Source code for openquake.risklib.scientific

# -*- coding: utf-8 -*-
# vim: tabstop=4 shiftwidth=4 softtabstop=4
#
# Copyright (C) 2012-2018 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.
#
# You should have received a copy of the GNU Affero General Public License
# along with OpenQuake. If not, see <http://www.gnu.org/licenses/>.

"""
This module includes the scientific API of the oq-risklib
"""
import abc
import copy
import bisect
import warnings
import collections

import numpy
from numpy.testing import assert_equal
from scipy import interpolate, stats, random

from openquake.baselib.general import CallableDict, group_array
from openquake.hazardlib.stats import compute_stats2
from openquake.risklib import utils

F32 = numpy.float32
U32 = numpy.uint32


[docs]def fine_graining(points, steps): """ :param points: a list of floats :param int steps: expansion steps (>= 2) >>> fine_graining([0, 1], steps=0) [0, 1] >>> fine_graining([0, 1], steps=1) [0, 1] >>> fine_graining([0, 1], steps=2) array([0. , 0.5, 1. ]) >>> fine_graining([0, 1], steps=3) array([0. , 0.33333333, 0.66666667, 1. ]) >>> fine_graining([0, 0.5, 0.7, 1], steps=2) array([0. , 0.25, 0.5 , 0.6 , 0.7 , 0.85, 1. ]) N points become S * (N - 1) + 1 points with S > 0 """ if steps < 2: return points ls = numpy.concatenate([numpy.linspace(x, y, num=steps + 1)[:-1] for x, y in utils.pairwise(points)]) return numpy.concatenate([ls, [points[-1]]])
# # Input models #
[docs]class VulnerabilityFunction(object): dtype = numpy.dtype([('iml', F32), ('loss_ratio', F32), ('cov', F32)]) def __init__(self, vf_id, imt, imls, mean_loss_ratios, covs=None, distribution="LN"): """ A wrapper around a probabilistic distribution function (currently only the log normal distribution is supported). It is meant to be pickeable to allow distributed computation. The only important method is `.__call__`, which applies the vulnerability function to a given set of ground motion fields and epsilons and return a loss matrix with N x R elements. :param str vf_id: Vulnerability Function ID :param str imt: Intensity Measure Type as a string :param list imls: Intensity Measure Levels for the vulnerability function. All values must be >= 0.0, values must be arranged in ascending order with no duplicates :param list mean_loss_ratios: Mean Loss ratio values, equal in length to imls, where value >= 0. :param list covs: Coefficients of Variation. Equal in length to mean loss ratios. All values must be >= 0.0. :param str distribution_name: The probabilistic distribution related to this function. """ self.id = vf_id self.imt = imt self._check_vulnerability_data( imls, mean_loss_ratios, covs, distribution) self.imls = numpy.array(imls) self.mean_loss_ratios = numpy.array(mean_loss_ratios) if covs is not None: self.covs = numpy.array(covs) else: self.covs = numpy.zeros(self.imls.shape) for lr, cov in zip(self.mean_loss_ratios, self.covs): if lr == 0.0 and cov > 0.0: msg = ("It is not valid to define a loss ratio = 0.0 with a " "corresponding coeff. of variation > 0.0") raise ValueError(msg) self.distribution_name = distribution # to be set in .init(), called also by __setstate__ (self.stddevs, self._mlr_i1d, self._covs_i1d, self.distribution) = None, None, None, None self.init()
[docs] def init(self): self.stddevs = self.covs * self.mean_loss_ratios self._mlr_i1d = interpolate.interp1d(self.imls, self.mean_loss_ratios) self._covs_i1d = interpolate.interp1d(self.imls, self.covs) self.set_distribution(None)
[docs] def set_distribution(self, epsilons=None): if (self.covs > 0).any(): self.distribution = DISTRIBUTIONS[self.distribution_name]() else: self.distribution = DegenerateDistribution() self.distribution.epsilons = (numpy.array(epsilons) if epsilons is not None else None)
[docs] def interpolate(self, gmvs): """ :param gmvs: array of intensity measure levels :returns: (interpolated loss ratios, interpolated covs, indices > min) """ # gmvs are clipped to max(iml) gmvs_curve = numpy.piecewise( gmvs, [gmvs > self.imls[-1]], [self.imls[-1], lambda x: x]) idxs = gmvs_curve >= self.imls[0] # indices over the minimum gmvs_curve = gmvs_curve[idxs] return self._mlr_i1d(gmvs_curve), self._cov_for(gmvs_curve), idxs
[docs] def sample(self, means, covs, idxs, epsilons): """ Sample the epsilons and apply the corrections to the means. This method is called only if there are nonzero covs. :param means: array of E' loss ratios :param covs: array of E' floats :param idxs: array of E booleans with E >= E' :param epsilons: array of E floats :returns: array of E' loss ratios """ if epsilons is None: return means self.set_distribution(epsilons) return self.distribution.sample(means, covs, means * covs, idxs)
# this is used in the tests, not in the engine code base def __call__(self, gmvs, epsilons): """ A small wrapper around .interpolate and .apply_to """ means, covs, idxs = self.interpolate(gmvs) # for gmvs < min(iml) we return a loss of 0 (default) ratios = numpy.zeros(len(gmvs)) ratios[idxs] = self.sample(means, covs, idxs, epsilons) return ratios
[docs] def strictly_increasing(self): """ :returns: a new vulnerability function that is strictly increasing. It is built by removing piece of the function where the mean loss ratio is constant. """ imls, mlrs, covs = [], [], [] previous_mlr = None for i, mlr in enumerate(self.mean_loss_ratios): if previous_mlr == mlr: continue else: mlrs.append(mlr) imls.append(self.imls[i]) covs.append(self.covs[i]) previous_mlr = mlr return self.__class__( self.id, self.imt, imls, mlrs, covs, self.distribution_name)
