def pre_execute(self):
"""
Associate the assets to the sites and build the riskinputs.
"""
if 'hazard_curves' in self.oqparam.inputs: # read hazard from file
haz_sitecol, haz_curves = readinput.get_hcurves(self.oqparam)
self.read_exposure() # define .assets_by_site
self.load_riskmodel()
self.sitecol, self.assets_by_site = self.assoc_assets_sites(
haz_sitecol)
curves_by_trt_gsim = {(0, 'FromFile'): haz_curves}
self.rlzs_assoc = logictree.trivial_rlzs_assoc()
self.save_mesh()
else: # compute hazard
super(ClassicalRiskCalculator, self).pre_execute()
logging.info('Preparing the risk input')
curves_by_trt_gsim = {}
for dset in self.datastore['curves_by_sm'].values():
for key, curves in dset.items():
trt_id, gsim = key.split('-')
curves_by_trt_gsim[int(trt_id), gsim] = curves.value
self.assetcol = riskinput.build_asset_collection(
self.assets_by_site, self.oqparam.time_event)
self.riskinputs = self.build_riskinputs(curves_by_trt_gsim)
self.monitor.oqparam = self.oqparam
self.N = sum(len(assets) for assets in self.assets_by_site)
self.L = len(self.riskmodel.loss_types)
self.R = len(self.rlzs_assoc.realizations)
self.I = self.oqparam.insured_losses
self.Q1 = len(self.oqparam.quantile_loss_curves) + 1
def build_riskinputs(self, hazards_by_key, eps=numpy.zeros(0)):
"""
:param hazards_by_key:
a dictionary key -> IMT -> array of length num_sites
:param eps:
a matrix of epsilons (possibly empty)
:returns:
a list of RiskInputs objects, sorted by IMT.
"""
# add asset.idx as side effect
riskinput.build_asset_collection(self.assets_by_site,
self.oqparam.time_event)
imtls = self.oqparam.imtls
with self.monitor('building riskinputs', autoflush=True):
riskinputs = []
idx_weight_pairs = [(i, len(assets))
for i, assets in enumerate(self.assets_by_site)
]
blocks = general.split_in_blocks(idx_weight_pairs,
self.oqparam.concurrent_tasks
or 1,
weight=operator.itemgetter(1))
for block in blocks:
indices = numpy.array([idx for idx, _weight in block])
reduced_assets = self.assets_by_site[indices]
reduced_eps = {} # for the assets belonging to the indices
if len(eps):
for assets in reduced_assets:
for asset in assets:
reduced_eps[asset.idx] = eps[asset.idx]
# collect the hazards by key into hazards by imt
hdata = collections.defaultdict(lambda: [{} for _ in indices])
for key, hazards_by_imt in hazards_by_key.items():
for imt in imtls:
hazards_by_site = hazards_by_imt[imt]
for i, haz in enumerate(hazards_by_site[indices]):
hdata[imt][i][key] = haz
# build the riskinputs
for imt in hdata:
ri = self.riskmodel.build_input(imt, hdata[imt],
reduced_assets,
reduced_eps)
if ri.weight > 0:
riskinputs.append(ri)
logging.info('Built %d risk inputs', len(riskinputs))
return sorted(riskinputs, key=self.riskinput_key)
def build_riskinputs(self, hazards_by_key, eps=numpy.zeros(0)):
"""
:param hazards_by_key:
a dictionary key -> IMT -> array of length num_sites
:param eps:
a matrix of epsilons (possibly empty)
:returns:
a list of RiskInputs objects, sorted by IMT.
