# coding: utf-8
# The Hazard Library
# Copyright (C) 2012-2023 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.source.rupture` defines classes
:class:`BaseRupture` and its subclasses
:class:`NonParametricProbabilisticRupture` and
:class:`ParametricProbabilisticRupture`
"""
import abc
import numpy
import math
import itertools
import json
from openquake.baselib import general, hdf5
from openquake.hazardlib import geo
from openquake.hazardlib.geo.nodalplane import NodalPlane
from openquake.hazardlib.geo.mesh import (
Mesh, RectangularMesh, surface_to_arrays)
from openquake.hazardlib.geo.point import Point
from openquake.hazardlib.geo.geodetic import geodetic_distance
from openquake.hazardlib.near_fault import (
get_plane_equation, projection_pp, directp, average_s_rad, isochone_ratio)
from openquake.hazardlib.geo.surface.base import BaseSurface
U8 = numpy.uint8
U16 = numpy.uint16
U32 = numpy.uint32
F32 = numpy.float32
F64 = numpy.float64
TWO16 = 2 ** 16
TWO32 = 2 ** 32
pmf_dt = numpy.dtype([
('prob', float),
('occ', U32)])
events_dt = numpy.dtype([
('id', U32),
('rup_id', U32),
('rlz_id', U16)])
rup_dt = numpy.dtype([
('seed', U32),
('mag', F32),
('rake', F32),
('lon', F32),
('lat', F32),
('dep', F32),
('multiplicity', U32),
('trt', hdf5.vstr),
('kind', hdf5.vstr),
('mesh', hdf5.vstr),
('extra', hdf5.vstr)])
rupture_dt = numpy.dtype([
('id', U32),
('seed', U32),
('source_id', '<S16'),
('trt_smr', U16),
('code', U8),
('n_occ', U32),
('mag', F32),
('rake', F32),
('occurrence_rate', F32),
('minlon', F32),
('minlat', F32),
('maxlon', F32),
('maxlat', F32),
('hypo', (F32, 3)),
('geom_id', U32),
('e0', U32)])
code2cls = {}
[docs]def to_csv_array(ruptures):
"""
:param ruptures: a list of ruptures
:returns: an array of ruptures suitable for serialization in CSV
"""
if not code2cls:
code2cls.update(BaseRupture.init())
arr = numpy.zeros(len(ruptures), rup_dt)
for rec, rup in zip(arr, ruptures):
# s0=number of multi surfaces, s1=number of rows, s2=number of columns
arrays = surface_to_arrays(rup.surface) # shape (s0, 3, s1, s2)
rec['seed'] = rup.seed
rec['mag'] = rup.mag
rec['rake'] = rup.rake
rec['lon'] = rup.hypocenter.x
rec['lat'] = rup.hypocenter.y
rec['dep'] = rup.hypocenter.z
rec['multiplicity'] = rup.multiplicity
rec['trt'] = rup.tectonic_region_type
rec['kind'] = ' '.join(cls.__name__ for cls in code2cls[rup.code])
rec['mesh'] = json.dumps(
[[[[float5(z) for z in y] for y in x] for x in array]
for array in arrays])
extra = {}
if hasattr(rup, 'probs_occur'):
extra['probs_occur'] = rup.probs_occur
else:
extra['occurrence_rate'] = rup.occurrence_rate
if hasattr(rup, 'weight'):
extra['weight'] = rup.weight
_fixfloat32(extra)
rec['extra'] = json.dumps(extra)
return arr
[docs]def to_arrays(geom):
"""
:param geom: an array [num_surfaces, shape_y, shape_z ..., coords]
:returns: a list of num_surfaces arrays with shape (3, shape_y, shape_z)
"""
arrays = []
num_surfaces = int(geom[0])
start = num_surfaces * 2 + 1
for i in range(1, 2 * num_surfaces, 2):
s1, s2 = int(geom[i]), int(geom[i + 1])
size = s1 * s2 * 3
array = geom[start:start + size].reshape(3, s1, s2)
arrays.append(array)
start += size
return arrays
def _get_rupture(rec, geom=None, trt=None):
