openquake.risklib package

openquake.risklib.riskinput module

class openquake.risklib.riskinput.RiskInput(hazard_getter, asset_df)

Bases: object

Site specific inputs.

Parameters:

• hazard_getter – a callable returning the hazard data for all realizations

• asset_df – a DataFrame of assets on the given site

openquake.risklib.riskinput.rsi2str(rlzi, sid, imt)

Convert a triple (XXXX, YYYY, ZZZ) into a string of the form ‘rlz-XXXX/sid-YYYY/ZZZ’

openquake.risklib.riskinput.str2rsi(key)

Convert a string of the form ‘rlz-XXXX/sid-YYYY/ZZZ’ into a triple (XXXX, YYYY, ZZZ)

openquake.risklib.riskmodels module

class openquake.risklib.riskmodels.CompositeRiskModel(oqparam, risklist, consdict=())

Bases: Mapping

A container (riskid, kind) -> riskmodel

Parameters:

• oqparam – an openquake.commonlib.oqvalidation.OqParam instance

• fragdict – a dictionary riskid -> loss_type -> fragility functions

• vulndict – a dictionary riskid -> loss_type -> vulnerability function

• consdict – a dictionary riskid -> loss_type -> consequence functions

asset_damage_dt(float_dmg_dist)

Returns:

a composite dtype with damages and consequences

check_risk_ids(inputs)

Check that there are no missing risk IDs for some risk functions

compute_csq(assets, dd5, tmap_df, oq)

Parameters:

• assets – asset array

• dd5 – distribution functions of shape (P, A, E, L, D)

• tmap_df – DataFrame corresponding to the given taxonomy

• oq – OqParam instance with .loss_types and .time_event

Returns:

a dict consequence_name, loss_type -> array[P, A, E]

eid_dmg_dt()

Returns:

a dtype (eid, dmg)

get_attrs()

get_consequences()

Returns:

the list of available consequences

get_dmg_csq()

Returns:

damage states (except no_damage) plus consequences

get_loss_ratios()

Returns:

a 1-dimensional composite array with loss ratios by loss type

get_outputs(asset_df, haz, sec_losses=(), rndgen=None, country='?')

Parameters:

• asset_df – a DataFrame of assets with the same taxonomy and country

• haz – a DataFrame of GMVs on the sites of the assets

• sec_losses – a list of functions

• rndgen – a MultiEventRNG instance

Returns:

a list of dictionaries loss_type-> output

init()

make_curve_params()

multi_damage_dt()

Returns:

composite datatype with fields peril-loss_type-damage_state

classmethod read(dstore, oqparam)

Parameters:

dstore – a DataStore instance

Returns:

a CompositeRiskModel instance

reduce(taxonomies)

Parameters:

taxonomies – a set of taxonomies

Returns:

a new CompositeRiskModel reduced to the given taxonomies

set_tmap(tmap_df, taxidx)

Set the attribute .tmap_df if the risk IDs in the taxonomy mapping are consistent with the fragility functions.

property taxonomy_dict

Returns:

a dict taxonomy string -> taxonomy index

tmap_df = ()

to_dframe()

Returns:

a DataFrame containing all risk functions

to_multi_damage(array5d)

Parameters:

array5d – array of shape (P, A, R, L, Dc)

Returns:

array of shape (A, R) of dtype multi_damage_dt

class openquake.risklib.riskmodels.PerilDict

Bases: dict

>>> pd = PerilDict({('groundshaking', 'structural'): .23})
>>> pd['structural']
0.23
>>> pd['structurl']
Traceback (most recent call last):
...
KeyError: ('groundshaking', 'structurl')

class openquake.risklib.riskmodels.RiskFuncList(iterable=(), /)

Bases: list

A list of risk functions with attributes .id, .loss_type, .kind

groupby_id()

Returns:

dictionary id -> peril -> loss_type -> risk_function

class openquake.risklib.riskmodels.RiskModel(calcmode, taxonomy, risk_functions, **kw)

Bases: object

Base class. Can be used in the tests as a mock.

