# -*- 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 os
import sys
import abc
import pdb
import logging
import operator
import traceback
from datetime import datetime
from shapely import wkt
import numpy
import pandas
from openquake.baselib import (
general, hdf5, datastore, __version__ as engine_version)
from openquake.baselib import parallel, python3compat
from openquake.baselib.performance import Monitor, init_performance
from openquake.hazardlib import InvalidFile, site, stats
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.source import rupture
from openquake.hazardlib.shakemap import get_sitecol_shakemap, to_gmfs
from openquake.risklib import riskinput, riskmodels
from openquake.commonlib import readinput, logictree, util
from openquake.calculators.export import export as exp
from openquake.calculators import getters
get_taxonomy = operator.attrgetter('taxonomy')
get_weight = operator.attrgetter('weight')
get_imt = operator.attrgetter('imt')
calculators = general.CallableDict(operator.attrgetter('calculation_mode'))
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 get_calc(job_ini, calc_id):
"""
Factory function returning a Calculator instance
:param job_ini: path to job.ini file
:param calc_id: calculation ID
"""
return calculators(readinput.get_oqparam(job_ini), calc_id)
# 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 fix_ones(pmap):
"""
Physically, an extremely small intensity measure level can have an
extremely large probability of exceedence, however that probability
cannot be exactly 1 unless the level is exactly 0. Numerically, the
PoE can be 1 and this give issues when calculating the damage (there
is a log(0) in
:class:`openquake.risklib.scientific.annual_frequency_of_exceedence`).
Here we solve the issue by replacing the unphysical probabilities 1
with .9999999999999999 (the float64 closest to 1).
"""
for sid in pmap:
array = pmap[sid].array
array[array == 1.] = .9999999999999999
return pmap
[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
MAXSITES = 1000
CORRELATION_MATRIX_TOO_LARGE = '''\
You have a correlation matrix which is too large: %%d sites > %d.
To avoid that, set a proper `region_grid_spacing` so that your exposure
takes less sites.''' % MAXSITES
[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.datastore = datastore.DataStore(calc_id)
init_performance(self.datastore.hdf5)
self._monitor = Monitor(
'%s.run' % self.__class__.__name__, measuremem=True,
h5=self.datastore)
# NB: using h5=self.datastore.hdf5 would mean losing the performance
# info about Calculator.run since the file will be closed later on
self.oqparam = oqparam
[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)
self.datastore['oqparam'] = self.oqparam # save the updated oqparam
attrs = self.datastore['/'].attrs
attrs['engine_version'] = engine_version
attrs['date'] = datetime.now().isoformat()[:19]
if 'checksum32' not in attrs:
attrs['checksum32'] = readinput.get_checksum32(
self.oqparam, self.datastore.hdf5)
logging.info('Checksum of the input files: %(checksum32)s', attrs)
self.datastore.flush()
[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
"""
with self._monitor:
self._monitor.username = kw.get('username', '')
if concurrent_tasks is None: # use the job.ini parameter
ct = self.oqparam.concurrent_tasks
else: # used the parameter passed in the command-line
ct = concurrent_tasks
if ct == 0: # disable distribution temporarily
oq_distribute = os.environ.get('OQ_DISTRIBUTE')
os.environ['OQ_DISTRIBUTE'] = 'no'
if ct != self.oqparam.concurrent_tasks:
# save the used concurrent_tasks
self.oqparam.concurrent_tasks = ct
self.save_params(**kw)
try:
if pre_execute:
self.pre_execute()
self.result = self.execute()
if self.result is not None:
self.post_execute(self.result)
self.export(kw.get('exports', ''))
except Exception:
if kw.get('pdb'): # post-mortem debug
tb = sys.exc_info()[2]
traceback.print_tb(tb)
pdb.post_mortem(tb)
else:
logging.critical('', exc_info=True)
raise
finally:
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.pmap = None
