Source code for openquake.calculators.export.risk

# -*- 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
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# 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 <http://www.gnu.org/licenses/>.
import itertools
import collections
import logging
import numpy

from openquake.baselib import hdf5, parallel, performance
from openquake.baselib.python3compat import encode
from openquake.baselib.general import (
    group_array, split_in_blocks, deprecated as depr)
from openquake.hazardlib import nrml
from openquake.hazardlib.stats import compute_stats2
from openquake.risklib import scientific
from openquake.calculators.extract import (
    extract, build_damage_dt, build_damage_array)
from openquake.calculators.export import export, loss_curves
from openquake.calculators.export.hazard import savez, get_mesh
from openquake.calculators import getters
from openquake.commonlib import writers, hazard_writers
from openquake.commonlib.util import (
    get_assets, compose_arrays, reader)

Output = collections.namedtuple('Output', 'ltype path array')
F32 = numpy.float32
F64 = numpy.float64
U16 = numpy.uint16
U32 = numpy.uint32
U64 = numpy.uint64
stat_dt = numpy.dtype([('mean', F32), ('stddev', F32)])


deprecated = depr('Use the csv exporter instead')


[docs]def add_quotes(values): # used to escape tags in CSV files return numpy.array([encode('"%s"' % val) for val in values], (bytes, 100))
[docs]def get_rup_data(ebruptures): dic = {} for ebr in ebruptures: point = ebr.rupture.surface.get_middle_point() dic[ebr.serial] = (ebr.rupture.mag, point.x, point.y, point.z) return dic
# ############################### exporters ############################## # # this is used by event_based_risk
[docs]@export.add(('agg_curves-rlzs', 'csv'), ('agg_curves-stats', 'csv')) def export_agg_curve_rlzs(ekey, dstore): oq = dstore['oqparam'] agg_curve = dstore[ekey[0]] periods = dstore.get_attr(ekey[0], 'return_periods') if ekey[0].endswith('stats'): tags = ['mean'] + ['quantile-%s' % q for q in oq.quantile_loss_curves] else: tags = ['rlz-%03d' % r for r in range(agg_curve.shape[1])] writer = writers.CsvWriter(fmt=writers.FIVEDIGITS) header = (('annual_frequency_of_exceedence', 'return_period') + agg_curve.dtype.names) for r, tag in enumerate(tags): d = compose_arrays(periods, agg_curve[:, r], 'return_period') data = compose_arrays(1 / periods, d, 'annual_frequency_of_exceedence') dest = dstore.build_fname('agg_loss', tag, 'csv') writer.save(data, dest, header) return writer.getsaved()
# this is used by event_based_risk and classical_risk
[docs]@export.add(('avg_losses-rlzs', 'csv'), ('avg_losses-stats', 'csv')) def export_avg_losses(ekey, dstore): """ :param ekey: export key, i.e. a pair (datastore key, fmt) :param dstore: datastore object """ dskey = ekey[0] oq = dstore['oqparam'] dt = oq.loss_dt() assets = get_assets(dstore) writer = writers.CsvWriter(fmt=writers.FIVEDIGITS) name, kind = dskey.split('-') if kind == 'stats': weights = dstore['csm_info'].rlzs['weight'] tags, stats = zip(*oq.risk_stats()) if dskey in dstore: # precomputed value = dstore[dskey].value else: # computed on the fly value = compute_stats2( dstore['avg_losses-rlzs'].value, stats, weights) else: # rlzs value = dstore[dskey].value # shape (A, R, LI) R = value.shape[1] tags = ['rlz-%03d' % r for r in range(R)] for tag, values in zip(tags, value.transpose(1, 0, 2)): dest = dstore.build_fname(name, tag, 'csv') array = numpy.zeros(len(values), dt) for l, lt in enumerate(dt.names): array[lt] = values[:, l] writer.save(compose_arrays(assets, array), dest) return writer.getsaved()
