# -*- coding: utf-8 -*-
# vim: tabstop=4 shiftwidth=4 softtabstop=4
#
# Copyright (C) 2014-2017 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 decode
from openquake.baselib.general import (
AccumDict, split_in_blocks, deprecated as depr)
from openquake.hazardlib.stats import compute_stats2
from openquake.risklib import scientific, riskinput
from openquake.calculators.export import export, loss_curves
from openquake.calculators.export.hazard import savez
from openquake.commonlib import writers, risk_writers, calc
from openquake.commonlib.util import (
get_assets, compose_arrays, reader)
from openquake.commonlib.risk_writers import (
DmgState, DmgDistPerTaxonomy, DmgDistPerAsset, DmgDistTotal,
ExposureData, Site)
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 taxonomies in CSV files
return numpy.array(['"%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
[docs]def copy_to(elt, rup_data, rup_ids):
"""
Copy information from the ruptures into the elt array for
the given rupture serials.
"""
assert len(elt) == len(rup_ids), (len(elt), len(rup_ids))
for i, serial in numpy.ndenumerate(rup_ids):
rec = elt[i]
rec['rup_id'] = serial
(rec['magnitude'], rec['centroid_lon'], rec['centroid_lat'],
rec['centroid_depth']) = rup_data[serial]
# ############################### exporters ############################## #
# this is used by event_based_risk
@export.add(('avg_losses-rlzs', 'csv'), ('avg_losses-stats', 'csv'))
[docs]def export_avg_losses(ekey, dstore):
"""
:param ekey: export key, i.e. a pair (datastore key, fmt)
:param dstore: datastore object
"""
oq = dstore['oqparam']
dt = oq.loss_dt()
assets = get_assets(dstore)
writer = writers.CsvWriter(fmt=writers.FIVEDIGITS)
name, kind = ekey[0].split('-')
value = dstore[name + '-rlzs'].value # shape (A, R, L')
if kind == 'stats':
weights = dstore['realizations']['weight']
tags, stats = zip(*oq.risk_stats())
value = compute_stats2(value, stats, weights)
else: # rlzs
tags = ['rlz-%03d' % r for r in range(len(dstore['realizations']))]
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
@export.add(('losses_by_asset', 'csv'))
[docs]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
@export.add(('losses_by_event', 'csv'))
[docs]def export_losses_by_event(ekey, dstore):
"""
:param ekey: export key, i.e. a pair (datastore key, fmt)
:param dstore: datastore object
"""
loss_dt = dstore['oqparam'].loss_dt()
all_losses = dstore[ekey[0]].value
rlzs = dstore['csm_info'].get_rlzs_assoc().realizations
writer = writers.CsvWriter(fmt=writers.FIVEDIGITS)
for rlz in rlzs:
dest = dstore.build_fname('losses_by_event', rlz, 'csv')
data = all_losses[:, rlz.ordinal].copy().view(loss_dt)
writer.save(data, dest)
return writer.getsaved()
@export.add(('losses_by_asset', 'npz'))
[docs]def export_losses_by_asset_npz(ekey, dstore):
"""
:param ekey: export key, i.e. a pair (datastore key, fmt)
:param dstore: datastore object
"""
loss_dt = dstore['oqparam'].loss_dt()
losses_by_asset = dstore[ekey[0]].value
rlzs = dstore['csm_info'].get_rlzs_assoc().realizations
assets = get_assets(dstore)
dic = {}
for rlz in rlzs:
# I am exporting the 'mean' and ignoring the 'stddev'
losses = losses_by_asset[:, rlz.ordinal]['mean'].copy() # shape (N, 1)
data = compose_arrays(assets, losses.view(loss_dt)[:, 0])
dic['rlz-%03d' % rlz.ordinal] = data
fname = dstore.export_path('%s.%s' % ekey)
savez(fname, **dic)
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
@export.add(('all_losses-rlzs', 'npz'))
[docs]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]
# this is used by classical_risk
@export.add(('agg_losses-rlzs', 'csv'))
[docs]def export_agg_losses(ekey, dstore):
