# -*- coding: utf-8 -*-
# vim: tabstop=4 shiftwidth=4 softtabstop=4
#
# Copyright (C) 2018-2023 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 abc
import copy
import time
import logging
import warnings
import itertools
import operator
import collections
import numpy
import shapely
from scipy.interpolate import interp1d
from openquake.baselib import config
from openquake.baselib.general import (
AccumDict, DictArray, RecordBuilder, split_in_slices, block_splitter,
sqrscale)
from openquake.baselib.performance import Monitor, split_array, kround0, compile
from openquake.baselib.python3compat import decode
from openquake.hazardlib import valid, imt as imt_module
from openquake.hazardlib.const import StdDev, OK_COMPONENTS
from openquake.hazardlib.tom import NegativeBinomialTOM, PoissonTOM
from openquake.hazardlib.stats import ndtr, truncnorm_sf
from openquake.hazardlib.site import SiteCollection, site_param_dt
from openquake.hazardlib.calc.filters import (
SourceFilter, IntegrationDistance, magdepdist,
get_dparam, get_distances, getdefault, MINMAG, MAXMAG)
from openquake.hazardlib.map_array import MapArray
from openquake.hazardlib.geo import multiline
from openquake.hazardlib.geo.mesh import Mesh
from openquake.hazardlib.geo.surface.planar import (
project, project_back, get_distances_planar)
U8 = numpy.uint8
I32 = numpy.int32
U32 = numpy.uint32
F16 = numpy.float16
F32 = numpy.float32
F64 = numpy.float64
TWO20 = 2**20
TWO16 = 2**16
TWO24 = 2**24
TWO32 = 2**32
STD_TYPES = (StdDev.TOTAL, StdDev.INTER_EVENT, StdDev.INTRA_EVENT)
KNOWN_DISTANCES = frozenset('''rrup rx_ry0 rx ry0 rjb rhypo repi rcdpp azimuth
azimuthcp rvolc clon_clat clon clat'''.split())
NUM_BINS = 256
DIST_BINS = sqrscale(80, 1000, NUM_BINS)
MEA = 0
STD = 1
bymag = operator.attrgetter('mag')
# These coordinates were provided by M Gerstenberger (personal
# communication, 10 August 2018)
cshm_polygon = shapely.geometry.Polygon([(171.6, -43.3), (173.2, -43.3),
(173.2, -43.9), (171.6, -43.9)])
def _get(surfaces, param, dparam, mask=slice(None)):
arr = numpy.array([dparam[sec.idx, param][mask] for sec in surfaces])
return arr # shape (S, N, ...)
def _get_tu(rup, dparam, mask):
tor = rup.surface.tor
arr = _get(rup.surface.surfaces, 'tuw', dparam, mask)
S, N = arr.shape[:2]
# keep the flipped values and then reorder the surface indices
# arr has shape (S, N, 2, 3) where 2 refer to the flipping
tuw = numpy.zeros((S, N, 3), F32)
for s in range(S):
idx = tor.soidx[s]
flip = int(tor.flipped[idx])
tuw[s] = arr[idx, :, flip, :] # shape (N, 3)
return multiline.get_tu(tor.shift, tuw)
[docs]def set_distances(ctx, rup, r_sites, param, dparam, mask, tu):
"""
Set the distance attributes on the context; also manages paired
attributes like clon_lat and rx_ry0.
"""
if dparam is None:
# no multifault
dists = get_distances(rup, r_sites, param)
if '_' in param:
p0, p1 = param.split('_') # clon_clat
setattr(ctx, p0, dists[:, 0])
setattr(ctx, p1, dists[:, 1])
else:
setattr(ctx, param, dists)
else:
# use the MultiLine object
u_max = rup.surface.msparam['u_max']
if param in ('rx', 'ry0'):
tut, uut = tu
'''
# sanity check with the right parameters t, u
t, u = rup.surface.tor.get_tu(r_sites)
numpy.testing.assert_allclose(tut, t)
numpy.testing.assert_allclose(uut, u)
'''
if param == 'rx':
ctx.rx = tut
elif param == 'ry0':
neg = uut < 0
ctx.ry0[neg] = numpy.abs(uut[neg])
big = uut > u_max
ctx.ry0[big] = uut[big] - u_max
elif param == 'rjb':
rjbs = _get(rup.surface.surfaces, 'rjb', dparam, mask)
ctx['rjb'] = numpy.min(rjbs, axis=0)
'''
# sanity check with the right rjb
rjb = rup.surface.get_joyner_boore_distance(r_sites)
numpy.testing.assert_allclose(ctx.rjb, rjb)
'''
elif param == 'clon_clat':
coos = _get(rup.surface.surfaces, 'clon_clat', dparam, mask)
# shape (numsections, numsites, 3)
m = Mesh(coos[:, :, 0], coos[:, :, 1]).get_closest_points(r_sites)
# shape (numsites, 3)
ctx['clon'] = m.lons
ctx['clat'] = m.lats
[docs]def round_dist(dst):
idx = numpy.searchsorted(DIST_BINS, dst)
idx[idx == NUM_BINS] -= 1
return DIST_BINS[idx]
[docs]def is_modifiable(gsim):
"""
:returns: True if it is a ModifiableGMPE
"""
return hasattr(gsim, 'gmpe') and hasattr(gsim, 'params')
[docs]def concat(ctxs):
"""
Concatenate context arrays.
