def view_delta_loss(token, dstore):
"""
Estimate the stocastic error on the loss curve by splitting the events
in odd and even. Example:
$ oq show delta_loss # consider the first loss type
"""
if ':' in token:
_, li = token.split(':')
li = int(li)
else:
li = 0
oq = dstore['oqparam']
efftime = oq.investigation_time * oq.ses_per_logic_tree_path * len(
dstore['weights'])
num_events = len(dstore['events'])
num_events0 = num_events // 2 + (num_events % 2)
num_events1 = num_events // 2
periods = return_periods(efftime, num_events)[1:-1]
K = dstore['risk_by_event'].attrs.get('K', 0)
df = dstore.read_df('risk_by_event', 'event_id', dict(agg_id=K,
loss_id=li))
if len(df) == 0: # for instance no fatalities
return {'delta': numpy.zeros(1)}
mod2 = df.index % 2
losses0 = df['loss'][mod2 == 0]
losses1 = df['loss'][mod2 == 1]
c0 = losses_by_period(losses0, periods, num_events0, efftime / 2)
c1 = losses_by_period(losses1, periods, num_events1, efftime / 2)
dic = dict(loss=losses_by_period(df['loss'], periods, num_events, efftime),
even=c0,
odd=c1,
delta=numpy.abs(c0 - c1) / (c0 + c1))
return pandas.DataFrame(dic, periods)
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):
"""
:param dstore: datastore for an event based risk calculation
:returns: a LossesByPeriodBuilder instance
"""
oq = dstore['oqparam']
weights = dstore['csm_info'].rlzs['weight']
eff_time = oq.investigation_time * oq.ses_per_logic_tree_path
num_events = dstore['gmdata']['events']
periods = oq.return_periods or scientific.return_periods(
eff_time, num_events.max())
return scientific.LossesByPeriodBuilder(numpy.array(periods), oq.loss_dt(),
weights, num_events, eff_time,
oq.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)
