# -*- coding: utf-8 -*-
# vim: tabstop=4 shiftwidth=4 softtabstop=4
#
# Copyright (C) 2015-2017 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/>.
import os
import copy
import time
import math
import os.path
import logging
import collections
import h5py
import numpy
from openquake.baselib.general import AccumDict
from openquake.baselib.python3compat import zip
from openquake.baselib import parallel
from openquake.hazardlib import nrml
from openquake.hazardlib.calc import stochastic
from openquake.risklib import riskinput
from openquake.commonlib import readinput, source, calc, util
from openquake.calculators import base, event_based, getters
from openquake.calculators.event_based_risk import (
EbriskCalculator, event_based_risk)
from openquake.hazardlib.geo.surface.multi import MultiSurface
from openquake.hazardlib.pmf import PMF
from openquake.hazardlib.geo.point import Point
from openquake.hazardlib.geo.geodetic import min_idx_dst, min_geodetic_distance
from openquake.hazardlib.geo.surface.planar import PlanarSurface
from openquake.hazardlib.geo.nodalplane import NodalPlane
from openquake.hazardlib.tom import PoissonTOM
from openquake.hazardlib.source.rupture import (
ParametricProbabilisticRupture, EBRupture)
from openquake.hazardlib.source.characteristic import CharacteristicFaultSource
from openquake.hazardlib.source.point import PointSource
from openquake.hazardlib.scalerel.wc1994 import WC1994
from openquake.hazardlib.calc.filters import SourceFilter, FarAwayRupture
from openquake.hazardlib.mfd import EvenlyDiscretizedMFD
from openquake.hazardlib.sourceconverter import SourceConverter
# ######################## rupture calculator ############################ #
U16 = numpy.uint16
U32 = numpy.uint32
U64 = numpy.uint64
F32 = numpy.float32
TWO16 = 2 ** 16
# DEFAULT VALUES FOR UCERF BACKGROUND MODELS
DEFAULT_MESH_SPACING = 1.0
DEFAULT_TRT = "Active Shallow Crust"
HDD = PMF([(0.2, 3.0), (0.6, 6.0), (0.2, 9.0)])
NPD = PMF([(0.15, NodalPlane(0.0, 90.0, 0.0)),
(0.15, NodalPlane(45.0, 90.0, 0.0)),
(0.15, NodalPlane(90.0, 90.0, 0.0)),
(0.15, NodalPlane(135.0, 90.0, 0.0)),
(0.05, NodalPlane(0.0, 45.0, 90.)),
(0.05, NodalPlane(45.0, 45.0, 90.)),
(0.05, NodalPlane(90.0, 45.0, 90.)),
(0.05, NodalPlane(135.0, 45.0, 90.)),
(0.05, NodalPlane(180.0, 45.0, 90.)),
(0.05, NodalPlane(225.0, 45.0, 90.)),
(0.05, NodalPlane(270.0, 45.0, 90.)),
(0.05, NodalPlane(325.0, 45.0, 90.))])
[docs]class ImperfectPlanarSurface(PlanarSurface):
"""
The planar surface class sets a narrow tolerance for the rectangular plane
to be distorted in cartesian space. Ruptures with aspect ratios << 1.0,
and with a dip of less than 90 degrees, cannot be generated in a manner
that is consistent with the definitions - and thus cannot be instantiated.
This subclass modifies the original planar surface class such that the
tolerance checks are over-ridden. We find that distance errors with respect
to a simple fault surface with a mesh spacing of 0.001 km are only on the
order of < 0.15 % for Rrup (< 2 % for Rjb, < 3.0E-5 % for Rx)
"""
IMPERFECT_RECTANGLE_TOLERANCE = numpy.inf
[docs]def get_rupture_dimensions(mag, nodal_plane, msr, rupture_aspect_ratio,
upper_seismogenic_depth, lower_seismogenic_depth):
"""
Calculate and return the rupture length and width
for given magnitude ``mag`` and nodal plane.
:param nodal_plane:
Instance of :class:`openquake.hazardlib.geo.nodalplane.NodalPlane`.
:returns:
Tuple of two items: rupture length in width in km.
The rupture area is calculated using method
:meth:`~openquake.hazardlib.scalerel.base.BaseMSR.get_median_area`
of source's
magnitude-scaling relationship. In any case the returned
dimensions multiplication is equal to that value. Than
the area is decomposed to length and width with respect
to source's rupture aspect ratio.