[docs] def mean_loss_ratios_with_steps(self, steps): """ Split the mean loss ratios, producing a new set of loss ratios. The new set of loss ratios always includes 0.0 and 1.0 :param int steps: the number of steps we make to go from one loss ratio to the next. For example, if we have [0.5, 0.7]:: steps = 1 produces [0.0, 0.5, 0.7, 1] steps = 2 produces [0.0, 0.25, 0.5, 0.6, 0.7, 0.85, 1] steps = 3 produces [0.0, 0.17, 0.33, 0.5, 0.57, 0.63, 0.7, 0.8, 0.9, 1] """ loss_ratios = self.mean_loss_ratios if min(loss_ratios) > 0.0: # prepend with a zero loss_ratios = numpy.concatenate([[0.0], loss_ratios]) if max(loss_ratios) < 1.0: # append a 1.0 loss_ratios = numpy.concatenate([loss_ratios, [1.0]]) return fine_graining(loss_ratios, steps)
def _cov_for(self, imls): """ Clip `imls` to the range associated with the support of the vulnerability function and returns the corresponding covariance values by linear interpolation. For instance if the range is [0.005, 0.0269] and the imls are [0.0049, 0.006, 0.027], the clipped imls are [0.005, 0.006, 0.0269]. """ return self._covs_i1d( numpy.piecewise( imls, [imls > self.imls[-1], imls < self.imls[0]], [self.imls[-1], self.imls[0], lambda x: x])) def __getstate__(self): return (self.id, self.imt, self.imls, self.mean_loss_ratios, self.covs, self.distribution_name) def __setstate__(self, state): self.id = state[0] self.imt = state[1] self.imls = state[2] self.mean_loss_ratios = state[3] self.covs = state[4] self.distribution_name = state[5] self.init() def _check_vulnerability_data(self, imls, loss_ratios, covs, distribution): assert_equal(imls, sorted(set(imls))) assert all(x >= 0.0 for x in imls) assert covs is None or len(covs) == len(imls) assert len(loss_ratios) == len(imls) assert all(x >= 0.0 for x in loss_ratios) assert covs is None or all(x >= 0.0 for x in covs) assert distribution in ["LN", "BT"]
[docs] @utils.memoized def loss_ratio_exceedance_matrix(self, steps): """ Compute the LREM (Loss Ratio Exceedance Matrix). :param int steps: Number of steps between loss ratios. """ # add steps between mean loss ratio values loss_ratios = self.mean_loss_ratios_with_steps(steps) # LREM has number of rows equal to the number of loss ratios # and number of columns equal to the number of imls lrem = numpy.empty((loss_ratios.size, self.imls.size), float) for row, loss_ratio in enumerate(loss_ratios): for col, (mean_loss_ratio, stddev) in enumerate( zip(self.mean_loss_ratios, self.stddevs)): lrem[row][col] = self.distribution.survival( loss_ratio, mean_loss_ratio, stddev) return loss_ratios, lrem
[docs] @utils.memoized def mean_imls(self): """ Compute the mean IMLs (Intensity Measure Level) for the given vulnerability function. :param vulnerability_function: the vulnerability function where the IMLs (Intensity Measure Level) are taken from. :type vuln_function: :py:class:`openquake.risklib.vulnerability_function.\ VulnerabilityFunction` """ return numpy.array( [max(0, self.imls[0] - (self.imls[1] - self.imls[0]) / 2.)] + [numpy.mean(pair) for pair in utils.pairwise(self.imls)] + [self.imls[-1] + (self.imls[-1] - self.imls[-2]) / 2.])
def __toh5__(self): """ :returns: a pair (array, attrs) suitable for storage in HDF5 format """ array = numpy.zeros(len(self.imls), self.dtype) array['iml'] = self.imls array['loss_ratio'] = self.mean_loss_ratios array['cov'] = self.covs return array, {'id': self.id, 'imt': self.imt, 'distribution_name': self.distribution_name} def __fromh5__(self, array, attrs): vars(self).update(attrs) self.imls = array['iml'] self.mean_loss_ratios = array['loss_ratio'] self.covs = array['cov'] def __repr__(self): return '<VulnerabilityFunction(%s, %s)>' % (self.id, self.imt)
[docs]class VulnerabilityFunctionWithPMF(VulnerabilityFunction): """ Vulnerability function with an explicit distribution of probabilities :param str vf_id: vulnerability function ID :param str imt: Intensity Measure Type :param imls: intensity measure levels (L) :param ratios: an array of mean ratios (M) :param probs: a matrix of probabilities of shape (M, L) """ def __init__(self, vf_id, imt, imls, loss_ratios, probs, seed=42): self.id = vf_id self.imt = imt self._check_vulnerability_data(imls, loss_ratios, probs) self.imls = imls self.loss_ratios = loss_ratios self.probs = probs self.seed = seed self.distribution_name = "PM" # to be set in .init(), called also by __setstate__ (self._probs_i1d, self.distribution) = None, None self.init() ls = [('iml', F32)] + [('prob-%s' % lr, F32) for lr in loss_ratios] self.dtype = numpy.dtype(ls)
[docs] def init(self): # the seed is reset in CompositeRiskModel.__init__ self._probs_i1d = interpolate.interp1d(self.imls, self.probs) self.set_distribution(None)
[docs] def set_distribution(self, epsilons=None): self.distribution = DISTRIBUTIONS[self.distribution_name]() self.distribution.epsilons = epsilons self.distribution.seed = self.seed
