def classical_split_filter(srcs, srcfilter, gsims, params, monitor):
"""
Split the given sources, filter the subsources and the compute the
PoEs. Yield back subtasks if the split sources contain more than
maxweight ruptures.
"""
# first check if we are sampling the sources
ss = int(os.environ.get('OQ_SAMPLE_SOURCES', 0))
if ss:
splits, stime = split_sources(srcs)
srcs = random_filtered_sources(splits, srcfilter, ss)
yield classical(srcs, srcfilter, gsims, params, monitor)
return
# NB: splitting all the sources improves the distribution significantly,
# compared to splitting only the big source
sources = []
with monitor("filtering/splitting sources"):
for src, _sites in srcfilter(srcs):
splits, _stime = split_sources([src])
sources.extend(srcfilter.filter(splits))
if sources:
yield from parallel.split_task(classical,
sources,
srcfilter,
gsims,
params,
monitor,
duration=params['task_duration'])
def start_ebrisk(rupgetter, srcfilter, param, monitor):
"""
Launcher for ebrisk tasks
"""
rupgetters = rupgetter.split(srcfilter)
if rupgetters:
yield from parallel.split_task(ebrisk,
rupgetters,
srcfilter,
param,
monitor,
duration=param['task_duration'])
def classical_split_filter(srcs, srcfilter, gsims, params, monitor):
"""
Split the given sources, filter the subsources and the compute the
PoEs. Yield back subtasks if the split sources contain more than
maxweight ruptures.
"""
# first check if we are sampling the sources
ss = int(os.environ.get('OQ_SAMPLE_SOURCES', 0))
if ss:
splits, stime = split_sources(srcs)
srcs = readinput.random_filtered_sources(splits, srcfilter, ss)
yield classical(srcs, srcfilter, gsims, params, monitor)
return
# NB: splitting all the sources improves the distribution significantly,
# compared to splitting only the big source
sources = []
with monitor("filtering/splitting sources"):
for src, _sites in srcfilter(srcs):
splits, _stime = split_sources([src])
sources.extend(srcfilter.filter(splits))
if sources:
tot = 0
sd = AccumDict(accum=numpy.zeros(3)) # nsites, nrupts, weight
for src in sources:
arr = numpy.array([src.nsites, src.num_ruptures, src.weight])
sd[src.id] += arr
tot += 1
source_data = numpy.array([(monitor.task_no, src_id, s / tot, r, w)
for src_id, (s, r, w) in sd.items()],
source_data_dt)
first = True
for out in parallel.split_task(classical,
sources,
srcfilter,
gsims,
params,
monitor,
duration=params['task_duration'],
weight=nrup):
if first:
out['source_data'] = source_data
first = False
yield out
