# -*- 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 itertools
import warnings
import logging
from unittest.mock import Mock
import numpy
from openquake.baselib import performance, parallel, hdf5
from openquake.hazardlib.source import rupture
from openquake.hazardlib import probability_map
from openquake.hazardlib.source.rupture import EBRupture, events_dt
from openquake.commonlib import util
TWO16 = 2 ** 16
TWO32 = numpy.float64(2 ** 32)
MAX_NBYTES = 1024**3
MAX_INT = 2 ** 31 - 1 # this is used in the random number generator
# in this way even on 32 bit machines Python will not have to convert
# the generated seed into a long integer
U8 = numpy.uint8
U16 = numpy.uint16
I32 = numpy.int32
U32 = numpy.uint32
F32 = numpy.float32
U64 = numpy.uint64
F64 = numpy.float64
code2cls = rupture.BaseRupture.init()
# ############## utilities for the classical calculator ############### #
# used only in the view global_hcurves
[docs]def convert_to_array(pmap, nsites, imtls, inner_idx=0):
"""
Convert the probability map into a composite array with header
of the form PGA-0.1, PGA-0.2 ...
:param pmap: probability map
:param nsites: total number of sites
:param imtls: a DictArray with IMT and levels
:returns: a composite array of lenght nsites
"""
lst = []
# build the export dtype, of the form PGA-0.1, PGA-0.2 ...
for imt, imls in imtls.items():
for iml in imls:
lst.append(('%s-%.3f' % (imt, iml), F32))
curves = numpy.zeros(nsites, numpy.dtype(lst))
for sid, pcurve in pmap.items():
curve = curves[sid]
idx = 0
for imt, imls in imtls.items():
for iml in imls:
curve['%s-%.3f' % (imt, iml)] = pcurve.array[idx, inner_idx]
idx += 1
return curves
[docs]def get_mean_curve(dstore, imt, site_id=0):
"""
Extract the mean hazard curve from the datastore for the first site.
"""
if 'hcurves-stats' in dstore: # shape (N, S, M, L1)
arr = dstore.sel('hcurves-stats', stat='mean', imt=imt)
else: # there is only 1 realization
arr = dstore.sel('hcurves-rlzs', rlz_id=0, imt=imt)
return arr[site_id, 0, 0]
[docs]def get_poe_from_mean_curve(dstore, imt, iml, site_id=0):
"""
Extract the poe corresponding to the given iml by looking at the mean
curve for the given imt. `iml` can also be an array.
"""
imls = dstore['oqparam'].imtls[imt]
mean_curve = get_mean_curve(dstore, imt, site_id)
return numpy.interp(imls, mean_curve)[iml]
# ######################### hazard maps ################################### #
# cutoff value for the poe
EPSILON = 1E-30
[docs]def compute_hazard_maps(curves, imls, poes):
"""
Given a set of hazard curve poes, interpolate hazard maps at the specified
``poes``.
:param curves:
Array of floats of shape N x L. Each row represents a curve, where the
values in the row are the PoEs (Probabilities of Exceedance)
corresponding to the ``imls``.
Each curve corresponds to a geographical location.
:param imls:
Intensity Measure Levels associated with these hazard ``curves``. Type
should be an array-like of floats.
:param poes:
Value(s) on which to interpolate a hazard map from the input
``curves``. Can be an array-like or scalar value (for a single PoE).
:returns:
An array of shape N x P, where N is the number of curves and P the
number of poes.
