def get_rlzs_assoc(oqparam):
"""
Extract the GSIM realizations from the gsim_logic_tree file, if present,
or build a single realization from the gsim attribute. It is only defined
for the scenario calculators.
:param oqparam:
an :class:`openquake.commonlib.oqvalidation.OqParam` instance
"""
if 'gsim_logic_tree' in oqparam.inputs:
gsim_lt = get_gsim_lt(oqparam, [])
if len(gsim_lt.values) != 1:
gsim_file = os.path.join(oqparam.base_path,
oqparam.inputs['gsim_logic_tree'])
raise InvalidFile(
'The gsim logic tree file %s must contain a single tectonic '
'region type, found %s instead ' %
(gsim_file, list(gsim_lt.values)))
trt = gsim_lt.values
rlzs = sorted(get_gsim_lt(oqparam, trt))
else:
rlzs = [
logictree.Realization(value=(str(oqparam.gsim), ),
weight=1,
lt_path=('', ),
ordinal=0,
lt_uid=('@', ))
]
return logictree.RlzsAssoc(rlzs)
def pre_execute(self):
"""
Read the curves and build the riskinputs.
"""
super(ClassicalDamageCalculator, self).pre_execute()
logging.info('Reading hazard curves from CSV')
sites, hcurves_by_imt = readinput.get_sitecol_hcurves(self.oqparam)
with self.monitor('assoc_assets_sites'):
sitecol, assets_by_site = self.assoc_assets_sites(sites)
num_assets = sum(len(assets) for assets in assets_by_site)
num_sites = len(sitecol)
logging.info('Associated %d assets to %d sites', num_assets, num_sites)
logging.info('Preparing the risk input')
self.riskinputs = self.build_riskinputs({
(0, 'FromFile'): hcurves_by_imt
})
fake_rlz = logictree.Realization(value=('FromFile', ),
weight=1,
lt_path=('', ),
ordinal=0,
lt_uid=('*', ))
self.rlzs_assoc = logictree.RlzsAssoc([fake_rlz])
def __fromh5__(self, dic, attrs):
# TODO: this is called more times than needed, maybe we should cache it
sm_data = dic['sm_data']
vars(self).update(attrs)
self.source_model_lt = dic['source_model_lt']
self.gsim_lt = dic['gsim_lt']
self.sm_rlzs = []
for sm_id, rec in enumerate(sm_data):
path = tuple(str(decode(rec['path'])).split('_'))
sm = logictree.Realization(rec['name'], rec['weight'], sm_id, path,
rec['samples'], rec['offset'])
self.sm_rlzs.append(sm)
def get_gmfs(calc):
"""
:param calc: a ScenarioDamage or ScenarioRisk calculator
:returns: a dictionary of gmfs
"""
if 'gmfs' in calc.oqparam.inputs: # from file
logging.info('Reading gmfs from file')
sitecol, calc.tags, gmfs_by_imt = readinput.get_gmfs(calc.oqparam)
calc.save_params() # save number_of_ground_motion_fields and sites
# reduce the gmfs matrices to the filtered sites
for imt in calc.oqparam.imtls:
gmfs_by_imt[imt] = gmfs_by_imt[imt][sitecol.indices]
logging.info('Preparing the risk input')
fake_rlz = logictree.Realization(
value=('FromFile',), weight=1, lt_path=('',),
ordinal=0, lt_uid=('*',))
calc.rlzs_assoc = logictree.RlzsAssoc([fake_rlz])
return sitecol, {(0, 'FromFile'): gmfs_by_imt}
# else from rupture
gmf = calc.datastore['gmfs/col00'].value
# NB: if the hazard site collection has N sites, the hazard
# filtered site collection for the nonzero GMFs has N' <= N sites
# whereas the risk site collection associated to the assets
# has N'' <= N' sites
if calc.datastore.parent:
haz_sitecol = calc.datastore.parent['sitecol'] # N' values
else:
haz_sitecol = calc.sitecol
risk_indices = set(calc.sitecol.indices) # N'' values
N = len(haz_sitecol.complete)
imt_dt = numpy.dtype([(imt, float) for imt in calc.oqparam.imtls])
gmf_by_idx = general.groupby(gmf, lambda row: row['idx'])
R = len(gmf_by_idx)
# build a matrix N x R for each GSIM realization
gmfs = {(trt_id, gsim): numpy.zeros((N, R), imt_dt)
for trt_id, gsim in calc.rlzs_assoc}
for rupid, rows in sorted(gmf_by_idx.items()):
assert len(haz_sitecol.indices) == len(rows), (
len(haz_sitecol.indices), len(rows))
for sid, gmv in zip(haz_sitecol.indices, rows):
if sid in risk_indices:
for trt_id, gsim in gmfs:
gmfs[trt_id, gsim][sid, rupid] = gmv[gsim]
return haz_sitecol, gmfs
def fake(cls, gsimlt=None):
"""
:returns:
a fake `FullLogicTree` instance with the given gsim logic tree
object; if None, builds automatically a fake gsim logic tree
"""
gsim_lt = gsimlt or logictree.GsimLogicTree.from_('[FromFile]')
fakeSM = logictree.Realization('scenario',
weight=1,
ordinal=0,
lt_path='b1',
samples=1)
info = object.__new__(cls)
info.source_model_lt = logictree.SourceModelLogicTree.fake()
info.gsim_lt = gsim_lt
info.sm_rlzs = [fakeSM]
return info
def read_gmfs_from_csv(calc):
"""
:param calc: a ScenarioDamage or ScenarioRisk calculator
:returns: riskinputs
"""
logging.info('Reading hazard curves from CSV')
gmfs_by_imt = readinput.get_gmfs(calc.oqparam, calc.sitecol.complete)
# reduce the gmfs matrices to the filtered sites
for imt in calc.oqparam.imtls:
gmfs_by_imt[imt] = gmfs_by_imt[imt][calc.sitecol.indices]
num_assets = calc.count_assets()
num_sites = len(calc.sitecol)
logging.info('Associated %d assets to %d sites', num_assets, num_sites)
logging.info('Preparing the risk input')
fake_rlz = logictree.Realization(value=('FromCsv', ),
weight=1,
lt_path=('', ),
ordinal=0,
lt_uid=('*', ))
calc.rlzs_assoc = logictree.RlzsAssoc([fake_rlz])
return {(0, 'FromCsv'): gmfs_by_imt}
def read_gmfs_from_file(calc):
"""
:param calc: a ScenarioDamage or ScenarioRisk calculator
:returns: riskinputs
"""
logging.info('Reading gmfs from file')
try:
sitecol = calc.sitecol.complete
except KeyError:
sitecol = None
calc.sitecol, calc.tags, gmfs_by_imt = readinput.get_gmfs(
calc.oqparam, sitecol)
calc.save_params() # save number_of_ground_motion_fields and sites
# reduce the gmfs matrices to the filtered sites
for imt in calc.oqparam.imtls:
gmfs_by_imt[imt] = gmfs_by_imt[imt][calc.sitecol.indices]
logging.info('Preparing the risk input')
fake_rlz = logictree.Realization(
value=('FromFile',), weight=1, lt_path=('',),
ordinal=0, lt_uid=('*',))
calc.rlzs_assoc = logictree.RlzsAssoc([fake_rlz])
return {(0, 'FromFile'): gmfs_by_imt}