Source code for openquake.risklib.scientific

# -*- coding: utf-8 -*-
# vim: tabstop=4 shiftwidth=4 softtabstop=4
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"""
This module includes the scientific API of the oq-risklib
"""
import copy
import bisect
import itertools
import collections
from functools import lru_cache

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

F64 = numpy.float64
F32 = numpy.float32
U32 = numpy.uint32
U16 = numpy.uint16
U8 = numpy.uint8
KNOWN_CONSEQUENCES = ['loss', 'ins_loss', 'losses', 'collapsed', 'injured',
                      'fatalities', 'homeless']


[docs]def pairwise(iterable): "s -> (s0,s1), (s1,s2), (s2, s3), ..." a, b = itertools.tee(iterable) # b ahead one step; if b is empty do not raise StopIteration next(b, None) return zip(a, b) # if a is empty will return an empty iter
[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 pairwise(points)]) return numpy.concatenate([ls, [points[-1]]])
# sampling functions
[docs]class Sampler(object): def __init__(self, distname, rng, lratios=(), cols=None): self.distname = distname self.rng = rng self.arange = numpy.arange(len(lratios)) # for the PM distribution self.lratios = lratios # for the PM distribution self.cols = cols # for the PM distribution
[docs] def get_losses(self, df, covs): vals = df['val'].to_numpy() if not self.rng or not covs: # fast lane losses = vals * df['mean'].to_numpy() else: # slow lane losses = vals * getattr(self, 'sample' + self.distname)(df) return losses
[docs] def sampleLN(self, df): means = df['mean'].to_numpy() covs = df['cov'].to_numpy() eids = df['eid'].to_numpy() return self.rng.lognormal(eids, means, covs)
[docs] def sampleBT(self, df): means = df['mean'].to_numpy() covs = df['cov'].to_numpy() eids = df['eid'].to_numpy() return self.rng.beta(eids, means, covs)
[docs] def samplePM(self, df): eids = df['eid'].to_numpy() allprobs = df[self.cols].to_numpy() pmf = [] for eid, probs in zip(eids, allprobs): # probs by asset if probs.sum() == 0: # oq-risk-tests/case_1g # means are zeros for events below the threshold pmf.append(0) else: pmf.append(stats.rv_discrete( name='pmf', values=(self.arange, probs), seed=self.rng.master_seed + eid).rvs()) return self.lratios[pmf]
# # Input models #
[docs]class VulnerabilityFunction(object): dtype = numpy.dtype([('iml', F64), ('loss_ratio', F64), ('cov', F64)]) seed = None # to be overridden def __init__(self, vf_id, imt, imls, mean_loss_ratios, covs=None, distribution="LN"): """ A wrapper around a probabilistic distribution function (currently, the Log-normal ("LN") and Beta ("BT") distributions are supported amongst the continuous probability distributions. For specifying a discrete probability distribution refer to the class VulnerabilityFunctionWithPMF. 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 and cov > 0: msg = ("It is not valid to define a mean loss ratio = 0 " "with a corresponding coefficient of variation > 0") raise ValueError(msg) if cov < 0: raise ValueError( 'Found a negative coefficient of variation in %s' % self.covs) if distribution == 'BT': if lr == 0: # possible with cov == 0 pass elif lr > 1: raise ValueError( 'The meanLRs must be below 1, got %s' % lr) elif cov ** 2 > 1 / lr - 1: # see https://github.com/gem/oq-engine/issues/4841 raise ValueError( 'The coefficient of variation %s > %s does not ' 'satisfy the requirement 0 < sig < sqrt[mu × (1 - mu)]' ' in %s' % (cov, numpy.sqrt(1 / lr - 1), self)) self.distribution_name = distribution
[docs] def init(self): # called by CompositeRiskModel and by __setstate__ self.covs = F64(self.covs) self.mean_loss_ratios = F64(self.mean_loss_ratios) 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)
