# -*- 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
import numpy
import pandas
from openquake.baselib import hdf5, parallel
from openquake.baselib.general import AccumDict, copyobj, humansize
from openquake.hazardlib.probability_map import ProbabilityMap
from openquake.hazardlib.stats import geom_avg_std, compute_stats
from openquake.hazardlib.calc.stochastic import sample_ruptures
from openquake.hazardlib.gsim.base import ContextMaker, FarAwayRupture
from openquake.hazardlib.calc.filters import nofilter, getdefault, SourceFilter
from openquake.hazardlib.calc.gmf import GmfComputer
from openquake.hazardlib.shakemap.conditioned_gmfs import ConditionedGmfComputer
from openquake.hazardlib import InvalidFile
from openquake.hazardlib.calc.stochastic import get_rup_array, rupture_dt
from openquake.hazardlib.site import SiteCollection
from openquake.hazardlib.source.rupture import (
RuptureProxy, EBRupture, get_ruptures)
from openquake.commonlib import (
calc, util, logs, readinput, logictree, datastore)
from openquake.risklib.riskinput import str2rsi, rsi2str
from openquake.commonlib.calc import get_mean_curve
from openquake.calculators import base, views
from openquake.calculators.getters import (
get_rupture_getters, sig_eps_dt, time_dt)
from openquake.calculators.classical import ClassicalCalculator
from openquake.engine import engine
U8 = numpy.uint8
U16 = numpy.uint16
U32 = numpy.uint32
F32 = numpy.float32
F64 = numpy.float64
TWO32 = numpy.float64(2 ** 32)
# ######################## GMF calculator ############################ #
[docs]def count_ruptures(src):
"""
Count the number of ruptures on a heavy source
"""
return {src.source_id: src.count_ruptures()}
[docs]def strip_zeros(gmf_df):
# remove the rows with all zero values
df = gmf_df[gmf_df.columns[3:]] # strip eid, sid, rlz
ok = df.to_numpy().sum(axis=1) > 0
return gmf_df[ok]
[docs]def event_based(proxies, full_lt, oqparam, dstore, monitor):
"""
Compute GMFs and optionally hazard curves
"""
alldata = AccumDict(accum=[])
sig_eps = []
times = [] # rup_id, nsites, dt
hcurves = {} # key -> poes
trt_smr = proxies[0]['trt_smr']
fmon = monitor('filtering ruptures', measuremem=False)
cmon = monitor('computing gmfs', measuremem=False)
with dstore:
trt = full_lt.trts[trt_smr // len(full_lt.sm_rlzs)]
sitecol = dstore['sitecol']
extra = sitecol.array.dtype.names
srcfilter = SourceFilter(
sitecol, oqparam.maximum_distance(trt))
rupgeoms = dstore['rupgeoms']
rlzs_by_gsim = full_lt._rlzs_by_gsim(trt_smr)
cmaker = ContextMaker(trt, rlzs_by_gsim, oqparam, extraparams=extra)
cmaker.min_mag = getdefault(oqparam.minimum_magnitude, trt)
for proxy in proxies:
t0 = time.time()
with fmon:
if proxy['mag'] < cmaker.min_mag:
continue
sids = srcfilter.close_sids(proxy, trt)
if len(sids) == 0: # filtered away
continue
proxy.geom = rupgeoms[proxy['geom_id']]
ebr = proxy.to_ebr(cmaker.trt) # after the geometry is set
if "station_data" in oqparam.inputs:
station_sites = dstore.read_df('station_sites')
station_data = dstore.read_df('station_data')
station_sites = SiteCollection.from_points(
lons=station_sites.lon.values,
lats=station_sites.lat.values)
station_sitemodel = station_sites.assoc(
sitecol, assoc_dist=None)
station_sitecol = SiteCollection.from_points(
lons=station_sites.lon,
lats=station_sites.lat,
sitemodel=station_sitemodel)
stnfilter = SourceFilter(
station_sitecol, oqparam.maximum_distance(trt))
stnids = stnfilter.close_sids(proxy, trt)
if len(stnids) < len(station_sites):
logging.warning('%d stations filtered away',
len(station_sites) - len(stnids))
if len(stnids) == 0: # all stations filtered away
continue
try:
computer = ConditionedGmfComputer(
ebr, srcfilter.sitecol.filtered(sids),
stnfilter.sitecol.filtered(stnids),
station_data.loc[stnids], oqparam.observed_imts,
cmaker, oqparam.correl_model, oqparam.cross_correl,
oqparam.ground_motion_correlation_params,
