# -*- coding: utf-8 -*-
# vim: tabstop=4 shiftwidth=4 softtabstop=4
#
# Copyright (C) 2013-2019 GEM Foundation
#
# OpenQuake is free software: you can redistribute it and/or modify it
# under the terms of the GNU Affero General Public License as published
# by the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# OpenQuake is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Affero General Public License for more details.
#
# You should have received a copy of the GNU Affero General Public License
# along with OpenQuake. If not, see <http://www.gnu.org/licenses/>.
import re
import functools
import numpy
from openquake.baselib.node import Node
from openquake.baselib.general import AccumDict
from openquake.hazardlib import valid, nrml, InvalidFile
from openquake.hazardlib.sourcewriter import obj_to_node
from openquake.risklib import scientific
U32 = numpy.uint32
F32 = numpy.float32
F64 = numpy.float64
COST_TYPE_REGEX = '|'.join(valid.cost_type.choices)
RISK_TYPE_REGEX = re.compile(
r'(%s|occupants|fragility)_([\w_]+)' % COST_TYPE_REGEX)
[docs]def get_risk_files(inputs):
"""
:param inputs: a dictionary key -> path name
:returns: a pair (file_type, {risk_type: path})
"""
rfs = {}
job_ini = inputs['job_ini']
for key in inputs:
if key == 'fragility':
# backward compatibily for .ini files with key fragility_file
# instead of structural_fragility_file
rfs['fragility/structural'] = inputs[
'structural_fragility'] = inputs[key]
del inputs['fragility']
elif key.endswith(('_fragility', '_vulnerability', '_consequence')):
match = RISK_TYPE_REGEX.match(key)
if match and 'retrofitted' not in key and 'consequence' not in key:
rfs['%s/%s' % (match.group(2), match.group(1))] = inputs[key]
elif match is None:
raise ValueError('Invalid key in %s: %s_file' % (job_ini, key))
return rfs
# ########################### vulnerability ############################## #
[docs]def filter_vset(elem):
return elem.tag.endswith('discreteVulnerabilitySet')
[docs]@obj_to_node.add('VulnerabilityFunction')
def build_vf_node(vf):
"""
Convert a VulnerabilityFunction object into a Node suitable
for XML conversion.
"""
nodes = [Node('imls', {'imt': vf.imt}, vf.imls),
Node('meanLRs', {}, vf.mean_loss_ratios),
Node('covLRs', {}, vf.covs)]
return Node(
'vulnerabilityFunction',
{'id': vf.id, 'dist': vf.distribution_name}, nodes=nodes)
[docs]def get_risk_models(oqparam, kind='vulnerability fragility consequence '
'vulnerability_retrofitted'):
"""
:param oqparam:
an OqParam instance
:param kind:
a space-separated string with the kinds of risk models to read
:returns:
a dictionary riskid -> loss_type, kind -> function
"""
kinds = kind.split()
rmodels = AccumDict()
for kind in kinds:
for key in sorted(oqparam.inputs):
mo = re.match('(occupants|%s)_%s$' % (COST_TYPE_REGEX, kind), key)
if mo:
