openquake.calculators package

Subpackages

• openquake.calculators.export package
  • Submodules
  • openquake.calculators.export.hazard module
  • openquake.calculators.export.risk module
  • Module contents

base module

class openquake.calculators.base.BaseCalculator(oqparam, calc_id)

Bases: object

Abstract base class for all calculators.

Parameters:

• oqparam – OqParam object
• monitor – monitor object
• calc_id – numeric calculation ID

accept_precalc = []

check_precalc(precalc_mode)

Defensive programming against users providing an incorrect pre-calculation ID (with --hazard-calculation-id).

Parameters:precalc_mode – calculation_mode of the previous calculation

core_task(*args)

Core routine running on the workers.

execute()

Execution phase. Usually will run in parallel the core function and return a dictionary with the results.

export(exports=None)

Export all the outputs in the datastore in the given export formats. Individual outputs are not exported if there are multiple realizations.

from_engine = False

gzip_inputs()

Gzipping the inputs and saving them in the datastore

is_stochastic = False

monitor(operation='', **kw)

Returns:a new Monitor instance

post_execute(result)

Post-processing phase of the aggregated output. It must be overridden with the export code. It will return a dictionary of output files.

pre_execute()

Initialization phase.

precalc = None

run(pre_execute=True, concurrent_tasks=None, remove=True, **kw)

Run the calculation and return the exported outputs.

save_params(**kw)

Update the current calculation parameters and save engine_version

class openquake.calculators.base.HazardCalculator(oqparam, calc_id)

Bases: openquake.calculators.base.BaseCalculator

Base class for hazard calculators based on source models

E

Returns:the number of stored events

N

Returns:the total number of sites

R

Returns:the number of realizations

check_floating_spinning()

check_overflow()

Overridden in event based

few_sites

Returns:True if there are less than max_sites_disagg

init()

To be overridden to initialize the datasets needed by the calculation

load_crmodel()

Read the risk models and set the attribute .crmodel. The crmodel can be empty for hazard calculations. Save the loss ratios (if any) in the datastore.

load_insurance_data(ins_types, ins_files)

Read the insurance files and populate the policy_dict

post_process()

For compatibility with the engine

pre_execute()

Check if there is a previous calculation ID. If yes, read the inputs by retrieving the previous calculation; if not, read the inputs directly.

read_exposure(haz_sitecol)

Read the exposure, the risk models and update the attributes .sitecol, .assetcol

read_inputs()

Read risk data and sources if any

save_crmodel()

Save the risk models in the datastore

save_multi_peril()

Defined in MultiRiskCalculator

src_filter(filename=None)

Returns:a SourceFilter/UcerfFilter

store_rlz_info(eff_ruptures)

Save info about the composite source model inside the full_lt dataset

store_source_info(calc_times)

Save (eff_ruptures, num_sites, calc_time) inside the source_info

exception openquake.calculators.base.InvalidCalculationID

Bases: Exception

Raised when running a post-calculation on top of an incompatible pre-calculation

class openquake.calculators.base.RiskCalculator(oqparam, calc_id)

Bases: openquake.calculators.base.HazardCalculator

Base class for all risk calculators. A risk calculator must set the attributes .crmodel, .sitecol, .assetcol, .riskinputs in the pre_execute phase.

build_riskinputs(kind)

Parameters:kind – kind of hazard getter, can be ‘poe’ or ‘gmf’ Returns:a list of RiskInputs objects, sorted by IMT.

combine(acc, res)

execute()

Parallelize on the riskinputs and returns a dictionary of results. Require a .core_task to be defined with signature (riskinputs, crmodel, param, monitor).

get_getter(kind, sid)

Parameters:

• kind – ‘poe’ or ‘gmf’
• sid – a site ID

Returns:

a PmapGetter or GmfDataGetter

read_shakemap(haz_sitecol, assetcol)

Enabled only if there is a shakemap_id parameter in the job.ini. Download, unzip, parse USGS shakemap files and build a corresponding set of GMFs which are then filtered with the hazard site collection and stored in the datastore.

openquake.calculators.base.build_hmaps(hcurves_by_kind, slice_, imtls, poes, monitor)

Build hazard maps from a slice of hazard curves. :returns: a pair ({kind: hmaps}, slice)

openquake.calculators.base.build_weights(realizations, imt_dt)

Returns:an array with the realization weights of shape R

openquake.calculators.base.check_amplification(dstore)

Make sure the amplification codes in the site collection match the ones in the amplification table

openquake.calculators.base.check_time_event(oqparam, occupancy_periods)

Check the time_event parameter in the datastore, by comparing with the periods found in the exposure.

openquake.calculators.base.consistent(dic1, dic2)

Check if two dictionaries with default are consistent:

>>> consistent({'PGA': 0.05, 'SA(0.3)': 0.05}, {'default': 0.05})
True
>>> consistent({'SA(0.3)': 0.1, 'SA(0.6)': 0.05},
... {'default': 0.1, 'SA(0.3)': 0.1, 'SA(0.6)': 0.05})
True

openquake.calculators.base.fix_ones(pmap)

Physically, an extremely small intensity measure level can have an extremely large probability of exceedence, however that probability cannot be exactly 1 unless the level is exactly 0. Numerically, the PoE can be 1 and this give issues when calculating the damage (there is a log(0) in openquake.risklib.scientific.annual_frequency_of_exceedence). Here we solve the issue by replacing the unphysical probabilities 1 with .9999999999999999 (the float64 closest to 1).

openquake.calculators.base.get_calc(job_ini, calc_id)

Factory function returning a Calculator instance

Parameters:

• job_ini – path to job.ini file
• calc_id – calculation ID

openquake.calculators.base.get_stats(seq)

openquake.calculators.base.import_gmfs(dstore, fname, sids)

Import in the datastore a ground motion field CSV file.

