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.
"""
cmaker = ContextMaker(
rupture.tectonic_region_type, [gsim],
dict(truncation_level=truncation_level,
imtls={str(imt): [1]
for imt in imts}))
rupture.rup_id = seed
gc = GmfComputer(rupture, sites, cmaker, correlation_model)
mean_stds = cmaker.get_mean_stds([gc.ctx])[:, 0]
res, _sig, _eps = gc.compute(gsim, realizations, mean_stds)
return {imt: res[m] for m, imt in enumerate(gc.imts)}
