# -*- coding: utf-8 -*-
# vim: tabstop=4 shiftwidth=4 softtabstop=4
#
# Copyright (C) 2012-2016 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 collections
import numpy
import scipy.stats
from openquake.baselib.python3compat import zip
from openquake.baselib.general import get_array
from openquake.hazardlib.const import StdDev
from openquake.hazardlib.calc import filters
from openquake.hazardlib.gsim.base import ContextMaker
from openquake.hazardlib.imt import from_string
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]class GmfComputer(object):
"""
Given an earthquake rupture, the ground motion field computer computes
ground shaking over a set of sites, by randomly sampling a ground
shaking intensity model.
:param rupture:
Rupture to calculate ground motion fields radiated from.
:param :class:`openquake.hazardlib.site.SiteCollection` sites:
Sites of interest to calculate GMFs.
:param imts:
a sorted list of Intensity Measure Type strings
:param truncation_level:
Float, number of standard deviations for truncation of the intensity
distribution, or ``None``.
: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.
"""
# The GmfComputer is called from the OpenQuake Engine. In that case
# the rupture is an higher level containing a
# :class:`openquake.hazardlib.source.rupture.Rupture` instance as an
# attribute. Then the `.compute(gsim, num_events)` method is called and
# a matrix of size (I, N, E) is returned, where I 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 and salted in such a
# way to produce different numbers even if the method is called twice
# with the same `gsim`. This ensures that different GMPE logic tree
# realizations produce different numbers even in the case of complex
# logic trees.
def __init__(self, rupture, sites, imts, gsims,
truncation_level=None, correlation_model=None, samples=0):
assert sites, sites
self.rupture = rupture
self.sites = sites
self.imts = [from_string(imt) for imt in imts]
self.gsims = gsims
self.truncation_level = truncation_level
self.correlation_model = correlation_model
self.samples = samples
# `rupture` can be a high level rupture object containing a low
# level hazardlib rupture object as a .rupture attribute
if hasattr(rupture, 'rupture'):
rupture = rupture.rupture
self.salt = collections.Counter() # associate a salt to the gsims
self.ctx = ContextMaker(gsims).make_contexts(sites, rupture)
[docs] def compute(self, gsim, num_events, seed=None):
"""
:param gsim: a GSIM instance
:param num_events: the number of seismic events
:param seed: a random seed or None
:returns: a 32 bit array of shape (num_imts, num_sites, num_events)
"""
if hasattr(self, 'salt'): # when called from the engine
seed = (seed or self.rupture.rupture.seed) + self.salt[gsim]
self.salt[gsim] += 1
if seed is not None:
numpy.random.seed(seed)
result = numpy.zeros(
(len(self.imts), len(self.sites), num_events), numpy.float32)
for imti, imt in enumerate(self.imts):
result[imti] = self._compute(None, gsim, num_events, imt)
return result
def _compute(self, seed, gsim, num_events, imt):
"""
:param seed: a random seed or None if the seed is already set
:param gsim: a GSIM instance
:param num_events: the number of seismic events
:param imt: an IMT instance
:returns: a 32 bit array of shape (num_sites, num_events)
"""
if seed is not None:
numpy.random.seed(seed)
sctx, rctx, dctx = self.ctx
if self.truncation_level == 0:
assert self.correlation_model is None
mean, _stddevs = gsim.get_mean_and_stddevs(
sctx, rctx, dctx, imt, stddev_types=[])
mean = gsim.to_imt_unit_values(mean)
mean.shape += (1, )
mean = mean.repeat(num_events, axis=1)
return mean
elif self.truncation_level is None:
distribution = scipy.stats.norm()
else:
assert self.truncation_level > 0
distribution = scipy.stats.truncnorm(
- self.truncation_level, self.truncation_level)
if gsim.DEFINED_FOR_STANDARD_DEVIATION_TYPES == \
set([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)
mean, [stddev_total] = gsim.get_mean_and_stddevs(
sctx, rctx, dctx, imt, [StdDev.TOTAL])
stddev_total = stddev_total.reshape(stddev_total.shape + (1, ))
mean = mean.reshape(mean.shape + (1, ))
total_residual = stddev_total * distribution.rvs(
size=(len(self.sites), num_events))
gmf = gsim.to_imt_unit_values(mean + total_residual)
else:
mean, [stddev_inter, stddev_intra] = gsim.get_mean_and_stddevs(
sctx, rctx, dctx, imt,
[StdDev.INTER_EVENT, StdDev.INTRA_EVENT])
stddev_intra = stddev_intra.reshape(stddev_intra.shape + (1, ))
stddev_inter = stddev_inter.reshape(stddev_inter.shape + (1, ))
mean = mean.reshape(mean.shape + (1, ))
intra_residual = stddev_intra * distribution.rvs(
size=(len(self.sites), num_events))
if self.correlation_model is not None:
ir = self.correlation_model.apply_correlation(
self.sites, imt, intra_residual)
# this fixes a mysterious bug: ir[row] is actually
# a matrix of shape (E, 1) and not a vector of size E
intra_residual = numpy.zeros(ir.shape)
for i, val in numpy.ndenumerate(ir):
intra_residual[i] = val
inter_residual = stddev_inter * distribution.rvs(
size=num_events)
gmf = gsim.to_imt_unit_values(
mean + intra_residual + inter_residual)
return gmf
# 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,
rupture_site_filter=filters.rupture_site_noop_filter,
seed=None):
"""
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. If a non-trivial
filtering function is passed, the final result is expanded and filled
with zeros in the places corresponding to the filtered out sites.
.. 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, or ``None``.
:param realizations:
Integer number of GMF realizations 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 rupture_site_filter:
Optional rupture-site filter function. See
:mod:`openquake.hazardlib.calc.filters`.
: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 realizations of ground shaking intensity
for all sites in the collection. First dimension represents
sites and second one is for realizations.
"""
r_sites = rupture_site_filter.affected(rupture, sites)
if r_sites is None:
return dict((imt, numpy.zeros((len(sites), realizations)))
for imt in imts)
gc = GmfComputer(rupture, r_sites, [str(imt) for imt in imts], [gsim],
truncation_level, correlation_model)
res = gc.compute(gsim, realizations, seed)
result = {}
for imti, imt in enumerate(gc.imts):
# makes sure the lenght of the arrays in output is the same as sites
if rupture_site_filter is not filters.rupture_site_noop_filter:
result[imt] = r_sites.expand(res[imti], placeholder=0)
else:
result[imt] = res[imti]
return result