def get_loss_builder(dstore, return_periods=None, loss_dt=None):
"""
:param dstore: datastore for an event based risk calculation
:returns: a LossCurvesMapsBuilder instance
"""
oq = dstore['oqparam']
weights = dstore['weights'][()]
try:
haz_time = dstore['gmf_data'].attrs['effective_time']
except KeyError:
haz_time = None
eff_time = oq.investigation_time * oq.ses_per_logic_tree_path * (
len(weights) if oq.collect_rlzs else 1)
if oq.collect_rlzs:
if haz_time and haz_time != eff_time:
raise ValueError('The effective time stored in gmf_data is %d, '
'which is inconsistent with %d' %
(haz_time, eff_time))
num_events = numpy.array([len(dstore['events'])])
weights = numpy.ones(1)
else:
num_events = numpy.bincount(dstore['events']['rlz_id'])
periods = return_periods or oq.return_periods or scientific.return_periods(
eff_time, num_events.max())
return scientific.LossCurvesMapsBuilder(oq.conditional_loss_poes,
numpy.array(periods), loss_dt
or oq.loss_dt(), weights,
dict(enumerate(num_events)),
eff_time,
oq.risk_investigation_time)
def get_loss_builder(dstore, return_periods=None, loss_dt=None):
"""
:param dstore: datastore for an event based risk calculation
:returns: a LossCurvesMapsBuilder instance
"""
oq = dstore['oqparam']
weights = dstore['weights'][()]
eff_time = oq.investigation_time * oq.ses_per_logic_tree_path
num_events = countby(dstore['events'][()], 'rlz')
periods = return_periods or oq.return_periods or scientific.return_periods(
eff_time, max(num_events.values()))
return scientific.LossCurvesMapsBuilder(
oq.conditional_loss_poes, numpy.array(periods),
loss_dt or oq.loss_dt(), weights, num_events,
eff_time, oq.risk_investigation_time)
