def hazard_curves_per_rupture_subset(
rupset_idx, ucerf_source, src_filter, imtls, cmaker,
truncation_level=None, bbs=(), monitor=Monitor()):
"""
Calculates the probabilities of exceedence from a set of rupture indices
"""
imtls = DictArray(imtls)
ctx_mon = monitor('making contexts', measuremem=False)
pne_mon = monitor('computing poes', measuremem=False)
disagg_mon = monitor('get closest points', measuremem=False)
pmap = ProbabilityMap(len(imtls.array), len(cmaker.gsims))
pmap.calc_times = []
pmap.eff_ruptures = 0
pmap.grp_id = ucerf_source.src_group_id
nsites = len(src_filter.sitecol)
with h5py.File(ucerf_source.source_file, "r") as hdf5:
t0 = time.time()
pmap |= ucerf_poe_map(hdf5, ucerf_source, rupset_idx,
src_filter.sitecol, imtls, cmaker,
truncation_level, bbs,
ctx_mon, pne_mon, disagg_mon)
pmap.calc_times.append(
(ucerf_source.source_id, nsites, time.time() - t0))
pmap.eff_ruptures += pne_mon.counts
return pmap
def ucerf_classical(rupset_idx, ucerf_source, src_filter, gsims, monitor):
"""
:param rupset_idx:
indices of the rupture sets
:param ucerf_source:
an object taking the place of a source for UCERF
:param src_filter:
a source filter returning the sites affected by the source
:param gsims:
a list of GSIMs
:param monitor:
a monitor instance
:returns:
a ProbabilityMap
"""
t0 = time.time()
truncation_level = monitor.oqparam.truncation_level
imtls = monitor.oqparam.imtls
ucerf_source.src_filter = src_filter # so that .iter_ruptures() work
# prefilter the sites close to the rupture set
with h5py.File(ucerf_source.control.source_file, "r") as hdf5:
mag = hdf5[ucerf_source.idx_set["mag_idx"]][rupset_idx].max()
ridx = set()
# find the combination of rupture sections used in this model
rup_index_key = "/".join(
[ucerf_source.idx_set["geol_idx"], "RuptureIndex"])
# determine which of the rupture sections used in this set of indices
rup_index = hdf5[rup_index_key]
for i in rupset_idx:
ridx.update(rup_index[i])
s_sites = ucerf_source.get_rupture_sites(hdf5, ridx, src_filter, mag)
if s_sites is None: # return an empty probability map
pm = ProbabilityMap(len(imtls.array), len(gsims))
pm.calc_times = [] # TODO: fix .calc_times
pm.eff_ruptures = {ucerf_source.src_group_id: 0}
pm.grp_id = ucerf_source.src_group_id
return pm
# compute the ProbabilityMap by using hazardlib.calc.hazard_curve.poe_map
ucerf_source.rupset_idx = rupset_idx
ucerf_source.num_ruptures = nruptures = len(rupset_idx)
cmaker = ContextMaker(gsims, src_filter.integration_distance)
imtls = DictArray(imtls)
ctx_mon = monitor('making contexts', measuremem=False)
pne_mons = [
monitor('%s.get_poes' % gsim, measuremem=False) for gsim in gsims
]
pmap = poe_map(ucerf_source, s_sites, imtls, cmaker, truncation_level,
ctx_mon, pne_mons)
nsites = len(s_sites)
pmap.calc_times = [(ucerf_source.source_id, nruptures * nsites, nsites,
time.time() - t0)]
pmap.grp_id = ucerf_source.src_group_id
pmap.eff_ruptures = {pmap.grp_id: ucerf_source.num_ruptures}
return pmap
def pmap_from_grp(sources,
source_site_filter,
imtls,
gsims,
truncation_level=None,
bbs=(),
monitor=Monitor()):
"""
Compute the hazard curves for a set of sources belonging to the same
tectonic region type for all the GSIMs associated to that TRT.
The arguments are the same as in :func:`calc_hazard_curves`, except
for ``gsims``, which is a list of GSIM instances.