Parameters:

• dstore – the datastore
• fname – the CSV file
• sids – the site IDs (complete)

Returns:

event_ids, num_rlzs

openquake.calculators.base.save_exposed_values(dstore, assetcol, lossnames, tagnames)

Store 2^n arrays where n is the number of tagNames. For instance with the tags country, occupancy it stores 2^2 = 4 arrays:

exposed_values/agg_country_occupancy # shape (T1, T2, L) exposed_values/agg_country # shape (T1, L) exposed_values/agg_occupancy # shape (T2, L) exposed_values/agg # shape (L,)

openquake.calculators.base.save_gmf_data(dstore, sitecol, gmfs, imts, events=())

Parameters:

• dstore – a openquake.baselib.datastore.DataStore instance
• sitecol – a openquake.hazardlib.site.SiteCollection instance
• gmfs – an array of shape (N, E, M)
• imts – a list of IMT strings
• events – E event IDs or the empty tuple

openquake.calculators.base.set_array(longarray, shortarray)

Parameters:

• longarray – a numpy array of floats of length L >= l
• shortarray – a numpy array of floats of length l

Fill longarray with the values of shortarray, starting from the left. If shortarry is shorter than longarray, then the remaining elements on the right are filled with numpy.nan values.

getters module

class openquake.calculators.getters.GmfDataGetter(dstore, sids, num_rlzs)

Bases: collections.abc.Mapping

A dictionary-like object {sid: dictionary by realization index}

get_hazard(gsim=None)

Parameters:gsim – ignored Returns:an dict rlzi -> datadict

init()

class openquake.calculators.getters.GmfGetter(rupgetter, srcfilter, oqparam, amplifier=None)

Bases: object

An hazard getter with methods .get_gmfdata and .get_hazard returning ground motion values.

compute_gmfs_curves(rlzs, monitor)

Parameters:rlzs – an array of shapeE Returns:a dict with keys gmfdata, indices, hcurves

gen_computers(mon)

Yield a GmfComputer instance for each non-discarded rupture

get_gmfdata(mon)

Returns:an array of the dtype (sid, eid, gmv)

get_hazard_by_sid(data=None)

Parameters:data – if given, an iterator of records of dtype gmf_dt Returns:sid -> records

imtls

imts

sids

class openquake.calculators.getters.PmapGetter(dstore, weights, sids=None, poes=())

Bases: object

Read hazard curves from the datastore for all realizations or for a specific realization.

Parameters:

• dstore – a DataStore instance or file system path to it
• sids – the subset of sites to consider (if None, all sites)

get(rlzi, grp=None)

Parameters:

• rlzi – a realization index
• grp – None (all groups) or a string of the form “grp-XX”

Returns:

the hazard curves for the given realization

get_hazard(gsim=None)

Parameters:gsim – ignored Returns:R probability curves for the given site

get_mean(grp=None)

Compute the mean curve as a ProbabilityMap

Parameters:grp – if not None must be a string of the form “grp-XX”; in that case returns the mean considering only the contribution for group XX

get_pcurve(s, r, g)

Parameters:

• s – site ID
• r – realization ID
• g – group ID

Returns:

a probability curves with shape L (or None, if missing)

get_pcurves(sid)

Returns:a list of R probability curves with shape L

imts

init()

Read the poes and set the .data attribute with the hazard curves

items(kind='')

Extract probability maps from the datastore, possibly generating on the fly the ones corresponding to the individual realizations. Yields pairs (tag, pmap).

Parameters:kind – the kind of PoEs to extract; if not given, returns the realization if there is only one or the statistics otherwise.

class openquake.calculators.getters.RuptureGetter(proxies, filename, grp_id, trt, samples, rlzs_by_gsim)

Bases: object

Parameters:

• proxies – a list of RuptureProxies
• filename – path to the HDF5 file containing a ‘rupgeoms’ dataset
• grp_id – source group index
• trt – tectonic region type string
• samples – number of samples of the group
• rlzs_by_gsim – dictionary gsim -> rlzs for the group

get_eid_rlz()

Returns:a composite array with the associations eid->rlz

get_proxies(min_mag=0)

Returns:a list of RuptureProxies

get_rupdict()

Returns:a dictionary with the parameters of the rupture

num_ruptures

openquake.calculators.getters.build_stat_curve(poes, imtls, stat, weights)

Build statistics by taking into account IMT-dependent weights

openquake.calculators.getters.gen_rgetters(dstore, slc=slice(None, None, None))

Yields:unfiltered RuptureGetters

openquake.calculators.getters.gen_rupture_getters(dstore, srcfilter, ct)

Parameters:

• dstore – a openquake.baselib.datastore.DataStore
• srcfilter – a openquake.hazardlib.calc.filters.SourceFilter
• ct – number of concurrent tasks

Yields:

filtered RuptureGetters

openquake.calculators.getters.get_ebruptures(dstore)

Extract EBRuptures from the datastore

openquake.calculators.getters.get_rupdict(dstore)

Returns:a dictionary rup_id->rup_dict

openquake.calculators.getters.group_by_rlz(data, rlzs)

Parameters:

• data – a composite array of D elements with a field eid
• rlzs – an array of E >= D elements

Returns:

a dictionary rlzi -> data for each realization

openquake.calculators.getters.sig_eps_dt(imts)

Returns:a composite data type for the sig_eps output

classical module

class openquake.calculators.classical.ClassicalCalculator(oqparam, calc_id)

Bases: openquake.calculators.base.HazardCalculator

Classical PSHA calculator

acc0()

Initial accumulator, a dict grp_id -> ProbabilityMap(L, G)

accept_precalc = ['classical']

agg_dicts(acc, dic)

Aggregate dictionaries of hazard curves by updating the accumulator.

