# -*- coding: utf-8 -*-
# vim: tabstop=4 shiftwidth=4 softtabstop=4
#
# Copyright (C) 2012-2017 GEM Foundation
#
# OpenQuake is free software: you can redistribute it and/or modify it
# under the terms of the GNU Affero General Public License as published
# by the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# OpenQuake is distributed in the hope that it will be useful,
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# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Affero General Public License for more details.
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# along with OpenQuake. If not, see <http://www.gnu.org/licenses/>.
""":mod:`openquake.hazardlib.calc.hazard_curve` implements
:func:`calc_hazard_curves`. Here is an example of a classical PSHA
parallel calculator computing the hazard curves per each realization in less
than 20 lines of code:
.. code-block:: python
import sys
import logging
from openquake.baselib import parallel
from openquake.hazardlib.calc.filters import SourceFilter
from openquake.hazardlib.calc.hazard_curve import calc_hazard_curves
from openquake.commonlib import readinput
def main(job_ini):
logging.basicConfig(level=logging.INFO)
oq = readinput.get_oqparam(job_ini)
sitecol = readinput.get_site_collection(oq)
src_filter = SourceFilter(sitecol, oq.maximum_distance)
csm = readinput.get_composite_source_model(oq).filter(src_filter)
rlzs_assoc = csm.info.get_rlzs_assoc()
sources = csm.get_sources()
for rlzno, gsim_by_trt in enumerate(rlzs_assoc.gsim_by_trt):
hcurves = calc_hazard_curves(sources, src_filter, oq.imtls,
gsim_by_trt, oq.truncation_level,
parallel.apply)
print('rlzno=%d, hcurves=%r' % (rlzno, hcurves))
if __name__ == '__main__':
main(sys.argv[1]) # path to a job.ini file
NB: the implementation in the engine is smarter and more
efficient. Here we start a parallel computation per each realization,
the engine manages all the realizations at once.
"""
from __future__ import division
import sys
import time
import operator
import collections
import numpy
from openquake.baselib.python3compat import raise_, zip
from openquake.baselib.performance import Monitor
from openquake.baselib.general import DictArray, groupby
from openquake.baselib.parallel import Sequential
from openquake.hazardlib.probability_map import ProbabilityMap
from openquake.hazardlib.gsim.base import ContextMaker, FarAwayRupture
from openquake.hazardlib.gsim.base import GroundShakingIntensityModel
from openquake.hazardlib.calc.filters import SourceFilter
from openquake.hazardlib.imt import from_string
from openquake.hazardlib.sourceconverter import SourceGroup
[docs]def zero_curves(num_sites, imtls):
"""
:param num_sites: the number of sites
:param imtls: the intensity measure levels dictionary
:returns: an array of zero curves with length num_sites
"""
# numpy dtype for the hazard curves
imt_dt = numpy.dtype([(imt, float, 1 if imls is None else len(imls))
for imt, imls in imtls.items()])
return numpy.zeros(num_sites, imt_dt)
[docs]def rupture_weight_pairs(src):
"""
Generator yielding (rupture, weight) for each rupture in the source
"""
if hasattr(src, 'weights'):
for pair in zip(src.iter_ruptures(), src.weights):
yield pair
weight = 1. / (src.num_ruptures or src.count_ruptures())
for rup in src.iter_ruptures():
yield rup, weight
# old version working only for independent sources
[docs]def calc_hazard_curves(
sources, source_site_filter, imtls, gsim_by_trt,
truncation_level=None, apply=Sequential.apply):
"""
Compute hazard curves on a list of sites, given a set of seismic sources
and a set of ground shaking intensity models (one per tectonic region type
considered in the seismic sources).
Probability of ground motion exceedance is computed using the following
formula ::
P(X≥x|T) = 1 - ∏ ∏ Prup_ij(X<x|T)
where ``P(X≥x|T)`` is the probability that the ground motion parameter
``X`` is exceeding level ``x`` one or more times in a time span ``T``, and
``Prup_ij(X<x|T)`` is the probability that the j-th rupture of the i-th
source is not producing any ground motion exceedance in time span ``T``.
The first product ``∏`` is done over sources, while the second one is done
over ruptures in a source.
The above formula computes the probability of having at least one ground
motion exceedance in a time span as 1 minus the probability that none of
the ruptures in none of the sources is causing a ground motion exceedance
in the same time span. The basic assumption is that seismic sources are
independent, and ruptures in a seismic source are also independent.
:param sources:
A sequence of seismic sources objects (instances of subclasses
of :class:`~openquake.hazardlib.source.base.BaseSeismicSource`).
:param source_site_filter:
A source filter over the site collection or the site collection itself
:param imtls:
Dictionary mapping intensity measure type strings
to lists of intensity measure levels.