"""
log_poes = numpy.log(poes)
if len(log_poes.shape) == 0:
# `poes` was passed in as a scalar;
# convert it to 1D array of 1 element
log_poes = log_poes.reshape(1)
P = len(log_poes)
if len(curves.shape) == 1:
# `curves` was passed as 1 dimensional array, there is a single site
curves = curves.reshape((1,) + curves.shape) # 1 x L
N, L = curves.shape # number of levels
if L != len(imls):
raise ValueError('The curves have %d levels, %d were passed' %
(L, len(imls)))
hmap = numpy.zeros((N, P))
with warnings.catch_warnings():
warnings.simplefilter("ignore")
# avoid RuntimeWarning: divide by zero for zero levels
imls = numpy.log(numpy.array(imls[::-1]))
for n, curve in enumerate(curves):
# the hazard curve, having replaced the too small poes with EPSILON
log_curve = numpy.log([max(poe, EPSILON) for poe in curve[::-1]])
for p, log_poe in enumerate(log_poes):
if log_poe > log_curve[-1]:
# special case when the interpolation poe is bigger than the
# maximum, i.e the iml must be smaller than the minimum;
# extrapolate the iml to zero as per
# https://bugs.launchpad.net/oq-engine/+bug/1292093;
# then the hmap goes automatically to zero
pass
else:
# exp-log interpolation, to reduce numerical errors
# see https://bugs.launchpad.net/oq-engine/+bug/1252770
hmap[n, p] = numpy.exp(numpy.interp(log_poe, log_curve, imls))
return hmap
[docs]def get_lvl(hcurve, imls, poe):
"""
:param hcurve: a hazard curve, i.e. array of L1 PoEs
:param imls: L1 intensity measure levels
:returns: index of the intensity measure level associated to the poe
>>> imls = numpy.array([.1, .2, .3, .4])
>>> hcurve = numpy.array([1., .99, .90, .8])
>>> get_lvl(hcurve, imls, 1)
0
>>> get_lvl(hcurve, imls, .99)
1
>>> get_lvl(hcurve, imls, .91)
2
>>> get_lvl(hcurve, imls, .8)
3
"""
[[iml]] = compute_hazard_maps(hcurve, imls, poe)
iml -= 1E-10 # small buffer
return numpy.searchsorted(imls, iml)
# ######################### GMF->curves #################################### #
# NB (MS): the approach used here will not work for non-poissonian models
def _gmvs_to_haz_curve(gmvs, imls, ses_per_logic_tree_path):
"""
Given a set of ground motion values (``gmvs``) and intensity measure levels
(``imls``), compute hazard curve probabilities of exceedance.
:param gmvs:
Am array of ground motion values, as floats.
:param imls:
A list of intensity measure levels, as floats.
:param ses_per_logic_tree_path:
Number of stochastic event sets: the larger, the best convergency
:returns:
Numpy array of PoEs (probabilities of exceedance).
"""
# convert to numpy array and redimension so that it can be broadcast with
# the gmvs for computing PoE values; there is a gmv for each rupture
# here is an example: imls = [0.03, 0.04, 0.05], gmvs=[0.04750576]
# => num_exceeding = [1, 1, 0] coming from 0.04750576 > [0.03, 0.04, 0.05]
imls = numpy.array(imls).reshape((len(imls), 1))
num_exceeding = numpy.sum(gmvs >= imls, axis=1)
poes = 1 - numpy.exp(- num_exceeding / ses_per_logic_tree_path)
return poes
[docs]def gmvs_to_poes(df, imtls, ses_per_logic_tree_path):
"""
:param df: a DataFrame with fields gmv_0, .. gmv_{M-1}
:param imtls: a dictionary imt -> imls with M IMTs and L levels
:param ses_per_logic_tree_path: a positive integer
:returns: an array of PoEs of shape (M, L)
"""
M = len(imtls)
L = len(imtls[next(iter(imtls))])
arr = numpy.zeros((M, L))
for m, imt in enumerate(imtls):
arr[m] = _gmvs_to_haz_curve(
df[f'gmv_{m}'].to_numpy(), imtls[imt], ses_per_logic_tree_path)
return arr
# ################## utilities for classical calculators ################ #
[docs]def make_hmaps(pmaps, imtls, poes):
"""
Compute the hazard maps associated to the passed probability maps.
