Source code for openquake.calculators.base

# -*- coding: utf-8 -*-
# vim: tabstop=4 shiftwidth=4 softtabstop=4
# Copyright (C) 2014-2018 GEM Foundation
# OpenQuake is free software: you can redistribute it and/or modify it
# under the terms of the GNU Affero General Public License as published
# by the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
# OpenQuake is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# GNU Affero General Public License for more details.
# You should have received a copy of the GNU Affero General Public License
# along with OpenQuake. If not, see <>.
import os
import sys
import abc
import pdb
import logging
import operator
import itertools
import traceback
from functools import partial
from datetime import datetime
from shapely import wkt
import numpy

from openquake.baselib import (
    config, general, hdf5, datastore, __version__ as engine_version)
from openquake.baselib.performance import perf_dt, Monitor
from openquake.hazardlib.calc.filters import SourceFilter, RtreeFilter
from openquake.risklib import riskinput, riskmodels
from openquake.commonlib import readinput, source, calc, writers
from openquake.baselib.parallel import Starmap
from openquake.hazardlib.shakemap import get_sitecol_shakemap, to_gmfs
from openquake.calculators.export import export as exp
from openquake.calculators.getters import GmfDataGetter, PmapGetter

get_taxonomy = operator.attrgetter('taxonomy')
get_weight = operator.attrgetter('weight')
get_trt = operator.attrgetter('src_group_id')
get_imt = operator.attrgetter('imt')

calculators = general.CallableDict(operator.attrgetter('calculation_mode'))
U16 = numpy.uint16
U32 = numpy.uint32
U64 = numpy.uint64
F32 = numpy.float32
TWO16 = 2 ** 16
logversion = True

[docs]class InvalidCalculationID(Exception): """ Raised when running a post-calculation on top of an incompatible pre-calculation """
PRECALC_MAP = dict( classical=['psha'], disaggregation=['psha'], scenario_risk=['scenario'], scenario_damage=['scenario'], classical_risk=['classical'], classical_bcr=['classical'], classical_damage=['classical'], event_based=['event_based', 'event_based_risk', 'ucerf_hazard'], event_based_risk=['event_based', 'event_based_risk', 'ucerf_hazard'], ucerf_classical=['ucerf_psha'], ucerf_hazard=['ucerf_hazard'])
[docs]def set_array(longarray, shortarray): """ :param longarray: a numpy array of floats of length L >= l :param 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. """ longarray[:len(shortarray)] = shortarray longarray[len(shortarray):] = numpy.nan
[docs]def check_precalc_consistency(calc_mode, precalc_mode): """ Defensive programming against users providing an incorrect pre-calculation ID (with ``--hazard-calculation-id``) :param calc_mode: calculation_mode of the current calculation :param precalc_mode: calculation_mode of the previous calculation """ ok_mode = PRECALC_MAP[calc_mode] if calc_mode != precalc_mode and precalc_mode not in ok_mode: raise InvalidCalculationID( 'In order to run a risk calculation of kind %r, ' 'you need to provide a calculation of kind %r, ' 'but you provided a %r instead' % (calc_mode, ok_mode, precalc_mode))
[docs]class BaseCalculator(metaclass=abc.ABCMeta): """ Abstract base class for all calculators. :param oqparam: OqParam object :param monitor: monitor object :param calc_id: numeric calculation ID """ from_engine = False # set by engine.run_calc is_stochastic = False # True for scenario and event based calculators def __init__(self, oqparam, calc_id=None): self.datastore = datastore.DataStore(calc_id) self._monitor = Monitor( '' % self.__class__.__name__, measuremem=True) self.oqparam = oqparam