Parameters:

• taxonomy – a taxonomy string

• risk_functions – a dict peril -> (loss_type, kind) -> risk_function

classical_bcr(peril, loss_type, assets, hazard, rng=None)

Parameters:

• loss_type – the loss type

• assets – a list of N assets of the same taxonomy

• hazard – a dictionary col -> hazard curve

• _eps – dummy parameter, unused

Returns:

a list of triples (eal_orig, eal_retro, bcr_result)

classical_damage(peril, loss_type, assets, hazard_curve, rng=None)

Parameters:

• loss_type – the loss type

• assets – a list of N assets of the same taxonomy

• hazard_curve – a dictionary col -> hazard curve

Returns:

an array of N x D elements

where N is the number of points and D the number of damage states.

classical_risk(peril, loss_type, assets, hazard_curve, rng=None)

Parameters:

• loss_type (str) – the loss type considered

• assets – assets is an iterator over A openquake.risklib.scientific.Asset instances

• hazard_curve – an array of poes

• eps – ignored, here only for API compatibility with other calculators

Returns:

a composite array (loss, poe) of shape (A, C)

compositemodel = None

ebrisk(peril, loss_type, assets, gmf_df, rndgen)

Returns:

a DataFrame with columns eid, eid, loss

event_based_damage(peril, loss_type, assets, gmf_df, rng=None)

Parameters:

• loss_type – the loss type

• assets – a list of A assets of the same taxonomy

• gmf_df – a DataFrame of GMFs

• epsilons – dummy parameter, unused

Returns:

an array of shape (A, E, D) elements

where N is the number of points, E the number of events and D the number of damage states.

event_based_risk(peril, loss_type, assets, gmf_df, rndgen)

Returns:

a DataFrame with columns eid, eid, loss

property loss_types

The list of loss types in the underlying vulnerability functions, in lexicographic order

scenario(peril, loss_type, assets, gmf_df, rndgen)

Returns:

a DataFrame with columns eid, eid, loss

scenario_damage(peril, loss_type, assets, gmf_df, rng=None)

Parameters:

• loss_type – the loss type

• assets – a list of A assets of the same taxonomy

• gmf_df – a DataFrame of GMFs

• epsilons – dummy parameter, unused

Returns:

an array of shape (A, E, D) elements

where N is the number of points, E the number of events and D the number of damage states.

scenario_risk(peril, loss_type, assets, gmf_df, rndgen)

Returns:

a DataFrame with columns eid, eid, loss

time_event = None

exception openquake.risklib.riskmodels.ValidationError

Bases: Exception

openquake.risklib.riskmodels.build_vf_node(vf)

Convert a VulnerabilityFunction object into a Node suitable for XML conversion.

openquake.risklib.riskmodels.filter_vset(elem)

openquake.risklib.riskmodels.get_risk_files(inputs)

Parameters:

inputs – a dictionary key -> path name

Returns:

a dictionary “peril/kind/cost_type” -> fname

openquake.risklib.riskmodels.get_risk_functions(oqparam)

Parameters:

oqparam – an OqParam instance

Returns:

a list of risk functions

openquake.risklib.riskmodels.get_riskcomputer(dic, limit_states=())

openquake.risklib.riskmodels.get_riskmodel(taxonomy, oqparam, risk_functions)

Return an instance of the correct risk model class, depending on the attribute calculation_mode of the object oqparam.

Parameters:

• taxonomy – a taxonomy string

• oqparam – an object containing the parameters needed by the RiskModel class

• extra – extra parameters to pass to the RiskModel class

openquake.risklib.riskmodels.group_by_lt(funclist)

Converts a list of objects with attribute .loss_type into a dictionary peril -> loss_type -> risk_function

openquake.risklib.riskmodels.rescale(curves, values)

Multiply the losses in each curve of kind (losses, poes) by the corresponding value.