oqparam.number_of_ground_motion_fields,
oqparam._amplifier, oqparam._sec_perils)
except FarAwayRupture:
# skip this rupture
continue
else:
try:
computer = GmfComputer(
ebr, srcfilter.sitecol.filtered(sids), cmaker,
oqparam.correl_model, oqparam.cross_correl,
oqparam._amplifier, oqparam._sec_perils)
except FarAwayRupture:
# skip this rupture
continue
with cmon:
data = computer.compute_all(sig_eps)
dt = time.time() - t0
times.append(
(computer.ebrupture.id, len(computer.ctx.sids), dt))
for key in data:
alldata[key].extend(data[key])
for key, val in sorted(alldata.items()):
if key in 'eid sid rlz':
alldata[key] = U32(alldata[key])
else:
alldata[key] = F32(alldata[key])
gmfdata = strip_zeros(pandas.DataFrame(alldata))
if len(gmfdata) and oqparam.hazard_curves_from_gmfs:
hc_mon = monitor('building hazard curves', measuremem=False)
for (sid, rlz), df in gmfdata.groupby(['sid', 'rlz']):
with hc_mon:
poes = calc.gmvs_to_poes(
df, oqparam.imtls, oqparam.ses_per_logic_tree_path)
for m, imt in enumerate(oqparam.imtls):
hcurves[rsi2str(rlz, sid, imt)] = poes[m]
times = numpy.array([tup + (monitor.task_no,) for tup in times], time_dt)
times.sort(order='rup_id')
if not oqparam.ground_motion_fields:
gmfdata = ()
return dict(gmfdata=gmfdata, hcurves=hcurves, times=times,
sig_eps=numpy.array(sig_eps, sig_eps_dt(oqparam.imtls)))
[docs]def compute_avg_gmf(gmf_df, weights, min_iml):
"""
:param gmf_df: a DataFrame with colums eid, sid, rlz, gmv...
:param weights: E weights associated to the realizations
:param min_iml: array of M minimum intensities
:returns: a dictionary site_id -> array of shape (2, M)
"""
dic = {}
E = len(weights)
M = len(min_iml)
for sid, df in gmf_df.groupby(gmf_df.index):
eid = df.pop('eid')
gmvs = numpy.ones((E, M), F32) * min_iml
gmvs[eid.to_numpy()] = df.to_numpy()
dic[sid] = geom_avg_std(gmvs, weights)
return dic
[docs]@base.calculators.add('event_based', 'scenario', 'ucerf_hazard')
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 = event_based
is_stochastic = True
accept_precalc = ['event_based', 'ebrisk', 'event_based_risk']
[docs] def init(self):
if self.oqparam.cross_correl.__class__.__name__ == 'GodaAtkinson2009':
logging.warning(
'The truncation_level param is ignored with GodaAtkinson2009')
if hasattr(self, 'csm'):
self.check_floating_spinning()
if hasattr(self.oqparam, 'maximum_distance'):
self.srcfilter = self.src_filter()
else:
self.srcfilter = nofilter
if not self.datastore.parent:
self.datastore.create_dset('ruptures', rupture_dt)
self.datastore.create_dset('rupgeoms', hdf5.vfloat32)
[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.full_lt.get_gsims_by_trt()
sources = self.csm.get_sources()
# weighting the heavy sources
nrups = parallel.Starmap(
count_ruptures, [(src,) for src in sources if src.code in b'AMC'],
progress=logging.debug
).reduce()
for src in sources:
try:
src.num_ruptures = nrups[src.source_id]
except KeyError:
src.num_ruptures = src.count_ruptures()
src.weight = src.num_ruptures
maxweight = sum(sg.weight for sg in self.csm.src_groups) / (
self.oqparam.concurrent_tasks or 1)
eff_ruptures = AccumDict(accum=0) # grp_id => potential ruptures
source_data = AccumDict(accum=[])
allargs = []
srcfilter = self.srcfilter
logging.info('Building ruptures')
for sg in self.csm.src_groups:
if not sg.sources:
continue
logging.info('Sending %s', sg)
cmaker = ContextMaker(sg.trt, gsims_by_trt[sg.trt], oq)
for src_group in sg.split(maxweight):
allargs.append((src_group, cmaker, srcfilter.sitecol))
self.datastore.swmr_on()
smap = parallel.Starmap(
sample_ruptures, allargs, h5=self.datastore.hdf5)
mon = self.monitor('saving ruptures')
self.nruptures = 0 # estimated classical ruptures within maxdist
for dic in smap:
# NB: dic should be a dictionary, but when the calculation dies
# for an OOM it can become None, thus giving a very confusing error
if dic is None:
raise MemoryError('You ran out of memory!')