[docs] def monitor(self, operation='', **kw): """ :returns: a new Monitor instance """ mon = self._monitor(operation, hdf5=self.datastore.hdf5) self._monitor.calc_id = mon.calc_id = self.datastore.calc_id vars(mon).update(kw) return mon
[docs] def save_params(self, **kw): """ Update the current calculation parameters and save engine_version """ if ('hazard_calculation_id' in kw and kw['hazard_calculation_id'] is None): del kw['hazard_calculation_id'] vars(self.oqparam).update(**kw) self.datastore['oqparam'] = self.oqparam # save the updated oqparam attrs = self.datastore['/'].attrs attrs['engine_version'] = engine_version attrs['date'] =[:19] if 'checksum32' not in attrs: attrs['checksum32'] = readinput.get_checksum32(self.oqparam) self.datastore.flush()
[docs] def set_log_format(self): """Set the format of the root logger""" fmt = '[%(asctime)s #{} %(levelname)s] %(message)s'.format( self.datastore.calc_id) for handler in logging.root.handlers: handler.setFormatter(logging.Formatter(fmt))
[docs] def run(self, pre_execute=True, concurrent_tasks=None, close=True, **kw): """ Run the calculation and return the exported outputs. """ global logversion with self._monitor: self._monitor.username = kw.get('username', '') self._monitor.hdf5 = self.datastore.hdf5 if 'performance_data' not in self.datastore: self.datastore.create_dset('performance_data', perf_dt) self.set_log_format() if logversion: # make sure this is logged only once'Running %s', self.oqparam.inputs['job_ini'])'Using engine version %s', engine_version) logversion = False if concurrent_tasks is None: # use the job.ini parameter ct = self.oqparam.concurrent_tasks else: # used the parameter passed in the command-line ct = concurrent_tasks if ct == 0: # disable distribution temporarily oq_distribute = os.environ.get('OQ_DISTRIBUTE') os.environ['OQ_DISTRIBUTE'] = 'no' if ct != self.oqparam.concurrent_tasks: # save the used concurrent_tasks self.oqparam.concurrent_tasks = ct self.save_params(**kw) Starmap.init(distribute=os.environ['OQ_DISTRIBUTE']) try: if pre_execute: self.pre_execute() self.result = self.execute() if self.result is not None: self.post_execute(self.result) self.before_export() self.export(kw.get('exports', '')) except Exception: if kw.get('pdb'): # post-mortem debug tb = sys.exc_info()[2] traceback.print_tb(tb) pdb.post_mortem(tb) else: logging.critical('', exc_info=True) raise finally: # cleanup globals if ct == 0: # restore OQ_DISTRIBUTE if oq_distribute is None: # was not set del os.environ['OQ_DISTRIBUTE'] else: os.environ['OQ_DISTRIBUTE'] = oq_distribute readinput.pmap = None readinput.exposure = None Starmap.shutdown() self._monitor.flush() if close: # in the engine we close later self.result = None try: self.datastore.close() except (RuntimeError, ValueError): # sometimes produces errors but they are difficult to # reproduce logging.warn('', exc_info=True) return getattr(self, 'exported', {})
[docs] def core_task(*args): """ Core routine running on the workers. """ raise NotImplementedError
[docs] @abc.abstractmethod def pre_execute(self): """ Initialization phase. """
[docs] @abc.abstractmethod def execute(self): """ Execution phase. Usually will run in parallel the core function and return a dictionary with the results. """
[docs] @abc.abstractmethod def post_execute(self, result): """ Post-processing phase of the aggregated output. It must be overridden with the export code. It will return a dictionary of output files. """