loss_type = mo.group(1) # the cost_type in the key
# can be occupants, structural, nonstructural, ...
rmodel = nrml.to_python(oqparam.inputs[key])
if len(rmodel) == 0:
raise InvalidFile('%s is empty!' % oqparam.inputs[key])
rmodels[loss_type, kind] = rmodel
if rmodel.lossCategory is None: # NRML 0.4
continue
cost_type = str(rmodel.lossCategory)
rmodel_kind = rmodel.__class__.__name__
kind_ = kind.replace('_retrofitted', '') # strip retrofitted
if not rmodel_kind.lower().startswith(kind_):
raise ValueError(
'Error in the file "%s_file=%s": is '
'of kind %s, expected %s' % (
key, oqparam.inputs[key], rmodel_kind,
kind.capitalize() + 'Model'))
if cost_type != loss_type:
raise ValueError(
'Error in the file "%s_file=%s": lossCategory is of '
'type "%s", expected "%s"' %
(key, oqparam.inputs[key],
rmodel.lossCategory, loss_type))
rdict = AccumDict(accum={})
rdict.limit_states = []
for (loss_type, kind), rm in sorted(rmodels.items()):
if kind == 'fragility':
# build a copy of the FragilityModel with different IM levels
newfm = rm.build(oqparam.continuous_fragility_discretization,
oqparam.steps_per_interval)
for (imt, riskid), ffl in sorted(newfm.items()):
if not rdict.limit_states:
rdict.limit_states.extend(rm.limitStates)
# we are rejecting the case of loss types with different
# limit states; this may change in the future
assert rdict.limit_states == rm.limitStates, (
rdict.limit_states, rm.limitStates)
rdict[riskid][loss_type, kind] = ffl
# TODO: see if it is possible to remove the attribute
# below, used in classical_damage
ffl.steps_per_interval = oqparam.steps_per_interval
elif kind == 'consequence':
for riskid, cf in sorted(rm.items()):
rdict[riskid][loss_type, kind] = cf
else: # vulnerability, vulnerability_retrofitted
cl_risk = oqparam.calculation_mode in (
'classical', 'classical_risk')
# only for classical_risk reduce the loss_ratios
# to make sure they are strictly increasing
for (imt, riskid), rf in sorted(rm.items()):
rdict[riskid][loss_type, kind] = (
rf.strictly_increasing() if cl_risk else rf)
return rdict
[docs]def get_values(loss_type, assets, time_event=None):
"""
:returns:
a numpy array with the values for the given assets, depending on the
loss_type.
"""
if loss_type == 'occupants':
return assets['occupants_%s' % time_event]
else:
return assets['value-' + loss_type]
loss_poe_dt = numpy.dtype([('loss', F64), ('poe', F64)])
[docs]def rescale(curves, values):
"""
Multiply the losses in each curve of kind (losses, poes) by the
corresponding value.
:param curves: an array of shape (A, 2, C)
:param values: an array of shape (A,)
"""
A, _, C = curves.shape
assert A == len(values), (A, len(values))
array = numpy.zeros((A, C), loss_poe_dt)
array['loss'] = [c * v for c, v in zip(curves[:, 0], values)]
array['poe'] = curves[:, 1]
return array
[docs]class RiskModel(object):
"""
Base class. Can be used in the tests as a mock.
:param taxonomy: a taxonomy string
:param risk_functions: a dict (loss_type, kind) -> risk_function
"""
time_event = None # used in scenario_risk
compositemodel = None # set by get_risk_model
def __init__(self, calcmode, taxonomy, risk_functions, **kw):
self.calcmode = calcmode
self.taxonomy = taxonomy
self.risk_functions = risk_functions
vars(self).update(kw)
steps = kw.get('lrem_steps_per_interval')
if calcmode in 'classical_risk':
self.loss_ratios = {
lt: tuple(vf.mean_loss_ratios_with_steps(steps))
for (lt, kind), vf in risk_functions.items()}
if calcmode == 'classical_bcr':
self.loss_ratios_orig = {
lt: tuple(vf.mean_loss_ratios_with_steps(steps))
for (lt, kind), vf in risk_functions.items()
if kind == 'vulnerability'}
self.loss_ratios_retro = {
lt: tuple(vf.mean_loss_ratios_with_steps(steps))
for (lt, kind), vf in risk_functions.items()
if kind == 'vulnerability_retrofitted'}
@property
def loss_types(self):
"""
