Source code for openquake.risklib.riskmodels

# -*- coding: utf-8 -*-
# vim: tabstop=4 shiftwidth=4 softtabstop=4
#
# Copyright (C) 2013-2018 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.
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# 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 inspect
import functools
import numpy

from openquake.baselib.node import Node
from openquake.baselib.general import CallableDict, AccumDict
from openquake.baselib.hdf5 import ArrayWrapper
from openquake.hazardlib import valid, nrml
from openquake.hazardlib.sourcewriter import obj_to_node
from openquake.risklib import utils, scientific

U32 = numpy.uint32
F32 = numpy.float32
F64 = numpy.float64
registry = CallableDict()

COST_TYPE_REGEX = '|'.join(valid.cost_type.choices)
RISK_TYPE_REGEX = re.compile(
    '(%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 = {} names = set() 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] names.add('fragility') del inputs['fragility'] continue 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] names.add(match.group(2)) if not names: return None, {} elif len(names) > 1: raise ValueError('Found inconsistent keys %s in the .ini file' % ', '.join(names)) return names.pop(), 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=None): """ :param oqparam: an OqParam instance :param kind: vulnerability|vulnerability_retrofitted|fragility|consequence; if None it is extracted from the oqparam.file_type attribute :returns: a dictionary taxonomy -> loss_type -> function """ kind = kind or oqparam.file_type rmodels = AccumDict() rmodels.limit_states = [] for key in sorted(oqparam.inputs): mo = re.match('(occupants|%s)_%s$' % (COST_TYPE_REGEX, kind), key) if mo: key_type = mo.group(1) # the cost_type in the key # can be occupants, structural, nonstructural, ... rmodel = nrml.to_python(oqparam.inputs[key]) rmodels[key_type] = 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 != key_type: raise ValueError( 'Error in the file "%s_file=%s": lossCategory is of type ' '"%s", expected "%s"' % (key, oqparam.inputs[key], rmodel.lossCategory, key_type)) rdict = AccumDict(accum={}) rdict.limit_states = [] if kind == 'fragility': limit_states = [] for loss_type, fm in sorted(rmodels.items()): # build a copy of the FragilityModel with different IM levels newfm = fm.build(oqparam.continuous_fragility_discretization, oqparam.steps_per_interval) for (imt, taxo), ffl in newfm.items(): if not limit_states: limit_states.extend(fm.limitStates) # we are rejecting the case of loss types with different # limit states; this may change in the future assert limit_states == fm.limitStates, ( limit_states, fm.limitStates) rdict[taxo][loss_type] = ffl # TODO: see if it is possible to remove the attribute # below, used in classical_damage ffl.steps_per_interval = oqparam.steps_per_interval rdict.limit_states = [str(ls) for ls in limit_states] elif kind == 'consequence': rdict = rmodels else: # vulnerability 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 loss_type, rm in rmodels.items(): for (imt, taxo), rf in rm.items(): rdict[taxo][loss_type] = ( 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. """ return numpy.array([a.value(loss_type, time_event) for a in assets])
[docs]class RiskModel(object): """ Base class. Can be used in the tests as a mock. """ time_event = None # used in scenario_risk compositemodel = None # set by get_risk_model kind = None # must be set in subclasses def __init__(self, taxonomy, risk_functions, insured_losses): self.taxonomy = taxonomy self.risk_functions = risk_functions self.insured_losses = insured_losses @property def loss_types(self): """ The list of loss types in the underlying vulnerability functions, in lexicographic order """ return sorted(self.risk_functions)
[docs] def get_loss_types(self, imt): """ :param imt: Intensity Measure Type string :returns: loss types with risk functions of the given imt """ return [lt for lt in self.loss_types if self.risk_functions[lt].imt == imt]
[docs] def get_output(self, assets, data_by_lt, epsgetter): """ :param assets: a list of assets with the same taxonomy :param data_by_lt: hazards for each loss type :param epsgetter: an epsilon getter function :returns: an ArrayWrapper of shape (L, ...) """ out = [self(lt, assets, data, epsgetter) for lt, data in zip(self.loss_types, data_by_lt)] return ArrayWrapper(numpy.array(out), dict(assets=assets))
def __toh5__(self): risk_functions = {lt: func for lt, func in self.risk_functions.items()} if hasattr(self, 'retro_functions'): for lt, func in self.retro_functions.items(): risk_functions[lt + '_retrofitted'] = func return 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__, list(self.risk_functions))