# rec: a dictionary or a record
# geom: if any, an array of floats32 convertible into a mesh
if not code2cls:
code2cls.update(BaseRupture.init())
if geom is None:
points = F32([rec['lons'], rec['lats'], rec['depths']]).flat
geom = numpy.concatenate([[1], [len(rec['lons']), 1], points])
# build surface
arrays = to_arrays(geom)
mesh = arrays[0]
rupture_cls, surface_cls = code2cls[rec['code']]
surface = object.__new__(surface_cls)
if surface_cls is geo.PlanarSurface:
surface = geo.PlanarSurface.from_array(mesh[:, 0, :])
elif surface_cls is geo.MultiSurface:
if all(array.shape == (3, 1, 4) for array in arrays):
# for PlanarSurfaces each array has shape (3, 1, 4)
surface.__init__([
geo.PlanarSurface.from_array(array[:, 0, :])
for array in arrays])
else:
# assume KiteSurfaces
surface.__init__([geo.KiteSurface(RectangularMesh(*array))
for array in arrays])
elif surface_cls is geo.GriddedSurface:
# fault surface, strike and dip will be computed
surface.strike = surface.dip = None
surface.mesh = Mesh(*mesh)
else:
# fault surface, strike and dip will be computed
surface.strike = surface.dip = None
surface.__init__(RectangularMesh(*mesh))
# build rupture
rupture = object.__new__(rupture_cls)
rupture.seed = rec['seed']
rupture.surface = surface
rupture.mag = rec['mag']
rupture.rake = rec['rake']
rupture.hypocenter = geo.Point(*rec['hypo'])
rupture.occurrence_rate = rec['occurrence_rate']
try:
rupture.probs_occur = rec['probs_occur']
except (KeyError, ValueError): # rec can be a numpy record
pass
rupture.tectonic_region_type = trt or rec['trt']
rupture.multiplicity = rec['n_occ']
return rupture
[docs]def float5(x):
# a float with 5 digits
return round(float(x), 5)
def _fixfloat32(dic):
# work around a TOML/numpy issue
for k, v in dic.items():
if isinstance(v, F32):
dic[k] = float5(v)
elif isinstance(v, tuple):
dic[k] = [float5(x) for x in v]
elif isinstance(v, numpy.ndarray):
if len(v.shape) == 3: # 3D array
dic[k] = [[[float5(z) for z in y] for y in x] for x in v]
elif len(v.shape) == 2: # 2D array
dic[k] = [[float5(y) for y in x] for x in v]
elif len(v.shape) == 1: # 1D array
dic[k] = [float5(x) for x in v]
else:
raise NotImplementedError
[docs]def to_checksum8(cls1, cls2):
"""
Convert a pair of classes into a numeric code (uint8)
"""
names = '%s,%s' % (cls1.__name__, cls2.__name__)
return sum(map(ord, names)) % 256
[docs]class BaseRupture(metaclass=abc.ABCMeta):
"""
Rupture object represents a single earthquake rupture.
:param mag:
Magnitude of the rupture.
:param rake:
Rake value of the rupture.
See :class:`~openquake.hazardlib.geo.nodalplane.NodalPlane`.
:param tectonic_region_type:
Rupture's tectonic regime. One of constants
in :class:`openquake.hazardlib.const.TRT`.
:param hypocenter:
A :class:`~openquake.hazardlib.geo.point.Point`, rupture's hypocenter.
:param surface:
An instance of subclass of
:class:`~openquake.hazardlib.geo.surface.base.BaseSurface`.
Object representing the rupture surface geometry.
:param rupture_slip_direction:
Angle describing rupture propagation direction in decimal degrees.
:raises ValueError:
If magnitude value is not positive, or tectonic region type is unknown.
NB: if you want to convert the rupture into XML, you should set the
attribute surface_nodes to an appropriate value.