:param gsim_by_trt:
Dictionary mapping tectonic region types (members
of :class:`openquake.hazardlib.const.TRT`) to
:class:`~openquake.hazardlib.gsim.base.GMPE` or
:class:`~openquake.hazardlib.gsim.base.IPE` objects.
:param truncation_level:
Float, number of standard deviations for truncation of the intensity
distribution.
:param apply:
Application function, for instance `parallel.apply`; by default use
`openquake.baselib.parallel.Sequential.apply`.
:returns:
An array of size N, where N is the number of sites, which elements
are records with fields given by the intensity measure types; the
size of each field is given by the number of levels in ``imtls``.
"""
imtls = DictArray(imtls)
if hasattr(source_site_filter, 'sitecol'): # a filter, as it should be
sites = source_site_filter.sitecol
else: # backward compatibility, a site collection was passed
sites = source_site_filter
source_site_filter = SourceFilter(sites, None)
pmap = apply(
pmap_from_grp, (sources, source_site_filter, imtls,
gsim_by_trt, truncation_level),
weight=operator.attrgetter('weight'),
key=operator.attrgetter('tectonic_region_type')
).reduce(operator.or_, ProbabilityMap(len(imtls.array), 1))
return pmap.convert(imtls, len(sites))
# NB: it is important for this to be fast since it is inside an inner loop
[docs]def get_probability_no_exceedance(
rupture, sctx, rctx, dctx, imtls, gsims, trunclevel):
"""
:param rupture: a Rupture instance
:param sctx: the corresponding SiteContext instance
:param rctx: the corresponding RuptureContext instance
:param dctx: the corresponding DistanceContext instance
:param imtls: a dictionary-like object providing the intensity levels
:param gsims: the list of GSIMs to use
:param trunclevel: the truncation level
:returns: an array of shape (num_sites, num_levels, num_gsims)
"""
pne_array = numpy.zeros((len(sctx.sites), len(imtls.array), len(gsims)))
for i, gsim in enumerate(gsims):
pnos = [] # list of arrays nsites x nlevels
for imt in imtls:
poes = gsim.get_poes(
sctx, rctx, dctx, from_string(imt), imtls[imt], trunclevel)
pnos.append(rupture.get_probability_no_exceedance(poes))
pne_array[:, :, i] = numpy.concatenate(pnos, axis=1)
return pne_array
[docs]def poe_map(src, s_sites, imtls, cmaker, trunclevel, bbs, rup_indep,
ctx_mon, pne_mon, disagg_mon):
"""
Compute the ProbabilityMap generated by the given source. Also,
store some information in the monitors and optionally in the
bounding boxes.
"""
pmap = ProbabilityMap.build(
len(imtls.array), len(cmaker.gsims), s_sites.sids, initvalue=rup_indep)
try:
for rup, weight in rupture_weight_pairs(src):
with ctx_mon: # compute distances
try:
sctx, rctx, dctx = cmaker.make_contexts(s_sites, rup)
except FarAwayRupture:
continue
with pne_mon: # compute probabilities and updates the pmap
pnes = get_probability_no_exceedance(
rup, sctx, rctx, dctx, imtls, cmaker.gsims, trunclevel)
for sid, pne in zip(sctx.sites.sids, pnes):
if rup_indep:
pmap[sid].array *= pne
else:
pmap[sid].array += pne * weight
# add optional disaggregation information (bounding boxes)
if bbs:
with disagg_mon:
sids = set(sctx.sites.sids)
jb_dists = dctx.rjb
closest_points = rup.surface.get_closest_points(
sctx.sites.mesh)
bs = [bb for bb in bbs if bb.site_id in sids]
# NB: the assert below is always true; we are
# protecting against possible refactoring errors
assert len(bs) == len(jb_dists) == len(closest_points)
for bb, dist, p in zip(bs, jb_dists, closest_points):
bb.update([dist], [p.longitude], [p.latitude])
except Exception as err:
etype, err, tb = sys.exc_info()
msg = 'An error occurred with source id=%s. Error: %s'
msg %= (src.source_id, str(err))
raise_(etype, msg, tb)
return ~pmap
# this is used by the engine
[docs]def pmap_from_grp(
sources, source_site_filter, imtls, gsims, truncation_level=None,
bbs=(), monitor=Monitor()):
"""
Compute the hazard curves for a set of sources belonging to the same
tectonic region type for all the GSIMs associated to that TRT.
The arguments are the same as in :func:`calc_hazard_curves`, except
for ``gsims``, which is a list of GSIM instances.
