def split_in_blocks(self, maxweight, sources):
"""
Split a set of sources in blocks of weight up to maxweight; heavy
sources (i.e. with weight > maxweight) are split.
:param maxweight: maximum weight of a block
:param sources: sources of the same source group
:yields: blocks of sources of weight around maxweight
"""
sources.sort(key=weight)
# yield light sources in blocks
light = [src for src in sources if src.weight <= maxweight]
for block in block_splitter(light, maxweight, weight):
yield block
# yield heavy sources in blocks
heavy = [src for src in sources if src.weight > maxweight]
for src in heavy:
srcs = [
s for s in source.split_source(src)
if self.src_filter.get_close_sites(s) is not None
]
for block in block_splitter(srcs, maxweight, weight):
yield block
def test(self):
npd = PMF([(0.5, NodalPlane(1, 20, 3)), (0.5, NodalPlane(2, 2, 4))])
hd = PMF([(1, 14)])
mesh = Mesh(numpy.array([0, 1]), numpy.array([0.5, 1]))
tom = PoissonTOM(50.)
mmfd = MultiMFD('incrementalMFD',
size=2,
min_mag=[4.5],
bin_width=[2.0],
occurRates=[[.3, .1], [.4, .2, .1]])
mps = MultiPointSource('mp1', 'multi point source',
'Active Shallow Crust', mmfd, 2.0, PeerMSR(),
1.0, tom, 10, 20, npd, hd, mesh)
mps.src_group_id = 1
# test the splitting
splits = list(split_source(mps))
self.assertEqual(len(splits), 2)
for split in splits:
self.assertEqual(split.src_group_id, mps.src_group_id)
got = obj_to_node(mps).to_str()
print(got)
self.assertEqual(
got, '''\
multiPointSource{id='mp1', name='multi point source', tectonicRegion='Active Shallow Crust'}
multiPointGeometry
gml:posList [0, 0.5, 1, 1.0]
upperSeismoDepth 10
lowerSeismoDepth 20
magScaleRel 'PeerMSR'
ruptAspectRatio 1.0
multiMFD{kind='incrementalMFD', size=2}
bin_width [2.0]
min_mag [4.5]
occurRates [0.29999999999999999, 0.10000000000000001, 0.40000000000000002, 0.20000000000000001, 0.10000000000000001]
lengths [2, 3]
nodalPlaneDist
nodalPlane{dip=20, probability=0.5, rake=3, strike=1}
nodalPlane{dip=2, probability=0.5, rake=4, strike=2}
hypoDepthDist
hypoDepth{depth=14, probability=1.0}
''')
def pmap_from_trt(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.
:returns:
a dictionary {grp_id: pmap} with attributes .grp_ids, .calc_times,
.eff_ruptures
"""
grp_ids = set()
for src in sources:
grp_ids.update(src.src_group_ids)
maxdist = src_filter.integration_distance
srcs = sum([split_source(src) for src in sources], []) # split first
with GroundShakingIntensityModel.forbid_instantiation():
imtls = param['imtls']
trunclevel = param.get('truncation_level')
cmaker = ContextMaker(gsims, maxdist)
ctx_mon = monitor('make_contexts', measuremem=False)
poe_mon = monitor('get_poes', measuremem=False)
pmap = AccumDict({
grp_id: ProbabilityMap(len(imtls.array), len(gsims))
for grp_id in grp_ids
})
pmap.calc_times = [] # pairs (src_id, delta_t)
pmap.eff_ruptures = AccumDict() # grp_id -> num_ruptures
for src, s_sites in src_filter(srcs): # filter now
t0 = time.time()
poemap = cmaker.poe_map(src, s_sites, imtls, trunclevel, ctx_mon,
poe_mon)
if poemap:
for grp_id in src.src_group_ids:
pmap[grp_id] |= poemap
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 += {
grp_id: getattr(poemap, 'eff_ruptures', 0)
for grp_id in src.src_group_ids
}
return pmap
def filter(self, src_filter): # called once per tile
"""
Generate a new CompositeSourceModel by filtering the sources on
the given site collection.
