# -*- coding: utf-8 -*-
# vim: tabstop=4 shiftwidth=4 softtabstop=4
#
# Copyright (C) 2018-2021 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 abc
import copy
import time
import logging
import warnings
import itertools
import functools
import collections
import numpy
import pandas
from scipy.interpolate import interp1d
try:
import numba
except ImportError:
numba = None
from openquake.baselib import hdf5, parallel
from openquake.baselib.general import (
AccumDict, DictArray, groupby, RecordBuilder)
from openquake.baselib.performance import Monitor
from openquake.hazardlib import imt as imt_module
from openquake.hazardlib.const import StdDev
from openquake.hazardlib.tom import registry
from openquake.hazardlib.site import site_param_dt
from openquake.hazardlib.calc.filters import MagDepDistance
from openquake.hazardlib.probability_map import ProbabilityMap
from openquake.hazardlib.geo.surface import PlanarSurface
STD_TYPES = (StdDev.TOTAL, StdDev.INTER_EVENT, StdDev.INTRA_EVENT)
KNOWN_DISTANCES = frozenset(
'rrup rx ry0 rjb rhypo repi rcdpp azimuth azimuth_cp rvolc closest_point'
.split())
[docs]class Timer(object):
"""
Timer used to save the time needed to process each source and to
postprocess it with ``Timer('timer.csv').read_df()``. To use it, run
the calculation on a single machine with
OQ_TIMER=timer.csv oq run job.ini
"""
fields = ['source_id', 'code', 'effrups', 'nsites', 'weight',
'numctxs', 'numsites', 'dt', 'task_no']
def __init__(self, fname):
self.fname = fname
[docs] def save(self, src, numctxs, numsites, dt, task_no):
# save the source info
if self.fname:
row = [src.source_id, src.code.decode('ascii'),
src.num_ruptures, src.nsites, src.weight,
numctxs, numsites, dt, task_no]
open(self.fname, 'a').write(','.join(map(str, row)) + '\n')
[docs] def read_df(self):
# method used to postprocess the information
df = pandas.read_csv(self.fname, names=self.fields, index_col=0)
df['speed'] = df['weight'] / df['dt']
return df.sort_values('dt')
# object used to measure the time needed to process each source
timer = Timer(os.environ.get('OQ_TIMER'))
[docs]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 == 'closest_point':
t = rupture.surface.get_closest_points(sites)
dist = numpy.vstack([t.lons, t.lats, t.depths]).T # shape (N, 3)
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
[docs]class FarAwayRupture(Exception):
"""Raised if the rupture is outside the maximum distance for all sites"""
[docs]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)
[docs]def use_recarray(gsims):
"""
:returns:
True if the `ctx` argument of gsim.compute is a recarray for all gsims
"""
return all(gsim.compute.__annotations__.get("ctx") is numpy.recarray
for gsim in gsims)
[docs]class ContextMaker(object):
"""
A class to manage the creation of contexts and to compute mean/stddevs
and possibly PoEs.
:param trt: a tectonic region type string
:param gsims: a list of GSIMs or a dictionary gsim -> rlz indices
:param param:
a dictionary of parameters like the maximum_distance, the IMTLs,
the investigation time, etc
NB: the trt can be different from the tectonic region type for which
the underlying GSIMs are defined. This is intentional.
"""
REQUIRES = ['DISTANCES', 'SITES_PARAMETERS', 'RUPTURE_PARAMETERS']
rup_indep = True
tom = None
@property
def dtype(self):
"""
:returns: dtype of the underlying ctx_builder
"""
return self.ctx_builder.dtype
def __init__(self, trt, gsims, param, monitor=Monitor()):
param = param
self.af = param.get('af', None)
self.max_sites_disagg = param.get('max_sites_disagg', 10)
self.disagg_by_src = param.get('disagg_by_src')
self.collapse_level = param.get('collapse_level', False)
self.trt = trt
self.gsims = gsims
self.maximum_distance = (
param.get('maximum_distance') or MagDepDistance({}))
self.minimum_distance = param.get('minimum_distance', 0)
self.investigation_time = param.get('investigation_time')
if self.investigation_time:
self.tom = registry['PoissonTOM'](self.investigation_time)
self.ses_seed = param.get('ses_seed', 42)
self.ses_per_logic_tree_path = param.get('ses_per_logic_tree_path', 1)
self.trunclevel = param.get('truncation_level')
self.num_epsilon_bins = param.get('num_epsilon_bins', 1)
self.grp_id = param.get('grp_id', 0)
self.effect = param.get('effect')
self.use_recarray = use_recarray(gsims)
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
psd = param.get('pointsource_distance')
if hasattr(psd, 'ddic'):
self.pointsource_distance = psd.ddic.get(trt, {})
if all(val == 0 for val in self.pointsource_distance.values()):
self.pointsource_distance = 0
else:
self.pointsource_distance = {}
if 'imtls' in param:
self.imtls = param['imtls']
elif 'hazard_imtls' in param:
self.imtls = DictArray(param['hazard_imtls'])
else:
raise KeyError('Missing imtls in ContextMaker!')
