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=None)

Bases: object

Abstract base class for all calculators.

Parameters:

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

before_export()

Set the attributes nbytes

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

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.

run(pre_execute=True, concurrent_tasks=None, close=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=None)

Bases: openquake.calculators.base.BaseCalculator

Base class for hazard calculators based on source models

block_splitter(sources, weight=operator.attrgetter('weight'), key=<function HazardCalculator.<lambda>>)

Parameters:

• sources – a list of sources
• weight – a weight function (default .weight)
• key – None or ‘src_group_id’

Returns:

an iterator over blocks of sources

can_read_parent()

Returns:the parent datastore if it is present and can be read from the workers, None otherwise

check_floating_spinning()

check_overflow()

Overridden in event based

get_min_iml(oq)

init()

To be overridden to initialize the datasets needed by the calculation

load_riskmodel()

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

post_process()

For compatibility with the engine

pre_execute(pre_calculator=None)

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

precalc = None

read_exposure(haz_sitecol=None)

Read the exposure, the riskmodel and update the attributes .sitecol, .assetcol

read_inputs()

Read risk data and sources if any

save_riskmodel()

Save the risk models in the datastore

src_filter

Returns:a SourceFilter/UcerfFilter

store_csm_info(eff_ruptures)

Save info about the composite source model inside the csm_info dataset

store_source_info(calc_times)

Save (weight, num_sites, calc_time) inside the source_info dataset

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=None)

Bases: openquake.calculators.base.HazardCalculator

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

R

Returns:the number of realizations as read from csm_info

build_riskinputs(kind, eps=None, num_events=0)

Parameters:

• kind – kind of hazard getter, can be ‘poe’ or ‘gmf’
• eps – a matrix of epsilons (or None)
• num_events – how many events there are

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, riskmodel, rlzs_assoc, monitor).

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.check_precalc_consistency(calc_mode, precalc_mode)

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

Parameters:

• calc_mode – calculation_mode of the current calculation
• precalc_mode – calculation_mode of the previous calculation

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.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_gmv_data(sids, gmfs)

Convert an array of shape (R, N, E, I) into an array of type gmv_data_dt

openquake.calculators.base.get_idxs(data, eid2idx)

Convert from event IDs to event indices.

Parameters:

• data – an array with a field eid
• eid2idx – a dictionary eid -> idx

Returns:

the array of event indices

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_gmf_data(dstore, sitecol, gmfs, imts, eids=())

Parameters:

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

openquake.calculators.base.save_gmfs(calculator)

Parameters:calculator – a scenario_risk/damage or event_based_risk calculator Returns:a pair (eids, R) where R is the number of realizations

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(rlzs_by_gsim, ebruptures, sitecol, oqparam, min_iml)

Bases: object

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

compute_gmfs_curves(monitor)

Returns:a dict with keys gmfdata, indices, hcurves

gen_gmv()

Compute the GMFs for the given realization and yields tuples of the form (sid, eid, imti, gmv).

get_hazard(data=None)

Parameters:data – if given, an iterator of records of dtype gmf_data_dt Returns:an array (rlzi, sid, imti) -> array(gmv, eid)

imtls

imts

init()

Initialize the computers. Should be called on the workers

sids

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

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)
• rlzs_assoc – a RlzsAssoc instance (if None, infers it)

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:an dict rlzi -> datadict

get_hcurves(imtls=None)

Parameters:imtls – intensity measure types and levels Returns:an array of (R, N) hazard curves

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_pmaps(sids)

Parameters:sids – an array of S site IDs Returns:a list of R probability maps

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.

pmap_by_grp

Returns:dictionary “grp-XXX” -> ProbabilityMap instance

weights

class openquake.calculators.getters.RuptureGetter(hdf5path, code2cls, rup_array, trt, samples, rlzs_by_gsim)

Bases: object

Iterable over ruptures.