If calculated rupture width being inclined by nodal plane's
dip angle would not fit in between upper and lower seismogenic
depth, the rupture width is shrunken to a maximum possible
and rupture length is extended to preserve the same area.
"""
area = msr.get_median_area(mag, nodal_plane.rake)
rup_length = math.sqrt(area * rupture_aspect_ratio)
rup_width = area / rup_length
seismogenic_layer_width = (lower_seismogenic_depth -
upper_seismogenic_depth)
max_width = (seismogenic_layer_width /
math.sin(math.radians(nodal_plane.dip)))
if rup_width > max_width:
rup_width = max_width
rup_length = area / rup_width
return rup_length, rup_width
[docs]def get_rupture_surface(mag, nodal_plane, hypocenter, msr,
rupture_aspect_ratio, upper_seismogenic_depth,
lower_seismogenic_depth, mesh_spacing=1.0):
"""
Create and return rupture surface object with given properties.
:param mag:
Magnitude value, used to calculate rupture dimensions,
see :meth:`_get_rupture_dimensions`.
:param nodal_plane:
Instance of :class:`openquake.hazardlib.geo.nodalplane.NodalPlane`
describing the rupture orientation.
:param hypocenter:
Point representing rupture's hypocenter.
:returns:
Instance of
:class:`~openquake.hazardlib.geo.surface.planar.PlanarSurface`.
"""
assert (upper_seismogenic_depth <= hypocenter.depth
and lower_seismogenic_depth >= hypocenter.depth)
rdip = math.radians(nodal_plane.dip)
# precalculated azimuth values for horizontal-only and vertical-only
# moves from one point to another on the plane defined by strike
# and dip:
azimuth_right = nodal_plane.strike
azimuth_down = (azimuth_right + 90) % 360
azimuth_left = (azimuth_down + 90) % 360
azimuth_up = (azimuth_left + 90) % 360
rup_length, rup_width = get_rupture_dimensions(
mag, nodal_plane, msr, rupture_aspect_ratio, upper_seismogenic_depth,
lower_seismogenic_depth)
# calculate the height of the rupture being projected
# on the vertical plane:
rup_proj_height = rup_width * math.sin(rdip)
# and it's width being projected on the horizontal one:
rup_proj_width = rup_width * math.cos(rdip)
# half height of the vertical component of rupture width
# is the vertical distance between the rupture geometrical
# center and it's upper and lower borders:
hheight = rup_proj_height / 2
# calculate how much shallower the upper border of the rupture
# is than the upper seismogenic depth:
vshift = upper_seismogenic_depth - hypocenter.depth + hheight
# if it is shallower (vshift > 0) than we need to move the rupture
# by that value vertically.
if vshift < 0:
# the top edge is below upper seismogenic depth. now we need
# to check that we do not cross the lower border.
vshift = lower_seismogenic_depth - hypocenter.depth - hheight
if vshift > 0:
# the bottom edge of the rupture is above the lower sesmogenic
# depth. that means that we don't need to move the rupture
# as it fits inside seismogenic layer.
vshift = 0
# if vshift < 0 than we need to move the rupture up by that value.
# now we need to find the position of rupture's geometrical center.
# in any case the hypocenter point must lie on the surface, however
# the rupture center might be off (below or above) along the dip.
rupture_center = hypocenter
if vshift != 0:
# we need to move the rupture center to make the rupture fit
# inside the seismogenic layer.
hshift = abs(vshift / math.tan(rdip))
rupture_center = rupture_center.point_at(
horizontal_distance=hshift, vertical_increment=vshift,
azimuth=(azimuth_up if vshift < 0 else azimuth_down)
)
# from the rupture center we can now compute the coordinates of the
# four coorners by moving along the diagonals of the plane. This seems
# to be better then moving along the perimeter, because in this case
# errors are accumulated that induce distorsions in the shape with
# consequent raise of exceptions when creating PlanarSurface objects
# theta is the angle between the diagonal of the surface projection
# and the line passing through the rupture center and parallel to the
# top and bottom edges. Theta is zero for vertical ruptures (because
# rup_proj_width is zero)
theta = math.degrees(
math.atan((rup_proj_width / 2.) / (rup_length / 2.))