"""
seed = 0 # set to a value > 0 by the engine
_code = {}
[docs] @classmethod
def init(cls):
"""
Initialize the class dictionary `._code` by encoding the
bidirectional correspondence between an integer in the range 0..255
(the code) and a pair of classes (rupture_class, surface_class).
This is useful when serializing the rupture to and from HDF5.
:returns: {code: pair of classes}
"""
rupture_classes = [BaseRupture] + list(
general.gen_subclasses(BaseRupture))
surface_classes = list(general.gen_subclasses(BaseSurface))
code2cls = {}
BaseRupture.str2code = {}
for rup, sur in itertools.product(rupture_classes, surface_classes):
chk = to_checksum8(rup, sur)
if chk in code2cls and code2cls[chk] != (rup, sur):
raise ValueError('Non-unique checksum %d for %s, %s' %
(chk, rup, sur))
cls._code[rup, sur] = chk
code2cls[chk] = rup, sur
BaseRupture.str2code['%s %s' % (rup.__name__, sur.__name__)] = chk
return code2cls
def __init__(self, mag, rake, tectonic_region_type, hypocenter,
surface, rupture_slip_direction=None, weight=None):
if not mag > 0:
raise ValueError('magnitude must be positive')
NodalPlane.check_rake(rake)
self.tectonic_region_type = tectonic_region_type
self.rake = rake
self.mag = mag
self.hypocenter = hypocenter
self.surface = surface
self.rupture_slip_direction = rupture_slip_direction
self.ruid = None
@property
def code(self):
"""Returns the code (integer in the range 0 .. 255) of the rupture"""
return self._code[self.__class__, self.surface.__class__]
[docs] def size(self):
"""
Dummy method for compatibility with the RuptureContext.
:returns: 1
"""
return 1
[docs] def sample_number_of_occurrences(self, n=1):
"""
Randomly sample number of occurrences from temporal occurrence model
probability distribution.
.. note::
This method 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
:returns:
numpy array of size n with number of rupture occurrences
"""
raise NotImplementedError
[docs]class NonParametricProbabilisticRupture(BaseRupture):
"""
Probabilistic rupture for which the probability distribution for rupture
occurrence is described through a generic probability mass function.
:param pmf:
Instance of :class:`openquake.hazardlib.pmf.PMF`. Values in the
abscissae represent number of rupture occurrences (in increasing order,
staring from 0) and values in the ordinates represent associated
probabilities. Example: if, for a given time span, a rupture has
probability ``0.8`` to not occurr, ``0.15`` to occur once, and
``0.05`` to occur twice, the ``pmf`` can be defined as ::
pmf = PMF([(0.8, 0), (0.15, 1), 0.05, 2)])
:raises ValueError:
If number of ruptures in ``pmf`` do not start from 0, are not defined
in increasing order, and if they are not defined with unit step
"""
def __init__(self, mag, rake, tectonic_region_type, hypocenter, surface,
pmf, rupture_slip_direction=None, weight=None):
occ = numpy.array([occ for (prob, occ) in pmf.data])
if not occ[0] == 0:
raise ValueError('minimum number of ruptures must be zero')
if not numpy.all(numpy.sort(occ) == occ):
raise ValueError(
'numbers of ruptures must be defined in increasing order')
if not numpy.all(numpy.diff(occ) == 1):
raise ValueError(
'numbers of ruptures must be defined with unit step')
super().__init__(
mag, rake, tectonic_region_type, hypocenter, surface,
rupture_slip_direction, weight)
# an array of probabilities with sum 1
self.probs_occur = numpy.array([prob for (prob, occ) in pmf.data])
if weight is not None:
self.weight = weight
[docs] def sample_number_of_occurrences(self, n=1):
"""
See :meth:`superclass method
<.rupture.BaseRupture.sample_number_of_occurrences>`
for spec of input and result values.
Uses 'Inverse Transform Sampling' method.
"""
# compute cdf from pmf
cdf = numpy.cumsum(self.probs_occur)
n_occ = numpy.digitize(numpy.random.random(n), cdf)
return n_occ
[docs]class ParametricProbabilisticRupture(BaseRupture):
"""
:class:`Rupture` associated with an occurrence rate and a temporal
occurrence model.