:returns: [] or [poisson_ctx] or [nonpoisson_ctx, ...]
"""
if not ctxs:
return []
ctx = ctxs[0]
out = []
# if ctx has probs_occur, it is assumed to be non-poissonian
if hasattr(ctx, 'probs_occur') and ctx.probs_occur.shape[1] >= 1:
# case 27, 29, 62, 65, 75, 78, 80
for shp in set(ctx.probs_occur.shape[1] for ctx in ctxs):
p_array = [p for p in ctxs if p.probs_occur.shape[1] == shp]
out.append(numpy.concatenate(p_array).view(numpy.recarray))
else:
out.append(numpy.concatenate(ctxs).view(numpy.recarray))
return out
[docs]def size(imtls):
"""
:returns: size of the dictionary of arrays imtls
"""
imls = imtls[next(iter(imtls))]
return len(imls) * len(imtls)
[docs]def trivial(ctx, name):
"""
:param ctx: a recarray
:param name: name of a parameter
:returns: True if the parameter is missing or single valued
"""
if name not in ctx.dtype.names:
return True
return len(numpy.unique(numpy.float32(ctx[name]))) == 1
[docs]class Oq(object):
"""
A mock for OqParam
"""
af = None
aristotle = False
cross_correl = None
mea_tau_phi = False
split_sources = True
use_rates = False
with_betw_ratio = None
infer_occur_rates = False
inputs = ()
def __init__(self, **hparams):
vars(self).update(hparams)
@property
def min_iml(self):
try:
imtls = self.imtls
except AttributeError:
imtls = self.hazard_imtls
return numpy.array([1E-10 for imt in imtls])
[docs] def get_reqv(self):
if 'reqv' not in self.inputs:
return
return {key: valid.RjbEquivalent(value)
for key, value in self.inputs['reqv'].items()}
[docs]class DeltaRatesGetter(object):
"""
Read the delta rates from an aftershock datastore
"""
def __init__(self, dstore):
self.dstore = dstore
def __call__(self, src_id):
with self.dstore.open('r') as dstore:
return dstore['delta_rates'][src_id]
# same speed as performance.kround, round more
[docs]def kround1(ctx, kfields):
kdist = 2. * ctx.mag**2 # heuristic collapse distance from 32 to 200 km
close = ctx.rrup < kdist
far = ~close
out = numpy.zeros(len(ctx), [(k, ctx.dtype[k]) for k in kfields])
for kfield in kfields:
kval = ctx[kfield]
if kfield == 'vs30':
out[kfield][close] = numpy.round(kval[close]) # round less
out[kfield][far] = numpy.round(kval[far], 1) # round more
elif kval.dtype == F64 and kfield != 'mag':
out[kfield][close] = F16(kval[close]) # round less
out[kfield][far] = numpy.round(kval[far]) # round more
else:
out[kfield] = ctx[kfield]
return out
[docs]def kround2(ctx, kfields):
kdist = 5. * ctx.mag**2 # from 80 to 500 km
close = ctx.rrup < kdist
far = ~close
out = numpy.zeros(len(ctx), [(k, ctx.dtype[k]) for k in kfields])
for kfield in kfields:
kval = ctx[kfield]
if kfield == 'rx': # can be negative
out[kfield] = numpy.round(kval)
elif kfield in KNOWN_DISTANCES:
out[kfield][close] = numpy.ceil(kval[close]) # round to 1 km
out[kfield][far] = round_dist(kval[far]) # round more
elif kfield == 'vs30':
out[kfield][close] = numpy.round(kval[close]) # round less
out[kfield][far] = numpy.round(kval[far], 1) # round more
elif kval.dtype == F64 and kfield != 'mag':
out[kfield][close] = F16(kval[close]) # round less
out[kfield][far] = numpy.round(kval[far]) # round more
else:
out[kfield] = ctx[kfield]
return out
kround = {0: kround0, 1: kround1, 2: kround2}
[docs]class FarAwayRupture(Exception):
"""Raised if the rupture is outside the maximum distance for all sites"""
[docs]def get_num_distances(gsims):
"""
:returns: the number of distances required for the given GSIMs
"""
dists = set()
for gsim in gsims:
dists.update(gsim.REQUIRES_DISTANCES)
return len(dists)