:returns: a ProbabilityMap instance
"""
if isinstance(sources, SourceGroup):
group = sources
sources = group.sources
trt = sources[0].tectonic_region_type
else: # list of sources
trt = sources[0].tectonic_region_type
group = SourceGroup(trt, sources, 'src_group', 'indep', 'indep')
try:
maxdist = source_site_filter.integration_distance[trt]
except:
maxdist = source_site_filter.integration_distance
if hasattr(gsims, 'keys'):
gsims = [gsims[trt]]
# check all the sources belong to the same tectonic region
trts = set(src.tectonic_region_type for src in sources)
assert len(trts) == 1, 'Multiple TRTs: %s' % ', '.join(trts)
with GroundShakingIntensityModel.forbid_instantiation():
imtls = DictArray(imtls)
cmaker = ContextMaker(gsims, maxdist)
ctx_mon = monitor('making contexts', measuremem=False)
pne_mon = monitor('computing poes', measuremem=False)
disagg_mon = monitor('get closest points', measuremem=False)
src_indep = group.src_interdep == 'indep'
pmap = ProbabilityMap(len(imtls.array), len(gsims))
pmap.calc_times = [] # pairs (src_id, delta_t)
pmap.grp_id = sources[0].src_group_id
for src, s_sites in source_site_filter(sources):
t0 = time.time()
poemap = poe_map(
src, s_sites, imtls, cmaker, truncation_level, bbs,
group.rup_interdep == 'indep', ctx_mon, pne_mon, disagg_mon)
if src_indep: # usual composition of probabilities
pmap |= poemap
else: # mutually exclusive probabilities
weight = float(group.srcs_weights[src.source_id])
for sid in poemap:
pmap[sid] += poemap[sid] * weight
pmap.calc_times.append(
(src.source_id, len(s_sites), time.time() - t0))
# storing the number of contributing ruptures too
pmap.eff_ruptures = {pmap.grp_id: pne_mon.counts}
return pmap
[docs]def calc_hazard_curves_ext(
groups, source_site_filter, imtls, gsim_by_trt, truncation_level=None,
apply=Sequential.apply):
"""
Compute hazard curves on a list of sites, given a set of seismic source
groups and a dictionary of ground shaking intensity models (one per
tectonic region type).
Probability of ground motion exceedance is computed in different ways
depending if the sources are independent or mutually exclusive.
:param group:
A sequence of groups of seismic sources objects (instances of
of :class:`~openquake.hazardlib.source.base.BaseSeismicSource`).
:param source_site_filter:
A source filter over the site collection or the site collection itself
:param imtls:
Dictionary mapping intensity measure type strings
to lists of intensity measure levels.
:param gsim_by_trt:
Dictionary mapping tectonic region types (members
of :class:`openquake.hazardlib.const.TRT`) to
:class:`~openquake.hazardlib.gsim.base.GMPE` or
:class:`~openquake.hazardlib.gsim.base.IPE` objects.
:param truncation_level:
Float, number of standard deviations for truncation of the intensity
distribution.
:param source_site_filter:
Optional source-site filter function. See
:mod:`openquake.hazardlib.calc.filters`.
:param maximum_distance:
The integration distance, if any
:returns:
An array of size N, where N is the number of sites, which elements
are records with fields given by the intensity measure types; the
size of each field is given by the number of levels in ``imtls``.
"""
# This is ensuring backward compatibility i.e. processing a list of
# sources
if not isinstance(groups[0], SourceGroup): # sent list of sources
dic = groupby(groups, operator.attrgetter('tectonic_region_type'))
groups = [SourceGroup(trt, dic[trt], 'src_group', 'indep', 'indep')
for trt in dic]
imtls = DictArray(imtls)
sitecol = source_site_filter.sitecol
pmap = ProbabilityMap(len(imtls.array), 1)
# Processing groups
for group in groups:
indep = group.src_interdep == 'indep'
# Prepare a dictionary
sources_by_trt = collections.defaultdict(list)
weights_by_trt = collections.defaultdict(dict)
# Fill the dictionary with sources for the different tectonic regions
# belonging to this group
if indep:
for src in group.sources:
sources_by_trt[src.tectonic_region_type].append(src)
weights_by_trt[src.tectonic_region_type][src.source_id] = 1
else:
for src in group.sources:
sources_by_trt[src.tectonic_region_type].append(src)
w = group.srcs_weights[src.source_id]
weights_by_trt[src.tectonic_region_type][src.source_id] = w
# Aggregate results. Note that for now we assume that source groups
# are independent.
for trt in sources_by_trt:
gsim = gsim_by_trt[trt]
# Create a temporary group
tmp_group = SourceGroup(trt,
sources_by_trt[trt],
'temp',
group.src_interdep,
group.rup_interdep,
weights_by_trt[trt].values(),
False)
if indep:
pmap |= pmap_from_grp(
tmp_group, source_site_filter, imtls, [gsim],
truncation_level)
else:
# since in this case the probability for each source have
# been already accounted, we use a weight equal to unity
pmap += pmap_from_grp(
tmp_group, sitecol, imtls, [gsim], truncation_level)
return pmap.convert(imtls, len(sitecol.complete))