# -*- coding: utf-8 -*-
# vim: tabstop=4 shiftwidth=4 softtabstop=4
#
# Copyright (C) 2014-2021 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 json
import logging
import itertools
import collections
import numpy
import pandas
from openquake.baselib import hdf5, writers, general
from openquake.hazardlib.stats import compute_stats2
from openquake.risklib import scientific
from openquake.calculators.extract import (
extract, build_damage_dt, build_csq_dt, build_damage_array, sanitize)
from openquake.calculators.export import export, loss_curves
from openquake.calculators.export.hazard import savez
from openquake.commonlib.util import get_assets, compose_arrays
Output = collections.namedtuple('Output', 'ltype path array')
F32 = numpy.float32
F64 = numpy.float64
U16 = numpy.uint16
U32 = numpy.uint32
stat_dt = numpy.dtype([('mean', F32), ('stddev', F32)])
[docs]def get_rup_data(ebruptures):
dic = {}
for ebr in ebruptures:
point = ebr.rupture.surface.get_middle_point()
dic[ebr.rup_id] = (ebr.rupture.mag, point.x, point.y, point.z)
return dic
# ############################### exporters ############################## #
[docs]def tag2idx(tags):
return {tag: i for i, tag in enumerate(tags)}
# this is used by event_based_risk and ebrisk
[docs]@export.add(('agg_curves-rlzs', 'csv'), ('agg_curves-stats', 'csv'))
def export_agg_curve_rlzs(ekey, dstore):
oq = dstore['oqparam']
lnames = numpy.array(oq.loss_names)
agg_tags = get_agg_tags(dstore, oq.aggregate_by)
aggvalue = dstore['agg_values'][()] # shape (K+1, L)
md = dstore.metadata
md['risk_investigation_time'] = (
oq.risk_investigation_time or oq.investigation_time)
writer = writers.CsvWriter(fmt=writers.FIVEDIGITS)
descr = hdf5.get_shape_descr(dstore[ekey[0]].attrs['json'])
name, suffix = ekey[0].split('-')
rlzs_or_stats = descr[suffix[:-1]]
aw = hdf5.ArrayWrapper(dstore[ekey[0]], descr, ('loss_value',))
dataf = aw.to_dframe().set_index(suffix[:-1])
for r, ros in enumerate(rlzs_or_stats):
md['kind'] = f'{name}-' + (
ros if isinstance(ros, str) else 'rlz-%03d' % ros)
try:
df = dataf[dataf.index == ros]
except KeyError:
logging.warning('No data for %s', md['kind'])
continue
dic = {col: df[col].to_numpy() for col in dataf.columns}
dic['loss_type'] = lnames[dic['lti']]
for tagname in oq.aggregate_by:
dic[tagname] = agg_tags[tagname][dic['agg_id']]
dic['loss_ratio'] = dic['loss_value'] / aggvalue[
dic['agg_id'], dic.pop('lti')]
dic['annual_frequency_of_exceedence'] = 1 / dic['return_period']
del dic['agg_id']
dest = dstore.build_fname(md['kind'], '', 'csv')
writer.save(pandas.DataFrame(dic), dest, comment=md)
return writer.getsaved()
# this is used by ebrisk
[docs]@export.add(('agg_losses-rlzs', 'csv'), ('agg_losses-stats', 'csv'))
def export_agg_losses(ekey, dstore):
"""
:param ekey: export key, i.e. a pair (datastore key, fmt)
:param dstore: datastore object
"""
dskey = ekey[0]
oq = dstore['oqparam']
aggregate_by = oq.aggregate_by if dskey.startswith('agg_') else []
name, value, rlzs_or_stats = _get_data(dstore, dskey, oq.hazard_stats())
# value has shape (K, R, L)
writer = writers.CsvWriter(fmt=writers.FIVEDIGITS)
tagcol = dstore['assetcol/tagcol']
aggtags = list(tagcol.get_aggkey(aggregate_by).values())
