# -*- coding: utf-8 -*-
# vim: tabstop=4 shiftwidth=4 softtabstop=4
#
# Copyright (C) 2018-2020 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 abc
import copy
import time
import pprint
import logging
import warnings
import operator
import itertools
import collections
import numpy
from scipy.interpolate import interp1d
from openquake.baselib import hdf5, parallel
from openquake.baselib.general import (
AccumDict, DictArray, groupby, groupby_bin)
from openquake.baselib.performance import Monitor
from openquake.hazardlib import imt as imt_module
from openquake.hazardlib.gsim import base
from openquake.hazardlib.calc.filters import IntegrationDistance
from openquake.hazardlib.probability_map import ProbabilityMap
from openquake.hazardlib.geo.surface import PlanarSurface
bymag = operator.attrgetter('mag')
bydist = operator.attrgetter('dist')
I16 = numpy.int16
F32 = numpy.float32
KNOWN_DISTANCES = frozenset(
'rrup rx ry0 rjb rhypo repi rcdpp azimuth azimuth_cp rvolc'.split())
def get_distances(rupture, sites, param):
"""
:param rupture: a rupture
:param sites: a mesh of points or a site collection
:param param: the kind of distance to compute (default rjb)
:returns: an array of distances from the given sites
"""
if not rupture.surface: # PointRupture
dist = rupture.hypocenter.distance_to_mesh(sites)
elif param == 'rrup':
dist = rupture.surface.get_min_distance(sites)
elif param == 'rx':
dist = rupture.surface.get_rx_distance(sites)
elif param == 'ry0':
dist = rupture.surface.get_ry0_distance(sites)
elif param == 'rjb':
dist = rupture.surface.get_joyner_boore_distance(sites)
elif param == 'rhypo':
dist = rupture.hypocenter.distance_to_mesh(sites)
elif param == 'repi':
dist = rupture.hypocenter.distance_to_mesh(sites, with_depths=False)
elif param == 'rcdpp':
dist = rupture.get_cdppvalue(sites)
elif param == 'azimuth':
dist = rupture.surface.get_azimuth(sites)
elif param == 'azimuth_cp':
dist = rupture.surface.get_azimuth_of_closest_point(sites)
elif param == "rvolc":
# Volcanic distance not yet supported, defaulting to zero
dist = numpy.zeros_like(sites.lons)
else:
raise ValueError('Unknown distance measure %r' % param)
dist.flags.writeable = False
return dist
class FarAwayRupture(Exception):
"""Raised if the rupture is outside the maximum distance for all sites"""
def get_num_distances(gsims):
"""
:returns: the number of distances required for the given GSIMs
"""
dists = set()
for gsim in gsims:
dists.update(gsim.REQUIRES_DISTANCES)
return len(dists)
class RupData(object):
"""
A class to collect rupture information into an AccumDict
"""
def __init__(self, cmaker, data=None):
self.cmaker = cmaker
self.data = AccumDict(accum=[]) if data is None else data
def add(self, ctxs, sites, grp_ids):
"""
Populate the inner AccumDict
:param ctxs: a list of pairs (rctx, dctx) associated to U ruptures
:param sites: a filtered site collection with N'<=N sites
:param grp_ids: a tuple of indices associated to the ruptures
"""
U, N = len(ctxs), len(sites.complete)
params = (sorted(self.cmaker.REQUIRES_DISTANCES | {'rrup'}) +
['lon', 'lat'])
data = {par + '_': numpy.ones((U, N), F32) * 9999 for par in params}
for par in data:
self.data[par].append(data[par])
for r, (rup, dctx) in enumerate(ctxs):
if numpy.isnan(rup.occurrence_rate): # for nonparametric ruptures
probs_occur = rup.probs_occur
else:
probs_occur = numpy.zeros(0, numpy.float64)
self.data['occurrence_rate'].append(rup.occurrence_rate)
self.data['probs_occur'].append(probs_occur)
self.data['weight'].append(rup.weight or numpy.nan)
self.data['grp_id'].append(grp_ids)
for rup_param in self.cmaker.REQUIRES_RUPTURE_PARAMETERS:
self.data[rup_param].append(getattr(rup, rup_param))
for dst_param in params: # including lon, lat
for s, dst in zip(sites.sids, getattr(dctx, dst_param)):
data[dst_param + '_'][r, s] = dst
def dictarray(self):
"""
:returns: key -> array
"""
dic = {}
for k, v in self.data.items():
if k.endswith('_'):
dic[k] = numpy.concatenate(v)
else:
dic[k] = numpy.array(v)
return dic
class ContextMaker(object):
"""
A class to manage the creation of contexts for distances, sites, rupture.