Parameters:

• curves – an array of shape (A, 2, C)

• values – an array of shape (A,)

openquake.risklib.scientific module

This module includes the scientific API of the oq-risklib

class openquake.risklib.scientific.ConsequenceModel(id, assetCategory, lossCategory, description, limitStates)

Bases: dict

Dictionary of consequence functions. You can access each function given its name with the square bracket notation.

Parameters:

• id (str) – ID of the model

• assetCategory (str) – asset category (i.e. buildings, population)

• lossCategory (str) – loss type (i.e. structural, contents, …)

• description (str) – description of the model

• limitStates – a list of limit state strings

kind = 'consequence'

class openquake.risklib.scientific.CurveParams(index, loss_type, curve_resolution, ratios, user_provided)

Bases: tuple

curve_resolution

Alias for field number 2

index

Alias for field number 0

loss_type

Alias for field number 1

ratios

Alias for field number 3

user_provided

Alias for field number 4

class openquake.risklib.scientific.FragilityFunctionContinuous(limit_state, mean, stddev, minIML, maxIML, nodamage=0)

Bases: object

kind = 'fragility'

class openquake.risklib.scientific.FragilityFunctionDiscrete(limit_state, imls, poes, no_damage_limit=None)

Bases: object

kind = 'fragility'

class openquake.risklib.scientific.FragilityFunctionList(array, **attrs)

Bases: list

A list of fragility functions with common attributes; there is a function for each limit state.

build(limit_states, discretization=20, steps_per_interval=1)

Parameters:

• limit_states – a sequence of limit states

• discretization – continouos fragility discretization parameter

• steps_per_interval – steps_per_interval parameter

Returns:

a populated FragilityFunctionList instance

kind = 'fragility'

mean_loss_ratios_with_steps(steps)

For compatibility with vulnerability functions

class openquake.risklib.scientific.FragilityModel(id, assetCategory, lossCategory, description, limitStates)

Bases: dict

Container for a set of fragility functions. You can access each function given the IMT and taxonomy with the square bracket notation.

Parameters:

• id (str) – ID of the model

• assetCategory (str) – asset category (i.e. buildings, population)

• lossCategory (str) – loss type (i.e. structural, contents, …)

• description (str) – description of the model

• limitStates – a list of limit state strings

class openquake.risklib.scientific.LossCurvesMapsBuilder(conditional_loss_poes, return_periods, loss_dt, weights, eff_time, risk_investigation_time, pla_factor=None)

Bases: object

Build losses curves and maps for all loss types at the same time.

Parameters:

• conditional_loss_poes – a list of PoEs, possibly empty

• return_periods – ordered array of return periods

• loss_dt – composite dtype for the loss types

• weights – weights of the realizations

• num_events – number of events for each realization

• eff_time – ses_per_logic_tree_path * hazard investigation time

build_curve(years, col, losses, agg_types, loss_type, ne)

Compute the requested curves (AEP and OEP curves only if years is not None)

class openquake.risklib.scientific.MultiEventRNG(master_seed, eids, asset_correlation=0)

Bases: 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)

beta(eids, means, covs)

Parameters:

• eids – event IDs

• means – array of floats in the range 0..1

• 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.

boolean_dist(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.])

discrete_dmg_dist(eids, fractions, numbers)

Converting fractions into discrete damage distributions using bincount and random.choice.

Parameters:

• eids – E event IDs

• fractions – array of shape (A, E, D)

• numbers – A asset numbers

Returns:

array of integers of shape (A, E, D)

lognormal(eids, means, covs)

Parameters:

• eids – event IDs

• means – array of floats in the range 0..1

• covs – array of floats with the same shape

Returns:

array of floats

class openquake.risklib.scientific.RiskComputer(crm, taxidx, country_str='?')

Bases: dict

A callable dictionary of risk models able to compute average losses according to the taxonomy mapping. It also computes secondary losses after the average (this is a hugely simplifying approximation).