Parameters:

• hdf5path – path to an HDF5 file with a dataset names ruptures
• rup_array – an array of rupture parameters with homogeneous grp_id

get_eid_rlz(monitor=None)

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

get_ruptures(srcfilter=None)

Returns:a list of EBRuptures filtered by bounding box

openquake.calculators.getters.get_code2cls(ruptures_attrs)

openquake.calculators.getters.get_maxloss_rupture(dstore, loss_type)

Parameters:

• dstore – a DataStore instance
• loss_type – a loss type string

Returns:

EBRupture instance corresponding to the maximum loss for the given loss type

openquake.calculators.getters.get_rupture_getters(dstore, slc=slice(None, None, None), split=0, hdf5cache=None)

Returns:a list of RuptureGetters

classical module

class openquake.calculators.classical.ClassicalCalculator(oqparam, calc_id=None)

Bases: openquake.calculators.base.HazardCalculator

Classical PSHA calculator

agg_dicts(acc, dic)

Aggregate dictionaries of hazard curves by updating the accumulator.

Parameters:

• acc – accumulator dictionary
• dic – dictionary with keys pmap, calc_times, eff_ruptures

calc_stats(parent)

core_task(group, src_filter, gsims, param, monitor=<Monitor [jenkins]>)

Compute the hazard curves for a set of sources belonging to the same tectonic region type for all the GSIMs associated to that TRT. The arguments are the same as in calc_hazard_curves(), except for gsims, which is a list of GSIM instances.

Returns:a dictionary {grp_id: pmap} with attributes .grp_ids, .calc_times, .eff_ruptures

execute()

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

gen_args()

Used in the case of large source model logic trees. :yields: (sources, sites, gsims) triples

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_stats(acc, pmap_by_kind)

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

Parameters:

• acc – ignored
• pmap_by_kind – a dictionary of ProbabilityMaps

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

zerodict()

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

class openquake.calculators.classical.PreCalculator(oqparam, calc_id=None)

Bases: openquake.calculators.classical.ClassicalCalculator

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

execute()

openquake.calculators.classical.build_hazard_stats(pgetter, hstats, individual_curves, monitor)

Parameters:

• pgetter – an openquake.commonlib.getters.PmapGetter
• hstats – a list of pairs (statname, statfunc)
• individual_curves – if True, also build the individual curves
• 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.get_src_ids(sources)

Returns:a string with the source IDs of the given sources, stripping the extension after the colon, if any

openquake.calculators.classical.saving_sources_by_task(iterargs, dstore)

Yield the iterargs again by populating ‘source_data’

classical_bcr module

class openquake.calculators.classical_bcr.ClassicalBCRCalculator(oqparam, calc_id=None)

Bases: openquake.calculators.classical_risk.ClassicalRiskCalculator

Classical BCR Risk calculator

core_task(riskinputs, riskmodel, param, monitor)

Compute and return the average losses for each asset.

Parameters:

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

post_execute(result)

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

Compute and return the average losses for each asset.

Parameters:

• riskinputs – openquake.risklib.riskinput.RiskInput objects
• riskmodel – 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=None)

Bases: openquake.calculators.classical_risk.ClassicalRiskCalculator

Scenario damage calculator

core_task(riskinputs, riskmodel, param, monitor)

Core function for a classical damage computation.

Parameters:

• riskinputs – openquake.risklib.riskinput.RiskInput objects
• riskmodel – 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, riskmodel, param, monitor)

Core function for a classical damage computation.

Parameters:

• riskinputs – openquake.risklib.riskinput.RiskInput objects
• riskmodel – 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=None)

Bases: openquake.calculators.base.RiskCalculator

Classical Risk calculator

core_task(riskinputs, riskmodel, param, monitor)

Compute and return the average losses for each asset.

Parameters:

• riskinputs – openquake.risklib.riskinput.RiskInput objects
• riskmodel – 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.

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

Compute and return the average losses for each asset.

Parameters:

• riskinputs – openquake.risklib.riskinput.RiskInput objects
• riskmodel – 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=None)

Bases: openquake.calculators.base.HazardCalculator

Classical PSHA disaggregation calculator

POE_TOO_BIG = "You are trying to disaggregate for poe=%s.\nHowever the source model #%d, '%s',\nproduces at most probabilities of %.7f for rlz=#%d, IMT=%s.\nThe disaggregation PoE is too big or your model is wrong,\nproducing too small PoEs."

agg_result(acc, result)

Collect the results coming from compute_disagg into self.results, a dictionary with key (sid, rlzi, poe, imt, trti) and values which are probability arrays.