"""
REQUIRES = ['DISTANCES', 'SITES_PARAMETERS', 'RUPTURE_PARAMETERS']
def __init__(self, trt, gsims, param=None, monitor=Monitor()):
param = param or {}
self.max_sites_disagg = param.get('max_sites_disagg', 10)
self.collapse_level = param.get('collapse_level', False)
self.point_rupture_bins = param.get('point_rupture_bins', 20)
self.trt = trt
self.gsims = gsims
self.maximum_distance = (
param.get('maximum_distance') or IntegrationDistance({}))
self.trunclevel = param.get('truncation_level')
self.effect = param.get('effect')
for req in self.REQUIRES:
reqset = set()
for gsim in gsims:
reqset.update(getattr(gsim, 'REQUIRES_' + req))
setattr(self, 'REQUIRES_' + req, reqset)
# self.pointsource_distance is a dict mag -> dist, possibly empty
if param.get('pointsource_distance'):
self.pointsource_distance = param['pointsource_distance'][trt]
else:
self.pointsource_distance = {}
self.filter_distance = 'rrup'
self.imtls = param.get('imtls', {})
self.imts = [imt_module.from_string(imt) for imt in self.imtls]
self.reqv = param.get('reqv')
if self.reqv is not None:
self.REQUIRES_DISTANCES.add('repi')
if hasattr(gsims, 'items'):
# gsims is actually a dict rlzs_by_gsim
# since the ContextMaker must be used on ruptures with the
# same TRT, given a realization there is a single gsim
self.gsim_by_rlzi = {}
for gsim, rlzis in gsims.items():
for rlzi in rlzis:
self.gsim_by_rlzi[rlzi] = gsim
self.mon = monitor
self.ctx_mon = monitor('make_contexts', measuremem=False)
self.loglevels = DictArray(self.imtls)
self.shift_hypo = param.get('shift_hypo')
with warnings.catch_warnings():
# avoid RuntimeWarning: divide by zero encountered in log
warnings.simplefilter("ignore")
for imt, imls in self.imtls.items():
if imt != 'MMI':
self.loglevels[imt] = numpy.log(imls)
def from_srcs(self, srcs, sites): # used in disagg.disaggregation
"""
:returns: a list of pairs (rctx, dctx)
"""
grp_ids = [0]
ctxs = []
for src in srcs:
rups = list(src.iter_ruptures(shift_hypo=self.shift_hypo))
for rup in rups:
self.add_rup_params(rup) # make the rupture context-like
ctxs.extend(self.make_ctxs(rups, sites, grp_ids, False))
return ctxs
def filter(self, sites, rup):
"""
Filter the site collection with respect to the rupture.
:param sites:
Instance of :class:`openquake.hazardlib.site.SiteCollection`.