def __getstate__(self): return (self.id, self.imt, self.imls, self.loss_ratios, self.probs, self.distribution_name, self.seed) def __setstate__(self, state): self.id = state[0] self.imt = state[1] self.imls = state[2] self.loss_ratios = state[3] self.probs = state[4] self.distribution_name = state[5] self.seed = state[6] self.init() def _check_vulnerability_data(self, imls, loss_ratios, probs): assert all(x >= 0.0 for x in imls) assert all(x >= 0.0 for x in loss_ratios) assert all([1.0 >= x >= 0.0 for x in y] for y in probs) assert probs.shape[0] == len(loss_ratios) assert probs.shape[1] == len(imls)
[docs] def interpolate(self, gmvs): """ :param gmvs: array of intensity measure levels :returns: (interpolated probabilities, None, indices > min) """ # gmvs are clipped to max(iml) gmvs_curve = numpy.piecewise( gmvs, [gmvs > self.imls[-1]], [self.imls[-1], lambda x: x]) idxs = gmvs_curve >= self.imls[0] # indices over the minimum gmvs_curve = gmvs_curve[idxs] return self._probs_i1d(gmvs_curve), None, idxs
[docs] def sample(self, probs, _covs, idxs, epsilons): """ Sample the .loss_ratios with the given probabilities. :param probs: array of E' floats :param _covs: ignored, it is there only for API consistency :param idxs: array of E booleans with E >= E' :param epsilons: array of E floats :returns: array of E' probabilities """ self.set_distribution(epsilons) return self.distribution.sample(self.loss_ratios, probs)
[docs] @utils.memoized def loss_ratio_exceedance_matrix(self, steps): """ Compute the LREM (Loss Ratio Exceedance Matrix). Required for the Classical Risk and BCR Calculators. Currently left unimplemented as the PMF format is used only for the Scenario and Event Based Risk Calculators. :param int steps: Number of steps between loss ratios. """
# TODO: to be implemented if the classical risk calculator # needs to support the pmf vulnerability format def __toh5__(self): """ :returns: a pair (array, attrs) suitable for storage in HDF5 format """ array = numpy.zeros(len(self.imls), self.dtype) array['iml'] = self.imls for i, lr in enumerate(self.loss_ratios): array['prob-%s' % lr] = self.probs[i] return array, {'id': self.id, 'imt': self.imt, 'distribution_name': self.distribution_name} def __fromh5__(self, array, attrs): lrs = [n.split('-')[1] for n in array.dtype.names if '-' in n] self.loss_ratios = map(float, lrs) self.imls = array['iml'] self.probs = array vars(self).update(attrs) def __repr__(self): return '<VulnerabilityFunctionWithPMF(%s, %s)>' % (self.id, self.imt)
# this is meant to be instantiated by riskmodels.get_risk_models
[docs]class VulnerabilityModel(dict): """ Container for a set of vulnerability functions. You can access each function given the IMT and taxonomy with the square bracket notation. :param str id: ID of the model :param str assetCategory: asset category (i.e. buildings, population) :param str lossCategory: loss type (i.e. structural, contents, ...) All such attributes are None for a vulnerability model coming from a NRML 0.4 file. """ def __init__(self, id=None, assetCategory=None, lossCategory=None): self.id = id self.assetCategory = assetCategory self.lossCategory = lossCategory def __repr__(self): return '<%s %s %s>' % ( self.__class__.__name__, self.lossCategory, sorted(self))
# ############################## fragility ############################### #
[docs]class FragilityFunctionContinuous(object): # FIXME (lp). Should be re-factored with LogNormalDistribution def __init__(self, limit_state, mean, stddev): self.limit_state = limit_state self.mean = mean self.stddev = stddev def __call__(self, imls): """ Compute the Probability of Exceedance (PoE) for the given Intensity Measure Levels (IMLs). """ variance = self.stddev ** 2.0 sigma = numpy.sqrt(numpy.log( (variance / self.mean ** 2.0) + 1.0)) mu = self.mean ** 2.0 / numpy.sqrt( variance + self.mean ** 2.0) return stats.lognorm.cdf(imls, sigma, scale=mu) def __getstate__(self): return dict(limit_state=self.limit_state, mean=self.mean, stddev=self.stddev) def __repr__(self): return '<%s(%s, %s, %s)>' % ( self.__class__.__name__, self.limit_state, self.mean, self.stddev)
[docs]class FragilityFunctionDiscrete(object): def __init__(self, limit_state, imls, poes, no_damage_limit=None): self.limit_state = limit_state self.imls = imls self.poes = poes self._interp = None self.no_damage_limit = no_damage_limit @property def interp(self): if self._interp is not None: return self._interp self._interp = interpolate.interp1d(self.imls, self.poes, bounds_error=False) return self._interp def __call__(self, imls): """ Compute the Probability of Exceedance (PoE) for the given Intensity Measure Levels (IMLs). """ highest_iml = self.imls[-1] imls = numpy.array(imls) if imls.sum() == 0.0: return numpy.zeros_like(imls) imls[imls > highest_iml] = highest_iml result = self.interp(imls) if self.no_damage_limit: result[imls < self.no_damage_limit] = 0 return result # so that the curve is pickeable def __getstate__(self): return dict(limit_state=self.limit_state, poes=self.poes, imls=self.imls, _interp=None, no_damage_limit=self.no_damage_limit) def __eq__(self, other): return (self.poes == other.poes and self.imls == other.imls and self.no_damage_limit == other.no_damage_limit) def __ne__(self, other): return not self == other def __repr__(self): return '<%s(%s, %s, %s)>' % ( self.__class__.__name__, self.limit_state, self.imls, self.poes)