"""
# add asset.idx as side effect
riskinput.build_asset_collection(
self.assets_by_site, self.oqparam.time_event)
imtls = self.oqparam.imtls
with self.monitor('building riskinputs', autoflush=True):
riskinputs = []
idx_weight_pairs = [
(i, len(assets))
for i, assets in enumerate(self.assets_by_site)]
blocks = general.split_in_blocks(
idx_weight_pairs,
self.oqparam.concurrent_tasks or 1,
weight=operator.itemgetter(1))
for block in blocks:
indices = numpy.array([idx for idx, _weight in block])
reduced_assets = self.assets_by_site[indices]
reduced_eps = {} # for the assets belonging to the indices
if len(eps):
for assets in reduced_assets:
for asset in assets:
reduced_eps[asset.idx] = eps[asset.idx]
# collect the hazards by key into hazards by imt
hdata = collections.defaultdict(lambda: [{} for _ in indices])
for key, hazards_by_imt in hazards_by_key.items():
for imt in imtls:
hazards_by_site = hazards_by_imt[imt]
for i, haz in enumerate(hazards_by_site[indices]):
hdata[imt][i][key] = haz
# build the riskinputs
for imt in hdata:
ri = self.riskmodel.build_input(
imt, hdata[imt], reduced_assets, reduced_eps)
if ri.weight > 0:
riskinputs.append(ri)
logging.info('Built %d risk inputs', len(riskinputs))
return sorted(riskinputs, key=self.riskinput_key)
def pre_execute(self):
"""
Read the precomputed ruptures (or compute them on the fly) and
prepare some datasets in the datastore.
"""
super(EventBasedRiskCalculator, self).pre_execute()
if not self.riskmodel: # there is no riskmodel, exit early
self.execute = lambda: None
self.post_execute = lambda result: None
return
oq = self.oqparam
if self.riskmodel.covs:
epsilon_sampling = oq.epsilon_sampling
else:
epsilon_sampling = 1 # only one ignored epsilon
correl_model = readinput.get_correl_model(oq)
gsims_by_col = self.rlzs_assoc.get_gsims_by_col()
assets_by_site = self.assets_by_site
# the following is needed to set the asset idx attribute
self.assetcol = riskinput.build_asset_collection(
assets_by_site, oq.time_event)
self.spec_indices = numpy.array([a['asset_ref'] in oq.specific_assets
for a in self.assetcol])
logging.info('Populating the risk inputs')
rup_by_tag = sum(self.datastore['sescollection'], AccumDict())
all_ruptures = [rup_by_tag[tag] for tag in sorted(rup_by_tag)]
for i, rup in enumerate(all_ruptures):
rup.ordinal = i
num_samples = min(len(all_ruptures), epsilon_sampling)
self.epsilon_matrix = eps = riskinput.make_eps(
assets_by_site, num_samples, oq.master_seed, oq.asset_correlation)
logging.info('Generated %d epsilons', num_samples * len(eps))
self.riskinputs = list(self.riskmodel.build_inputs_from_ruptures(
self.sitecol.complete, all_ruptures, gsims_by_col,
oq.truncation_level, correl_model, eps,
oq.concurrent_tasks or 1))
logging.info('Built %d risk inputs', len(self.riskinputs))
# preparing empty datasets
loss_types = self.riskmodel.loss_types
self.L = len(loss_types)
self.R = len(self.rlzs_assoc.realizations)
self.outs = OUTPUTS
self.datasets = {}
# ugly: attaching an attribute needed in the task function
self.monitor.num_outputs = len(self.outs)
self.monitor.num_assets = self.count_assets()
for o, out in enumerate(self.outs):
self.datastore.hdf5.create_group(out)
for l, loss_type in enumerate(loss_types):
for r, rlz in enumerate(self.rlzs_assoc.realizations):
key = '/%s/%s' % (loss_type, rlz.uid)
if o == AGGLOSS: # loss tables
dset = self.datastore.create_dset(out + key, elt_dt)
elif o == SPECLOSS: # specific losses
dset = self.datastore.create_dset(out + key, ela_dt)
self.datasets[o, l, r] = dset
def execute(self):
"""
Parallelize on the riskinputs and returns a dictionary of results.
Require a `.core_func` to be defined with signature
(riskinputs, riskmodel, rlzs_assoc, monitor).