:param occurrence_rate:
Number of times rupture happens per year.
:param temporal_occurrence_model:
Temporal occurrence model assigned for this rupture. Should
be an instance of :class:`openquake.hazardlib.tom.PoissonTOM`.
:raises ValueError:
If occurrence rate is not positive.
"""
def __init__(self, mag, rake, tectonic_region_type, hypocenter, surface,
occurrence_rate, temporal_occurrence_model,
rupture_slip_direction=None):
if not occurrence_rate > 0:
raise ValueError('occurrence rate must be positive')
super().__init__(
mag, rake, tectonic_region_type, hypocenter, surface,
rupture_slip_direction)
self.temporal_occurrence_model = temporal_occurrence_model
self.occurrence_rate = occurrence_rate
[docs] def get_probability_one_or_more_occurrences(self):
"""
Return the probability of this rupture to occur one or more times.
Uses
:meth:`~openquake.hazardlib.tom.PoissonTOM.get_probability_one_or_more_occurrences`
of an assigned temporal occurrence model.
"""
tom = self.temporal_occurrence_model
rate = self.occurrence_rate
return tom.get_probability_one_or_more_occurrences(rate)
[docs] def get_probability_one_occurrence(self):
"""
Return the probability of this rupture to occur exactly one time.
Uses :meth:
`openquake.hazardlib.tom.PoissonTOM.get_probability_n_occurrences`
of an assigned temporal occurrence model.
"""
tom = self.temporal_occurrence_model
rate = self.occurrence_rate
return tom.get_probability_n_occurrences(rate, 1)
[docs] def sample_number_of_occurrences(self, n=1):
"""
Draw a random sample from the distribution and return a number
of events to occur as an array of integers of size n.
Uses :meth:
`openquake.hazardlib.tom.PoissonTOM.sample_number_of_occurrences`
of an assigned temporal occurrence model.
"""
r = self.occurrence_rate * self.temporal_occurrence_model.time_span
return numpy.random.poisson(r, n)
[docs] def get_dppvalue(self, site):
"""
Get the directivity prediction value, DPP at
a given site as described in Spudich et al. (2013).
:param site:
:class:`~openquake.hazardlib.geo.point.Point` object
representing the location of the target site
:returns:
A float number, directivity prediction value (DPP).
"""
origin = self.surface.get_resampled_top_edge()[0]
dpp_multi = []
index_patch = self.surface.hypocentre_patch_index(
self.hypocenter, self.surface.get_resampled_top_edge(),
self.surface.mesh.depths[0][0], self.surface.mesh.depths[-1][0],
self.surface.get_dip())
idx_nxtp = True
hypocenter = self.hypocenter
while idx_nxtp:
# E Plane Calculation
p0, p1, p2, p3 = self.surface.get_fault_patch_vertices(
self.surface.get_resampled_top_edge(),
self.surface.mesh.depths[0][0],
self.surface.mesh.depths[-1][0],
self.surface.get_dip(), index_patch=index_patch)
[normal, dist_to_plane] = get_plane_equation(
p0, p1, p2, origin)
pp = projection_pp(site, normal, dist_to_plane, origin)
pd, e, idx_nxtp = directp(
p0, p1, p2, p3, hypocenter, origin, pp)
pd_geo = origin.point_at(
(pd[0] ** 2 + pd[1] ** 2) ** 0.5, -pd[2],
numpy.degrees(math.atan2(pd[0], pd[1])))
# determine the lower bound of E path value
f1 = geodetic_distance(p0.longitude,
p0.latitude,
p1.longitude,
p1.latitude)
f2 = geodetic_distance(p2.longitude,
p2.latitude,
p3.longitude,
p3.latitude)
if f1 > f2:
f = f1
else:
f = f2
fs, rd, r_hyp = average_s_rad(site, hypocenter, origin,
pp, normal, dist_to_plane, e, p0,
p1, self.rupture_slip_direction)
cprime = isochone_ratio(e, rd, r_hyp)
dpp_exp = cprime * numpy.maximum(e, 0.1 * f) *\
numpy.maximum(fs, 0.2)
dpp_multi.append(dpp_exp)
# check if go through the next patch of the fault
index_patch = index_patch + 1
if (len(self.surface.get_resampled_top_edge())
<= 2) and (index_patch >=
len(self.surface.get_resampled_top_edge())):
idx_nxtp = False
elif index_patch >= len(self.surface.get_resampled_top_edge()):
idx_nxtp = False
elif idx_nxtp:
hypocenter = pd_geo
idx_nxtp = True
# calculate DPP value of the site.
dpp = numpy.log(numpy.sum(dpp_multi))
return dpp
[docs] def get_cdppvalue(self, target, buf=1.0, delta=0.01, space=2.):
"""
Get the directivity prediction value, centered DPP(cdpp) at
a given site as described in Spudich et al. (2013), and this cdpp is
used in Chiou and Young (2014) GMPE for near-fault directivity
term prediction.
:param target_site:
A mesh object representing the location of the target sites.
:param buf:
A buffer distance in km to extend the mesh borders
:param delta:
The distance between two adjacent points in the mesh
:param space:
The tolerance for the distance of the sites (default 2 km)
:returns:
The centered directivity prediction value of Chiou and Young
"""
min_lon, max_lon, max_lat, min_lat = self.surface.get_bounding_box()
min_lon -= buf
max_lon += buf
min_lat -= buf
max_lat += buf
lons = numpy.arange(min_lon, max_lon + delta, delta)
# ex shape (233,)
lats = numpy.arange(min_lat, max_lat + delta, delta)
# ex shape (204,)
mesh = RectangularMesh(*numpy.meshgrid(lons, lats))
mesh_rup = self.surface.get_min_distance(mesh).reshape(mesh.shape)
# ex shape (204, 233)
target_rup = self.surface.get_min_distance(target)
# ex shape (2,)
cdpp = numpy.zeros_like(target.lons)
for i, (target_lon, target_lat) in enumerate(
zip(target.lons, target.lats)):
# indices around target_rup[i]
around = (mesh_rup <= target_rup[i] + space) & (
mesh_rup >= target_rup[i] - space)
dpp_target = self.get_dppvalue(Point(target_lon, target_lat))
dpp_mean = numpy.mean(
[self.get_dppvalue(Point(lon, lat))
for lon, lat in zip(mesh.lons[around], mesh.lats[around])])
cdpp[i] = dpp_target - dpp_mean
return cdpp
[docs]class PointSurface:
"""
A fake surface used in PointRuptures.
The parameters `hypocenter` and `strike` are determined by
collapsing the corresponding parameters in the original PointSource.
"""
def __init__(self, hypocenter, strike, dip):
self.hypocenter = hypocenter
self.strike = strike
self.dip = dip
[docs] def get_strike(self):
return self.strike
[docs] def get_dip(self):
return self.dip
[docs] def get_top_edge_depth(self):
return self.hypocenter.depth
[docs] def get_width(self):
return 0
[docs] def get_closest_points(self, mesh):
"""
:returns: N times the hypocenter if N is the number of points
"""
N = len(mesh)
lons = numpy.full(N, self.hypocenter.x)
lats = numpy.full(N, self.hypocenter.y)
deps = numpy.full(N, self.hypocenter.z)
return Mesh(lons, lats, deps)
def __bool__(self):
return False
[docs]class PointRupture(ParametricProbabilisticRupture):
"""
A rupture coming from a far away PointSource, so that the finite
size effects can be neglected.