try:
self.min_iml = param['min_iml']
except KeyError:
self.min_iml = [0. for imt in self.imtls]
self.reqv = param.get('reqv')
if self.reqv is not None:
self.REQUIRES_DISTANCES.add('repi')
reqs = (sorted(self.REQUIRES_RUPTURE_PARAMETERS) +
sorted(self.REQUIRES_SITES_PARAMETERS) +
sorted(self.REQUIRES_DISTANCES))
dic = {}
for req in reqs:
if req in site_param_dt:
dt = site_param_dt[req]
if isinstance(dt, tuple): # (string_, size)
dic[req] = b''
else:
dic[req] = dt(0)
else:
dic[req] = 0.
dic['sids'] = numpy.uint32(0)
self.ctx_builder = RecordBuilder(**dic)
self.loglevels = DictArray(self.imtls) if self.imtls else {}
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)
self.init_monitoring(monitor)
[docs] def init_monitoring(self, monitor):
# instantiating child monitors, may be called in the workers
self.ctx_mon = monitor('make_contexts', measuremem=False)
self.gmf_mon = monitor('computing mean_std', measuremem=False)
self.poe_mon = monitor('get_poes', measuremem=False)
self.pne_mon = monitor('composing pnes', measuremem=False)
self.task_no = getattr(monitor, 'task_no', 0)
[docs] def read_ctxs(self, dstore, slc=None):
"""
:param dstore: a DataStore instance
:param slice: a slice of contexts with the same grp_id
:returns: a list of contexts plus N lists of contexts for each site
"""
sitecol = dstore['sitecol'].complete
if slc is None:
slc = dstore['rup/grp_id'][:] == self.grp_id
params = {n: dstore['rup/' + n][slc] for n in dstore['rup']}
ctxs = []
for u in range(len(params['mag'])):
ctx = RuptureContext()
for par, arr in params.items():
if par.endswith('_'):
par = par[:-1]
setattr(ctx, par, arr[u])
for par in sitecol.array.dtype.names:
setattr(ctx, par, sitecol[par][ctx.sids])
ctxs.append(ctx)
return ctxs
[docs] def recarray(self, ctxs):
"""
:params ctxs: a list of contexts
:returns: a recarray
"""
C = sum(len(ctx) for ctx in ctxs)
ra = self.ctx_builder.zeros(C).view(numpy.recarray)
start = 0
for ctx in ctxs:
slc = slice(start, start + len(ctx))
for par in self.ctx_builder.names:
getattr(ra, par)[slc] = getattr(ctx, par)
ra.sids[slc] = ctx.sids
start = slc.stop
return ra
[docs] def get_ctx_params(self):
"""
:returns: the interesting attributes of the context
"""
params = {'occurrence_rate', 'sids_', 'src_id',
'probs_occur_', 'clon_', 'clat_', 'rrup_'}
params.update(self.REQUIRES_RUPTURE_PARAMETERS)
for dparam in self.REQUIRES_DISTANCES:
params.add(dparam + '_')
return params
[docs] def from_srcs(self, srcs, sitecol): # used in disagg.disaggregation
"""
:param srcs: a list of Source objects
:param sitecol: a SiteCollection instance
:returns: a list RuptureContexts
"""
allctxs = []
for i, src in enumerate(srcs):
src.id = i
rctxs = []
for rup in src.iter_ruptures(shift_hypo=self.shift_hypo):
rctxs.append(self.make_rctx(rup))
allctxs.extend(self.get_ctxs(rctxs, sitecol, src.id))
return allctxs
[docs] 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, 'rrup')
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([('rrup', distances)])
[docs] def make_rctx(self, rupture):
"""
Add .REQUIRES_RUPTURE_PARAMETERS to the rupture
"""
ctx = RuptureContext()
vars(ctx).update(vars(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(ctx, param, value)
return ctx
[docs] def make_contexts(self, sites, rupture):
"""
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`
:returns:
Tuple of three items: rupture, sites and distances context.