[docs]class FragilityFunctionList(list): """ A list of fragility functions with common attributes; there is a function for each limit state. """ # NB: the list is populated after instantiation by .append calls def __init__(self, array, **attrs): self.array = array vars(self).update(attrs)
[docs] def mean_loss_ratios_with_steps(self, steps): """For compatibility with vulnerability functions""" return fine_graining(self.imls, steps)
[docs] def build(self, limit_states, discretization, steps_per_interval): """ :param limit_states: a sequence of limit states :param discretization: continouos fragility discretization parameter :param steps_per_interval: steps_per_interval parameter :returns: a populated FragilityFunctionList instance """ new = copy.copy(self) add_zero = (self.format == 'discrete' and self.nodamage is not None and self.nodamage < self.imls[0]) new.imls = build_imls(new, discretization) if steps_per_interval > 1: new.interp_imls = build_imls( # passed to classical_damage new, discretization, steps_per_interval) for i, ls in enumerate(limit_states): data = self.array[i] if self.format == 'discrete': if add_zero: new.append(FragilityFunctionDiscrete( ls, [self.nodamage] + self.imls, numpy.concatenate([[0.], data]), self.nodamage)) else: new.append(FragilityFunctionDiscrete( ls, self.imls, data, self.nodamage)) else: # continuous new.append(FragilityFunctionContinuous( ls, data['mean'], data['stddev'])) return new
def __toh5__(self): return self.array, {k: v for k, v in vars(self).items() if k != 'array' and v is not None} def __fromh5__(self, array, attrs): self.array = array vars(self).update(attrs) def __repr__(self): kvs = ['%s=%s' % item for item in vars(self).items()] return '<FragilityFunctionList %s>' % ', '.join(kvs)
ConsequenceFunction = collections.namedtuple( 'ConsequenceFunction', 'id dist params')
[docs]class ConsequenceModel(dict): """ Container for a set of consequence functions. You can access each function given its name with the square bracket notation. :param str id: ID of the model :param str assetCategory: asset category (i.e. buildings, population) :param str lossCategory: loss type (i.e. structural, contents, ...) :param str description: description of the model :param limitStates: a list of limit state strings :param consequence_functions: a dictionary name -> ConsequenceFunction """ def __init__(self, id, assetCategory, lossCategory, description, limitStates): self.id = id self.assetCategory = assetCategory self.lossCategory = lossCategory self.description = description self.limitStates = limitStates def __repr__(self): return '<%s %s %s %s>' % ( self.__class__.__name__, self.lossCategory, ', '.join(self.limitStates), ' '.join(sorted(self)))
[docs]def build_imls(ff, continuous_fragility_discretization, steps_per_interval=0): """ Build intensity measure levels from a fragility function. If the function is continuous, they are produced simply as a linear space between minIML and maxIML. If the function is discrete, they are generated with a complex logic depending on the noDamageLimit and the parameter steps per interval. :param ff: a fragility function object :param continuous_fragility_discretization: .ini file parameter :param steps_per_interval: .ini file parameter :returns: generated imls """ if ff.format == 'discrete': imls = ff.imls if ff.nodamage is not None and ff.nodamage < imls[0]: imls = [ff.nodamage] + imls if steps_per_interval > 1: gen_imls = fine_graining(imls, steps_per_interval) else: gen_imls = imls else: # continuous gen_imls = numpy.linspace(ff.minIML, ff.maxIML, continuous_fragility_discretization) return gen_imls
# this is meant to be instantiated by riskmodels.get_fragility_model
[docs]class FragilityModel(dict): """ Container for a set of fragility functions. You can access each function given the IMT and taxonomy with the square bracket notation. :param str id: ID of the model :param str assetCategory: asset category (i.e. buildings, population) :param str lossCategory: loss type (i.e. structural, contents, ...) :param str description: description of the model :param limitStates: a list of limit state strings """ def __init__(self, id, assetCategory, lossCategory, description, limitStates): self.id = id self.assetCategory = assetCategory self.lossCategory = lossCategory self.description = description self.limitStates = limitStates def __repr__(self): return '<%s %s %s %s>' % ( self.__class__.__name__, self.lossCategory, self.limitStates, sorted(self))
[docs] def build(self, continuous_fragility_discretization, steps_per_interval): """ Return a new FragilityModel instance, in which the values have been replaced with FragilityFunctionList instances. :param continuous_fragility_discretization: configuration parameter :param steps_per_interval: configuration parameter """ newfm = copy.copy(self) for key, ffl in self.items(): newfm[key] = ffl.build(self.limitStates, continuous_fragility_discretization, steps_per_interval) return newfm
# # Distribution & Sampling # DISTRIBUTIONS = CallableDict()
[docs]class Distribution(metaclass=abc.ABCMeta): """ A Distribution class models continuous probability distribution of random variables used to sample losses of a set of assets. It is usually registered with a name (e.g. LN, BT, PM) by using :class:`openquake.baselib.general.CallableDict` """
