmfs')
agg_mon = self.monitor('aggregating hcurves')
with sav_mon:
data = result.pop('gmfdata')
if len(data):
idxs = base.get_idxs(data, self.eid2idx) # this has to be fast
data['eid'] = idxs # replace eid with idx
self.datastore.extend('gmf_data/data', data)
sig_eps = result.pop('sig_eps')
sig_eps['eid'] = base.get_idxs(sig_eps, self.eid2idx)
self.datastore.extend('gmf_data/sigma_epsilon', sig_eps)
# it is important to save the number of bytes while the
# computation is going, to see the progress
update_nbytes(self.datastore, 'gmf_data/data', data)
for sid, start, stop in result['indices']:
self.indices[sid, 0].append(start + self.offset)
self.indices[sid, 1].append(stop + self.offset)
self.offset += len(data)
if self.offset >= TWO32:
raise RuntimeError(
'The gmf_data table has more than %d rows' % TWO32)
imtls = self.oqparam.imtls
with agg_mon:
for key, poes in result.get('hcurves', {}).items():
r, sid, imt = str2rsi(key)
array = acc[r].setdefault(sid, 0).array[imtls(imt), 0]
array[:] = 1. - (1. - array) * (1. - poes)
sav_mon.flush()
agg_mon.flush()
self.datastore.flush()
return acc
[docs] def save_events(self, rup_array):
"""
:param rup_array: an array of ruptures with fields grp_id
:returns: a list of RuptureGetters
"""
# this is very fast compared to saving the ruptures
eids = rupture.get_eids(
rup_array, self.samples_by_grp, self.num_rlzs_by_grp)
self.check_overflow() # check the number of events
events = numpy.zeros(len(eids), rupture.events_dt)
# when computing the events all ruptures must be considered,
# including the ones far away that will be discarded later on
rgetters = self.gen_rupture_getters()
# build the associations eid -> rlz in parallel
smap = parallel.Starmap(RuptureGetter.get_eid_rlz,
((rgetter,) for rgetter in rgetters),
self.monitor('get_eid_rlz'),
progress=logging.debug)
i = 0
for eid_rlz in smap: # 30 million of events associated in 1 minute!
for er in eid_rlz:
events[i] = er
i += 1
if i >= TWO32:
raise ValueError('There are more than %d events!' % i)
events.sort(order='id') # fast too
n_unique_events = len(numpy.unique(events['id']))
assert n_unique_events == len(events), (n_unique_events, len(events))
self.datastore['events'] = events
[docs] def check_overflow(self):
"""
Raise a ValueError if the number of sites is larger than 65,536 or the
number of IMTs is larger than 256 or the number of ruptures is larger
than 4,294,967,296. The limits are due to the numpy dtype used to
store the GMFs (gmv_dt). They could be relaxed in the future.
"""
oq = self.oqparam
max_ = dict(sites=TWO32, events=TWO32, imts=2**8)
num_ = dict(events=self.E, imts=len(self.oqparam.imtls))
if self.sitecol:
num_['sites'] = n = len(self.sitecol)
if (oq.calculation_mode == 'event_based'
and oq.ground_motion_fields and n > oq.max_sites_per_gmf):
raise ValueError(
'You cannot compute the GMFs for %d > %d sites' %
(n, oq.max_sites_per_gmf))
for var in num_:
if num_[var] > max_[var]:
raise ValueError(
'The %s calculator is restricted to %d %s, got %d' %
(oq.calculation_mode, max_[var], var, num_[var]))
[docs] def set_param(self, **kw):
oq = self.oqparam
# set the minimum_intensity
if hasattr(self, 'riskmodel') and not oq.minimum_intensity:
# infer it from the risk models if not directly set in job.ini
oq.minimum_intensity = self.riskmodel.min_iml
min_iml = oq.min_iml
if min_iml.sum() == 0:
logging.warning('The GMFs are not filtered: '
'you may want to set a minimum_intensity')
else:
logging.info('minimum_intensity=%s', oq.minimum_intensity)
self.param.update(
oqparam=oq,
gmf=oq.ground_motion_fields,
truncation_level=oq.truncation_level,
ruptures_per_block=oq.ruptures_per_block,
imtls=oq.imtls, filter_distance=oq.filter_distance,
ses_per_logic_tree_path=oq.ses_per_logic_tree_path, **kw)
[docs] def execute(self):
oq = self.oqparam
self.set_param()
self.offset = 0
self.indices = collections.defaultdict(list) # sid, idx -> indices
if oq.hazard_calculation_id and 'ruptures' in self.datastore:
# from ruptures
self.datastore.parent = util.read(oq.hazard_calculation_id)
self.init_logic_tree(self.csm_info)
else:
# from sources
self.build_events_from_sources()
if (oq.ground_motion_fields is False and
oq.hazard_curves_from_gmfs is False):
return {}
if not oq.imtls:
raise InvalidFile('There are no intensity measure types in %s' %