[docs] def interpolate(self, gmf_df, col): """ :param gmf_df: DataFrame of GMFs :returns: DataFrame of interpolated loss ratios and covs """ gmvs = gmf_df[col].to_numpy() dic = dict(eid=gmf_df.eid.to_numpy(), mean=numpy.zeros(len(gmvs)), cov=numpy.zeros(len(gmvs))) # gmvs are clipped to max(iml) gmvs_curve = numpy.piecewise( gmvs, [gmvs > self.imls[-1]], [self.imls[-1], lambda x: x]) ok = gmvs_curve >= self.imls[0] # indices over the minimum curve_ok = gmvs_curve[ok] dic['mean'][ok] = self._mlr_i1d(curve_ok) dic['cov'][ok] = self._cov_for(curve_ok) return pandas.DataFrame(dic, gmf_df.sid)
[docs] def survival(self, loss_ratio, mean, stddev): """ Compute the survival probability based on the underlying distribution. """ if self.distribution_name == 'LN': # 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) elif self.distribution_name == 'BT': return stats.beta.sf(loss_ratio, *_alpha_beta(mean, stddev)) else: raise NotImplementedError(self.distribution_name)
def __call__(self, asset_df, gmf_df, col, rng=None, minloss=0): """ :param asset_df: a DataFrame with A assets :param gmf_df: a DataFrame of GMFs for the given assets :param rng: a MultiEventRNG or None :returns: a DataFrame with columns eid, aid, loss """ if asset_df is None: # in the tests asset_df = pandas.DataFrame(dict(aid=0, val=1), [0]) ratio_df = self.interpolate(gmf_df, col) # really fast if self.distribution_name == 'PM': # special case lratios = F64(self.loss_ratios) cols = [col for col in ratio_df.columns if isinstance(col, int)] else: lratios = () cols = None df = ratio_df.join(asset_df, how='inner') sampler = Sampler(self.distribution_name, rng, lratios, cols) covs = not hasattr(self, 'covs') or self.covs.any() losses = sampler.get_losses(df, covs) ok = losses > minloss if self.distribution_name == 'PM': # special case variances = numpy.zeros(len(losses)) else: variances = (losses * df['cov'].to_numpy())**2 return pandas.DataFrame(dict(eid=df.eid[ok], aid=df.aid[ok], variance=variances[ok], loss=losses[ok]))
[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", "PM"]
[docs] @lru_cache() def loss_ratio_exceedance_matrix(self, loss_ratios): """ Compute the LREM (Loss Ratio Exceedance Matrix). """ # 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((len(loss_ratios), len(self.imls))) 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.survival( loss_ratio, mean_loss_ratio, stddev) return lrem
[docs] @lru_cache() 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 pairwise(self.imls)] + [self.imls[-1] + (self.imls[-1] - self.imls[-2]) / 2.])
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): 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.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): self._probs_i1d = interpolate.interp1d(self.imls, self.probs)
def __getstate__(self): return (self.id, self.imt, self.imls, self.loss_ratios, self.probs, self.distribution_name) 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.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) # MN: in the test gmvs_curve is of shape (5,), self.probs of shape (7, 8) # self.imls of shape (8,) and the returned means have shape (5, 7)
[docs] def interpolate(self, gmf_df, col): """ :param gmvs: DataFrame of GMFs :param col: name of the column to consider :returns: DataFrame of interpolated probabilities """ # gmvs are clipped to max(iml) M = len(self.probs) gmvs = gmf_df[col].to_numpy() dic = {m: numpy.zeros_like(gmvs) for m in range(M)} dic['eid'] = gmf_df.eid.to_numpy() gmvs_curve = numpy.piecewise( gmvs, [gmvs > self.imls[-1]], [self.imls[-1], lambda x: x]) ok = gmvs_curve >= self.imls[0] # indices over the minimum for m, probs in enumerate(self._probs_i1d(gmvs_curve[ok])): dic[m][ok] = probs return pandas.DataFrame(dic, gmf_df.sid)
[docs] @lru_cache() def loss_ratio_exceedance_matrix(self, loss_ratios): """ 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 __repr__(self): return '<VulnerabilityFunctionWithPMF(%s, %s)>' % (self.id, self.imt)
