def _hmap4(rlzs, iml_disagg, imtls, poes_disagg, curves):
# an ArrayWrapper of shape (N, M, P, Z)
N, Z = rlzs.shape
P = len(poes_disagg)
M = len(imtls)
arr = numpy.empty((N, M, P, Z))
for m, imt in enumerate(imtls):
for (s, z), rlz in numpy.ndenumerate(rlzs):
curve = curves[s][z]
if poes_disagg == (None, ):
arr[s, m, 0, z] = imtls[imt]
elif curve:
rlz = rlzs[s, z]
max_poe = curve[imt].max()
arr[s, m, :,
z] = calc.compute_hazard_maps(curve[imt], imtls[imt],
poes_disagg)
for iml, poe in zip(arr[s, m, :, z], poes_disagg):
if iml == 0:
logging.warning(
'Cannot disaggregate for site %d, %s, '
'poe=%s, rlz=%d: the hazard is zero', s, imt, poe,
rlz)
elif poe > max_poe:
logging.warning(POE_TOO_BIG, s, poe, max_poe, rlz, imt)
return hdf5.ArrayWrapper(arr, {'rlzs': rlzs})
def hazard_maps(self, curves):
"""
Compute the hazard maps associated to the curves
"""
maps = zero_maps(
len(self.sitecol), self.oqparam.imtls, self.oqparam.poes)
for imt in curves.dtype.fields:
# build a matrix of size (N, P)
data = calc.compute_hazard_maps(
curves[imt], self.oqparam.imtls[imt], self.oqparam.poes)
for poe, hmap in zip(self.oqparam.poes, data.T):
maps['%s-%s' % (imt, poe)] = hmap
return maps
def hazard_maps(self, curves):
"""
Compute the hazard maps associated to the curves
"""
maps = zero_maps(len(self.sitecol), self.oqparam.imtls,
self.oqparam.poes)
for imt in curves.dtype.fields:
# build a matrix of size (N, P)
data = calc.compute_hazard_maps(curves[imt],
self.oqparam.imtls[imt],
self.oqparam.poes)
for poe, hmap in zip(self.oqparam.poes, data.T):
maps['%s-%s' % (imt, poe)] = hmap
return maps
def test_compute_hazard_map(self):
curves = [
[0.8, 0.5, 0.1],
[0.98, 0.15, 0.05],
[0.6, 0.5, 0.4],
[0.1, 0.01, 0.001],
[0.8, 0.2, 0.1],
]
imls = [0.005, 0.007, 0.0098]
poe = 0.2
expected = [[0.00847798, 0.00664814, 0.0098, 0, 0.007]]
actual = calc.compute_hazard_maps(numpy.array(curves), imls, poe)
aaae(expected, actual.T)
def test_compute_hazard_map_multi_poe(self):
curves = [
[0.8, 0.5, 0.1],
[0.98, 0.15, 0.05],
[0.6, 0.5, 0.4],
[0.1, 0.01, 0.001],
[0.8, 0.2, 0.1],
]
imls = [0.005, 0.007, 0.0098]
poes = [0.1, 0.2]
expected = [[0.0098, 0.00792555, 0.0098, 0.005, 0.0098],
[0.00847798, 0.00664814, 0.0098, 0, 0.007]]
actual = calc.compute_hazard_maps(numpy.array(curves), imls, poes)
aaae(expected, actual.T)
def test_compute_hazard_map(self):
curves = [
[0.8, 0.5, 0.1],
[0.98, 0.15, 0.05],
[0.6, 0.5, 0.4],
[0.1, 0.01, 0.001],
[0.8, 0.2, 0.1],
]
imls = [0.005, 0.007, 0.0098]
poe = 0.2
expected = [[0.00847798, 0.00664814, 0.0098, 0, 0.007]]
actual = calc.compute_hazard_maps(curves, imls, poe)
aaae(expected, actual.T)
def test_compute_hazard_map_multi_poe(self):
curves = [
[0.8, 0.5, 0.1],
[0.98, 0.15, 0.05],
[0.6, 0.5, 0.4],
