def save_hmaps(self):
"""
Save hazard maps generated from the hazard curves
"""
oq = self.oqparam
if oq.poes:
mon = self.monitor('computing hazard maps')
logging.info('Computing hazard maps for PoEs=%s', oq.poes)
with mon:
N = len(self.sitecol.complete)
ct = oq.concurrent_tasks or 1
if 'hcurves' in self.datastore:
kinds = self.datastore['hcurves']
hmaps_dt = numpy.dtype(
[('%s-%s' % (imt, poe), F32)
for imt in oq.imtls for poe in oq.poes])
for kind in kinds:
self.datastore.create_dset(
'hmaps/' + kind, hmaps_dt, (N,), fillvalue=None)
allargs = []
for slc in general.split_in_slices(N, ct):
hcurves_by_kind = {
kind: self.datastore['hcurves/' + kind][slc]
for kind in kinds}
allargs.append((hcurves_by_kind, slc,
oq.imtls, oq.poes, mon))
for dic, slc in Starmap(build_hmaps, allargs):
for kind, hmaps in dic.items():
self.datastore['hmaps/' + kind][slc] = hmaps
else: # single realization
pg = PmapGetter(self.datastore, self.rlzs_assoc)
self.datastore['hmaps/mean'] = calc.make_hmap_array(
pg.get_mean(), oq.imtls, oq.poes, N)
def save_hmaps(self):
"""
Save hazard maps generated from the hazard curves
"""
oq = self.oqparam
if oq.poes:
logging.info('Computing hazard maps for PoEs=%s', oq.poes)
with self.monitor('computing hazard maps',
autoflush=True, measuremem=True):
N = len(self.sitecol.complete)
if 'hcurves' in self.datastore:
# TODO: we could parallelize this branch
for kind in self.datastore['hcurves']:
self.datastore['hmaps/' + kind] = calc.make_hmap_array(
self.datastore['hcurves/' + kind],
oq.imtls, oq.poes, N)
else: # single realization
pg = PmapGetter(self.datastore, self.rlzs_assoc)
self.datastore['hmaps/mean'] = calc.make_hmap_array(
pg.get_mean(), oq.imtls, oq.poes, N)