# this is used by scenario_risk
[docs]@export.add(('losses_by_asset', 'csv')) def export_losses_by_asset(ekey, dstore): """ :param ekey: export key, i.e. a pair (datastore key, fmt) :param dstore: datastore object """ loss_dt = dstore['oqparam'].loss_dt(stat_dt) losses_by_asset = dstore[ekey[0]].value rlzs = dstore['csm_info'].get_rlzs_assoc().realizations assets = get_assets(dstore) writer = writers.CsvWriter(fmt=writers.FIVEDIGITS) for rlz in rlzs: losses = losses_by_asset[:, rlz.ordinal] dest = dstore.build_fname('losses_by_asset', rlz, 'csv') data = compose_arrays(assets, losses.copy().view(loss_dt)[:, 0]) writer.save(data, dest) return writer.getsaved()
# this is used by scenario_risk
[docs]@export.add(('losses_by_event', 'csv')) def export_losses_by_event(ekey, dstore): """ :param ekey: export key, i.e. a pair (datastore key, fmt) :param dstore: datastore object """ dtlist = [('eid', U64), ('rlzi', U16)] + dstore['oqparam'].loss_dt_list() writer = writers.CsvWriter(fmt=writers.FIVEDIGITS) dest = dstore.build_fname('losses_by_event', '', 'csv') writer.save(dstore['losses_by_event'].value.view(dtlist), dest) return writer.getsaved()
[docs]@export.add(('losses_by_asset', 'npz')) def export_losses_by_asset_npz(ekey, dstore): """ :param ekey: export key, i.e. a pair (datastore key, fmt) :param dstore: datastore object """ fname = dstore.export_path('%s.%s' % ekey) savez(fname, **dict(extract(dstore, 'losses_by_asset'))) return [fname]
def _compact(array): # convert an array of shape (a, e) into an array of shape (a,) dt = array.dtype a, e = array.shape lst = [] for name in dt.names: lst.append((name, (dt[name], e))) return array.view(numpy.dtype(lst)).reshape(a) # used by scenario_risk
[docs]@export.add(('all_losses-rlzs', 'npz')) def export_all_losses_npz(ekey, dstore): rlzs = dstore['csm_info'].get_rlzs_assoc().realizations assets = get_assets(dstore) losses = dstore['all_losses-rlzs'] dic = {} for rlz in rlzs: rlz_losses = _compact(losses[:, :, rlz.ordinal]) data = compose_arrays(assets, rlz_losses) dic['all_losses-%03d' % rlz.ordinal] = data fname = dstore.build_fname('all_losses', 'rlzs', 'npz') savez(fname, **dic) return [fname]
[docs]@export.add(('rup_loss_table', 'xml')) def export_maxloss_ruptures(ekey, dstore): """ :param ekey: export key, i.e. a pair (datastore key, fmt) :param dstore: datastore object """ oq = dstore['oqparam'] mesh = get_mesh(dstore['sitecol']) fnames = [] for loss_type in oq.loss_dt().names: ebr = getters.get_maxloss_rupture(dstore, loss_type) root = hazard_writers.rupture_to_element(ebr.export(mesh)) dest = dstore.export_path('rupture-%s.xml' % loss_type) with open(dest, 'wb') as fh: nrml.write(list(root), fh) fnames.append(dest) return fnames