# this is meant to be instantiated by riskmodels.get_risk_functions
[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): def __init__(self, limit_state, mean, stddev, minIML, maxIML, nodamage=0): self.limit_state = limit_state self.mean = mean self.stddev = stddev self.minIML = minIML self.maxIML = maxIML self.no_damage_limit = nodamage def __call__(self, imls): """ Compute the Probability of Exceedance (PoE) for the given Intensity Measure Levels (IMLs). """ # it is essentially to make a copy of the intensity measure levels, # otherwise the minIML feature in continuous fragility functions will # change the levels, thus breaking case_master for OQ_DISTRIBUTE=no if self.minIML or self.maxIML: imls = numpy.array(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) if self.maxIML: imls[imls > self.maxIML] = self.maxIML if self.minIML: imls[imls < self.minIML] = self.minIML result = stats.lognorm.cdf(imls, sigma, scale=mu) if self.no_damage_limit: result[imls <= self.no_damage_limit] = 0 return result 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 if len(imls) != len(poes): raise ValueError('%s: %d levels but %d poes' % ( limit_state, len(imls), len(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) new.clear() add_zero = (self.format == 'discrete' and self.nodamage 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: if len(self.imls) == len(data): # add no_damage imls = [self.nodamage] + self.imls else: # already added imls = self.imls new.append(FragilityFunctionDiscrete( ls, imls, numpy.concatenate([[0.], data]), self.nodamage)) else: new.append(FragilityFunctionDiscrete( ls, self.imls, data, self.nodamage)) else: # continuous new.append(FragilityFunctionContinuous( ls, data[0], data[1], # mean and stddev self.minIML, self.maxIML, self.nodamage)) return new
def __repr__(self): kvs = ['%s=%s' % item for item in vars(self).items()] return '<FragilityFunctionList %s>' % ', '.join(kvs)
[docs]class ConsequenceModel(dict): """ Dictionary 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 """ 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 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))
# ########################### random generators ########################### def _alpha_beta(mean, stddev): c = (1 - mean) / stddev ** 2 - 1 / mean return c * mean ** 2, c * (mean - mean ** 2)
[docs]class MultiEventRNG(object): """ An object ``MultiEventRNG(master_seed, eids, asset_correlation=0)`` has a method ``.get(A, eids)`` which returns a matrix of (A, E) normally distributed random numbers. If the ``asset_correlation`` is 1 the numbers are the same. >>> rng = MultiEventRNG( ... master_seed=42, eids=[0, 1, 2], asset_correlation=1) >>> eids = numpy.array([1] * 3) >>> means = numpy.array([.5] * 3) >>> covs = numpy.array([.1] * 3) >>> rng.lognormal(eids, means, covs) array([0.38892466, 0.38892466, 0.38892466]) >>> rng.beta(eids, means, covs) array([0.4372343 , 0.57308132, 0.56392573]) >>> fractions = numpy.array([[[.8, .1, .1]]]) >>> rng.discrete_dmg_dist([0], fractions, [10]) array([[[8, 2, 0]]], dtype=uint32) """ def __init__(self, master_seed, eids, asset_correlation=0): self.master_seed = master_seed self.asset_correlation = asset_correlation self.rng = {} for eid in eids: # NB: int below is necessary for totally mysterious reasons: # a calculation on cluster1 #41904 failed with a floating # point seed, but I cannot reproduce the issue ph = numpy.random.Philox(int(self.master_seed + eid)) self.rng[eid] = numpy.random.Generator(ph) def _get_eps(self, eid, corrcache): if self.asset_correlation: try: return corrcache[eid] except KeyError: corrcache[eid] = eps = self.rng[eid].normal() return eps return self.rng[eid].normal()
[docs] def lognormal(self, eids, means, covs): """ :param eids: event IDs :param means: array of floats in the range 0..1 :param covs: array of floats with the same shape :returns: array of floats """ corrcache = {} eps = numpy.array([self._get_eps(eid, corrcache) for eid in eids]) sigma = numpy.sqrt(numpy.log(1 + covs ** 2)) div = numpy.sqrt(1 + covs ** 2) return means * numpy.exp(eps * sigma) / div