:raises ValueError:
If any of declared required parameters (site, rupture and
distance parameters) is unknown.
"""
sites, dctx = self.filter(sites, rupture)
for param in self.REQUIRES_DISTANCES - {'rrup'}:
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)
return self.make_rctx(rupture), sites, dctx
[docs] def get_ctxs(self, ruptures, sites, src_id, mon=Monitor()):
"""
:param ruptures:
a list of ruptures generated by the same source
:param sites:
a (filtered) SiteCollection
:param src_id:
the ID of the source (for debugging purposes)
:param mon:
a Monitor object
:returns:
fat RuptureContexts
"""
ctxs = []
fewsites = len(sites.complete) <= self.max_sites_disagg
for rup in ruptures:
with mon:
try:
ctx, r_sites, dctx = self.make_contexts(
getattr(rup, 'sites', sites), rup)
except FarAwayRupture:
continue
for par in self.REQUIRES_SITES_PARAMETERS:
setattr(ctx, par, r_sites[par])
ctx.sids = r_sites.sids
ctx.src_id = src_id
for par in self.REQUIRES_DISTANCES | {'rrup'}:
setattr(ctx, par, getattr(dctx, par))
if fewsites:
# get closest point on the surface
closest = rup.surface.get_closest_points(sites.complete)
ctx.clon = closest.lons[ctx.sids]
ctx.clat = closest.lats[ctx.sids]
ctxs.append(ctx)
return ctxs
# this is used with pointsource_distance approximation for close distances,
# when there are many ruptures affecting few sites
[docs] def collapse_the_ctxs(self, ctxs):
"""
Collapse contexts with similar parameters and distances.
:param ctxs: a list of pairs (rup, dctx)
:returns: collapsed contexts
"""
if len(ctxs) == 1:
return ctxs
if self.collapse_level >= 3: # hack, ignore everything except mag
rrp = ['mag']
rnd = 0 # round distances to 1 km
else:
rrp = self.REQUIRES_RUPTURE_PARAMETERS
rnd = 1 # round distances to 100 m
def params(ctx):
lst = []
for par in rrp:
lst.append(getattr(ctx, par))
for dst in self.REQUIRES_DISTANCES:
lst.extend(numpy.round(getattr(ctx, dst), rnd))
return tuple(lst)
out = []
for values in groupby(ctxs, params).values():
out.extend(_collapse(values))
return out
[docs] 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]
ctx = RuptureContext()
for par in self.REQUIRES_RUPTURE_PARAMETERS:
setattr(ctx, par, 0)
for dst in self.REQUIRES_DISTANCES:
setattr(ctx, dst, numpy.array([dist]))
for par in self.REQUIRES_SITES_PARAMETERS:
setattr(ctx, par, getattr(sitecol1, par))
ctx.sids = sitecol1.sids
ctx.mag = mag
ctx.width = .01 # 10 meters to avoid warnings in abrahamson_2014
try:
maxmean = max(ms[0].max() for ms in self.get_mean_stds(
[ctx], StdDev.TOTAL))
# shape NM
except ValueError: # magnitude outside of supported range
continue
else:
gmv[m, d] = numpy.exp(maxmean)
return gmv
[docs] def get_pmap(self, ctxs, probmap=None):
"""
:param ctxs: a list of contexts
:param probmap: if not None, update it
:returns: a new ProbabilityMap if probmap is None
"""
tom = self.tom
rup_indep = self.rup_indep
if probmap is None: # create new pmap
pmap = ProbabilityMap(self.imtls.size, len(self.gsims))
