# -*- coding: utf-8 -*-
# vim: tabstop=4 shiftwidth=4 softtabstop=4
#
# Copyright (C) 2012-2023 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/>.
"""
Module :mod:`~openquake.hazardlib.calc.gmf` exports
:func:`ground_motion_fields`.
"""
import numpy
import pandas
from openquake.baselib.general import AccumDict
from openquake.baselib.performance import Monitor, compile
from openquake.hazardlib.const import StdDev
from openquake.hazardlib.source.rupture import EBRupture, get_eid_rlz
from openquake.hazardlib.cross_correlation import NoCrossCorrelation
from openquake.hazardlib.gsim.base import ContextMaker, FarAwayRupture
from openquake.hazardlib.imt import from_string
U32 = numpy.uint32
F32 = numpy.float32
[docs]class CorrelationButNoInterIntraStdDevs(Exception):
def __init__(self, corr, gsim):
self.corr = corr
self.gsim = gsim
def __str__(self):
return '''\
You cannot use the correlation model %s with the GSIM %s, \
that defines only the total standard deviation. If you want to use a \
correlation model you have to select a GMPE that provides the inter and \
intra event standard deviations.''' % (
self.corr.__class__.__name__, self.gsim.__class__.__name__)
[docs]@compile(["float64[:,:](float64[:,:], boolean)",
"float64[:](float64[:], boolean)",
"float64(float64, boolean)"])
def exp(vals, notMMI):
"""
Exponentiate the values unless the IMT is MMI
"""
if notMMI:
return numpy.exp(vals)
return vals
[docs]@compile("(float32[:,:,:],float64[:,:],float64[:],float64[:],int64)")
def set_max_min(array, mean, max_iml, min_iml, mmi_index):
N, M, E = array.shape
# manage max_iml
for m in range(M):
iml = max_iml[m]
for n in range(N):
maxval = exp(mean[m, n], m!=mmi_index)
for e in range(E):
val = array[n, m, e]
if val > iml:
array[n, m, e] = maxval
# manage min_iml
for n in range(N):
for e in range(E):
# set to zero only if all IMTs are below the thresholds
if (array[n, :, e] < min_iml).all():
array[n, :, e] = 0
[docs]@compile("(uint32[:],uint32[:],uint32[:],uint32[:])")
def build_eid_sid_rlz(allrlzs, sids, eids, rlzs):
eid_sid_rlz = numpy.zeros((3, len(sids) * len(eids)), U32)
idx = 0
for rlz in allrlzs:
for eid in eids[rlzs == rlz]:
for sid in sids:
eid_sid_rlz[0, idx] = eid
eid_sid_rlz[1, idx] = sid
eid_sid_rlz[2, idx] = rlz
idx += 1
return eid_sid_rlz
[docs]class GmfComputer(object):
"""
Given an earthquake rupture, the GmfComputer computes
ground shaking over a set of sites, by randomly sampling a ground
shaking intensity model.
:param rupture:
EBRupture to calculate ground motion fields radiated from.
:param :class:`openquake.hazardlib.site.SiteCollection` sitecol:
a complete SiteCollection
:param cmaker:
a :class:`openquake.hazardlib.gsim.base.ContextMaker` instance
:param correlation_model:
Instance of a spatial correlation model object. See
:mod:`openquake.hazardlib.correlation`. Can be ``None``, in which
case non-correlated ground motion fields are calculated.
Correlation model is not used if ``truncation_level`` is zero.
:param cross_correl:
Instance of a cross correlation model object. See
:mod:`openquake.hazardlib.cross_correlation`. Can be ``None``, in which
case non-cross-correlated ground motion fields are calculated.
:param amplifier:
None or an instance of Amplifier
:param sec_perils:
Tuple of secondary perils. See
:mod:`openquake.hazardlib.sep`. Can be ``None``, in which
case no secondary perils need to be evaluated.
"""