aggtags.append(('*total*',) * len(aggregate_by))
expvalue = dstore['agg_values'][()] # shape (K+1, L)
tagnames = tuple(aggregate_by)
header = ('loss_type',) + tagnames + (
'loss_value', 'exposed_value', 'loss_ratio')
md = dstore.metadata
md.update(dict(investigation_time=oq.investigation_time,
risk_investigation_time=oq.risk_investigation_time or
oq.investigation_time))
for r, ros in enumerate(rlzs_or_stats):
ros = ros if isinstance(ros, str) else 'rlz-%03d' % ros
rows = []
for (k, l), loss in numpy.ndenumerate(value[:, r]):
if loss: # many tag combinations are missing
evalue = expvalue[k, l]
row = aggtags[k] + (loss, evalue, loss / evalue)
rows.append((oq.loss_names[l],) + row)
dest = dstore.build_fname(name, ros, 'csv')
writer.save(rows, dest, header, comment=md)
return writer.getsaved()
def _get_data(dstore, dskey, stats):
name, kind = dskey.split('-') # i.e. ('avg_losses', 'stats')
if kind == 'stats':
weights = dstore['weights'][()]
if dskey in set(dstore): # precomputed
rlzs_or_stats = list(stats)
statfuncs = [stats[ros] for ros in stats]
value = dstore[dskey][()] # shape (A, S, LI)
else: # compute on the fly
rlzs_or_stats, statfuncs = zip(*stats.items())
value = compute_stats2(
dstore[name + '-rlzs'][()], statfuncs, weights)
else: # rlzs
value = dstore[dskey][()] # shape (A, R, LI)
R = value.shape[1]
rlzs_or_stats = ['rlz-%03d' % r for r in range(R)]
return name, value, rlzs_or_stats
# this is used by event_based_risk, classical_risk and scenario_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 = [(ln, F32) for ln in oq.loss_names]
name, value, rlzs_or_stats = _get_data(dstore, dskey, oq.hazard_stats())
writer = writers.CsvWriter(fmt=writers.FIVEDIGITS)
assets = get_assets(dstore)
md = dstore.metadata
md.update(dict(investigation_time=oq.investigation_time,
risk_investigation_time=oq.risk_investigation_time
or oq.investigation_time))
for ros, values in zip(rlzs_or_stats, value.transpose(1, 0, 2)):
dest = dstore.build_fname(name, ros, 'csv')
array = numpy.zeros(len(values), dt)
for li, ln in enumerate(oq.loss_names):
array[ln] = values[:, li]
writer.save(compose_arrays(assets, array), dest, comment=md,
renamedict=dict(id='asset_id'))
return writer.getsaved()
[docs]@export.add(('src_loss_table', 'csv'))
def export_src_loss_table(ekey, dstore):
"""
:param ekey: export key, i.e. a pair (datastore key, fmt)
:param dstore: datastore object
"""
oq = dstore['oqparam']
md = dstore.metadata
md.update(dict(investigation_time=oq.investigation_time,
risk_investigation_time=oq.risk_investigation_time or
oq.investigation_time))
aw = hdf5.ArrayWrapper.from_(dstore['src_loss_table'], 'loss_value')
dest = dstore.build_fname('src_loss_table', '', 'csv')
writer = writers.CsvWriter(fmt=writers.FIVEDIGITS)
writer.save(aw.to_dframe(), dest, comment=md)
return writer.getsaved()
# this is used by all GMF-based risk calculators
# NB: it exports only the event loss table, i.e. the totals
[docs]@export.add(('risk_by_event', 'csv'))
def export_event_loss_table(ekey, dstore):
"""
:param ekey: export key, i.e. a pair (datastore key, fmt)
:param dstore: datastore object
"""
oq = dstore['oqparam']
writer = writers.CsvWriter(fmt=writers.FIVEDIGITS)