rup_array = dic['rup_array']
if len(rup_array) == 0:
continue
if dic['source_data']:
source_data += dic['source_data']
if dic['eff_ruptures']:
eff_ruptures += dic['eff_ruptures']
with mon:
n = len(rup_array)
rup_array['id'] = numpy.arange(
self.nruptures, self.nruptures + n)
self.nruptures += n
hdf5.extend(self.datastore['ruptures'], rup_array)
hdf5.extend(self.datastore['rupgeoms'], rup_array.geom)
if len(self.datastore['ruptures']) == 0:
raise RuntimeError('No ruptures were generated, perhaps the '
'investigation time is too short')
# don't change the order of the 3 things below!
self.store_source_info(source_data)
self.store_rlz_info(eff_ruptures)
imp = calc.RuptureImporter(self.datastore)
with self.monitor('saving ruptures and events'):
imp.import_rups_events(
self.datastore.getitem('ruptures')[()], get_rupture_getters)
[docs] def agg_dicts(self, acc, result):
"""
:param acc: accumulator dictionary
:param result: an AccumDict with events, ruptures, gmfs and hcurves
"""
if result is None: # instead of a dict
raise MemoryError('You ran out of memory!')
sav_mon = self.monitor('saving gmfs')
agg_mon = self.monitor('aggregating hcurves')
primary = self.oqparam.get_primary_imtls()
sec_imts = self.oqparam.get_sec_imts()
with sav_mon:
df = result.pop('gmfdata')
if len(df):
dset = self.datastore['gmf_data/sid']
times = result.pop('times')
[task_no] = numpy.unique(times['task_no'])
rupids = list(times['rup_id'])
self.datastore['gmf_data/time_by_rup'][rupids] = times
if self.N >= calc.SLICE_BY_EVENT_NSITES:
sbe = calc.build_slice_by_event(
df.eid.to_numpy(), self.offset)
hdf5.extend(self.datastore['gmf_data/slice_by_event'], sbe)
hdf5.extend(dset, df.sid.to_numpy())
hdf5.extend(self.datastore['gmf_data/eid'], df.eid.to_numpy())
for m in range(len(primary)):
hdf5.extend(self.datastore[f'gmf_data/gmv_{m}'],
df[f'gmv_{m}'])
for sec_imt in sec_imts:
hdf5.extend(self.datastore[f'gmf_data/{sec_imt}'],
df[sec_imt])
sig_eps = result.pop('sig_eps')
hdf5.extend(self.datastore['gmf_data/sigma_epsilon'], sig_eps)
self.offset += len(df)
imtls = self.oqparam.imtls
with agg_mon:
for key, poes in result.get('hcurves', {}).items():
r, sid, imt = str2rsi(key)
array = acc[r].array[sid, imtls(imt), 0]
array[:] = 1. - (1. - array) * (1. - poes)
self.datastore.flush()
return acc
def _read_scenario_ruptures(self):
oq = self.oqparam
gsim_lt = readinput.get_gsim_lt(self.oqparam)
G = gsim_lt.get_num_paths()
if oq.calculation_mode.startswith('scenario'):
ngmfs = oq.number_of_ground_motion_fields
if oq.inputs['rupture_model'].endswith('.xml'):
# check the number of branchsets
bsets = len(gsim_lt._ltnode)
if bsets > 1:
raise InvalidFile(
'%s for a scenario calculation must contain a single '
'branchset, found %d!' % (oq.inputs['job_ini'], bsets))
[(trt, rlzs_by_gsim)] = gsim_lt.get_rlzs_by_gsim_trt().items()
rup = readinput.get_rupture(oq)
oq.mags_by_trt = {trt: ['%.2f' % rup.mag]}
self.cmaker = ContextMaker(trt, rlzs_by_gsim, oq)
if self.N > oq.max_sites_disagg: # many sites, split rupture
ebrs = [EBRupture(copyobj(rup, seed=rup.seed + i),