[docs] @abc.abstractmethod def sample(self, means, covs, stddevs, idxs): """ :returns: sample a set of losses :param means: an array of mean losses :param covs: an array of covariances :param stddevs: an array of stddevs """ raise NotImplementedError
[docs] @abc.abstractmethod def survival(self, loss_ratio, mean, stddev): """ Return the survival function of the distribution with `mean` and `stddev` applied to `loss_ratio` """ raise NotImplementedError
[docs]class DegenerateDistribution(Distribution): """ The degenerate distribution. E.g. a distribution with a delta corresponding to the mean. """
[docs] def sample(self, means, _covs, _stddev, _idxs): return means
[docs] def survival(self, loss_ratio, mean, _stddev): return numpy.piecewise( loss_ratio, [loss_ratio > mean or not mean], [0, 1])
[docs]def make_epsilons(matrix, seed, correlation): """ Given a matrix N * R returns a matrix of the same shape N * R obtained by applying the multivariate_normal distribution to N points and R samples, by starting from the given seed and correlation. """ if seed is not None: numpy.random.seed(seed) asset_count = len(matrix) samples = len(matrix[0]) if not correlation: # avoid building the covariance matrix return numpy.random.normal(size=(samples, asset_count)).transpose() means_vector = numpy.zeros(asset_count) covariance_matrix = ( numpy.ones((asset_count, asset_count)) * correlation + numpy.diag(numpy.ones(asset_count)) * (1 - correlation)) return numpy.random.multivariate_normal( means_vector, covariance_matrix, samples).transpose()
[docs]@DISTRIBUTIONS.add('LN') class LogNormalDistribution(Distribution): """ Model a distribution of a random variable whoose logarithm are normally distributed. :attr epsilons: An array of random numbers generated with :func:`numpy.random.multivariate_normal` with size E """ def __init__(self, epsilons=None): self.epsilons = epsilons
[docs] def sample(self, means, covs, _stddevs, idxs): if self.epsilons is None: raise ValueError("A LogNormalDistribution must be initialized " "before you can use it") eps = self.epsilons[idxs] sigma = numpy.sqrt(numpy.log(covs ** 2.0 + 1.0)) probs = means / numpy.sqrt(1 + covs ** 2) * numpy.exp(eps * sigma) return probs
[docs] def survival(self, loss_ratio, mean, stddev): # scipy does not handle correctly the limit case stddev = 0. # In that case, when `mean` > 0 the survival function # approaches to a step function, otherwise (`mean` == 0) we # returns 0 if stddev == 0: return numpy.piecewise( loss_ratio, [loss_ratio > mean or not mean], [0, 1]) variance = stddev ** 2.0 sigma = numpy.sqrt(numpy.log((variance / mean ** 2.0) + 1.0)) mu = mean ** 2.0 / numpy.sqrt(variance + mean ** 2.0) return stats.lognorm.sf(loss_ratio, sigma, scale=mu)
[docs]@DISTRIBUTIONS.add('BT') class BetaDistribution(Distribution):
[docs] def sample(self, means, _covs, stddevs, _idxs=None): alpha = self._alpha(means, stddevs) beta = self._beta(means, stddevs) return numpy.random.beta(alpha, beta, size=None)
[docs] def survival(self, loss_ratio, mean, stddev): return stats.beta.sf(loss_ratio, self._alpha(mean, stddev), self._beta(mean, stddev))
@staticmethod def _alpha(mean, stddev): return ((1 - mean) / stddev ** 2 - 1 / mean) * mean ** 2 @staticmethod def _beta(mean, stddev): return ((1 - mean) / stddev ** 2 - 1 / mean) * (mean - mean ** 2)
[docs]@DISTRIBUTIONS.add('PM') class DiscreteDistribution(Distribution): seed = None # to be set
[docs] def sample(self, loss_ratios, probs): ret = [] r = numpy.arange(len(loss_ratios)) for i in range(probs.shape[1]): random.seed(self.seed + i) # the seed is set inside the loop to avoid block-size dependency pmf = stats.rv_discrete(name='pmf', values=(r, probs[:, i])).rvs() ret.append(loss_ratios[pmf]) return ret
[docs] def survival(self, loss_ratios, probs): """ Required for the Classical Risk and BCR Calculators. Currently left unimplemented as the PMF format is used only for the Scenario and Event Based Risk Calculators. :param int steps: number of steps between loss ratios. """ # TODO: to be implemented if the classical risk calculator # needs to support the pmf vulnerability format return
# # Event Based # CurveParams = collections.namedtuple( 'CurveParams', ['index', 'loss_type', 'curve_resolution', 'ratios', 'user_provided']) # # Scenario Damage #
[docs]def scenario_damage(fragility_functions, gmvs): """ :param fragility_functions: a list of D - 1 fragility functions :param gmvs: an array of E ground motion values :returns: an array of (D, E) damage fractions """ lst = [numpy.ones_like(gmvs)] for f, ff in enumerate(fragility_functions): # D - 1 functions lst.append(ff(gmvs)) lst.append(numpy.zeros_like(gmvs)) # convert a (D + 1, E) array into a (D, E) array return pairwise_diff(numpy.array(lst))
# # Classical Damage #
[docs]def annual_frequency_of_exceedence(poe, t_haz): """ :param poe: array of probabilities of exceedence :param t_haz: hazard investigation time :returns: array of frequencies (with +inf values where poe=1) """ with warnings.catch_warnings(): warnings.simplefilter("ignore") # avoid RuntimeWarning: divide by zero encountered in log return - numpy.log(1. - poe) / t_haz