:param rup:
Instance of
:class:`openquake.hazardlib.source.rupture.BaseRupture`
:returns:
(filtered sites, distance context)
"""
distances = get_distances(rup, sites, self.filter_distance)
mdist = self.maximum_distance(self.trt, rup.mag)
mask = distances <= mdist
if mask.any():
sites, distances = sites.filter(mask), distances[mask]
else:
raise FarAwayRupture('%d: %d km' % (rup.rup_id, distances.min()))
return sites, DistancesContext([(self.filter_distance, distances)])
def get_dctx(self, sites, rup):
"""
:param sites: :class:`openquake.hazardlib.site.SiteCollection`
:param rup: :class:`openquake.hazardlib.source.rupture.BaseRupture`
:returns: :class:`DistancesContext`
"""
distances = get_distances(rup, sites, self.filter_distance)
mdist = self.maximum_distance(self.trt, rup.mag)
if (distances > mdist).all():
raise FarAwayRupture('%d: %d km' % (rup.rup_id, distances.min()))
return DistancesContext([(self.filter_distance, distances)])
def add_rup_params(self, rupture):
"""
Add .REQUIRES_RUPTURE_PARAMETERS to the rupture
"""
for param in self.REQUIRES_RUPTURE_PARAMETERS:
if param == 'mag':
value = rupture.mag
elif param == 'strike':
value = rupture.surface.get_strike()
elif param == 'dip':
value = rupture.surface.get_dip()
elif param == 'rake':
value = rupture.rake
elif param == 'ztor':
value = rupture.surface.get_top_edge_depth()
elif param == 'hypo_lon':
value = rupture.hypocenter.longitude
elif param == 'hypo_lat':
value = rupture.hypocenter.latitude
elif param == 'hypo_depth':
value = rupture.hypocenter.depth
elif param == 'width':
value = rupture.surface.get_width()
else:
raise ValueError('%s requires unknown rupture parameter %r' %
(type(self).__name__, param))
setattr(rupture, param, value)
def make_contexts(self, sites, rupture, filt=True):
"""
Filter the site collection with respect to the rupture and
create context objects.
:param sites:
Instance of :class:`openquake.hazardlib.site.SiteCollection`.
:param rupture:
Instance of
:class:`openquake.hazardlib.source.rupture.BaseRupture`
:param boolean filt:
If True filter the sites
:returns:
Tuple of two items: sites and distances context.
:raises ValueError:
If any of declared required parameters (site, rupture and
distance parameters) is unknown.
"""
if filt:
sites, dctx = self.filter(sites, rupture)
else:
dctx = self.get_dctx(sites, rupture)
for param in self.REQUIRES_DISTANCES - set([self.filter_distance]):
distances = get_distances(rupture, sites, param)
setattr(dctx, param, distances)
reqv_obj = (self.reqv.get(self.trt) if self.reqv else None)
if reqv_obj and isinstance(rupture.surface, PlanarSurface):
reqv = reqv_obj.get(dctx.repi, rupture.mag)
if 'rjb' in self.REQUIRES_DISTANCES:
dctx.rjb = reqv
if 'rrup' in self.REQUIRES_DISTANCES:
dctx.rrup = numpy.sqrt(reqv**2 + rupture.hypocenter.depth**2)
self.add_rup_params(rupture)
return sites, dctx
def make_ctxs(self, ruptures, sites, grp_ids, filt):
"""
:returns:
a list of triples (rctx, sctx, dctx) if filt is True,
a list of pairs (rctx, dctx) if filt is False
"""
ctxs = []
for rup in ruptures:
try:
sctx, dctx = self.make_contexts(sites, rup, filt)
except FarAwayRupture:
continue
rup.grp_ids = grp_ids
if filt:
ctxs.append((rup, sctx, dctx))
else:
closest = rup.surface.get_closest_points(sites)
dctx.lon = closest.lons
dctx.lat = closest.lats
ctxs.append((rup, dctx))
return ctxs
def max_intensity(self, sitecol1, mags, dists):
"""
:param sitecol1: a SiteCollection instance with a single site