"""
# add fatalities as side effect
riskinput.build_asset_collection(
self.assets_by_site, self.oqparam.time_event)
self.monitor.oqparam = self.oqparam
if self.pre_calculator == 'event_based_rupture':
self.monitor.assets_by_site = self.assets_by_site
self.monitor.num_assets = self.count_assets()
res = apply_reduce(
self.core_func.__func__,
(self.riskinputs, self.riskmodel, self.rlzs_assoc, self.monitor),
concurrent_tasks=self.oqparam.concurrent_tasks,
weight=get_weight, key=self.riskinput_key)
return res
def execute(self):
"""
Parallelize on the riskinputs and returns a dictionary of results.
Require a `.core_func` to be defined with signature
(riskinputs, riskmodel, rlzs_assoc, monitor).
"""
# add fatalities as side effect
riskinput.build_asset_collection(
self.assets_by_site, self.oqparam.time_event)
self.monitor.oqparam = self.oqparam
if self.pre_calculator == 'event_based_rupture':
self.monitor.assets_by_site = self.assets_by_site
self.monitor.num_assets = self.count_assets()
res = apply_reduce(
self.core_func.__func__,
(self.riskinputs, self.riskmodel, self.rlzs_assoc, self.monitor),
concurrent_tasks=self.oqparam.concurrent_tasks,
weight=get_weight, key=self.riskinput_key)
return res
def test_assetcol(self):
expected = writetmp('''\
asset_ref:|S100:,site_id:uint32:,taxonomy:uint32:,fatalities:float64:,structural:float64:,deductible~structural:float64:,insurance_limit~structural:float64:
a0,0,1,10,3000,.25,1.0
a1,1,0,20,2000,0.25,0.5
a2,2,2,30,1000,0.2,0.8
a3,2,1,0,5000,2.0,6.0
a4,3,1,50,500000,2.0,6.0
''')
assetcol = riskinput.build_asset_collection(self.assets_by_site)
numpy.testing.assert_equal(assetcol,
readers.read_composite_array(expected))
def test_assetcol(self):
expected = writetmp('''\
asset_ref:|S20:,site_id:uint32:,structural:float64:,deductible~structural:float64:,insurance_limit~structural:float64:
a0,0,3000,25,100
a1,1,2000,0.1,0.2
a2,2,1000,0.02,0.08
a3,2,5000,1000,3000
a4,3,500000,1000,3000
''')
assetcol = riskinput.build_asset_collection(self.assets_by_site)
numpy.testing.assert_equal(
assetcol, readers.read_composite_array(expected))
def test_assetcol(self):
expected = writetmp('''\
asset_ref:|S100:,site_id:uint32:,taxonomy:uint32:,fatalities:float64:,structural:float64:,deductible~structural:float64:,insurance_limit~structural:float64:
a0,0,1,10,3000,.25,1.0
a1,1,0,20,2000,0.25,0.5
a2,2,2,30,1000,0.2,0.8
a3,2,1,0,5000,2.0,6.0
a4,3,1,50,500000,2.0,6.0
''')
assetcol = riskinput.build_asset_collection(self.assets_by_site)
numpy.testing.assert_equal(
assetcol, readers.read_composite_array(expected))
def read_exposure_sitecol(self):
"""
Read the exposure (if any) and then the site collection, possibly
extracted from the exposure.