[docs]def rescale(curves, values): """ Multiply the losses in each curve of kind (losses, poes) by the corresponding value. """ n = len(curves) assert n == len(values), (n, len(values)) losses = [curves[i, 0] * values[i] for i in range(n)] poes = curves[:, 1] return numpy.array([[losses[i], poes[i]] for i in range(n)])
[docs]@registry.add('classical_risk', 'classical', 'disaggregation') class Classical(RiskModel): """ Classical PSHA-Based RiskModel. Computes loss curves and insured curves. """ kind = 'vulnerability' def __init__(self, taxonomy, vulnerability_functions, hazard_imtls, lrem_steps_per_interval, conditional_loss_poes, poes_disagg, insured_losses=False): """ :param imt: Intensity Measure Type for this riskmodel :param taxonomy: Taxonomy for this riskmodel :param vulnerability_functions: Dictionary of vulnerability functions by loss type :param hazard_imtls: The intensity measure types and levels of the hazard computation :param lrem_steps_per_interval: Configuration parameter :param poes_disagg: Probability of Exceedance levels used for disaggregate losses by taxonomy. :param bool insured_losses: ignored since insured loss curves are not implemented See :func:`openquake.risklib.scientific.classical` for a description of the other parameters. """ self.taxonomy = taxonomy self.risk_functions = vulnerability_functions self.hazard_imtls = hazard_imtls self.lrem_steps_per_interval = lrem_steps_per_interval self.conditional_loss_poes = conditional_loss_poes self.poes_disagg = poes_disagg self.insured_losses = insured_losses self.loss_ratios = { lt: vf.mean_loss_ratios_with_steps(self.lrem_steps_per_interval) for lt, vf in self.risk_functions.items()} def __call__(self, loss_type, assets, hazard_curve, _eps=None): """ :param str loss_type: the loss type considered :param assets: assets is an iterator over N :class:`openquake.risklib.scientific.Asset` instances :param hazard_curve: an array of poes :param _eps: ignored, here only for API compatibility with other calculators :returns: an array of shape (C, N, 2) """ n = len(assets) vf = self.risk_functions[loss_type] imls = self.hazard_imtls[vf.imt] values = get_values(loss_type, assets) lrcurves = numpy.array( [scientific.classical( vf, imls, hazard_curve, self.lrem_steps_per_interval)] * n) # if in the future we wanted to implement insured_losses the # following lines could be useful # deductibles = [a.deductible(loss_type) for a in assets] # limits = [a.insurance_limit(loss_type) for a in assets] # insured_curves = rescale( # utils.numpy_map(scientific.insured_loss_curve, # lrcurves, deductibles, limits), values) return rescale(lrcurves, values).transpose(2, 0, 1)
# transpose array from shape (N, 2, C) -> (C, N, 2) # otherwise .get_output would fail
[docs]@registry.add('event_based_risk', 'event_based', 'event_based_rupture', 'ucerf_rupture', 'ucerf_hazard', 'ucerf_risk') class ProbabilisticEventBased(RiskModel): """ Implements the Probabilistic Event Based riskmodel. Computes loss ratios and event IDs. """ kind = 'vulnerability' def __init__( self, taxonomy, vulnerability_functions, conditional_loss_poes, insured_losses=False): """ See :func:`openquake.risklib.scientific.event_based` for a description of the input parameters. """ self.taxonomy = taxonomy self.risk_functions = vulnerability_functions self.conditional_loss_poes = conditional_loss_poes self.insured_losses = insured_losses def __call__(self, loss_type, assets, gmvs_eids, epsgetter): """ :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 epsgetter: a callable returning the correct epsilons for the given gmvs :returns: a :class: `openquake.risklib.scientific.ProbabilisticEventBased.Output` instance. """ gmvs, eids = gmvs_eids E = len(gmvs) I = self.insured_losses + 1 A = len(assets) loss_ratios = numpy.zeros((A, E, I), F32) vf = self.risk_functions[loss_type] means, covs, idxs = vf.interpolate(gmvs) for i, asset in enumerate(assets): epsilons = epsgetter(asset.ordinal, eids) ratios = vf.sample(means, covs, idxs, epsilons) loss_ratios[i, idxs, 0] = ratios if self.insured_losses and loss_type != 'occupants': loss_ratios[i, idxs, 1] = scientific.insured_losses( ratios, asset.deductible(loss_type), asset.insurance_limit(loss_type)) return loss_ratios
