def preclassical(srcs, srcfilter, params, monitor):
"""
Weight the sources. Also split them if split_sources is true. If
ps_grid_spacing is set, grid the point sources before weighting them.
NB: srcfilter can be on a reduced site collection for performance reasons
"""
# src.id -> nrups, nsites, time, task_no
calc_times = AccumDict(accum=numpy.zeros(4, F32))
sources = []
grp_id = srcs[0].grp_id
trt = srcs[0].tectonic_region_type
md = params['maximum_distance'](trt)
pd = (params['pointsource_distance'](trt)
if params['pointsource_distance'] else 0)
with monitor('splitting sources'):
# this can be slow
for src in srcs:
t0 = time.time()
if srcfilter.sitecol:
src.nsites = len(srcfilter.close_sids(src))
else:
src.nsites = 1 # don't discard
# NB: it is crucial to split only the close sources, for
# performance reasons (think of Ecuador in SAM)q
splits = split_source(src) if (params['split_sources']
and src.nsites) else [src]
sources.extend(splits)
nrups = src.count_ruptures() if src.nsites else 0
dt = time.time() - t0
calc_times[src.id] += F32([nrups, src.nsites, dt, 0])
for arr in calc_times.values():
arr[3] = monitor.task_no
dic = grid_point_sources(sources, params['ps_grid_spacing'], monitor)
with monitor('weighting sources'):
# this is normally fast
for src in dic[grp_id]:
if not src.nsites: # filtered out
src.nsites = EPS
is_ps = isinstance(src, PointSource)
if is_ps and srcfilter.sitecol:
# NB: using cKDTree would not help, performance-wise
cdist = srcfilter.sitecol.get_cdist(src.location)
src.nsites = (cdist <= md + pd).sum() or EPS
src.num_ruptures = src.count_ruptures()
if pd and is_ps:
nphc = src.count_nphc()
if nphc > 1:
close = (cdist <= pd * BUFFER).sum()
far = src.nsites - close
factor = (close + (far + EPS) / nphc) / (close + far + EPS)
src.num_ruptures *= factor
dic['calc_times'] = calc_times
dic['before'] = len(sources)
dic['after'] = len(dic[grp_id])
if params['ps_grid_spacing']:
dic['ps_grid/%02d' % monitor.task_no] = [
src for src in dic[grp_id] if src.nsites > EPS
]
return dic
def preclassical(srcs, sites, cmaker, monitor):
"""
Weight the sources. Also split them if split_sources is true. If
ps_grid_spacing is set, grid the point sources before weighting them.
NB: srcfilter can be on a reduced site collection for performance reasons
"""
split_sources = []
spacing = cmaker.ps_grid_spacing
grp_id = srcs[0].grp_id
if sites is None:
# in csm2rup just split the sources and count the ruptures
for src in srcs:
ss = split_source(src)
if len(ss) > 1:
for ss_ in ss:
ss_.nsites = 1
split_sources.extend(ss)
src.num_ruptures = src.count_ruptures()
dic = {grp_id: split_sources}
dic['before'] = len(srcs)
dic['after'] = len(dic[grp_id])
return dic
with monitor('splitting sources'):
sf = SourceFilter(sites, cmaker.maximum_distance)
for src in srcs:
# NB: this is approximate, since the sites are sampled
src.nsites = len(sf.close_sids(src)) # can be 0
# NB: it is crucial to split only the close sources, for
# performance reasons (think of Ecuador in SAM)
splits = split_source(src) if (cmaker.split_sources
and src.nsites) else [src]
split_sources.extend(splits)
dic = grid_point_sources(split_sources, spacing, monitor)
with monitor('weighting sources'):
# this is also prefiltering again, to have a good representative
# of what will be done during the classical phase
cmaker.set_weight(dic[grp_id], sf)
dic['before'] = len(split_sources)
dic['after'] = len(dic[grp_id])
if spacing:
dic['ps_grid/%02d' % monitor.task_no] = dic[grp_id]
return dic
def test(self):
# make sure the et_id is transferred also for single split
# sources, since this caused hard to track bugs
fname = gettemp(characteric_source)
[[char]] = nrml.to_python(fname)
char.id = 1
char.et_id = 1
os.remove(fname)
[src] = split_source(char)
self.assertEqual(char.id, src.id)
self.assertEqual(char.source_id, src.source_id)
self.assertEqual(char.et_id, src.et_id)