# this is used by event_based_risk
[docs]@export.add(('agg_loss_table', 'csv')) @depr('This exporter will be removed soon') def export_agg_losses_ebr(ekey, dstore): """ :param ekey: export key, i.e. a pair (datastore key, fmt) :param dstore: datastore object """ if 'ruptures' not in dstore: logging.warn('There are no ruptures in the datastore') return [] name, ext = export.keyfunc(ekey) agg_losses = dstore['losses_by_event'] has_rup_data = 'ruptures' in dstore extra_list = [('magnitude', F32), ('centroid_lon', F32), ('centroid_lat', F32), ('centroid_depth', F32)] if has_rup_data else [] oq = dstore['oqparam'] lti = oq.lti dtlist = ([('event_id', U64), ('rup_id', U32), ('year', U32), ('rlzi', U16)] + extra_list + oq.loss_dt_list()) elt_dt = numpy.dtype(dtlist) elt = numpy.zeros(len(agg_losses), elt_dt) writer = writers.CsvWriter(fmt=writers.FIVEDIGITS) events_by_rupid = group_array(dstore['events'].value, 'rup_id') rup_data = {} event_by_eid = {} # eid -> event # populate rup_data and event_by_eid ruptures_by_grp = getters.get_ruptures_by_grp(dstore) # TODO: avoid reading the events twice for grp_id, ruptures in ruptures_by_grp.items(): for ebr in ruptures: for event in events_by_rupid[ebr.serial]: event_by_eid[event['eid']] = event if has_rup_data: rup_data.update(get_rup_data(ruptures)) for r, row in enumerate(agg_losses): rec = elt[r] event = event_by_eid[row['eid']] rec['event_id'] = event['eid'] rec['year'] = event['year'] rec['rlzi'] = row['rlzi'] if rup_data: rec['rup_id'] = rup_id = event['rup_id'] (rec['magnitude'], rec['centroid_lon'], rec['centroid_lat'], rec['centroid_depth']) = rup_data[rup_id] for lt, i in lti.items(): rec[lt] = row['loss'][i] elt.sort(order=['year', 'event_id', 'rlzi']) dest = dstore.build_fname('agg_losses', 'all', 'csv') writer.save(elt, dest) return writer.getsaved()
# this is used by classical_risk and event_based_risk
[docs]@export.add(('loss_curves', 'csv')) def export_loss_curves(ekey, dstore): if '/' not in ekey[0]: # full loss curves are not exportable logging.error('Use the command oq export loss_curves/rlz-0 to export ' 'the first realization') return [] what = ekey[0].split('/', 1)[1] return loss_curves.LossCurveExporter(dstore).export('csv', what)
# this is used by classical_risk and event_based_risk
[docs]@export.add(('loss_curves-stats', 'csv')) def export_loss_curves_stats(ekey, dstore): num_rlzs = dstore['csm_info'].get_num_rlzs() kind = 'stats' if num_rlzs > 1 else 'rlzs' return export_loss_curves(('loss_curves/' + kind, 'csv'), dstore)
# used by classical_risk and event_based_risk
[docs]@export.add(('loss_maps-rlzs', 'csv'), ('loss_maps-stats', 'csv')) def export_loss_maps_csv(ekey, dstore): kind = ekey[0].split('-')[1] # rlzs or stats assets = get_assets(dstore) value = get_loss_maps(dstore, kind) if kind == 'rlzs': tags = dstore['csm_info'].get_rlzs_assoc().realizations else: oq = dstore['oqparam'] tags = ['mean'] + ['quantile-%s' % q for q in oq.quantile_loss_curves] writer = writers.CsvWriter(fmt=writers.FIVEDIGITS) for tag, values in zip(tags, value.T): fname = dstore.build_fname('loss_maps', tag, ekey[1]) writer.save(compose_arrays(assets, values), fname) return writer.getsaved()
# used by classical_risk and event_based_risk
[docs]@export.add(('loss_maps-rlzs', 'npz'), ('loss_maps-stats', 'npz')) def export_loss_maps_npz(ekey, dstore): kind = ekey[0].split('-')[1] # rlzs or stats assets = get_assets(dstore) value = get_loss_maps(dstore, kind) R = dstore['csm_info'].get_num_rlzs() if kind == 'rlzs': tags = ['rlz-%03d' % r for r in range(R)] else: oq = dstore['oqparam'] tags = ['mean'] + ['quantile-%s' % q for q in oq.quantile_loss_curves] fname = dstore.export_path('%s.%s' % ekey) dic = {} for tag, values in zip(tags, value.T): dic[tag] = compose_arrays(assets, values) savez(fname, **dic) return [fname]