[docs]def classical_damage( fragility_functions, hazard_imls, hazard_poes, investigation_time, risk_investigation_time): """ :param fragility_functions: a list of fragility functions for each damage state :param hazard_imls: Intensity Measure Levels :param hazard_poes: hazard curve :param investigation_time: hazard investigation time :param risk_investigation_time: risk investigation time :returns: an array of M probabilities of occurrence where M is the numbers of damage states. """ spi = fragility_functions.steps_per_interval if spi and spi > 1: # interpolate imls = numpy.array(fragility_functions.interp_imls) min_val, max_val = hazard_imls[0], hazard_imls[-1] numpy.putmask(imls, imls < min_val, min_val) numpy.putmask(imls, imls > max_val, max_val) poes = interpolate.interp1d(hazard_imls, hazard_poes)(imls) else: imls = (hazard_imls if fragility_functions.format == 'continuous' else fragility_functions.imls) poes = numpy.array(hazard_poes) afe = annual_frequency_of_exceedence(poes, investigation_time) annual_frequency_of_occurrence = pairwise_diff( pairwise_mean([afe[0]] + list(afe) + [afe[-1]])) poes_per_damage_state = [] for ff in fragility_functions: frequency_of_exceedence_per_damage_state = numpy.dot( annual_frequency_of_occurrence, list(map(ff, imls))) poe_per_damage_state = 1. - numpy.exp( - frequency_of_exceedence_per_damage_state * risk_investigation_time) poes_per_damage_state.append(poe_per_damage_state) poos = pairwise_diff([1] + poes_per_damage_state + [0]) return poos
# # Classical #
[docs]def classical(vulnerability_function, hazard_imls, hazard_poes, steps=10): """ :param vulnerability_function: an instance of :py:class:`openquake.risklib.scientific.VulnerabilityFunction` representing the vulnerability function used to compute the curve. :param hazard_imls: the hazard intensity measure type and levels :type hazard_poes: the hazard curve :param int steps: Number of steps between loss ratios. """ assert len(hazard_imls) == len(hazard_poes), ( len(hazard_imls), len(hazard_poes)) vf = vulnerability_function imls = vf.mean_imls() loss_ratios, lrem = vf.loss_ratio_exceedance_matrix(steps) # saturate imls to hazard imls min_val, max_val = hazard_imls[0], hazard_imls[-1] numpy.putmask(imls, imls < min_val, min_val) numpy.putmask(imls, imls > max_val, max_val) # interpolate the hazard curve poes = interpolate.interp1d(hazard_imls, hazard_poes)(imls) # compute the poos pos = pairwise_diff(poes) lrem_po = numpy.empty(lrem.shape) for idx, po in enumerate(pos): lrem_po[:, idx] = lrem[:, idx] * po # column * po return numpy.array([loss_ratios, lrem_po.sum(axis=1)])
[docs]def conditional_loss_ratio(loss_ratios, poes, probability): """ Return the loss ratio corresponding to the given PoE (Probability of Exceendance). We can have four cases: 1. If `probability` is in `poes` it takes the bigger corresponding loss_ratios. 2. If it is in `(poe1, poe2)` where both `poe1` and `poe2` are in `poes`, then we perform a linear interpolation on the corresponding losses 3. if the given probability is smaller than the lowest PoE defined, it returns the max loss ratio . 4. if the given probability is greater than the highest PoE defined it returns zero. :param loss_ratios: an iterable over non-decreasing loss ratio values (float) :param poes: an iterable over non-increasing probability of exceedance values (float) :param float probability: the probability value used to interpolate the loss curve """ rpoes = poes[::-1] if probability > poes[0]: # max poes return 0.0 elif probability < poes[-1]: # min PoE return loss_ratios[-1] if probability in poes: return max([loss for i, loss in enumerate(loss_ratios) if probability == poes[i]]) else: interval_index = bisect.bisect_right(rpoes, probability) if interval_index == len(poes): # poes are all nan return float('nan') elif interval_index == 1: # boundary case x1, x2 = poes[-2:] y1, y2 = loss_ratios[-2:] else: x1, x2 = poes[-interval_index-1:-interval_index + 1] y1, y2 = loss_ratios[-interval_index-1:-interval_index + 1] return (y2 - y1) / (x2 - x1) * (probability - x1) + y1
# # Insured Losses #
[docs]def insured_losses(losses, deductible, insured_limit): """ :param losses: an array of ground-up loss ratios :param float deductible: the deductible limit in fraction form :param float insured_limit: the insured limit in fraction form Compute insured losses for the given asset and losses, from the point of view of the insurance company. For instance: >>> insured_losses(numpy.array([3, 20, 101]), 5, 100) array([ 0, 15, 95]) - if the loss is 3 (< 5) the company does not pay anything - if the loss is 20 the company pays 20 - 5 = 15 - if the loss is 101 the company pays 100 - 5 = 95 """ return numpy.piecewise( losses, [losses < deductible, losses > insured_limit], [0, insured_limit - deductible, lambda x: x - deductible])
[docs]def insured_loss_curve(curve, deductible, insured_limit): """ Compute an insured loss ratio curve given a loss ratio curve :param curve: an array 2 x R (where R is the curve resolution) :param float deductible: the deductible limit in fraction form :param float insured_limit: the insured limit in fraction form >>> losses = numpy.array([3, 20, 101]) >>> poes = numpy.array([0.9, 0.5, 0.1]) >>> insured_loss_curve(numpy.array([losses, poes]), 5, 100) array([[ 3. , 20. ], [ 0.85294118, 0.5 ]]) """ losses, poes = curve[:, curve[0] <= insured_limit] limit_poe = interpolate.interp1d( *curve, bounds_error=False, fill_value=1)(deductible) return numpy.array([ losses, numpy.piecewise(poes, [poes > limit_poe], [limit_poe, lambda x: x])])