# NB: asset correlation is ignored
[docs] def beta(self, eids, means, covs): """ :param eids: event IDs :param means: array of floats in the range 0..1 :param covs: array of floats with the same shape :returns: array of floats following the beta distribution This function works properly even when some or all of the stddevs are zero: in that case it returns the means since the distribution becomes extremely peaked. It also works properly when some one or all of the means are zero, returning zero in that case. """ # NB: you should not expect a smooth limit for the case of on cov->0 # since the random number generator will advance of a different number # of steps with cov == 0 and cov != 0 res = numpy.array(means) ok = (means != 0) & (covs != 0) # nonsingular values alpha, beta = _alpha_beta(means[ok], means[ok] * covs[ok]) res[ok] = [self.rng[eid].beta(alpha[i], beta[i]) for i, eid in enumerate(eids[ok])] return res
[docs] def discrete_dmg_dist(self, eids, fractions, numbers): """ Converting fractions into discrete damage distributions using bincount and random.choice. :param eids: E event IDs :param fractions: array of shape (A, E, D) :param numbers: A asset numbers :returns: array of integers of shape (A, E, D) """ A, E, D = fractions.shape assert len(eids) == E, (len(eids), E) assert len(numbers) == A, (len(eids), A) ddd = numpy.zeros(fractions.shape, U32) for e, eid in enumerate(eids): choice = self.rng[eid].choice for a, n in enumerate(numbers): frac = fractions[a, e] # shape D states = choice(D, n, p=frac/frac.sum()) # n states # ex. [0, 0, 0, 1, 0, 0, 0, 1, 0, 0], 8 times 0, 2 times 1 ddd[a, e] = numpy.bincount(states, minlength=D) return ddd
[docs] def boolean_dist(self, probs, num_sims): """ Convert E probabilities into an array of (E, S) booleans, being S the number of secondary simulations. >>> rng = MultiEventRNG(master_seed=42, eids=[0, 1, 2]) >>> dist = rng.boolean_dist(probs=[.1, .2, 0.], num_sims=100) >>> dist.sum(axis=1) # around 10% and 20% respectively array([12., 17., 0.]) """ E = len(self.rng) assert len(probs) == E, (len(probs), E) booldist = numpy.zeros((E, num_sims)) for e, eid, prob in zip(range(E), self.rng, probs): if prob > 0: booldist[e] = self.rng[eid].random(num_sims) < prob return booldist
# # 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 arr = pairwise_diff(numpy.array(lst)) arr[arr < 1E-7] = 0 # sanity check return arr
# # 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) """ assert not (poe == 1).any() # avoid log(0) return - numpy.log(1-poe) / t_haz
[docs]def classical_damage( fragility_functions, hazard_imls, hazard_poes, investigation_time, risk_investigation_time, steps_per_interval=1): """ :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 :param steps_per_interval: steps per interval :returns: an array of M probabilities of occurrence where M is the numbers of damage states. """ if steps_per_interval > 1: # interpolate imls = numpy.array(fragility_functions._interp_imls) min_val, max_val = hazard_imls[0], hazard_imls[-1] assert min_val > 0, hazard_imls # sanity check imls[imls < min_val] = min_val imls[imls > max_val] = max_val poes = interpolate.interp1d(hazard_imls, hazard_poes)(imls) else: imls = hazard_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:
[docs] fx = annual_frequency_of_occurrence @ ff(imls) poe_per_damage_state = 1. - numpy.exp(-fx * risk_investigation_time) poes_per_damage_state.append(poe_per_damage_state) poos = pairwise_diff([1] + poes_per_damage_state + [0]) return poos