"""
:param ekey: export key, i.e. a pair (datastore key, fmt)
:param dstore: datastore object
"""
agg_losses = dstore[ekey[0]].value
rlzs = dstore['csm_info'].get_rlzs_assoc().realizations
etags = calc.build_etags(dstore['events'], 0)
writer = writers.CsvWriter(fmt=writers.FIVEDIGITS)
for rlz in rlzs:
losses = agg_losses[:, rlz.ordinal]
dest = dstore.build_fname('agg_losses', rlz, 'csv')
data = compose_arrays(etags, losses)
writer.save(data, dest)
return writer.getsaved()
# this is used by event_based_risk
@export.add(('agg_loss_table', 'csv'))
[docs]def export_agg_losses_ebr(ekey, dstore):
"""
:param ekey: export key, i.e. a pair (datastore key, fmt)
:param dstore: datastore object
"""
loss_types = dstore.get_attr('composite_risk_model', 'loss_types')
name, ext = export.keyfunc(ekey)
agg_losses = dstore[name]
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']
dtlist = ([('event_id', U64), ('rup_id', U32), ('year', U32)] +
extra_list + oq.loss_dt_list())
elt_dt = numpy.dtype(dtlist)
csm_info = dstore['csm_info']
rlzs_assoc = csm_info.get_rlzs_assoc()
writer = writers.CsvWriter(fmt=writers.FIVEDIGITS)
for sm_id, rlzs in rlzs_assoc.rlzs_by_smodel.items():
# populate rup_data and event_by_eid
rup_data = {}
event_by_grp = {} # grp_id -> eid -> event
for grp_id in csm_info.get_grp_ids(sm_id):
event_by_grp[grp_id] = event_by_eid = {}
try:
events = dstore['events/grp-%02d' % grp_id]
except KeyError:
continue
for event in events:
event_by_eid[event['eid']] = event
if has_rup_data:
rup_data.update(
get_rup_data(calc.get_ruptures(dstore, grp_id)))
for rlz in rlzs:
rlzname = 'rlz-%03d' % rlz.ordinal
if rlzname not in agg_losses:
continue
data = agg_losses[rlzname].value
eids = data['eid']
losses = data['loss']
eids_, years, serials = get_eids_years_serials(event_by_grp, eids)
elt = numpy.zeros(len(eids), elt_dt)
elt['event_id'] = eids_
elt['year'] = years
if rup_data:
copy_to(elt, rup_data, serials)
for i, ins in enumerate(
['', '_ins'] if oq.insured_losses else ['']):
for l, loss_type in enumerate(loss_types):
elt[loss_type + ins][:] = losses[:, l, i]
elt.sort(order=['year', 'event_id'])
dest = dstore.build_fname('agg_losses', rlz, 'csv')
writer.save(elt, dest)
return writer.getsaved()
[docs]def get_eids_years_serials(events_by_grp, eids):
etags = []
years = []
serials = []
for eid in eids:
for grp_id, event_by_eid in events_by_grp.items():
try:
event = event_by_eid[eid]
except KeyError:
continue
else:
etags.append(event['eid'])
years.append(event['year'])
serials.append(event['rup_id'])
break
return numpy.array(etags), numpy.array(years), numpy.array(serials)
# this is used by classical_risk
@export.add(('loss_curves', 'csv'))
[docs]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)
@export.add(('dmg_by_asset', 'xml'))
@deprecated
[docs]def export_damage(ekey, dstore):
loss_types = dstore.get_attr('composite_risk_model', 'loss_types')
damage_states = dstore.get_attr('composite_risk_model', 'damage_states')
rlzs = dstore['csm_info'].get_rlzs_assoc().realizations
dmg_by_asset = dstore['dmg_by_asset'] # shape (N, L, R)
assetcol = dstore['assetcol/array'].value
aref = dstore['asset_refs'].value
dmg_states = [DmgState(s, i) for i, s in enumerate(damage_states)]
D = len(dmg_states)
N, R = dmg_by_asset.shape
L = len(loss_types)
fnames = []
for l, r in itertools.product(range(L), range(R)):
lt = loss_types[l]
rlz = rlzs[r]
dd_asset = []
for n, ass in enumerate(assetcol):
assref = decode(aref[ass['idx']])
dist = dmg_by_asset[n, r][lt]
site = Site(ass['lon'], ass['lat'])
for ds in range(D):
dd_asset.append(
DmgDistPerAsset(
ExposureData(assref, site), dmg_states[ds],
