def _gen_riskinputs(self, kind, eps, num_events):
assets_by_site = self.assetcol.assets_by_site()
dstore = self.can_read_parent() or self.datastore
for sid, assets in enumerate(assets_by_site):
if len(assets) == 0:
continue
# build the riskinputs
if kind == 'poe': # hcurves, shape (R, N)
getter = PmapGetter(dstore, self.rlzs_assoc, [sid])
getter.num_rlzs = self.R
else: # gmf
getter = GmfDataGetter(dstore, [sid], self.R,
self.oqparam.imtls)
if dstore is self.datastore:
# read the hazard data in the controller node
getter.init()
else:
# the datastore must be closed to avoid the HDF5 fork bug
assert dstore.hdf5 == (), '%s is not closed!' % dstore
for block in general.block_splitter(assets, 1000):
# dictionary of epsilons for the reduced assets
reduced_eps = {
ass.ordinal: eps[ass.ordinal]
for ass in block if eps is not None and len(eps)
}
yield riskinput.RiskInput(getter, [block], reduced_eps)
def _gen_riskinputs(self, kind, eps, num_events):
rinfo_dt = numpy.dtype([('sid', U16), ('num_assets', U16)])
rinfo = []
assets_by_site = self.assetcol.assets_by_site()
dstore = self.can_read_parent() or self.datastore
for sid, assets in enumerate(assets_by_site):
if len(assets) == 0:
continue
# build the riskinputs
if kind == 'poe': # hcurves, shape (R, N)
getter = PmapGetter(dstore, self.rlzs_assoc, [sid])
getter.num_rlzs = self.R
else: # gmf
getter = GmfDataGetter(dstore, [sid], self.R)
if dstore is self.datastore:
# read the hazard data in the controller node
getter.init()
else:
# the datastore must be closed to avoid the HDF5 fork bug
assert dstore.hdf5 == (), '%s is not closed!' % dstore
for block in general.block_splitter(
assets, self.oqparam.assets_per_site_limit):
# dictionary of epsilons for the reduced assets
reduced_eps = {
ass.ordinal: eps[ass.ordinal]
for ass in block if eps is not None and len(eps)
}
yield riskinput.RiskInput(getter, [block], reduced_eps)
rinfo.append((sid, len(block)))
if len(block) >= TWO16:
logging.error('There are %d assets on site #%d!', len(block),
sid)
self.datastore['riskinput_info'] = numpy.array(rinfo, rinfo_dt)
def build_riskinputs(self, kind, eps=None, num_events=0):
"""
:param kind:
kind of hazard getter, can be 'poe' or 'gmf'
:param eps:
a matrix of epsilons (or None)
:param num_events:
how many events there are
:returns:
a list of RiskInputs objects, sorted by IMT.
"""
logging.info('There are %d realizations', self.R)
imtls = self.oqparam.imtls
if not set(self.oqparam.risk_imtls) & set(imtls):
rsk = ', '.join(self.oqparam.risk_imtls)
haz = ', '.join(imtls)
raise ValueError('The IMTs in the risk models (%s) are disjoint '
"from the IMTs in the hazard (%s)" % (rsk, haz))
num_tasks = self.oqparam.concurrent_tasks or 1
if not hasattr(self, 'assetcol'):
self.assetcol = self.datastore['assetcol']
self.riskmodel.taxonomy = self.assetcol.tagcol.taxonomy
assets_by_site = self.assetcol.assets_by_site()
with self.monitor('building riskinputs', autoflush=True):
riskinputs = []
sid_weight_pairs = [(sid, len(assets))
for sid, assets in enumerate(assets_by_site)]
blocks = general.split_in_blocks(sid_weight_pairs,
num_tasks,
weight=operator.itemgetter(1))
dstore = self.can_read_parent() or self.datastore
for block in blocks:
sids = numpy.array([sid for sid, _weight in block])
reduced_assets = assets_by_site[sids]
# dictionary of epsilons for the reduced assets
reduced_eps = {}
for assets in reduced_assets:
for ass in assets:
if eps is not None and len(eps):
reduced_eps[ass.ordinal] = eps[ass.ordinal]
# build the riskinputs
if kind == 'poe': # hcurves, shape (R, N)
getter = PmapGetter(dstore, sids, self.rlzs_assoc)
getter.num_rlzs = self.R
else: # gmf
getter = GmfDataGetter(dstore, sids, self.R, num_events)
if dstore is self.datastore:
# read the hazard data in the controller node
logging.info('Reading hazard')
getter.init()
else:
# the datastore must be closed to avoid the HDF5 fork bug
assert dstore.hdf5 == (), '%s is not closed!' % dstore
ri = riskinput.RiskInput(getter, reduced_assets, reduced_eps)
if ri.weight > 0:
riskinputs.append(ri)
assert riskinputs
logging.info('Built %d risk inputs', len(riskinputs))
return riskinputs
def _gen_riskinputs(self, kind, eps, num_events):
num_tasks = self.oqparam.concurrent_tasks or 1
assets_by_site = self.assetcol.assets_by_site()
if kind == 'poe':
indices = None
else:
indices = self.datastore['gmf_data/indices'].value
dstore = self.can_read_parent() or self.datastore
sid_weight = []
for sid, assets in enumerate(assets_by_site):
if len(assets) == 0:
continue
elif indices is None:
weight = len(assets)
else:
idx = indices[sid]
if indices.dtype.names: # engine < 3.2
num_gmfs = sum(stop - start for start, stop in idx)
else: # engine >= 3.2
num_gmfs = (idx[1] - idx[0]).sum()
weight = len(assets) * (num_gmfs or 1)
sid_weight.append((sid, weight))
for block in general.split_in_blocks(sid_weight,
num_tasks,
weight=operator.itemgetter(1)):
sids = numpy.array([sid for sid, _weight in block])
reduced_assets = assets_by_site[sids]
# dictionary of epsilons for the reduced assets
reduced_eps = {}
for assets in reduced_assets:
for ass in assets:
if eps is not None and len(eps):
reduced_eps[ass.ordinal] = eps[ass.ordinal]
# build the riskinputs
if kind == 'poe': # hcurves, shape (R, N)
getter = PmapGetter(dstore, self.rlzs_assoc, sids)
getter.num_rlzs = self.R
else: # gmf
getter = GmfDataGetter(dstore, sids, self.R, num_events,
self.oqparam.imtls)
if dstore is self.datastore:
# read the hazard data in the controller node
getter.init()
else:
# the datastore must be closed to avoid the HDF5 fork bug
assert dstore.hdf5 == (), '%s is not closed!' % dstore
ri = riskinput.RiskInput(getter, reduced_assets, reduced_eps)
ri.weight = block.weight
yield ri
def test_case_11(self):
self.assert_curves_ok([
'hazard_curve-mean.csv', 'hazard_curve-smltp_b1_b2-gsimltp_b1.csv',
'hazard_curve-smltp_b1_b3-gsimltp_b1.csv',
'hazard_curve-smltp_b1_b4-gsimltp_b1.csv',
'quantile_curve-0.1.csv', 'quantile_curve-0.9.csv'
], case_11.__file__)
# checking PmapGetter.get_pcurve
pgetter = PmapGetter(self.calc.datastore, self.calc.weights)
poes = pgetter.get_hcurves(pgetter.init())[0]
mean = self.calc.datastore.sel('hcurves-stats', stat='mean', sid=0)
mean2 = poes.T @ numpy.array([w['weight'] for w in self.calc.weights])
aac(mean2.flat, mean.flat)
check_disagg_by_src(self.calc.datastore)