Parameters:

• crm – a CompositeRiskModel

• asset_df – a DataFrame of assets with the same taxonomy

get_dd5(adf, gmf_df, rng=None, C=0, crm=None)

Parameters:

• adf – DataFrame of assets on the given site with the same taxonomy

• gmf_df – GMFs on the given site for E events

• rng – MultiEvent random generator or None

• C – Number of consequences

Returns:

damage distribution of shape (P, A, E, L, D+C)

output(asset_df, haz, sec_losses=(), rndgen=None)

Compute averages by using the taxonomy mapping

Parameters:

• asset_df – assets on the same site with the same taxonomy

• haz – a DataFrame of GMFs or an array of PoEs

• sec_losses – a list of functions updating the loss dict

• rndgen – None or MultiEventRNG instance

Yields:

dictionaries {loss_type: loss_output}

pprint()

todict()

Returns:

a literal dict describing the RiskComputer

class openquake.risklib.scientific.Sampler(distname, rng, lratios=(), cols=None)

Bases: object

get_losses(df, covs)

sampleBT(df)

sampleLN(df)

samplePM(df)

class openquake.risklib.scientific.VulnerabilityFunction(vf_id, imt, imls, mean_loss_ratios, covs=None, distribution='LN')

Bases: object

dtype = dtype([('iml', '<f8'), ('loss_ratio', '<f8'), ('cov', '<f8')])

init()

interpolate(gmf_df, col)

Parameters:

gmf_df – DataFrame of GMFs

Returns:

DataFrame of interpolated loss ratios and covs

kind = 'vulnerability'

loss_ratio_exceedance_matrix(loss_ratios)

Compute the LREM (Loss Ratio Exceedance Matrix).

mean_imls()

Compute the mean IMLs (Intensity Measure Level) for the given vulnerability function.

Parameters:

vulnerability_function – the vulnerability function where the IMLs (Intensity Measure Level) are taken from.

mean_loss_ratios_with_steps(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

Parameters:

steps (int) –

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]

seed = None

strictly_increasing()

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.

survival(loss_ratio, mean, stddev)

Compute the survival probability based on the underlying distribution.

class openquake.risklib.scientific.VulnerabilityFunctionWithPMF(vf_id, imt, imls, loss_ratios, probs)

Bases: VulnerabilityFunction

Vulnerability function with an explicit distribution of probabilities

Parameters:

• vf_id (str) – vulnerability function ID

• imt (str) – Intensity Measure Type

• imls – intensity measure levels (L)

• ratios – an array of mean ratios (M)

• probs – a matrix of probabilities of shape (M, L)

init()

interpolate(gmf_df, col)

Parameters:

• gmvs – DataFrame of GMFs

• col – name of the column to consider

Returns:

DataFrame of interpolated probabilities

loss_ratio_exceedance_matrix(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.

Parameters:

steps (int) – Number of steps between loss ratios.

class openquake.risklib.scientific.VulnerabilityModel(id=None, assetCategory=None, lossCategory=None)

Bases: dict

Container for a set of vulnerability functions. You can access each function given the IMT and taxonomy with the square bracket notation.

Parameters:

• id (str) – ID of the model

• assetCategory (str) – asset category (i.e. buildings, population)

• lossCategory (str) – loss type (i.e. structural, contents, …)

All such attributes are None for a vulnerability model coming from a NRML 0.4 file.

openquake.risklib.scientific.annual_frequency_of_exceedence(poe, t_haz)

Parameters:

• poe – array of probabilities of exceedence in time t_haz

• t_haz – hazard investigation time

Returns:

array of frequencies (with +inf values where poe=1)

openquake.risklib.scientific.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.

openquake.risklib.scientific.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

openquake.risklib.scientific.broadcast(func, composite_array, *args)

Broadcast an array function over a composite array

openquake.risklib.scientific.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.

Parameters:

• ff – a fragility function object

• continuous_fragility_discretization – .ini file parameter

• steps_per_interval – .ini file parameter

Returns:

generated imls

openquake.risklib.scientific.build_loss_curve_dt(curve_resolution, insurance_losses=False)

Parameters:

• curve_resolution – dictionary loss_type -> curve_resolution

• insurance_losses – configuration parameter

Returns:

loss_curve_dt

openquake.risklib.scientific.classical(vulnerability_function, hazard_imls, hazard_poes, loss_ratios, investigation_time, risk_investigation_time)

Parameters:

• vulnerability_function – an instance of openquake.risklib.scientific.VulnerabilityFunction representing the vulnerability function used to compute the curve.