)
hor_dist = math.sqrt(
(rup_length / 2.) ** 2 + (rup_proj_width / 2.) ** 2
)
left_top = rupture_center.point_at(
horizontal_distance=hor_dist,
vertical_increment=-rup_proj_height / 2,
azimuth=(nodal_plane.strike + 180 + theta) % 360
)
right_top = rupture_center.point_at(
horizontal_distance=hor_dist,
vertical_increment=-rup_proj_height / 2,
azimuth=(nodal_plane.strike - theta) % 360
)
left_bottom = rupture_center.point_at(
horizontal_distance=hor_dist,
vertical_increment=rup_proj_height / 2,
azimuth=(nodal_plane.strike + 180 - theta) % 360
)
right_bottom = rupture_center.point_at(
horizontal_distance=hor_dist,
vertical_increment=rup_proj_height / 2,
azimuth=(nodal_plane.strike + theta) % 360
)
return PlanarSurface(mesh_spacing, nodal_plane.strike, nodal_plane.dip,
left_top, right_top, right_bottom, left_bottom)
[docs]def generate_background_ruptures(tom, locations, occurrence, mag, npd,
hdd, upper_seismogenic_depth,
lower_seismogenic_depth, msr=WC1994(),
aspect=1.5, trt=DEFAULT_TRT):
"""
:param tom:
Temporal occurrence model as instance of :class:
openquake.hazardlib.tom.TOM
:param numpy.ndarray locations:
Array of locations [Longitude, Latitude] of the point sources
:param numpy.ndarray occurrence:
Annual rates of occurrence
:param float mag:
Magnitude
:param npd:
Nodal plane distribution as instance of :class:
openquake.hazardlib.pmf.PMF
:param hdd:
Hypocentral depth distribution as instance of :class:
openquake.hazardlib.pmf.PMF
:param float upper_seismogenic_depth:
Upper seismogenic depth (km)
:param float lower_seismogenic_depth:
Lower seismogenic depth (km)
:param msr:
Magnitude scaling relation
:param float aspect:
Aspect ratio
:param str trt:
Tectonic region type
:returns:
List of ruptures
"""
ruptures = []
n_vals = len(locations)
depths = hdd.sample_pairs(n_vals)
nodal_planes = npd.sample_pairs(n_vals)
for i, (x, y) in enumerate(locations):
hypocentre = Point(x, y, depths[i][1])
surface = get_rupture_surface(mag, nodal_planes[i][1],
hypocentre, msr, aspect,
upper_seismogenic_depth,
lower_seismogenic_depth)
rupture_probability = (occurrence[i] * nodal_planes[i][0] *
depths[i][0])
ruptures.append(ParametricProbabilisticRupture(
mag, nodal_planes[i][1].rake, trt, hypocentre, surface,
PointSource, rupture_probability, tom))
return ruptures
[docs]def sample_background_model(
hdf5, branch_key, tom, seed, filter_idx, min_mag, npd, hdd,
upper_seismogenic_depth, lower_seismogenic_depth, msr=WC1994(),
aspect=1.5, trt=DEFAULT_TRT):
"""
Generates a rupture set from a sample of the background model
:param branch_key:
Key to indicate the branch for selecting the background model
:param tom:
Temporal occurrence model as instance of :class:
openquake.hazardlib.tom.TOM
:param seed:
Random seed to use in the call to tom.sample_number_of_occurrences
:param filter_idx:
Sites for consideration (can be None!)