else: # update passed probmap
pmap = probmap
for ctx, poes in zip(ctxs, self.gen_poes(ctxs)):
# pnes and poes of shape (N, L, G)
with self.pne_mon:
pnes = get_probability_no_exceedance(ctx, poes, tom)
for sid, pne in zip(ctx.sids, pnes):
probs = pmap.setdefault(sid, self.rup_indep).array
if rup_indep:
probs *= pne
else: # rup_mutex
probs += (1. - pne) * ctx.weight
if probmap is None: # return the new pmap
return ~pmap if rup_indep else pmap
# called by gen_poes and by the GmfComputer
[docs] def get_mean_stds(self, ctxs, stdtype=StdDev.ALL):
"""
:param ctxs: a list of contexts
:param stdtype: a standard deviation type
:returns: a list of G arrays of shape (O, M, N) with mean and stddevs
"""
if not hasattr(self, 'imts'):
self.imts = tuple(imt_module.from_string(im) for im in self.imtls)
ctxs = [ctx.roundup(self.minimum_distance) for ctx in ctxs]
N = sum(len(ctx.sids) for ctx in ctxs)
M = len(self.imtls)
out = []
if self.use_recarray:
ctxs = [self.recarray(ctxs)]
for g, gsim in enumerate(self.gsims):
if stdtype is None or self.trunclevel == 0:
stypes = ()
elif stdtype == StdDev.EVENT:
if gsim.DEFINED_FOR_STANDARD_DEVIATION_TYPES == {StdDev.TOTAL}:
stypes = (StdDev.TOTAL,)
else:
stypes = (StdDev.INTER_EVENT, StdDev.INTRA_EVENT)
elif stdtype == StdDev.ALL:
stypes = tuple(sdt for sdt in STD_TYPES if sdt in
gsim.DEFINED_FOR_STANDARD_DEVIATION_TYPES)
else:
stypes = (stdtype,)
S = len(stypes)
arr = numpy.zeros((1 + S, M, N))
compute = gsim.__class__.__dict__.get('compute')
if compute: # new api
outs = numpy.zeros((4, M, N))
start = 0
for ctx in ctxs:
slc = slice(start, start + len(ctx))
compute(gsim, ctx, self.imts, *outs[:, :, slc])
start = slc.stop
arr[0] = outs[0]
for s, stype in enumerate(stypes, 1):
if stype == StdDev.TOTAL:
arr[s] = outs[1]
elif stype == StdDev.INTER_EVENT:
arr[s] = outs[2]
elif stype == StdDev.INTRA_EVENT:
arr[s] = outs[3]
else: # legacy api
start = 0
for ctx in ctxs:
stop = start + len(ctx.sids)
for m, imt in enumerate(self.imts):
mean, stds = gsim.get_mean_and_stddevs(
ctx, ctx, ctx, imt, stypes)
arr[0, m, start:stop] = mean
for s in range(S):
arr[1 + s, m, start:stop] = stds[s]
start = stop
out.append(arr)
return out
[docs] def gen_poes(self, ctxs):
"""
:param ctxs: a list of C context objects
:yields: poes of shape (N, L, G)
"""
from openquake.hazardlib.site_amplification import get_poes_site
with self.gmf_mon:
mean_stdt = self.get_mean_stds(ctxs, StdDev.TOTAL)
s = 0
for ctx in ctxs:
with self.poe_mon:
n = len(ctx)
poes = numpy.zeros((n, self.loglevels.size, len(self.gsims)))
for g, gsim in enumerate(self.gsims):
ms = mean_stdt[g][:, :, s:s+n]
# builds poes of shape (n, L, G)
if self.af: # kernel amplification method
poes[:, :, g] = get_poes_site(ms, self, ctx)
else: # regular case
poes[:, :, g] = gsim.get_poes(ms, self, ctx)
yield poes
s += n
# see contexts_tests.py for examples of collapse
[docs]def combine_pmf(o1, o2):
"""
Combine probabilities of occurrence; used to collapse nonparametric
ruptures.