[docs] def export(self, exports=None): """ Export all the outputs in the datastore in the given export formats. Individual outputs are not exported if there are multiple realizations. """ self.exported = getattr(self.precalc, 'exported', {}) if isinstance(exports, tuple): fmts = exports elif exports: # is a string fmts = exports.split(',') elif isinstance(self.oqparam.exports, tuple): fmts = self.oqparam.exports else: # is a string fmts = self.oqparam.exports.split(',') keys = set(self.datastore) has_hcurves = 'hcurves' in self.datastore or 'poes' in self.datastore if has_hcurves: keys.add('hcurves') for fmt in fmts: if not fmt: continue for key in sorted(keys): # top level keys if 'rlzs' in key and self.R > 1: continue # skip individual curves self._export((key, fmt)) if has_hcurves and self.oqparam.hazard_maps: self._export(('hmaps', fmt)) if has_hcurves and self.oqparam.uniform_hazard_spectra: self._export(('uhs', fmt))
def _export(self, ekey): if ekey not in exp or self.exported.get(ekey): # already exported return with self.monitor('export'): self.exported[ekey] = fnames = exp(ekey, self.datastore) if fnames:'exported %s: %s', ekey[0], fnames)
[docs] def before_export(self): """ Set the attributes nbytes """ # sanity check that eff_ruptures have been set, i.e. are not -1 csm_info = self.datastore['csm_info'] for sm in csm_info.source_models: for sg in sm.src_groups: assert sg.eff_ruptures != -1, sg for key in self.datastore: self.datastore.set_nbytes(key) self.datastore.flush()
[docs]def check_time_event(oqparam, occupancy_periods): """ Check the `time_event` parameter in the datastore, by comparing with the periods found in the exposure. """ time_event = oqparam.time_event if time_event and time_event not in occupancy_periods: raise ValueError( 'time_event is %s in %s, but the exposure contains %s' % (time_event, oqparam.inputs['job_ini'], ', '.join(occupancy_periods)))
[docs]class HazardCalculator(BaseCalculator): """ Base class for hazard calculators based on source models """ precalc = None
[docs] def filter_csm(self): """ :returns: (filtered CompositeSourceModel, SourceFilter) """ oq = self.oqparam mon = self.monitor('preprocess') self.hdf5cache = self.datastore.hdf5cache() src_filter = SourceFilter(self.sitecol.complete, oq.maximum_distance, self.hdf5cache) param = dict(concurrent_tasks=oq.concurrent_tasks) if 'EventBased' in self.__class__.__name__: param['filter_distance'] = oq.filter_distance param['ses_per_logic_tree_path'] = oq.ses_per_logic_tree_path param['gsims_by_trt'] = self.csm.gsim_lt.values else: # use processpool in classical param['distribute'] = 'processpool' if oq.prefilter_sources == 'no':'Not prefiltering the sources') return src_filter, self.csm dist = param.get('distribute', os.environ['OQ_DISTRIBUTE']) if oq.prefilter_sources == 'rtree' and dist in ('no', 'processpool'): # rtree can be used only with processpool, otherwise one gets an # RTreeError: Error in "Index_Create": Spatial Index Error: # IllegalArgumentException: SpatialIndex::DiskStorageManager: # Index/Data file cannot be read/writen.'Preprocessing the sources with rtree') prefilter = RtreeFilter(self.sitecol.complete, oq.maximum_distance, self.hdf5cache) sources_by_grp = prefilter.pfilter( self.csm.get_sources(), param, mon) csm = else:'Preprocessing the sources') sources_by_grp = src_filter.pfilter( self.csm.get_sources(), param, mon) csm ='There are %d realizations', return src_filter, csm
[docs] def can_read_parent(self): """ :returns: the parent datastore if it is present and can be read from the workers, None otherwise """ read_access = ( config.distribution.oq_distribute in ('no', 'processpool') or hdf5cache = getattr(self, 'hdf5cache', None) if hdf5cache and read_access: return hdf5cache elif (self.oqparam.hazard_calculation_id and read_access and 'gmf_data' not in self.datastore.hdf5): self.datastore.parent.close() # make sure it is closed return self.datastore.parent logging.warn('With a parent calculation reading the hazard ' 'would be faster')