:param float min_mag:
Minimim magnitude for consideration of background sources
:param npd:
Nodal plane distribution as instance of :class:
openquake.hazardlib.pmf.PMF
:param hdd:
Hypocentral depth distribution as instance of :class:
openquake.hazardlib.pmf.PMF
:param float aspect:
Aspect ratio
:param float upper_seismogenic_depth:
Upper seismogenic depth (km)
:param float lower_seismogenic_depth:
Lower seismogenic depth (km)
:param msr:
Magnitude scaling relation
:param float integration_distance:
Maximum distance from rupture to site for consideration
"""
bg_magnitudes = hdf5["/".join(["Grid", branch_key, "Magnitude"])].value
# Select magnitudes above the minimum magnitudes
mag_idx = bg_magnitudes >= min_mag
mags = bg_magnitudes[mag_idx]
rates = hdf5["/".join(["Grid", branch_key, "RateArray"])][filter_idx, :]
rates = rates[:, mag_idx]
valid_locs = hdf5["Grid/Locations"][filter_idx, :]
# Sample remaining rates
sampler = tom.sample_number_of_occurrences(rates, seed)
background_ruptures = []
background_n_occ = []
for i, mag in enumerate(mags):
rate_idx = numpy.where(sampler[:, i])[0]
rate_cnt = sampler[rate_idx, i]
occurrence = rates[rate_idx, i]
locations = valid_locs[rate_idx, :]
ruptures = generate_background_ruptures(
tom, locations, occurrence,
mag, npd, hdd, upper_seismogenic_depth,
lower_seismogenic_depth, msr, aspect, trt)
background_ruptures.extend(ruptures)
background_n_occ.extend(rate_cnt.tolist())
return background_ruptures, background_n_occ
[docs]class UCERFSource(object):
"""
:param source_file:
Path to an existing HDF5 file containing the UCERF model
:param str id:
Valid branch of UCERF
:param float investigation_time:
Investigation time of event set (years)
:param start_date:
Starting date of the investigation (None for time independent)
:param float min_mag:
Minimim magnitude for consideration of background sources
:param npd:
Nodal plane distribution as instance of :class:
openquake.hazardlib.pmf.PMF
:param hdd:
Hypocentral depth distribution as instance of :class:
openquake.hazardlib.pmf.PMF
:param float aspect:
Aspect ratio
:param float upper_seismoge nic_depth:
Upper seismogenic depth (km)
:param float lower_seismogenic_depth:
Lower seismogenic depth (km)
:param msr:
Magnitude scaling relation
:param float mesh_spacing:
Spacing (km) of fault mesh
:param str trt:
Tectonic region type
:param float integration_distance:
Maximum distance from rupture to site for consideration
"""
tectonic_region_type = DEFAULT_TRT
def __init__(
self, source_file, id, investigation_time, start_date, min_mag,
npd=NPD, hdd=HDD, aspect=1.5, upper_seismogenic_depth=0.0,
lower_seismogenic_depth=15.0, msr=WC1994(), mesh_spacing=1.0,
trt="Active Shallow Crust", integration_distance=1000):
assert os.path.exists(source_file), source_file
self.source_file = source_file
self.source_id = id
self.inv_time = investigation_time
self.start_date = start_date
self.tom = self._get_tom()
self.min_mag = min_mag
self.npd = npd
self.hdd = hdd
self.aspect = aspect
self.usd = upper_seismogenic_depth
self.lsd = lower_seismogenic_depth
self.msr = msr
self.mesh_spacing = mesh_spacing
self.tectonic_region_type = trt
self.num_ruptures = 0 # not set yet
[docs] def count_ruptures(self):
"""
The length of the rupture array if the branch_id is set, else 0
"""
return len(self.mags) if hasattr(self, 'mags') else 0
[docs] def new(self, grp_id, branch_id):
"""
:param grp_id: ordinal of the source group
:param branch_name: name of the UCERF branch
:param branch_id: string associated to the branch
:returns: a new UCERFSource associated to the branch_id
"""
new = copy.copy(self)
new.src_group_id = grp_id
new.source_id = branch_id
new.idx_set = build_idx_set(branch_id, self.start_date)
with h5py.File(self.source_file, "r") as hdf5:
# read from datasets like
# FM0_0/MEANFS/MEANMSR/Magnitude
# FM0_0/MEANFS/MEANMSR/Rates/MeanRates
# FM0_0/MEANFS/Rake
new.mags = hdf5[new.idx_set["mag"]].value
new.rate = hdf5[new.idx_set["rate"]].value
new.rake = hdf5[new.idx_set["rake"]].value
new.num_ruptures = len(new.mags)
return new
[docs] def get_min_max_mag(self):
"""
Called when updating the SourceGroup
"""
return self.min_mag, None
def _get_tom(self):
"""
Returns the temporal occurence model as a Poisson TOM
"""
return PoissonTOM(self.inv_time)
[docs] def get_ridx(self, iloc):
"""List of rupture indices for the given iloc"""
with h5py.File(self.source_file, "r") as hdf5:
return hdf5[self.idx_set["geol"] + "/RuptureIndex"][iloc]
[docs] def get_centroids(self, ridx):
"""
:returns: array of centroids for the given rupture index
"""
centroids = []
with h5py.File(self.source_file, "r") as hdf5:
for idx in ridx:
trace = "{:s}/{:s}".format(self.idx_set["sec"], str(idx))
centroids.append(hdf5[trace + "/Centroids"].value)
return numpy.concatenate(centroids)
[docs] def gen_trace_planes(self, ridx):
"""
:yields: trace and rupture planes for the given rupture index
"""
with h5py.File(self.source_file, "r") as hdf5:
for idx in ridx:
trace = "{:s}/{:s}".format(self.idx_set["sec"], str(idx))
plane = hdf5[trace + "/RupturePlanes"][:].astype("float64")
yield trace, plane
@property
def weight(self):
"""
Weight of the source, equal to the number of ruptures contained
"""
return self.num_ruptures
[docs] def get_rupture_sites(self, ridx, src_filter, mag):
"""
Determines if a rupture is likely to be inside the integration distance
by considering the set of fault plane centroids and returns the
affected sites if any.
