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_checks()

Checks to run after the pre_execute but before the execute

pre_execute()

Initialization phase.

precalc = None

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

Run the calculation and return the exported outputs.

Parameters:

• pre_execute – set it to False to avoid running pre_execute
• concurrent_tasks – set it to 0 to disable parallelization
• remove – set it to False to remove the hdf5cache file (if any)
• shutdown – set it to True to shutdown the ProcessPool

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

import_perils()

Defined in MultiRiskCalculator

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

src_filter()

Returns:a SourceFilter

store_rlz_info(rel_ruptures)

Save info about the composite source model inside the full_lt dataset

Parameters:rel_ruptures – dictionary TRT -> number of relevant ruptures

store_source_info(calc_times, nsites=False)

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)

Combine the outputs assuming acc and res are dictionaries

execute()

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

Returns:an array with the realization weights of shape R

openquake.calculators.base.check_amplification(ampl_df, sitecol)

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

Parameters:

• ampl_df – the amplification table as a pandas DataFrame
• sitecol – the site collection

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.create_gmf_data(dstore, M, sec_imts=(), data=None)

Create and possibly populate the datasets in the gmf_data group

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, oqparam, sids)

Import in the datastore a ground motion field CSV file.

Parameters:

• dstore – the datastore
• oqparam – an OqParam instance
• sids – the complete site IDs

Returns:

event_ids

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

Store agg_keys, agg_values. :returns: the aggkey dictionary key -> tags

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(sid, df, num_events, num_rlzs)

Bases: object

An object with an .init() and .get_hazard() method

get_hazard(gsim=None)

Parameters:gsim – ignored Returns:the underlying DataFrame

init()

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

Bases: object

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

compute_gmfs_curves(monitor)

Returns:a dict with keys gmfdata, hcurves

gen_computers(mon)

Yield a GmfComputer instance for each non-discarded rupture

get_gmfdata(mon=<Monitor [jenkins]>)

Returns:a DataFrame with fields eid, sid, gmv_…

get_hazard(gsim=None)

Parameters:gsim – ignored Returns:DataFrame

imtls

imts

sids

class openquake.calculators.getters.PmapGetter(dstore, weights, sids, imtls=(), 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)

L

M

N

R

get_hazard(gsim=None)

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

get_hcurves(pmap, rlzs_by_gsim)

Parameters:pmap_by_et_id – a dictionary of ProbabilityMaps by group ID Returns:an array of PoEs of shape (N, R, M, L)

get_mean()

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_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

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

Bases: object

Parameters:

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

get_proxies(min_mag=0)

Returns:a list of RuptureProxies

get_rupdict()

Returns:a dictionary with the parameters of the rupture

num_ruptures

split(srcfilter, maxw)

Yields:RuptureProxies with weight < maxw

class openquake.calculators.getters.ZeroGetter(sid, rlzs, R)

Bases: openquake.calculators.getters.GmfDataGetter

An object with an .init() and .get_hazard() method

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

Build statistics by taking into account IMT-dependent weights

openquake.calculators.getters.gen_rupture_getters(dstore, ct=0, slc=slice(None, None, None))

Parameters:

• dstore – a openquake.baselib.datastore.DataStore
• ct – number of concurrent tasks

Yields:

RuptureGetters

openquake.calculators.getters.get_ebruptures(dstore)

Extract EBRuptures from the datastore

openquake.calculators.getters.get_gmfgetter(dstore, rup_id)

Returns:GmfGetter associated to the given rupture

openquake.calculators.getters.get_slice_by_g(rlzs_by_gsim_list)

Returns:a list of slices

openquake.calculators.getters.sig_eps_dt(imts)

Returns:a composite data type for the sig_eps output

openquake.calculators.getters.strip_zeros(gmf_df)

classical module

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

Bases: openquake.calculators.base.HazardCalculator

Classical PSHA calculator

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, rlzs_by_gsim, params, monitor)

Read the SourceFilter and call the classical calculator in hazardlib

create_dsets()

Store some empty datasets in the datastore

execute()

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

get_args(grp_ids, hazard)

Returns:a list of Starmap arguments

get_source_ids()

Returns:the unique source IDs contained in the composite model

init()

post_execute(dummy)