Parameters:

• acc – accumulator dictionary
• dic – dict with keys pmap, calc_times, rup_data

calc_stats()

core_task(srcs, srcfilter, gsims, params, monitor)

Split the given sources, filter the subsources and the compute the PoEs. Yield back subtasks if the split sources contain more than maxweight ruptures.

execute()

Run in parallel core_task(sources, sitecol, monitor), by parallelizing on the sources according to their weight and tectonic region type.

post_execute(pmap_by_grp_id)

Collect the hazard curves by realization and export them.

Parameters:pmap_by_grp_id – a dictionary grp_id -> hazard curves

save_hazard(acc, pmap_by_kind)

Works by side effect by saving hcurves and hmaps on the datastore

Parameters:

• acc – ignored
• pmap_by_kind – a dictionary of ProbabilityMaps

kind can be (‘hcurves’, ‘mean’), (‘hmaps’, ‘mean’), …

submit_tasks(smap)

Submit tasks to the passed Starmap

class openquake.calculators.classical.PreCalculator(oqparam, calc_id)

Bases: openquake.calculators.classical.ClassicalCalculator

Calculator to filter the sources and compute the number of effective ruptures

core_task(srcs, srcfilter, gsims, params, monitor)

Split and prefilter the sources

openquake.calculators.classical.build_hazard(pgetter, N, hstats, individual_curves, max_sites_disagg, amplifier, monitor)

Parameters:

• pgetter – an openquake.commonlib.getters.PmapGetter
• N – the total number of sites
• hstats – a list of pairs (statname, statfunc)
• individual_curves – if True, also build the individual curves
• max_sites_disagg – if there are less sites than this, store rup info
• amplifier – instance of Amplifier or None
• monitor – instance of Monitor

Returns:

a dictionary kind -> ProbabilityMap

The “kind” is a string of the form ‘rlz-XXX’ or ‘mean’ of ‘quantile-XXX’ used to specify the kind of output.

openquake.calculators.classical.classical_split_filter(srcs, srcfilter, gsims, params, monitor)

Split the given sources, filter the subsources and the compute the PoEs. Yield back subtasks if the split sources contain more than maxweight ruptures.

openquake.calculators.classical.get_extreme_poe(array, imtls)

Parameters:

• array – array of shape (L, G) with L=num_levels, G=num_gsims
• imtls – DictArray imt -> levels

Returns:

the maximum PoE corresponding to the maximum level for IMTs and GSIMs

openquake.calculators.classical.preclassical(srcs, srcfilter, gsims, params, monitor)

Split and prefilter the sources

classical_bcr module

class openquake.calculators.classical_bcr.ClassicalBCRCalculator(oqparam, calc_id)

Bases: openquake.calculators.classical_risk.ClassicalRiskCalculator

Classical BCR Risk calculator

accept_precalc = ['classical']

core_task(riskinputs, crmodel, param, monitor)

Compute and return the average losses for each asset.

Parameters:

• riskinputs – openquake.risklib.riskinput.RiskInput objects
• crmodel – a openquake.risklib.riskinput.CompositeRiskModel instance
• param – dictionary of extra parameters
• monitor – openquake.baselib.performance.Monitor instance

post_execute(result)

pre_execute()

openquake.calculators.classical_bcr.classical_bcr(riskinputs, crmodel, param, monitor)

Compute and return the average losses for each asset.

Parameters:

• riskinputs – openquake.risklib.riskinput.RiskInput objects
• crmodel – a openquake.risklib.riskinput.CompositeRiskModel instance
• param – dictionary of extra parameters
• monitor – openquake.baselib.performance.Monitor instance

classical_damage module

class openquake.calculators.classical_damage.ClassicalDamageCalculator(oqparam, calc_id)

Bases: openquake.calculators.classical_risk.ClassicalRiskCalculator

Scenario damage calculator

accept_precalc = ['classical']

core_task(riskinputs, crmodel, param, monitor)

Core function for a classical damage computation.

Parameters:

• riskinputs – openquake.risklib.riskinput.RiskInput objects
• crmodel – a openquake.risklib.riskinput.CompositeRiskModel instance
• param – dictionary of extra parameters
• monitor – openquake.baselib.performance.Monitor instance

Returns:

a nested dictionary lt_idx, rlz_idx -> asset_idx -> <damage array>

post_execute(result)

Export the result in CSV format.

Parameters:result – a dictionary (l, r) -> asset_ordinal -> fractions per damage state

openquake.calculators.classical_damage.classical_damage(riskinputs, crmodel, param, monitor)

Core function for a classical damage computation.

Parameters:

• riskinputs – openquake.risklib.riskinput.RiskInput objects
• crmodel – a openquake.risklib.riskinput.CompositeRiskModel instance
• param – dictionary of extra parameters
• monitor – openquake.baselib.performance.Monitor instance

Returns:

a nested dictionary lt_idx, rlz_idx -> asset_idx -> <damage array>

classical_risk module

class openquake.calculators.classical_risk.ClassicalRiskCalculator(oqparam, calc_id)

Bases: openquake.calculators.base.RiskCalculator

Classical Risk calculator

accept_precalc = ['classical']

core_task(riskinputs, crmodel, param, monitor)

Compute and return the average losses for each asset.

Parameters:

• riskinputs – openquake.risklib.riskinput.RiskInput objects
• crmodel – a openquake.risklib.riskinput.CompositeRiskModel instance
• param – dictionary of extra parameters
• monitor – openquake.baselib.performance.Monitor instance

post_execute(result)

Saving loss curves in the datastore.

Parameters:result – aggregated result of the task classical_risk

pre_execute()

Associate the assets to the sites and build the riskinputs.

precalc = 'classical'

openquake.calculators.classical_risk.classical_risk(riskinputs, crmodel, param, monitor)

Compute and return the average losses for each asset.

Parameters:

• riskinputs – openquake.risklib.riskinput.RiskInput objects
• crmodel – a openquake.risklib.riskinput.CompositeRiskModel instance
• param – dictionary of extra parameters
• monitor – openquake.baselib.performance.Monitor instance

disaggregation module

Disaggregation calculator core functionality

class openquake.calculators.disaggregation.DisaggregationCalculator(oqparam, calc_id)

Bases: openquake.calculators.base.HazardCalculator

Classical PSHA disaggregation calculator

accept_precalc = ['classical', 'disaggregation']

agg_result(acc, result)

Collect the results coming from compute_disagg into self.results.