:param ridx:
List of indices composing the rupture sections
:param src_filter:
SourceFilter instance
:param mag:
Magnitude of the rupture for consideration
:returns:
The sites affected by the rupture (or None)
"""
centroids = self.get_centroids(ridx)
distance = min_geodetic_distance(
centroids[:, 0], centroids[:, 1],
src_filter.sitecol.lons, src_filter.sitecol.lats)
idist = src_filter.integration_distance(DEFAULT_TRT, mag)
return src_filter.sitecol.filter(distance <= idist)
[docs] def get_background_sids(self, src_filter):
"""
We can apply the filtering of the background sites as a pre-processing
step - this is done here rather than in the sampling of the ruptures
themselves
"""
branch_key = self.idx_set["grid_key"]
idist = src_filter.integration_distance(DEFAULT_TRT)
lons, lats = src_filter.sitecol.lons, src_filter.sitecol.lats
with h5py.File(self.source_file, 'r') as hdf5:
bg_locations = hdf5["Grid/Locations"].value
n_locations = bg_locations.shape[0]
distances = min_idx_dst(lons, lats, numpy.zeros_like(lons),
bg_locations[:, 0], bg_locations[:, 1],
numpy.zeros(n_locations))[1]
# Add buffer equal to half of length of median area from Mmax
mmax_areas = self.msr.get_median_area(
hdf5["/".join(["Grid", branch_key, "MMax"])].value, 0.0)
# for instance hdf5['Grid/FM0_0_MEANFS_MEANMSR/MMax']
mmax_lengths = numpy.sqrt(mmax_areas / self.aspect)
ok = distances <= (0.5 * mmax_lengths + idist)
# get list of indices from array of booleans
return numpy.where(ok)[0].tolist()
[docs] def get_ucerf_rupture(self, iloc, src_filter):
"""
:param iloc:
Location of the rupture plane in the hdf5 file
:param src_filter:
Sites for consideration and maximum distance
"""
mesh_spacing = self.mesh_spacing
trt = self.tectonic_region_type
ridx = self.get_ridx(iloc)
mag = self.mags[iloc]
surface_set = []
r_sites = self.get_rupture_sites(ridx, src_filter, mag)
if r_sites is None:
return None
for trace, plane in self.gen_trace_planes(ridx):
# build simple fault surface
for jloc in range(0, plane.shape[2]):
top_left = Point(
plane[0, 0, jloc], plane[0, 1, jloc], plane[0, 2, jloc])
top_right = Point(
plane[1, 0, jloc], plane[1, 1, jloc], plane[1, 2, jloc])
bottom_right = Point(
plane[2, 0, jloc], plane[2, 1, jloc], plane[2, 2, jloc])
bottom_left = Point(
plane[3, 0, jloc], plane[3, 1, jloc], plane[3, 2, jloc])
try:
surface_set.append(
ImperfectPlanarSurface.from_corner_points(
mesh_spacing, top_left, top_right,
bottom_right, bottom_left))
except ValueError as err:
raise ValueError(err, trace, top_left, top_right,
bottom_right, bottom_left)
rupture = ParametricProbabilisticRupture(
mag, self.rake[iloc], trt,
surface_set[len(surface_set) // 2].get_middle_point(),
MultiSurface(surface_set), CharacteristicFaultSource,
self.rate[iloc], self.tom)
return rupture
[docs] def generate_event_set(self, background_sids, src_filter, seed):
"""
Generates the event set corresponding to a particular branch
"""
# get rates from file
with h5py.File(self.source_file, 'r') as hdf5:
occurrences = self.tom.sample_number_of_occurrences(
self.rate, seed)
indices = numpy.where(occurrences)[0]
logging.debug(
'Considering "%s", %d ruptures', self.source_id, len(indices))
# get ruptures from the indices
ruptures = []
rupture_occ = []
for iloc, n_occ in zip(indices, occurrences[indices]):
ucerf_rup = self.get_ucerf_rupture(iloc, src_filter)
if ucerf_rup:
ruptures.append(ucerf_rup)
rupture_occ.append(n_occ)
# sample background sources
background_ruptures, background_n_occ = sample_background_model(
hdf5, self.idx_set["grid_key"], self.tom, seed,
background_sids, self.min_mag, self.npd, self.hdd, self.usd,