:param o1: probability distribution of length n1
:param o2: probability distribution of length n2
:returns: probability distribution of length n1 + n2 - 1
>>> combine_pmf([.99, .01], [.98, .02])
array([9.702e-01, 2.960e-02, 2.000e-04])
"""
n1 = len(o1)
n2 = len(o2)
o = numpy.zeros(n1 + n2 - 1)
for i in range(n1):
for j in range(n2):
o[i + j] += o1[i] * o2[j]
return o
def _collapse(ctxs):
# collapse a list of contexts into a single context
if len(ctxs) < 2: # nothing to collapse
return ctxs
prups, nrups, out = [], [], []
for ctx in ctxs:
if numpy.isnan(ctx.occurrence_rate): # nonparametric
nrups.append(ctx)
else: # parametric
prups.append(ctx)
if len(prups) > 1:
ctx = copy.copy(prups[0])
ctx.occurrence_rate = sum(r.occurrence_rate for r in prups)
out.append(ctx)
else:
out.extend(prups)
if len(nrups) > 1:
ctx = copy.copy(nrups[0])
ctx.probs_occur = functools.reduce(
combine_pmf, (n.probs_occur for n in nrups))
out.append(ctx)
else:
out.extend(nrups)
return out
[docs]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()))
[docs]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.N = len(self.srcfilter.sitecol.complete)
self.group = group
self.src_mutex = getattr(group, 'src_interdep', None) == 'mutex'
self.cmaker.rup_indep = getattr(group, 'rup_interdep', None) != 'mutex'
self.fewsites = self.N <= cmaker.max_sites_disagg
[docs] def count_bytes(self, ctxs):
# # usuful for debugging memory issues
rparams = len(self.cmaker.REQUIRES_RUPTURE_PARAMETERS)
sparams = len(self.cmaker.REQUIRES_SITES_PARAMETERS) + 1
dparams = len(self.cmaker.REQUIRES_DISTANCES)
nbytes = 0
for ctx in ctxs:
nsites = len(ctx.sids)
nbytes += 8 * rparams
nbytes += 8 * sparams * nsites
nbytes += 8 * dparams * nsites
return nbytes
def _ruptures(self, src, filtermag=None):
return src.iter_ruptures(
shift_hypo=self.shift_hypo, mag=filtermag)
def _get_ctxs(self, rups, sites, srcid):
ctxs = self.cmaker.get_ctxs(rups, sites, srcid, self.ctx_mon)
if self.collapse_level > 1:
ctxs = self.cmaker.collapse_the_ctxs(ctxs)
out = []
for ctx in ctxs:
self.numsites += len(ctx.sids)
self.numctxs += 1
if self.fewsites: # keep the contexts in memory
self.rupdata.append(ctx)
out.append(ctx)
return out
def _make_src_indep(self):
# sources with the same ID
pmap = ProbabilityMap(self.imtls.size, len(self.gsims))
# split the sources only if there is more than 1 site
filt = (self.srcfilter.split_less if self.N == 1
else self.srcfilter.split)
for src, sites in filt(self.group):
t0 = time.time()
if self.fewsites:
sites = sites.complete
self.numctxs = 0
self.numsites = 0
rups = self._gen_rups(src, sites)
self.cmaker.get_pmap(self._get_ctxs(rups, sites, src.id), pmap)
dt = time.time() - t0
self.calc_times[src.id] += numpy.array(
[self.numctxs, self.numsites, dt])
timer.save(src, self.numctxs, self.numsites, dt,
self.cmaker.task_no)
return ~pmap if self.cmaker.rup_indep else pmap
def _make_src_mutex(self):
pmap = ProbabilityMap(self.imtls.size, len(self.gsims))
for src, sites in self.srcfilter.filter(self.group):
t0 = time.time()
self.numctxs = 0
self.numsites = 0
rups = self._ruptures(src)
pm = ProbabilityMap(self.cmaker.imtls.size, len(self.cmaker.gsims))
self.cmaker.get_pmap(self._get_ctxs(rups, sites, src.id), pm)
p = pm
if self.cmaker.rup_indep:
p = ~p
p *= src.mutex_weight
pmap += p
dt = time.time() - t0
self.calc_times[src.id] += numpy.array(
[self.numctxs, self.numsites, dt])
timer.save(src, self.numctxs, self.numsites, dt,
self.cmaker.task_no)
return pmap
[docs] def dictarray(self, ctxs):
dic = {} # par -> array
z = numpy.zeros(0)
for par in self.cmaker.get_ctx_params():
pa = par[:-1] if par.endswith('_') else par
dic[par] = numpy.array([getattr(ctx, pa, z) for ctx in ctxs])
return dic
[docs] def make(self):
self.rupdata = []
# AccumDict of arrays with 3 elements nrups, nsites, calc_time
self.calc_times = AccumDict(accum=numpy.zeros(3, numpy.float32))