Parameters:

• acc – dictionary sid -> trti -> 8D array
• result – dictionary with the result coming from a task

build_disagg_by_src(rlzs)

check_poes_disagg(curves, rlzs)

Raise an error if the given poes_disagg are too small compared to the hazard curves.

execute()

Performs the disaggregation

full_disaggregation()

Run the disaggregation phase.

get_curve(sid, rlzs)

Get the hazard curves for the given site ID and realizations.

Parameters:

• sid – site ID
• rlzs – a matrix of indices of shape Z

Returns:

a list of Z arrays of PoEs

init()

post_execute(results)

Save all the results of the disaggregation. NB: the number of results to save is #sites * #rlzs * #disagg_poes * #IMTs.

Parameters:results – a dictionary sid -> trti -> disagg matrix

precalc = 'classical'

save_bin_edges()

Save disagg-bins

save_disagg_results(results, **attrs)

Save the computed PMFs in the datastore

Parameters:

• results – an 8D-matrix of shape (T, .., E, M, P)
• attrs – dictionary of attributes to add to the dataset

openquake.calculators.disaggregation.agg_probs(*probs)

Aggregate probabilities withe the usual formula 1 - (1 - P1) … (1 - Pn)

openquake.calculators.disaggregation.compute_disagg(dstore, idxs, cmaker, iml4, trti, bin_edges, monitor)

:param dstore

a DataStore instance

Parameters:

• idxs – an array of indices to ruptures
• cmaker – a openquake.hazardlib.gsim.base.ContextMaker instance
• iml4 – an ArrayWrapper of shape (N, M, P, Z)
• trti – tectonic region type index
• bin_egdes – a quintet (mag_edges, dist_edges, lon_edges, lat_edges, eps_edges)
• monitor – monitor of the currently running job

Returns:

a dictionary sid -> 8D-array

openquake.calculators.disaggregation.get_indices(dstore, concurrent_tasks)

event_based module

class openquake.calculators.event_based.EventBasedCalculator(oqparam, calc_id)

Bases: openquake.calculators.base.HazardCalculator

Event based PSHA calculator generating the ground motion fields and the hazard curves from the ruptures, depending on the configuration parameters.

acc0()

Initial accumulator, a dictionary (grp_id, gsim) -> curves

accept_precalc = ['event_based', 'ebrisk', 'event_based_risk']

agg_dicts(acc, result)

Parameters:

• acc – accumulator dictionary
• result – an AccumDict with events, ruptures, gmfs and hcurves

build_events_from_sources(srcfilter)

Prefilter the composite source model and store the source_info

check_overflow()

Raise a ValueError if the number of sites is larger than 65,536 or the number of IMTs is larger than 256 or the number of ruptures is larger than 4,294,967,296. The limits are due to the numpy dtype used to store the GMFs (gmv_dt). There also a limit of max_potential_gmfs on the number of sites times the number of events, to avoid producing too many GMFs. In that case split the calculation or be smarter.

core_task(rupgetter, srcfilter, param, monitor)

Compute GMFs and optionally hazard curves

execute()

init()

init_logic_tree(full_lt)

is_stochastic = True

post_execute(result)

save_events(rup_array)

Parameters:rup_array – an array of ruptures with fields grp_id Returns:a list of RuptureGetters

set_param(**kw)

openquake.calculators.event_based.compute_gmfs(rupgetter, srcfilter, param, monitor)

Compute GMFs and optionally hazard curves

openquake.calculators.event_based.get_mean_curves(dstore)

Extract the mean hazard curves from the datastore, as a composite array of length nsites.

event_based_risk module

class openquake.calculators.event_based_risk.EbrCalculator(oqparam, calc_id)

Bases: openquake.calculators.base.RiskCalculator

Event based PSHA calculator generating the total losses by taxonomy

accept_precalc = ['event_based', 'event_based_risk', 'ebrisk']

build_datasets(builder)

combine(dummy, res)

Parameters:

• dummy – unused parameter
• res – a result dictionary

core_task(riskinputs, crmodel, param, monitor)

Parameters:

• riskinputs – openquake.risklib.riskinput.RiskInput objects
• crmodel – a openquake.risklib.riskinput.CompositeRiskModel instance
• param – a dictionary of parameters
• monitor – openquake.baselib.performance.Monitor instance

Returns:

a dictionary of numpy arrays of shape (L, R)

is_stochastic = True

post_execute(result)

Save risk data and build the aggregate loss curves

pre_execute()

precalc = 'event_based'

save_losses(dic)

Save the event loss tables incrementally.

Parameters:dic – dictionary with agglosses, avglosses

openquake.calculators.event_based_risk.event_based_risk(riskinputs, crmodel, param, monitor)

Parameters:

• riskinputs – openquake.risklib.riskinput.RiskInput objects
• crmodel – a openquake.risklib.riskinput.CompositeRiskModel instance
• param – a dictionary of parameters
• monitor – openquake.baselib.performance.Monitor instance

Returns:

a dictionary of numpy arrays of shape (L, R)

reportwriter module

Utilities to build a report writer generating a .rst report for a calculation

class openquake.calculators.reportwriter.ReportWriter(dstore)

Bases: object

A particularly smart view over the datastore

add(name, obj=None)

Add the view named name to the report text

make_report()

Build the report and return a restructed text string

save(fname)

