def make(self, src, sites, pmap, rup_data):
"""
:param src: a hazardlib source
:param sites: the sites affected by it
:returns: the probability map generated by the source
"""
with self.cmaker.mon('iter_ruptures', measuremem=False):
self.mag_rups = [(mag, list(rups))
for mag, rups in itertools.groupby(
src.iter_ruptures(shift_hypo=self.shift_hypo),
key=operator.attrgetter('mag'))]
rupdata = RupData(self.cmaker)
totrups, numrups, nsites = 0, 0, 0
L, G = len(self.imtls.array), len(self.gsims)
poemap = ProbabilityMap(L, G)
for rups, sites in self._gen_rups_sites(src, sites):
with self.ctx_mon:
ctxs = self.cmaker.make_ctxs(rups, sites)
if ctxs:
totrups += len(ctxs)
ctxs = self.collapse(ctxs)
numrups += len(ctxs)
for rup, r_sites, dctx in ctxs:
if self.fewsites: # store rupdata
rupdata.add(rup, r_sites, dctx)
sids, poes = self._sids_poes(rup, r_sites, dctx, src.id)
with self.pne_mon:
pnes = rup.get_probability_no_exceedance(poes)
if self.rup_indep:
for sid, pne in zip(sids, pnes):
poemap.setdefault(sid, self.rup_indep).array *= pne
else:
for sid, pne in zip(sids, pnes):
poemap.setdefault(
sid,
self.rup_indep).array += (1. -
pne) * rup.weight
nsites += len(sids)
poemap.totrups = totrups
poemap.numrups = numrups
poemap.nsites = nsites
self._update(pmap, poemap, src)
if len(rupdata.data):
for gid in src.src_group_ids:
rup_data['grp_id'].extend([gid] * numrups)
for k, v in rupdata.data.items():
rup_data[k].extend(v)
return poemap
def get_pmap(self, src, s_sites, rup_indep=True):
"""
:param src: a hazardlib source
:param s_sites: the sites affected by it
:returns: the probability map generated by the source
"""
imts = self.imts
sitecol = s_sites.complete
N, M = len(sitecol), len(imts)
fewsites = N <= self.max_sites_disagg
rupdata = RupData(self)
nrups, nsites = 0, 0
L, G = len(self.imtls.array), len(self.gsims)
poemap = ProbabilityMap(L, G)
for rup, sites in self._gen_rup_sites(src, s_sites):
try:
with self.ctx_mon:
r_sites, dctx = self.make_contexts(sites, rup)
except FarAwayRupture:
continue
with self.gmf_mon:
mean_std = numpy.zeros((2, len(r_sites), M, G))
for g, gsim in enumerate(self.gsims):
dctx_ = dctx.roundup(gsim.minimum_distance)
mean_std[:, :, :,
g] = gsim.get_mean_std(r_sites, rup, dctx_, imts)
with self.poe_mon:
pairs = zip(r_sites.sids, self._make_pnes(rup, mean_std))
with self.pne_mon:
if rup_indep:
for sid, pne in pairs:
poemap.setdefault(sid, rup_indep).array *= pne
else:
for sid, pne in pairs:
poemap.setdefault(
sid, rup_indep).array += (1. - pne) * rup.weight
nrups += 1
nsites += len(r_sites)
if fewsites: # store rupdata
rupdata.add(rup, src.id, r_sites, dctx)
poemap.nrups = nrups
poemap.nsites = nsites
poemap.data = rupdata.data
return poemap
def get_pmap(self, src, s_sites, rup_indep=True):
"""
:param src: a hazardlib source
:param s_sites: the sites affected by it
:returns: the probability map generated by the source
"""
imts = self.imts
fewsites = len(s_sites.complete) <= self.max_sites_disagg
rupdata = RupData(self)
nrups, nsites = 0, 0
L, G = len(self.imtls.array), len(self.gsims)
poemap = ProbabilityMap(L, G)
dists = []
for rup, sites, maxdist in self._gen_rup_sites(src, s_sites):
if maxdist is not None:
dists.append(maxdist)
try:
with self.ctx_mon:
r_sites, dctx = self.make_contexts(sites, rup, maxdist)
except FarAwayRupture:
continue
with self.gmf_mon:
mean_std = base.get_mean_std( # shape (2, N, M, G)
r_sites, rup, dctx, imts, self.gsims)
with self.poe_mon:
pairs = zip(r_sites.sids, self._make_pnes(rup, mean_std))
with self.pne_mon:
if rup_indep:
for sid, pne in pairs:
poemap.setdefault(sid, rup_indep).array *= pne
else:
for sid, pne in pairs:
poemap.setdefault(sid, rup_indep).array += (
1.-pne) * rup.weight
nrups += 1
nsites += len(r_sites)
if fewsites: # store rupdata
rupdata.add(rup, src.id, r_sites, dctx)
poemap.nrups = nrups
poemap.nsites = nsites
poemap.maxdist = numpy.mean(dists) if dists else None
poemap.data = rupdata.data
return poemap
def make(self):
"""
:param src: a hazardlib source
:param s_sites: the sites affected by it
:returns: the probability map generated by the source
"""
totrups, numrups, nsites = 0, 0, 0
L, G = len(self.imtls.array), len(self.gsims)
poemap = ProbabilityMap(L, G)
dists = []
for rups, sites, mdist in self._gen_rups_sites():
if mdist is not None:
dists.append(mdist)
with self.ctx_mon:
ctxs = self.cmaker.make_ctxs(rups, sites, mdist)
if ctxs:
totrups += len(ctxs)
ctxs = self.collapse(ctxs)
numrups += len(ctxs)
for rup, r_sites, dctx in ctxs:
sids, poes = self._sids_poes(rup, r_sites, dctx)
with self.pne_mon:
pnes = rup.get_probability_no_exceedance(poes)
if self.rup_indep:
for sid, pne in zip(sids, pnes):
poemap.setdefault(sid, self.rup_indep).array *= pne
else:
for sid, pne in zip(sids, pnes):
poemap.setdefault(
sid,
self.rup_indep).array += (1. -
pne) * rup.weight
nsites += len(sids)
poemap.totrups = totrups
poemap.numrups = numrups
poemap.nsites = nsites
poemap.maxdist = numpy.mean(dists) if dists else None
poemap.data = self.rupdata.data
return poemap