:param pmaps: a list of Pmaps of shape (N, M, L1)
:param imtls: DictArray with M intensity measure types
:param poes: P PoEs where to compute the maps
:returns: a list of Pmaps with size (N, M, P)
"""
M, P = len(imtls), len(poes)
hmaps = []
for pmap in pmaps:
hmap = probability_map.ProbabilityMap(pmaps[0].sids, M, P).fill(0)
for m, imt in enumerate(imtls):
data = compute_hazard_maps(
pmap.array[:, m], imtls[imt], poes) # (N, P)
for idx, imls in enumerate(data):
for p, iml in enumerate(imls):
hmap.array[idx, m, p] = iml
hmaps.append(hmap)
return hmaps
[docs]def make_uhs(hmap, info):
"""
Make Uniform Hazard Spectra curves for each location.
:param hmap:
array of shape (N, M, P)
:param info:
a dictionary with keys poes, imtls, uhs_dt
:returns:
a composite array containing uniform hazard spectra
"""
uhs = numpy.zeros(len(hmap), info['uhs_dt'])
for p, poe in enumerate(info['poes']):
for m, imt in enumerate(info['imtls']):
if imt.startswith(('PGA', 'SA')):
uhs['%.6f' % poe][imt] = hmap[:, m, p]
return uhs
[docs]class RuptureImporter(object):
"""
Import an array of ruptures correctly, i.e. by populating the datasets
ruptures, rupgeoms, events.
"""
def __init__(self, dstore):
self.datastore = dstore
self.oqparam = dstore['oqparam']
try:
self.N = len(dstore['sitecol'])
except KeyError: # missing sitecol
self.N = 0
[docs] def get_eid_rlz(self, proxies, rlzs_by_gsim):
"""
:returns: a composite array with the associations eid->rlz
"""
eid_rlz = []
for rup in proxies:
ebr = EBRupture(
Mock(seed=rup['seed']), rup['source_id'],
rup['trt_smr'], rup['n_occ'], e0=rup['e0'],
scenario='scenario' in self.oqparam.calculation_mode)
for rlz_id, eids in ebr.get_eids_by_rlz(rlzs_by_gsim).items():
for eid in eids:
eid_rlz.append((eid, rup['id'], rlz_id))
return numpy.array(eid_rlz, events_dt)
[docs] def import_rups_events(self, rup_array, get_rupture_getters):
"""
Import an array of ruptures and store the associated events.
:returns: (number of imported ruptures, number of imported events)
"""
oq = self.oqparam
logging.info('Reordering the ruptures and storing the events')
# order the ruptures by seed
rup_array.sort(order='seed')
nr = len(rup_array)
seeds, counts = numpy.unique(rup_array['seed'], return_counts=True)
if len(seeds) != nr:
dupl = seeds[counts > 1]
logging.debug('The following %d rupture seeds are duplicated: %s',
len(dupl), dupl)
rup_array['geom_id'] = rup_array['id']
rup_array['id'] = numpy.arange(nr)
if len(self.datastore['ruptures']):
self.datastore['ruptures'].resize((0,))
hdf5.extend(self.datastore['ruptures'], rup_array)
rgetters = get_rupture_getters( # fast
self.datastore, self.oqparam.concurrent_tasks)
self._save_events(rup_array, rgetters)
nr, ne = len(rup_array), rup_array['n_occ'].sum()
if oq.investigation_time:
eff_time = (oq.investigation_time * oq.ses_per_logic_tree_path *
len(self.datastore['weights']))
mag = numpy.average(rup_array['mag'], weights=rup_array['n_occ'])
logging.info('There are {:_d} events and {:_d} ruptures in {:_d} '
'years (mean mag={:.2f})'.format(
ne, nr, int(eff_time), mag))
def _save_events(self, rup_array, rgetters):
# this is very fast compared to saving the ruptures
E = rup_array['n_occ'].sum()
self.check_overflow(E) # check the number of events
events = numpy.zeros(E, rupture.events_dt)