Save the report

title = {'params': 'Parameters', 'inputs': 'Input files', 'full_lt': 'Composite source model', 'required_params_per_trt': 'Required parameters per tectonic region type', 'ruptures_events': 'Specific information for event based', 'job_info': 'Data transfer', 'biggest_ebr_gmf': 'Maximum memory allocated for the GMFs', 'avglosses_data_transfer': 'Estimated data transfer for the avglosses', 'exposure_info': 'Exposure model', 'slow_sources': 'Slowest sources', 'task:classical_split_filter:0': 'Fastest task', 'task:classical_split_filter:-1': 'Slowest task', 'task_info': 'Information about the tasks', 'times_by_source_class': 'Computation times by source typology', 'performance': 'Slowest operations'}

openquake.calculators.reportwriter.build_report(job_ini, output_dir=None)

Write a report.csv file with information about the calculation without running it

Parameters:

• job_ini – full pathname of the job.ini file
• output_dir – the directory where the report is written (default the input directory)

openquake.calculators.reportwriter.indent(text)

scenario module

class openquake.calculators.scenario.ScenarioCalculator(oqparam, calc_id)

Bases: openquake.calculators.base.HazardCalculator

Scenario hazard calculator

execute()

Compute the GMFs and return a dictionary gsim -> array(N, E, I)

init()

is_stochastic = True

post_execute(gmfa)

pre_execute()

Read the site collection and initialize GmfComputer and seeds

scenario_damage module

class openquake.calculators.scenario_damage.EventBasedDamageCalculator(oqparam, calc_id)

Bases: openquake.calculators.scenario_damage.ScenarioDamageCalculator

Event Based Damage calculator, able to compute dmg_by_asset, dmg_by_event and consequences.

accept_precalc = ['event_based', 'event_based_risk']

core_task(riskinputs, crmodel, param, monitor)

Core function for a damage computation.

Parameters:

• riskinputs – openquake.risklib.riskinput.RiskInput objects
• crmodel – a openquake.risklib.riskinput.CompositeRiskModel instance
• monitor – openquake.baselib.performance.Monitor instance
• param – dictionary of extra parameters

Returns:

a dictionary {‘d_asset’: [(l, r, a, mean-stddev), …],

’d_event’: dict eid -> array of shape (L, D) + optional consequences}

d_asset and d_tag are related to the damage distributions.

precalc = 'event_based'

class openquake.calculators.scenario_damage.ScenarioDamageCalculator(oqparam, calc_id)

Bases: openquake.calculators.base.RiskCalculator

Scenario damage calculator

accept_precalc = ['scenario']

combine(acc, res)

core_task(riskinputs, crmodel, param, monitor)

Core function for a damage computation.

Parameters:

• riskinputs – openquake.risklib.riskinput.RiskInput objects
• crmodel – a openquake.risklib.riskinput.CompositeRiskModel instance
• monitor – openquake.baselib.performance.Monitor instance
• param – dictionary of extra parameters

Returns:

a dictionary {‘d_asset’: [(l, r, a, mean-stddev), …],

’d_event’: dict eid -> array of shape (L, D) + optional consequences}

d_asset and d_tag are related to the damage distributions.

is_stochastic = True

post_execute(result)

Compute stats for the aggregated distributions and save the results on the datastore.

pre_execute()

precalc = 'scenario'

openquake.calculators.scenario_damage.approx_ddd(fractions, n, seed=None)

Converting fractions into uint16 discrete damage distributions using round

openquake.calculators.scenario_damage.bin_ddd(fractions, n, seed)

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

openquake.calculators.scenario_damage.floats_in(numbers)

Parameters:numbers – an array of numbers Returns:number of non-uint32 number

openquake.calculators.scenario_damage.scenario_damage(riskinputs, crmodel, param, monitor)

Core function for a damage computation.

Parameters:

• riskinputs – openquake.risklib.riskinput.RiskInput objects
• crmodel – a openquake.risklib.riskinput.CompositeRiskModel instance
• monitor – openquake.baselib.performance.Monitor instance
• param – dictionary of extra parameters

Returns:

a dictionary {‘d_asset’: [(l, r, a, mean-stddev), …],

’d_event’: dict eid -> array of shape (L, D) + optional consequences}

d_asset and d_tag are related to the damage distributions.

scenario_risk module

class openquake.calculators.scenario_risk.ScenarioRiskCalculator(oqparam, calc_id)

Bases: openquake.calculators.base.RiskCalculator

Run a scenario risk calculation

accept_precalc = ['scenario']

combine(acc, res)

Combine the outputs from scenario_risk and incrementally store the asset loss table

core_task(riskinputs, crmodel, param, monitor)

Core function for a scenario computation.

Parameters:

• riskinput – a of openquake.risklib.riskinput.RiskInput object
• crmodel – a openquake.risklib.riskinput.CompositeRiskModel instance
• param – dictionary of extra parameters
• monitor – openquake.baselib.performance.Monitor instance

Returns:

a dictionary { ‘agg’: array of shape (E, L, R, 2), ‘avg’: list of tuples (lt_idx, rlz_idx, asset_ordinal, statistics) } where E is the number of simulated events, L the number of loss types, R the number of realizations and statistics is an array of shape (n, R, 4), with n the number of assets in the current riskinput object

is_stochastic = True

post_execute(result)

Compute stats for the aggregated distributions and save the results on the datastore.

pre_execute()

Compute the GMFs, build the epsilons, the riskinputs, and a dictionary with the unit of measure, used in the export phase.

precalc = 'scenario'

openquake.calculators.scenario_risk.ael_dt(loss_names, rlz=False)

Returns:(asset_id, event_id, loss) or (asset_id, event_id, rlzi, loss)

openquake.calculators.scenario_risk.scenario_risk(riskinputs, crmodel, param, monitor)

Core function for a scenario computation.