dist['mean'][ds], dist['stddev'][ds]))
f1 = export_dmg_xml(('dmg_dist_per_asset', 'xml'), dstore,
dmg_states, dd_asset, lt, rlz)
max_damage = dmg_states[-1]
# the collapse map is extracted from the damage distribution per asset
# (dda) by taking the value corresponding to the maximum damage
collapse_map = [dda for dda in dd_asset if dda.dmg_state == max_damage]
f2 = export_dmg_xml(('collapse_map', 'xml'), dstore,
dmg_states, collapse_map, lt, rlz)
fnames.extend(sum((f1 + f2).values(), []))
return sorted(fnames)
@export.add(('dmg_by_taxon', 'xml'))
@deprecated
[docs]def export_damage_taxon(ekey, dstore):
loss_types = dstore.get_attr('composite_risk_model', 'loss_types')
damage_states = dstore.get_attr('composite_risk_model', 'damage_states')
rlzs = dstore['csm_info'].get_rlzs_assoc().realizations
dmg_by_taxon = dstore['dmg_by_taxon'] # shape (T, L, R)
taxonomies = dstore['assetcol/taxonomies']
dmg_states = [DmgState(s, i) for i, s in enumerate(damage_states)]
D = len(dmg_states)
T, R = dmg_by_taxon.shape
L = len(loss_types)
fnames = []
for l, r in itertools.product(range(L), range(R)):
lt = loss_types[l]
rlz = rlzs[r]
dd_taxo = []
for t in range(T):
dist = dmg_by_taxon[t, r][lt]
for ds in range(D):
dd_taxo.append(
DmgDistPerTaxonomy(
taxonomies[t], dmg_states[ds],
dist['mean'][ds], dist['stddev'][ds]))
f = export_dmg_xml(('dmg_dist_per_taxonomy', 'xml'),
dstore, dmg_states, dd_taxo, lt, rlz)
fnames.extend(sum(f.values(), []))
return sorted(fnames)
@export.add(('dmg_total', 'xml'))
@deprecated
[docs]def export_damage_total(ekey, dstore):
loss_types = dstore.get_attr('composite_risk_model', 'loss_types')
damage_states = dstore.get_attr('composite_risk_model', 'damage_states')
rlzs = dstore['csm_info'].get_rlzs_assoc().realizations
dmg_total = dstore['dmg_total']
R, = dmg_total.shape
L = len(loss_types)
dmg_states = [DmgState(s, i) for i, s in enumerate(damage_states)]
D = len(dmg_states)
fnames = []
for l, r in itertools.product(range(L), range(R)):
lt = loss_types[l]
rlz = rlzs[r]
dd_total = []
for ds in range(D):
dist = dmg_total[r][lt]
dd_total.append(DmgDistTotal(
dmg_states[ds], dist['mean'][ds], dist['stddev'][ds]))
f = export_dmg_xml(('dmg_dist_total', 'xml'), dstore,
dmg_states, dd_total, lt, rlz)
fnames.extend(sum(f.values(), []))
return sorted(fnames)
# used by classical_risk and event_based_risk
@export.add(('loss_maps-rlzs', 'csv'), ('loss_maps-stats', 'csv'))
[docs]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
@export.add(('loss_maps-rlzs', 'npz'), ('loss_maps-stats', 'npz'))
[docs]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 = len(dstore['realizations'])
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]
@export.add(('damages-rlzs', 'csv'))
[docs]def export_rlzs_by_asset_csv(ekey, dstore):
rlzs = dstore['csm_info'].get_rlzs_assoc().realizations
assets = get_assets(dstore)
value = dstore[ekey[0]].value # matrix N x R or T x R
writer = writers.CsvWriter(fmt=writers.FIVEDIGITS)
for rlz, values in zip(rlzs, value.T):
fname = dstore.build_fname(ekey[0], rlz, ekey[1])
writer.save(compose_arrays(assets, values), fname)
return writer.getsaved()
@export.add(('losses_total', 'csv'))
[docs]def export_losses_total_csv(ekey, dstore):
rlzs = dstore['csm_info'].get_rlzs_assoc().realizations
value = dstore[ekey[0]].value
writer = writers.CsvWriter(fmt=writers.FIVEDIGITS)
for rlz, values in zip(rlzs, value):
fname = dstore.build_fname(ekey[0], rlz, ekey[1])
writer.save(numpy.array([values], value.dtype), fname)
return writer.getsaved()
[docs]def build_damage_dt(dstore):
"""
:param dstore: a datastore instance
:returns: a composite dtype loss_type -> (mean_ds1, stdv_ds1, ...)
