# The Hazard Library
# Copyright (C) 2012-2014, GEM Foundation
#
# This program 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.
#
# This program 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 this program. 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.hazardlib.const import StdDev
from openquake.hazardlib.calc import filters
from openquake.hazardlib.gsim.base import gsim_imt_dt, 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. The usage is::
gmfcomputer = GmfComputer(rupture, r_sites, imts, gsims,
truncation_level, correlation_model)
gmf1 = gmfcomputer.compute(seed1)
gmf2 = gmfcomputer.compute(seed2)
:param :class:`openquake.hazardlib.source.rupture.Rupture` rupture:
Rupture to calculate ground motion fields radiated from.
:param :class:`openquake.hazardlib.site.SiteCollection` sites:
Sites of interest to calculate GMFs.
:param imts:
Sorted list of intensity measure type strings
:param gsims:
Ground-shaking intensity models, instances of subclass of either
:class:`~openquake.hazardlib.gsim.base.GMPE` or
:class:`~openquake.hazardlib.gsim.base.IPE`, sorted lexicographically.
: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.
"""
def __init__(self, rupture, sites, imts, gsims,
truncation_level=None, correlation_model=None):
assert sites and imts, (sites, imts)
self.rupture = rupture
self.sites = sites
self.imts = list(map(from_string, imts))
self.gsims = gsims
self.truncation_level = truncation_level
self.correlation_model = correlation_model
self.ctx = ContextMaker(gsims).make_contexts(sites, rupture)
self.gmf_dt = gsim_imt_dt(gsims, imts)
def _compute(self, seed, gsim, realizations):
# the method doing the real stuff; use compute instead
if seed is not None:
numpy.random.seed(seed)
result = collections.OrderedDict()
sctx, rctx, dctx = self.ctx
if self.truncation_level == 0:
assert self.correlation_model is None
for imt in self.imts:
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(realizations, axis=1)
result[str(imt)] = mean
return result
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)
for imt in self.imts:
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), realizations)
)
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), realizations)
)
if self.correlation_model is not None:
intra_residual = self.correlation_model.apply_correlation(
self.sites, imt, intra_residual
)
inter_residual = stddev_inter * distribution.rvs(
size=realizations)
gmf = gsim.to_imt_unit_values(
mean + intra_residual + inter_residual)
result[str(imt)] = gmf
return result
[docs] def compute(self, seeds):
"""
Compute the ground motion field for the given sites and seeds.
:param seeds:
S seeds for the numpy random number generator
:returns:
a list of numpy arrays of dtype gmf_dt and length num_sites
"""
n = len(self.sites)
indices = self.sites.indices
gmfs = []
for seed in seeds:
gmfa = numpy.zeros(n, self.gmf_dt)
for gsim in self.gsims:
gs = str(gsim)
for imt, value in self._compute(
seed, gsim, realizations=1).items():
# 1 realization, get the 0-th colum of the v-array
array = list(map(float, value[:, 0]))
# NB: with correlation, the value is a numpy.matrix
# not an array, flatten does not work and the only
# way to extract the numbers is the map before!
# something is wrong and must be fixed in the future
for i, gmv in enumerate(array):
gmfa[i]['idx'] = indices[i]
gmfa[i][gs][imt] = gmv
gmfs.append(gmfa)
return gmfs
# 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.
"""
ruptures_sites = list(rupture_site_filter([(rupture, sites)]))
if not ruptures_sites:
return dict((imt, numpy.zeros((len(sites), realizations)))
for imt in imts)
[(rupture, sites)] = ruptures_sites
gc = GmfComputer(rupture, sites, list(map(str, imts)), [gsim],
truncation_level, correlation_model)
result = gc._compute(seed, gsim, realizations)
for imt, gmf in result.items():
# 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] = sites.expand(gmf, placeholder=0)
return {from_string(imt): result[imt] for imt in result}