"""
def __init__(self, mag, rake, tectonic_region_type, hypocenter, strike,
dip, occurrence_rate, temporal_occurrence_model, zbot):
self.tectonic_region_type = tectonic_region_type
self.hypocenter = hypocenter
self.mag = mag
self.strike = strike
self.rake = rake
self.dip = dip
self.occurrence_rate = occurrence_rate
self.temporal_occurrence_model = temporal_occurrence_model
self.surface = PointSurface(hypocenter, strike, dip)
self.zbot = zbot
[docs]def get_geom(surface, is_from_fault_source, is_multi_surface,
is_gridded_surface):
"""
The following fields can be interpreted different ways,
depending on the value of `is_from_fault_source`. If
`is_from_fault_source` is True, each of these fields should
contain a 2D numpy array (all of the same shape). Each triple
of (lon, lat, depth) for a given index represents the node of
a rectangular mesh. If `is_from_fault_source` is False, each
of these fields should contain a sequence (tuple, list, or
numpy array, for example) of 4 values. In order, the triples
of (lon, lat, depth) represent top left, top right, bottom
left, and bottom right corners of the the rupture's planar
surface. Update: There is now a third case. If the rupture
originated from a characteristic fault source with a
multi-planar-surface geometry, `lons`, `lats`, and `depths`
will contain one or more sets of 4 points, similar to how
planar surface geometry is stored (see above).
:param surface: a Surface instance
:param is_from_fault_source: a boolean
:param is_multi_surface: a boolean
"""
if is_from_fault_source:
# for simple and complex fault sources,
# rupture surface geometry is represented by a mesh
surf_mesh = surface.mesh
lons = surf_mesh.lons
lats = surf_mesh.lats
depths = surf_mesh.depths
else:
if is_multi_surface:
# `list` of
# openquake.hazardlib.geo.surface.planar.PlanarSurface
# objects:
surfaces = surface.surfaces
# lons, lats, and depths are arrays with len == 4*N,
# where N is the number of surfaces in the
# multisurface for each `corner_*`, the ordering is:
# - top left
# - top right
# - bottom left
# - bottom right
lons = numpy.concatenate([x.corner_lons for x in surfaces])
lats = numpy.concatenate([x.corner_lats for x in surfaces])
depths = numpy.concatenate([x.corner_depths for x in surfaces])
elif is_gridded_surface:
# the surface mesh has shape (1, N)
lons = surface.mesh.lons[0]
lats = surface.mesh.lats[0]
depths = surface.mesh.depths[0]
else:
# For area or point source,
# rupture geometry is represented by a planar surface,
# defined by 3D corner points
lons = numpy.zeros((4))
lats = numpy.zeros((4))
depths = numpy.zeros((4))
# NOTE: It is important to maintain the order of these
# corner points. TODO: check the ordering
for i, corner in enumerate((surface.top_left,
surface.top_right,
surface.bottom_left,
surface.bottom_right)):
lons[i] = corner.longitude
lats[i] = corner.latitude
depths[i] = corner.depth
return lons, lats, depths
[docs]class ExportedRupture(object):
"""
Simplified Rupture class with attributes rupid, events_by_ses, indices
and others, used in export.
:param rupid: rupture.seed ID
:param events_by_ses: dictionary ses_idx -> event records
:param indices: site indices
"""
def __init__(self, rupid, n_occ, events_by_ses, indices=None):
self.rupid = rupid
self.n_occ = n_occ
self.events_by_ses = events_by_ses
self.indices = indices
[docs]class EBRupture(object):
"""
An event based rupture. It is a wrapper over a hazardlib rupture
object.
