def compute_disagg(dstore, idxs, cmaker, iml3, trti, bin_edges, oq, monitor):
# see https://bugs.launchpad.net/oq-engine/+bug/1279247 for an explanation
# of the algorithm used
"""
:param dstore
a DataStore instance
:param idxs:
an array of indices to ruptures
:param cmaker:
a :class:`openquake.hazardlib.gsim.base.ContextMaker` instance
:param iml3:
an ArrayWrapper of shape (N, P, Z) with an attribute imt
:param trti:
tectonic region type index
:param bin_egdes:
a quintet (mag_edges, dist_edges, lon_edges, lat_edges, eps_edges)
:param monitor:
monitor of the currently running job
:returns:
a dictionary sid -> 8D-array
"""
with monitor('reading rupdata', measuremem=True):
dstore.open('r')
sitecol = dstore['sitecol']
rupdata = {k: dstore['rup/' + k][idxs] for k in dstore['rup']}
RuptureContext.temporal_occurrence_model = PoissonTOM(
oq.investigation_time)
pne_mon = monitor('disaggregate_pne', measuremem=False)
mat_mon = monitor('build_disagg_matrix', measuremem=True)
gmf_mon = monitor('disagg mean_std', measuremem=False)
for sid, iml2 in zip(sitecol.sids, iml3):
singlesite = sitecol.filtered([sid])
bins = disagg.get_bins(bin_edges, sid)
gsim_by_z = {}
for z in range(iml3.shape[-1]):
try:
gsim = cmaker.gsim_by_rlzi[iml3.rlzs[sid, z]]
except KeyError:
pass
else:
gsim_by_z[z] = gsim
ctxs = []
ok, = numpy.where(
rupdata['rrup_'][:, sid] <= cmaker.maximum_distance(cmaker.trt))
for ridx in ok: # consider only the ruptures close to the site
ctx = RuptureContext((par, rupdata[par][ridx])
for par in rupdata if not par.endswith('_'))
for par in rupdata:
if par.endswith('_'):
setattr(ctx, par[:-1], rupdata[par][ridx, [sid]])
ctxs.append(ctx)
if not ctxs:
continue
eps3 = disagg._eps3(cmaker.trunclevel, oq.num_epsilon_bins)
matrix = numpy.zeros([len(b) - 1 for b in bins] + list(iml2.shape))
for z, gsim in gsim_by_z.items():
with gmf_mon:
ms = disagg.get_mean_stdv(singlesite, ctxs, iml3.imt, gsim)
bdata = disagg.disaggregate(
ms, ctxs, iml3.imt, iml2[:, z], eps3, pne_mon)
if bdata.pnes.sum():
with mat_mon:
matrix[..., z] = disagg.build_disagg_matrix(bdata, bins)
if matrix.any():
yield {'trti': trti, 'imti': iml3.imti, sid: matrix}
def compute_disagg(dstore, slc, cmaker, hmap4, magi, bin_edges, monitor):
# see https://bugs.launchpad.net/oq-engine/+bug/1279247 for an explanation
# of the algorithm used
"""
:param dstore:
a DataStore instance
:param slc:
a slice of ruptures
:param cmaker:
a :class:`openquake.hazardlib.gsim.base.ContextMaker` instance
:param hmap4:
an ArrayWrapper of shape (N, M, P, Z)
:param magi:
magnitude bin indices
:param bin_egdes:
a quartet (dist_edges, lon_edges, lat_edges, eps_edges)
:param monitor:
monitor of the currently running job
:returns:
a dictionary sid, imti -> 6D-array
"""
with monitor('reading contexts', measuremem=True):
dstore.open('r')
allctxs = cmaker.read_ctxs(dstore, slc)
for magidx, ctx in zip(magi, allctxs):
ctx.magi = magidx
dis_mon = monitor('disaggregate', measuremem=False)
ms_mon = monitor('disagg mean_std', measuremem=True)
N, M, P, Z = hmap4.shape
g_by_z = AccumDict(accum={}) # dict s -> z -> g
for g, rlzs in enumerate(cmaker.gsims.values()):
for (s, z), r in numpy.ndenumerate(hmap4.rlzs):
if r in rlzs:
g_by_z[s][z] = g
eps3 = disagg._eps3(cmaker.trunclevel, cmaker.num_epsilon_bins)
imts = [from_string(im) for im in cmaker.imtls]
for magi, ctxs in groupby(allctxs, operator.attrgetter('magi')).items():
res = {'trti': cmaker.trti, 'magi': magi}
with ms_mon:
# compute mean and std (N * U * M * G * 16 bytes)
disagg.set_mean_std(ctxs, imts, cmaker.gsims)
# disaggregate by site, IMT
for s, iml3 in enumerate(hmap4):
close = [ctx for ctx in ctxs if ctx.magi == magi and s in ctx.sids]
if not g_by_z[s] or not close:
# g_by_z[s] is empty in test case_7
continue
# dist_bins, lon_bins, lat_bins, eps_bins
bins = (bin_edges[1], bin_edges[2][s], bin_edges[3][s],
bin_edges[4])
iml2 = dict(zip(imts, iml3))
with dis_mon:
# 7D-matrix #distbins, #lonbins, #latbins, #epsbins, M, P, Z
matrix = disagg.disaggregate(close, cmaker.tom, g_by_z[s],
iml2, eps3, s, bins) # 7D-matrix
for m in range(M):
mat6 = matrix[..., m, :, :]
if mat6.any():
res[s, m] = output(mat6)
yield res
def compute_disagg(dstore, rctx, cmaker, hmap4, trti, bin_edges, oq, monitor):