[docs] def check_overflow(self): """Overridden in event based"""
[docs] def check_floating_spinning(self): f, s = self.csm.get_floating_spinning_factors() if f != 1:'Rupture floating factor=%s', f) if s != 1:'Rupture spinning factor=%s', s)
[docs] def read_inputs(self): """ Read risk data and sources if any """ oq = self.oqparam self._read_risk_data() self.check_overflow() # check if self.sitecol is too large if 'source' in oq.inputs and oq.hazard_calculation_id is None: self.csm = readinput.get_composite_source_model(oq, self.monitor()) if oq.disagg_by_src: self.csm = self.csm.grp_by_src() with self.monitor('splitting sources', measuremem=1, autoflush=1):'Splitting sources') self.csm.split_all(oq.minimum_magnitude) self.rup_data = {} self.init() # do this at the end of pre-execute
[docs] def pre_execute(self, 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. """ oq = self.oqparam if 'gmfs' in oq.inputs: # read hazard from file assert not oq.hazard_calculation_id, ( 'You cannot use --hc together with gmfs_file') self.read_inputs() save_gmfs(self) elif 'hazard_curves' in oq.inputs: # read hazard from file assert not oq.hazard_calculation_id, ( 'You cannot use --hc together with hazard_curves') haz_sitecol = readinput.get_site_collection(oq) # NB: horrible: get_site_collection calls get_pmap_from_nrml # that sets oq.investigation_time, so it must be called first self.load_riskmodel() # must be after get_site_collection self.read_exposure(haz_sitecol) # define .assets_by_site self.datastore['poes/grp-00'] = readinput.pmap self.datastore['sitecol'] = self.sitecol self.datastore['assetcol'] = self.assetcol self.datastore['csm_info'] = fake = source.CompositionInfo.fake() self.rlzs_assoc = fake.get_rlzs_assoc() elif oq.hazard_calculation_id: parent = check_precalc_consistency( oq.calculation_mode, parent['oqparam'].calculation_mode) self.datastore.parent = parent # copy missing parameters from the parent params = {name: value for name, value in vars(parent['oqparam']).items() if name not in vars(self.oqparam)} self.save_params(**params) self.read_inputs() oqp = parent['oqparam'] if oqp.investigation_time != oq.investigation_time: raise ValueError( 'The parent calculation was using investigation_time=%s' ' != %s' % (oqp.investigation_time, oq.investigation_time)) if oqp.minimum_intensity != oq.minimum_intensity: raise ValueError( 'The parent calculation was using minimum_intensity=%s' ' != %s' % (oqp.minimum_intensity, oq.minimum_intensity)) elif pre_calculator: calc = calculators[pre_calculator]( self.oqparam, self.datastore.calc_id) self.param = calc.param self.sitecol = calc.sitecol self.assetcol = calc.assetcol self.riskmodel = calc.riskmodel self.rlzs_assoc = calc.rlzs_assoc else: self.read_inputs()
[docs] def init(self): """ To be overridden to initialize the datasets needed by the calculation """ if not self.oqparam.risk_imtls: if self.datastore.parent: self.oqparam.risk_imtls = ( self.datastore.parent['oqparam'].risk_imtls) elif not self.oqparam.imtls: raise ValueError('Missing intensity_measure_types!') if self.precalc: self.rlzs_assoc = self.precalc.rlzs_assoc elif 'csm_info' in self.datastore: self.rlzs_assoc = self.datastore['csm_info'].get_rlzs_assoc() elif hasattr(self, 'csm'): self.check_floating_spinning() self.rlzs_assoc = self.datastore['csm_info'] = else: # build a fake; used by risk-from-file calculators self.datastore['csm_info'] = fake = source.CompositionInfo.fake() self.rlzs_assoc = fake.get_rlzs_assoc()
