# -*- coding: utf-8 -*-
# vim: tabstop=4 shiftwidth=4 softtabstop=4
#
# Copyright (C) 2014-2023 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 os
import sys
import abc
import pdb
import json
import time
import inspect
import logging
import operator
import traceback
from datetime import datetime
from shapely import wkt
import psutil
import h5py
import numpy
import pandas
from openquake.baselib import general, hdf5
from openquake.baselib import performance, parallel, python3compat
from openquake.baselib.performance import Monitor
from openquake.hazardlib import (
InvalidFile, site, stats, logictree, source_reader)
from openquake.hazardlib.site_amplification import Amplifier
from openquake.hazardlib.site_amplification import AmplFunction
from openquake.hazardlib.calc.filters import SourceFilter, getdefault
from openquake.hazardlib.calc.disagg import to_rates
from openquake.hazardlib.source import rupture
from openquake.hazardlib.shakemap.maps import get_sitecol_shakemap
from openquake.hazardlib.shakemap.gmfs import to_gmfs
from openquake.risklib import riskinput, riskmodels, reinsurance
from openquake.commonlib import readinput, datastore, logs
from openquake.calculators.export import export as exp
from openquake.calculators import getters, postproc
get_taxonomy = operator.attrgetter('taxonomy')
get_weight = operator.attrgetter('weight')
get_imt = operator.attrgetter('imt')
calculators = general.CallableDict(operator.attrgetter('calculation_mode'))
U8 = numpy.uint8
U16 = numpy.uint16
U32 = numpy.uint32
F32 = numpy.float32
TWO16 = 2 ** 16
TWO32 = 2 ** 32
stats_dt = numpy.dtype([('mean', F32), ('std', F32),
('min', F32), ('max', F32),
('len', U16)])
[docs]def check_imtls(this, parent):
"""
Fix the hazard_imtls of two calculations if possible
"""
for imt, imls in this.items():
if len(imls) != len(parent[imt]) or (imls != parent[imt]).any():
raise ValueError('The intensity measure levels %s are different '
'from the parent levels %s for %s' % (
imls, parent[imt], imt))
# this is used for the minimum_intensity dictionaries
[docs]def consistent(dic1, dic2):
"""
Check if two dictionaries with default are consistent:
>>> consistent({'PGA': 0.05, 'SA(0.3)': 0.05}, {'default': 0.05})
True
>>> consistent({'SA(0.3)': 0.1, 'SA(0.6)': 0.05},
... {'default': 0.1, 'SA(0.3)': 0.1, 'SA(0.6)': 0.05})
True
"""
if dic1 == dic2:
return True
v1 = set(dic1.values())
v2 = set(dic2.values())
missing = set(dic2) - set(dic1) - {'default'}
if len(v1) == 1 and len(v2) == 1 and v1 == v2:
# {'PGA': 0.05, 'SA(0.3)': 0.05} is consistent with {'default': 0.05}
return True
return not missing
[docs]def get_stats(seq):
std = numpy.nan if len(seq) == 1 else numpy.std(seq, ddof=1)
tup = (numpy.mean(seq), std, numpy.min(seq), numpy.max(seq), len(seq))
return numpy.array(tup, stats_dt)
[docs]class InvalidCalculationID(Exception):
"""
Raised when running a post-calculation on top of an incompatible
pre-calculation
"""
[docs]def build_weights(realizations):
"""
:returns: an array with the realization weights of shape R
"""
arr = numpy.array([rlz.weight['default'] for rlz in realizations])
return arr
[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]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
"""
precalc = None
accept_precalc = []
from_engine = False # set by engine.run_calc
is_stochastic = False # True for scenario and event based calculators
def __init__(self, oqparam, calc_id):
self.oqparam = oqparam
self.datastore = datastore.new(calc_id, oqparam)
self.engine_version = logs.dbcmd('engine_version')
# save the version in the monitor, to be used in the version
# check in the workers
self._monitor = Monitor(
'%s.run' % self.__class__.__name__, measuremem=True,
h5=self.datastore, version=self.engine_version
if parallel.oq_distribute() == 'zmq' else None)
# NB: using h5=self.datastore.hdf5 would mean losing the performance
# info about Calculator.run since the file will be closed later on
[docs] def pre_checks(self):
"""
Checks to run after the pre_execute but before the execute
"""
[docs] def monitor(self, operation='', **kw):
"""
:returns: a new Monitor instance
"""
mon = self._monitor(operation, h5=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)
if isinstance(self.oqparam.risk_imtls, dict):
# always except in case_shakemap
self.datastore['oqparam'] = self.oqparam
attrs = self.datastore['/'].attrs
attrs['engine_version'] = self.engine_version
attrs['date'] = datetime.now().isoformat()[:19]
if 'checksum32' not in attrs:
attrs['input_size'] = size = self.oqparam.get_input_size()
attrs['checksum32'] = check = readinput.get_checksum32(
self.oqparam, self.datastore.hdf5)
logging.info(f'Checksum of the inputs: {check} '
f'(total size {general.humansize(size)})')
[docs] def check_precalc(self, precalc_mode):
"""
Defensive programming against users providing an incorrect
pre-calculation ID (with ``--hazard-calculation-id``).
:param precalc_mode:
calculation_mode of the previous calculation
"""
calc_mode = self.oqparam.calculation_mode
ok_mode = self.accept_precalc
if calc_mode != precalc_mode and precalc_mode not in ok_mode:
raise InvalidCalculationID(
'In order to run a 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] def run(self, pre_execute=True, concurrent_tasks=None, remove=True,
shutdown=False, **kw):
"""
Run the calculation and return the exported outputs.