Parameters:

• acc – dictionary k -> dic accumulating the results
• result – dictionary with the result coming from a task

build_disagg_by_src(iml4)

Parameters:

• dstore – a datastore
• iml4 – 4D array of IMLs with shape (N, 1, M, P)

build_stats(results, hstats)

Parameters:

• results – dict key -> 6D disagg_matrix
• hstats – (statname, statfunc) pairs

check_poes_disagg(curves)

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

execute()

Performs the disaggregation

full_disaggregation(curves)

Run the disaggregation phase.

Parameters:curves – a list of hazard curves, one per site

The curves can be all None if iml_disagg is set in the job.ini

get_NRPM()

Returns:(num_sites, num_rlzs, num_poes, num_imts)

get_curves(sid)

Get all the relevant hazard curves for the given site ordinal. Returns a dictionary rlz_id -> curve_by_imt.

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, rlzi, poe, imt) -> trti -> disagg matrix

pre_execute()

save_bin_edges()

Save disagg-bins

save_disagg_result(dskey, results)

Save the computed PMFs in the datastore

Parameters:

• dskey – dataset key; can be ‘disagg’ or ‘disagg-stats’
• results – a dictionary sid, rlz, poe, imt -> 6D disagg_matrix

openquake.calculators.disaggregation.agg_probs(*probs)

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

openquake.calculators.disaggregation.compute_disagg(src_filter, sources, cmaker, iml4, trti, bin_edges, oqparam, monitor)

Parameters:

• src_filter – a openquake.hazardlib.calc.filter.SourceFilter instance
• sources – list of hazardlib source objects
• cmaker – a openquake.hazardlib.gsim.base.ContextMaker instance
• iml4 – an array of intensities of shape (N, R, M, P)
• trti (dict) – tectonic region type index
• bin_egdes – a dictionary site_id -> edges
• oqparam – the parameters in the job.ini file
• monitor – monitor of the currently running job

Returns:

a dictionary of probability arrays, with composite key (sid, rlzi, poe, imt, iml, trti).

event_based module

class openquake.calculators.event_based.EventBasedCalculator(oqparam, calc_id=None)

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.

agg_dicts(acc, result)

Parameters:

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

build_ruptures(sources, param, monitor=<Monitor [jenkins]>)

Parameters:

• sources – a sequence of (prefiltered) sources of the same group
• param – a dictionary of additional parameters including ses_per_logic_tree_path
• monitor – monitor instance

Yields:

dictionaries with keys rup_array, calc_times, eff_ruptures

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). They could be relaxed in the future.

core_task(rupgetter, srcfilter, param, monitor)

Compute GMFs and optionally hazard curves

csm_info

Returns:a cached CompositionInfo object

execute()

from_sources(par)

Prefilter the composite source model and store the source_info

get_rupture_getters()

Returns:a list of RuptureGetters

init()

init_logic_tree(csm_info)

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

save_gmf_bytes()

Save the attribute nbytes in the gmf_data datasets

zerodict()

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

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.

openquake.calculators.event_based.store_rlzs_by_grp(dstore)

Save in the datastore a composite array with fields (grp_id, gsim_id, rlzs)

openquake.calculators.event_based.update_nbytes(dstore, key, array)

event_based_risk module

class openquake.calculators.event_based_risk.EbrCalculator(oqparam, calc_id=None)

Bases: openquake.calculators.base.RiskCalculator

Event based PSHA calculator generating the total losses by taxonomy

build_datasets(builder)

combine(dummy, res)

Parameters:

• dummy – unused parameter
• res – a result dictionary

core_task(riskinputs, riskmodel, param, monitor)

Parameters:

• riskinputs – openquake.risklib.riskinput.RiskInput objects
• riskmodel – 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)

epsilon_getter()

Returns:a callable (start, stop) producing a slice of epsilons

is_stochastic = True

post_execute(result)

Save risk data and build the aggregate loss curves

postproc()

Build aggregate loss curves in process

pre_execute()

save_losses(dic)

Save the event loss tables incrementally.

