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 event_based(proxies, full_lt, oqparam, dstore, monitor):
"""
Compute GMFs and optionally hazard curves
"""
alldata = AccumDict(accum=[])
sig_eps = []
times = [] # rup_id, nsites, dt
hcurves = {} # key -> poes
trt_smr = proxies[0]['trt_smr']
fmon = monitor('filtering ruptures', measuremem=False)
cmon = monitor('computing gmfs', measuremem=False)
with dstore:
trt = full_lt.trts[trt_smr // len(full_lt.sm_rlzs)]
srcfilter = SourceFilter(dstore['sitecol'],
oqparam.maximum_distance(trt))
rupgeoms = dstore['rupgeoms']
rlzs_by_gsim = full_lt._rlzs_by_gsim(trt_smr)
param = vars(oqparam).copy()
param['imtls'] = oqparam.imtls
param['min_iml'] = oqparam.min_iml
param['maximum_distance'] = oqparam.maximum_distance(trt)
cmaker = ContextMaker(trt, rlzs_by_gsim, param)
min_mag = getdefault(oqparam.minimum_magnitude, trt)
for proxy in proxies:
t0 = time.time()
with fmon:
if proxy['mag'] < min_mag:
continue
sids = srcfilter.close_sids(proxy, trt)
if len(sids) == 0: # filtered away
continue
proxy.geom = rupgeoms[proxy['geom_id']]
ebr = proxy.to_ebr(cmaker.trt) # after the geometry is set
try:
computer = GmfComputer(ebr,
srcfilter.sitecol.filtered(sids),
cmaker, oqparam.correl_model,
oqparam.cross_correl,
oqparam._amplifier,
oqparam._sec_perils)
except FarAwayRupture:
continue
with cmon:
data = computer.compute_all(sig_eps)
dt = time.time() - t0
times.append((computer.ebrupture.id, len(computer.ctx.sids), dt))
for key in data:
alldata[key].extend(data[key])
for key, val in sorted(alldata.items()):
if key in 'eid sid rlz':
alldata[key] = U32(alldata[key])
else:
alldata[key] = F32(alldata[key])
gmfdata = strip_zeros(pandas.DataFrame(alldata))
if len(gmfdata) and oqparam.hazard_curves_from_gmfs:
hc_mon = monitor('building hazard curves', measuremem=False)
for (sid, rlz), df in gmfdata.groupby(['sid', 'rlz']):
with hc_mon:
poes = calc.gmvs_to_poes(df, oqparam.imtls,
oqparam.ses_per_logic_tree_path)
for m, imt in enumerate(oqparam.imtls):
hcurves[rsi2str(rlz, sid, imt)] = poes[m]
times = numpy.array([tup + (monitor.task_no, ) for tup in times], time_dt)
times.sort(order='rup_id')
if not oqparam.ground_motion_fields:
gmfdata = ()
return dict(gmfdata=gmfdata,
hcurves=hcurves,
times=times,
sig_eps=numpy.array(sig_eps, sig_eps_dt(oqparam.imtls)))