def post_execute(self, dummy):
oq = self.oqparam
size = self.datastore.getsize('agg_loss_table')
logging.info('Building aggregated curves from %s of agg_loss_table',
general.humansize(size))
builder = get_loss_builder(self.datastore)
alt_df = self.datastore.read_df('agg_loss_table')
del alt_df['event_id']
dic = general.AccumDict(accum=[])
columns = sorted(
set(alt_df.columns) - {'agg_id', 'loss_id', 'variance'})
periods = [0] + list(builder.return_periods)
for (agg_id,
loss_id), df in alt_df.groupby([alt_df.agg_id, alt_df.loss_id]):
tots = [df[col].sum() * oq.time_ratio for col in columns]
curves = [
builder.build_curve(df[col].to_numpy()) for col in columns
]
for p, period in enumerate(periods):
dic['agg_id'].append(agg_id)
dic['loss_id'].append(loss_id)
dic['return_period'].append(period)
if p == 0:
for col, tot in zip(columns, tots):
dic[col].append(tot)
else:
for col, curve in zip(columns, curves):
dic[col].append(curve[p - 1])
fix_dtype(dic, U16, ['agg_id'])
fix_dtype(dic, U8, ['loss_id'])
fix_dtype(dic, U32, ['return_period'])
fix_dtype(dic, F32, columns)
ls = ' '.join(self.crmodel.damage_states[1:])
self.datastore.create_df('aggcurves', dic.items(), limit_states=ls)
def __init__(self, dstore):
self.dstore = dstore
self.oq = dstore['oqparam']
try:
self.builder = get_loss_builder(dstore)
except KeyError: # no 'events' for non event_based_risk
pass
self.assetcol = dstore['assetcol']
arefs = [decode(aref) for aref in self.assetcol.asset_refs]
self.str2asset = dict(zip(arefs, self.assetcol))
self.asset_refs = arefs
self.loss_types = dstore.get_attr('risk_model', 'loss_types')
self.R = dstore['csm_info'].get_num_rlzs()
def execute(self):
"""
Compute risk from GMFs or ruptures depending on what is stored
"""
self.builder = get_loss_builder(self.datastore) # check
eids = self.datastore['gmf_data/eid'][:]
logging.info('Processing {:_d} rows of gmf_data'.format(len(eids)))
self.dmgcsq = zero_dmgcsq(self.assetcol, self.crmodel)
self.datastore.swmr_on()
smap = parallel.Starmap(event_based_damage,
self.gen_args(eids),
h5=self.datastore.hdf5)
smap.monitor.save('assets', self.assetcol.to_dframe())
smap.monitor.save('crmodel', self.crmodel)
return smap.reduce(self.combine)