readinput.exposure = None
readinput.gmfs = None
readinput.eids = None
readinput.smlt_cache.clear()
readinput.gsim_lt_cache.clear()
# remove temporary hdf5 file, if any
if os.path.exists(self.datastore.tempname) and remove:
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):
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')
for fmt in fmts:
if not fmt:
continue
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 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
"""
[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 total number of sites
"""
return len(self.sitecol.complete) if self.sitecol else None
@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):
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 self.oqparam.pointsource_distance is None
and 'classical' in self.oqparam.calculation_mode):
logging.info(
'You are not using the pointsource_distance approximation:\n'
'https://docs.openquake.org/oq-engine/advanced/common-mistakes.html#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
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:
if not oq.inputs['gmfs'].endswith('.csv'):
raise NotImplementedError(
'Importer for %s' % oq.inputs['gmfs'])
E = len(import_gmfs(self.datastore, oq,
self.sitecol.complete.sids))
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.load_crmodel() # must be after get_site_collection
self.read_exposure(haz_sitecol) # define .assets_by_site
poes = fix_ones(readinput.pmap).array(len(haz_sitecol))
self.datastore['_poes'] = poes.transpose(2, 0, 1) # shape GNL
self.datastore['assetcol'] = self.assetcol
self.datastore['full_lt'] = fake = logictree.FullLogicTree.fake()
self.datastore['rlzs_by_g'] = sum(
fake.get_rlzs_by_grp().values(), [])
with hdf5.File(self.datastore.tempname, 'a') as t:
t['oqparam'] = oq
self.realizations = fake.get_realizations()
self.save_crmodel()
self.datastore.swmr_on()
elif oq.hazard_calculation_id:
parent = util.read(oq.hazard_calculation_id)
self.check_precalc(parent['oqparam'].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)}
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))
missing_imts = set(oq.risk_imtls) - set(oqp.imtls)
if 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.pre_checks = lambda: self.__class__.pre_checks(calc)
calc.run(remove=False)
for name in ('csm param sitecol assetcol crmodel realizations '
'policy_name policy_dict 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 'full_lt' in self.datastore:
full_lt = self.datastore['full_lt']
self.realizations = full_lt.get_realizations()
if oq.hazard_calculation_id and 'gsim_logic_tree' in oq.inputs:
# redefine the realizations by reading the weights from the
# gsim_logic_tree_file that could be different from the parent
full_lt.gsim_lt = logictree.GsimLogicTree(
oq.inputs['gsim_logic_tree'], set(full_lt.trts))
elif hasattr(self, 'csm'):
self.check_floating_spinning()
self.realizations = self.csm.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
"""
try:
return self.csm.full_lt.get_num_rlzs()
except AttributeError: # no self.csm
return self.datastore['full_lt'].get_num_rlzs()
[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
with self.monitor('reading exposure'):
self.sitecol, self.assetcol, discarded = (
readinput.get_sitecol_assetcol(
oq, haz_sitecol, self.crmodel.loss_types))
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))
self.policy_name = ''
self.policy_dict = {}
if oq.inputs.get('insurance'):
k, v = zip(*oq.inputs['insurance'].items())
self.load_insurance_data(k, v)
return readinput.exposure
[docs] def load_insurance_data(self, ins_types, ins_files):
"""
Read the insurance files and populate the policy_dict
"""
for loss_type, fname in zip(ins_types, ins_files):
array = hdf5.read_csv(
fname, {'insurance_limit': float, 'deductible': float,
None: object}).array
policy_name = array.dtype.names[0]
policy_idx = getattr(self.assetcol.tagcol, policy_name + '_idx')
insurance = numpy.zeros((len(policy_idx), 2))
for pol, ded, lim in array[