Parameters:

• riskinput – a of openquake.risklib.riskinput.RiskInput object
• crmodel – a openquake.risklib.riskinput.CompositeRiskModel instance
• param – dictionary of extra parameters
• monitor – openquake.baselib.performance.Monitor instance

Returns:

a dictionary { ‘agg’: array of shape (E, L, R, 2), ‘avg’: list of tuples (lt_idx, rlz_idx, asset_ordinal, statistics) } where E is the number of simulated events, L the number of loss types, R the number of realizations and statistics is an array of shape (n, R, 4), with n the number of assets in the current riskinput object

openquake.calculators.scenario_risk.value(asset, loss_type)

ucerf_event_classical module

views module

class openquake.calculators.views.Source(source_id, code, num_ruptures, checksum)

Bases: tuple

checksum

Alias for field number 3

code

Alias for field number 1

num_ruptures

Alias for field number 2

source_id

Alias for field number 0

openquake.calculators.views.avglosses_data_transfer(token, dstore)

Determine the amount of average losses transferred from the workers to the controller node in a risk calculation.

openquake.calculators.views.build_links(items)

openquake.calculators.views.form(value)

Format numbers in a nice way.

>>> form(0)
'0'
>>> form(0.0)
'0.0'
>>> form(0.0001)
'1.000E-04'
>>> form(1003.4)
'1_003'
>>> form(103.4)
'103'
>>> form(9.3)
'9.30000'
>>> form(-1.2)
'-1.2'

openquake.calculators.views.portfolio_loss(dstore)

openquake.calculators.views.rst_links(*fnames)

openquake.calculators.views.rst_table(data, header=None, fmt=None)

Build a .rst table from a matrix.

>>> tbl = [['a', 1], ['b', 2]]
>>> print(rst_table(tbl, header=['Name', 'Value']))
==== =====
Name Value
==== =====
a    1    
b    2    
==== =====

openquake.calculators.views.stats(name, array, *extras)

Returns statistics from an array of numbers.

Parameters:name – a descriptive string Returns:(name, mean, std, min, max, len)

openquake.calculators.views.sum_table(records)

Used to compute summaries. The records are assumed to have numeric fields, except the first field which is ignored, since it typically contains a label. Here is an example:

>>> sum_table([('a', 1), ('b', 2)])
['total', 3]

openquake.calculators.views.view_act_ruptures_by_src(token, dstore)

Display the actual number of ruptures by source in event based calculations

openquake.calculators.views.view_assets_by_site(token, dstore)

Display statistical information about the distribution of the assets

openquake.calculators.views.view_bad_ruptures(token, dstore)

Display the ruptures degenerating to a point

openquake.calculators.views.view_contents(token, dstore)

Returns the size of the contents of the datastore and its total size

openquake.calculators.views.view_eff_ruptures(token, dstore)

openquake.calculators.views.view_elt(token, dstore)

Display the event loss table averaged by event

openquake.calculators.views.view_events_by_mag(token, dstore)

Show how many events there are for each magnitude

openquake.calculators.views.view_exposure_info(token, dstore)

Display info about the exposure model

openquake.calculators.views.view_extreme_groups(token, dstore)

Show the source groups contributing the most to the highest IML

openquake.calculators.views.view_full_lt(token, dstore)

openquake.calculators.views.view_fullreport(token, dstore)

Display an .rst report about the computation

openquake.calculators.views.view_global_gmfs(token, dstore)

Display GMFs averaged on everything for debugging purposes

openquake.calculators.views.view_global_hcurves(token, dstore)

Display the global hazard curves for the calculation. They are used for debugging purposes when comparing the results of two calculations. They are the mean over the sites of the mean hazard curves.

openquake.calculators.views.view_global_hmaps(token, dstore)

Display the global hazard maps for the calculation. They are used for debugging purposes when comparing the results of two calculations. They are the mean over the sites of the mean hazard maps.

openquake.calculators.views.view_global_poes(token, dstore)

Display global probabilities averaged on all sites and all GMPEs

openquake.calculators.views.view_gmv_by_rup(token, dstore)

Display a synthetic gmv per rupture serial for debugging purposes

openquake.calculators.views.view_gmvs(token, dstore)

Show the GMVs on a given site ID

openquake.calculators.views.view_gmvs_to_hazard(token, dstore)

Show the number of GMFs over the highest IML

openquake.calculators.views.view_hmap(token, dstore)

Display the highest 20 points of the mean hazard map. Called as $ oq show hmap:0.1 # 10% PoE

openquake.calculators.views.view_inputs(token, dstore)

openquake.calculators.views.view_job_info(token, dstore)

Determine the amount of data transferred from the controller node to the workers and back in a classical calculation.

openquake.calculators.views.view_maximum_intensity(token, dstore)

Show intensities at minimum and maximum distance for the highest magnitude

openquake.calculators.views.view_mean_disagg(token, dstore)

Display mean quantities for the disaggregation. Useful for checking differences between two calculations.

openquake.calculators.views.view_num_units(token, dstore)

Display the number of units by taxonomy

openquake.calculators.views.view_params(token, dstore)

openquake.calculators.views.view_performance(token, dstore)

Display performance information

openquake.calculators.views.view_pmap(token, dstore)

Display the mean ProbabilityMap associated to a given source group name

openquake.calculators.views.view_portfolio_loss(token, dstore)

The mean and stddev loss for the full portfolio for each loss type, extracted from the event loss table, averaged over the realizations

openquake.calculators.views.view_portfolio_losses(token, dstore)

The losses for the full portfolio, for each realization and loss type, extracted from the event loss table.

openquake.calculators.views.view_required_params_per_trt(token, dstore)

Display the parameters needed by each tectonic region type

openquake.calculators.views.view_ruptures_events(token, dstore)

openquake.calculators.views.view_short_source_info(token, dstore, maxrows=20)

openquake.calculators.views.view_slow_ruptures(token, dstore, maxrows=25)

Show the slowest ruptures

openquake.calculators.views.view_slow_sources(token, dstore, maxrows=20)

Returns the slowest sources

openquake.calculators.views.view_task_durations(token, dstore)

Display the raw task durations. Here is an example of usage:

$ oq show task_durations:classical

openquake.calculators.views.view_task_ebrisk(token, dstore)