[0.1, 0.01, 0.001],
[0.8, 0.2, 0.1],
]
imls = [0.005, 0.007, 0.0098]
poes = [0.1, 0.2]
expected = [
[0.0098, 0.00792555, 0.0098, 0.005, 0.0098],
[0.00847798, 0.00664814, 0.0098, 0, 0.007]
]
actual = calc.compute_hazard_maps(curves, imls, poes)
aaae(expected, actual.T)
def execute(self):
"""
Compute the conditional spectrum
"""
oq = self.oqparam
self.full_lt = self.datastore['full_lt']
self.trts = list(self.full_lt.gsim_lt.values)
self.imts = list(oq.imtls)
imti = self.imts.index(oq.imt_ref)
self.M = len(self.imts)
dstore = (self.datastore.parent
if self.datastore.parent else self.datastore)
totrups = len(dstore['rup/mag'])
logging.info('Reading {:_d} ruptures'.format(totrups))
rdt = [('grp_id', U16), ('nsites', U16), ('idx', U32)]
rdata = numpy.zeros(totrups, rdt)
rdata['idx'] = numpy.arange(totrups)
rdata['grp_id'] = dstore['rup/grp_id'][:]
rdata['nsites'] = [len(sids) for sids in dstore['rup/sids_']]
totweight = rdata['nsites'].sum()
trt_smrs = dstore['trt_smrs'][:]
rlzs_by_gsim = self.full_lt.get_rlzs_by_gsim_list(trt_smrs)
G_ = sum(len(rbg) for rbg in rlzs_by_gsim)
self.periods = [from_string(imt).period for imt in self.imts]
if oq.imls_ref:
self.imls = oq.imls_ref
else: # extract imls from the "mean" hazard map
curve = self.datastore.sel('hcurves-stats', stat='mean')[0, 0,
imti]
[self.imls] = compute_hazard_maps(curve, oq.imtls[oq.imt_ref],
oq.poes) # there is 1 site
self.P = P = len(self.imls)
self.datastore.create_dset('cs-rlzs', float, (P, self.R, 2, self.M))
self.datastore.set_shape_descr('cs-rlzs',
poe_id=P,
rlz_id=self.R,
cs=2,
m=self.M)
self.datastore.create_dset('cond-spectra', float, (P, 2, self.M))
self.datastore.set_shape_descr('cond-spectra',
poe_id=P,
cs=['spec', 'std'],
period=self.periods)
self.datastore.create_dset('_c', float, (G_, P, 2, self.M))
self.datastore.create_dset('_s', float, (
G_,
P,
))
G = max(len(rbg) for rbg in rlzs_by_gsim)
maxw = 2 * 1024**3 / (16 * G * self.M) # at max 2 GB
maxweight = min(numpy.ceil(totweight / (oq.concurrent_tasks or 1)),
maxw)
U = 0
Ta = 0
self.cmakers = read_cmakers(self.datastore)
self.datastore.swmr_on()
smap = parallel.Starmap(conditional_spectrum, h5=self.datastore.hdf5)
# IMPORTANT!! we rely on the fact that the classical part
# of the calculation stores the ruptures in chunks of constant
# grp_id, therefore it is possible to build (start, stop) slices
for block in general.block_splitter(rdata, maxweight,
operator.itemgetter('nsites'),
operator.itemgetter('grp_id')):
Ta += 1
grp_id = block[0]['grp_id']
G = len(rlzs_by_gsim[grp_id])
cmaker = self.cmakers[grp_id]
U = max(U, block.weight)
slc = slice(block[0]['idx'], block[-1]['idx'] + 1)
smap.submit((dstore, slc, cmaker, imti, self.imls))
return smap.reduce()