:param pre_execute: set it to False to avoid running pre_execute
:param concurrent_tasks: set it to 0 to disable parallelization
:param remove: set it to False to remove the hdf5cache file (if any)
:param shutdown: set it to True to shutdown the ProcessPool
"""
oq = self.oqparam
with self._monitor:
self._monitor.username = kw.get('username', '')
if concurrent_tasks is None: # use the job.ini parameter
ct = oq.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 != oq.concurrent_tasks:
# save the used concurrent_tasks
oq.concurrent_tasks = ct
if self.precalc is None:
logging.info('Running %s with concurrent_tasks = %d',
self.__class__.__name__, ct)
self.save_params(**kw)
try:
if pre_execute:
self.pre_execute()
self.result = self.execute()
if self.result is not None:
self.post_execute(self.result)
self.post_process()
self.export(kw.get('exports', ''))
except Exception as exc:
if kw.get('pdb'): # post-mortem debug
tb = sys.exc_info()[2]
traceback.print_tb(tb)
pdb.post_mortem(tb)
else:
raise exc from None
finally:
if shutdown:
parallel.Starmap.shutdown()
# 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.Global.reset()
# remove temporary hdf5 file, if any
if os.path.exists(self.datastore.tempname):
if remove and oq.calculation_mode != 'preclassical':
# removing in preclassical with multiFaultSources
# would break --hc which is reading the temp file
os.remove(self.datastore.tempname)
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, 'exported', {})
if isinstance(exports, tuple):
fmts = exports
elif exports: # is a string
fmts = exports.split(',')
elif isinstance(self.oqparam.exports, (tuple, list)):
fmts = self.oqparam.exports
else: # is a string
fmts = self.oqparam.exports.split(',')
keys = set(self.datastore) | {'fullreport'}
has_hcurves = ('hcurves-stats' in self.datastore or
'hcurves-rlzs' in self.datastore)
if has_hcurves:
keys.add('hcurves')
if 'ruptures' in self.datastore and len(self.datastore['ruptures']):
keys.add('event_based_mfd')
elif 'ruptures' in keys:
keys.remove('ruptures')
for fmt in fmts:
if not fmt:
continue
if fmt == 'csv':
self._export(('realizations', fmt))
for key in sorted(keys): # top level keys
if 'rlzs' in key and self.R > 1:
if (key[:-4] + 'stats') in self.datastore:
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'):
try:
self.exported[ekey] = fnames = exp(ekey, self.datastore)
except Exception as exc:
fnames = []
logging.error('Could not export %s: %s', ekey, exc)
if fnames:
logging.info('exported %s: %s', ekey[0], fnames)
def __repr__(self):
return '<%s#%d>' % (self.__class__.__name__, self.datastore.calc_id)
[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 != 'avg' 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]def check_amplification(ampl_df, sitecol):
"""
Make sure the amplification codes in the site collection match the
ones in the amplification table.
:param ampl_df: the amplification table as a pandas DataFrame
:param sitecol: the site collection
"""
codeset = set(ampl_df.index)
if len(codeset) == 1:
# there is a single amplification function, there is no need to
# extend the sitecol with an ampcode field
return
codes = set(sitecol.ampcode)
missing = codes - codeset
if missing:
raise ValueError('The site collection contains references to missing '
'amplification functions: %s' % b' '.join(missing).
decode('utf8'))
[docs]class HazardCalculator(BaseCalculator):
"""
Base class for hazard calculators based on source models
"""
af = None
amplifier = None
[docs] def src_filter(self):
"""
:returns: a SourceFilter
"""
oq = self.oqparam
if getattr(self, 'sitecol', None):
sitecol = self.sitecol.complete
else: # can happen to the ruptures-only calculator
sitecol = None
return SourceFilter(sitecol, oq.maximum_distance)
@property
def E(self):
"""
:returns: the number of stored events
"""
try:
return len(self.datastore['events'])
except KeyError:
return 0
@property
def N(self):
"""
:returns: the number of sites
"""
if hasattr(self, 'sitecol'):
return len(self.sitecol) if self.sitecol else 0
if 'sitecol' not in self.datastore:
return 0
return len(self.datastore['sitecol'])
@property
def few_sites(self):
"""
:returns: True if there are less than max_sites_disagg
"""
return len(self.sitecol.complete) <= self.oqparam.max_sites_disagg
[docs] def check_overflow(self):
"""Overridden in event based"""
[docs] def check_floating_spinning(self):
oq = self.oqparam
f, s = self.csm.get_floating_spinning_factors()
if f != 1:
logging.info('Rupture floating factor = %s', f)
if s != 1:
logging.info('Rupture spinning factor = %s', s)
if (f * s >= 1.5 and oq.no_pointsource_distance
and ('classical' in oq.calculation_mode or
'disaggregation' in oq.calculation_mode)):
logging.info(
'You are not using the pointsource_distance approximation:\n'
'https://docs.openquake.org/oq-engine/advanced/general.html#'
'pointsource-distance')
elif 'classical' in oq.calculation_mode:
if oq.ps_grid_spacing:
logging.info('Using pointsource_distance=%s + %d',
oq.pointsource_distance, int(oq.ps_grid_spacing))
else:
logging.info('Using pointsource_distance=%s',
oq.pointsource_distance)
[docs] def import_perils(self):
"""Defined in MultiRiskCalculator"""
[docs] def pre_execute(self):
"""
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