if self.src_mutex:
pmap = self._make_src_mutex()
else:
pmap = self._make_src_indep()
dic = {'pmap': pmap,
'rup_data': self.dictarray(self.rupdata),
'calc_times': self.calc_times,
'task_no': self.task_no,
'grp_id': self.group[0].grp_id}
if self.disagg_by_src:
dic['source_id'] = self.group[0].source_id
return dic
def _gen_rups(self, src, sites):
# yield ruptures, each one with a .sites attribute
def rups(rupiter, sites):
for rup in rupiter:
rup.sites = sites
yield rup
bigps = getattr(src, 'location', None) and src.count_nphc() > 1
if bigps and self.pointsource_distance == 0:
# finite size effects are averaged always
yield from rups(src.avg_ruptures(), sites)
elif bigps and self.pointsource_distance:
# finite site effects are averaged for sites over the
# pointsource_distance from the rupture (if any)
cdist = sites.get_cdist(src.location)
for ar in src.avg_ruptures():
pdist = self.pointsource_distance['%.2f' % ar.mag]
close = sites.filter(cdist <= pdist)
far = sites.filter(cdist > pdist)
if self.fewsites:
if close is None: # all is far, common for small mag
yield from rups([ar], sites)
else: # something is close
yield from rups(self._ruptures(src, ar.mag), sites)
else: # many sites
if close is None: # all is far
yield from rups([ar], far)
elif far is None: # all is close
yield from rups(self._ruptures(src, ar.mag), close)
else: # some sites are far, some are close
yield from rups([ar], far)
yield from rups(self._ruptures(src, ar.mag), close)
else: # just add the ruptures
yield from rups(self._ruptures(src), sites)
[docs]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
[docs]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))
# used in the SMTK
def __len__(self):
return len(self.sids)
[docs]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)
[docs] 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
[docs]def get_dists(ctx):
"""
Extract the distance parameters from a context.
:returns: a dictionary dist_name -> distances
"""
return {par: dist for par, dist in vars(ctx).items()
if par in KNOWN_DISTANCES}
[docs]def full_context(sites, rup, dctx=None):
"""
:returns: a full RuptureContext with all the relevant attributes
"""
self = RuptureContext()
for par, val in vars(rup).items():
setattr(self, par, val)
if hasattr(sites, 'array'): # is a SiteCollection
for par in sites.array.dtype.names:
setattr(self, par, sites[par])
else: # sites is a SitesContext
for par, val in vars(sites).items():
setattr(self, par, val)
if dctx:
for par, val in vars(dctx).items():
setattr(self, par, val)
return self
[docs]def get_mean_stds(gsims, ctx, imts, stdtype=StdDev.ALL):
"""
:param gsims: a list of G GSIMs
:param ctx: a RuptureContext or a recarray of size N
:param imts: a list of M IMTs
:param stdtype: a standard deviation type (TOTAL, EVENT, etc)
:returns:
an array of shape (G, O, M, N) obtained by applying the
given GSIMs, ctx amd imts
"""
imtls = {imt.string: [0] for imt in imts}
cmaker = ContextMaker('*', gsims, {'imtls': imtls})
return numpy.array(cmaker.get_mean_stds([ctx], stdtype))
# mock of a rupture used in the tests and in the SMTK
[docs]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')
def __init__(self, param_pairs=()):
for param, value in param_pairs:
setattr(self, param, value)
[docs] def size(self):
"""
If the context is a multi rupture context, i.e. it contains an array
of magnitudes and it refers to a single site, returns the size of
the array, otherwise returns 1.
"""
nsites = len(self.sids)
if nsites == 1 and isinstance(self.mag, numpy.ndarray):
return len(self.mag)
return nsites
# used in acme_2019
def __len__(self):
return len(self.sids)
[docs] def roundup(self, minimum_distance):
"""
If the minimum_distance is nonzero, returns a copy of the
RuptureContext with updated distances, i.e. the ones below
minimum_distance are rounded up to the minimum_distance. Otherwise,
returns the original.
