def _update_pmap(self, ctxs, pmap=None):
# compute PoEs and update pmap
if pmap is None: # for src_indep
pmap = self.pmap
for rup, r_sites, dctx in ctxs:
# this must be fast since it is inside an inner loop
with self.gmf_mon:
mean_std = base.get_mean_std( # shape (2, N, M, G)
r_sites, rup, dctx, self.imts, self.gsims)
with self.poe_mon:
ll = self.loglevels
poes = base.get_poes(mean_std, ll, self.trunclevel, self.gsims)
for g, gsim in enumerate(self.gsims):
for m, imt in enumerate(ll):
if hasattr(gsim, 'weight') and gsim.weight[imt] == 0:
# set by the engine when parsing the gsim logictree
# when 0 ignore the gsim: see _build_trts_branches
poes[:, ll(imt), g] = 0
with self.pne_mon:
# pnes and poes of shape (N, L, G)
pnes = rup.get_probability_no_exceedance(poes)
for grp_id in rup.grp_ids:
p = pmap[grp_id]
if self.rup_indep:
for sid, pne in zip(r_sites.sids, pnes):
p.setdefault(sid, 1.).array *= pne
else: # rup_mutex
for sid, pne in zip(r_sites.sids, pnes):
p.setdefault(sid,
0.).array += (1. - pne) * rup.weight
def _update_pmap(self, ctxs, pmap=None):
# compute PoEs and update pmap
if pmap is None: # for src_indep
pmap = self.pmap
rup_indep = self.rup_indep
for ctx in ctxs:
# this must be fast since it is inside an inner loop
with self.gmf_mon:
# shape (2, N, M, G)
mean_std = ctx.get_mean_std(self.imts, self.gsims)
with self.poe_mon:
ll = self.loglevels
af = self.cmaker.af
if af:
[sitecode] = ctx.sites['ampcode'] # single-site only
else:
sitecode = None
poes = base.get_poes(mean_std, ll, self.trunclevel, self.gsims,
af, ctx.mag, sitecode, ctx.rrup)
for g, gsim in enumerate(self.gsims):
for m, imt in enumerate(ll):
if hasattr(gsim, 'weight') and gsim.weight[imt] == 0:
# set by the engine when parsing the gsim logictree
# when 0 ignore the gsim: see _build_trts_branches
poes[:, ll(imt), g] = 0
with self.pne_mon:
# pnes and poes of shape (N, L, G)
pnes = ctx.get_probability_no_exceedance(poes)
for sid, pne in zip(ctx.sids, pnes):
probs = pmap.setdefault(sid, rup_indep).array
if rup_indep:
probs *= pne
else: # rup_mutex
probs += (1. - pne) * ctx.weight
def _make_pnes(self, rupture, mean_std):
ll = self.loglevels
poes = base.get_poes(mean_std, ll, self.trunclevel, self.gsims)
for g, gsim in enumerate(self.gsims):
for m, imt in enumerate(ll):
if hasattr(gsim, 'weight') and gsim.weight[imt] == 0:
# set by the engine when parsing the gsim logictree;
# when 0 ignore the gsim: see _build_trts_branches
poes[:, ll(imt), g] = 0
return rupture.get_probability_no_exceedance(poes)
def _sids_poes(self, rup, r_sites, dctx, srcid):
# return sids and poes of shape (N, L, G)
# NB: this must be fast since it is inside an inner loop
with self.gmf_mon:
mean_std = base.get_mean_std( # shape (2, N, M, G)
r_sites, rup, dctx, self.imts, self.gsims)
with self.poe_mon:
ll = self.loglevels
poes = base.get_poes(mean_std, ll, self.trunclevel, self.gsims)
for g, gsim in enumerate(self.gsims):
for m, imt in enumerate(ll):
if hasattr(gsim, 'weight') and gsim.weight[imt] == 0:
# set by the engine when parsing the gsim logictree;
# when 0 ignore the gsim: see _build_trts_branches
poes[:, ll(imt), g] = 0
return r_sites.sids, poes
def make_pmap(ctxs, gsims, imtls, trunclevel, investigation_time):
RuptureContext.temporal_occurrence_model = PoissonTOM(investigation_time)
# easy case of independent ruptures, useful for debugging
imts = [from_string(im) for im in imtls]
loglevels = DictArray(imtls)
for imt, imls in imtls.items():
if imt != 'MMI':
loglevels[imt] = numpy.log(imls)
pmap = ProbabilityMap(len(loglevels.array), len(gsims))
for ctx in ctxs:
mean_std = ctx.get_mean_std(imts, gsims) # shape (2, N, M, G)
poes = base.get_poes(mean_std, loglevels, trunclevel, gsims, None,
ctx.mag, None, ctx.rrup) # (N, L, G)
pnes = ctx.get_probability_no_exceedance(poes)
for sid, pne in zip(ctx.sids, pnes):
pmap.setdefault(sid, 1.).array *= pne
return ~pmap