# # Benefit Cost Ratio Analysis #
[docs]def bcr(eal_original, eal_retrofitted, interest_rate, asset_life_expectancy, asset_value, retrofitting_cost): """ Compute the Benefit-Cost Ratio. BCR = (EALo - EALr)(1-exp(-r*t))/(r*C) Where: * BCR -- Benefit cost ratio * EALo -- Expected annual loss for original asset * EALr -- Expected annual loss for retrofitted asset * r -- Interest rate * t -- Life expectancy of the asset * C -- Retrofitting cost """ return ((eal_original - eal_retrofitted) * asset_value * (1 - numpy.exp(- interest_rate * asset_life_expectancy)) / (interest_rate * retrofitting_cost))
# ####################### statistics #################################### #
[docs]def pairwise_mean(values): "Averages between a value and the next value in a sequence" return numpy.array([numpy.mean(pair) for pair in utils.pairwise(values)])
[docs]def pairwise_diff(values): "Differences between a value and the next value in a sequence" return numpy.array([x - y for x, y in utils.pairwise(values)])
[docs]def mean_std(fractions): """ Given an N x M matrix, returns mean and std computed on the rows, i.e. two M-dimensional vectors. """ return numpy.mean(fractions, axis=0), numpy.std(fractions, axis=0, ddof=1)
[docs]def loss_maps(curves, conditional_loss_poes): """ :param curves: an array of loss curves :param conditional_loss_poes: a list of conditional loss poes :returns: a composite array of loss maps with the same shape """ loss_maps_dt = numpy.dtype([('poe-%s' % poe, F32) for poe in conditional_loss_poes]) loss_maps = numpy.zeros(curves.shape, loss_maps_dt) for idx, curve in numpy.ndenumerate(curves): for poe in conditional_loss_poes: loss_maps['poe-%s' % poe][idx] = conditional_loss_ratio( curve['losses'], curve['poes'], poe) return loss_maps
[docs]def broadcast(func, composite_array, *args): """ Broadcast an array function over a composite array """ dic = {} dtypes = [] for name in composite_array.dtype.names: dic[name] = func(composite_array[name], *args) dtypes.append((name, dic[name].dtype)) res = numpy.zeros(dic[name].shape, numpy.dtype(dtypes)) for name in dic: res[name] = dic[name] return res
# TODO: remove this from openquake.risklib.qa_tests.bcr_test
[docs]def average_loss(losses_poes): """ Given a loss curve with `poes` over `losses` defined on a given time span it computes the average loss on this period of time. :note: As the loss curve is supposed to be piecewise linear as it is a result of a linear interpolation, we compute an exact integral by using the trapeizodal rule with the width given by the loss bin width. """ losses, poes = losses_poes return numpy.dot(-pairwise_diff(losses), pairwise_mean(poes))
[docs]def normalize_curves_eb(curves): """ A more sophisticated version of normalize_curves, used in the event based calculator. :param curves: a list of pairs (losses, poes) :returns: first losses, all_poes """ # we assume non-decreasing losses, so losses[-1] is the maximum loss non_zero_curves = [(losses, poes) for losses, poes in curves if losses[-1] > 0] if not non_zero_curves: # no damage. all zero curves return curves[0][0], numpy.array([poes for _losses, poes in curves]) else: # standard case max_losses = [losses[-1] for losses, _poes in non_zero_curves] reference_curve = non_zero_curves[numpy.argmax(max_losses)] loss_ratios = reference_curve[0] curves_poes = [interpolate.interp1d( losses, poes, bounds_error=False, fill_value=0)(loss_ratios) for losses, poes in curves] # fix degenerated case with flat curve for cp in curves_poes: if numpy.isnan(cp[0]): cp[0] = 0 return loss_ratios, numpy.array(curves_poes)
[docs]def build_loss_curve_dt(curve_resolution, insured_losses=False): """ :param curve_resolution: dictionary loss_type -> curve_resolution :param insured_losses: configuration parameter :returns: loss_curve_dt """ lc_list = [] for lt in sorted(curve_resolution): C = curve_resolution[lt] pairs = [('losses', (F32, C)), ('poes', (F32, C))] lc_dt = numpy.dtype(pairs) lc_list.append((str(lt), lc_dt)) if insured_losses: for lt in sorted(curve_resolution): C = curve_resolution[lt] pairs = [('losses', (F32, C)), ('poes', (F32, C))] lc_dt = numpy.dtype(pairs) lc_list.append((str(lt) + '_ins', lc_dt)) loss_curve_dt = numpy.dtype(lc_list) if lc_list else None return loss_curve_dt
[docs]def return_periods(eff_time, num_losses): """ :param eff_time: ses_per_logic_tree_path * investigation_time :param num_losses: used to determine the minimum period :returns: an array of 32 bit periods Here are a few examples: >>> return_periods(1, 1) Traceback (most recent call last): ... AssertionError: eff_time too small: 1 >>> return_periods(2, 2) array([1, 2], dtype=uint32) >>> return_periods(2, 10) array([1, 2], dtype=uint32) >>> return_periods(100, 2) array([ 50, 100], dtype=uint32) >>> return_periods(1000, 1000) array([ 1, 2, 5, 10, 20, 50, 100, 200, 500, 1000], dtype=uint32) """ assert eff_time >= 2, 'eff_time too small: %s' % eff_time assert num_losses >= 2, 'num_losses too small: %s' % num_losses min_time = eff_time / num_losses period = 1 periods = [] loop = True while loop: for val in [1, 2, 5]: time = period * val if time >= min_time: if time > eff_time: loop = False break periods.append(time) period *= 10 return U32(periods)