"""
inputs = self.oqparam.inputs
if 'gmfs' in inputs and self.oqparam.sites:
haz_sitecol = self.sitecol = readinput.get_site_collection(
self.oqparam)
if 'scenario_' in self.oqparam.calculation_mode:
self.gmfs = get_gmfs(self)
haz_sitecol = self.sitecol
if 'exposure' in inputs:
logging.info('Reading the exposure')
with self.monitor('reading exposure', autoflush=True):
self.exposure = readinput.get_exposure(self.oqparam)
self.sitecol, self.assets_by_site = (
readinput.get_sitecol_assets(self.oqparam, self.exposure))
self.cost_types = self.exposure.cost_types
self.taxonomies = numpy.array(
sorted(self.exposure.taxonomies), '|S100')
num_assets = self.count_assets()
if self.datastore.parent:
haz_sitecol = self.datastore.parent['sitecol']
elif 'gmfs' in inputs:
pass # haz_sitecol is already defined
# TODO: think about the case hazard_curves in inputs
else:
haz_sitecol = None
if haz_sitecol is not None and haz_sitecol != self.sitecol:
with self.monitor('assoc_assets_sites'):
self.sitecol, self.assets_by_site = \
self.assoc_assets_sites(haz_sitecol.complete)
ok_assets = self.count_assets()
num_sites = len(self.sitecol)
logging.warn('Associated %d assets to %d sites, %d discarded',
ok_assets, num_sites, num_assets - ok_assets)
elif (self.datastore.parent and 'exposure' in
OqParam.from_(self.datastore.parent.attrs).inputs):
logging.info('Re-using the already imported exposure')
else: # no exposure
logging.info('Reading the site collection')
with self.monitor('reading site collection', autoflush=True):
self.sitecol = readinput.get_site_collection(self.oqparam)
# save mesh and asset collection
self.save_mesh()
if hasattr(self, 'assets_by_site'):
self.assetcol = riskinput.build_asset_collection(
self.assets_by_site, self.oqparam.time_event)
def read_exposure_sitecol(self):
"""
Read the exposure (if any) and then the site collection, possibly
extracted from the exposure.
"""
logging.info('Reading the site collection')
with self.monitor('reading site collection', autoflush=True):
haz_sitecol = readinput.get_site_collection(self.oqparam)
inputs = self.oqparam.inputs
if 'exposure' in inputs:
logging.info('Reading the exposure')
with self.monitor('reading exposure', autoflush=True):
self.exposure = readinput.get_exposure(self.oqparam)
self.sitecol, self.assets_by_site = (
readinput.get_sitecol_assets(self.oqparam, self.exposure))
if len(self.exposure.cost_types):
self.cost_types = self.exposure.cost_types
self.taxonomies = numpy.array(
sorted(self.exposure.taxonomies), '|S100')
num_assets = self.count_assets()
if self.datastore.parent:
haz_sitecol = self.datastore.parent['sitecol']
if haz_sitecol is not None and haz_sitecol != self.sitecol:
with self.monitor('assoc_assets_sites'):
self.sitecol, self.assets_by_site = \
self.assoc_assets_sites(haz_sitecol.complete)
ok_assets = self.count_assets()
num_sites = len(self.sitecol)
logging.warn('Associated %d assets to %d sites, %d discarded',
ok_assets, num_sites, num_assets - ok_assets)
elif (self.datastore.parent and 'exposure' in
OqParam.from_(self.datastore.parent.attrs).inputs):
logging.info('Re-using the already imported exposure')
else: # no exposure
self.sitecol = haz_sitecol
# save mesh and asset collection
self.save_mesh()
if hasattr(self, 'assets_by_site'):
self.assetcol = riskinput.build_asset_collection(
self.assets_by_site, self.oqparam.time_event)
spec = set(self.oqparam.specific_assets)
unknown = spec - set(self.assetcol['asset_ref'])
if unknown:
raise ValueError('The specific asset(s) %s are not in the '
'exposure' % ', '.join(unknown))
def read_exposure_sitecol(self):
"""
Read the exposure (if any) and then the site collection, possibly
extracted from the exposure.