The list of loss types in the underlying vulnerability functions,
in lexicographic order
"""
return sorted(lt for (lt, kind) in self.risk_functions)
def __call__(self, loss_type, assets, gmvs, eids, epsilons):
meth = getattr(self, self.calcmode)
res = meth(loss_type, assets, gmvs, eids, epsilons)
return res
def __toh5__(self):
return self.risk_functions, {'taxonomy': self.taxonomy}
def __fromh5__(self, dic, attrs):
vars(self).update(attrs)
self.risk_functions = dic
def __repr__(self):
return '<%s %s>' % (self.__class__.__name__, self.taxonomy)
# ######################## calculation methods ######################### #
[docs] def classical_risk(
self, loss_type, assets, hazard_curve, eids=None, eps=None):
"""
:param str loss_type:
the loss type considered
:param assets:
assets is an iterator over A
:class:`openquake.risklib.scientific.Asset` instances
:param hazard_curve:
an array of poes
:param eids:
ignored, here only for API compatibility with other calculators
:param eps:
ignored, here only for API compatibility with other calculators
:returns:
a composite array (loss, poe) of shape (A, C)
"""
n = len(assets)
vf = self.risk_functions[loss_type, 'vulnerability']
lratios = self.loss_ratios[loss_type]
imls = self.hazard_imtls[vf.imt]
values = get_values(loss_type, assets)
lrcurves = numpy.array(
[scientific.classical(vf, imls, hazard_curve, lratios)] * n)
return rescale(lrcurves, values)
[docs] def event_based_risk(self, loss_type, assets, gmvs, eids, epsilons):
"""
:param str loss_type:
the loss type considered
:param assets:
a list of assets on the same site and with the same taxonomy
:param gmvs_eids:
a pair (gmvs, eids) with E values each
:param epsilons:
a matrix of epsilons of shape (A, E) (or an empty tuple)
:returns:
an array of loss ratios of shape (A, E)
"""
E = len(gmvs)
A = len(assets)
loss_ratios = numpy.zeros((A, E), F32)
vf = self.risk_functions[loss_type, 'vulnerability']
means, covs, idxs = vf.interpolate(gmvs)
if len(means) == 0: # all gmvs are below the minimum imls, 0 ratios
pass
elif self.ignore_covs or covs.sum() == 0 or len(epsilons) == 0:
# the ratios are equal for all assets
ratios = vf.sample(means, covs, idxs, None) # right shape
for a in range(A):
loss_ratios[a, idxs] = ratios
else:
# take into account the epsilons
for a, asset in enumerate(assets):
loss_ratios[a, idxs] = vf.sample(
means, covs, idxs, epsilons[a])
return loss_ratios
ebrisk = event_based_risk
[docs] def classical_bcr(self, loss_type, assets, hazard, eids=None, eps=None):
"""
:param loss_type: the loss type
:param assets: a list of N assets of the same taxonomy
:param hazard: an hazard curve
:param _eps: dummy parameter, unused
:param _eids: dummy parameter, unused
:returns: a list of triples (eal_orig, eal_retro, bcr_result)
"""
if loss_type != 'structural':
raise NotImplemented('retrofitted is not defined for ' + loss_type)
n = len(assets)
self.assets = assets
vf = self.risk_functions[loss_type, 'vulnerability']
imls = self.hazard_imtls[vf.imt]
vf_retro = self.risk_functions[loss_type, 'vulnerability_retrofitted']
curves_orig = functools.partial(
scientific.classical, vf, imls,
loss_ratios=self.loss_ratios_orig[loss_type])
curves_retro = functools.partial(
scientific.classical, vf_retro, imls,
loss_ratios=self.loss_ratios_retro[loss_type])
original_loss_curves = numpy.array([curves_orig(hazard)] * n)
retrofitted_loss_curves = numpy.array([curves_retro(hazard)] * n)
eal_original = numpy.array([scientific.average_loss(lc)
for lc in original_loss_curves])
eal_retrofitted = numpy.array([scientific.average_loss(lc)
for lc in retrofitted_loss_curves])
bcr_results = [
scientific.bcr(
eal_original[i], eal_retrofitted[i],
self.interest_rate, self.asset_life_expectancy,
asset['value-' + loss_type], asset['retrofitted'])
for i, asset in enumerate(assets)]