[docs] def read_exposure(self, haz_sitecol=None): """ Read the exposure, the riskmodel and update the attributes .sitecol, .assetcol """ with self.monitor('reading exposure', autoflush=True): self.sitecol, self.assetcol = readinput.get_sitecol_assetcol( self.oqparam, haz_sitecol, self.riskmodel.loss_types) readinput.exposure = None # reset the global
[docs] def get_min_iml(self, oq): # set the minimum_intensity if hasattr(self, 'riskmodel') and not oq.minimum_intensity: # infer it from the risk models if not directly set in job.ini oq.minimum_intensity = self.riskmodel.get_min_iml() min_iml = calc.fix_minimum_intensity( oq.minimum_intensity, oq.imtls) if min_iml.sum() == 0: logging.warn('The GMFs are not filtered: ' 'you may want to set a minimum_intensity') else:'minimum_intensity=%s', oq.minimum_intensity) return min_iml
[docs] def load_riskmodel(self): """ 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. """'Reading the risk model if present') self.riskmodel = rm = readinput.get_risk_model(self.oqparam) if not self.riskmodel: parent = self.datastore.parent if 'composite_risk_model' in parent: self.riskmodel = riskinput.read_composite_risk_model(parent) return self.save_params() # re-save oqparam # save the risk models and loss_ratios in the datastore self.datastore['composite_risk_model'] = rm attrs = self.datastore.getitem('composite_risk_model').attrs attrs['min_iml'] = hdf5.array_of_vstr(sorted(rm.get_min_iml().items())) self.datastore.set_nbytes('composite_risk_model') self.datastore.hdf5.flush()
def _read_risk_data(self): # read the exposure (if any), the risk model (if any) and then the # site collection, possibly extracted from the exposure. oq = self.oqparam self.load_riskmodel() # must be called first if oq.hazard_calculation_id: with as dstore: haz_sitecol = dstore['sitecol'].complete else: haz_sitecol = readinput.get_site_collection(oq) if hasattr(self, 'rup'): # for scenario we reduce the site collection to the sites # within the maximum distance from the rupture haz_sitecol, _dctx = self.cmaker.filter( haz_sitecol, self.rup) haz_sitecol.make_complete() oq_hazard = (self.datastore.parent['oqparam'] if self.datastore.parent else None) if 'exposure' in oq.inputs: self.read_exposure(haz_sitecol) self.datastore['assetcol'] = self.assetcol elif 'assetcol' in self.datastore.parent: assetcol = self.datastore.parent['assetcol'] if oq.region: region = wkt.loads(self.oqparam.region) self.sitecol = haz_sitecol.within(region) if oq.shakemap_id or 'shakemap' in oq.inputs: self.sitecol, self.assetcol = self.read_shakemap( haz_sitecol, assetcol) self.datastore['assetcol'] = self.assetcol'Extracted %d/%d assets', len(self.assetcol), len(assetcol)) elif hasattr(self, 'sitecol') and general.not_equal( self.sitecol.sids, haz_sitecol.sids): self.assetcol = assetcol.reduce(self.sitecol) self.datastore['assetcol'] = self.assetcol'Extracted %d/%d assets', len(self.assetcol), len(assetcol)) else: self.assetcol = assetcol else: # no exposure self.sitecol = haz_sitecol'Read %d hazard sites', len(self.sitecol)) if oq_hazard: parent = self.datastore.parent if 'assetcol' in parent: check_time_event(oq, parent['assetcol'].occupancy_periods) if oq_hazard.time_event and oq_hazard.time_event != oq.time_event: raise ValueError( 'The risk configuration file has time_event=%s but the ' 'hazard was computed with time_event=%s' % ( oq.time_event, oq_hazard.time_event)) if self.oqparam.job_type == 'risk': taxonomies = set(taxo for taxo in self.assetcol.tagcol.taxonomy if taxo != '?') # check