dest = dstore.build_fname('risk_by_event', '', 'csv')
md = dstore.metadata
if 'scenario' not in oq.calculation_mode:
md.update(dict(investigation_time=oq.investigation_time,
risk_investigation_time=oq.risk_investigation_time
or oq.investigation_time))
events = dstore['events'][()]
K = dstore.get_attr('risk_by_event', 'K', 0)
try:
lstates = dstore.get_attr('risk_by_event', 'limit_states').split()
except KeyError: # ebrisk, no limit states
lstates = []
lnames = numpy.array(oq.loss_names)
df = dstore.read_df('risk_by_event', 'agg_id', dict(agg_id=K))
df['loss_type'] = lnames[df.loss_id.to_numpy()]
del df['loss_id']
if 'variance' in df.columns:
del df['variance']
ren = {'dmg_%d' % i: lstate for i, lstate in enumerate(lstates, 1)}
df.rename(columns=ren, inplace=True)
evs = events[df.event_id.to_numpy()]
if 'scenario' not in oq.calculation_mode:
df['rup_id'] = evs['rup_id']
if 'scenario' not in oq.calculation_mode and 'year' in evs.dtype.names:
df['year'] = evs['year']
df.sort_values(['event_id', 'loss_type'], inplace=True)
writer.save(df, dest, comment=md)
return writer.getsaved()
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)
# this is used by classical_risk
[docs]@export.add(('loss_curves-rlzs', 'csv'), ('loss_curves-stats', 'csv'),
('loss_curves', 'csv'))
def export_loss_curves(ekey, dstore):
if '/' in ekey[0]:
kind = ekey[0].split('/', 1)[1]
else:
kind = ekey[0].split('-', 1)[1] # rlzs or stats
return loss_curves.LossCurveExporter(dstore).export('csv', kind)
# used by classical_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)
oq = dstore['oqparam']
if kind == 'rlzs':
rlzs_or_stats = dstore['full_lt'].get_realizations()
else:
rlzs_or_stats = oq.hazard_stats()
writer = writers.CsvWriter(fmt=writers.FIVEDIGITS)
md = dstore.metadata
for i, ros in enumerate(rlzs_or_stats):
if hasattr(ros, 'ordinal'): # is a realization
ros = 'rlz-%d' % ros.ordinal
fname = dstore.build_fname('loss_maps', ros, ekey[1])
md.update(
dict(kind=ros, risk_investigation_time=oq.risk_investigation_time
or oq.investigation_time))
writer.save(compose_arrays(assets, value[:, i]), fname, comment=md,
renamedict=dict(id='asset_id'))
return writer.getsaved()
# used by classical_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['full_lt'].get_num_rlzs()
if kind == 'rlzs':
rlzs_or_stats = ['rlz-%03d' % r for r in range(R)]
else:
oq = dstore['oqparam']
rlzs_or_stats = oq.hazard_stats()
fname = dstore.export_path('%s.%s' % ekey)
dic = {}
for i, ros in enumerate(rlzs_or_stats):
dic[ros] = compose_arrays(assets, value[:, i])
savez(fname, **dic)
return [fname]
[docs]def modal_damage_array(data, damage_dt):
# determine the damage state with the highest probability
A, L, D = data.shape
dmgstate = damage_dt['structural'].names
arr = numpy.zeros(A, [('modal-ds-' + lt, hdf5.vstr)
for lt in damage_dt.names])
for li, loss_type in enumerate(damage_dt.names):
arr['modal-ds-' + loss_type] = [dmgstate[data[a, li].argmax()]
for a in range(A)]
return arr
# used by event_based_damage, scenario_damage, classical_damage
[docs]@export.add(('damages-rlzs', 'csv'), ('damages-stats', 'csv'))
def export_damages_csv(ekey, dstore):
oq = dstore['oqparam']
ebd = oq.calculation_mode == 'event_based_damage'