[policy_name, 'deductible', 'insurance_limit']]:
insurance[policy_idx[pol]] = ded, lim
self.policy_dict[loss_type] = insurance
if self.policy_name and policy_name != self.policy_name:
raise ValueError(
'The file %s contains %s as policy field, but we were '
'expecting %s' % (fname, policy_name, self.policy_name))
else:
self.policy_name = policy_name
[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
logging.info('Reading the risk model if present')
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_dframe('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 oq.ground_motion_fields):
raise InvalidFile('There are no intensity measure types in %s' %
oq.inputs['job_ini'])
if oq.hazard_calculation_id:
with util.read(oq.hazard_calculation_id) as dstore:
haz_sitecol = dstore['sitecol'].complete
if ('amplification' in oq.inputs and
'ampcode' not in haz_sitecol.array.dtype.names):
haz_sitecol.add_col('ampcode', site.ampcode_dt)
else:
haz_sitecol = readinput.get_site_collection(oq, self.datastore)
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:
exposure = self.read_exposure(haz_sitecol)
self.datastore['assetcol'] = self.assetcol
self.datastore['cost_calculator'] = exposure.cost_calculator
if hasattr(readinput.exposure, 'exposures'):
self.datastore['assetcol/exposures'] = (
numpy.array(exposure.exposures, hdf5.vstr))
elif 'assetcol' in self.datastore.parent:
assetcol = self.datastore.parent['assetcol']
if oq.region:
region = wkt.loads(oq.region)
self.sitecol = haz_sitecol.within(region)
if oq.shakemap_id or 'shakemap' in oq.inputs:
self.sitecol, self.assetcol = self.read_shakemap(
haz_sitecol, assetcol)
self.datastore['sitecol'] = self.sitecol
self.datastore['assetcol'] = self.assetcol
logging.info('Extracted %d/%d assets',
len(self.assetcol), len(assetcol))
nsites = len(self.sitecol)
if (oq.spatial_correlation != 'no' and
nsites > MAXSITES): # hard-coded, heuristic
raise ValueError(CORRELATION_MATRIX_TOO_LARGE % nsites)
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
else: # no exposure
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 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':
tmap = readinput.taxonomy_mapping(
self.oqparam, self.assetcol.tagcol.taxonomy)
self.crmodel.tmap = tmap
taxonomies = set()
for ln in oq.loss_names:
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 taxonomies %s '
'which are not in the risk model' % missing)
if len(self.crmodel.taxonomies) > len(taxonomies):
logging.info('Reducing risk model from %d to %d taxonomies',
len(self.crmodel.taxonomies), len(taxonomies))
self.crmodel = self.crmodel.reduce(taxonomies)
self.crmodel.tmap = tmap
self.crmodel.reduce_cons_model(self.assetcol.tagcol)
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)
self.sitecol.complete.assoc(sm, assoc_dist)
self.datastore['sitecol'] = self.sitecol
# store amplification functions if any
self.af = None
if 'amplification' in oq.inputs:
logging.info('Reading %s', oq.inputs['amplification'])
df = readinput.get_amplification(oq)
check_amplification(df, self.sitecol)
self.amplifier = Amplifier(oq.imtls, df, oq.soil_intensities)
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)
self.amplifier = None
else:
self.amplifier = None
# manage secondary perils
sec_perils = oq.get_sec_perils()
for sp in sec_perils:
sp.prepare(self.sitecol) # add columns as needed
mal = {lt: getdefault(oq.minimum_asset_loss, lt)
for lt in oq.loss_names}
if mal:
logging.info('minimum_asset_loss=%s', mal)
self.param = dict(individual_curves=oq.individual_curves,
ps_grid_spacing=oq.ps_grid_spacing,
collapse_level=oq.collapse_level,
split_sources=oq.split_sources,
avg_losses=oq.avg_losses,
amplifier=self.amplifier,
sec_perils=sec_perils,
ses_seed=oq.ses_seed,
minimum_asset_loss=mal)
# compute exposure stats
if hasattr(self, 'assetcol'):
save_agg_values(
self.datastore, self.assetcol, oq.loss_names, oq.aggregate_by)
[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
"""
oq = self.oqparam
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?')
