def pre_execute(self):
oq = self.oqparam
self.reaggreate = False
if oq.hazard_calculation_id and not self.datastore.parent:
self.datastore.parent = datastore.read(oq.hazard_calculation_id)
assetcol = self.datastore['assetcol']
self.aggkey = base.save_agg_values(self.datastore, assetcol,
oq.loss_names, oq.aggregate_by)
aggby = self.datastore.parent['oqparam'].aggregate_by
self.reaggreate = oq.aggregate_by != aggby
if self.reaggreate:
self.num_tags = dict(
zip(aggby, assetcol.tagcol.agg_shape(aggby)))
else:
assetcol = self.datastore['assetcol']
self.aggkey = assetcol.tagcol.get_aggkey(oq.aggregate_by)
self.L = len(oq.loss_names)
def pre_execute(self):
oq = self.oqparam
ds = self.datastore
self.reaggreate = False
if oq.hazard_calculation_id and not ds.parent:
ds.parent = datastore.read(oq.hazard_calculation_id)
assetcol = ds['assetcol']
self.aggkey = base.save_agg_values(ds, assetcol, oq.loss_names,
oq.aggregate_by)
aggby = ds.parent['oqparam'].aggregate_by
self.reaggreate = aggby and oq.aggregate_by != aggby
if self.reaggreate:
self.num_tags = dict(
zip(aggby, assetcol.tagcol.agg_shape(aggby)))
else:
assetcol = ds['assetcol']
self.aggkey = assetcol.tagcol.get_aggkey(oq.aggregate_by)
self.L = len(oq.loss_names)
size = general.humansize(ds.getsize('agg_loss_table'))
logging.info('Stored %s in the agg_loss_table', size)
def pre_execute(self):
oq = self.oqparam
ds = self.datastore
self.reaggreate = False
if oq.hazard_calculation_id and not ds.parent:
ds.parent = datastore.read(oq.hazard_calculation_id)
self.aggkey = base.save_agg_values(ds, self.assetcol,
oq.loss_types, oq.aggregate_by)
aggby = ds.parent['oqparam'].aggregate_by
self.reaggreate = aggby and oq.aggregate_by != aggby
if self.reaggreate:
self.num_tags = dict(
zip(aggby, self.assetcol.tagcol.agg_shape(aggby)))
else:
assetcol = ds['assetcol']
self.aggkey = assetcol.tagcol.get_aggkey(oq.aggregate_by)
self.L = len(oq.loss_types)
if self.R > 1:
self.num_events = numpy.bincount(ds['events']['rlz_id'],
minlength=self.R) # events by rlz
else:
self.num_events = numpy.array([len(ds['events'])])