:param mags: a sequence of magnitudes
:param dists: a sequence of distances
:returns: an array of GMVs of shape (#mags, #dists)
"""
assert len(sitecol1) == 1, sitecol1
nmags, ndists = len(mags), len(dists)
gmv = numpy.zeros((nmags, ndists))
for m, d in itertools.product(range(nmags), range(ndists)):
mag, dist = mags[m], dists[d]
rup = RuptureContext()
for par in self.REQUIRES_RUPTURE_PARAMETERS:
setattr(rup, par, 0)
rup.mag = mag
rup.width = .01 # 10 meters to avoid warnings in abrahamson_2014
dctx = DistancesContext(
(dst, numpy.array([dist])) for dst in self.REQUIRES_DISTANCES)
means = []
for gsim in self.gsims:
try:
mean = base.get_mean_std( # shape (2, N, M, G) -> M
sitecol1, rup, dctx, self.imts, [gsim])[0, 0, :, 0]
except ValueError: # magnitude outside of supported range
continue
means.append(mean.max())
if means:
gmv[m, d] = numpy.exp(max(means))
return gmv
def _collapse(rups):
# collapse a list of ruptures into a single rupture
if len(rups) < 2:
return rups
rup = copy.copy(rups[0])
rup.occurrence_rate = sum(r.occurrence_rate for r in rups)
return [rup]
def print_finite_size(rups):
"""
Used to print the number of finite-size ruptures
"""
c = collections.Counter()
for rup in rups:
if rup.surface:
c['%.2f' % rup.mag] += 1
print(c)
print('total finite size ruptures = ', sum(c.values()))
class PmapMaker(object):
"""
A class to compute the PoEs from a given source
"""
def __init__(self, cmaker, srcfilter, group):
vars(self).update(vars(cmaker))
self.cmaker = cmaker
self.srcfilter = srcfilter
self.group = group
self.src_mutex = getattr(group, 'src_interdep', None) == 'mutex'
self.rup_indep = getattr(group, 'rup_interdep', None) != 'mutex'
self.fewsites = len(srcfilter.sitecol) <= cmaker.max_sites_disagg
self.poe_mon = cmaker.mon('get_poes', measuremem=False)
self.pne_mon = cmaker.mon('composing pnes', measuremem=False)
self.gmf_mon = cmaker.mon('computing mean_std', measuremem=False)
def _gen_ctxs(self, rups, sites, grp_ids):
# generate triples (rup, sites, dctx)
rup_param = not numpy.isnan([r.occurrence_rate for r in rups]).any()
collapse_level = self.rup_indep and rup_param and self.collapse_level
if (collapse_level and len(sites.complete) == 1 and
self.pointsource_distance != {}):
rups = self.collapse_point_ruptures(rups, sites)
# print_finite_size(rups)
ctxs = self.cmaker.make_ctxs(rups, sites, grp_ids, filt=False)
if collapse_level > 1:
ctxs = self.collapse_the_ctxs(ctxs)
self.numrups += len(ctxs)
if ctxs:
self.rupdata.add(ctxs, sites, grp_ids)
for rup, dctx in ctxs:
mask = (dctx.rrup <= self.maximum_distance(
rup.tectonic_region_type, rup.mag))
r_sites = sites.filter(mask)
for name in self.REQUIRES_DISTANCES:
setattr(dctx, name, getattr(dctx, name)[mask])
self.numsites += len(r_sites)
yield rup, r_sites, dctx
def _update_pmap(self, ctxs, pmap=None):
# compute PoEs and update pmap
if pmap is None: # for src_indep
pmap = self.pmap
for rup, r_sites, dctx in ctxs:
# this must be fast since it is inside an inner loop
with self.gmf_mon:
mean_std = base.get_mean_std( # shape (2, N, M, G)
r_sites, rup, dctx, self.imts, self.gsims)
with self.poe_mon:
ll = self.loglevels
poes = base.get_poes(mean_std, ll, self.trunclevel, self.gsims)
for g, gsim in enumerate(self.gsims):
for m, imt in enumerate(ll):
if hasattr(gsim, 'weight') and gsim.weight[imt] == 0:
# set by the engine when parsing the gsim logictree
# when 0 ignore the gsim: see _build_trts_branches
poes[:, ll(imt), g] = 0
with self.pne_mon:
# pnes and poes of shape (N, L, G)
pnes = rup.get_probability_no_exceedance(poes)
for grp_id in rup.grp_ids:
p = pmap[grp_id]
if self.rup_indep:
for sid, pne in zip(r_sites.sids, pnes):
p.setdefault(sid, 1.).array *= pne
else: # rup_mutex
for sid, pne in zip(r_sites.sids, pnes):
p.setdefault(sid, 0.).array += (
1.-pne) * rup.weight
def _ruptures(self, src, filtermag=None):
with self.cmaker.mon('iter_ruptures', measuremem=False):
return list(src.iter_ruptures(shift_hypo=self.shift_hypo,
mag=filtermag))
def _make_src_indep(self):
# srcs with the same source_id and grp_ids
for srcs, sites in self.srcfilter.get_sources_sites(self.group):
t0 = time.time()
src_id = srcs[0].source_id
grp_ids = numpy.array(srcs[0].grp_ids)
self.numrups = 0
self.numsites = 0
if self.fewsites:
# we can afford using a lot of memory to store the ruptures
rups = self._get_rups(srcs, sites)
# print_finite_size(rups)
with self.ctx_mon:
ctxs = list(self._gen_ctxs(rups, sites, grp_ids))
self._update_pmap(ctxs)
else:
# many sites: keep in memory less ruptures
for src in srcs:
for rup in self._get_rups([src], sites):
with self.ctx_mon:
ctxs = self.cmaker.make_ctxs(
[rup], rup.sites, grp_ids, filt=True)
self.numrups += len(ctxs)
self.numsites += sum(len(ctx[1]) for ctx in ctxs)
self._update_pmap(ctxs)
self.calc_times[src_id] += numpy.array(
[self.numrups, self.numsites, time.time() - t0])
return AccumDict((grp_id, ~p if self.rup_indep else p)
for grp_id, p in self.pmap.items())
def _make_src_mutex(self):
for src, sites in self.srcfilter(self.group):
t0 = time.time()
self.totrups += src.num_ruptures
self.numrups = 0
self.numsites = 0
rups = self._ruptures(src)
with self.ctx_mon:
L, G = len(self.cmaker.imtls.array), len(self.cmaker.gsims)
pmap = {grp_id: ProbabilityMap(L, G) for grp_id in src.grp_ids}
ctxs = self.cmaker.make_ctxs(
rups, sites, numpy.array(src.grp_ids), filt=True)
self.numrups += len(ctxs)
self.numsites += sum(len(ctx[1]) for ctx in ctxs)
self._update_pmap(ctxs, pmap)
for grp_id in src.grp_ids:
p = pmap[grp_id]
if self.rup_indep:
p = ~p
p *= src.mutex_weight
self.pmap[grp_id] += p
self.calc_times[src.source_id] += numpy.array(
[self.numrups, self.numsites, time.time() - t0])
return self.pmap
def make(self):
self.rupdata = RupData(self.cmaker)
imtls = self.cmaker.imtls
L, G = len(imtls.array), len(self.gsims)
self.pmap = AccumDict(accum=ProbabilityMap(L, G)) # grp_id -> pmap
# AccumDict of arrays with 3 elements nrups, nsites, calc_time
self.calc_times = AccumDict(accum=numpy.zeros(3, numpy.float32))
self.totrups = 0
if self.src_mutex:
pmap = self._make_src_mutex()
else:
pmap = self._make_src_indep()
return (pmap, self.rupdata.dictarray(), self.calc_times,
dict(totrups=self.totrups))
def collapse_point_ruptures(self, rups, sites):
"""
Collapse ruptures more distant than the pointsource_distance
"""
pointlike, output = [], []
for rup in rups:
if not rup.surface:
pointlike.append(rup)
else:
output.append(rup)
for mag, mrups in groupby(pointlike, bymag).items():
if len(mrups) == 1: # nothing to do
output.extend(mrups)
continue
mdist = self.maximum_distance(self.trt, mag)
coll = []
for rup in mrups: # called on a single site
rup.dist = get_distances(rup, sites, 'rrup').min()
if rup.dist <= mdist:
coll.append(rup)
for rs in groupby_bin(coll, self.point_rupture_bins, bydist):
# group together ruptures in the same distance bin
output.extend(_collapse(rs))
return output
def collapse_the_ctxs(self, ctxs):
"""
Collapse contexts with similar parameters and distances.