[docs]@registry.add('classical_bcr') class ClassicalBCR(RiskModel): kind = 'vulnerability' def __init__(self, taxonomy, vulnerability_functions_orig, vulnerability_functions_retro, hazard_imtls, lrem_steps_per_interval, interest_rate, asset_life_expectancy): self.taxonomy = taxonomy self.risk_functions = vulnerability_functions_orig self.insured_losses = False # not implemented self.retro_functions = vulnerability_functions_retro self.assets = [] # set a __call__ time self.interest_rate = interest_rate self.asset_life_expectancy = asset_life_expectancy self.hazard_imtls = hazard_imtls self.lrem_steps_per_interval = lrem_steps_per_interval def __call__(self, loss_type, assets, hazard, _eps=None, _eids=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] imls = self.hazard_imtls[vf.imt] vf_retro = self.retro_functions[loss_type] curves_orig = functools.partial(scientific.classical, vf, imls, steps=self.lrem_steps_per_interval) curves_retro = functools.partial(scientific.classical, vf_retro, imls, steps=self.lrem_steps_per_interval) original_loss_curves = utils.numpy_map(curves_orig, [hazard] * n) retrofitted_loss_curves = utils.numpy_map(curves_retro, [hazard] * n) eal_original = utils.numpy_map( scientific.average_loss, original_loss_curves) eal_retrofitted = utils.numpy_map( scientific.average_loss, 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]@registry.add('scenario_risk', 'scenario') class Scenario(RiskModel): """ Implements the Scenario riskmodel. Computes the loss matrix. """ kind = 'vulnerability' def __init__(self, taxonomy, vulnerability_functions, insured_losses, time_event=None): self.taxonomy = taxonomy self.risk_functions = vulnerability_functions self.insured_losses = insured_losses self.time_event = time_event def __call__(self, loss_type, assets, gmvs_eids, epsgetter): gmvs, eids = gmvs_eids epsilons = [epsgetter(asset.ordinal, eids) for asset in assets] 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 # there may be assets without a value missing_value = not ok.all() if missing_value: assets = assets[ok] epsilons = epsilons[ok] E = len(epsilons[0]) I = self.insured_losses + 1 # a matrix of A x E x I elements loss_matrix = numpy.empty((len(assets), E, I)) loss_matrix.fill(numpy.nan) vf = self.risk_functions[loss_type] means, covs, idxs = vf.interpolate(gmvs) loss_ratio_matrix = numpy.zeros((len(assets), E)) for i, eps in enumerate(epsilons): loss_ratio_matrix[i, idxs] = vf.sample(means, covs, idxs, eps) loss_matrix[:, :, 0] = (loss_ratio_matrix.T * values).T if self.insured_losses and loss_type != "occupants": deductibles = [a.deductible(loss_type) for a in assets] limits = [a.insurance_limit(loss_type) for a in assets] insured_loss_ratio_matrix = utils.numpy_map( scientific.insured_losses, loss_ratio_matrix, deductibles, limits) loss_matrix[:, :, 1] = (insured_loss_ratio_matrix.T * values).T return loss_matrix
[docs]@registry.add('scenario_damage') class Damage(RiskModel): """ Implements the ScenarioDamage riskmodel. Computes the damages. """ kind = 'fragility' def __init__(self, taxonomy, fragility_functions): self.taxonomy = taxonomy self.risk_functions = fragility_functions self.insured_losses = False # not implemented def __call__(self, loss_type, assets, gmvs_eids, _eps=None): """ :param loss_type: the loss type :param assets: a list of N assets of the same taxonomy :param gmvs_eids: pairs (gmvs, eids), each one with E elements :param _eps: dummy parameter, unused :returns: N arrays of E x D 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] damages = scientific.scenario_damage(ffs, gmvs_eids[0]) # shape (D, E) damages[damages < 1E-7] = 0 # sanity check return [damages.T] * len(assets)
[docs]@registry.add('classical_damage') class ClassicalDamage(Damage): """ Implements the ClassicalDamage riskmodel. Computes the damages. """ kind = 'fragility' def __init__(self, taxonomy, fragility_functions, hazard_imtls, investigation_time, risk_investigation_time): self.taxonomy = taxonomy self.risk_functions = fragility_functions self.insured_losses = False # not implemented self.hazard_imtls = hazard_imtls self.investigation_time = investigation_time self.risk_investigation_time = risk_investigation_time assert risk_investigation_time, risk_investigation_time def __call__(self, loss_type, assets, hazard_curve, _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] 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 view 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 """ riskmodel_class = registry[oqparam.calculation_mode] # arguments needed to instantiate the riskmodel class argnames = inspect.getargspec(riskmodel_class.__init__).args[3:] # arguments extracted from oqparam known_args = set(name for name, value in inspect.getmembers(oqparam.__class__) if isinstance(value, valid.Param)) all_args = {} for argname in argnames: if argname in known_args: all_args[argname] = getattr(oqparam, argname) if 'hazard_imtls' in argnames: # special case all_args['hazard_imtls'] = oqparam.imtls all_args.update(extra) missing = set(argnames) - set(all_args) if missing: raise TypeError('Missing parameter: %s' % ', '.join(missing)) return riskmodel_class(taxonomy, **all_args)