:param sitecol: a SiteCollection instance
:para src_filter: a SourceFilter instance
"""
ngsims = {trt: len(gs) for trt, gs in self.gsim_lt.values.items()}
source_models = []
weight = 0
for sm in self.source_models:
src_groups = []
for src_group in sm.src_groups:
mutex = getattr(src_group, 'src_interdep', None) == 'mutex'
self.add_infos(src_group.sources) # unsplit sources
sources = []
for src in src_group.sources:
if hasattr(src, '__iter__') and not mutex:
# MultiPoint, AreaSource, NonParametric
# NB: source.split_source is cached
sources.extend(source.split_source(src))
else:
# mutex sources cannot be split
sources.append(src)
sg = copy.copy(src_group)
sg.sources = []
for src, _sites in src_filter(sources):
sg.sources.append(src)
src.ngsims = ngsims[src.tectonic_region_type]
weight += src.weight
src_groups.append(sg)
newsm = logictree.SourceModel(sm.names, sm.weight, sm.path,
src_groups, sm.num_gsim_paths,
sm.ordinal, sm.samples)
source_models.append(newsm)
new = self.__class__(self.gsim_lt, self.source_model_lt, source_models)
new.weight = weight
new.src_filter = src_filter
return new
def pmap_from_grp(group, 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 dictionary {grp_id: ProbabilityMap instance}
"""
mutex_weight = {
src.source_id: weight
for src, weight in zip(group.sources, group.srcs_weights)
}
maxdist = src_filter.integration_distance
srcs = sum([split_source(src) for src in group.sources], [])
with GroundShakingIntensityModel.forbid_instantiation():
imtls = param['imtls']
trunclevel = param.get('truncation_level')
cmaker = ContextMaker(gsims, maxdist)
ctx_mon = monitor('make_contexts', measuremem=False)
poe_mon = monitor('get_poes', measuremem=False)
pmap = ProbabilityMap(len(imtls.array), len(gsims))
calc_times = [] # pairs (src_id, delta_t)
for src, s_sites in src_filter(srcs):
t0 = time.time()
poemap = cmaker.poe_map(src, s_sites, imtls, trunclevel, ctx_mon,
poe_mon, group.rup_interdep == 'indep')
weight = mutex_weight[src.source_id]
for sid in poemap:
pcurve = pmap.setdefault(sid, 0)
pcurve += poemap[sid] * weight
calc_times.append(
(src.source_id, src.weight, len(s_sites), time.time() - t0))
if group.grp_probability is not None:
pmap *= group.grp_probability
acc = AccumDict({group.id: pmap})
# adding the number of contributing ruptures too
acc.eff_ruptures = {group.id: ctx_mon.counts}
acc.calc_times = calc_times
return acc
def classical(group, 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 dictionary {grp_id: pmap} with attributes .grp_ids, .calc_times,
.eff_ruptures
"""
if getattr(group, 'src_interdep', None) == 'mutex':
mutex_weight = {
src.source_id: weight
for src, weight in zip(group.sources, group.srcs_weights)
}
srcs = group.sources
else:
mutex_weight = None
srcs = sum([split_source(src) for src in group], [])
grp_ids = set()
for src in group:
grp_ids.update(src.src_group_ids)
maxdist = src_filter.integration_distance
with GroundShakingIntensityModel.forbid_instantiation():
imtls = param['imtls']
trunclevel = param.get('truncation_level')
cmaker = ContextMaker(gsims, maxdist)
ctx_mon = monitor('make_contexts', measuremem=False)
poe_mon = monitor('get_poes', measuremem=False)
pmap = AccumDict({
grp_id: ProbabilityMap(len(imtls.array), len(gsims))
for grp_id in grp_ids
})
# AccumDict of arrays with 4 elements weight, nsites, calc_time, split
pmap.calc_times = AccumDict(accum=numpy.zeros(4))
pmap.eff_ruptures = AccumDict() # grp_id -> num_ruptures
for src, s_sites in src_filter(srcs): # filter now
t0 = time.time()
indep = group.rup_interdep == 'indep' if mutex_weight else True
poemap = cmaker.poe_map(src, s_sites, imtls, trunclevel, ctx_mon,
poe_mon, indep)
if mutex_weight: # mutex sources
weight = mutex_weight[src.source_id]
for sid in poemap:
pcurve = pmap[group.id].setdefault(sid, 0)
pcurve += poemap[sid] * weight
elif poemap:
for grp_id in src.src_group_ids:
pmap[grp_id] |= poemap
src_id = src.source_id.split(':', 1)[0]
pmap.calc_times[src_id] += numpy.array(
[src.weight, len(s_sites),
time.time() - t0, 1])
# storing the number of contributing ruptures too
pmap.eff_ruptures += {
grp_id: getattr(poemap, 'eff_ruptures', 0)
for grp_id in src.src_group_ids
}
if mutex_weight and group.grp_probability is not None:
pmap[group.id] *= group.grp_probability
return pmap