"""
damage_states = dstore.get_attr('composite_risk_model', 'damage_states')
dt_list = []
for ds in damage_states:
dt_list.append(('%s_mean' % ds, F32))
dt_list.append(('%s_stdv' % ds, F32))
damage_dt = numpy.dtype(dt_list)
loss_types = dstore.get_attr('composite_risk_model', 'loss_types')
return numpy.dtype([(lt, damage_dt) for lt in loss_types])
[docs]def build_damage_array(data, damage_dt):
"""
:param data: an array of length N with fields 'mean' and 'stddev'
:param damage_dt: a damage composite data type loss_type -> states
:returns: a composite array of length N and dtype damage_dt
"""
L = len(data) if data.shape else 1
dmg = numpy.zeros(L, damage_dt)
for lt in damage_dt.names:
for i, ms in numpy.ndenumerate(data[lt]):
lst = []
for m, s in zip(ms['mean'], ms['stddev']):
lst.append(m)
lst.append(s)
dmg[lt][i] = tuple(lst)
return dmg
@export.add(('dmg_by_asset', 'csv'))
[docs]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()
@export.add(('dmg_by_taxon', 'csv'))
[docs]def export_dmg_by_taxon_csv(ekey, dstore):
damage_dt = build_damage_dt(dstore)
taxonomies = add_quotes(dstore['assetcol/taxonomies'].value)
rlzs = dstore['csm_info'].get_rlzs_assoc().realizations
data = dstore[ekey[0]]
writer = writers.CsvWriter(fmt='%.6E')
for rlz in rlzs:
dmg_by_taxon = build_damage_array(data[:, rlz.ordinal], damage_dt)
fname = dstore.build_fname(ekey[0], rlz, ekey[1])
array = compose_arrays(taxonomies, dmg_by_taxon, 'taxonomy')
writer.save(array, fname)
return writer.getsaved()
@export.add(('dmg_total', 'csv'))
[docs]def export_dmg_totalcsv(ekey, dstore):
damage_dt = build_damage_dt(dstore)
rlzs = dstore['csm_info'].get_rlzs_assoc().realizations
dset = dstore[ekey[0]]
writer = writers.CsvWriter(fmt='%.6E')
for rlz in rlzs:
dmg_total = build_damage_array(dset[rlz.ordinal], damage_dt)
fname = dstore.build_fname(ekey[0], rlz, ekey[1])
data = [['loss_type', 'damage_state', 'damage_value']]
for loss_type in dmg_total.dtype.names:
tot = dmg_total[loss_type]
for name in tot.dtype.names:
data.append((loss_type, name, tot[name]))
writer.save(data, fname)
return writer.getsaved()
[docs]def export_dmg_xml(key, dstore, damage_states, dmg_data, lt, rlz):
"""
Export damage outputs in XML format.
:param key:
dmg_dist_per_asset|dmg_dist_per_taxonomy|dmg_dist_total|collapse_map
:param dstore:
the datastore
:param damage_states:
the list of damage states
:param dmg_data:
a list [(loss_type, unit, asset_ref, mean, stddev), ...]