# # Classical # def classical(vulnerability_function, hazard_imls, hazard_poes, loss_ratios): """ :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 loss_ratios: a tuple of C loss ratios :returns: an array of shape (2, C) """ assert len(hazard_imls) == len(hazard_poes), ( len(hazard_imls), len(hazard_poes)) vf = vulnerability_function imls = vf.mean_imls() lrem = vf.loss_ratio_exceedance_matrix(loss_ratios) # saturate imls to hazard imls min_val, max_val = hazard_imls[0], hazard_imls[-1] imls[imls < min_val] = min_val 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)])
# used in classical_risk only
[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: non-decreasing loss ratio values (float32) :param poes: non-increasing probabilities of exceedance values (float32) :param float probability: the probability value used to interpolate the loss curve """ assert len(loss_ratios) >= 3, loss_ratios probability = numpy.float32(probability) if not isinstance(loss_ratios, numpy.ndarray): loss_ratios = numpy.float32(loss_ratios) if not isinstance(poes, numpy.ndarray): poes = numpy.float32(poes) 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 loss_ratios[probability == poes].max() 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 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 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. """ n = fractions.shape[0] if n == 1: # avoid warnings when computing the stddev return fractions[0], numpy.ones_like(fractions[0]) * numpy.nan 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(lc): """ Given a loss curve array with `poe` and `loss` fields, computes the average loss on a 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 = (lc['loss'], lc['poe']) if lc.dtype.names else lc
[docs] return -pairwise_diff(losses) @ pairwise_mean(poes)
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 periods of dtype uint32 Here are a few examples: >>> return_periods(1, 1) Traceback (most recent call last): ... ValueError: 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) """ if eff_time < 2: raise ValueError('eff_time too small: %s' % eff_time) if num_losses < 2: raise ValueError('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=None, eff_time=None): """ :param losses: simulated losses :param return_periods: return periods of interest :param num_events: the number of events (>= number of 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 /num_events. On the right of the interval they have NaN values; on the left zero values. If num_events is not passed, it is inferred from the number of losses; if eff_time is not passed, it is inferred from the longest return period. 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) array([ 0. , 0. , 0. , 3.5, 8. , 13. , 23. ]) """ P = len(return_periods) assert len(losses) if isinstance(losses, list): losses = numpy.array(losses) num_losses = len(losses) if num_events is None: num_events = num_losses elif num_events < num_losses: raise ValueError( 'There are not enough events (%d<%d) to compute the loss curve' % (num_events, num_losses)) if eff_time is None: eff_time = return_periods[-1] losses = numpy.sort(losses) # num_losses < num_events: just add zeros num_zeros = num_events - num_losses if num_zeros: newlosses = numpy.zeros(num_events, losses.dtype) newlosses[num_events - num_losses:num_events] = losses losses = newlosses periods = eff_time / numpy.arange(num_events, 0., -1) num_left = sum(1 for rp in return_periods if rp < periods[0]) num_right = sum(1 for rp in return_periods if rp > periods[-1]) rperiods = [rp for rp in return_periods if periods[0] <= rp <= periods[-1]] curve = numpy.zeros(len(return_periods), losses.dtype) logr, logp = numpy.log(rperiods), numpy.log(periods) curve[num_left:P - num_right] = numpy.interp(logr, logp, losses) curve[P - num_right:] = numpy.nan return curve
[docs]class LossCurvesMapsBuilder(object): """ Build losses curves and maps for all loss types at the same time. :param conditional_loss_poes: a list of PoEs, possibly empty :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, conditional_loss_poes, return_periods, loss_dt, weights, num_events, eff_time, risk_investigation_time): self.conditional_loss_poes = conditional_loss_poes self.return_periods = return_periods self.loss_dt = loss_dt self.weights = weights self.num_events = num_events self.eff_time = eff_time if return_periods.sum() == 0: self.poes = 1 else: self.poes = 1. - numpy.exp( - risk_investigation_time / return_periods) # used in post_risk