:returns: a ProbabilityMap instance
"""
if isinstance(sources, SourceGroup):
group = sources
sources = group.sources
trt = sources[0].tectonic_region_type
else: # list of sources
trt = sources[0].tectonic_region_type
group = SourceGroup(trt, sources, 'src_group', 'indep', 'indep')
try:
maxdist = source_site_filter.integration_distance[trt]
except:
maxdist = source_site_filter.integration_distance
if hasattr(gsims, 'keys'): # dictionary trt -> gsim
gsims = [gsims[trt]]
with GroundShakingIntensityModel.forbid_instantiation():
imtls = DictArray(imtls)
cmaker = ContextMaker(gsims, maxdist)
ctx_mon = monitor('making contexts', measuremem=False)
pne_mon = monitor('computing poes', measuremem=False)
disagg_mon = monitor('get closest points', measuremem=False)
src_indep = group.src_interdep == 'indep'
pmap = ProbabilityMap(len(imtls.array), len(gsims))
pmap.calc_times = [] # pairs (src_id, delta_t)
pmap.grp_id = sources[0].src_group_id
for src, s_sites in source_site_filter(sources):
t0 = time.time()
poemap = poe_map(src, s_sites, imtls, cmaker, truncation_level,
bbs, group.rup_interdep == 'indep', ctx_mon,
pne_mon, disagg_mon)
if src_indep: # usual composition of probabilities
pmap |= poemap
else: # mutually exclusive probabilities
weight = float(group.srcs_weights[src.source_id])
for sid in poemap:
pmap[sid] += poemap[sid] * weight
pmap.calc_times.append(
(src.source_id, len(s_sites), time.time() - t0))
# storing the number of contributing ruptures too
pmap.eff_ruptures = {pmap.grp_id: pne_mon.counts}
return pmap
def pmap_from_grp(
sources, source_site_filter, imtls, gsims, truncation_level=None,
bbs=(), monitor=Monitor()):
"""
Compute the hazard curves for a set of sources belonging to the same
tectonic region type for all the GSIMs associated to that TRT.
The arguments are the same as in :func:`calc_hazard_curves`, except
for ``gsims``, which is a list of GSIM instances.
:returns: a ProbabilityMap instance
"""
if isinstance(sources, SourceGroup):
group = sources
sources = group.src_list
else:
group = SourceGroup(sources, 'src_group', 'indep', 'indep')
sources = group.src_list
trt = sources[0].tectonic_region_type
try:
maxdist = source_site_filter.integration_distance[trt]
except:
maxdist = source_site_filter.integration_distance
if hasattr(gsims, 'keys'):
gsims = [gsims[trt]]
# check all the sources belong to the same tectonic region
trts = set(src.tectonic_region_type for src in sources)
assert len(trts) == 1, 'Multiple TRTs: %s' % ', '.join(trts)
with GroundShakingIntensityModel.forbid_instantiation():
imtls = DictArray(imtls)
cmaker = ContextMaker(gsims, maxdist)
ctx_mon = monitor('making contexts', measuremem=False)
pne_mon = monitor('computing poes', measuremem=False)
disagg_mon = monitor('get closest points', measuremem=False)
src_indep = group.src_interdep == 'indep'
pmap = ProbabilityMap(len(imtls.array), len(gsims))
pmap.calc_times = [] # pairs (src_id, delta_t)
pmap.grp_id = sources[0].src_group_id
for src, s_sites in source_site_filter(sources):
t0 = time.time()
poemap = poe_map(
src, s_sites, imtls, cmaker, truncation_level, bbs,
group.rup_interdep == 'indep', ctx_mon, pne_mon, disagg_mon)
if src_indep: # usual composition of probabilities
pmap |= poemap
else: # mutually exclusive probabilities
weight = float(group.srcs_weights[src.source_id])
for sid in poemap:
pmap[sid] += poemap[sid] * weight
pmap.calc_times.append(
(src.source_id, len(s_sites), time.time() - t0))