self.datastore['full_lt'] = self.full_lt
else: # scenario
self.full_lt = self.datastore['full_lt']
R = self.R
logging.info('There are %d realization(s)', R)
if oq.imtls:
self.datastore['weights'] = arr = build_weights(self.realizations)
self.datastore.set_attrs('weights', nbytes=arr.nbytes)
if ('event_based' in oq.calculation_mode and R >= TWO16
or R >= TWO32):
raise ValueError(
'The logic tree has too many realizations (%d), use sampling '
'instead' % R)
elif R > 10000:
logging.warning(
'The logic tree has %d realizations(!), please consider '
'sampling it', R)
# check for gsim logic tree reduction
discard_trts = []
for trt in self.full_lt.gsim_lt.values:
if rel_ruptures.get(trt, 0) == 0:
discard_trts.append(trt)
if (discard_trts and 'scenario' not in oq.calculation_mode
and 'event_based' not in oq.calculation_mode
and 'ebrisk' not in oq.calculation_mode
and not oq.is_ucerf()):
msg = ('No sources for some TRTs: you should set\n'
'discard_trts = %s\nin %s') % (', '.join(discard_trts),
oq.inputs['job_ini'])
logging.warning(msg)
[docs] def store_source_info(self, calc_times, nsites=False):
"""
Save (eff_ruptures, num_sites, calc_time) inside the source_info
"""
self.csm.update_source_info(calc_times, nsites)
recs = [tuple(row) for row in self.csm.source_info.values()]
hdf5.extend(self.datastore['source_info'],
numpy.array(recs, readinput.source_info_dt))
return [rec[0] for rec in recs] # return source_ids
[docs] def post_process(self):
"""For compatibility with the engine"""
[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.
"""
[docs] def read_shakemap(self, haz_sitecol, assetcol):
"""
Enabled only if there is a shakemap_id parameter in the job.ini.
Download, unzip, parse USGS shakemap files and build a corresponding
set of GMFs which are then filtered with the hazard site collection
and stored in the datastore.
"""
oq = self.oqparam
E = oq.number_of_ground_motion_fields
oq.risk_imtls = oq.imtls or self.datastore.parent['oqparam'].imtls
logging.info('Getting/reducing shakemap')
with self.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
smap = oq.shakemap_id if oq.shakemap_id else numpy.load(
oq.inputs['shakemap'])
sitecol, shakemap, discarded = get_sitecol_shakemap(
smap, oq.imtls, haz_sitecol,
oq.asset_hazard_distance['default'],
oq.discard_assets)
if len(discarded):
self.datastore['discarded'] = discarded
assetcol.reduce_also(sitecol)
logging.info('Building GMFs')
with self.monitor('building/saving GMFs'):
imts, gmfs = to_gmfs(
shakemap, oq.spatial_correlation, oq.cross_correlation,
oq.site_effects, oq.truncation_level, E, oq.random_seed,
oq.imtls)
N, E, M = gmfs.shape
events = numpy.zeros(E, rupture.events_dt)
events['id'] = numpy.arange(E, dtype=U32)
self.datastore['events'] = events
# convert into an array of dtype gmv_data_dt
lst = [(sitecol.sids[s], ei) + tuple(gmfs[s, ei])
for s in numpy.arange(N, dtype=U32)
for ei, event in enumerate(events)]
oq.hazard_imtls = {imt: [0] for imt in imts}
data = numpy.array(lst, oq.gmf_data_dt())
create_gmf_data(self.datastore, len(imts), data=data)
return sitecol, assetcol
def _gen_riskinputs_gmf(self, dstore):
out = []
if 'gmf_data' not in dstore: # needed for case_shakemap
dstore.close()
dstore = self.datastore
if 'gmf_data' not in dstore:
raise InvalidFile('No gmf_data: did you forget gmfs_csv in %s?'