self.t0 = time.time()
if 'gmfs' in oq.inputs or 'multi_peril' in oq.inputs:
# read hazard from files
assert not oq.hazard_calculation_id, (
'You cannot use --hc together with gmfs_file')
with self.monitor('importing inputs', measuremem=True):
self.read_inputs()
if 'gmfs' in oq.inputs:
self.datastore['full_lt'] = logictree.FullLogicTree.fake()
if oq.inputs['gmfs'].endswith('.csv'):
eids = import_gmfs_csv(self.datastore, oq, self.sitecol)
elif oq.inputs['gmfs'].endswith('.hdf5'):
eids = import_gmfs_hdf5(self.datastore, oq)
else:
raise NotImplementedError(
'Importer for %s' % oq.inputs['gmfs'])
E = len(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
else:
self.import_perils()
self.save_crmodel()
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, self.datastore.hdf5)
self.load_crmodel() # must be after get_site_collection
self.read_exposure(haz_sitecol) # define .assets_by_site
df = readinput.Global.pmap.to_dframe()
df.rate = to_rates(df.rate)
self.datastore.create_df('_rates', df)
self.datastore['assetcol'] = self.assetcol
self.datastore['full_lt'] = fake = logictree.FullLogicTree.fake()
self.datastore['trt_rlzs'] = U32([[0]])
self.realizations = fake.get_realizations()
self.save_crmodel()
self.datastore.swmr_on()
elif oq.hazard_calculation_id:
parent = datastore.read(oq.hazard_calculation_id)
oqparent = parent['oqparam']
if 'weights' in parent:
weights = numpy.unique(parent['weights'][:])
if (oq.job_type == 'risk' and oq.collect_rlzs and
len(weights) > 1):
raise ValueError(
'collect_rlzs=true can be specified only if '
'the realizations have identical weights')
if oqparent.imtls:
check_imtls(self.oqparam.imtls, oqparent.imtls)
self.check_precalc(oqparent.calculation_mode)
self.datastore.parent = parent
# copy missing parameters from the parent
if 'concurrent_tasks' not in vars(self.oqparam):
self.oqparam.concurrent_tasks = (
self.oqparam.__class__.concurrent_tasks.default)
params = {name: value for name, value in
vars(parent['oqparam']).items()
if name not in vars(self.oqparam)
and name != 'ground_motion_fields'}
if params:
self.save_params(**params)
with self.monitor('importing inputs', measuremem=True):
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))
hstats, rstats = list(oqp.hazard_stats()), list(oq.hazard_stats())
if hstats != rstats:
raise ValueError(
'The parent calculation had stats %s != %s' %
(hstats, rstats))
sec_imts = set(oq.sec_imts)
missing_imts = set(oq.risk_imtls) - sec_imts - set(oqp.imtls)
if oqp.imtls and missing_imts:
raise ValueError(
'The parent calculation is missing the IMT(s) %s' %
', '.join(missing_imts))
self.save_crmodel()
elif self.__class__.precalc:
calc = calculators[self.__class__.precalc](
self.oqparam, self.datastore.calc_id)
calc.from_engine = self.from_engine
calc.run(remove=False)
calc.datastore.close()
for name in (
'csm param sitecol assetcol crmodel realizations max_weight '
'amplifier policy_df treaty_df full_lt exported'
).split():
if hasattr(calc, name):
setattr(self, name, getattr(calc, name))
else:
with self.monitor('importing inputs', measuremem=True):
self.read_inputs()
self.save_crmodel()
[docs] def init(self):
"""
To be overridden to initialize the datasets needed by the calculation
"""
oq = self.oqparam
if not oq.risk_imtls:
if self.datastore.parent:
oq.risk_imtls = (
self.datastore.parent['oqparam'].risk_imtls)
if hasattr(self, 'csm'):
self.check_floating_spinning()
self.realizations = self.csm.full_lt.get_realizations()
elif 'full_lt' in self.datastore:
# for instance in classical damage case_8a
self.realizations = self.datastore['full_lt'].get_realizations()
else: # build a fake; used by risk-from-file calculators
self.datastore['full_lt'] = fake = logictree.FullLogicTree.fake()
self.realizations = fake.get_realizations()
@general.cached_property
def R(self):
"""
:returns: the number of realizations
"""
if self.oqparam.collect_rlzs and self.oqparam.job_type == 'risk':
return 1
elif 'weights' in self.datastore:
return len(self.datastore['weights'])
try:
return self.csm.full_lt.get_num_paths()
except AttributeError: # no self.csm
return self.datastore['full_lt'].get_num_paths()
[docs] def read_exposure(self, haz_sitecol): # after load_risk_model
"""
Read the exposure, the risk models and update the attributes
.sitecol, .assetcol
"""
oq = self.oqparam
(self.sitecol,
self.assetcol,
discarded) = readinput.get_sitecol_assetcol(
oq, haz_sitecol, self.crmodel.loss_types, self.datastore)
# this is overriding the sitecol in test_case_miriam
self.datastore['sitecol'] = self.sitecol
if len(discarded):
self.datastore['discarded'] = discarded
if 'scenario' in oq.calculation_mode:
# this is normal for the case of scenario from rupture
logging.info('%d assets were discarded because too far '
'from the rupture; use `oq show discarded` '
'to show them and `oq plot_assets` to plot '
'them' % len(discarded))
elif not oq.discard_assets: # raise an error
self.datastore['assetcol'] = self.assetcol
raise RuntimeError(
'%d assets were discarded; use `oq show discarded` to'
' show them and `oq plot_assets` to plot them' %
len(discarded))
if 'insurance' in oq.inputs:
self.load_insurance_data(oq.inputs['insurance'].items())
elif 'reinsurance' in oq.inputs:
self.load_insurance_data(oq.inputs['reinsurance'].items())
return readinput.Global.exposure
[docs] def load_insurance_data(self, lt_fnames):
"""
Read the insurance files and populate the policy_df
"""
oq = self.oqparam
policy_acc = general.AccumDict(accum=[])
# here is an example of policy_idx: {'?': 0, 'B': 1, 'A': 2}
if 'reinsurance' in oq.inputs:
loss_type = list(lt_fnames)[0][0]
policy_df, treaty_df, fieldmap = readinput.get_reinsurance(
oq, self.assetcol)
treaties = set(treaty_df.id)
assert len(treaties) == len(treaty_df), 'Not unique treaties'
self.datastore.create_df('treaty_df', treaty_df,
field_map=json.dumps(fieldmap))
self.treaty_df = treaty_df
# add policy_grp column
for _, pol in policy_df.iterrows():
grp = reinsurance.build_policy_grp(pol, treaty_df)
policy_acc['policy_grp'].append(grp)
for col in policy_df.columns:
policy_acc[col].extend(policy_df[col])
policy_acc['loss_type'].extend([loss_type] * len(policy_df))
else: # insurance
policy_idx = self.assetcol.tagcol.policy_idx
for loss_type, fname in lt_fnames:
# `deductible` and `insurance_limit` as fractions
policy_df = pandas.read_csv(fname, keep_default_na=False)
policy_df['policy'] = [
policy_idx[pol] for pol in policy_df.policy]
for col in ['deductible', 'insurance_limit']:
reinsurance.check_fractions(
[col], [policy_df[col].to_numpy()], fname)
for col in policy_df.columns:
policy_acc[col].extend(policy_df[col])
policy_acc['loss_type'].extend([loss_type] * len(policy_df))
assert policy_acc
self.policy_df = pandas.DataFrame(policy_acc)
self.datastore.create_df('policy', self.policy_df)
[docs] def load_crmodel(self):
# to be called before read_exposure
# NB: this is called even if there is no risk model
"""
Read the risk models and set the attribute .crmodel.
The crmodel can be empty for hazard calculations.
Save the loss ratios (if any) in the datastore.
"""
oq = self.oqparam
self.crmodel = readinput.get_crmodel(oq)
if not self.crmodel:
parent = self.datastore.parent
if 'crm' in parent:
self.crmodel = riskmodels.CompositeRiskModel.read(parent, oq)
return
if oq.ground_motion_fields and not oq.imtls:
raise InvalidFile('No intensity_measure_types specified in %s' %
self.oqparam.inputs['job_ini'])
self.save_params() # re-save oqparam
[docs] def save_crmodel(self):
"""
Save the risk models in the datastore
"""
if len(self.crmodel):
logging.info('Storing risk model')
attrs = self.crmodel.get_attrs()
self.datastore.create_df('crm', self.crmodel.to_dframe(),
'gzip', **attrs)
def _read_risk_data(self):
# read the risk model (if any), the exposure (if any) and then the
# site collection, possibly extracted from the exposure.
oq = self.oqparam
self.load_crmodel() # must be called first
if (not oq.imtls and 'shakemap' not in oq.inputs and 'ins_loss'
not in oq.inputs and oq.ground_motion_fields):
raise InvalidFile('There are no intensity measure types in %s' %
oq.inputs['job_ini'])
elif oq.hazard_calculation_id:
haz_sitecol = read_parent_sitecol(oq, self.datastore)
else:
if 'gmfs' in oq.inputs and oq.inputs['gmfs'].endswith('.hdf5'):
with hdf5.File(oq.inputs['gmfs']) as f:
haz_sitecol = f['sitecol']
else:
haz_sitecol = readinput.get_site_collection(
oq, self.datastore.hdf5)
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()
if 'site_model' in oq.inputs:
self.datastore['site_model'] = readinput.get_site_model(oq)
oq_hazard = (self.datastore.parent['oqparam']
if self.datastore.parent else None)
if 'exposure' in oq.inputs and 'assetcol' not in self.datastore.parent:
exposure = self.read_exposure(haz_sitecol)
self.datastore['assetcol'] = self.assetcol
self.datastore['exposure'] = exposure
if hasattr(readinput.Global.exposure, 'exposures'):
self.datastore.getitem('assetcol')['exposures'] = numpy.array(
exposure.exposures, hdf5.vstr)
elif 'assetcol' in self.datastore.parent:
logging.info('Reusing hazard exposure')
haz_sitecol = read_parent_sitecol(oq, self.datastore)
assetcol = self.datastore.parent['assetcol']
assetcol.update_tagcol(oq.aggregate_by)
if oq.region:
region = wkt.loads(oq.region)
self.sitecol = haz_sitecol.within(region)
if oq.shakemap_id or 'shakemap' in oq.inputs or oq.shakemap_uri:
self.sitecol, self.assetcol = read_shakemap(
self, haz_sitecol, assetcol)
self.datastore['sitecol'] = self.sitecol
self.datastore['assetcol'] = self.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
logging.info('Extracted %d/%d assets',
len(self.assetcol), len(assetcol))
else:
self.assetcol = assetcol
self.sitecol = haz_sitecol
if ('site_id' in oq.aggregate_by and 'site_id' not
in assetcol.tagcol.tagnames):
assetcol.tagcol.add_tagname('site_id')
assetcol.tagcol.site_id.extend(range(self.N))
else: # no exposure
if oq.hazard_calculation_id: # read the sitecol of the child
self.sitecol = readinput.get_site_collection(
oq, self.datastore.hdf5)
self.datastore['sitecol'] = self.sitecol
else:
self.sitecol = haz_sitecol
if self.sitecol and oq.imtls:
logging.info('Read N=%d hazard sites and L=%d hazard levels',
len(self.sitecol), oq.imtls.size)
if oq_hazard:
parent = self.datastore.parent
if 'assetcol' in parent:
check_time_event(oq, parent['assetcol'].occupancy_periods)
elif oq.job_type == 'risk' and 'exposure' not in oq.inputs:
raise ValueError('Missing exposure both in hazard and risk!')