[docs]@export.add(('damages-rlzs', 'csv'), ('damages-stats', 'csv')) def export_damages_csv(ekey, dstore): rlzs = dstore['csm_info'].get_rlzs_assoc().realizations oq = dstore['oqparam'] assets = get_assets(dstore) value = dstore[ekey[0]].value # matrix N x R or T x R writer = writers.CsvWriter(fmt=writers.FIVEDIGITS) if ekey[0].endswith('stats'): tags = ['mean'] + ['quantile-%s' % q for q in oq.quantile_loss_curves] else: tags = ['rlz-%03d' % r for r in range(len(rlzs))] for tag, values in zip(tags, value.T): fname = dstore.build_fname('damages', tag, ekey[1]) writer.save(compose_arrays(assets, values), fname) return writer.getsaved()
[docs]@export.add(('dmg_by_asset', 'csv')) def export_dmg_by_asset_csv(ekey, dstore): damage_dt = build_damage_dt(dstore) rlzs = dstore['csm_info'].get_rlzs_assoc().realizations data = dstore[ekey[0]] writer = writers.CsvWriter(fmt='%.6E') assets = get_assets(dstore) for rlz in rlzs: dmg_by_asset = build_damage_array(data[:, rlz.ordinal], damage_dt) fname = dstore.build_fname(ekey[0], rlz, ekey[1]) writer.save(compose_arrays(assets, dmg_by_asset), fname) return writer.getsaved()
[docs]@export.add(('dmg_by_asset', 'npz')) def export_dmg_by_asset_npz(ekey, dstore): fname = dstore.export_path('%s.%s' % ekey) savez(fname, **dict(extract(dstore, 'dmg_by_asset'))) return [fname]
[docs]@export.add(('dmg_by_event', 'csv')) def export_dmg_by_event(ekey, dstore): """ :param ekey: export key, i.e. a pair (datastore key, fmt) :param dstore: datastore object """ damage_dt = build_damage_dt(dstore, mean_std=False) all_losses = dstore[ekey[0]].value eids = dstore['events']['eid'] rlzs = dstore['csm_info'].get_rlzs_assoc().realizations writer = writers.CsvWriter(fmt=writers.FIVEDIGITS) for rlz in rlzs: dest = dstore.build_fname('dmg_by_event', rlz, 'csv') data = all_losses[:, rlz.ordinal].copy().view(damage_dt).squeeze() writer.save(compose_arrays(eids, data, 'event_id'), dest) return writer.getsaved()
# emulate a Django point
[docs]class Location(object): def __init__(self, x, y): self.x, self.y = x, y self.wkt = 'POINT(%s %s)' % (x, y)
[docs]def indices(*sizes): return itertools.product(*map(range, sizes))
[docs]def get_loss_maps(dstore, kind): """ :param dstore: a DataStore instance :param kind: 'rlzs' or 'stats' """ oq = dstore['oqparam'] name = 'loss_maps-%s' % kind if name in dstore: # event_based risk return dstore[name].value.view(oq.loss_maps_dt()) name = 'loss_curves-%s' % kind if name in dstore: # classical_risk loss_curves = dstore[name] loss_maps = scientific.broadcast( scientific.loss_maps, loss_curves, oq.conditional_loss_poes) return loss_maps raise KeyError('loss_maps/loss_curves missing in %s' % dstore)
agg_dt = numpy.dtype([('unit', (bytes, 6)), ('mean', F32), ('stddev', F32)]) # this is used by scenario_risk
[docs]@export.add(('agglosses-rlzs', 'csv')) def export_agglosses(ekey, dstore): oq = dstore['oqparam'] loss_dt = oq.loss_dt() cc = dstore['assetcol/cost_calculator'] unit_by_lt = cc.units unit_by_lt['occupants'] = 'people' agglosses = dstore[ekey[0]] fnames = [] for rlz in dstore['csm_info'].get_rlzs_assoc().realizations: loss = agglosses[rlz.ordinal] losses = [] header = ['loss_type', 'unit', 'mean', 'stddev'] for l, lt in enumerate(loss_dt.names): unit = unit_by_lt[lt.replace('_ins', '')] mean = loss[l]['mean'] stddev = loss[l]['stddev'] losses.append((lt, unit, mean, stddev)) dest = dstore.build_fname('agglosses', rlz, 'csv') writers.write_csv(dest, losses, header=header) fnames.append(dest) return sorted(fnames)