Parameters:dic – dictionary with agglosses, avglosses

openquake.calculators.event_based_risk.build_loss_tables(dstore)

Compute the total losses by rupture and losses by rlzi.

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

Parameters:

• riskinputs – openquake.risklib.riskinput.RiskInput objects
• riskmodel – 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', 'csm_info': 'Composite source model', 'dupl_sources': 'Duplicated sources', 'required_params_per_trt': 'Required parameters per tectonic region type', 'ruptures_per_trt': 'Number of ruptures per tectonic region type', 'ruptures_events': 'Specific information for event based', 'rlzs_assoc': 'Realizations per (TRT, GSIM)', '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', 'short_source_info': 'Slowest sources', 'task_hazard:0': 'Fastest task', 'task_hazard:-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=None)

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(dummy)

pre_execute()

Read the site collection and initialize GmfComputer and seeds

scenario_damage module

class openquake.calculators.scenario_damage.ScenarioDamageCalculator(oqparam, calc_id=None)

Bases: openquake.calculators.base.RiskCalculator

Scenario damage calculator

core_task(riskinputs, riskmodel, param, monitor)

Core function for a damage computation.

Parameters:

• riskinputs – openquake.risklib.riskinput.RiskInput objects
• riskmodel – 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’: damage array of shape R, L, E, D, ‘c_asset’: [(l, r, a, mean-stddev), …], ‘c_event’: damage array of shape R, L, E}

d_asset and d_tag are related to the damage distributions whereas c_asset and c_tag are the consequence distributions. If there is no consequence model c_asset is an empty list and c_tag is a zero-valued array.

is_stochastic = True

post_execute(result)

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

pre_execute()

openquake.calculators.scenario_damage.dist_by_asset(data, multi_stat_dt, number)

Parameters:

• data – array of shape (N, R, L, 2, …)
• multi_stat_dt – numpy dtype for statistical outputs
• number – expected number of units per asset

Returns:

array of shape (N, R) with records of type multi_stat_dt

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

Core function for a damage computation.

Parameters:

• riskinputs – openquake.risklib.riskinput.RiskInput objects
• riskmodel – 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’: damage array of shape R, L, E, D, ‘c_asset’: [(l, r, a, mean-stddev), …], ‘c_event’: damage array of shape R, L, E}

d_asset and d_tag are related to the damage distributions whereas c_asset and c_tag are the consequence distributions. If there is no consequence model c_asset is an empty list and c_tag is a zero-valued array.

scenario_risk module

class openquake.calculators.scenario_risk.ScenarioRiskCalculator(oqparam, calc_id=None)

Bases: openquake.calculators.base.RiskCalculator

Run a scenario risk calculation

core_task(riskinputs, riskmodel, param, monitor)

Core function for a scenario computation.

Parameters:

• riskinput – a of openquake.risklib.riskinput.RiskInput object
• riskmodel – 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.

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

Core function for a scenario computation.

Parameters:

• riskinput – a of openquake.risklib.riskinput.RiskInput object
• riskmodel – 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

ucerf_event_classical module

class openquake.calculators.ucerf_classical.UcerfClassicalCalculator(oqparam, calc_id=None)

Bases: openquake.calculators.classical.ClassicalCalculator

UCERF classical calculator.

execute()

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

pre_execute()

ucerf_event_based module

class openquake.calculators.ucerf_event_based.UCERFHazardCalculator(oqparam, calc_id=None)

Bases: openquake.calculators.event_based.EventBasedCalculator

Event based PSHA calculator generating the ruptures and GMFs together

build_ruptures(sources, param, monitor)

Parameters:

• sources – a list with a single UCERF source
• param – extra parameters
• monitor – a Monitor instance

Returns:

an AccumDict grp_id -> EBRuptures

pre_execute()

parse the logic tree and source model input

openquake.calculators.ucerf_event_based.build_ruptures(sources, param, monitor)

Parameters:

• sources – a list with a single UCERF source
• param – extra parameters
• monitor – a Monitor instance

Returns:

an AccumDict grp_id -> EBRuptures

openquake.calculators.ucerf_event_based.generate_event_set(ucerf, background_sids, src_filter, ses_idx, seed)