if (oq_hazard.time_event != 'avg' 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 oq.job_type == 'risk':
taxs = python3compat.decode(self.assetcol.tagcol.taxonomy)
tmap = readinput.taxonomy_mapping(self.oqparam, taxs)
self.crmodel.set_tmap(tmap)
taxonomies = set()
for ln in oq.loss_types:
for items in self.crmodel.tmap[ln]:
for taxo, weight in items:
if taxo != '?':
taxonomies.add(taxo)
# check that we are covering all the taxonomies in the exposure
missing = taxonomies - set(self.crmodel.taxonomies)
if self.crmodel and missing:
raise RuntimeError(
'The exposure contains the taxonomy strings '
'%s which are not in the fragility/vulnerability/'
'consequence model' % missing)
self.crmodel.check_risk_ids(oq.inputs)
if len(self.crmodel.taxonomies) > len(taxonomies):
logging.info(
'Reducing risk model from %d to %d taxonomy strings',
len(self.crmodel.taxonomies), len(taxonomies))
self.crmodel = self.crmodel.reduce(taxonomies)
self.crmodel.tmap = tmap
if hasattr(self, 'sitecol') and self.sitecol:
if 'site_model' in oq.inputs:
assoc_dist = (oq.region_grid_spacing * 1.414
if oq.region_grid_spacing else 5) # Graeme's 5km
sm = readinput.get_site_model(oq)
if oq.prefer_global_site_params:
self.sitecol.set_global_params(oq)
else:
# use the site model parameters
self.sitecol.assoc(sm, assoc_dist)
if oq.override_vs30:
# override vs30, z1pt0 and z2pt5
names = self.sitecol.array.dtype.names
self.sitecol.array['vs30'] = oq.override_vs30
if 'z1pt0' in names:
self.sitecol.calculate_z1pt0()
if 'z2pt5' in names:
self.sitecol.calculate_z2pt5()
self.datastore['sitecol'] = self.sitecol
if self.sitecol is not self.sitecol.complete:
self.datastore['complete'] = self.sitecol.complete
elif 'complete' in self.datastore.parent:
# fix: the sitecol is not complete
self.sitecol.complete = self.datastore.parent['complete']
# store amplification functions if any
if 'amplification' in oq.inputs:
logging.info('Reading %s', oq.inputs['amplification'])
df = AmplFunction.read_df(oq.inputs['amplification'])
check_amplification(df, self.sitecol)
if oq.amplification_method == 'kernel':
# TODO: need to add additional checks on the main calculation
# methodology since the kernel method is currently tested only
# for classical PSHA
self.af = AmplFunction.from_dframe(df)
else:
self.amplifier = Amplifier(oq.imtls, df, oq.soil_intensities)
# manage secondary perils
sec_perils = oq.get_sec_perils()
for sp in sec_perils:
sp.prepare(self.sitecol) # add columns as needed
if sec_perils:
self.datastore['sitecol'] = self.sitecol
mal = {lt: getdefault(oq.minimum_asset_loss, lt)
for lt in oq.loss_types}
if mal:
logging.info('minimum_asset_loss=%s', mal)
oq._amplifier = self.amplifier
oq._sec_perils = sec_perils
# compute exposure stats
if hasattr(self, 'assetcol'):
save_agg_values(
self.datastore, self.assetcol, oq.loss_types,
oq.aggregate_by, oq.max_aggregations)
[docs] def store_rlz_info(self, rel_ruptures):
"""
Save info about the composite source model inside the full_lt dataset
:param rel_ruptures: dictionary TRT -> number of relevant ruptures
"""
if hasattr(self, 'full_lt'): # no scenario
self.realizations = self.full_lt.get_realizations()
if not self.realizations:
raise RuntimeError('Empty logic tree: too much filtering?')