"""
if 'exposure' in self.oqparam.inputs:
logging.info('Reading the exposure')
with self.monitor('reading exposure', autoflush=True):
self.exposure = readinput.get_exposure(self.oqparam)
self.sitecol, self.assets_by_site = (
readinput.get_sitecol_assets(self.oqparam, self.exposure))
self.cost_types = self.exposure.cost_types
self.taxonomies = numpy.array(
sorted(self.exposure.taxonomies), '|S100')
num_assets = self.count_assets()
mesh = readinput.get_mesh(self.oqparam)
if self.datastore.parent:
parent_mesh = self.datastore.parent['sitemesh'].value
if mesh is None:
mesh = Mesh(parent_mesh['lon'], parent_mesh['lat'])
if mesh is not None:
sites = readinput.get_site_collection(self.oqparam, mesh)
with self.monitor('assoc_assets_sites'):
self.sitecol, self.assets_by_site = \
self.assoc_assets_sites(sites)
ok_assets = self.count_assets()
num_sites = len(self.sitecol)
logging.warn('Associated %d assets to %d sites, %d discarded',
ok_assets, num_sites, num_assets - ok_assets)
if (self.is_stochastic and self.datastore.parent and
self.datastore.parent['sitecol'] != self.sitecol):
logging.warn(
'The hazard sites are different from the risk sites %s!=%s'
% (self.datastore.parent['sitecol'], self.sitecol))
else: # no exposure
logging.info('Reading the site collection')
with self.monitor('reading site collection', autoflush=True):
self.sitecol = readinput.get_site_collection(self.oqparam)
# save mesh and asset collection
self.save_mesh()
if hasattr(self, 'assets_by_site'):
self.assetcol = riskinput.build_asset_collection(
self.assets_by_site, self.oqparam.time_event)
def read_exposure_sitecol(self):
"""
Read the exposure (if any) and then the site collection, possibly
extracted from the exposure.
"""
inputs = self.oqparam.inputs
if 'exposure' in inputs:
logging.info('Reading the exposure')
with self.monitor('reading exposure', autoflush=True):
self.exposure = readinput.get_exposure(self.oqparam)
self.sitecol, self.assets_by_site = (
readinput.get_sitecol_assets(self.oqparam, self.exposure))
self.cost_types = self.exposure.cost_types
self.taxonomies = numpy.array(
sorted(self.exposure.taxonomies), '|S100')
num_assets = self.count_assets()
if self.datastore.parent:
haz_sitecol = self.datastore.parent['sitecol']
elif 'gmfs' in inputs:
haz_sitecol = readinput.get_site_collection(self.oqparam)
# TODO: think about the case hazard_curves in inputs
else:
haz_sitecol = None
if haz_sitecol is not None and haz_sitecol != self.sitecol:
with self.monitor('assoc_assets_sites'):
self.sitecol, self.assets_by_site = \
self.assoc_assets_sites(haz_sitecol.complete)
ok_assets = self.count_assets()
num_sites = len(self.sitecol)
logging.warn('Associated %d assets to %d sites, %d discarded',
ok_assets, num_sites, num_assets - ok_assets)
elif (self.datastore.parent and 'exposure' in
self.datastore.parent['oqparam'].inputs):
logging.info('Re-using the already imported exposure')
else: # no exposure
logging.info('Reading the site collection')
with self.monitor('reading site collection', autoflush=True):
self.sitecol = readinput.get_site_collection(self.oqparam)
# save mesh and asset collection
self.save_mesh()
if hasattr(self, 'assets_by_site'):
self.assetcol = riskinput.build_asset_collection(
self.assets_by_site, self.oqparam.time_event)
def _info(name, filtersources, weightsources):
if name in base.calculators:
print(textwrap.dedent(base.calculators[name].__doc__.strip()))
elif name == 'gsims':
for gs in gsim.get_available_gsims():
print(gs)
elif name.endswith('.xml'):
print(nrml.read(name).to_str())
elif name.endswith(('.ini', '.zip')):
oqparam = readinput.get_oqparam(name)
if 'exposure' in oqparam.inputs:
expo = readinput.get_exposure(oqparam)
sitecol, assets_by_site = readinput.get_sitecol_assets(
oqparam, expo)
elif filtersources or weightsources:
sitecol, assets_by_site = readinput.get_site_collection(
oqparam), []
else:
sitecol, assets_by_site = None, []
if 'source_model_logic_tree' in oqparam.inputs:
print('Reading the source model...')