# The GmfComputer is called from the OpenQuake Engine. In that case
# the rupture is an EBRupture instance containing a
# :class:`openquake.hazardlib.source.rupture.Rupture` instance as an
# attribute. Then the `.compute(gsim, num_events, ms)` method is called and
# a matrix of size (M, N, E) is returned, where M is the number of
# IMTs, N the number of affected sites and E the number of events. The
# seed is extracted from the underlying rupture.
def __init__(self, rupture, sitecol, cmaker, correlation_model=None,
cross_correl=None, amplifier=None, sec_perils=()):
if len(sitecol) == 0:
raise ValueError('No sites')
elif len(cmaker.imtls) == 0:
raise ValueError('No IMTs')
elif len(cmaker.gsims) == 0:
raise ValueError('No GSIMs')
self.cmaker = cmaker
self.imts = [from_string(imt) for imt in cmaker.imtls]
self.cmaker = cmaker
self.gsims = sorted(cmaker.gsims)
self.correlation_model = correlation_model
self.amplifier = amplifier
self.sec_perils = sec_perils
self.ebrupture = rupture
self.seed = rupture.seed
rupture = rupture.rupture # the underlying rupture
ctxs = list(cmaker.get_ctx_iter([rupture], sitecol))
if not ctxs:
raise FarAwayRupture
[self.ctx] = ctxs
self.N = len(self.ctx)
if correlation_model: # store the filtered sitecol
self.sites = sitecol.complete.filtered(self.ctx.sids)
self.cross_correl = cross_correl or NoCrossCorrelation(
cmaker.truncation_level)
self.gmv_fields = [f'gmv_{m}' for m in range(len(cmaker.imts))]
[docs] def init_eid_rlz_sig_eps(self):
"""
Initialize the attributes eid, rlz, sig, eps with shapes E, E, EM, EM
"""
self.rlzs = numpy.concatenate(list(self.cmaker.gsims.values()))
self.eid, self.rlz = get_eid_rlz(
vars(self.ebrupture), self.rlzs, self.cmaker.scenario)
self.E = E = len(self.eid)
self.M = M = len(self.gmv_fields)
self.sig = numpy.zeros((E, M), F32) # same for all events
self.eps = numpy.zeros((E, M), F32) # not the same
[docs] def build_sig_eps(self, se_dt):
"""
:returns: a structured array of size E with fields
(eid, rlz_id, sig_inter_IMT, eps_inter_IMT)
"""
sig_eps = numpy.zeros(self.E, se_dt)
sig_eps['eid'] = self.eid
sig_eps['rlz_id'] = self.rlz
for m, imt in enumerate(self.cmaker.imtls):
sig_eps[f'sig_inter_{imt}'] = self.sig[:, m]
sig_eps[f'eps_inter_{imt}'] = self.eps[:, m]
return sig_eps
[docs] def update(self, data, array, rlzs, mean_stds, max_iml=None):
"""
Updates the data dictionary with the values coming from the array
of GMVs. Also indirectly updates the arrays .sig and .eps.
"""
min_iml = self.cmaker.min_iml
mag = self.ebrupture.rupture.mag
mean = mean_stds[0]
if len(mean.shape) == 3: # shape (M, N, 1) for conditioned gmfs
mean = mean[:, :, 0]
mmi_index = -1
for m, imt in enumerate(self.cmaker.imtls):
if imt == 'MMI':
mmi_index = m
if max_iml is None:
M = len(self.cmaker.imts)
max_iml = numpy.full(M, numpy.inf, float)
set_max_min(array, mean, max_iml, min_iml, mmi_index)
data['gmv'].append(array)
if self.sec_perils:
n = 0
for rlz in rlzs:
eids = self.eid[self.rlz == rlz]
E = len(eids)
for e, eid in enumerate(eids):
gmfa = array[:, :, n + e].T # shape (M, N)
for sp in self.sec_perils:
o = sp.compute(mag, zip(self.imts, gmfa), self.ctx)
for outkey, outarr in zip(sp.outputs, o):
data[outkey].append(outarr)
n += E
[docs] def strip_zeros(self, data):
"""
:returns: a DataFrame with the nonzero GMVs
"""
# building an array of shape (3, NE)
eid_sid_rlz = build_eid_sid_rlz(
self.rlzs, self.ctx.sids, self.eid, self.rlz)
for key, val in sorted(data.items()):
data[key] = numpy.concatenate(data[key], axis=-1, dtype=F32)
gmv = data.pop('gmv') # shape (N, M, E)
ok = gmv.sum(axis=1).T.reshape(-1) > 0
for m, gmv_field in enumerate(self.gmv_fields):
data[gmv_field] = gmv[:, m].T.reshape(-1)
# build dataframe
df = pandas.DataFrame(data)
df['eid'] = eid_sid_rlz[0]
df['sid'] = eid_sid_rlz[1]
df['rlz'] = eid_sid_rlz[2]
# remove the rows with all zero values
return df[ok]
[docs] def compute_all(self, mean_stds, max_iml=None,
cmon=Monitor(), umon=Monitor()):
"""
:returns: DataFrame with fields eid, rlz, sid, gmv_X, ...