else: # scenario
self.full_lt = self.datastore['full_lt']
self.datastore['weights'] = arr = build_weights(self.realizations)
self.datastore.set_attrs('weights', nbytes=arr.nbytes)
if rel_ruptures:
self.check_discardable(rel_ruptures)
[docs] def check_discardable(self, rel_ruptures):
"""
Check if logic tree reduction is possible
"""
keep_trts = set()
nrups = []
for grp_id, trt_smrs in enumerate(self.csm.get_trt_smrs()):
trti, smrs = numpy.divmod(trt_smrs, 2**24)
trt = self.full_lt.trts[trti[0]]
nr = rel_ruptures.get(grp_id, 0)
nrups.append(nr)
if nr:
keep_trts.add(trt)
self.datastore['est_rups_by_grp'] = U32(nrups)
discard_trts = set(self.full_lt.trts) - keep_trts
if discard_trts and self.oqparam.calculation_mode == 'disaggregation':
self.oqparam.discard_trts = discard_trts
elif discard_trts:
msg = ('No sources for some TRTs: you should set\n'
'discard_trts = %s\nin %s') % (
', '.join(discard_trts), self.oqparam.inputs['job_ini'])
logging.warning(msg)
# to be called after csm.fix_src_offset()
[docs] def store_source_info(self, source_data):
"""
Save (eff_ruptures, num_sites, calc_time) inside the source_info
"""
# called first in preclassical, then called again in classical
first_time = 'source_info' not in self.datastore
if first_time:
source_reader.create_source_info(self.csm, self.datastore.hdf5)
self.csm.update_source_info(source_data)
recs = [tuple(row) for row in self.csm.source_info.values()]
self.datastore['source_info'][:] = numpy.array(
recs, source_reader.source_info_dt)
[docs] def post_process(self):
"""
Run postprocessing function, if any
"""
oq = self.oqparam
if oq.postproc_func:
modname, funcname = oq.postproc_func.rsplit('.', 1)
mod = getattr(postproc, modname)
func = getattr(mod, funcname)
if 'csm' in inspect.getfullargspec(func).args:
if hasattr(self, 'csm'): # already there
csm = self.csm
else: # read the csm from the parent calculation
csm = self.datastore.parent['_csm']
csm.full_lt = self.datastore.parent['full_lt'].init()
oq.postproc_args['csm'] = csm
func(self.datastore, **oq.postproc_args)
[docs]class RiskCalculator(HazardCalculator):
"""
Base class for all risk calculators. A risk calculator must set the
attributes .crmodel, .sitecol, .assetcol, .riskinputs in the
pre_execute phase.
"""
# used only for classical_risk and classical_damage
def _gen_riskinputs(self, dstore):
full_lt = dstore['full_lt'].init()
out = []
asset_df = self.assetcol.to_dframe('site_id')
slices = performance.get_slices(dstore['_rates/sid'][:])
for sid, assets in asset_df.groupby(asset_df.index):
# hcurves, shape (R, N)
getter = getters.PmapGetter(
dstore, full_lt, slices.get(sid, []), self.oqparam.imtls)
for slc in general.split_in_slices(
len(assets), self.oqparam.assets_per_site_limit):
out.append(riskinput.RiskInput(getter, assets[slc]))
if slc.stop - slc.start >= TWO16:
logging.error('There are %d assets on site #%d!',
slc.stop - slc.start, sid)
return out
[docs] def execute(self):
"""
Parallelize on the riskinputs and returns a dictionary of results.
Require a `.core_task` to be defined with signature
(riskinputs, crmodel, param, monitor).
"""
if not hasattr(self, 'riskinputs'): # in the reportwriter
return
ct = self.oqparam.concurrent_tasks or 1
maxw = sum(ri.weight for ri in self.riskinputs) / ct
self.datastore.swmr_on()
smap = parallel.Starmap(
self.core_task.__func__, h5=self.datastore.hdf5)
smap.monitor.save('crmodel', self.crmodel)
for block in general.block_splitter(
self.riskinputs, maxw, get_weight, sort=True):
smap.submit((block, self.oqparam))
return smap.reduce(self.combine, self.acc)
[docs] def combine(self, acc, res):
"""
Combine the outputs assuming acc and res are dictionaries
"""
if res is None:
raise MemoryError('You ran out of memory!')
return acc + res
[docs]def import_gmfs_csv(dstore, oqparam, sitecol):
"""
Import in the datastore a ground motion field CSV file.
:param dstore: the datastore
:param oqparam: an OqParam instance
:param sitecol: the site collection
:returns: event_ids
"""
fname = oqparam.inputs['gmfs']
dtdict = {'sid': U32,
'eid': U32,
'custom_site_id': (numpy.bytes_, 8),
None: F32}
array = hdf5.read_csv(
fname, dtdict,
renamedict=dict(site_id='sid', event_id='eid', rlz_id='rlzi')
).array
names = array.dtype.names # rlz_id, sid, ...