if weightsources:
sp = source.SourceFilterWeighter
elif filtersources:
sp = source.SourceFilter
else:
sp = source.BaseSourceProcessor # do nothing
csm = readinput.get_composite_source_model(oqparam, sitecol, sp)
assoc = csm.get_rlzs_assoc()
dstore = datastore.Fake(vars(oqparam),
rlzs_assoc=assoc,
composite_source_model=csm,
sitecol=sitecol)
_print_info(dstore, filtersources, weightsources)
if len(assets_by_site):
assetcol = riskinput.build_asset_collection(assets_by_site)
dic = groupby(assetcol, operator.attrgetter('taxonomy'))
for taxo, num in dic.items():
print('taxonomy #%d, %d assets' % (taxo, num))
print('total assets = %d' % len(assetcol))
else:
print("No info for '%s'" % name)
def _info(name, filtersources, weightsources):
if name in base.calculators:
print(textwrap.dedent(base.calculators[name].__doc__.strip()))
elif name == 'gsims':
for gs in gsim.get_available_gsims():
print(gs)
elif name.endswith('.xml'):
print(nrml.read(name).to_str())
elif name.endswith(('.ini', '.zip')):
oqparam = readinput.get_oqparam(name)
if 'exposure' in oqparam.inputs:
expo = readinput.get_exposure(oqparam)
sitecol, assets_by_site = readinput.get_sitecol_assets(
oqparam, expo)
elif filtersources or weightsources:
sitecol, assets_by_site = readinput.get_site_collection(
oqparam), []
else:
sitecol, assets_by_site = None, []
if 'source_model_logic_tree' in oqparam.inputs:
print('Reading the source model...')
if weightsources:
sp = source.SourceFilterWeighter
elif filtersources:
sp = source.SourceFilter
else:
sp = source.BaseSourceProcessor # do nothing
csm = readinput.get_composite_source_model(oqparam, sitecol, sp)
assoc = csm.get_rlzs_assoc()
_print_info(
dict(rlzs_assoc=assoc, oqparam=oqparam,
composite_source_model=csm, sitecol=sitecol),
filtersources, weightsources)
if len(assets_by_site):
assetcol = riskinput.build_asset_collection(assets_by_site)
dic = groupby(assetcol, operator.attrgetter('taxonomy'))
for taxo, num in dic.items():
print('taxonomy #%d, %d assets' % (taxo, num))
print('total assets = %d' % len(assetcol))
else:
print("No info for '%s'" % name)
def _info(name, filtersources, weightsources):
if name in base.calculators:
print(textwrap.dedent(base.calculators[name].__doc__.strip()))
elif name == "gsims":
for gs in gsim.get_available_gsims():
print(gs)
elif name.endswith(".xml"):
print(nrml.read(name).to_str())
elif name.endswith((".ini", ".zip")):
oqparam = readinput.get_oqparam(name)
if "exposure" in oqparam.inputs:
expo = readinput.get_exposure(oqparam)
sitecol, assets_by_site = readinput.get_sitecol_assets(oqparam, expo)
elif filtersources or weightsources:
sitecol, assets_by_site = readinput.get_site_collection(oqparam), []
else:
sitecol, assets_by_site = None, []
if "source_model_logic_tree" in oqparam.inputs:
print("Reading the source model...")
if weightsources:
sp = source.SourceFilterWeighter
elif filtersources:
sp = source.SourceFilter
else:
sp = source.BaseSourceProcessor # do nothing
csm = readinput.get_composite_source_model(oqparam, sitecol, sp)
assoc = csm.get_rlzs_assoc()
dstore = datastore.Fake(vars(oqparam), rlzs_assoc=assoc, composite_source_model=csm, sitecol=sitecol)
_print_info(dstore, filtersources, weightsources)
if len(assets_by_site):
assetcol = riskinput.build_asset_collection(assets_by_site)
dic = groupby(assetcol, operator.attrgetter("taxonomy"))
for taxo, num in dic.items():
print("taxonomy #%d, %d assets" % (taxo, num))
print("total assets = %d" % len(assetcol))
else:
print("No info for '%s'" % name)
def pre_execute(self):
"""
Read the precomputed ruptures (or compute them on the fly) and
prepare some datasets in the datastore.