# storing the number of contributing ruptures too
pmap.eff_ruptures = {pmap.grp_id: pne_mon.counts}
return pmap
def ucerf_classical_hazard_by_rupture_set(
rupset_idx, branchname, ucerf_source, src_group_id, src_filter,
gsims, monitor):
"""
:param rupset_idx:
indices of the rupture sets
:param branchname:
name of the branch
:param ucerf_source:
an object taking the place of a source for UCERF
:param src_group_id:
source group index
:param src_filter:
a source filter returning the sites affected by the source
:param gsims:
a list of GSIMs
:param monitor:
a monitor instance
:returns:
an AccumDict rlz -> curves
"""
truncation_level = monitor.oqparam.truncation_level
imtls = monitor.oqparam.imtls
max_dist = monitor.oqparam.maximum_distance[DEFAULT_TRT]
# Apply the initial rupture to site filtering
rupset_idx, s_sites = \
ucerf_source.filter_sites_by_distance_from_rupture_set(
rupset_idx, src_filter.sitecol, max_dist)
if len(s_sites):
cmaker = ContextMaker(gsims, max_dist)
pmap = hazard_curves_per_rupture_subset(
rupset_idx, ucerf_source, src_filter, imtls, cmaker,
truncation_level, bbs=[], monitor=monitor)
else:
pmap = ProbabilityMap(len(imtls.array), len(gsims))
pmap.calc_times = []
pmap.eff_ruptures = {src_group_id: 0}
pmap.grp_id = ucerf_source.src_group_id
return pmap
def pmap_from_grp(sources, src_filter, gsims, param, monitor=Monitor()):
"""
Compute the hazard curves for a set of sources belonging to the same
tectonic region type for all the GSIMs associated to that TRT.
The arguments are the same as in :func:`calc_hazard_curves`, except
for ``gsims``, which is a list of GSIM instances.
:returns: a ProbabilityMap instance
"""
if isinstance(sources, SourceGroup):
group = sources
sources = group.sources
trt = sources[0].tectonic_region_type
mutex_weight = {
src.source_id: weight
for src, weight in zip(group.sources, group.srcs_weights)
}
else: # list of sources
trt = sources[0].tectonic_region_type
group = SourceGroup(trt, sources, 'src_group', 'indep', 'indep')
grp_id = sources[0].src_group_id
maxdist = src_filter.integration_distance
if hasattr(gsims, 'keys'): # dictionary trt -> gsim
gsims = [gsims[trt]]
srcs = []
for src in sources:
if hasattr(src, '__iter__'): # MultiPointSource
srcs.extend(src)
else:
srcs.append(src)
del sources
with GroundShakingIntensityModel.forbid_instantiation():
imtls = param['imtls']
trunclevel = param.get('truncation_level')
cmaker = ContextMaker(gsims, maxdist)
ctx_mon = monitor('making contexts', measuremem=False)
pne_mons = [
monitor('%s.get_poes' % gsim, measuremem=False) for gsim in gsims
]
src_indep = group.src_interdep == 'indep'
pmap = ProbabilityMap(len(imtls.array), len(gsims))
pmap.calc_times = [] # pairs (src_id, delta_t)
pmap.grp_id = grp_id
for src, s_sites in src_filter(srcs):
t0 = time.time()
poemap = poe_map(src, s_sites, imtls, cmaker, trunclevel, ctx_mon,
pne_mons, group.rup_interdep == 'indep')
if src_indep: # usual composition of probabilities
pmap |= poemap
else: # mutually exclusive probabilities
weight = mutex_weight[src.source_id]
for sid in poemap:
pcurve = pmap.setdefault(sid, 0)
pcurve += poemap[sid] * weight
pmap.calc_times.append(
(src.source_id, src.weight, len(s_sites), time.time() - t0))
# storing the number of contributing ruptures too
pmap.eff_ruptures = {pmap.grp_id: pne_mons[0].counts}
if group.grp_probability is not None:
return pmap * group.grp_probability
return pmap