:param ctxs: a list of pairs (rup, dctx)
:returns: collapsed contexts
"""
def params(ctx):
rup, dctx = ctx
lst = []
for par in self.REQUIRES_RUPTURE_PARAMETERS:
lst.append(getattr(rup, par))
for dst in self.REQUIRES_DISTANCES:
lst.extend(numpy.round(getattr(dctx, dst)))
return tuple(lst)
out = []
for values in groupby(ctxs, params).values():
if len(values) == 1:
out.append(values[0])
else:
[rup] = _collapse([rup for rup, dctx in values])
dctx = values[0][1] # get the first dctx
out.append((rup, dctx))
return out
def _get_rups(self, srcs, sites):
# returns a list of ruptures, each one with a .sites attribute
rups = []
def _add(rupiter, sites):
for rup in rupiter:
rup.sites = sites
rups.append(rup)
for src in srcs:
self.totrups += src.num_ruptures
loc = getattr(src, 'location', None)
if loc and self.pointsource_distance == 0:
# all finite size effects are ignored
_add(src.point_ruptures(), sites)
elif loc and self.pointsource_distance:
# finite site effects are ignored only for sites over the
# pointsource_distance from the rupture (if any)
for pr in src.point_ruptures():
pdist = self.pointsource_distance['%.2f' % pr.mag]
close, far = sites.split(pr.hypocenter, pdist)
if self.fewsites:
if close is None: # all is far, common for small mag
_add([pr], sites)
else: # something is close
_add(self._ruptures(src, pr.mag), sites)
else: # many sites
if close is None: # all is far
_add([pr], far)
elif far is None: # all is close
_add(self._ruptures(src, pr.mag), close)
else: # some sites are far, some are close
_add([pr], far)
_add(self._ruptures(src, pr.mag), close)
else: # just add the ruptures
_add(self._ruptures(src), sites)
return rups
class BaseContext(metaclass=abc.ABCMeta):
"""
Base class for context object.
"""
def __eq__(self, other):
"""
Return True if ``other`` has same attributes with same values.
"""
if isinstance(other, self.__class__):
if self._slots_ == other._slots_:
oks = []
for s in self._slots_:
a, b = getattr(self, s, None), getattr(other, s, None)
if a is None and b is None:
ok = True
elif a is None and b is not None:
ok = False
elif a is not None and b is None:
ok = False
elif hasattr(a, 'shape') and hasattr(b, 'shape'):
if a.shape == b.shape:
ok = numpy.allclose(a, b)
else:
ok = False
else:
ok = a == b
oks.append(ok)
return numpy.all(oks)
return False
# mock of a site collection used in the tests and in the SMTK
class SitesContext(BaseContext):
"""
Sites calculation context for ground shaking intensity models.
Instances of this class are passed into
:meth:`GroundShakingIntensityModel.get_mean_and_stddevs`. They are
intended to represent relevant features of the sites collection.
Every GSIM class is required to declare what :attr:`sites parameters
<GroundShakingIntensityModel.REQUIRES_SITES_PARAMETERS>` does it need.
Only those required parameters are made available in a result context
object.
"""
# _slots_ is used in hazardlib check_gsim and in the SMTK
def __init__(self, slots='vs30 vs30measured z1pt0 z2pt5'.split(),
sitecol=None):
self._slots_ = slots
if sitecol is not None:
self.sids = sitecol.sids
for slot in slots:
setattr(self, slot, getattr(sitecol, slot))
class DistancesContext(BaseContext):
"""
Distances context for ground shaking intensity models.
Instances of this class are passed into
:meth:`GroundShakingIntensityModel.get_mean_and_stddevs`. They are
intended to represent relevant distances between sites from the collection
and the rupture. Every GSIM class is required to declare what
:attr:`distance measures <GroundShakingIntensityModel.REQUIRES_DISTANCES>`
does it need. Only those required values are calculated and made available
in a result context object.
"""
_slots_ = ('rrup', 'rx', 'rjb', 'rhypo', 'repi', 'ry0', 'rcdpp',
'azimuth', 'hanging_wall', 'rvolc')
def __init__(self, param_dist_pairs=()):
for param, dist in param_dist_pairs:
setattr(self, param, dist)
def roundup(self, minimum_distance):
"""
If the minimum_distance is nonzero, returns a copy of the
DistancesContext with updated distances, i.e. the ones below
minimum_distance are rounded up to the minimum_distance. Otherwise,
returns the original DistancesContext unchanged.
"""
if not minimum_distance:
return self
ctx = DistancesContext()
for dist, array in vars(self).items():
small_distances = array < minimum_distance
if small_distances.any():
array = numpy.array(array) # make a copy first
array[small_distances] = minimum_distance
array.flags.writeable = False
setattr(ctx, dist, array)
return ctx
# mock of a rupture used in the tests and in the SMTK
class RuptureContext(BaseContext):
"""
Rupture calculation context for ground shaking intensity models.
Instances of this class are passed into
:meth:`GroundShakingIntensityModel.get_mean_and_stddevs`. They are
intended to represent relevant features of a single rupture. Every
GSIM class is required to declare what :attr:`rupture parameters
<GroundShakingIntensityModel.REQUIRES_RUPTURE_PARAMETERS>` does it need.