AggCurve = collections.namedtuple( 'AggCurve', ['losses', 'poes', 'average_loss', 'stddev_loss'])
[docs]def get_paths(rlz): """ :param rlz: a logic tree realization (composite or simple) :returns: a dict {'source_model_tree_path': string, 'gsim_tree_path': string} """ dic = {} if hasattr(rlz, 'sm_lt_path'): # composite realization dic['source_model_tree_path'] = '_'.join(rlz.sm_lt_path) dic['gsim_tree_path'] = '_'.join(rlz.gsim_lt_path) else: # simple GSIM realization dic['source_model_tree_path'] = '' dic['gsim_tree_path'] = '_'.join(rlz.lt_path) return dic
[docs]@export.add(('bcr-rlzs', 'csv'), ('bcr-stats', 'csv')) def export_bcr_map(ekey, dstore): oq = dstore['oqparam'] assets = get_assets(dstore) bcr_data = dstore[ekey[0]] N, R = bcr_data.shape if ekey[0].endswith('stats'): tags = ['mean'] + ['quantile-%s' % q for q in oq.quantile_loss_curves] else: tags = ['rlz-%03d' % r for r in range(R)] fnames = [] writer = writers.CsvWriter(fmt=writers.FIVEDIGITS) for t, tag in enumerate(tags): path = dstore.build_fname('bcr', tag, 'csv') writer.save(compose_arrays(assets, bcr_data[:, t]), path) fnames.append(path) return writer.getsaved()
[docs]@reader def get_loss_ratios(lrgetter, monitor): with lrgetter.dstore: loss_ratios = lrgetter.get_all() # list of arrays of dtype lrs_dt return list(zip(lrgetter.aids, loss_ratios))
[docs]@export.add(('asset_loss_table', 'hdf5')) @depr('This exporter will be removed soon') def export_asset_loss_table(ekey, dstore): """ Export in parallel the asset loss table from the datastore. NB1: for large calculation this may run out of memory NB2: due to an heisenbug in the parallel reading of .hdf5 files this works reliably only if the datastore has been created by a different process The recommendation is: *do not use this exporter*: rather, study its source code and write what you need. Every postprocessing is different. """ key, fmt = ekey oq = dstore['oqparam'] assetcol = dstore['assetcol'] arefs = assetcol.asset_refs avals = assetcol.values() loss_types = dstore.get_attr('all_loss_ratios', 'loss_types').split() dtlist = [(lt, F32) for lt in loss_types] if oq.insured_losses: for lt in loss_types: dtlist.append((lt + '_ins', F32)) lrs_dt = numpy.dtype([('rlzi', U16), ('losses', dtlist)]) fname = dstore.export_path('%s.%s' % ekey) monitor = performance.Monitor(key, fname) aids = range(len(assetcol)) allargs = [(getters.LossRatiosGetter(dstore, block), monitor) for block in split_in_blocks(aids, oq.concurrent_tasks)] dstore.close() # avoid OSError: Can't read data (Wrong b-tree signature) L = len(loss_types) with hdf5.File(fname, 'w') as f: nbytes = 0 total = numpy.zeros(len(dtlist), F32) for pairs in parallel.Starmap(get_loss_ratios, allargs): for aid, data in pairs: asset = assetcol[aid] avalue = avals[aid] for l, lt in enumerate(loss_types): aval = avalue[lt] for i in range(oq.insured_losses + 1): data['ratios'][:, l + L * i] *= aval aref = arefs[asset.ordinal] f[b'asset_loss_table/' + aref] = data.view(lrs_dt) total += data['ratios'].sum(axis=0) nbytes += data.nbytes f['asset_loss_table'].attrs['loss_types'] = ' '.join(loss_types) f['asset_loss_table'].attrs['total'] = total f['asset_loss_table'].attrs['nbytes'] = nbytes return [fname]