self.lsd, self.msr, self.aspect, self.tectonic_region_type)
ruptures.extend(background_ruptures)
rupture_occ.extend(background_n_occ)
return ruptures, rupture_occ
[docs] def iter_ruptures(self):
"""
Yield ruptures for the current set of indices (.rupset_idx)
"""
try: # the source has set a subset of indices
rupset_idx = self.rupset_idx
except AttributeError: # use all indices
rupset_idx = numpy.arange(self.num_ruptures)
for ridx in rupset_idx:
if self.rate[ridx]: # ruptures may have have zero rate
rup = self.get_ucerf_rupture(ridx, self.src_filter)
if rup:
yield rup
[docs] def get_background_sources(self, src_filter):
"""
Turn the background model of a given branch into a set of point sources
:param src_filter:
SourceFilter instance
"""
background_sids = self.get_background_sids(src_filter)
with h5py.File(self.source_file, "r") as hdf5:
grid_loc = "/".join(["Grid", self.idx_set["grid_key"]])
mags = hdf5[grid_loc + "/Magnitude"].value
mmax = hdf5[grid_loc + "/MMax"][background_sids]
rates = hdf5[grid_loc + "/RateArray"][background_sids, :]
locations = hdf5["Grid/Locations"][background_sids, :]
sources = []
for i, bg_idx in enumerate(background_sids):
src_id = "_".join([self.idx_set["grid_key"], str(bg_idx)])
src_name = "|".join([self.idx_set["total_key"], str(bg_idx)])
# Get MFD
mag_idx = numpy.logical_and(
mags >= self.min_mag, mags < mmax[i])
src_mags = mags[mag_idx]
src_rates = rates[i, :]
src_mfd = EvenlyDiscretizedMFD(
src_mags[0], src_mags[1] - src_mags[0],
src_rates[mag_idx].tolist())
ps = PointSource(
src_id, src_name, self.tectonic_region_type, src_mfd,
self.mesh_spacing, self.msr, self.aspect, self.tom,
self.usd, self.lsd,
Point(locations[i, 0], locations[i, 1]),
self.npd, self.hdd)
ps.src_group_id = self.src_group_id
sources.append(ps)
return sources
[docs] def filter_sites_by_distance_to_source(self, integration_distance, sites):
# do not filter
return sites
[docs]def build_idx_set(branch_id, start_date):
"""
Builds a dictionary of keys based on the branch code
"""
code_set = branch_id.split("/")
code_set.insert(3, "Rates")
idx_set = {
"sec": "/".join([code_set[0], code_set[1], "Sections"]),
"mag": "/".join([code_set[0], code_set[1], code_set[2], "Magnitude"])}
idx_set["rate"] = "/".join(code_set)
idx_set["rake"] = "/".join([code_set[0], code_set[1], "Rake"])
idx_set["msr"] = "-".join(code_set[:3])
idx_set["geol"] = code_set[0]
if start_date: # time-dependent source
idx_set["grid_key"] = "_".join(
branch_id.replace("/", "_").split("_")[:-1])
else: # time-independent source
idx_set["grid_key"] = branch_id.replace("/", "_")
idx_set["total_key"] = branch_id.replace("/", "|")
return idx_set
# #################################################################### #
[docs]@util.reader
def compute_ruptures(sources, src_filter, gsims, param, monitor):
"""
:param sources: a list with a single UCERF source
:param src_filter: a SourceFilter instance
:param gsims: a list of GSIMs
:param param: extra parameters
:param monitor: a Monitor instance
:returns: an AccumDict grp_id -> EBRuptures
"""
[src] = sources
res = AccumDict()
res.calc_times = AccumDict()
serial = 1
sampl_mon = monitor('sampling ruptures', measuremem=True)
filt_mon = monitor('filtering ruptures', measuremem=False)
res.trt = DEFAULT_TRT
t0 = time.time()
ebruptures = []
background_sids = src.get_background_sids(src_filter)
sitecol = src_filter.sitecol
idist = src_filter.integration_distance
for sample in range(param['samples']):
for ses_idx, ses_seed in param['ses_seeds']:
seed = sample * TWO16 + ses_seed
with sampl_mon:
rups, n_occs = src.generate_event_set(
background_sids, src_filter, seed)
