Source code for openquake.hazardlib.calc.gmf

# -*- coding: utf-8 -*-
# vim: tabstop=4 shiftwidth=4 softtabstop=4
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# Copyright (C) 2012-2018 GEM Foundation
#
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"""
Module :mod:`~openquake.hazardlib.calc.gmf` exports
:func:`ground_motion_fields`.
"""
import numpy
import scipy.stats

from openquake.hazardlib.const import StdDev
from openquake.hazardlib.gsim.base import ContextMaker
from openquake.hazardlib.imt import from_string


[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]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` sitecol: a complete SiteCollection :param imts: a sorted list of Intensity Measure Type strings :param cmaker: a :class:`openquake.hazardlib.gsim.base.ContextMaker` instance :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. def __init__(self, rupture, sitecol, imts, cmaker, truncation_level=None, correlation_model=None): if len(sitecol) == 0: raise ValueError('No sites') elif len(imts) == 0: raise ValueError('No IMTs') elif len(cmaker.gsims) == 0: raise ValueError('No GSIMs') self.rupture = rupture self.imts = [from_string(imt) for imt in imts] self.gsims = sorted(cmaker.gsims) self.truncation_level = truncation_level self.correlation_model = correlation_model # `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 try: self.sctx, self.dctx = rupture.sctx, rupture.dctx except AttributeError: self.sctx, self.dctx = cmaker.make_contexts(sitecol, rupture) self.sids = self.sctx.sids if correlation_model: # store the filtered sitecol self.sites = sitecol.filtered(self.sids)
[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) """ try: # read the seed from self.rupture.rupture if possible seed = seed or self.rupture.rupture.seed except AttributeError: pass if seed is not None: numpy.random.seed(seed) result = numpy.zeros( (len(self.imts), len(self.sids), 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) """ rctx = getattr(self.rupture, 'rupture', self.rupture) if seed is not None: numpy.random.seed(seed) dctx = self.dctx.roundup(gsim.minimum_distance) if self.truncation_level == 0: assert self.correlation_model is None mean, _stddevs = gsim.get_mean_and_stddevs( self.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( self.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.sids), num_events)) gmf = gsim.to_imt_unit_values(mean + total_residual) else: mean, [stddev_inter, stddev_intra] = gsim.get_mean_and_stddevs( self.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.sids), 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, 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. .. 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 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. """ gc = GmfComputer(rupture, sites, [str(imt) for imt in imts], ContextMaker([gsim]), truncation_level, correlation_model) res = gc.compute(gsim, realizations, seed) return {imt: res[imti] for imti, imt in enumerate(gc.imts)}