return list(zip(eal_original, eal_retrofitted, bcr_results))
[docs] def scenario_risk(self, loss_type, assets, gmvs, eids, epsilons):
"""
:returns: an array of shape (A, E)
"""
values = get_values(loss_type, assets, self.time_event)
ok = ~numpy.isnan(values)
if not ok.any():
# there are no assets with a value
return numpy.zeros(0)
# there may be assets without a value
missing_value = not ok.all()
if missing_value:
assets = assets[ok]
epsilons = epsilons[ok]
E = len(eids)
# a matrix of A x E elements
loss_matrix = numpy.empty((len(assets), E))
loss_matrix.fill(numpy.nan)
vf = self.risk_functions[loss_type, 'vulnerability']
means, covs, idxs = vf.interpolate(gmvs)
loss_ratio_matrix = numpy.zeros((len(assets), E))
if len(epsilons):
for a, eps in enumerate(epsilons):
loss_ratio_matrix[a, idxs] = vf.sample(means, covs, idxs, eps)
else:
ratios = vf.sample(means, covs, idxs, numpy.zeros(len(means), F32))
for a in range(len(assets)):
loss_ratio_matrix[a, idxs] = ratios
loss_matrix[:, :] = (loss_ratio_matrix.T * values).T
return loss_matrix
scenario = scenario_risk
[docs] def scenario_damage(self, loss_type, assets, gmvs, eids=None, eps=None):
"""
:param loss_type: the loss type
:param assets: a list of A assets of the same taxonomy
:param gmvs_eids: pairs (gmvs, eids), each one with E elements
:param _eps: dummy parameter, unused
:returns: an array of shape (A, E, D + 1) elements
where N is the number of points, E the number of events
and D the number of damage states.
"""
ffs = self.risk_functions[loss_type, 'fragility']
damages = scientific.scenario_damage(ffs, gmvs).T
E, D = damages.shape
dmg_csq = numpy.zeros((E, D + 1))
dmg_csq[:, :D] = damages
c_model = self.risk_functions.get((loss_type, 'consequence'))
if c_model: # compute consequences
means = [0] + [par[0] for par in c_model.params]
# NB: we add a 0 in front for nodamage state
[docs] dmg_csq[:, D] = damages @ means # consequence ratio
return numpy.array([dmg_csq] * len(assets))
def classical_damage(
self, loss_type, assets, hazard_curve, eids=None, eps=None):
"""
:param loss_type: the loss type
:param assets: a list of N assets of the same taxonomy
:param hazard_curve: an hazard curve array
:returns: an array of N assets and an array of N x D elements
where N is the number of points and D the number of damage states.
"""
ffl = self.risk_functions[loss_type, 'fragility']
hazard_imls = self.hazard_imtls[ffl.imt]
damage = scientific.classical_damage(
ffl, hazard_imls, hazard_curve,
investigation_time=self.investigation_time,
risk_investigation_time=self.risk_investigation_time)
return [a['number'] * damage for a in assets]
# NB: the approach used here relies on the convention of having the
# names of the arguments of the riskmodel class to be equal to the
# names of the parameter in the oqparam object. This is seen as a
# feature, since it forces people to be consistent with the names,
# in the spirit of the 'convention over configuration' philosophy
[docs]def get_riskmodel(taxonomy, oqparam, **extra):
"""
Return an instance of the correct riskmodel class, depending on the
attribute `calculation_mode` of the object `oqparam`.
:param taxonomy:
a taxonomy string
:param oqparam:
an object containing the parameters needed by the riskmodel class
:param extra:
extra parameters to pass to the riskmodel class
"""
extra['hazard_imtls'] = oqparam.imtls
extra['investigation_time'] = oqparam.investigation_time
extra['risk_investigation_time'] = oqparam.risk_investigation_time
extra['lrem_steps_per_interval'] = oqparam.lrem_steps_per_interval
extra['ignore_covs'] = oqparam.ignore_covs
extra['time_event'] = oqparam.time_event
if oqparam.calculation_mode == 'classical_bcr':
extra['interest_rate'] = oqparam.interest_rate
extra['asset_life_expectancy'] = oqparam.asset_life_expectancy
return RiskModel(oqparam.calculation_mode, taxonomy, **extra)