that we are covering all the taxonomies in the exposure missing = taxonomies - set(self.riskmodel.taxonomies) if self.riskmodel and missing: raise RuntimeError('The exposure contains the taxonomies %s ' 'which are not in the risk model' % missing) # same check for the consequence models, if any consequence_models = riskmodels.get_risk_models( self.oqparam, 'consequence') for lt, cm in consequence_models.items(): missing = taxonomies - set(cm) if missing: raise ValueError( 'Missing consequenceFunctions for %s' % ' '.join(missing)) if hasattr(self, 'sitecol'): self.datastore['sitecol'] = self.sitecol.complete # used in the risk calculators self.param = dict(individual_curves=oq.individual_curves)
[docs] def count_eff_ruptures(self, result_dict, src_group_id): """ Returns the number of ruptures in the src_group (after filtering) or 0 if the src_group has been filtered away. :param result_dict: a dictionary with keys (grp_id, gsim) :param src_group_id: the source group ID """ return result_dict.eff_ruptures.get(src_group_id, 0)
[docs] def store_source_info(self, infos, acc): # save the calculation times per each source if infos: rows = sorted( infos.values(), key=operator.attrgetter('calc_time'), reverse=True) array = numpy.zeros(len(rows), source.SourceInfo.dt) for i, row in enumerate(rows): for name in array.dtype.names: value = getattr(row, name) if name == 'num_sites': value /= (row.num_split or 1) elif name == 'grp_id' and isinstance(value, list): # same ID sources; store only the first value = value[0] array[i][name] = value self.datastore['source_info'] = array infos.clear() partial(self.count_eff_ruptures, acc)) self.rlzs_assoc = if not self.rlzs_assoc: raise RuntimeError('Empty logic tree: too much filtering?') self.datastore['csm_info'] = R = len(self.rlzs_assoc.realizations) if 'event_based' in self.oqparam.calculation_mode and R >= TWO16: # rlzi is 16 bit integer in the GMFs, so there is hard limit or R raise ValueError( 'The logic tree has %d realizations, the maximum ' 'is %d' % (R, TWO16)) elif R > 10000: logging.warn( 'The logic tree has %d realizations(!), please consider ' 'sampling it', R) if 'source_info' in self.datastore: # the table is missing for UCERF, we should fix that self.datastore.set_attrs( 'source_info', nbytes=array.nbytes, has_dupl_sources=self.csm.has_dupl_sources) self.datastore.flush()
[docs] def post_process(self): """For compatibility with the engine"""
[docs]def build_hmaps(hcurves_by_kind, slice_, imtls, poes, monitor): """ Build hazard maps from a slice of hazard curves. :returns: a pair ({kind: hmaps}, slice) """ dic = {} for kind, hcurves in hcurves_by_kind.items(): dic[kind] = calc.make_hmap_array(hcurves, imtls, poes, len(hcurves)) return dic, slice_
[docs]class RiskCalculator(HazardCalculator): """ Base class for all risk calculators. A risk calculator must set the attributes .riskmodel, .sitecol, .assetcol, .riskinputs in the pre_execute phase. """ @property def R(self): """ :returns: the number of realizations as read from `csm_info` """ try: return self._R except AttributeError: self._R = self.datastore['csm_info'].get_num_rlzs() return self._R