:param lt:
loss type string
:param rlz:
a realization object
"""
dest = dstore.build_fname('%s-%s' % (key[0], lt), rlz, key[1])
risk_writers.DamageWriter(damage_states).to_nrml(key[0], dmg_data, dest)
return AccumDict({key: [dest]})
# exports for scenario_risk
LossMap = collections.namedtuple('LossMap', 'location asset_ref value std_dev')
LossCurve = collections.namedtuple(
'LossCurve', 'location asset_ref poes losses loss_ratios '
'average_loss stddev_loss')
# 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
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)
# used by event_based_risk and classical_risk
@export.add(('loss_maps-rlzs', 'xml'), ('loss_maps-rlzs', 'geojson'))
@deprecated
[docs]def export_loss_maps_rlzs_xml_geojson(ekey, dstore):
oq = dstore['oqparam']
cc = dstore['assetcol/cost_calculator']
unit_by_lt = cc.units
unit_by_lt['occupants'] = 'people'
rlzs = dstore['csm_info'].get_rlzs_assoc().realizations
loss_maps = get_loss_maps(dstore, 'rlzs')
assetcol = dstore['assetcol/array'].value
aref = dstore['asset_refs'].value
R = len(rlzs)
fnames = []
export_type = ekey[1]
writercls = (risk_writers.LossMapGeoJSONWriter
if export_type == 'geojson' else
risk_writers.LossMapXMLWriter)
loss_types = loss_maps.dtype.names
for lt in loss_types:
loss_maps_lt = loss_maps[lt]
for r in range(R):
lmaps = loss_maps_lt[:, r]
for poe in oq.conditional_loss_poes:
rlz = rlzs[r]
unit = unit_by_lt[lt[:-4] if lt.endswith('_ins') else lt]
root = ekey[0][:-5] # strip -rlzs
name = '%s-%s-poe-%s' % (root, lt, poe)
fname = dstore.build_fname(name, rlz, ekey[1])
data = []
poe_str = 'poe-%s' % poe
for ass, stat in zip(assetcol, lmaps[poe_str]):
loc = Location(ass['lon'], ass['lat'])
lm = LossMap(loc, decode(aref[ass['idx']]), stat, None)
data.append(lm)
writer = writercls(
fname, oq.investigation_time, poe=poe, loss_type=lt,
unit=unit,
risk_investigation_time=oq.risk_investigation_time,
**get_paths(rlz))
writer.serialize(data)
fnames.append(fname)
return sorted(fnames)
# used by classical_risk and event_based_risk
# NB: loss_maps-stats are NOT computed as stats of loss_maps-rlzs,
# instead they are extracted directly from loss_maps-stats
@export.add(('loss_maps-stats', 'xml'), ('loss_maps-stats', 'geojson'))
@deprecated
[docs]def export_loss_maps_stats_xml_geojson(ekey, dstore):
loss_maps = get_loss_maps(dstore, 'stats')
N, S = loss_maps.shape
assetcol = dstore['assetcol/array'].value
aref = dstore['asset_refs'].value
fnames = []
export_type = ekey[1]
writercls = (risk_writers.LossMapGeoJSONWriter
if export_type == 'geojson' else
risk_writers.LossMapXMLWriter)
for writer, (ltype, poe, s, insflag) in _gen_writers(
dstore, writercls, ekey[0]):
ins = '_ins' if insflag else ''
if ltype not in loss_maps.dtype.names:
continue
array = loss_maps[ltype + ins][:, s]
curves = []
poe_str = 'poe-%s' % poe
for ass, val in zip(assetcol, array[poe_str]):
loc = Location(ass['lon'], ass['lat'])
curve = LossMap(loc, decode(aref[ass['idx']]), val, None)
curves.append(curve)
writer.serialize(curves)
fnames.append(writer._dest)
return sorted(fnames)
agg_dt = numpy.dtype([('unit', (bytes, 6)), ('mean', F32), ('stddev', F32)])
# this is used by scenario_risk
@export.add(('agglosses-rlzs', 'csv'))
[docs]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
def _gen_writers(dstore, writercls, root):
# build Writer instances
name = writercls.__name__
if 'XML' in name:
ext = 'xml'
elif 'JSON' in name:
ext = 'geojson'
else:
raise ValueError('Unsupported writer class: %s' % writercls)
oq = dstore['oqparam']
rlzs = dstore['csm_info'].get_rlzs_assoc().realizations
cc = dstore['assetcol/cost_calculator']
poes = oq.conditional_loss_poes if 'maps' in root else [None]
for poe in poes:
poe_str = '-%s' % poe if poe is not None else ''
for l, loss_type in enumerate(cc.loss_types):
for ins in range(oq.insured_losses + 1):