with filt_mon:
for rup, n_occ in zip(rups, n_occs):
rup.seed = seed
try:
r_sites, rrup = idist.get_closest(sitecol, rup)
except FarAwayRupture:
continue
indices = (numpy.arange(len(r_sites)) if r_sites.indices
is None else r_sites.indices)
events = []
for _ in range(n_occ):
events.append((0, src.src_group_id, ses_idx, sample))
if events:
evs = numpy.array(events, stochastic.event_dt)
ebruptures.append(
EBRupture(rup, indices, evs, serial))
serial += 1
res.num_events = stochastic.set_eids(ebruptures)
res[src.src_group_id] = ebruptures
res.calc_times[src.src_group_id] = {
src.source_id:
numpy.array([src.weight, len(sitecol), time.time() - t0, 1])}
if not param['save_ruptures']:
res.events_by_grp = {grp_id: event_based.get_events(res[grp_id])
for grp_id in res}
res.eff_ruptures = {src.src_group_id: src.num_ruptures}
return res
[docs]def get_composite_source_model(oq):
"""
:param oq: :class:`openquake.commonlib.oqvalidation.OqParam` instance
:returns: a `class:`openquake.commonlib.source.CompositeSourceModel`
"""
[src_group] = nrml.to_python(
oq.inputs["source_model"],
SourceConverter(oq.investigation_time, oq.rupture_mesh_spacing))
source_models = []
gsim_lt = readinput.get_gsim_lt(oq, [DEFAULT_TRT])
smlt = readinput.get_source_model_lt(oq)
for sm in smlt.gen_source_models(gsim_lt):
sg = copy.copy(src_group)
sg.id = sm.ordinal
sm.src_groups = [sg]
sg.sources = [sg[0].new(sm.ordinal, sm.names)]
source_models.append(sm)
return source.CompositeSourceModel(gsim_lt, smlt, source_models,
oq.optimize_same_id_sources)
[docs]@base.calculators.add('ucerf_rupture')
class UCERFRuptureCalculator(event_based.EventBasedRuptureCalculator):
"""
Event based PSHA calculator generating the ruptures only
"""
core_task = compute_ruptures
[docs] def pre_execute(self):
"""
parse the logic tree and source model input
"""
logging.warn('%s is still experimental', self.__class__.__name__)
oq = self.oqparam
self.read_risk_data() # read the site collection
self.csm = get_composite_source_model(oq)
self.csm.src_filter = SourceFilter(self.sitecol, oq.maximum_distance)
logging.info('Found %d source model logic tree branches',
len(self.csm.source_models))
self.datastore['csm_info'] = self.csm_info = self.csm.info
self.rlzs_assoc = self.csm_info.get_rlzs_assoc()
self.infos = []
self.eid = collections.Counter() # sm_id -> event_id
self.sm_by_grp = self.csm_info.get_sm_by_grp()
if not self.oqparam.imtls:
raise ValueError('Missing intensity_measure_types!')
self.rupser = calc.RuptureSerializer(self.datastore)
self.precomputed_gmfs = False
[docs] def gen_args(self, csm, monitor):
"""
Generate a task for each branch
"""
oq = self.oqparam
allargs = [] # it is better to return a list; if there is single
# branch then `parallel.Starmap` will run the task in core
for sm_id in range(len(csm.source_models)):
ssm = csm.get_model(sm_id)
[sm] = ssm.source_models
gsims = ssm.gsim_lt.values[DEFAULT_TRT]
srcs = ssm.get_sources()
for src in srcs:
src.nsites = len(self.sitecol) # not filtered here
for ses_idx in range(1, oq.ses_per_logic_tree_path + 1):
ses_seeds = [(ses_idx, oq.ses_seed + ses_idx)]
param = dict(ses_seeds=ses_seeds, samples=sm.samples,
save_ruptures=oq.save_ruptures)
allargs.append(
(srcs, self.csm.src_filter, gsims, param, monitor))
return allargs
[docs]class List(list):
"""Trivial container returned by compute_losses"""
[docs]@util.reader
def compute_losses(ssm, src_filter, param, riskmodel,
imts, trunc_level, correl_model, min_iml, monitor):
"""
Compute the losses for a single source model. Returns the ruptures
as an attribute `.ruptures_by_grp` of the list of losses.