:param rupture: the underlying rupture
:param str source_id: ID of the source that generated the rupture
:param int trt_smr: an integer describing TRT and source model realization
:param int n_occ: number of occurrences of the rupture
:param int e0: initial event ID (default 0)
:param bool scenario: True for scenario ruptures, default False
"""
def __init__(self, rupture, source_id, trt_smr, n_occ=1,
id=None, e0=0, scenario=False):
assert rupture.seed > 0 # sanity check
self.rupture = rupture
self.source_id = source_id
self.trt_smr = trt_smr
self.n_occ = n_occ
self.id = id # id of the rupture on the DataStore
self.e0 = e0
self.scenario = scenario
@property
def tectonic_region_type(self):
return self.rupture.tectonic_region_type
@property
def seed(self):
"""
Seed of the rupture
"""
return self.rupture.seed
[docs] def get_eids_by_rlz(self, rlzs_by_gsim):
"""
:params rlzs_by_gsim: a dictionary gsims -> rlzs array
:returns: a dictionary rlz index -> eids array
"""
dic = {}
rlzs = numpy.concatenate(list(rlzs_by_gsim.values()))
if self.scenario:
all_eids = numpy.arange(self.n_occ, dtype=U32) + self.e0
splits = numpy.array_split(all_eids, len(rlzs))
for rlz_id, eids in zip(rlzs, splits):
dic[rlz_id] = eids
else: # event_based
j = 0
histo = general.random_histogram(self.n_occ, len(rlzs), self.seed)
for rlz, n in zip(rlzs, histo):
dic[rlz] = numpy.arange(j, j + n, dtype=U32) + self.e0
j += n
return dic
[docs] def get_eids(self):
"""
:returns: an array of event IDs
"""
return numpy.arange(self.n_occ, dtype=U32)
def __repr__(self):
return '<%s %d[%d]>' % (
self.__class__.__name__, self.seed, self.n_occ)
[docs]class RuptureProxy(object):
"""
A proxy for a rupture record.
:param rec: a record with the rupture parameters
"""
def __init__(self, rec, scenario=False):
self.rec = rec
self.scenario = scenario
def __getitem__(self, name):
return self.rec[name]
# NB: requires the .geom attribute to be set
[docs] def to_ebr(self, trt):
"""
:returns: EBRupture instance associated to the underlying rupture
"""
# not implemented: rupture_slip_direction
rupture = _get_rupture(self.rec, self.geom, trt)
ebr = EBRupture(rupture, self['source_id'], self['trt_smr'],
self['n_occ'], self['id'], self['e0'], self.scenario)
return ebr
def __repr__(self):
src = self['source_id'].decode('ascii')
return '<%s#%d[%s], w=%d>' % (self.__class__.__name__,
self['id'], src, self['n_occ'])
[docs]def get_ruptures(fname_csv):
"""
Read ruptures in CSV format and return an ArrayWrapper.
:param fname_csv: path to the CSV file
"""
if not BaseRupture._code:
BaseRupture.init() # initialize rupture codes
code = BaseRupture.str2code
aw = hdf5.read_csv(fname_csv, rup_dt)
rups = []
geoms = []
n_occ = 1
for u, row in enumerate(aw.array):
hypo = row['lon'], row['lat'], row['dep']
dic = json.loads(row['extra'])
meshes = F32(json.loads(row['mesh'])) # num_surfaces 3D arrays
num_surfaces = len(meshes)
shapes = []
points = []
minlons = []
maxlons = []
minlats = []
maxlats = []
for mesh in meshes:
shapes.extend(mesh.shape[1:])
points.extend(mesh.flatten()) # lons + lats + deps
minlons.append(mesh[0].min())
minlats.append(mesh[1].min())
maxlons.append(mesh[0].max())
maxlats.append(mesh[1].max())
rec = numpy.zeros(1, rupture_dt)[0]
rec['seed'] = row['seed']
rec['minlon'] = minlon = min(minlons)
rec['minlat'] = minlat = min(minlats)
rec['maxlon'] = maxlon = max(maxlons)
rec['maxlat'] = maxlat = max(maxlats)
rec['mag'] = row['mag']
rec['hypo'] = hypo
rate = dic.get('occurrence_rate', numpy.nan)
tup = (u, row['seed'], 'no-source', aw.trts.index(row['trt']),
code[row['kind']], n_occ, row['mag'], row['rake'], rate,
minlon, minlat, maxlon, maxlat, hypo, u, 0)
rups.append(tup)
geoms.append(numpy.concatenate([[num_surfaces], shapes, points]))
if not rups:
return ()
dic = dict(geom=numpy.array(geoms, object), trts=aw.trts)
# NB: PMFs for nonparametric ruptures are missing
return hdf5.ArrayWrapper(numpy.array(rups, rupture_dt), dic)