"""
if not minimum_distance:
return self
ctx = copy.copy(self)
for dist, array in vars(self).items():
if dist in KNOWN_DISTANCES:
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
[docs]def get_probability_no_exceedance(rup, poes, tom):
"""
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 rup:
an object with attributes .occurrence_rate and possibly .probs_occur
: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>`
:param tom:
temporal occurrence model instance, used only if the rupture
is parametric
"""
if numpy.isnan(rup.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``.
prob_no_exceed = numpy.float64(
[v * (1 - poes) ** i for i, v in enumerate(rup.probs_occur)]
).sum(axis=0)
return numpy.clip(prob_no_exceed, 0., 1.) # avoid numeric issues
# parametric rupture
return tom.get_probability_no_exceedance(rup.occurrence_rate, poes)
[docs]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)
[docs] 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
[docs] 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
[docs]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 MagDepDistance 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))
# not used at the moment
[docs]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
if psd is not None:
psd.interp(mags)
psd = psd.ddic
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.ddic[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))
return aw
# not used right now
[docs]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, indices in srcfilter.filter(sources):
sites = srcfilter.sitecol.filtered(indices)
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)}
[docs]def read_cmakers(dstore, full_lt=None):
"""
:param dstore: a DataStore-like object
:param full_lt: a FullLogicTree instance, if given
:returns: a list of ContextMaker instance, one per source group
"""
from openquake.hazardlib.site_amplification import AmplFunction
cmakers = []
oq = dstore['oqparam']
full_lt = full_lt or dstore['full_lt']
trt_smrs = dstore['trt_smrs'][:]
toms = dstore['toms'][:]
rlzs_by_gsim_list = full_lt.get_rlzs_by_gsim_list(trt_smrs)
trts = list(full_lt.gsim_lt.values)
num_eff_rlzs = len(full_lt.sm_rlzs)
start = 0
# some ugly magic on the pointsource_distance
if oq.pointsource_distance:
mags = dstore['source_mags']
psd = MagDepDistance.new(str(oq.pointsource_distance))
psd.interp({trt: mags[trt][:] for trt in mags})
oq.pointsource_distance = psd
for grp_id, rlzs_by_gsim in enumerate(rlzs_by_gsim_list):
trti = trt_smrs[grp_id][0] // num_eff_rlzs
trt = trts[trti]
if ('amplification' in oq.inputs and
oq.amplification_method == 'kernel'):
df = AmplFunction.read_df(oq.inputs['amplification'])
af = AmplFunction.from_dframe(df)
else:
af = None
cmaker = ContextMaker(
trt, rlzs_by_gsim,
{'truncation_level': oq.truncation_level,
'collapse_level': int(oq.collapse_level),
'num_epsilon_bins': oq.num_epsilon_bins,
'investigation_time': oq.investigation_time,
'maximum_distance': oq.maximum_distance,
'pointsource_distance': oq.pointsource_distance,
'minimum_distance': oq.minimum_distance,
'ses_seed': oq.ses_seed,
'ses_per_logic_tree_path': oq.ses_per_logic_tree_path,
'max_sites_disagg': oq.max_sites_disagg,
'disagg_by_src': oq.disagg_by_src,
'min_iml': oq.min_iml,
'imtls': oq.imtls,
'reqv': oq.get_reqv(),
'shift_hypo': oq.shift_hypo,
'af': af,
'grp_id': grp_id})
cmaker.tom = registry[toms[grp_id]](oq.investigation_time)
cmaker.trti = trti
cmaker.start = start
start += len(rlzs_by_gsim)
cmakers.append(cmaker)
return cmakers
[docs]def read_cmaker(dstore, trt_smr):
"""
:param dstore: a DataStore-like object
:returns: a ContextMaker instance
"""
oq = dstore['oqparam']
full_lt = dstore['full_lt']
trts = list(full_lt.gsim_lt.values)
trt = trts[trt_smr // len(full_lt.sm_rlzs)]
rlzs_by_gsim = full_lt.get_rlzs_by_gsim()[trt_smr]
mags = dstore['source_mags']
md = MagDepDistance.new(str(oq.maximum_distance))
md.interp({trt: mags[trt][:] for trt in mags})
cmaker = ContextMaker(
trt, rlzs_by_gsim,
{'truncation_level': oq.truncation_level,
'collapse_level': int(oq.collapse_level),
'num_epsilon_bins': oq.num_epsilon_bins,
'investigation_time': oq.investigation_time,
'maximum_distance': md,
'minimum_distance': oq.minimum_distance,
'ses_seed': oq.ses_seed,
'ses_per_logic_tree_path': oq.ses_per_logic_tree_path,
'max_sites_disagg': oq.max_sites_disagg,
'disagg_by_src': oq.disagg_by_src,
'min_iml': oq.min_iml,
'imtls': oq.imtls,
'reqv': oq.get_reqv(),
'shift_hypo': oq.shift_hypo})
return cmaker