Generates the event set corresponding to a particular branch

openquake.calculators.ucerf_event_based.sample_background_model(hdf5, branch_key, tom, seed, filter_idx, min_mag, npd, hdd, upper_seismogenic_depth, lower_seismogenic_depth, msr=<WC1994>, aspect=1.5, trt='Active Shallow Crust')

Generates a rupture set from a sample of the background model

Parameters:

• branch_key – Key to indicate the branch for selecting the background model
• tom – Temporal occurrence model as instance of :class: openquake.hazardlib.tom.TOM
• seed – Random seed to use in the call to tom.sample_number_of_occurrences
• filter_idx – Sites for consideration (can be None!)
• min_mag (float) – Minimim magnitude for consideration of background sources
• npd – Nodal plane distribution as instance of :class: openquake.hazardlib.pmf.PMF
• hdd – Hypocentral depth distribution as instance of :class: openquake.hazardlib.pmf.PMF
• aspect (float) – Aspect ratio
• upper_seismogenic_depth (float) – Upper seismogenic depth (km)
• lower_seismogenic_depth (float) – Lower seismogenic depth (km)
• msr – Magnitude scaling relation
• integration_distance (float) – Maximum distance from rupture to site for consideration

views module

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.classify_gsim_lt(gsim_lt)

Returns:“trivial”, “simple” or “complex”

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

Display the data transferred in an event based risk calculation

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.performance_view(dstore)

Returns the performance view as a numpy array.

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.sum_tbl(tbl, kfield, vfields)

Aggregate a composite array and compute the totals on a given key.

>>> dt = numpy.dtype([('name', (bytes, 10)), ('value', int)])
>>> tbl = numpy.array([('a', 1), ('a', 2), ('b', 3)], dt)
>>> sum_tbl(tbl, 'name', ['value'])['value']
array([3, 3])

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

Display statistical information about the distribution of the assets

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

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

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

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

Display the duplicated sources from source_info

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

Display the event loss table averaged by event

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

Display info about the exposure model

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_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_mean_avg_losses(token, dstore)

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_ruptures_per_trt(token, dstore)

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

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_hazard(token, dstore)

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

$ oq show task_hazard:0  # the fastest task
$ oq show task_hazard:-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_task_risk(token, dstore)

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

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

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), extract(dstore, ‘asset_values/0’) etc.

add(key, cache=False)

openquake.calculators.extract.barray(iterlines)

Array of bytes

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

Parameters:

• data – an array of length N with fields ‘mean’ and ‘stddev’
• 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.curves_by_tag(dstore, tag)

Statistical loss curves by tag. For instance call

$ oq extract curves_by_tag/occupancy

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

Extracts an HDF5 path object from the datastore, for instance extract(‘sitecol’, dstore). It is also possibly to extract the attributes, for instance with extract(‘sitecol.attrs’, dstore).

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

Aggregate loss curves of the given loss type and tags for event based risk calculations. Use it as /extract/agg_curves/structural?taxonomy=RC&zipcode=20126

Returns:array of shape (S, P), being P the number of return periods and S the number of statistics

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 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_by(dstore, what)

/extract/aggregate_by/taxonomy,occupancy/curves/structural yield pairs (<stat>, <array of shape (T, O, S, P)>)

/extract/aggregate_by/taxonomy,occupancy/avg_losses/structural yield pairs (<stat>, <array of shape (T, O, S)>)

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_asset_values(dstore, sid)

Extract an array of asset values for the given sid. Use it as /extract/asset_values/0

Returns:(aid, loss_type1, …, loss_typeN) composite array

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

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

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

Extracts hazard curves and possibly hazard maps and/or uniform hazard spectra. Use it as /extract/hazard/mean or /extract/hazard/rlz-0, etc

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

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

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

Extracts hazard maps. Use it as /extract/hmaps/mean or /extract/hmaps/rlz-0, etc

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

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

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

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_uhs(dstore, what)

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

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.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.losses_by_tag(dstore, tag)

Statistical average losses by tag. For instance call

$ oq extract losses_by_tag/occupancy