"""
super(EventBasedRiskCalculator, self).pre_execute()
if not self.riskmodel: # there is no riskmodel, exit early
self.execute = lambda: None
self.post_execute = lambda result: None
return
oq = self.oqparam
epsilon_sampling = oq.epsilon_sampling
correl_model = readinput.get_correl_model(oq)
gsims_by_col = self.rlzs_assoc.get_gsims_by_col()
assets_by_site = self.assets_by_site
# the following is needed to set the asset idx attribute
self.assetcol = riskinput.build_asset_collection(
assets_by_site, oq.time_event)
logging.info('Populating the risk inputs')
rup_by_tag = sum(self.datastore['sescollection'], AccumDict())
all_ruptures = [rup_by_tag[tag] for tag in sorted(rup_by_tag)]
num_samples = min(len(all_ruptures), epsilon_sampling)
eps_dict = riskinput.make_eps_dict(
assets_by_site, num_samples, oq.master_seed, oq.asset_correlation)
logging.info('Generated %d epsilons', num_samples * len(eps_dict))
self.epsilon_matrix = numpy.array(
[eps_dict[a['asset_ref']] for a in self.assetcol])
self.riskinputs = list(self.riskmodel.build_inputs_from_ruptures(
self.sitecol.complete, all_ruptures, gsims_by_col,
oq.truncation_level, correl_model, eps_dict,
oq.concurrent_tasks or 1))
logging.info('Built %d risk inputs', len(self.riskinputs))
# preparing empty datasets
loss_types = self.riskmodel.loss_types
self.L = len(loss_types)
self.R = len(self.rlzs_assoc.realizations)
self.outs = OUTPUTS
self.datasets = {}
self.monitor.oqparam = self.oqparam
# ugly: attaching an attribute needed in the task function
self.monitor.num_outputs = len(self.outs)
# attaching two other attributes used in riskinput.gen_outputs
self.monitor.assets_by_site = self.assets_by_site
self.monitor.num_assets = N = self.count_assets()
for o, out in enumerate(self.outs):
self.datastore.hdf5.create_group(out)
for l, loss_type in enumerate(loss_types):
cb = self.riskmodel.curve_builders[l]
build_curves = len(cb.ratios)
for r, rlz in enumerate(self.rlzs_assoc.realizations):
key = '/%s/rlz-%03d' % (loss_type, rlz.ordinal)
if o in (ELT, ILT): # loss tables
dset = self.datastore.create_dset(out + key, elt_dt)
else: # risk curves
if not build_curves:
continue
dset = self.datastore.create_dset(
out + key, cb.poes_dt, N)
self.datasets[o, l, r] = dset
if o in (FRC, IRC) and build_curves:
grp = self.datastore['%s/%s' % (out, loss_type)]
grp.attrs['loss_ratios'] = cb.ratios
def pre_execute(self):
"""
Read the precomputed ruptures (or compute them on the fly) and
prepare some datasets in the datastore.
"""
super(EventBasedRiskCalculator, self).pre_execute()
if not self.riskmodel: # there is no riskmodel, exit early
self.execute = lambda: None
self.post_execute = lambda result: None
return
oq = self.oqparam
epsilon_sampling = oq.epsilon_sampling
correl_model = readinput.get_correl_model(oq)
gsims_by_col = self.rlzs_assoc.get_gsims_by_col()
assets_by_site = self.assets_by_site
# the following is needed to set the asset idx attribute
self.assetcol = riskinput.build_asset_collection(
assets_by_site, oq.time_event)
logging.info('Populating the risk inputs')
rup_by_tag = sum(self.datastore['sescollection'], AccumDict())
all_ruptures = [rup_by_tag[tag] for tag in sorted(rup_by_tag)]
num_samples = min(len(all_ruptures), epsilon_sampling)
eps_dict = riskinput.make_eps_dict(assets_by_site, num_samples,
oq.master_seed,
oq.asset_correlation)
logging.info('Generated %d epsilons', num_samples * len(eps_dict))
self.epsilon_matrix = numpy.array(
[eps_dict[a['asset_ref']] for a in self.assetcol])
self.riskinputs = list(
self.riskmodel.build_inputs_from_ruptures(