if names[0] == 'rlzi': # backward compatibility
names = names[1:] # discard the field rlzi
names = [n for n in names if n != 'custom_site_id']
imts = [name.lstrip('gmv_')
for name in names if name not in ('sid', 'eid')]
oqparam.hazard_imtls = {imt: [0] for imt in imts}
missing = set(oqparam.imtls) - set(imts)
if missing:
raise ValueError('The calculation needs %s which is missing from %s' %
(', '.join(missing), fname))
imt2idx = {imt: i for i, imt in enumerate(oqparam.imtls)}
arr = numpy.zeros(len(array), oqparam.gmf_data_dt())
for name in names:
if name.startswith('gmv_'):
try:
m = imt2idx[name[4:]]
except KeyError: # the file contains more than enough IMTs
pass
else:
arr[f'gmv_{m}'][:] = array[name]
else:
arr[name] = array[name]
if 'sid' not in names:
# there is a custom_site_id instead
customs = sitecol.complete.custom_site_id
to_sid = {csi: sid for sid, csi in enumerate(customs)}
for csi in numpy.unique(array['custom_site_id']):
ok = array['custom_site_id'] == csi
arr['sid'][ok] = to_sid[csi]
n = len(numpy.unique(arr[['sid', 'eid']]))
if n != len(array):
raise ValueError('Duplicated site_id, event_id in %s' % fname)
# store the events
eids = numpy.unique(array['eid'])
eids.sort()
if eids[0] != 0:
raise ValueError('The event_id must start from zero in %s' % fname)
E = len(eids)
events = numpy.zeros(E, rupture.events_dt)
events['id'] = eids
logging.info('Storing %d events, all relevant', E)
dstore['events'] = events
# store the GMFs
dic = general.group_array(arr, 'sid')
offset = 0
gmvlst = []
for sid in sitecol.complete.sids:
n = len(dic.get(sid, []))
if n:
offset += n
gmvs = dic[sid]
gmvlst.append(gmvs)
data = numpy.concatenate(gmvlst)
data.sort(order='eid')
create_gmf_data(dstore, oqparam.get_primary_imtls(),
oqparam.sec_imts, data=data)
dstore['weights'] = numpy.ones(1)
return eids
def _getset_attrs(oq):
# read effective_time, num_events and imts from oq.inputs['gmfs']
# if the format of the file is old (v3.11) also sets the attributes
# investigation_time and ses_per_logic_tree_path on `oq`
with hdf5.File(oq.inputs['gmfs'], 'r') as f:
attrs = f['gmf_data'].attrs
etime = attrs.get('effective_time')
num_events = attrs.get('num_events')
if etime is None: # engine == 3.11
R = len(f['weights'])
num_events = len(f['events'])
arr = f.getitem('oqparam')
it = arr['par_name'] == b'investigation_time'
it = float(arr[it]['par_value'][0])
oq.investigation_time = it
ses = arr['par_name'] == b'ses_per_logic_tree_path'
ses = int(arr[ses]['par_value'][0])
oq.ses_per_logic_tree_path = ses
etime = it * ses * R
imts = []
for name in arr['par_name']:
if name.startswith(b'hazard_imtls.'):
imts.append(name[13:].decode('utf8'))
else: # engine >= 3.12
imts = attrs['imts'].split()
return dict(effective_time=etime, num_events=num_events, imts=imts)
[docs]def import_gmfs_hdf5(dstore, oqparam):
"""
Import in the datastore a ground motion field HDF5 file.
:param dstore: the datastore
:param oqparam: an OqParam instance
:returns: event_ids
"""
# NB: once we tried to use ExternalLinks to avoid copying the GMFs,
# but: you cannot access an external link if the file it points to is
# already open, therefore you cannot run in parallel two calculations
# starting from the same GMFs; moreover a calc_XXX.hdf5 downloaded
# from the webui would be small but orphan of the GMFs; moreover
# users changing the name of the external file or changing the
# ownership would break calc_XXX.hdf5; therefore we copy everything
# even if bloated (also because of SURA issues having the external
# file under NFS and calc_XXX.hdf5 in the local filesystem)
with hdf5.File(oqparam.inputs['gmfs'], 'r') as f:
f.copy('gmf_data', dstore.hdf5)
dstore['sitecol'] = f['sitecol'] # complete by construction
attrs = _getset_attrs(oqparam)
oqparam.hazard_imtls = {imt: [0] for imt in attrs['imts']}
# store the events
E = attrs['num_events']
events = numpy.zeros(E, rupture.events_dt)
rel = numpy.unique(dstore['gmf_data/eid'])
e = len(rel)
assert E >= e, (E, e)
events['id'] = numpy.concatenate([rel, numpy.arange(E-e) + rel.max() + 1])
logging.info('Storing %d events, %d relevant', E, e)
dstore['events'] = events
n = oqparam.number_of_logic_tree_samples
if n:
dstore['weights'] = numpy.full(n, 1/n)
else:
dstore['weights'] = numpy.ones(1)
return events['id']
[docs]def create_gmf_data(dstore, prim_imts, sec_imts=(), data=None):
"""
Create and possibly populate the datasets in the gmf_data group
"""
oq = dstore['oqparam']
R = dstore['full_lt'].get_num_paths()
M = len(prim_imts)
N = 0 if data is None else data['sid'].max() + 1
items = [('sid', U32 if N == 0 else data['sid']),
('eid', U32 if N == 0 else data['eid'])]
for m in range(M):
col = f'gmv_{m}'
items.append((col, F32 if data is None else data[col]))
for imt in sec_imts:
items.append((str(imt), F32 if N == 0 else data[imt]))
if oq.investigation_time:
eff_time = oq.investigation_time * oq.ses_per_logic_tree_path * R
else:
eff_time = 0
dstore.create_df('gmf_data', items) # not gzipping for speed
dstore.set_attrs('gmf_data', num_events=len(dstore['events']),
imts=' '.join(map(str, prim_imts)),
investigation_time=oq.investigation_time or 0,
effective_time=eff_time)
if data is not None:
_df = pandas.DataFrame(dict(items))
avg_gmf = numpy.zeros((2, N, M + len(sec_imts)), F32)
for sid, df in _df.groupby(_df.sid):
df.pop('eid')
df.pop('sid')
avg_gmf[:, sid] = stats.avg_std(df.to_numpy())
dstore['avg_gmf'] = avg_gmf
[docs]def save_agg_values(dstore, assetcol, lossnames, aggby, maxagg):
"""
Store agg_keys, agg_values.
:returns: the aggkey dictionary key -> tags
"""
if aggby:
aggids, aggtags = assetcol.build_aggids(aggby, maxagg)
logging.info('Storing %d aggregation keys', len(aggids))
agg_keys = [','.join(tags) for tags in aggtags]
dstore['agg_keys'] = numpy.array(agg_keys, hdf5.vstr)
if 'assetcol' not in set(dstore):
dstore['assetcol'] = assetcol
if assetcol.get_value_fields():
dstore['agg_values'] = assetcol.get_agg_values(aggby, maxagg)
[docs]def store_shakemap(calc, sitecol, shakemap, gmf_dict):
"""
Store a ShakeMap array as a gmf_data dataset.