dmg_dt = build_damage_dt(dstore)
rlzs = dstore['full_lt'].get_realizations()
orig = dstore[ekey[0]][:] # shape (A, R, L, D)
writer = writers.CsvWriter(fmt='%.6E')
assets = get_assets(dstore)
md = dstore.metadata
if oq.investigation_time:
rit = oq.risk_investigation_time or oq.investigation_time
md.update(dict(investigation_time=oq.investigation_time,
risk_investigation_time=rit))
D = len(oq.limit_states) + 1
R = 1 if oq.collect_rlzs else len(rlzs)
if ekey[0].endswith('stats'):
rlzs_or_stats = oq.hazard_stats()
else:
rlzs_or_stats = ['rlz-%03d' % r for r in range(R)]
name = ekey[0].split('-')[0]
if oq.calculation_mode != 'classical_damage':
name = 'avg_' + name
for i, ros in enumerate(rlzs_or_stats):
if ebd: # export only the consequences from damages-rlzs, i == 0
rate = len(dstore['events']) * oq.time_ratio / len(rlzs)
data = orig[:, i] * rate
A, L, Dc = data.shape
if Dc == D: # no consequences, export nothing
return
csq_dt = build_csq_dt(dstore)
damages = numpy.zeros(A, csq_dt)
for a in range(A):
for li, lt in enumerate(csq_dt.names):
damages[lt][a] = tuple(data[a, li, D:Dc])
fname = dstore.build_fname('avg_risk', ros, ekey[1])
else: # scenario_damage, classical_damage
if oq.modal_damage_state:
damages = modal_damage_array(orig[:, i], dmg_dt)
else:
damages = build_damage_array(orig[:, i], dmg_dt)
fname = dstore.build_fname(name, ros, ekey[1])
writer.save(compose_arrays(assets, damages), fname,
comment=md, renamedict=dict(id='asset_id'))
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))
def _to_loss_maps(array, loss_maps_dt):
# convert a 4D array into a 2D array of dtype loss_maps_dt
A, R, C, LI = array.shape
lm = numpy.zeros((A, R), loss_maps_dt)
for li, name in enumerate(loss_maps_dt.names):
for p, poe in enumerate(loss_maps_dt[name].names):
lm[name][poe] = array[:, :, p, li]
return lm
[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 _to_loss_maps(dstore[name][()], oq.loss_maps_dt())
name = 'loss_curves-%s' % kind
if name in dstore: # classical_risk
# the loss maps are built on the fly from the loss curves
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', 'csv'))
def export_agglosses(ekey, dstore):
oq = dstore['oqparam']
loss_dt = oq.loss_dt()
cc = dstore['cost_calculator']
unit_by_lt = cc.units
unit_by_lt['occupants'] = 'people'
agglosses = dstore[ekey[0]]
losses = []
header = ['rlz_id', 'loss_type', 'unit', 'mean', 'stddev']
for r in range(len(agglosses)):
for li, lt in enumerate(loss_dt.names):
unit = unit_by_lt[lt]
mean = agglosses[r, li]['mean']
stddev = agglosses[r, li]['stddev']
losses.append((r, lt, unit, mean, stddev))
dest = dstore.build_fname('agglosses', '', 'csv')
writers.write_csv(dest, losses, header=header, comment=dstore.metadata)
return [dest]
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'):
rlzs_or_stats = oq.hazard_stats()
else:
rlzs_or_stats = ['rlz-%03d' % r for r in range(R)]
fnames = []
writer = writers.CsvWriter(fmt=writers.FIVEDIGITS)
for t, ros in enumerate(rlzs_or_stats):
path = dstore.build_fname('bcr', ros, 'csv')
writer.save(compose_arrays(assets, bcr_data[:, t]), path,
renamedict=dict(id='asset_id'))
fnames.append(path)
return writer.getsaved()
[docs]@export.add(('aggregate_by', 'csv'))