if root.endswith('-rlzs'):
for rlz in rlzs:
dest = dstore.build_fname(
'%s-%s%s%s' %
(root[:-5], # strip -rlzs
loss_type, poe_str, '_ins' if ins else ''),
rlz, ext)
yield writercls(
dest, oq.investigation_time, poe=poe,
loss_type=loss_type, unit=cc.units[loss_type],
risk_investigation_time=oq.risk_investigation_time,
**get_paths(rlz)), (
loss_type, poe, rlz.ordinal, ins)
elif root.endswith('-stats'):
pairs = [('mean', None)] + [
('quantile-%s' % q, q)
for q in oq.quantile_loss_curves]
for ordinal, (statname, statvalue) in enumerate(pairs):
prefix = root[:-6] # strip -stats
key = '%s-%s%s%s' % (statname, loss_type, poe_str,
'_ins' if ins else '')
dest = dstore.build_fname(prefix, key, ext)
yield writercls(
dest, oq.investigation_time,
poe=poe, loss_type=loss_type,
risk_investigation_time=oq.risk_investigation_time,
statistics='mean' if ordinal == 0 else 'quantile',
quantile_value=statvalue,
unit=cc.units[loss_type],
), (loss_type, poe, ordinal, ins)
# this is used by event_based_risk
@export.add(('agg_curve-rlzs', 'xml'), ('agg_curve-stats', 'xml'))
[docs]def export_agg_curve_rlzs(ekey, dstore):
agg_curve = dstore[ekey[0]]
fnames = []
for writer, (loss_type, poe, r, ins) in _gen_writers(
dstore, risk_writers.AggregateLossCurveXMLWriter, ekey[0]):
rec = agg_curve[loss_type][ins, r]
curve = AggCurve(rec['losses'], rec['poes'], rec['avg'], None)
writer.serialize(curve)
fnames.append(writer._dest)
return sorted(fnames)
# this is used by classical risk
@export.add(('loss_curves-stats', 'xml'),
('loss_curves-stats', 'geojson'))
@deprecated
[docs]def export_loss_curves_stats(ekey, dstore):
assetcol = dstore['assetcol/array'].value
aref = dstore['asset_refs'].value
loss_curves = dstore[ekey[0]]
ok_loss_types = loss_curves.dtype.names
[loss_ratios] = dstore['loss_ratios']
fnames = []
writercls = (risk_writers.LossCurveGeoJSONWriter
if ekey[0] == 'geojson' else
risk_writers.LossCurveXMLWriter)
for writer, (ltype, poe, s, insflag) in _gen_writers(
dstore, writercls, ekey[0]):
if ltype not in ok_loss_types:
continue # ignore loss type
ins = '_ins' if insflag else ''
array = loss_curves[ltype][:, s]
curves = []
for ass, rec in zip(assetcol, array):
loc = Location(ass['lon'], ass['lat'])
curve = LossCurve(loc, decode(aref[ass['idx']]), rec['poes' + ins],
rec['losses' + ins], loss_ratios[ltype],
rec['avg' + ins], None)
curves.append(curve)
writer.serialize(curves)
fnames.append(writer._dest)
return sorted(fnames)
# used by scenario_damage
@export.add(('losses_by_taxon', 'csv'))
[docs]def export_csq_by_taxon_csv(ekey, dstore):
taxonomies = add_quotes(dstore['assetcol/taxonomies'].value)
rlzs = dstore['csm_info'].get_rlzs_assoc().realizations
value = dstore[ekey[0]].value # matrix T x R
writer = writers.CsvWriter(fmt=writers.FIVEDIGITS)
for rlz, values in zip(rlzs, value.T):
fname = dstore.build_fname(ekey[0], rlz, ekey[1])
writer.save(compose_arrays(taxonomies, values, 'taxonomy'), fname)
return writer.getsaved()
# used by event_based_risk and scenario_risk
@export.add(('losses_by_taxon-rlzs', 'csv'), ('losses_by_taxon-stats', 'csv'))
[docs]def export_losses_by_taxon_csv(ekey, dstore):
oq = dstore['oqparam']
taxonomies = add_quotes(dstore['assetcol/taxonomies'].value)
rlzs = dstore['csm_info'].get_rlzs_assoc().realizations
loss_types = oq.loss_dt().names
key, kind = ekey[0].split('-')
value = dstore[key + '-rlzs'].value
if kind == 'stats':
weights = dstore['realizations']['weight']
tags, stats = zip(*oq.risk_stats())
value = compute_stats2(value, stats, weights)
else: # rlzs
tags = rlzs
writer = writers.CsvWriter(fmt=writers.FIVEDIGITS)
dt = numpy.dtype([('taxonomy', taxonomies.dtype)] + oq.loss_dt_list())
for tag, values in zip(tags, value.transpose(1, 0, 2)):
fname = dstore.build_fname(key, tag, ekey[1])
array = numpy.zeros(len(values), dt)
array['taxonomy'] = taxonomies