Compute the statistical 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

set_psd()

Set the pointsource_distance

store_info(psd)

class openquake.calculators.classical.Hazard(dstore, full_lt, pgetter, srcidx)

Bases: object

Helper class for storing the PoEs

init(pmaps, grp_id)

Initialize the pmaps dictionary with zeros, if needed

store_disagg(pmaps=None)

Store data inside disagg_by_src/disagg_by_grp

store_poes(grp_id, pmap)

Store the pmap of the given group inside the _poes dataset

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(srcs, rlzs_by_gsim, params, monitor)

Read the SourceFilter and call the classical calculator in hazardlib

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.get_source_id(src)

openquake.calculators.classical.make_hmap_png(hmap, lons, lats)

Parameters:

• hmap – a dictionary with keys calc_id, m, p, imt, poe, inv_time, array
• lons – an array of longitudes
• lats – an array of latitudes

Returns:

an Image object containing the hazard map

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

Weight the sources. Also split them if split_sources is true. If ps_grid_spacing is set, grid the point sources before weighting them.

NB: srcfilter can be on a reduced site collection for performance reasons

openquake.calculators.classical.run_preclassical(csm, oqparam, h5)

Parameters:

• csm – a CompositeSourceModel with attribute .srcfilter
• oqparam – the parameters in job.ini file
• h5 – a DataStore instance

openquake.calculators.classical.store_ctxs(dstore, rupdata, grp_id)

Store contexts with the same magnitude in the datastore

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, param, monitor)

Compute and return the average losses for each asset.

Parameters:

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

post_execute(result)

pre_execute()

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

Compute and return the average losses for each asset.

Parameters:

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

Core function for a classical damage computation.

Parameters:

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

Yields:

dictionaries asset_ordinal -> damage(R, L, D)

post_execute(result)

Export the result in CSV format.

Parameters:result – a dictionary asset_ordinal -> array(R, L, D)

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

Core function for a classical damage computation.

Parameters:

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

Yields:

dictionaries asset_ordinal -> damage(R, L, D)

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, param, monitor)

Compute and return the average losses for each asset.

Parameters:

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

Compute and return the average losses for each asset.

Parameters:

• riskinputs – openquake.risklib.riskinput.RiskInput objects
• 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, magi -> 6D array
• result – dictionary with the result coming from a task

compute()

Submit disaggregation tasks and return the results

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

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, imti, kind -> trti -> disagg matrix

pre_checks()

Checks on the number of sites, atomic groups and size of the disaggregation matrix.

precalc = 'classical'

save_bin_edges(all_edges)

Save disagg-bins

save_disagg_results(results)

Save the computed PMFs in the datastore

Parameters:results – a dict s, m, k -> 6D-matrix of shape (T, Ma, Lo, La, P, Z) or (T, Ma, D, E, P, Z) depending if k is 0 or k is 1

openquake.calculators.disaggregation.compute_disagg(dstore, slc, cmaker, hmap4, trti, magi, bin_edges, monitor)

Parameters:

• dstore – a DataStore instance
• slc – a slice of ruptures
• cmaker – a openquake.hazardlib.gsim.base.ContextMaker instance
• hmap4 – an ArrayWrapper of shape (N, M, P, Z)
• trti – tectonic region type index
• magi – magnitude bin indices
• bin_egdes – a quartet (dist_edges, lon_edges, lat_edges, eps_edges)
• monitor – monitor of the currently running job

Returns:

a dictionary sid, imti -> 6D-array

openquake.calculators.disaggregation.get_outputs_size(shapedic, disagg_outputs)

Returns:the total size of the outputs

openquake.calculators.disaggregation.output(mat6)

Parameters:mat6 – a 6D matrix with axis (D, Lo, La, E, P, Z) Returns:two matrices of shape (D, E, P, Z) and (Lo, La, P, Z)

openquake.calculators.disaggregation.output_dict(shapedic, disagg_outputs)

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 (et_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()

Prefilter the composite source model and store the source_info

core_task(rupgetter, param, monitor)

Compute GMFs and optionally hazard curves

execute()

init()

is_stochastic = True

post_execute(result)

save_avg_gmf()