# when computing the events all ruptures must be considered,
# including the ones far away that will be discarded later on
# build the associations eid -> rlz sequentially or in parallel
# this is very fast: I saw 30 million events associated in 1 minute!
iterargs = ((rg.proxies, rg.rlzs_by_gsim) for rg in rgetters)
if len(events) < 1E5:
it = itertools.starmap(self.get_eid_rlz, iterargs)
else:
it = parallel.Starmap(
self.get_eid_rlz, iterargs, progress=logging.debug)
i = 0
for eid_rlz in it:
for er in eid_rlz:
events[i] = er
i += 1
if i >= TWO32:
raise ValueError('There are more than %d events!' % i)
events.sort(order='rup_id') # fast too
# sanity check
n_unique_events = len(numpy.unique(events[['id', 'rup_id']]))
assert n_unique_events == len(events), (n_unique_events, len(events))
events['id'] = numpy.arange(len(events))
# set event year and event ses starting from 1
nses = self.oqparam.ses_per_logic_tree_path
extra = numpy.zeros(len(events), [('year', U32), ('ses_id', U32)])
numpy.random.seed(self.oqparam.ses_seed)
if self.oqparam.investigation_time:
itime = int(self.oqparam.investigation_time)
extra['year'] = numpy.random.choice(itime, len(events)) + 1
extra['ses_id'] = numpy.random.choice(nses, len(events)) + 1
self.datastore['events'] = util.compose_arrays(events, extra)
cumsum = self.datastore['ruptures']['n_occ'].cumsum()
rup_array['e0'][1:] = cumsum[:-1]
self.datastore['ruptures']['e0'] = rup_array['e0']
[docs] def check_overflow(self, E):
"""
Raise a ValueError if the number of IMTs is larger than 256 or the
number of events is larger than 4,294,967,296. The limits
are due to the numpy dtype used to store the GMFs
(gmv_dt). There also a limit of `max_potential_gmfs` on the
number of sites times the number of events, to avoid producing
too many GMFs. In that case split the calculation or be
smarter.
"""
oq = self.oqparam
if len(oq.imtls) > 256:
raise ValueError('The event_based calculator is restricted '
'to 256 imts, got %d' % len(oq.imtls))
if E > TWO32:
raise ValueError('The event_based calculator is restricted '
'to 2^32 events, got %d' % E)
max_ = dict(sites=TWO32, events=TWO32, imts=2**8)
num_ = dict(events=E, imts=len(self.oqparam.imtls))
num_['sites'] = self.N
if oq.calculation_mode == 'event_based' and oq.ground_motion_fields:
if self.N * E > oq.max_potential_gmfs:
raise ValueError(
'A GMF calculation with {:_d} sites and {:_d} events is '
'forbidden unless you raise `max_potential_gmfs` to {:_d}'.
format(self.N, int(E), int(self.N * E)))
for var in num_:
if num_[var] > max_[var]:
raise ValueError(
'The %s calculator is restricted to %d %s, got %d' %
(oq.calculation_mode, max_[var], var, num_[var]))
##############################################################
# logic for building the GMF slices used in event_based_risk #
##############################################################
SLICE_BY_EVENT_NSITES = 1000
slice_dt = numpy.dtype([('start', int), ('stop', int), ('eid', U32)])
[docs]def build_slice_by_event(eids, offset=0):
arr = performance.idx_start_stop(eids)
sbe = numpy.zeros(len(arr), slice_dt)
sbe['eid'] = arr[:, 0]
sbe['start'] = arr[:, 1] + offset
sbe['stop'] = arr[:, 2] + offset
return sbe
[docs]def starmap_from_gmfs(task_func, oq, dstore):
"""
:param task_func: function or generator with signature (gmf_df, oq, dstore)
:param oq: an OqParam instance
:param dstore: DataStore instance where the GMFs are stored
:returns: a Starmap object used for event based calculations
"""
data = dstore['gmf_data']
try:
sbe = data['slice_by_event'][:]
except KeyError:
sbe = build_slice_by_event(data['eid'][:])
nrows = sbe[-1]['stop'] - sbe[0]['start']
maxweight = numpy.ceil(nrows / (oq.concurrent_tasks or 1))
dstore.swmr_on() # before the Starmap
smap = parallel.Starmap.apply(
task_func, (sbe, oq, dstore),
weight=lambda rec: rec['stop']-rec['start'],
maxweight=numpy.clip(maxweight, 1000, 10_000_000),
h5=dstore.hdf5)
return smap