• hazard_imls – the hazard intensity measure type and levels

• loss_ratios – a tuple of C loss ratios

• investigation_time – hazard investigation time

• risk_investigation_time – risk investigation time

Returns:

an array of shape (2, C)

openquake.risklib.scientific.classical_damage(fragility_functions, hazard_imls, hazard_poes, investigation_time, risk_investigation_time, steps_per_interval=1)

Parameters:

• fragility_functions – a list of fragility functions for each damage state

• hazard_imls – Intensity Measure Levels

• hazard_poes – hazard curve

• investigation_time – hazard investigation time

• risk_investigation_time – risk investigation time

• steps_per_interval – steps per interval

Returns:

an array of D probabilities of occurrence where D is the numbers of damage states.

openquake.risklib.scientific.compose_dds(dmg_dists)

Compose an array of N damage distributions:

>>> compose_dds([[.6, .2, .1, .1], [.5, .3 ,.1, .1]])
array([0.3 , 0.34, 0.17, 0.19])

openquake.risklib.scientific.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.

Parameters:

• loss_ratios – non-decreasing loss ratio values (float32)

• poes – non-increasing probabilities of exceedance values (float32)

• probability (float) – the probability value used to interpolate the loss curve

openquake.risklib.scientific.consequence(consequence, assets, coeff, loss_type, time_event)

Parameters:

• consequence – kind of consequence

• assets – asset array (shape A)

• coeff – composite array of coefficients of shape (A, E)

• time_event – time event string

Returns:

array of shape (A, E)

openquake.risklib.scientific.dds_to_poes(dmg_dists)

Convert an array of damage distributions into an array of PoEs

>>> dds_to_poes([[.7, .2, .1], [0., 0., 1.0]])
array([[1. , 0.3, 0.1],
       [1. , 1. , 1. ]])

openquake.risklib.scientific.fine_graining(points, steps)

Parameters:

• points – a list of floats

• steps (int) – 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

openquake.risklib.scientific.fix_losses(orig_losses, num_events, eff_time=0, sorting=True)

Possibly add zeros and sort the passed losses.

Parameters:

• orig_losses – an array of size num_losses

• num_events – an integer >= num_losses

Returns:

three arrays of size num_events

openquake.risklib.scientific.get_agg_value(consequence, agg_values, agg_id, xltype, time_event)

Returns:

sum of the values corresponding to agg_id for the given consequence

openquake.risklib.scientific.insurance_loss_curve(curve, deductible, insurance_limit)

Compute an insured loss ratio curve given a loss ratio curve

Parameters:

• curve – an array 2 x R (where R is the curve resolution)

• deductible (float) – the deductible limit in fraction form

• insurance_limit (float) – the insured limit in fraction form

>>> losses = numpy.array([3, 20, 101])
>>> poes = numpy.array([0.9, 0.5, 0.1])
>>> insurance_loss_curve(numpy.array([losses, poes]), 5, 100)
array([[ 3.        , 20.        ],
       [ 0.85294118,  0.5       ]])

openquake.risklib.scientific.insurance_losses(asset_df, losses_by_lt, policy_df)

Parameters:

• asset_df – DataFrame of assets

• losses_by_lt – loss_type -> DataFrame[eid, aid, variance, loss]

• policy_df – a DataFrame of policies

openquake.risklib.scientific.insured_losses(losses, deductible, insurance_limit)

Parameters:

• losses – array of ground-up losses

• deductible – array of deductible values

• insurance_limit – array of insurance limit values

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]),
...                numpy.array([5, 5, 5]), numpy.array([100, 100, 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

openquake.risklib.scientific.loss_maps(curves, conditional_loss_poes)

Parameters:

• curves – an array of loss curves

• conditional_loss_poes – a list of conditional loss poes

Returns:

a composite array of loss maps with the same shape

openquake.risklib.scientific.losses_by_period(losses, return_periods, num_events, eff_time=None, sorting=True, name='curve', pla_factor=None)

Parameters:

• losses – simulated losses as an array, list or DataFrame column

• return_periods – return periods of interest

• num_events – the number of events (>= number of losses)

• eff_time – investigation_time * ses_per_logic_tree_path

Returns:

a dictionary with the interpolated losses for the return periods, possibly with NaNs and possibly also a post-loss-amplified curve

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)
{'curve': array([ 0. ,  0. ,  0. ,  3.5,  8. , 13. , 23. ])}

openquake.risklib.scientific.maximum_probable_loss(losses, return_period, eff_time, sorting=True)

Returns:

Maximum Probable Loss at the given return period

>>> losses = [1000., 0., 2000., 1500., 780., 900., 1700., 0., 100., 200.]
>>> float(maximum_probable_loss(losses, 2000, 10_000))
900.0

openquake.risklib.scientific.mean_std(fractions)

Given an N x M matrix, returns mean and std computed on the rows, i.e. two M-dimensional vectors.

openquake.risklib.scientific.normalize_curves_eb(curves)

A more sophisticated version of normalize_curves, used in the event based calculator.

Parameters:

curves – a list of pairs (losses, poes)

Returns:

first losses, all_poes

openquake.risklib.scientific.pairwise(iterable)

Parameters:

iterable – a sequence of N values (s0, s1, …)

Returns:

N-1 pairs (s0, s1), (s1, s2), (s2, s3), …

>>> list(pairwise('ABC'))
[('A', 'B'), ('B', 'C')]

openquake.risklib.scientific.pairwise_diff(values, addlast=False)

Differences between a value and the next value in a sequence. If addlast is set the last value is added to the difference, i.e. N values are returned instead of N-1.

openquake.risklib.scientific.pairwise_mean(values)

Averages between a value and the next value in a sequence

openquake.risklib.scientific.pla_factor(df)

Post-Loss-Amplification factor interpolator. To be instantiated with a DataFrame with columns return_period and pla_factor.

openquake.risklib.scientific.probability_of_exceedance(afoe, t_risk)

Parameters:

• afoe – array of annual frequencies of exceedence

• t_risk – risk investigation time

Returns:

array of probabilities of exceedance in time t_risk

openquake.risklib.scientific.return_periods(eff_time, num_losses)

Parameters:

• eff_time – ses_per_logic_tree_path * investigation_time

• 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)

openquake.risklib.scientific.scenario_damage(fragility_functions, gmvs)

Parameters:

• fragility_functions – a list of D - 1 fragility functions

• gmvs – an array of E ground motion values

Returns:

an array of (D, E) damage fractions

openquake.risklib.scientific.total_losses(asset_df, losses_by_lt, kind, ideduc=False)

Parameters:

• asset_df – DataFrame of assets

• losses_by_lt – lt -> DataFrame[eid, aid]

• kind – kind of total loss (i.e. “structural+nonstructural”)

• ideduc – if True compute the insurance claim

Module contents

Checks on the risk inputs

In the regular workflow the engine processes the inputs in the following order:

1. the risk functions are read

2. the site collection is read

3. the exposure is read and associated to the sites

4. the taxonomy mapping is read, the taxonomies are checked against the exposure taxonomies and the risk IDs checked against the risk functions

5. the consequence functions are checked in set_tmap

The perils are checked and you can get errors like

ValueError: Invalid key in job_r.ini: groundshking_fragility_file

The keys are checked and you can get errors like

ValueError: Unknown key groundshaking_fraglity_file in job_r.ini

The consequences are checked and for an unknown peril you will get an error

InvalidFile: consequences.csv: unknown peril=’groundsaking’ at line=1

The consequences are checked and for an unknown taxonomy you will get a warning:

In consequences.csv there are taxonomies missing in the exposure: {‘Concrete’}