[docs]def losses_by_period(losses, return_periods, num_events, eff_time): """ :param losses: array of simulated losses :param return_periods: return periods of interest :param num_events: the number of events (must be more than the losses) :param eff_time: investigation_time * ses_per_logic_tree_path :returns: interpolated losses for the return periods, possibly with NaN NB: the return periods must be ordered integers >= 1. The interpolated losses are defined inside the interval min_time < time < eff_time where min_time = eff_time /len(losses). Outside the interval they have NaN values. Here is an example: >>> losses = [3, 2, 3.5, 4, 3, 23, 11, 2, 1, 4, 5, 7, 8, 9, 13] >>> losses_by_period(losses, [1, 2, 5, 10, 20, 50, 100], 20, 100) array([ nan, nan, 0. , 3.5, 8. , 13. , 23. ]) """ if num_events < len(losses): raise ValueError( 'There are not enough events to compute the loss curves: %d' % num_events) losses = numpy.sort(losses) num_zeros = num_events - len(losses) if num_zeros: losses = numpy.concatenate( [numpy.zeros(num_zeros, losses.dtype), losses]) periods = eff_time / numpy.arange(num_events, 0., -1) rperiods = [rp if periods[0] <= rp <= periods[-1] else numpy.nan for rp in return_periods] curve = numpy.interp(numpy.log(rperiods), numpy.log(periods), losses) return curve
[docs]class LossesByPeriodBuilder(object): """ Build losses by period for all loss types at the same time. :param return_periods: ordered array of return periods :param loss_dt: composite dtype for the loss types :param weights: weights of the realizations :param num_events: number of events for each realization :param eff_time: ses_per_logic_tree_path * hazard investigation time """ def __init__(self, return_periods, loss_dt, weights, num_events, eff_time, risk_investigation_time): self.return_periods = return_periods self.loss_dt = loss_dt self.weights = weights self.num_events = num_events self.eff_time = eff_time self.poes = 1. - numpy.exp(- risk_investigation_time / return_periods)
[docs] def pair(self, array, stats): """ :return (array, array_stats) if stats, else (array, None) """ if len(self.weights) > 1 and stats: statnames, statfuncs = zip(*stats) array_stats = compute_stats2(array, statfuncs, self.weights) else: array_stats = None return array, array_stats
# used in the EbrPostCalculator
[docs] def build_all(self, asset_values, loss_ratios, stats=()): """ :param asset_values: a list of asset values :param loss_ratios: an array of dtype lrs_dt :param stats: list of pairs [(statname, statfunc), ...] :returns: two composite arrays of shape (A, R, P) and (A, S, P) """ # loss_ratios from lrgetter.get_all A = len(asset_values) R = len(self.weights) P = len(self.return_periods) array = numpy.zeros((A, R, P), self.loss_dt) for a, asset_value in enumerate(asset_values): r_recs = group_array(loss_ratios[a], 'rlzi').items() for li, lt in enumerate(self.loss_dt.names): aval = asset_value[lt.replace('_ins', '')] for r, recs in r_recs: array[a, r][lt] = aval * losses_by_period( recs['ratios'][:, li], self.return_periods, self.num_events[r], self.eff_time) return self.pair(array, stats)
# used in the LossCurvesExporter
[docs] def build_rlz(self, asset_values, loss_ratios, rlzi): """ :param asset_values: a list of asset values :param loss_ratios: a dictionary aid -> array of shape (E, LI) :returns: a composite array of shape (A, P) """ # loss_ratios from lrgetter.get, aid -> list of ratios A, P = len(asset_values), len(self.return_periods) array = numpy.zeros((A, P), self.loss_dt) for a, asset_value in enumerate(asset_values): try: ratios = loss_ratios[a] # shape (E, LI) except KeyError: # no loss ratios > 0 for the given asset continue for li, lt in enumerate(self.loss_dt.names): aval = asset_value[lt.replace('_ins', '')] array[a][lt] = aval * losses_by_period( ratios[:, li], self.return_periods, self.num_events[rlzi], self.eff_time) return array
[docs] def build(self, losses_by_event, stats=()): """ :param losses_by_event: the aggregate loss table as an array :param stats: list of pairs [(statname, statfunc), ...] :returns: two arrays of dtype loss_dt values with shape (P, R) and (P, S) """ P, R = len(self.return_periods), len(self.weights) array = numpy.zeros((P, R), self.loss_dt) dic = group_array(losses_by_event, 'rlzi') for r in dic: num_events = self.num_events[r] losses = dic[r]['loss'] for lti, lt in enumerate(self.loss_dt.names): ls = losses[:, lti].flatten() # flatten only in ucerf # NB: do not use squeeze or the gmf_ebrisk tests will break lbp = losses_by_period( ls, self.return_periods, num_events, self.eff_time) array[:, r][lt] = lbp return self.pair(array, stats)
[docs] def build_maps(self, losses, clp, stats=()): """ :param losses: an array of shape (A, R, P) :param clp: a list of C conditional loss poes :param stats: list of pairs [(statname, statfunc), ...] :returns: an array of loss_maps of shape (A, R, C, LI) """ shp = losses.shape[:2] + (len(clp), len(losses.dtype)) # (A, R, C, LI) array = numpy.zeros(shp, F32) for lti, lt in enumerate(losses.dtype.names): for a, losses_ in enumerate(losses[lt]): for r, ls in enumerate(losses_): for c, poe in enumerate(clp): clratio = conditional_loss_ratio(ls, self.poes, poe) array[a, r, c, lti] = clratio return self.pair(array, stats)