Display info about ebrisk tasks:

$ oq show task_ebrisk:-1 # the slowest task

openquake.calculators.views.view_task_hazard(token, dstore)

Display info about a given task. Here are a few examples of usage:

$ oq show task:classical:0  # the fastest task
$ oq show task:classical:-1  # the slowest task

openquake.calculators.views.view_task_info(token, dstore)

Display statistical information about the tasks performance. It is possible to get full information about a specific task with a command like this one, for a classical calculation:

$ oq show task_info:classical

openquake.calculators.views.view_times_by_source_class(token, dstore)

Returns the calculation times depending on the source typology

openquake.calculators.views.view_totlosses(token, dstore)

This is a debugging view. You can use it to check that the total losses, i.e. the losses obtained by summing the average losses on all assets are indeed equal to the aggregate losses. This is a sanity check for the correctness of the implementation.

extract module

class openquake.calculators.extract.Extract

Bases: dict

A callable dictionary of functions with a single instance called extract. Then extract(dstore, fullkey) dispatches to the function determined by the first part of fullkey (a slash-separated string) by passing as argument the second part of fullkey.

For instance extract(dstore, ‘sitecol’).

add(key, cache=False)

class openquake.calculators.extract.Extractor(calc_id)

Bases: object

A class to extract data from a calculation.

Parameters:calc_id – a calculation ID

NB: instantiating the Extractor opens the datastore.

close()

Close the datastore

get(what, asdict=False)

Parameters:what – what to extract Returns:an ArrayWrapper instance or a dictionary if asdict is True

exception openquake.calculators.extract.NotFound

Bases: Exception

class openquake.calculators.extract.RuptureData(trt, samples, gsims)

Bases: object

Container for information about the ruptures of a given tectonic region type.

to_array(proxies)

Convert a list of rupture proxies into an array of dtype RuptureRata.dt

exception openquake.calculators.extract.WebAPIError

Bases: RuntimeError

Wrapper for an error on a WebAPI server

class openquake.calculators.extract.WebExtractor(calc_id, server=None, username=None, password=None)

Bases: openquake.calculators.extract.Extractor

A class to extract data from the WebAPI.

Parameters:

• calc_id – a calculation ID
• server – hostname of the webapi server (can be ‘’)
• username – login username (can be ‘’)
• password – login password (can be ‘’)

NB: instantiating the WebExtractor opens a session.

close()

Close the session

dump(fname)

Dump the remote datastore on a local path.

get(what)

Parameters:what – what to extract Returns:an ArrayWrapper instance

openquake.calculators.extract.barray(iterlines)

Array of bytes

openquake.calculators.extract.build_damage_array(data, damage_dt)

Parameters:

• data – an array of shape (A, L, 1, D) or (A, L, 2, D)
• damage_dt – a damage composite data type loss_type -> states

Returns:

a composite array of length N and dtype damage_dt

openquake.calculators.extract.build_damage_dt(dstore, mean_std=True)

Parameters:

• dstore – a datastore instance
• mean_std – a flag (default True)

Returns:

a composite dtype loss_type -> (mean_ds1, stdv_ds1, …) or loss_type -> (ds1, ds2, …) depending on the flag mean_std

openquake.calculators.extract.cast(loss_array, loss_dt)

openquake.calculators.extract.crm_attrs(dstore, what)

Returns:the attributes of the risk model, i.e. limit_states, loss_types, min_iml and covs, needed by the risk exporters.

openquake.calculators.extract.disagg_output(dstore, imt, sid, poe_id, rlz=None)

Returns:a datagroup

openquake.calculators.extract.extract_(dstore, dspath)

Extracts an HDF5 path object from the datastore, for instance extract(dstore, ‘sitecol’).

openquake.calculators.extract.extract_agg_curves(dstore, what)

Aggregate loss curves from the ebrisk calculator:

/extract/agg_curves? kind=stats&absolute=1&loss_type=occupants&occupancy=RES

Returns an array of shape (P, S, 1…) or (P, R, 1…)

openquake.calculators.extract.extract_agg_damages(dstore, what)

Aggregate damages of the given loss type and tags. Use it as /extract/agg_damages/structural?taxonomy=RC&zipcode=20126

Returns:array of shape (R, D), being R the number of realizations and D the number of damage states, or an array of length 0 if there is no data for the given tags

openquake.calculators.extract.extract_agg_losses(dstore, what)

Aggregate losses of the given loss type and tags. Use it as /extract/agg_losses/structural?taxonomy=RC&zipcode=20126 /extract/agg_losses/structural?taxonomy=RC&zipcode=*

Returns:an array of shape (T, R) if one of the tag names has a * value an array of shape (R,), being R the number of realizations an array of length 0 if there is no data for the given tags

openquake.calculators.extract.extract_aggregate(dstore, what)

/extract/aggregate/avg_losses? kind=mean&loss_type=structural&tag=taxonomy&tag=occupancy

openquake.calculators.extract.extract_asset_risk(dstore, what)

Extract an array of assets + risk fields, optionally filtered by tag. Use it as /extract/asset_risk?taxonomy=RC&taxonomy=MSBC&occupancy=RES

openquake.calculators.extract.extract_asset_tags(dstore, tagname)

Extract an array of asset tags for the given tagname. Use it as /extract/asset_tags or /extract/asset_tags/taxonomy

openquake.calculators.extract.extract_assets(dstore, what)

Extract an array of assets, optionally filtered by tag. Use it as /extract/assets?taxonomy=RC&taxonomy=MSBC&occupancy=RES

openquake.calculators.extract.extract_curves(dstore, what, tot)

Porfolio loss curves from the ebrisk calculator:

/extract/tot_curves? kind=stats&absolute=1&loss_type=occupants

Returns an array of shape (P, S) or (P, R)

openquake.calculators.extract.extract_disagg(dstore, what)