[docs] def read_shakemap(self, 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. """ oq = self.oqparam E = oq.number_of_ground_motion_fields oq.risk_imtls = oq.imtls or self.datastore.parent['oqparam'].imtls extra = self.riskmodel.get_extra_imts(oq.risk_imtls) if extra: logging.warn('There are risk functions for not available IMTs ' 'which will be ignored: %s' % extra)'Getting/reducing shakemap') with self.monitor('getting/reducing shakemap'): smap = oq.shakemap_id if oq.shakemap_id else numpy.load( oq.inputs['shakemap']) sitecol, shakemap = get_sitecol_shakemap( smap, oq.imtls, haz_sitecol, oq.asset_hazard_distance or oq.region_grid_spacing) assetcol = assetcol.reduce_also(sitecol)'Building GMFs') with self.monitor('building/saving GMFs'): gmfs = to_gmfs(shakemap, oq.cross_correlation, oq.site_effects, oq.truncation_level, E, oq.random_seed, oq.imtls) save_gmf_data(self.datastore, sitecol, gmfs) events = numpy.zeros(E, readinput.stored_event_dt) events['eid'] = numpy.arange(E, dtype=U64) self.datastore['events'] = events return sitecol, assetcol
[docs] def build_riskinputs(self, kind, eps=None, num_events=0): """ :param kind: kind of hazard getter, can be 'poe' or 'gmf' :param eps: a matrix of epsilons (or None) :param num_events: how many events there are :returns: a list of RiskInputs objects, sorted by IMT. """'Building risk inputs from %d realization(s)', self.R) imtls = self.oqparam.imtls if not set(self.oqparam.risk_imtls) & set(imtls): rsk = ', '.join(self.oqparam.risk_imtls) haz = ', '.join(imtls) raise ValueError('The IMTs in the risk models (%s) are disjoint ' "from the IMTs in the hazard (%s)" % (rsk, haz)) if not hasattr(self, 'assetcol'): self.assetcol = self.datastore['assetcol'] self.riskmodel.taxonomy = self.assetcol.tagcol.taxonomy with self.monitor('building riskinputs', autoflush=True): riskinputs = list(self._gen_riskinputs(kind, eps, num_events)) assert riskinputs'Built %d risk inputs', len(riskinputs)) return riskinputs
def _gen_riskinputs(self, kind, eps, num_events): assets_by_site = self.assetcol.assets_by_site() dstore = self.can_read_parent() or self.datastore for sid, assets in enumerate(assets_by_site): if len(assets) == 0: continue # build the riskinputs if kind == 'poe': # hcurves, shape (R, N) getter = PmapGetter(dstore, self.rlzs_assoc, [sid]) getter.num_rlzs = self.R else: # gmf getter = GmfDataGetter(dstore, [sid], self.R, self.oqparam.imtls) if dstore is self.datastore: # read the hazard data in the controller node getter.init() else: # the datastore must be closed to avoid the HDF5 fork bug assert dstore.hdf5 == (), '%s is not closed!' % dstore for block in general.block_splitter(assets, 1000): # dictionary of epsilons for the reduced assets reduced_eps = {ass.ordinal: eps[ass.ordinal] for ass in block if eps is not None and len(eps)} yield riskinput.RiskInput(getter, [block], reduced_eps)
[docs] def execute(self): """ Parallelize on the riskinputs and returns a dictionary of results. Require a `.core_task` to be defined with signature (riskinputs, riskmodel, rlzs_assoc, monitor). """ if not hasattr(self, 'riskinputs'): # in the reportwriter return res = Starmap.apply( self.core_task.__func__, (self.riskinputs, self.riskmodel, self.param, self.monitor()), concurrent_tasks=self.oqparam.concurrent_tasks or 1, weight=get_weight ).reduce(self.combine) return res
[docs] def combine(self, acc, res): return acc + res
[docs]def get_gmv_data(sids, gmfs): """ Convert an array of shape (R, N, E, I) into an array of type gmv_data_dt """ R, N, E, I = gmfs.shape gmv_data_dt = numpy.dtype( [('rlzi', U16), ('sid', U32), ('eid', U64), ('gmv', (F32, (I,)))]) # NB: ordering of the loops: first site, then event, then realization # it is such that save_gmf_data saves the indices correctly for each sid it = ((r, sids[s], eid, gmfa[s, eid]) for s, eid in itertools.product( numpy.arange(N, dtype=U32), numpy.arange(E, dtype=U64)) for r, gmfa in enumerate(gmfs)) return numpy.fromiter(it, gmv_data_dt)