oq.inputs['job_ini'])
iterargs = ((rgetter, self.src_filter, self.param)
for rgetter in self.gen_rupture_getters())
# call compute_gmfs in parallel
acc = parallel.Starmap(
self.core_task.__func__, iterargs, self.monitor()
).reduce(self.agg_dicts, self.acc0())
if self.indices:
N = len(self.sitecol.complete)
logging.info('Saving gmf_data/indices')
with self.monitor('saving gmf_data/indices', measuremem=True,
autoflush=True):
self.datastore['gmf_data/imts'] = ' '.join(oq.imtls)
dset = self.datastore.create_dset(
'gmf_data/indices', hdf5.vuint32,
shape=(N, 2), fillvalue=None)
num_evs = self.datastore.create_dset(
'gmf_data/events_by_sid', U32, (N,))
for sid in self.sitecol.complete.sids:
start = numpy.array(self.indices[sid, 0])
stop = numpy.array(self.indices[sid, 1])
dset[sid, 0] = start
dset[sid, 1] = stop
num_evs[sid] = (stop - start).sum()
num_evs = num_evs[()]
avg_events_by_sid = num_evs.sum() / N
logging.info('Found ~%d GMVs per site', avg_events_by_sid)
self.datastore.set_attrs(
'gmf_data', avg_events_by_sid=avg_events_by_sid,
max_events_by_sid=num_evs.max())
elif oq.ground_motion_fields:
raise RuntimeError('No GMFs were generated, perhaps they were '
'all below the minimum_intensity threshold')
return acc
[docs] def post_execute(self, result):
oq = self.oqparam
if not oq.ground_motion_fields and not oq.hazard_curves_from_gmfs:
return
N = len(self.sitecol.complete)
L = len(oq.imtls.array)
if result and oq.hazard_curves_from_gmfs:
rlzs = self.rlzs_assoc.realizations
# compute and save statistics; this is done in process and can
# be very slow if there are thousands of realizations
weights = [rlz.weight for rlz in rlzs]
# NB: in the future we may want to save to individual hazard
# curves if oq.individual_curves is set; for the moment we
# save the statistical curves only
hstats = oq.hazard_stats()
S = len(hstats)
pmaps = list(result.values())
R = len(weights)
if len(pmaps) != R:
# this should never happen, unless I break the
# logic tree reduction mechanism during refactoring
raise AssertionError('Expected %d pmaps, got %d' %
(len(weights), len(pmaps)))
if oq.individual_curves:
logging.info('Saving individual hazard curves')
self.datastore.create_dset('hcurves-rlzs', F32, (N, R, L))
self.datastore.set_attrs('hcurves-rlzs', nbytes=N * R * L * 4)
if oq.poes:
P = len(oq.poes)
M = len(oq.imtls)
ds = self.datastore.create_dset(
'hmaps-rlzs', F32, (N, R, M, P))
self.datastore.set_attrs(
'hmaps-rlzs', nbytes=N * R * P * M * 4)
for r, pmap in enumerate(pmaps):
arr = numpy.zeros((N, L), F32)
for sid in pmap:
arr[sid] = pmap[sid].array[:, 0]
self.datastore['hcurves-rlzs'][:, r] = arr
if oq.poes:
hmap = calc.make_hmap(pmap, oq.imtls, oq.poes)
for sid in hmap:
ds[sid, r] = hmap[sid].array
if S:
logging.info('Computing statistical hazard curves')
self.datastore.create_dset('hcurves-stats', F32, (N, S, L))
self.datastore.set_attrs('hcurves-stats', nbytes=N * S * L * 4)
if oq.poes:
P = len(oq.poes)
M = len(oq.imtls)
ds = self.datastore.create_dset(
'hmaps-stats', F32, (N, S, M, P))
self.datastore.set_attrs(
'hmaps-stats', nbytes=N * S * P * M * 4)
for s, stat in enumerate(hstats):
pmap = compute_pmap_stats(
pmaps, [hstats[stat]], weights, oq.imtls)
arr = numpy.zeros((N, L), F32)
for sid in pmap:
arr[sid] = pmap[sid].array[:, 0]
self.datastore['hcurves-stats'][:, s] = arr
if oq.poes:
hmap = calc.make_hmap(pmap, oq.imtls, oq.poes)
for sid in hmap:
ds[sid, s] = hmap[sid].array
if self.datastore.parent:
self.datastore.parent.open('r')
if oq.compare_with_classical: # compute classical curves
export_dir = os.path.join(oq.export_dir, 'cl')
if not os.path.exists(export_dir):
os.makedirs(export_dir)
oq.export_dir = export_dir
job_id = logs.init('job')
oq.calculation_mode = 'classical'
self.cl = ClassicalCalculator(oq, job_id)
# TODO: perhaps it is possible to avoid reprocessing the source
# model, however usually this is quite fast and do not dominate
# the computation
self.cl.run(close=False)
engine.expose_outputs(self.cl.datastore)
cl_mean_curves = get_mean_curves(self.cl.datastore)
eb_mean_curves = get_mean_curves(self.datastore)
self.rdiff, index = util.max_rel_diff_index(
cl_mean_curves, eb_mean_curves)
logging.warning('Relative difference with the classical '
'mean curves: %d%% at site index %d',
self.rdiff * 100, index)