"""
self.init_eid_rlz_sig_eps()
rng = numpy.random.default_rng(self.seed)
data = AccumDict(accum=[])
for g, (gs, rlzs) in enumerate(self.cmaker.gsims.items()):
idxs, = numpy.where(numpy.isin(self.rlz, rlzs))
E = len(idxs)
if E == 0: # crucial for performance
continue
with cmon:
array = self.compute(gs, idxs, mean_stds[:, g], rng) # NME
with umon:
self.update(data, array, rlzs, mean_stds[:, g], max_iml)
with umon:
return self.strip_zeros(data)
[docs] def compute(self, gsim, idxs, mean_stds, rng):
"""
:param gsim: GSIM used to compute mean_stds
:param idxs: affected indices
:param mean_stds: array of shape (4, M, N)
:param rng: random number generator for the rupture
:returns: a 32 bit array of shape (N, M, E)
"""
# sets self.eps
M = len(self.imts)
E = len(idxs)
result = numpy.zeros(
(len(self.imts), len(self.ctx.sids), E), F32)
ccdist = self.cross_correl.distribution
# build arrays of random numbers of shape (M, N, E) and (M, E)
intra_eps = [ccdist.rvs((self.N, E), rng) for _ in range(M)]
self.eps[idxs] = self.cross_correl.get_inter_eps(self.imts, E, rng).T
for m, imt in enumerate(self.imts):
try:
result[m] = self._compute(
mean_stds[:, m], m, imt, gsim, intra_eps[m], idxs)
except Exception as exc:
raise RuntimeError(
'(%s, %s, %s): %s' %
(gsim, imt, exc.__class__.__name__, exc)
).with_traceback(exc.__traceback__)
if self.amplifier:
self.amplifier.amplify_gmfs(
self.ctx.ampcode, result, self.imts, self.seed)
return result.transpose(1, 0, 2)
def _compute(self, mean_stds, m, imt, gsim, intra_eps, idxs):
# sets self.sig, returns gmf
im = imt.string
mean, sig, tau, phi = mean_stds
if self.cmaker.truncation_level <= 1E-9:
# for truncation_level = 0 there is only mean, no stds
if self.correlation_model:
raise ValueError('truncation_level=0 requires '
'no correlation model')
gmf = exp(mean, im!='MMI')[:, None].repeat(len(idxs), axis=1)
elif gsim.DEFINED_FOR_STANDARD_DEVIATION_TYPES == {StdDev.TOTAL}:
# If the GSIM provides only total standard deviation, we need
# to compute mean and total standard deviation at the sites
# of interest.
# In this case, we also assume no correlation model is used.
if self.correlation_model:
raise CorrelationButNoInterIntraStdDevs(
self.correlation_model, gsim)
gmf = exp(mean[:, None] + sig[:, None] * intra_eps, im!='MMI')
self.sig[idxs, m] = numpy.nan
else:
# the [:, None] is used to implement multiplication by row;
# for instance if a = [1 2], b = [[1 2] [3 4]] then
# a[:, None] * b = [[1 2] [6 8]] which is the expected result;
# otherwise one would get multiplication by column [[1 4] [3 8]]
intra_res = phi[:, None] * intra_eps # shape (N, E)
if self.correlation_model is not None:
intra_res = self.correlation_model.apply_correlation(
self.sites, imt, intra_res, phi)
if len(intra_res.shape) == 1: # a vector
intra_res = intra_res[:, None]
inter_res = tau[:, None] * self.eps[idxs, m]
# shape (N, 1) * E => (N, E)
gmf = exp(mean[:, None] + intra_res + inter_res, im!='MMI')
self.sig[idxs, m] = tau.max() # from shape (N, 1) => scalar
return gmf # shapes (N, E)
# this is not used in the engine; it is still useful for usage in IPython
# when demonstrating hazardlib capabilities
[docs]def ground_motion_fields(rupture, sites, imts, gsim, truncation_level,
realizations, correlation_model=None, seed=0):
"""
Given an earthquake rupture, the ground motion field calculator computes
ground shaking over a set of sites, by randomly sampling a ground shaking
intensity model. A ground motion field represents a possible 'realization'
of the ground shaking due to an earthquake rupture.
.. note::
This calculator is using random numbers. In order to reproduce the
same results numpy random numbers generator needs to be seeded, see
http://docs.scipy.org/doc/numpy/reference/generated/numpy.random.seed.html
:param openquake.hazardlib.source.rupture.Rupture rupture:
Rupture to calculate ground motion fields radiated from.
:param openquake.hazardlib.site.SiteCollection sites:
Sites of interest to calculate GMFs.
:param imts:
List of intensity measure type objects (see
:mod:`openquake.hazardlib.imt`).
:param gsim:
Ground-shaking intensity model, instance of subclass of either
:class:`~openquake.hazardlib.gsim.base.GMPE` or
:class:`~openquake.hazardlib.gsim.base.IPE`.
:param truncation_level:
Float, number of standard deviations for truncation of the intensity
distribution
:param realizations:
Integer number of GMF simulations to compute.
:param correlation_model:
Instance of correlation model object. See
:mod:`openquake.hazardlib.correlation`. Can be ``None``, in which case
non-correlated ground motion fields are calculated. Correlation model
is not used if ``truncation_level`` is zero.
:param int seed:
The seed used in the numpy random number generator
:returns:
Dictionary mapping intensity measure type objects (same
as in parameter ``imts``) to 2d numpy arrays of floats,
representing different simulations of ground shaking intensity
for all sites in the collection. First dimension represents
sites and second one is for simulations.
"""
cmaker = ContextMaker(rupture.tectonic_region_type, {gsim: U32([0])},
dict(truncation_level=truncation_level,
imtls={str(imt): numpy.array([0.])
for imt in imts}))
cmaker.scenario = True
ebr = EBRupture(
rupture, source_id=0, trt_smr=0, n_occ=realizations, id=0, e0=0)
ebr.seed = seed
gc = GmfComputer(ebr, sites, cmaker, correlation_model)
mean_stds = cmaker.get_mean_stds([gc.ctx])[:, 0] # shape (4, M, N)
gc.init_eid_rlz_sig_eps()
res = gc.compute(gsim, U32([0]), mean_stds,
numpy.random.default_rng(seed))
return {imt: res[:, m] for m, imt in enumerate(gc.imts)}