:param ssm: CompositeSourceModel containing a single source model
:param sitecol: a SiteCollection instance
:param param: a dictionary of parameters
:param riskmodel: a RiskModel instance
:param imts: a list of Intensity Measure Types
:param trunc_level: truncation level
:param correl_model: correlation model
:param min_iml: vector of minimum intensities, one per IMT
:param monitor: a Monitor instance
:returns: a List containing the losses by taxonomy and some attributes
"""
[grp] = ssm.src_groups
res = List()
gsims = ssm.gsim_lt.values[DEFAULT_TRT]
ruptures_by_grp = compute_ruptures(
grp, src_filter, gsims, param, monitor)
[(grp_id, ebruptures)] = ruptures_by_grp.items()
rlzs_assoc = ssm.info.get_rlzs_assoc()
samples = ssm.info.get_samples_by_grp()
num_rlzs = len(rlzs_assoc.realizations)
rlzs_by_gsim = rlzs_assoc.get_rlzs_by_gsim(DEFAULT_TRT)
getter = getters.GmfGetter(
rlzs_by_gsim, ebruptures, src_filter.sitecol, imts, min_iml,
src_filter.integration_distance, trunc_level, correl_model,
samples[grp_id])
ri = riskinput.RiskInput(getter, param['assetcol'].assets_by_site())
res.append(event_based_risk(ri, riskmodel, param, monitor))
res.sm_id = ssm.sm_id
res.num_events = len(ri.hazard_getter.eids)
start = res.sm_id * num_rlzs
res.rlz_slice = slice(start, start + num_rlzs)
res.events_by_grp = ruptures_by_grp.events_by_grp
res.eff_ruptures = ruptures_by_grp.eff_ruptures
return res
[docs]@base.calculators.add('ucerf_hazard')
class UCERFHazardCalculator(event_based.EventBasedCalculator):
"""
Runs a standard event based calculation starting from UCERF ruptures
"""
pre_calculator = 'ucerf_rupture'
[docs]@base.calculators.add('ucerf_risk')
class UCERFRiskCalculator(EbriskCalculator):
"""
Event based risk calculator for UCERF, parallelizing on the source models
"""
pre_execute = UCERFRuptureCalculator.__dict__['pre_execute']
[docs] def gen_args(self):
"""
Yield the arguments required by build_ruptures, i.e. the
source models, the asset collection, the riskmodel and others.
"""
oq = self.oqparam
self.L = len(self.riskmodel.lti)
self.I = oq.insured_losses + 1
correl_model = oq.get_correl_model()
min_iml = self.get_min_iml(oq)
imts = list(oq.imtls)
elt_dt = numpy.dtype([('eid', U64), ('rlzi', U16),
('loss', (F32, (self.L, self.I)))])
monitor = self.monitor('compute_losses')
for sm in self.csm.source_models:
if sm.samples > 1:
logging.warn('Sampling in ucerf_risk is untested')
ssm = self.csm.get_model(sm.ordinal)
for ses_idx in range(1, oq.ses_per_logic_tree_path + 1):
param = dict(ses_seeds=[(ses_idx, oq.ses_seed + ses_idx)],
samples=sm.samples, assetcol=self.assetcol,
save_ruptures=False,
ses_ratio=oq.ses_ratio,
avg_losses=oq.avg_losses,
elt_dt=elt_dt,
asset_loss_table=False,
insured_losses=oq.insured_losses)
yield (ssm, self.csm.src_filter, param,
self.riskmodel, imts, oq.truncation_level,
correl_model, min_iml, monitor)
[docs] def execute(self):
self.riskmodel.taxonomy = self.assetcol.tagcol.taxonomy
num_rlzs = len(self.rlzs_assoc.realizations)
self.grp_trt = self.csm_info.grp_by("trt")
res = parallel.Starmap(compute_losses, self.gen_args()).submit_all()
self.vals = self.assetcol.values()
self.eff_ruptures = AccumDict(accum=0)
num_events = self.save_results(res, num_rlzs)
self.csm.info.update_eff_ruptures(self.eff_ruptures)
self.datastore['csm_info'] = self.csm.info
return num_events