[docs] def build_curve(self, losses, rlzi=0): return losses_by_period( losses, self.return_periods, self.num_events[rlzi], self.eff_time)
[docs]class InsuredLosses(object): """ There is an insured loss for each loss type in the policy dictionary. """ def __init__(self, policy_name, policy_dict): self.policy_name = policy_name self.policy_dict = policy_dict self.sec_names = ['ins_loss']
[docs] def update(self, lt, out, asset_df): """ :param lt: a loss type string :param out: a dictionary of dataframes keyed by loss_type :param asset_df: a DataFrame of assets with index "ordinal" """ o = out[lt] o['ins_loss'] = numpy.zeros(len(o)) if lt in self.policy_dict and len(o): policy = self.policy_dict[lt] for (eid, aid), df in o.iterrows(): asset = asset_df.loc[aid] avalue = asset['value-' + lt] policy_idx = asset[self.policy_name] ded, lim = policy[policy_idx] ins = insured_losses(df.loss, ded * avalue, lim * avalue) o.loc[eid, aid]['ins_loss'] = ins
# not used anymore
[docs]def make_epsilons(matrix, seed, correlation): """ Given a matrix of shape (A, E) returns a matrix of the same shape obtained by applying the multivariate_normal distribution to A points and E samples, by starting from the given seed and correlation. """ if seed is not None: numpy.random.seed(seed) A = len(matrix) E = len(matrix[0]) if not correlation: # avoid building the covariance matrix return numpy.random.normal(size=(E, A)).transpose() means_vector = numpy.zeros(A) covariance_matrix = (numpy.ones((A, A)) * correlation + numpy.diag(numpy.ones(A)) * (1 - correlation)) return numpy.random.multivariate_normal( means_vector, covariance_matrix, E).transpose()
# ####################### Consequences ##################################### #
[docs]def consequence(consequence, coeffs, asset, dmgdist, loss_type): """ :param consequence: kind of consequence :param coeffs: coefficients per damage state :param asset: asset record :param dmgdist: an array of probabilies of shape (E, D - 1) :param loss_type: loss type string :returns: array of shape E """ if consequence not in KNOWN_CONSEQUENCES: raise NotImplementedError(consequence) elif consequence == 'losses':
[docs] return dmgdist @ coeffs * asset['value-' + loss_type] elif consequence == 'collapsed': return dmgdist @ coeffs * asset['value-number'] elif consequence == 'injured': return dmgdist @ coeffs * asset['occupants_night'] elif consequence == 'fatalities': return dmgdist @ coeffs * asset['occupants_night'] elif consequence == 'homeless': return dmgdist @ coeffs * asset['occupants_night']
def get_agg_value(consequence, agg_values, agg_id, loss_type): """ :returns: sum of the values corresponding to agg_id for the given consequence """ aval = agg_values[agg_id] if consequence not in KNOWN_CONSEQUENCES: raise NotImplementedError(consequence) elif consequence in ('loss', 'ins_loss', 'losses'): return aval[loss_type] elif consequence == 'collapsed': return aval['number'] elif consequence == 'injured': return aval['occupants_night'] elif consequence == 'fatalities': return aval['occupants_night'] elif consequence == 'homeless': return aval['occupants_night']
if __name__ == '__main__': # plots of the beta distribution in terms of mean and stddev # see https://en.wikipedia.org/wiki/Beta_distribution import matplotlib.pyplot as plt x = numpy.arange(0, 1, .01) def beta(mean, stddev): a, b = _alpha_beta(numpy.array([mean]*100), numpy.array([stddev]*100)) return stats.beta.pdf(x, a, b) rng = MultiEventRNG(42, [1]) ones = numpy.ones(100) vals = rng.beta(1, .5 * ones, .05 * ones) print(vals.mean(), vals.std()) # print(vals) vals = rng.beta(1, .5 * ones, .01 * ones) print(vals.mean(), vals.std()) # print(vals) plt.plot(x, beta(.5, .05), label='.5[.05]') plt.plot(x, beta(.5, .01), label='.5[.01]') plt.legend() plt.show()