def compute_hazard(sources, src_filter, rlzs_by_gsim, param, monitor):
"""
:param sources: a list with a single UCERF source
:param src_filter: a SourceFilter instance
:param rlzs_by_gsim: a dictionary gsim -> rlzs
:param param: extra parameters
:param monitor: a Monitor instance
:returns: an AccumDict grp_id -> EBRuptures
"""
[src] = sources
res = AccumDict()
res.calc_times = []
serial = 1
sampl_mon = monitor('sampling ruptures', measuremem=True)
filt_mon = monitor('filtering ruptures', measuremem=False)
res.trt = DEFAULT_TRT
ebruptures = []
background_sids = src.get_background_sids(src_filter)
sitecol = src_filter.sitecol
cmaker = ContextMaker(rlzs_by_gsim, src_filter.integration_distance)
for sample in range(param['samples']):
for ses_idx, ses_seed in param['ses_seeds']:
seed = sample * TWO16 + ses_seed
with sampl_mon:
rups, n_occs = generate_event_set(src, background_sids,
src_filter, seed)
with filt_mon:
for rup, n_occ in zip(rups, n_occs):
rup.serial = serial
try:
rup.sctx, rup.dctx = cmaker.make_contexts(sitecol, rup)
indices = rup.sctx.sids
except FarAwayRupture:
continue
events = []
for _ in range(n_occ):
events.append((0, src.src_group_id, ses_idx, sample))
if events:
evs = numpy.array(events, stochastic.event_dt)
ebruptures.append(EBRupture(rup, src.id, indices, evs))
serial += 1
res.num_events = len(stochastic.set_eids(ebruptures))
res['ruptures'] = {src.src_group_id: ebruptures}
if param['save_ruptures']:
res.ruptures_by_grp = {src.src_group_id: ebruptures}
else:
res.events_by_grp = {
src.src_group_id: event_based.get_events(ebruptures)
}
res.eff_ruptures = {src.src_group_id: src.num_ruptures}
if param.get('gmf'):
getter = getters.GmfGetter(rlzs_by_gsim, ebruptures, sitecol,
param['oqparam'], param['min_iml'],
param['samples'])
res.update(getter.compute_gmfs_curves(monitor))
return res
def compute_ruptures(sources, src_filter, gsims, param, monitor):
"""
:param sources: a list with a single UCERF source
:param src_filter: a SourceFilter instance
:param gsims: a list of GSIMs
:param param: extra parameters
:param monitor: a Monitor instance
:returns: an AccumDict grp_id -> EBRuptures
"""
[src] = sources
res = AccumDict()
res.calc_times = []
serial = 1
sampl_mon = monitor('sampling ruptures', measuremem=True)
filt_mon = monitor('filtering ruptures', measuremem=False)
res.trt = DEFAULT_TRT
ebruptures = []
background_sids = src.get_background_sids(src_filter)
sitecol = src_filter.sitecol
cmaker = ContextMaker(gsims, src_filter.integration_distance)
for sample in range(param['samples']):
for ses_idx, ses_seed in param['ses_seeds']:
seed = sample * TWO16 + ses_seed
with sampl_mon:
rups, n_occs = generate_event_set(src, background_sids,
src_filter, seed)
with filt_mon:
for rup, n_occ in zip(rups, n_occs):
rup.serial = serial
rup.seed = seed
try:
rup.sctx, rup.dctx = cmaker.make_contexts(sitecol, rup)
indices = rup.sctx.sids
except FarAwayRupture:
continue
events = []
for _ in range(n_occ):
events.append((0, src.src_group_id, ses_idx, sample))
if events:
evs = numpy.array(events, stochastic.event_dt)
ebruptures.append(EBRupture(rup, indices, evs))
serial += 1
res.num_events = len(stochastic.set_eids(ebruptures))
res[src.src_group_id] = ebruptures
if not param['save_ruptures']:
res.events_by_grp = {
grp_id: event_based.get_events(res[grp_id])
for grp_id in res
}
res.eff_ruptures = {src.src_group_id: src.num_ruptures}
return res
def pt_src_are(self, pt_src, gsim, weight, lnSA, monitor):
"""
Returns the vector-valued Annual Rate of Exceedance for one single point-source
:param pt_src: single instance of class "openquake.hazardlib.source.area.PointSource"
:param gsim: tuple, containing (only one?) instance of Openquake GSIM class
:param: weight, weight to be multiplied to ARE estimate
:param lnSA: list, natural logarithm of acceleration values for each spectral period.
Note : Values should be ordered in the same order than self.periods
"""
annual_rate = 0
# Loop over ruptures:
# i.e. one rupture for each combination of (mag, nodal plane, hypocentral depth):
for r in pt_src.iter_ruptures():
# NOTE: IF ACCOUNTING FOR "pointsource_distance" IN THE INI FILE, ONE SHOULD USE THE
# "point_ruptures()" METHOD BELOW:
# Loop over ruptures, one rupture for each magnitude ( neglect floating and combination on
# nodal plane and hypocentral depth):
## for r in pt_src.point_ruptures():
# Note: Seismicity rate evenly distributed over all point sources
# Seismicity rate also accounts for FMD (i.e. decreasing for
# increasing magnitude value)
# Filter the site collection with respect to the rupture and prepare context objects:
context_maker = ContextMaker(r.tectonic_region_type, gsim)
site_ctx, dist_ctx = context_maker.make_contexts(self.sites, r)
rup_ctx = RuptureContext()
rup_ctx.mag = r.mag
rup_ctx.rake = r.rake
assert len(gsim)==1
annual_rate += r.occurrence_rate * weight * self.gm_poe(gsim[0],
dist_ctx,
rup_ctx,
site_ctx,
lnSA)
return annual_rate