[docs]def save_gmdata(calc, n_rlzs): """ Save a composite array `gmdata` in the datastore. :param calc: a calculator with a dictionary .gmdata {rlz: data} :param n_rlzs: the total number of realizations """ n_sites = len(calc.sitecol) dtlist = ([(imt, F32) for imt in calc.oqparam.imtls] + [('events', U32)]) array = numpy.zeros(n_rlzs, dtlist) for rlzi in sorted(calc.gmdata): data = calc.gmdata[rlzi] # (imts, events) events = data[-1] gmv = data[:-1] / events / n_sites array[rlzi] = tuple(gmv) + (events,) calc.datastore['gmdata'] = array
[docs]def save_gmfs(calculator): """ :param calculator: a scenario_risk/damage or event_based_risk calculator :returns: a pair (eids, R) where R is the number of realizations """ dstore = calculator.datastore oq = calculator.oqparam'Reading gmfs from file') if oq.inputs['gmfs'].endswith('.csv'): # TODO: check if import_gmfs can be removed eids, num_rlzs, calculator.gmdata = import_gmfs( dstore, oq.inputs['gmfs'], calculator.sitecol.complete.sids) save_gmdata(calculator, calculator.R) else: # XML eids, gmfs = readinput.eids, readinput.gmfs E = len(eids) calculator.eids = eids if hasattr(oq, 'number_of_ground_motion_fields'): if oq.number_of_ground_motion_fields != E: raise RuntimeError( 'Expected %d ground motion fields, found %d' % (oq.number_of_ground_motion_fields, E)) else: # set the number of GMFs from the file oq.number_of_ground_motion_fields = E # NB: save_gmfs redefine oq.sites in case of GMFs from XML or CSV if oq.inputs['gmfs'].endswith('.xml'): haz_sitecol = readinput.get_site_collection(oq) R, N, E, I = gmfs.shape save_gmf_data(dstore, haz_sitecol, gmfs[:, haz_sitecol.sids], eids)
[docs]def save_gmf_data(dstore, sitecol, gmfs, eids=()): """ :param dstore: a :class:`openquake.baselib.datastore.DataStore` instance :param sitecol: a :class:`` instance :param gmfs: an array of shape (R, N, E, M) :param eids: E event IDs or the empty tuple """ offset = 0 dstore['gmf_data/data'] = gmfa = get_gmv_data(sitecol.sids, gmfs) dic = general.group_array(gmfa, 'sid') lst = [] all_sids = sitecol.complete.sids for sid in all_sids: rows = dic.get(sid, ()) n = len(rows) lst.append((offset, offset + n)) offset += n dstore['gmf_data/indices'] = numpy.array(lst, U32) dstore.set_attrs('gmf_data', num_gmfs=len(gmfs)) if len(eids): # store the events events = numpy.zeros(len(eids), readinput.stored_event_dt) events['eid'] = eids dstore['events'] = events
[docs]def import_gmfs(dstore, fname, sids): """ Import in the datastore a ground motion field CSV file. :param dstore: the datastore :param fname: the CSV file :param sids: the site IDs (complete) :returns: event_ids, num_rlzs """ array = writers.read_composite_array(fname).array n_imts = len(array.dtype.names[3:]) # rlzi, sid, eid, gmv_PGA, ... gmf_data_dt = numpy.dtype( [('rlzi', U16), ('sid', U32), ('eid', U64), ('gmv', (F32, (n_imts,)))]) # store the events eids = numpy.unique(array['eid']) eids.sort() events = numpy.zeros(len(eids), readinput.stored_event_dt) events['eid'] = eids dstore['events'] = events # store the GMFs dic = general.group_array(array.view(gmf_data_dt), 'sid') lst = [] offset = 0 for sid in sids: n = len(dic.get(sid, [])) lst.append((offset, offset + n)) if n: offset += n dstore.extend('gmf_data/data', dic[sid]) dstore['gmf_data/indices'] = numpy.array(lst, U32) # FIXME: if there is no data for the maximum realization # the inferred number of realizations will be wrong num_rlzs = array['rlzi'].max() + 1 # compute gmdata dic = general.group_array(array.view(gmf_data_dt), 'rlzi') gmdata = {r: numpy.zeros(n_imts + 1, F32) for r in range(num_rlzs)} for r in dic: gmv = dic[r]['gmv'] rec = gmdata[r] # (imt1, ..., imtM, nevents) rec[:-1] += gmv.sum(axis=0) rec[-1] += len(gmv) return eids, num_rlzs, gmdata