self.sitecol.complete, all_ruptures, gsims_by_col,
oq.truncation_level, correl_model, eps_dict,
oq.concurrent_tasks or 1))
logging.info('Built %d risk inputs', len(self.riskinputs))
# preparing empty datasets
loss_types = self.riskmodel.loss_types
self.L = len(loss_types)
self.R = len(self.rlzs_assoc.realizations)
self.outs = OUTPUTS
self.datasets = {}
self.monitor.oqparam = self.oqparam
# ugly: attaching an attribute needed in the task function
self.monitor.num_outputs = len(self.outs)
# attaching two other attributes used in riskinput.gen_outputs
self.monitor.assets_by_site = self.assets_by_site
self.monitor.num_assets = N = self.count_assets()
for o, out in enumerate(self.outs):
self.datastore.hdf5.create_group(out)
for l, loss_type in enumerate(loss_types):
cb = self.riskmodel.curve_builders[l]
build_curves = len(cb.ratios)
for r, rlz in enumerate(self.rlzs_assoc.realizations):
key = '/%s/rlz-%03d' % (loss_type, rlz.ordinal)
if o in (ELT, ILT): # loss tables
dset = self.datastore.create_dset(out + key, elt_dt)
else: # risk curves
if not build_curves:
continue
dset = self.datastore.create_dset(
out + key, cb.poes_dt, N)
self.datasets[o, l, r] = dset
if o in (FRC, IRC) and build_curves:
grp = self.datastore['%s/%s' % (out, loss_type)]
grp.attrs['loss_ratios'] = cb.ratios
def pre_execute(self):
"""
Read the precomputed ruptures (or compute them on the fly) and
prepare some datasets in the datastore.
"""
super(EventBasedRiskCalculator, self).pre_execute()
if not self.riskmodel: # there is no riskmodel, exit early
self.execute = lambda: None
self.post_execute = lambda result: None
return
oq = self.oqparam
if self.riskmodel.covs:
epsilon_sampling = oq.epsilon_sampling
else:
epsilon_sampling = 1 # only one ignored epsilon
correl_model = readinput.get_correl_model(oq)
gsims_by_col = self.rlzs_assoc.get_gsims_by_col()
assets_by_site = self.assets_by_site
# the following is needed to set the asset idx attribute
self.assetcol = riskinput.build_asset_collection(
assets_by_site, oq.time_event)
self.spec_indices = numpy.array(
[a['asset_ref'] in oq.specific_assets for a in self.assetcol])
logging.info('Populating the risk inputs')
rup_by_tag = sum(self.datastore['sescollection'], AccumDict())
all_ruptures = [rup_by_tag[tag] for tag in sorted(rup_by_tag)]
for i, rup in enumerate(all_ruptures):
rup.ordinal = i
num_samples = min(len(all_ruptures), epsilon_sampling)
self.epsilon_matrix = eps = riskinput.make_eps(assets_by_site,
num_samples,
oq.master_seed,
oq.asset_correlation)
logging.info('Generated %d epsilons', num_samples * len(eps))
self.riskinputs = list(
self.riskmodel.build_inputs_from_ruptures(
self.sitecol.complete, all_ruptures, gsims_by_col,
oq.truncation_level, correl_model, eps, oq.concurrent_tasks
or 1))
logging.info('Built %d risk inputs', len(self.riskinputs))
# preparing empty datasets
loss_types = self.riskmodel.loss_types
self.L = len(loss_types)
self.R = len(self.rlzs_assoc.realizations)
self.outs = OUTPUTS
self.datasets = {}
# ugly: attaching an attribute needed in the task function
self.monitor.num_outputs = len(self.outs)
self.monitor.num_assets = self.count_assets()
for o, out in enumerate(self.outs):
self.datastore.hdf5.create_group(out)
for l, loss_type in enumerate(loss_types):
for r, rlz in enumerate(self.rlzs_assoc.realizations):
key = '/%s/%s' % (loss_type, rlz.uid)
if o == AGGLOSS: # loss tables
dset = self.datastore.create_dset(out + key, elt_dt)
elif o == SPECLOSS: # specific losses
dset = self.datastore.create_dset(out + key, ela_dt)
self.datasets[o, l, r] = dset