def export_aggregate_by_csv(ekey, dstore):
"""
:param ekey: export key, i.e. a pair (datastore key, fmt)
:param dstore: datastore object
"""
token, what = ekey[0].split('/', 1)
aw = extract(dstore, 'aggregate/' + what)
fnames = []
writer = writers.CsvWriter(fmt=writers.FIVEDIGITS)
path = '%s.%s' % (sanitize(ekey[0]), ekey[1])
fname = dstore.export_path(path)
writer.save(aw.to_dframe(), fname)
fnames.append(fname)
return fnames
[docs]@export.add(('asset_risk', 'csv'))
def export_asset_risk_csv(ekey, dstore):
"""
:param ekey: export key, i.e. a pair (datastore key, fmt)
:param dstore: datastore object
"""
writer = writers.CsvWriter(fmt=writers.FIVEDIGITS)
path = '%s.%s' % (sanitize(ekey[0]), ekey[1])
fname = dstore.export_path(path)
md = json.loads(extract(dstore, 'exposure_metadata').json)
tostr = {'taxonomy': md['taxonomy']}
for tagname in md['tagnames']:
tostr[tagname] = md[tagname]
tagnames = sorted(set(md['tagnames']) - {'id'})
arr = extract(dstore, 'asset_risk').array
rows = []
lossnames = sorted(name for name in arr.dtype.names if 'loss' in name)
expnames = [name for name in arr.dtype.names if name not in md['tagnames']
and 'loss' not in name and name not in 'lon lat']
colnames = tagnames + ['lon', 'lat'] + expnames + lossnames
# sanity check
assert len(colnames) == len(arr.dtype.names)
for rec in arr:
row = []
for name in colnames:
value = rec[name]
try:
row.append(tostr[name][value])
except KeyError:
row.append(value)
rows.append(row)
writer.save(rows, fname, colnames)
return [fname]
[docs]@export.add(('agg_risk', 'csv'))
def export_agg_risk_csv(ekey, dstore):
"""
:param ekey: export key, i.e. a pair (datastore key, fmt)
:param dstore: datastore object
"""
writer = writers.CsvWriter(fmt=writers.FIVEDIGITS)
path = '%s.%s' % (sanitize(ekey[0]), ekey[1])
fname = dstore.export_path(path)
dset = dstore['agg_risk']
writer.save(dset[()], fname, dset.dtype.names)
return [fname]
[docs]@export.add(('aggcurves', 'csv'))
def export_aggcurves_csv(ekey, dstore):
"""
:param ekey: export key, i.e. a pair (datastore key, fmt)
:param dstore: datastore object
"""
oq = dstore['oqparam']
E = len(dstore['events'])
R = len(dstore['weights'])
lossnames = numpy.array(oq.loss_names)
aggtags = get_agg_tags(dstore, oq.aggregate_by)
df = dstore.read_df('aggcurves')
consequences = [col for col in df.columns
if col in scientific.KNOWN_CONSEQUENCES]
for tagname, tags in aggtags.items():
df[tagname] = tags[df.agg_id]
df['loss_type'] = lossnames[df.loss_id.to_numpy()]
del df['loss_id']
dest = dstore.export_path('%s.%s' % ekey)
writer = writers.CsvWriter(fmt=writers.FIVEDIGITS)
md = dstore.metadata
md['risk_investigation_time'] = (oq.risk_investigation_time or
oq.investigation_time)
md['num_events'] = E
md['effective_time'] = (
oq.investigation_time * oq.ses_per_logic_tree_path * R)
md['limit_states'] = dstore.get_attr('aggcurves', 'limit_states')
# aggcurves
del df['agg_id']
edic = general.AccumDict(accum=[])
for cons in consequences:
for col in df.columns:
if col not in scientific.KNOWN_CONSEQUENCES:
edic[col].extend(df[col])
elif col == cons:
edic['conseq_value'].extend(df[col])
edic['conseq_type'].extend([col] * len(df))
writer.save(pandas.DataFrame(edic), dest, comment=md)
return [dest]