def get_crmodel(oqparam):
"""
Return a :class:`openquake.risklib.riskinput.CompositeRiskModel` instance
:param oqparam:
an :class:`openquake.commonlib.oqvalidation.OqParam` instance
"""
riskdict = get_risk_models(oqparam)
oqparam.set_risk_imtls(riskdict)
if 'consequence' in oqparam.inputs:
# build consdict of the form cname_by_tagname -> tag -> array
consdict = {}
for by, fname in oqparam.inputs['consequence'].items():
dtypedict = {by: str, 'cname': str, 'loss_type': str, None: float}
dic = group_array(hdf5.read_csv(fname, dtypedict).array, 'cname')
for cname, group in dic.items():
bytag = {tag: _cons_coeffs(grp, riskdict.limit_states)
for tag, grp in group_array(group, by).items()}
consdict['%s_by_%s' % (cname, by)] = bytag
else:
# legacy approach, extract the consequences from the risk models
consdict = {'losses_by_taxonomy': {}}
for taxo, dic in riskdict.items():
coeffs_by_lt = {lt: dic.pop((lt, kind)) for lt, kind in list(dic)
if kind == 'consequence'}
if coeffs_by_lt:
dtlist = [(lt, F32) for lt in coeffs_by_lt]
coeffs = numpy.zeros(len(riskdict.limit_states), dtlist)
for lt, cf in coeffs_by_lt.items():
coeffs[lt] = cf
consdict['losses_by_taxonomy'][taxo] = coeffs
crm = riskmodels.CompositeRiskModel(oqparam, riskdict, consdict)
return crm
def get_crmodel(oqparam):
"""
Return a :class:`openquake.risklib.riskinput.CompositeRiskModel` instance
:param oqparam:
an :class:`openquake.commonlib.oqvalidation.OqParam` instance
"""
risklist = get_risk_functions(oqparam)
if not oqparam.limit_states and risklist.limit_states:
oqparam.limit_states = risklist.limit_states
elif 'damage' in oqparam.calculation_mode and risklist.limit_states:
assert oqparam.limit_states == risklist.limit_states
consdict = {}
if 'consequence' in oqparam.inputs:
# build consdict of the form cname_by_tagname -> tag -> array
for by, fname in oqparam.inputs['consequence'].items():
dtypedict = {by: str, 'cname': str, 'loss_type': str, None: float}
dic = group_array(hdf5.read_csv(fname, dtypedict).array, 'cname')
for cname, group in dic.items():
bytag = {
tag: _cons_coeffs(grp, risklist.limit_states)
for tag, grp in group_array(group, by).items()
}
consdict['%s_by_%s' % (cname, by)] = bytag
crm = riskmodels.CompositeRiskModel(oqparam, risklist, consdict)
return crm
def get_crmodel(oqparam):
"""
Return a :class:`openquake.risklib.riskinput.CompositeRiskModel` instance
:param oqparam:
an :class:`openquake.commonlib.oqvalidation.OqParam` instance
"""
riskdict = get_risk_models(oqparam)
oqparam.set_risk_imtls(riskdict)
crm = riskmodels.CompositeRiskModel(oqparam, riskdict)
return crm
def get_crmodel(oqparam):
"""
Return a :class:`openquake.risklib.riskinput.CompositeRiskModel` instance
:param oqparam:
an :class:`openquake.commonlib.oqvalidation.OqParam` instance
"""
risklist = get_risk_functions(oqparam)
if not oqparam.limit_states and risklist.limit_states:
oqparam.limit_states = risklist.limit_states
elif 'damage' in oqparam.calculation_mode and risklist.limit_states:
assert oqparam.limit_states == risklist.limit_states
loss_types = oqparam.loss_dt().names
consdict = {}
if 'consequence' in oqparam.inputs:
# build consdict of the form consequence_by_tagname -> tag -> array
for by, fnames in oqparam.inputs['consequence'].items():
if isinstance(fnames, str): # single file
fnames = [fnames]
dtypedict = {
by: str,
'consequence': str,
'loss_type': str,
None: float
}
# i.e. files collapsed.csv, fatalities.csv, ... with headers
# taxonomy,consequence,loss_type,slight,moderate,extensive
arrays = []
for fname in fnames:
arr = hdf5.read_csv(fname, dtypedict).array
arrays.append(arr)
for no, row in enumerate(arr, 2):
if row['loss_type'] not in loss_types:
msg = '%s: %s is not a recognized loss type, line=%d'
raise InvalidFile(msg % (fname, row['loss_type'], no))
array = numpy.concatenate(arrays)
dic = group_array(array, 'consequence')
for consequence, group in dic.items():
if consequence not in scientific.KNOWN_CONSEQUENCES:
raise InvalidFile('Unknown consequence %s in %s' %
(consequence, fnames))
bytag = {
tag: _cons_coeffs(grp, loss_types, risklist.limit_states)
for tag, grp in group_array(group, by).items()
}
consdict['%s_by_%s' % (consequence, by)] = bytag
# for instance consdict['collapsed_by_taxonomy']['W_LFM-DUM_H3']
# is [(0.05,), (0.2 ,), (0.6 ,), (1. ,)] for damage state and structural
crm = riskmodels.CompositeRiskModel(oqparam, risklist, consdict)
return crm
