def save_disagg_results(self, results):
"""
Save the computed PMFs in the datastore
:param results:
a dict s, m, k -> 6D-matrix of shape (T, Ma, Lo, La, P, Z) or
(T, Ma, D, E, P, Z) depending if k is 0 or k is 1
"""
oq = self.oqparam
out = output_dict(self.shapedic, oq.disagg_outputs)
count = numpy.zeros(len(self.sitecol), U16)
_disagg_trt = numpy.zeros(self.N, [(trt, float) for trt in self.trts])
vcurves = [] # hazard curves with a vertical section for large poes
for (s, m, k), mat6 in sorted(results.items()):
# NB: k is an index with value 0 (MagDistEps) or 1 (MagLonLat)
imt = self.imts[m]
for p, poe in enumerate(self.poes_disagg):
mat5 = mat6[..., p, :]
if k == 0 and m == 0 and poe == self.poes_disagg[-1]:
# mat5 has shape (T, Ma, D, E, Z)
_disagg_trt[s] = tuple(pprod(mat5[..., 0], axis=(1, 2, 3)))
poe2 = pprod(mat5, axis=(0, 1, 2, 3))
self.datastore['poe4'][s, m, p] = poe2 # shape Z
poe_agg = poe2.mean()
if (poe and abs(1 - poe_agg / poe) > .1 and not count[s]
and self.hmap4[s, m, p].any()):
logging.warning(
'Site #%d, IMT=%s: poe_agg=%s is quite different from '
'the expected poe=%s, perhaps not enough levels',
s, imt, poe_agg, poe)
vcurves.append(self.curves[s])
count[s] += 1
mat4 = agg_probs(*mat5) # shape (Ma D E Z) or (Ma Lo La Z)
for key in oq.disagg_outputs:
if key == 'Mag' and k == 0:
out[key][s, m, p, :] = pprod(mat4, axis=(1, 2))
elif key == 'Dist' and k == 0:
out[key][s, m, p, :] = pprod(mat4, axis=(0, 2))
elif key == 'TRT' and k == 0:
out[key][s, m, p, :] = pprod(mat5, axis=(1, 2, 3))
elif key == 'Mag_Dist' and k == 0:
out[key][s, m, p, :] = pprod(mat4, axis=2)
elif key == 'Mag_Dist_Eps' and k == 0:
out[key][s, m, p, :] = mat4
elif key == 'Lon_Lat' and k == 1:
out[key][s, m, p, :] = pprod(mat4, axis=0)
elif key == 'Mag_Lon_Lat' and k == 1:
out[key][s, m, p, :] = mat4
elif key == 'Lon_Lat_TRT' and k == 1:
out[key][s, m, p, :] = pprod(mat5, axis=1).transpose(
1, 2, 0, 3) # T Lo La Z -> Lo La T Z
# shape NMP..Z
self.datastore['disagg'] = out
# below a dataset useful for debugging, at minimum IMT and maximum RP
self.datastore['_disagg_trt'] = _disagg_trt
if len(vcurves):
NML1 = len(vcurves), self.M, oq.imtls.size // self.M
self.datastore['_vcurves'] = numpy.array(vcurves).reshape(NML1)
self.datastore['_vcurves'].attrs['sids'] = numpy.where(count)[0]
def save_disagg_results(self, results, out):
"""
Save the computed PMFs in the datastore
:param results:
a dict s -> 9D-matrix of shape (T, Ma, D, Lo, La, E, M, P, Z)
:para out:
a dict kind -> PMF matrix to be populated
"""
outputs = self.oqparam.disagg_outputs
for s, mat9 in results.items():
if s not in self.ok_sites:
continue
for p, poe in enumerate(self.poes_disagg):
mat8 = mat9[..., p, :]
poe2 = pprod(mat8, axis=(0, 1, 2, 3, 4, 5))
self.datastore['poe4'][s, :, p] = poe2 # shape (M, Z)
poe_agg = poe2.mean()
if poe and abs(1 - poe_agg / poe) > .1:
logging.warning(
'Site #%d: poe_agg=%s is quite different from the '
'expected poe=%s; perhaps the number of intensity '
'measure levels is too small?', s, poe_agg, poe)
for m, imt in enumerate(self.imts):
mat7 = mat8[..., m, :]
mat6 = agg_probs(*mat7) # 6D
for key in outputs:
pmf = disagg.pmf_map[key](
mat7 if key.endswith('TRT') else mat6)
out[key][s, m, p, :] = pmf
def check_disagg_by_src(dstore):
"""
Make sure that by composing disagg_by_src one gets the hazard curves
"""
mean = dstore.sel('hcurves-stats', stat='mean')[:, 0] # N, M, L
dbs = dstore.sel('disagg_by_src') # N, R, M, L, Ns
poes = general.pprod(dbs, axis=4) # N, R, M, L
weights = dstore['weights'][:]
mean2 = numpy.einsum('sr...,r->s...', poes, weights) # N, M, L
aac(mean, mean2, atol=1E-6)
def output(mat6):
"""
:param mat6: a 6D matrix with axis (D, Lo, La, E, P, Z)
:returns: two matrices of shape (D, E, P, Z) and (Lo, La, P, Z)
"""
return pprod(mat6, axis=(1, 2)), pprod(mat6, axis=(0, 3))