% self.oqparam.inputs['job_ini'])
rlzs = dstore['events']['rlz_id']
gmf_df = dstore.read_df('gmf_data', 'sid')
logging.info('Grouping the GMFs by site ID')
by_sid = dict(list(gmf_df.groupby(gmf_df.index)))
for sid, assets in enumerate(self.assetcol.assets_by_site()):
if len(assets) == 0:
continue
try:
df = by_sid[sid]
except KeyError:
getter = getters.ZeroGetter(sid, rlzs, self.R)
else:
df['rlz'] = rlzs[df.eid.to_numpy()]
getter = getters.GmfDataGetter(sid, df, len(rlzs), self.R)
if len(dstore['gmf_data/eid']) == 0:
raise RuntimeError(
'There are no GMFs available: perhaps you did set '
'ground_motion_fields=False or a large minimum_intensity')
for block in general.block_splitter(
assets, self.oqparam.assets_per_site_limit):
out.append(riskinput.RiskInput(getter, numpy.array(block)))
if len(block) >= TWO16:
logging.error('There are %d assets on site #%d!',
len(block), sid)
return out
def _gen_riskinputs_poe(self, dstore):
out = []
assets_by_site = self.assetcol.assets_by_site()
for sid, assets in enumerate(assets_by_site):
if len(assets) == 0:
continue
# hcurves, shape (R, N)
ws = [rlz.weight for rlz in self.realizations]
getter = getters.PmapGetter(dstore, ws, [sid], self.oqparam.imtls)
for block in general.block_splitter(
assets, self.oqparam.assets_per_site_limit):
out.append(riskinput.RiskInput(getter, numpy.array(block)))
if len(block) >= TWO16:
logging.error('There are %d assets on site #%d!',
len(block), 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):
for ri in block:
# we must use eager reading for performance reasons:
# concurrent reading on the workers would be extra-slow;
# also, I could not get lazy reading to work with
# the SWMR mode for event_based_risk
if not isinstance(ri.hazard_getter, getters.PmapGetter):
ri.hazard_getter.init()
smap.submit((block, self.param))
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(dstore, oqparam, sids):
"""
Import in the datastore a ground motion field CSV file.
:param dstore: the datastore
:param oqparam: an OqParam instance
:param sids: the complete site IDs
:returns: event_ids
"""
fname = oqparam.inputs['gmfs']
array = hdf5.read_csv(fname, {'sid': U32, 'eid': U32, None: F32},
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
imts = [name.lstrip('gmv_') for name in names[2:]]
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]
n = len(numpy.unique(array[['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
dstore['events'] = events
# store the GMFs
dic = general.group_array(arr, 'sid')
offset = 0
gmvlst = []
for sid in sids:
n = len(dic.get(sid, []))
if n:
offset += n
gmvs = dic[sid]
gmvlst.append(gmvs)
data = numpy.concatenate(gmvlst)
create_gmf_data(dstore, len(oqparam.get_primary_imtls()),
oqparam.get_sec_imts(), data=data)
dstore['weights'] = numpy.ones(1)
return eids
[docs]def create_gmf_data(dstore, M, sec_imts=(), data=None):
"""
Create and possibly populate the datasets in the gmf_data group
"""
n = 0 if data is None else len(data['sid'])
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]))
dstore.create_dframe('gmf_data', items, 'gzip')
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, tagnames):
"""
Store agg_keys, agg_values.
:returns: the aggkey dictionary key -> tags
"""
lst = []
if tagnames:
aggkey = assetcol.tagcol.get_aggkey(tagnames)
logging.info('Storing %d aggregation keys', len(aggkey))
dt = [(name + '_', U16) for name in tagnames] + [
(name, hdf5.vstr) for name in tagnames]
kvs = []
for key, val in aggkey.items():
val = tuple(python3compat.decode(val))
kvs.append(key + val)
lst.append(' '.join(val))
dstore['agg_keys'] = numpy.array(kvs, dt)
lst.append('*total*')
loss_names = dstore['oqparam'].loss_names
dstore['agg_values'] = assetcol.get_agg_values(lossnames, tagnames)
dstore.set_shape_descr('agg_values', aggregation=lst, loss_type=loss_names)
return aggkey if tagnames else {}