def get_assets_by_taxo(assets, tempname=None):
"""
:param assets: an array of assets
:param tempname: hdf5 file where the epsilons are (or None)
:returns: assets_by_taxo with attributes eps and idxs
"""
assets_by_taxo = AccumDict(group_array(assets, 'taxonomy'))
assets_by_taxo.assets = assets
assets_by_taxo.idxs = numpy.argsort(
numpy.concatenate([a['ordinal'] for a in assets_by_taxo.values()]))
assets_by_taxo.eps = {}
if tempname is None: # no epsilons
return assets_by_taxo
# otherwise read the epsilons and group them by taxonomy
with hdf5.File(tempname, 'r') as h5:
dset = h5['epsilon_matrix']
for taxo, assets in assets_by_taxo.items():
lst = [dset[aid] for aid in assets['ordinal']]
assets_by_taxo.eps[taxo] = numpy.array(lst)
return assets_by_taxo
def out_by_lr(self, imt, assets, hazard, epsilons, eids=None):
"""
:param imt: restrict the risk functions to this IMT
:param assets: an array of assets of homogeneous taxonomy
:param hazard: a dictionary rlz -> hazard
:param epsilons: an array of epsilons per each asset
:param eids: rupture indices (only for event based)
:returns: a dictionary (l, r) -> output
"""
out_by_lr = AccumDict()
out_by_lr.assets = assets
out_by_lr.eids = eids
loss_types = self.get_loss_types(imt)
# extract the realizations from the first asset
for rlz in sorted(hazard):
r = rlz.ordinal
for loss_type in loss_types:
out = self(loss_type, assets, hazard[rlz], epsilons, eids)
if out:
l = self.compositemodel.lti[loss_type]
out.hid = r
out.weight = rlz.weight
out_by_lr[l, r] = out
return out_by_lr
def out_by_lr(self, imt, assets, hazard, epsgetter):
"""
:param imt: restrict the risk functions to this IMT
:param assets: an array of assets of homogeneous taxonomy
:param hazard: a dictionary rlz -> hazard
:param epsgetter: a callable returning epsilons for the given eids
:returns: a dictionary (l, r) -> output
"""
out_by_lr = AccumDict()
out_by_lr.assets = assets
loss_types = self.get_loss_types(imt)
for rlz in sorted(hazard):
haz = hazard[rlz]
if len(haz) == 0:
continue
r = rlz.ordinal
for loss_type in loss_types:
out = self(loss_type, assets, haz, epsgetter)
if out: # can be None in scenario_risk with no valid values
l = self.compositemodel.lti[loss_type]
out.hid = r
out.weight = rlz.weight
out_by_lr[l, r] = out
return out_by_lr
def out_by_lr(self, imt, assets, hazard, epsgetter):
"""
:param imt: restrict the risk functions to this IMT
:param assets: an array of assets of homogeneous taxonomy
:param hazard: a dictionary rlz -> hazard
:param epsgetter: a callable returning epsilons for the given eids
:returns: a dictionary (l, r) -> output
"""
out_by_lr = AccumDict()
out_by_lr.assets = assets
loss_types = self.get_loss_types(imt)
for rlz in sorted(hazard):
haz = hazard[rlz]
if len(haz) == 0:
continue
r = rlz.ordinal
for loss_type in loss_types:
out = self(loss_type, assets, haz, epsgetter)
if out: # can be None in scenario_risk with no valid values
l = self.compositemodel.lti[loss_type]
out.hid = r
out.weight = rlz.weight
out_by_lr[l, r] = out
return out_by_lr