"""
logging.info('Building GMFs')
oq = calc.oqparam
with calc.monitor('building/saving GMFs'):
if oq.site_effects == 'no':
vs30 = None # do not amplify
elif oq.site_effects == 'shakemap':
vs30 = shakemap['vs30']
elif oq.site_effects == 'sitemodel':
vs30 = sitecol.vs30
imts, gmfs = to_gmfs(shakemap, gmf_dict, vs30,
oq.truncation_level,
oq.number_of_ground_motion_fields,
oq.random_seed, oq.imtls)
N, E, M = gmfs.shape
events = numpy.zeros(E, rupture.events_dt)
events['id'] = numpy.arange(E, dtype=U32)
calc.datastore['events'] = events
# convert into an array of dtype gmv_data_dt
lst = [(sitecol.sids[s], ei) + tuple(gmfs[s, ei])
for ei, event in enumerate(events)
for s in numpy.arange(N, dtype=U32)]
oq.hazard_imtls = {str(imt): [0] for imt in imts}
data = numpy.array(lst, oq.gmf_data_dt())
create_gmf_data(calc.datastore, imts, data=data)
[docs]def read_shakemap(calc, 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 = calc.oqparam
imtls = oq.imtls or calc.datastore.parent['oqparam'].imtls
oq.risk_imtls = {imt: list(imls) for imt, imls in imtls.items()}
logging.info('Getting/reducing shakemap')
with calc.monitor('getting/reducing shakemap'):
# for instance for the test case_shakemap the haz_sitecol
# has sids in range(0, 26) while sitecol.sids is
# [8, 9, 10, 11, 13, 15, 16, 17, 18];
# the total assetcol has 26 assets on the total sites
# and the reduced assetcol has 9 assets on the reduced sites
if oq.shakemap_id:
uridict = {'kind': 'usgs_id', 'id': oq.shakemap_id}
elif 'shakemap' in oq.inputs:
uridict = {'kind': 'file_npy', 'fname': oq.inputs['shakemap']}
else:
uridict = oq.shakemap_uri
sitecol, shakemap, discarded = get_sitecol_shakemap(
uridict, oq.risk_imtls, haz_sitecol,
oq.asset_hazard_distance['default'])
if len(discarded):
calc.datastore['discarded'] = discarded
assetcol.reduce_also(sitecol)
logging.info('Extracted %d assets', len(assetcol))
# assemble dictionary to decide on the calculation method for the gmfs
if 'MMI' in oq.imtls:
# calculations with MMI should be executed
if len(oq.imtls) == 1:
# only MMI intensities
if oq.spatial_correlation != 'no' or oq.cross_correlation != 'no':
logging.warning('Calculations with MMI intensities do not '
'support correlation. No correlations '
'are applied.')
gmf_dict = {'kind': 'mmi'}
else:
# there are also other intensities than MMI
raise RuntimeError(
'There are the following intensities in your model: %s '
'Models mixing MMI and other intensities are not supported. '
% ', '.join(oq.imtls.keys()))
else:
# no MMI intensities, calculation with or without correlation
if oq.spatial_correlation != 'no' or oq.cross_correlation != 'no':
# cross correlation and/or spatial correlation after S&H
gmf_dict = {'kind': 'Silva&Horspool',
'spatialcorr': oq.spatial_correlation,
'crosscorr': oq.cross_correlation,
'cholesky_limit': oq.cholesky_limit}
else:
# no correlation required, basic calculation is faster
gmf_dict = {'kind': 'basic'}
store_shakemap(calc, sitecol, shakemap, gmf_dict)
return sitecol, assetcol
[docs]def read_parent_sitecol(oq, dstore):
"""
:returns: the hazard site collection in the parent calculation
"""
with datastore.read(oq.hazard_calculation_id) as parent:
if 'sitecol' in parent:
haz_sitecol = parent['sitecol'].complete
else:
haz_sitecol = readinput.get_site_collection(oq, dstore.hdf5)
if ('amplification' in oq.inputs and
'ampcode' not in haz_sitecol.array.dtype.names):
haz_sitecol.add_col('ampcode', site.ampcode_dt)
return haz_sitecol
[docs]def create_risk_by_event(calc):
"""
Created an empty risk_by_event with keys event_id, agg_id, loss_id
and fields for damages, losses and consequences
"""
oq = calc.oqparam
dstore = calc.datastore
try:
K = len(dstore['agg_keys'])
except KeyError:
K = 0
crmodel = calc.crmodel
if 'risk' in oq.calculation_mode:
fields = [('loss', F32)]
descr = [('event_id', U32), ('agg_id', U32), ('loss_id', U8),
('variance', F32)] + fields
dstore.create_df('risk_by_event', descr, K=K, L=len(oq.loss_types))
else: # damage + consequences
dmgs = ' '.join(crmodel.damage_states[1:])
descr = ([('event_id', U32), ('agg_id', U32), ('loss_id', U8)] +
[(dc, F32) for dc in crmodel.get_dmg_csq()])
dstore.create_df('risk_by_event', descr, K=K,
L=len(oq.loss_types), limit_states=dmgs)
[docs]def run_calc(job_ini, **kw):
"""
Helper to run calculations programmatically.
:param job_ini: path to a job.ini file or dictionary of parameters
:param kw: parameters to override
:returns: a Calculator instance
"""
with logs.init(job_ini) as log:
log.params.update(kw)
calc = calculators(log.get_oqparam(), log.calc_id)
calc.run()
return calc