Compute and save avg_gmf, unless there are too many GMFs

set_param(**kw)

openquake.calculators.event_based.compute_avg_gmf(gmf_df, weights, min_iml)

Parameters:

• gmf_df – a DataFrame with colums eid, sid, rlz, gmv…
• weights – E weights associated to the realizations
• min_iml – array of M minimum intensities

Returns:

a dictionary site_id -> array of shape (2, M)

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

Compute GMFs and optionally hazard curves

openquake.calculators.event_based.get_mean_curves(dstore, imt)

Extract the mean hazard curves from the datastore, as an array of shape (N, L1)

post_risk module

class openquake.calculators.post_risk.PostRiskCalculator(oqparam, calc_id)

Bases: openquake.calculators.base.RiskCalculator

Compute losses and loss curves starting from an event loss table.

execute()

post_execute(dummy)

Sanity check on tot_losses

pre_execute()

openquake.calculators.post_risk.get_loss_builder(dstore, return_periods=None, loss_dt=None)

Parameters:dstore – datastore for an event based risk calculation Returns:a LossCurvesMapsBuilder instance

openquake.calculators.post_risk.get_src_loss_table(dstore, L)

Returns:(source_ids, array of losses of shape (Ns, L))

openquake.calculators.post_risk.post_risk(builder, kr_losses, monitor)

Returns:dictionary kr -> L loss curves

openquake.calculators.post_risk.reagg_idxs(num_tags, tagnames)

Parameters:

• num_tags – dictionary tagname -> number of tags with that tagname
• tagnames – subset of tagnames of interest

Returns:

T = T1 x … X TN indices with repetitions

Reaggregate indices. Consider for instance a case with 3 tagnames, taxonomy (4 tags), region (3 tags) and country (2 tags):

>>> num_tags = dict(taxonomy=4, region=3, country=2)

There are T = T1 x T2 x T3 = 4 x 3 x 2 = 24 combinations. The function will return 24 reaggregated indices with repetions depending on the selected subset of tagnames.

For instance reaggregating by taxonomy and region would give:

>>> list(reagg_idxs(num_tags, ['taxonomy', 'region']))  # 4x3
[0, 0, 1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6, 7, 7, 8, 8, 9, 9, 10, 10, 11, 11]

Reaggregating by taxonomy and country would give:

>>> list(reagg_idxs(num_tags, ['taxonomy', 'country']))  # 4x2
[0, 1, 0, 1, 0, 1, 2, 3, 2, 3, 2, 3, 4, 5, 4, 5, 4, 5, 6, 7, 6, 7, 6, 7]

Reaggregating by region and country would give:

>>> list(reagg_idxs(num_tags, ['region', 'country']))  # 3x2
[0, 1, 2, 3, 4, 5, 0, 1, 2, 3, 4, 5, 0, 1, 2, 3, 4, 5, 0, 1, 2, 3, 4, 5]

Here is an example of single tag aggregation:

>>> list(reagg_idxs(num_tags, ['taxonomy']))  # 4
[0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3]

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:start_classical:0': 'Fastest task', 'task:start_classical:-1': 'Slowest task', 'task_info': 'Information about the tasks', 'eff_ruptures': '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_damage module

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

Bases: openquake.calculators.base.RiskCalculator

Damage calculator

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

combine(acc, res)

Combine the results and grows dd_data

core_task(riskinputs, param, monitor)

Core function for a damage computation.

Parameters:

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

Returns:

a dictionary of arrays

is_stochastic = True

post_execute(result)

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

pre_execute()

precalc = 'event_based'

sanity_check()

Sanity check on the total number of buildings

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.run_sec_sims(damages, haz, sec_sims, seed)

Parameters:

• damages – array of shape (E, D) for a given asset
• haz – dataframe of size E with a probability field
• sec_sims – pair (probability field, number of simulations)
• seed – random seed to use

Run secondary simulations and update the array damages

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

Core function for a damage computation.

Parameters:

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

Returns:

a dictionary of arrays

scenario_risk module

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

Bases: openquake.calculators.base.RiskCalculator

Run a scenario/event_based risk calculation

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

combine(acc, res)

core_task(riskinputs, param, monitor)

Core function for a scenario_risk/event_based_risk computation.