'NA', 0, G, e0=i * G, scenario=True)
for i in range(ngmfs)]
else: # keep a single rupture with a big occupation number
ebrs = [EBRupture(rup, 'NA', 0, G * ngmfs, rup.seed,
scenario=True)]
srcfilter = SourceFilter(self.sitecol, oq.maximum_distance(trt))
aw = get_rup_array(ebrs, srcfilter)
if len(aw) == 0:
raise RuntimeError(
'The rupture is too far from the sites! Please check the '
'maximum_distance and the position of the rupture')
elif oq.inputs['rupture_model'].endswith('.csv'):
aw = get_ruptures(oq.inputs['rupture_model'])
if len(gsim_lt.values) == 1: # fix for scenario_damage/case_12
aw['trt_smr'] = 0 # a single TRT
if oq.calculation_mode.startswith('scenario'):
# rescale n_occ by ngmfs and nrlzs
aw['n_occ'] *= ngmfs * gsim_lt.get_num_paths()
else:
raise InvalidFile("Something wrong in %s" % oq.inputs['job_ini'])
rup_array = aw.array
hdf5.extend(self.datastore['rupgeoms'], aw.geom)
if len(rup_array) == 0:
raise RuntimeError(
'There are no sites within the maximum_distance'
' of %s km from the rupture' % oq.maximum_distance(
rup.tectonic_region_type)(rup.mag))
fake = logictree.FullLogicTree.fake(gsim_lt)
self.realizations = fake.get_realizations()
self.datastore['full_lt'] = fake
self.store_rlz_info({}) # store weights
self.save_params()
imp = calc.RuptureImporter(self.datastore)
imp.import_rups_events(rup_array, get_rupture_getters)
[docs] def execute(self):
oq = self.oqparam
dstore = self.datastore
if oq.ground_motion_fields and oq.min_iml.sum() == 0:
logging.warning('The GMFs are not filtered: '
'you may want to set a minimum_intensity')
elif oq.minimum_intensity:
logging.info('minimum_intensity=%s', oq.minimum_intensity)
else:
logging.info('min_iml=%s', oq.min_iml)
self.offset = 0
if oq.hazard_calculation_id: # from ruptures
dstore.parent = datastore.read(oq.hazard_calculation_id)
elif hasattr(self, 'csm'): # from sources
self.build_events_from_sources()
if (oq.ground_motion_fields is False and
oq.hazard_curves_from_gmfs is False):
return {}
elif 'rupture_model' not in oq.inputs:
logging.warning(
'There is no rupture_model, the calculator will just '
'import data without performing any calculation')
fake = logictree.FullLogicTree.fake()
dstore['full_lt'] = fake # needed to expose the outputs
dstore['weights'] = [1.]
return {}
else: # scenario
self._read_scenario_ruptures()
if (oq.ground_motion_fields is False and
oq.hazard_curves_from_gmfs is False):
return {}
if oq.ground_motion_fields:
imts = oq.get_primary_imtls()
nrups = len(dstore['ruptures'])
base.create_gmf_data(dstore, imts, oq.get_sec_imts())
dstore.create_dset('gmf_data/sigma_epsilon', sig_eps_dt(oq.imtls))
dstore.create_dset('gmf_data/time_by_rup',
time_dt, (nrups,), fillvalue=None)
if self.N >= calc.SLICE_BY_EVENT_NSITES:
dstore.create_dset('gmf_data/slice_by_event', calc.slice_dt)
# event_based in parallel
nr = len(dstore['ruptures'])
logging.info('Reading {:_d} ruptures'.format(nr))
scenario = 'scenario' in oq.calculation_mode
proxies = [RuptureProxy(rec, scenario)
for rec in dstore['ruptures'][:]]
if "station_data" in oq.inputs:
# this is meant to be used in conditioned scenario calculations with
# a single rupture; we are taking the first copy of the rupture
# (remember: _read_scenario_ruptures makes num_gmfs copies to
# parallelize, but the conditioning process is computationally
# expensive, so we want to avoid repeating it num_gmfs times)
# TODO: this is ugly and must be improved upon!
proxies = proxies[0:1]
full_lt = self.datastore['full_lt']
dstore.swmr_on() # must come before the Starmap
smap = parallel.Starmap.apply_split(
self.core_task.__func__, (proxies, full_lt, oq, self.datastore),
key=operator.itemgetter('trt_smr'),
weight=operator.itemgetter('n_occ'),
h5=dstore.hdf5,
concurrent_tasks=oq.concurrent_tasks or 1,
duration=oq.time_per_task,
outs_per_task=oq.outs_per_task)
if oq.hazard_curves_from_gmfs:
self.L = oq.imtls.size
acc0 = {r: ProbabilityMap(self.sitecol.sids, self.L, 1).fill(0)
for r in range(self.R)}
else:
acc0 = {}
acc = smap.reduce(self.agg_dicts, acc0)
if 'gmf_data' not in dstore:
return acc
if oq.ground_motion_fields:
with self.monitor('saving avg_gmf', measuremem=True):
self.save_avg_gmf()
return acc
[docs] def save_avg_gmf(self):
"""
Compute and save avg_gmf, unless there are too many GMFs
"""
size = self.datastore.getsize('gmf_data')
maxsize = self.oqparam.gmf_max_gb * 1024 ** 3
logging.info(f'Stored {humansize(size)} of GMFs')
if size > maxsize:
logging.warning(
f'There are more than {humansize(maxsize)} of GMFs,'
' not computing avg_gmf')
return numpy.unique(self.datastore['gmf_data/eid'][:])
rlzs = self.datastore['events']['rlz_id']
self.weights = self.datastore['weights'][:][rlzs]
gmf_df = self.datastore.read_df('gmf_data', 'sid')
for sec_imt in self.oqparam.get_sec_imts(): # ignore secondary perils
del gmf_df[sec_imt]
rel_events = gmf_df.eid.unique()
e = len(rel_events)
if e == 0:
raise RuntimeError(
'No GMFs were generated, perhaps they were '
'all below the minimum_intensity threshold')
elif e < len(self.datastore['events']):
self.datastore['relevant_events'] = rel_events
logging.info('Stored {:_d} relevant event IDs'.format(e))
# really compute and store the avg_gmf
M = len(self.oqparam.min_iml)
avg_gmf = numpy.zeros((2, self.N, M), F32)
for sid, avgstd in compute_avg_gmf(
gmf_df, self.weights, self.oqparam.min_iml).items():
avg_gmf[:, sid] = avgstd
self.datastore['avg_gmf'] = avg_gmf
return rel_events
[docs] def post_execute(self, pmap_by_rlz):
oq = self.oqparam
if (not pmap_by_rlz or not oq.ground_motion_fields and not
oq.hazard_curves_from_gmfs):
return
N = len(self.sitecol.complete)
M = len(oq.imtls) # 0 in scenario
L = oq.imtls.size
L1 = L // (M or 1)
# check seed dependency unless the number of GMFs is huge
if 'gmf_data' in self.datastore and self.datastore.getsize(
'gmf_data/gmv_0') < 4E9:
logging.info('Checking stored GMFs')
msg = views.view('extreme_gmvs', self.datastore)
logging.warning(msg)
if oq.hazard_curves_from_gmfs:
rlzs = self.datastore['full_lt'].get_realizations()
# compute and save statistics; this is done in process and can
# be very slow if there are thousands of realizations
weights = [rlz.weight['weight'] for rlz in rlzs]
# NB: in the future we may want to save to individual hazard
# curves if oq.individual_rlzs is set; for the moment we
# save the statistical curves only
hstats = oq.hazard_stats()
S = len(hstats)
R = len(weights)
pmaps = [p.reshape(N, M, L1) for p in pmap_by_rlz.values()]
if oq.individual_rlzs:
logging.info('Saving individual hazard curves')
self.datastore.create_dset('hcurves-rlzs', F32, (N, R, M, L1))
self.datastore.set_shape_descr(
'hcurves-rlzs', site_id=N, rlz_id=R,
imt=list(oq.imtls), lvl=numpy.arange(L1))
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_shape_descr(
'hmaps-rlzs', site_id=N, rlz_id=R,
imt=list(oq.imtls), poe=oq.poes)
for r in range(R):
self.datastore['hcurves-rlzs'][:, r] = pmaps[r].array
if oq.poes:
[hmap] = calc.make_hmaps([pmaps[r]], oq.imtls, oq.poes)
ds[:, r] = hmap.array
if S:
logging.info('Computing statistical hazard curves')
self.datastore.create_dset('hcurves-stats', F32, (N, S, M, L1))
self.datastore.set_shape_descr(
'hcurves-stats', site_id=N, stat=list(hstats),
imt=list(oq.imtls), lvl=numpy.arange(L1))
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_shape_descr(
'hmaps-stats', site_id=N, stat=list(hstats),
imt=list(oq.imtls), poes=oq.poes)
for s, stat in enumerate(hstats):
smap = ProbabilityMap(self.sitecol.sids, L1, M)
[smap.array] = compute_stats(
numpy.array([p.array for p in pmaps]),
[hstats[stat]], weights)
self.datastore['hcurves-stats'][:, s] = smap.array
if oq.poes:
[hmap] = calc.make_hmaps([smap], oq.imtls, oq.poes)
ds[:, s] = hmap.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
oq.calculation_mode = 'classical'
with logs.init('job', vars(oq)) as log:
self.cl = ClassicalCalculator(oq, log.calc_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()
engine.expose_outputs(self.cl.datastore)
all = slice(None)
for imt in oq.imtls:
cl_mean_curves = get_mean_curve(self.datastore, imt, all)
eb_mean_curves = get_mean_curve(self.datastore, imt, all)
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, imt=%s',
self.rdiff * 100, index, imt)