def event_based_risk(riskinputs, param, monitor):
"""
:param riskinputs:
:class:`openquake.risklib.riskinput.RiskInput` objects
:param param:
a dictionary of parameters
:param monitor:
:class:`openquake.baselib.performance.Monitor` instance
:returns:
a dictionary of numpy arrays of shape (L, R)
"""
crmodel = monitor.read('crmodel')
L = len(crmodel.lti)
tempname = param['tempname']
for ri in riskinputs:
with monitor('getting hazard'):
ri.hazard_getter.init()
hazard = ri.hazard_getter.get_hazard()
mon = monitor('build risk curves', measuremem=False)
A = len(ri.aids)
R = ri.hazard_getter.num_rlzs
try:
avg = numpy.zeros((A, R, L), F32)
except MemoryError:
raise MemoryError('Building array avg of shape (%d, %d, %d)' %
(A, R, L))
result = dict(aids=ri.aids, avglosses=avg)
acc = AccumDict() # accumulator eidx -> agglosses
aid2idx = {aid: idx for idx, aid in enumerate(ri.aids)}
if 'builder' in param:
builder = param['builder']
P = len(builder.return_periods)
all_curves = numpy.zeros((A, R, P), builder.loss_dt)
# update the result dictionary and the agg array with each output
for out in ri.gen_outputs(crmodel, monitor, tempname, hazard):
if len(out.eids) == 0: # this happens for sites with no events
continue
r = out.rlzi
agglosses = numpy.zeros((len(out.eids), L), F32)
for l, loss_type in enumerate(crmodel.loss_types):
loss_ratios = out[loss_type]
if loss_ratios is None: # for GMFs below the minimum_intensity
continue
avalues = riskmodels.get_values(loss_type, ri.assets)
for a, asset in enumerate(ri.assets):
aval = avalues[a]
aid = asset['ordinal']
idx = aid2idx[aid]
ratios = loss_ratios[a] # length E
# average losses
avg[idx, r,
l] = (ratios.sum(axis=0) * param['ses_ratio'] * aval)
# agglosses
agglosses[:, l] += ratios * aval
if 'builder' in param:
with mon: # this is the heaviest part
try:
all_curves[idx, r][loss_type] = (
builder.build_curve(aval, ratios, r))
except ValueError:
pass # not enough event to compute the curve
# NB: I could yield the agglosses per output, but then I would
# have millions of small outputs with big data transfer and slow
# saving time
acc += dict(zip(out.eids, agglosses))
if 'builder' in param:
clp = param['conditional_loss_poes']
result['curves-rlzs'], result['curves-stats'] = builder.pair(
all_curves, param['stats'])
if R > 1 and param['individual_curves'] is False:
del result['curves-rlzs']
if clp:
result['loss_maps-rlzs'], result['loss_maps-stats'] = (
builder.build_maps(all_curves, clp, param['stats']))
if R > 1 and param['individual_curves'] is False:
del result['loss_maps-rlzs']
# store info about the GMFs, must be done at the end
result['agglosses'] = (numpy.array(list(acc)),
numpy.array(list(acc.values())))
yield result
def event_based_risk(riskinputs, riskmodel, param, monitor):
"""
:param riskinputs:
:class:`openquake.risklib.riskinput.RiskInput` objects
:param riskmodel:
a :class:`openquake.risklib.riskinput.CompositeRiskModel` instance
:param param:
a dictionary of parameters
:param monitor:
:class:`openquake.baselib.performance.Monitor` instance
:returns:
a dictionary of numpy arrays of shape (L, R)
"""
L = len(riskmodel.lti)
epspath = param['epspath']
for ri in riskinputs:
with monitor('getting hazard'):
ri.hazard_getter.init()
hazard = ri.hazard_getter.get_hazard()
mon = monitor('build risk curves', measuremem=False)
A = len(ri.aids)
R = ri.hazard_getter.num_rlzs
try:
avg = numpy.zeros((A, R, L), F32)
except MemoryError:
raise MemoryError(
'Building array avg of shape (%d, %d, %d)' % (A, R, L))
result = dict(aids=ri.aids, avglosses=avg)
acc = AccumDict() # accumulator eidx -> agglosses
aid2idx = {aid: idx for idx, aid in enumerate(ri.aids)}
if 'builder' in param:
builder = param['builder']
P = len(builder.return_periods)
all_curves = numpy.zeros((A, R, P), builder.loss_dt)
# update the result dictionary and the agg array with each output
for out in riskmodel.gen_outputs(ri, monitor, epspath, hazard):
if len(out.eids) == 0: # this happens for sites with no events
continue
r = out.rlzi
agglosses = numpy.zeros((len(out.eids), L), F32)
for l, loss_type in enumerate(riskmodel.loss_types):
loss_ratios = out[loss_type]
if loss_ratios is None: # for GMFs below the minimum_intensity
continue
avalues = riskmodels.get_values(loss_type, ri.assets)
for a, asset in enumerate(ri.assets):
aval = avalues[a]
aid = asset['ordinal']
idx = aid2idx[aid]
ratios = loss_ratios[a] # length E
# average losses
avg[idx, r, l] = (
ratios.sum(axis=0) * param['ses_ratio'] * aval)
# agglosses
agglosses[:, l] += ratios * aval
if 'builder' in param:
with mon: # this is the heaviest part
all_curves[idx, r][loss_type] = (
builder.build_curve(aval, ratios, r))
# NB: I could yield the agglosses per output, but then I would
# have millions of small outputs with big data transfer and slow
# saving time
acc += dict(zip(out.eids, agglosses))
if 'builder' in param:
clp = param['conditional_loss_poes']
result['curves-rlzs'], result['curves-stats'] = builder.pair(
all_curves, param['stats'])
if R > 1 and param['individual_curves'] is False:
del result['curves-rlzs']
if clp:
result['loss_maps-rlzs'], result['loss_maps-stats'] = (
builder.build_maps(all_curves, clp, param['stats']))
if R > 1 and param['individual_curves'] is False:
del result['loss_maps-rlzs']
# store info about the GMFs, must be done at the end
result['agglosses'] = (numpy.array(list(acc)),
numpy.array(list(acc.values())))
yield result