Parameters:

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

Returns:

a dictionary { ‘alt’: AggLoggTable instance ‘losses_by_asset’: list of tuples (lt_idx, rlz_idx, asset_ordinal, totloss)}

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 = 'event_based'

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

Core function for a scenario_risk/event_based_risk computation.

Parameters:

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

Returns:

a dictionary { ‘alt’: AggLoggTable instance ‘losses_by_asset’: list of tuples (lt_idx, rlz_idx, asset_ordinal, totloss)}

openquake.calculators.scenario_risk.run_sec_sims(out, loss_types, sec_sims, seed)

Parameters:

• out – a dictionary of losses
• loss_types – the loss types
• sec_sims – pair (probability field, number of simulations)
• seed – random seed to use

Run secondary simulations and update the losses

views module

class openquake.calculators.views.GmpeExtractor(dstore)

Bases: object

extract(et_ids, rlz_ids)

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.binning_error(values, eids, nbins=10)

Parameters:

• values – E values
• eids – E integer event indices

Returns:

std/mean for the sums of the values

Group the values in nbins depending on the eids and returns the variability of the sums relative to the mean.

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.get_gmv0(dstore)

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

The damages and errors for the full portfolio, extracted from the asset damage table.

openquake.calculators.views.rst_links(*fnames)

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

Build a .rst table from a matrix or a DataFrame

>>> 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, rel_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_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_disagg_times(token, dstore)

Display slow tasks for disaggregation

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

Show how many event based ruptures there are for each magnitude

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

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

Show the number of events per realization multiplied by risk_time/eff_time

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(token, dstore)

Show sites where the mean hazard map reaches maximum values

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

Display table of extreme GMVs with fields (eid, gmv_0, sid, rlz. rup)

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 on the first IMT averaged on everything for debugging purposes

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

Display the global hazard for the calculation. This is used for debugging purposes when comparing the results of two calculations.

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_gmf(token, dstore)

Display a mean gmf for debugging purposes

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

Display a gmf relative error for seed dependency

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_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_portfolio_damage(token, dstore)

The mean full portfolio damage for each loss type, extracted from the average damages

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

The mean portfolio loss for each loss type, extracted from the event loss table.

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_sum(token, dstore)

Show the sum of an array on the first axis; used to debug the damages

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_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.

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

Sanity check on avg_losses and avg_gmf

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, 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, 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)

Parameters:dstore – a datastore instance Returns:a composite dtype loss_type -> (ds1, ds2, …)

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.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&custom_site_id=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_loss_table(dstore, what)

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&custom_site_id=20126 /extract/agg_losses/structural?taxonomy=RC&custom_site_id=*

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_avg_gmf(dstore, what)

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

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

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

Extract the disagg_by_src information Example: http://127.0.0.1:8800/v1/calc/30/extract/disagg_by_src?site_id=0&imt_id=0&rlz_id=0&lvl_id=-1

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

Extract a disaggregation layer containing all sites and outputs Example: http://127.0.0.1:8800/v1/calc/30/extract/disagg_layer?

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

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

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

Returns a dictionary gsim -> event_ids for the first TRT Example: http://127.0.0.1:8800/v1/calc/30/extract/eids_by_gsim

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_extreme_event(dstore, eidx)

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

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

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

Extract information about the gridded sources (requires ps_grid_spacing) Use it as /extract/gridded_sources?task_no=0. Returns a json string id -> lonlats

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&imt=PGA or /extract/hcurves?kind=rlz-0&imt=SA(1.0)

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_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_oqparam(dstore, dummy)

Extract job parameters as a JSON npz. Use it as /extract/oqparam

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

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

openquake.calculators.extract.extract_relevant_events(dstore, dummy=None)

Extract the relevant events Example: http://127.0.0.1:8800/v1/calc/30/extract/events

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_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_ruptures(dstore, what)

Extract some information about the ruptures, including the boundary. Example: http://127.0.0.1:8800/v1/calc/30/extract/ruptures?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?field=vs30

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_task_info(dstore, what)

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

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

Aggregate 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_uhs(dstore, what)

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

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

Extract the realization weights

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.norm(qdict, params)

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 ‘-‘