Only those required parameters are made available in a result context
object.
"""
_slots_ = (
'mag', 'strike', 'dip', 'rake', 'ztor', 'hypo_lon', 'hypo_lat',
'hypo_depth', 'width', 'hypo_loc')
temporal_occurrence_model = None # to be set
def __init__(self, param_pairs=()):
for param, value in param_pairs:
setattr(self, param, value)
def get_probability_no_exceedance(self, poes):
"""
Compute and return the probability that in the time span for which the
rupture is defined, the rupture itself never generates a ground motion
value higher than a given level at a given site.
Such calculation is performed starting from the conditional probability
that an occurrence of the current rupture is producing a ground motion
value higher than the level of interest at the site of interest.
The actual formula used for such calculation depends on the temporal
occurrence model the rupture is associated with.
The calculation can be performed for multiple intensity measure levels
and multiple sites in a vectorized fashion.
:param poes:
2D numpy array containing conditional probabilities the the a
rupture occurrence causes a ground shaking value exceeding a
ground motion level at a site. First dimension represent sites,
second dimension intensity measure levels. ``poes`` can be obtained
calling the :func:`func <openquake.hazardlib.gsim.base.get_poes>
"""
if numpy.isnan(self.occurrence_rate): # nonparametric rupture
# Uses the formula
#
# ∑ p(k|T) * p(X<x|rup)^k
#
# where `p(k|T)` is the probability that the rupture occurs k times
# in the time span `T`, `p(X<x|rup)` is the probability that a
# rupture occurrence does not cause a ground motion exceedance, and
# thesummation `∑` is done over the number of occurrences `k`.
#
# `p(k|T)` is given by the attribute probs_occur and
# `p(X<x|rup)` is computed as ``1 - poes``.
# Converting from 1d to 2d
if len(poes.shape) == 1:
poes = numpy.reshape(poes, (-1, len(poes)))
p_kT = self.probs_occur
prob_no_exceed = numpy.array(
[v * ((1 - poes) ** i) for i, v in enumerate(p_kT)])
prob_no_exceed = numpy.sum(prob_no_exceed, axis=0)
if isinstance(prob_no_exceed, numpy.ndarray):
prob_no_exceed[prob_no_exceed > 1.] = 1. # sanity check
prob_no_exceed[poes == 0.] = 1. # avoid numeric issues
return prob_no_exceed
# parametric rupture
tom = self.temporal_occurrence_model
return tom.get_probability_no_exceedance(self.occurrence_rate, poes)
class Effect(object):
"""
Compute the effect of a rupture of a given magnitude and distance.
:param effect_by_mag: a dictionary magstring -> intensities
:param dists: array of distances, one per each intensity
:param cdist: collapse distance
"""
def __init__(self, effect_by_mag, dists, collapse_dist=None):
self.effect_by_mag = effect_by_mag
self.dists = dists
self.nbins = len(dists)
def collapse_value(self, collapse_dist):
"""
:returns: intensity at collapse distance
"""
# get the maximum magnitude with a cutoff at 7
for mag in self.effect_by_mag:
if mag > '7.00':
break
effect = self.effect_by_mag[mag]
idx = numpy.searchsorted(self.dists, collapse_dist)
return effect[idx-1 if idx == self.nbins else idx]
def __call__(self, mag, dist):
di = numpy.searchsorted(self.dists, dist)
if di == self.nbins:
di = self.nbins
eff = self.effect_by_mag['%.2f' % mag][di]
return eff
# this is used to compute the magnitude-dependent pointsource_distance
def dist_by_mag(self, intensity):
"""
:returns: a dict magstring -> distance
"""
dst = {} # magnitude -> distance
for mag, intensities in self.effect_by_mag.items():
if intensity < intensities.min():
dst[mag] = self.dists[-1] # largest distance
elif intensity > intensities.max():
dst[mag] = self.dists[0] # smallest distance
else:
dst[mag] = interp1d(intensities, self.dists)(intensity)
return dst
def get_effect_by_mag(mags, sitecol1, gsims_by_trt, maximum_distance, imtls):
"""
:param mags: an ordered list of magnitude strings with format %.2f
:param sitecol1: a SiteCollection with a single site
:param gsims_by_trt: a dictionary trt -> gsims
:param maximum_distance: an IntegrationDistance object
:param imtls: a DictArray with intensity measure types and levels
:returns: a dict magnitude-string -> array(#dists, #trts)
"""
trts = list(gsims_by_trt)
ndists = 51
gmv = numpy.zeros((len(mags), ndists, len(trts)))
param = dict(maximum_distance=maximum_distance, imtls=imtls)
for t, trt in enumerate(trts):
dist_bins = maximum_distance.get_dist_bins(trt, ndists)
cmaker = ContextMaker(trt, gsims_by_trt[trt], param)
gmv[:, :, t] = cmaker.max_intensity(
sitecol1, [float(mag) for mag in mags], dist_bins)
return dict(zip(mags, gmv))
# used in calculators/classical.py
def get_effect(mags, sitecol1, gsims_by_trt, oq):
"""
:params mags:
a dictionary trt -> magnitudes
:param sitecol1:
a SiteCollection with a single site
:param gsims_by_trt:
a dictionary trt -> gsims
:param oq:
an object with attributes imtls, minimum_intensity,
maximum_distance and pointsource_distance
:returns:
an ArrayWrapper trt -> effect_by_mag_dst and a nested dictionary
trt -> mag -> dist with the effective pointsource_distance
Updates oq.maximum_distance.magdist
"""
assert list(mags) == list(gsims_by_trt), 'Missing TRTs!'
dist_bins = {trt: oq.maximum_distance.get_dist_bins(trt)
for trt in gsims_by_trt}
aw = hdf5.ArrayWrapper((), {})
# computing the effect make sense only if all IMTs have the same
# unity of measure; for simplicity we will consider only PGA and SA
psd = (oq.pointsource_distance.interp(mags)
if oq.pointsource_distance is not None else {})
if psd:
logging.info('Computing effect of the ruptures')
allmags = set()
for trt in mags:
allmags.update(mags[trt])
eff_by_mag = parallel.Starmap.apply(
get_effect_by_mag, (sorted(allmags), sitecol1, gsims_by_trt,
oq.maximum_distance, oq.imtls)
).reduce()
effect = {}
for t, trt in enumerate(mags):
arr = numpy.array([eff_by_mag[mag][:, t] for mag in mags[trt]])
setattr(aw, trt, arr) # shape (#mags, #dists)
setattr(aw, trt + '_dist_bins', dist_bins[trt])
effect[trt] = Effect(dict(zip(mags[trt], arr)), dist_bins[trt])
minint = oq.minimum_intensity.get('default', 0)
for trt, eff in effect.items():
if minint:
oq.maximum_distance.magdist[trt] = eff.dist_by_mag(minint)
# build a dict trt -> mag -> dst
if psd and set(psd[trt].values()) == {-1}:
maxdist = oq.maximum_distance[trt]
psd[trt] = eff.dist_by_mag(eff.collapse_value(maxdist))
dic = {trt: [(float(mag), int(dst)) for mag, dst in psd[trt].items()]
for trt in psd if trt != 'default'}
logging.info('Using pointsource_distance=\n%s', pprint.pformat(dic))
return aw, psd
# not used right now
def ruptures_by_mag_dist(sources, srcfilter, gsims, params, monitor):
"""
:returns: a dictionary trt -> mag string -> counts by distance
"""
assert len(srcfilter.sitecol) == 1
trt = sources[0].tectonic_region_type
dist_bins = srcfilter.integration_distance.get_dist_bins(trt)
nbins = len(dist_bins)
mags = set('%.2f' % mag for src in sources for mag in src.get_mags())
dic = {mag: numpy.zeros(len(dist_bins), int) for mag in sorted(mags)}
cmaker = ContextMaker(trt, gsims, params, monitor)
for src, sites in srcfilter(sources):
for rup in src.iter_ruptures(shift_hypo=cmaker.shift_hypo):
try:
sctx, dctx = cmaker.make_contexts(sites, rup)
except FarAwayRupture:
continue
di = numpy.searchsorted(dist_bins, dctx.rrup[0])
if di == nbins:
di = nbins - 1
dic['%.2f' % rup.mag][di] += 1
return {trt: AccumDict(dic)}