# see https://bugs.launchpad.net/oq-engine/+bug/1279247 for an explanation
# of the algorithm used
"""
:param dstore:
a DataStore instance
:param rctx:
an array of rupture parameters
:param cmaker:
a :class:`openquake.hazardlib.gsim.base.ContextMaker` instance
:param hmap4:
an ArrayWrapper of shape (N, M, P, Z)
:param trti:
tectonic region type index
:param magi:
magnitude bin index
:param bin_egdes:
a quartet (dist_edges, lon_edges, lat_edges, eps_edges)
:param monitor:
monitor of the currently running job
:returns:
a dictionary sid, imti -> 6D-array
"""
RuptureContext.temporal_occurrence_model = PoissonTOM(
oq.investigation_time)
with monitor('reading contexts', measuremem=True):
dstore.open('r')
ctxs, close_ctxs = read_ctxs(
dstore, rctx, req_site_params=cmaker.REQUIRES_SITES_PARAMETERS)
magi = numpy.searchsorted(bin_edges[0], rctx[0]['mag']) - 1
if magi == -1: # when the magnitude is on the edge
magi = 0
dis_mon = monitor('disaggregate', measuremem=False)
ms_mon = monitor('disagg mean_std', measuremem=True)
N, M, P, Z = hmap4.shape
g_by_z = AccumDict(accum={}) # dict s -> z -> g
for g, rlzs in enumerate(cmaker.gsims.values()):
for (s, z), r in numpy.ndenumerate(hmap4.rlzs):
if r in rlzs:
g_by_z[s][z] = g
eps3 = disagg._eps3(cmaker.trunclevel, oq.num_epsilon_bins)
res = {'trti': trti, 'magi': magi}
imts = [from_string(im) for im in oq.imtls]
with ms_mon:
# compute mean and std for a single IMT to save memory
# the size is N * U * G * 16 bytes
disagg.set_mean_std(ctxs, imts, cmaker.gsims)
# disaggregate by site, IMT
for s, iml3 in enumerate(hmap4):
if not g_by_z[s] or not close_ctxs[s]:
# g_by_z[s] is empty in test case_7
continue
# dist_bins, lon_bins, lat_bins, eps_bins
bins = (bin_edges[1], bin_edges[2][s], bin_edges[3][s], bin_edges[4])
iml2 = dict(zip(imts, iml3))
with dis_mon:
# 7D-matrix #distbins, #lonbins, #latbins, #epsbins, M, P, Z
matrix = disagg.disaggregate(close_ctxs[s], g_by_z[s], iml2, eps3,
s, bins) # 7D-matrix
for m in range(M):
mat6 = matrix[..., m, :, :]
if mat6.any():
res[s, m] = output(mat6)
return res
def compute_disagg(dstore, idxs, cmaker, iml4, trti, magi, bin_edges, oq,
monitor):
# see https://bugs.launchpad.net/oq-engine/+bug/1279247 for an explanation
# of the algorithm used
"""
:param dstore:
a DataStore instance
:param idxs:
an array of indices to ruptures
:param cmaker:
a :class:`openquake.hazardlib.gsim.base.ContextMaker` instance
:param iml4:
an ArrayWrapper of shape (N, M, P, Z)
:param trti:
tectonic region type index
:param magi:
magnitude bin index
:param bin_egdes:
a quartet (dist_edges, lon_edges, lat_edges, eps_edges)
:param monitor:
monitor of the currently running job
:returns:
a dictionary sid -> 7D-array
"""
res = {'trti': trti, 'magi': magi}
with monitor('reading rupdata', measuremem=True):
dstore.open('r')
sitecol = dstore['sitecol']
# NB: using dstore['rup/' + k][idxs] would be ultraslow!
a, b = idxs.min(), idxs.max() + 1
rupdata = {k: dstore['rup/' + k][a:b][idxs-a] for k in dstore['rup']}
RuptureContext.temporal_occurrence_model = PoissonTOM(
oq.investigation_time)
pne_mon = monitor('disaggregate_pne', measuremem=False)
mat_mon = monitor('build_disagg_matrix', measuremem=False)
ms_mon = monitor('disagg mean_std', measuremem=True)
eps3 = disagg._eps3(cmaker.trunclevel, oq.num_epsilon_bins)
with ms_mon:
ctxs = _prepare_ctxs(rupdata, cmaker, sitecol) # ultra-fast
mean_std = disagg.get_mean_std(ctxs, oq.imtls, cmaker.gsims)
for s, iml3 in enumerate(iml4):
iml2dict = {imt: iml3[m] for m, imt in enumerate(oq.imtls)}
# z indices by gsim
M, P, Z = iml3.shape
zs_by_g = AccumDict(accum=[])
for g, rlzs in enumerate(cmaker.gsims.values()):
for z in range(Z):
if iml4.rlzs[s, z] in rlzs:
zs_by_g[g].append(z)
# sanity check: the zs are disjoint
counts = numpy.zeros(Z, numpy.uint8)
for zs in zs_by_g.values():
counts[zs] += 1
assert (counts <= 1).all(), counts
# dist_bins, lon_bins, lat_bins, eps_bins
bins = bin_edges[0], bin_edges[1][s], bin_edges[2][s], bin_edges[3]
# build 7D-matrix #distbins, #lonbins, #latbins, #epsbins, M, P, Z
matrix = disagg.disaggregate(
ctxs, mean_std, zs_by_g, iml2dict, eps3, s, bins, pne_mon, mat_mon)
if matrix.any():
res[s] = matrix
return res