Extract a disaggregation output Example: http://127.0.0.1:8800/v1/calc/30/extract/ disagg?kind=Mag_Dist&imt=PGA&poe_id=0&site_id=1&rlz=0

openquake.calculators.extract.extract_disagg_layer(dstore, what)

Extract a disaggregation output containing all sites for the first realization or the mean. Example: http://127.0.0.1:8800/v1/calc/30/extract/ disagg_layer?kind=Mag_Dist&imt=PGA&poe_id=0

openquake.calculators.extract.extract_dmg_by_asset_npz(dstore, what)

openquake.calculators.extract.extract_effect(dstore, what)

Extracts the effect of ruptures. Use it as /extract/effect

openquake.calculators.extract.extract_event_info(dstore, eidx)

Extract information about the given event index. Example: http://127.0.0.1:8800/v1/calc/30/extract/event_info/0

openquake.calculators.extract.extract_exposure_metadata(dstore, what)

Extract the loss categories and the tags of the exposure. Use it as /extract/exposure_metadata

openquake.calculators.extract.extract_gmf_npz(dstore, what)

openquake.calculators.extract.extract_gsims_by_trt(dstore, what)

Extract the dictionary gsims_by_trt

openquake.calculators.extract.extract_hcurves(dstore, what)

Extracts hazard curves. Use it as /extract/hcurves?kind=mean or /extract/hcurves?kind=rlz-0, /extract/hcurves?kind=stats, /extract/hcurves?kind=rlzs etc

openquake.calculators.extract.extract_hmaps(dstore, what)

Extracts hazard maps. Use it as /extract/hmaps?imt=PGA

openquake.calculators.extract.extract_losses_by_asset(dstore, what)

openquake.calculators.extract.extract_losses_by_event(dstore, what)

openquake.calculators.extract.extract_mean_std_curves(dstore, what)

Yield imls/IMT and poes/IMT containg mean and stddev for all sites

openquake.calculators.extract.extract_mfd(dstore, what)

Display num_ruptures by magnitude for event based calculations. Example: http://127.0.0.1:8800/v1/calc/30/extract/event_based_mfd?kind=mean

openquake.calculators.extract.extract_num_events(dstore, what)

Returns:the number of events (if any)

openquake.calculators.extract.extract_realizations(dstore, dummy)

Extract an array of realizations. Use it as /extract/realizations

openquake.calculators.extract.extract_rups_by_mag_dist(dstore, what)

Extracts the number of ruptures by mag, dist. Use it as /extract/rups_by_mag_dist

openquake.calculators.extract.extract_rupture(dstore, rup_id)

Extract information about the given event index. Example: http://127.0.0.1:8800/v1/calc/30/extract/rupture/1066

openquake.calculators.extract.extract_rupture_info(dstore, what)

Extract some information about the ruptures, including the boundary. Example: http://127.0.0.1:8800/v1/calc/30/extract/rupture_info?min_mag=6

openquake.calculators.extract.extract_sitecol(dstore, what)

Extracts the site collection array (not the complete object, otherwise it would need to be pickled). Use it as /extract/sitecol

openquake.calculators.extract.extract_sources(dstore, what)

Extract information about a source model. Use it as /extract/sources?limit=10 or /extract/sources?source_id=1&source_id=2 or /extract/sources?code=A&code=B

openquake.calculators.extract.extract_src_loss_table(dstore, loss_type)

Extract the source loss table for a give loss type, ordered in decreasing order. Example: http://127.0.0.1:8800/v1/calc/30/extract/src_loss_table/structural

openquake.calculators.extract.extract_task_info(dstore, what)

Extracts the task distribution. Use it as /extract/task_info?kind=classical

openquake.calculators.extract.extract_uhs(dstore, what)

Extracts uniform hazard spectra. Use it as /extract/uhs?kind=mean or /extract/uhs?kind=rlz-0, etc

openquake.calculators.extract.get_info(dstore)

Returns:{‘stats’: dic, ‘loss_types’: dic, ‘num_rlzs’: R}

openquake.calculators.extract.get_loss_type_tags(what)

openquake.calculators.extract.get_mesh(sitecol, complete=True)

Returns:a lon-lat or lon-lat-depth array depending if the site collection is at sea level or not

openquake.calculators.extract.get_ruptures_within(dstore, bbox)

Extract the ruptures within the given bounding box, a string minlon,minlat,maxlon,maxlat. Example: http://127.0.0.1:8800/v1/calc/30/extract/ruptures_with/8,44,10,46

openquake.calculators.extract.hazard_items(dic, mesh, *extras, **kw)

Parameters:

• dic – dictionary of arrays of the same shape
• mesh – a mesh array with lon, lat fields of the same length
• extras – optional triples (field, dtype, values)
• kw – dictionary of parameters (like investigation_time)

Returns:

a list of pairs (key, value) suitable for storage in .npz format

openquake.calculators.extract.lit_eval(string)

ast.literal_eval the string if possible, otherwise returns it unchanged

openquake.calculators.extract.parse(query_string, info={})

Returns:a normalized query_dict as in the following examples:

>>> parse('kind=stats', {'stats': {'mean': 0, 'max': 1}})
{'kind': ['mean', 'max'], 'k': [0, 1], 'rlzs': False}
>>> parse('kind=rlzs', {'stats': {}, 'num_rlzs': 3})
{'kind': ['rlz-000', 'rlz-001', 'rlz-002'], 'k': [0, 1, 2], 'rlzs': True}
>>> parse('kind=mean', {'stats': {'mean': 0, 'max': 1}})
{'kind': ['mean'], 'k': [0], 'rlzs': False}
>>> parse('kind=rlz-3&imt=PGA&site_id=0', {'stats': {}})
{'kind': ['rlz-3'], 'imt': ['PGA'], 'site_id': [0], 'k': [3], 'rlzs': True}

openquake.calculators.extract.sanitize(query_string)

Replace /, ?, & characters with underscores and ‘=’ with ‘-‘