# NB: minimum_magnitude is ignored
def _interp(param, name, trt):
try:
mdd = param[name]
except KeyError:
return magdepdist([(MINMAG, 1000), (MAXMAG, 1000)])
if isinstance(mdd, IntegrationDistance):
return mdd(trt)
elif isinstance(mdd, dict):
if mdd:
magdist = getdefault(mdd, trt)
else:
magdist = [(MINMAG, 1000), (MAXMAG, 1000)]
return magdepdist(magdist)
return mdd
[docs]def simple_cmaker(gsims, imts, **params):
"""
:returns: a simplified ContextMaker for use in the tests
"""
dic = dict(imtls={imt: [0] for imt in imts})
dic.update(**params)
return ContextMaker('*', gsims, dic)
# ############################ genctxs ################################## #
# generator of quartets (rup_index, mag, planar_array, sites)
def _quartets(cmaker, src, sitecol, cdist, magdist, planardict):
minmag = cmaker.maximum_distance.x[0]
maxmag = cmaker.maximum_distance.x[-1]
# splitting by magnitude
if src.count_nphc() == 1:
# one rupture per magnitude
for m, (mag, pla) in enumerate(planardict.items()):
if minmag < mag < maxmag:
yield m, mag, pla, sitecol
else:
for m, rup in enumerate(src.iruptures()):
mag = rup.mag
if mag > maxmag or mag < minmag:
continue
arr = [rup.surface.array.reshape(-1, 3)]
pla = planardict[mag]
# NB: having a good psdist is essential for performance!
psdist = src.get_psdist(m, mag, cmaker.pointsource_distance,
magdist)
close = sitecol.filter(cdist <= psdist)
far = sitecol.filter(cdist > psdist)
if cmaker.fewsites:
if close is None: # all is far, common for small mag
yield m, mag, arr, sitecol
else: # something is close
yield m, mag, pla, sitecol
else: # many sites
if close is None: # all is far
yield m, mag, arr, far
elif far is None: # all is close
yield m, mag, pla, close
else: # some sites are far, some are close
yield m, mag, arr, far
yield m, mag, pla, close
# helper used to populate contexts for planar ruptures
def _get_ctx_planar(cmaker, zeroctx, mag, planar, sites, src_id, tom):
# computing distances
rrup, xx, yy = project(planar, sites.xyz) # (3, U, N)
# get the closest points on the surface
if cmaker.fewsites or 'clon' in cmaker.REQUIRES_DISTANCES:
closest = project_back(planar, xx, yy) # (3, U, N)
# set distances
zeroctx['rrup'] = rrup
for par in cmaker.REQUIRES_DISTANCES - {'rrup'}:
zeroctx[par] = get_distances_planar(planar, sites, par)
for par in cmaker.REQUIRES_DISTANCES:
dst = zeroctx[par]
if cmaker.minimum_distance:
dst[dst < cmaker.minimum_distance] = cmaker.minimum_distance
# ctx has shape (U, N), ctxt (N, U)
ctxt = zeroctx.T # smart trick taking advantage of numpy magic
ctxt['src_id'] = src_id
# setting rupture parameters
for par in cmaker.ruptparams:
if par == 'mag':
ctxt[par] = mag
elif par == 'occurrence_rate':
ctxt[par] = planar.wlr[:, 2] # shape U-> (N, U)
elif par == 'width':
ctxt[par] = planar.wlr[:, 0]
elif par == 'strike':
ctxt[par] = planar.sdr[:, 0]
elif par == 'dip':
ctxt[par] = planar.sdr[:, 1]
elif par == 'rake':
ctxt[par] = planar.sdr[:, 2]
elif par == 'ztor': # top edge depth
ctxt[par] = planar.corners[:, 2, 0]
elif par == 'zbot': # bottom edge depth
ctxt[par] = planar.corners[:, 2, 3]
elif par == 'hypo_lon':
ctxt[par] = planar.hypo[:, 0]
elif par == 'hypo_lat':
ctxt[par] = planar.hypo[:, 1]
elif par == 'hypo_depth':
ctxt[par] = planar.hypo[:, 2]
if cmaker.fewsites:
zeroctx['clon'] = closest[0]
zeroctx['clat'] = closest[1]
# setting site parameters
for par in cmaker.siteparams:
zeroctx[par] = sites.array[par] # shape N-> (U, N)
if hasattr(tom, 'get_pmf'): # NegativeBinomialTOM