for l, lt in enumerate(loss_types):
array[lt] = values[:, l]
writer.save(array, fname)
return writer.getsaved()
# this is used by classical_risk
@export.add(('loss_curves-rlzs', 'xml'),
('loss_curves-rlzs', 'geojson'))
@depr('Use `oq export loss_curves/rlz-XX` instead')
[docs]def export_loss_curves_rlzs(ekey, dstore):
assetcol = dstore['assetcol/array'].value
aref = dstore['asset_refs'].value
loss_curves = dstore[ekey[0]]
fnames = []
writercls = (risk_writers.LossCurveGeoJSONWriter
if ekey[0] == 'geojson' else
risk_writers.LossCurveXMLWriter)
for writer, (lt, poe, r, insflag) in _gen_writers(
dstore, writercls, ekey[0]):
ins = '_ins' if insflag else ''
array = loss_curves[lt][:, r]
curves = []
for ass, data in zip(assetcol, array):
loc = Location(ass['lon'], ass['lat'])
losses = data['losses' + ins]
poes = data['poes' + ins]
avg = data['avg' + ins]
if lt == 'occupants':
loss_ratios = losses / ass['occupants']
else:
loss_ratios = losses / ass['value-' + lt]
curve = LossCurve(loc, aref[ass['idx']], poes,
losses, loss_ratios, avg, None)
curves.append(curve)
writer.serialize(curves)
fnames.append(writer._dest)
return sorted(fnames)
BcrData = collections.namedtuple(
'BcrData', ['location', 'asset_ref', 'average_annual_loss_original',
'average_annual_loss_retrofitted', 'bcr'])
# this is used by classical_bcr
@export.add(('bcr-rlzs', 'xml'))
@deprecated
[docs]def export_bcr_map_rlzs(ekey, dstore):
assetcol = dstore['assetcol/array'].value
aref = dstore['asset_refs'].value
bcr_data = dstore['bcr-rlzs']
N, R = bcr_data.shape
oq = dstore['oqparam']
realizations = dstore['csm_info'].get_rlzs_assoc().realizations
loss_types = dstore.get_attr('composite_risk_model', 'loss_types')
writercls = risk_writers.BCRMapXMLWriter
fnames = []
for rlz in realizations:
for l, loss_type in enumerate(loss_types):
rlz_data = bcr_data[loss_type][:, rlz.ordinal]
path = dstore.build_fname('bcr-%s' % loss_type, rlz, 'xml')
writer = writercls(
path, oq.interest_rate, oq.asset_life_expectancy, loss_type,
**get_paths(rlz))
data = []
for ass, value in zip(assetcol, rlz_data):
loc = Location(ass['lon'], ass['lat'])
data.append(BcrData(loc, decode(aref[ass['idx']]),
value['annual_loss_orig'],
value['annual_loss_retro'],
value['bcr']))
writer.serialize(data)
fnames.append(path)
return sorted(fnames)
@export.add(('bcr-rlzs', 'csv'))
[docs]def export_bcr_map(ekey, dstore):
assetcol = dstore['assetcol/array'].value
aref = dstore['asset_refs'].value
bcr_data = dstore['bcr-rlzs']
N, R = bcr_data.shape
realizations = dstore['csm_info'].get_rlzs_assoc().realizations
loss_types = dstore.get_attr('composite_risk_model', 'loss_types')
fnames = []
writer = writers.CsvWriter(fmt=writers.FIVEDIGITS)
for rlz in realizations:
for l, loss_type in enumerate(loss_types):
rlz_data = bcr_data[loss_type][:, rlz.ordinal]
path = dstore.build_fname('bcr-%s' % loss_type, rlz, 'csv')
data = [['lon', 'lat', 'asset_ref', 'average_annual_loss_original',
'average_annual_loss_retrofitted', 'bcr']]
for ass, value in zip(assetcol, rlz_data):
data.append((ass['lon'], ass['lat'],
decode(aref[ass['idx']]),
value['annual_loss_orig'],
value['annual_loss_retro'],
value['bcr']))
writer.save(data, path)
fnames.append(path)
return writer.getsaved()
# TODO: add export_bcr_map_stats
@reader
[docs]def get_loss_ratios(lrgetter, aids, monitor):
with lrgetter.dstore:
loss_ratios = lrgetter.get_all(aids) # list of arrays of dtype lrs_dt
return zip(aids, loss_ratios)
@export.add(('asset_loss_table', 'hdf5'))
[docs]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 = dstore['asset_refs'].value
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)
lrgetter = riskinput.LossRatiosGetter(dstore)
aids = range(len(assetcol))
allargs = [(lrgetter, list(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.idx]
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]