# read Probability Mass Function from model and reshape it
# into predetermined shape of probs_occur
pmf = tom.get_pmf(planar.wlr[:, 2],
n_max=zeroctx['probs_occur'].shape[2])
zeroctx['probs_occur'] = pmf[:, numpy.newaxis, :]
return zeroctx.flatten() # shape N*U
[docs]def genctxs_Pp(src, sitecol, cmaker):
"""
Context generator for point sources and collapsed point sources
"""
dd = cmaker.defaultdict.copy()
tom = getattr(src, 'temporal_occurrence_model', None)
if tom and isinstance(tom, NegativeBinomialTOM):
if hasattr(src, 'pointsources'): # CollapsedPointSource
maxrate = max(max(ps.mfd.occurrence_rates)
for ps in src.pointsources)
else: # regular source
maxrate = max(src.mfd.occurrence_rates)
p_size = tom.get_pmf(maxrate).shape[1]
dd['probs_occur'] = numpy.zeros(p_size)
else:
dd['probs_occur'] = numpy.zeros(0)
builder = RecordBuilder(**dd)
cmaker.siteparams = [par for par in sitecol.array.dtype.names
if par in dd]
cmaker.ruptparams = cmaker.REQUIRES_RUPTURE_PARAMETERS | {'occurrence_rate'}
with cmaker.ir_mon:
# building planar geometries
planardict = src.get_planar(cmaker.shift_hypo)
magdist = {mag: cmaker.maximum_distance(mag)
for mag, rate in src.get_annual_occurrence_rates()}
# cmaker.maximum_distance(mag) can be 0 if outside the mag range
maxmag = max(mag for mag, dist in magdist.items() if dist > 0)
maxdist = magdist[maxmag]
cdist = sitecol.get_cdist(src.location)
# NB: having a decent max_radius is essential for performance!
mask = cdist <= maxdist + src.max_radius(maxdist)
sitecol = sitecol.filter(mask)
if sitecol is None:
return []
for magi, mag, planarlist, sites in _quartets(
cmaker, src, sitecol, cdist[mask], magdist, planardict):
if not planarlist:
continue
elif len(planarlist) > 1: # when using ps_grid_spacing
pla = numpy.concatenate(planarlist).view(numpy.recarray)
else:
pla = planarlist[0]
offset = src.offset + magi * len(pla)
zctx = builder.zeros((len(pla), len(sites))) # shape (N, U)
if cmaker.fewsites:
rup_ids = zctx['rup_id'].T # numpy trick, shape (U, N)
rup_ids[:] = numpy.arange(offset, offset+len(pla))
# building contexts
ctx = _get_ctx_planar(cmaker, zctx, mag, pla, sites, src.id, tom)
ctxt = ctx[ctx.rrup < magdist[mag]]
if len(ctxt):
yield ctxt
def _build_dparam(src, sitecol, cmaker):
dparams = {'rjb', 'tuw'}
if cmaker.fewsites:
dparams |= {'clon_clat'}
sections = src.get_sections(src.get_unique_idxs())
out = {}
for sec in sections:
out[sec.idx, 'rrup'] = get_dparam(sec, sitecol, 'rrup')
for param in dparams:
out[sec.idx, param] = get_dparam(sec, sitecol, param)
# use multi_fault_test to debug this
# from openquake.baselib.general import getsizeof
# print(getsizeof(out))
return out
# this is the critical function for the performance of the classical calculator
# the performance is dominated by the CPU cache, i.e. large arrays are slow
# the only way to speedup is to reduce the maximum_distance, then the array
# will become shorter in the N dimension (number of affected sites), or to
# collapse the ruptures, then truncnorm_sf will be called less times
@compile("(float64[:,:,:], float64[:,:], float64, float32[:,:])")
def _set_poes(mean_std, loglevels, phi_b, out):
L1 = loglevels.size // len(loglevels)
for m, levels in enumerate(loglevels):
mL1 = m * L1
mea, std = mean_std[:, m] # shape N
for lvl, iml in enumerate(levels):
out[mL1 + lvl] = truncnorm_sf(phi_b, (iml - mea) / std)
# ############################ ContextMaker ############################### #
def _fix(gsimdict, betw):
if betw:
out = {}
for gsim, uints in gsimdict.items():
if len(gsim.DEFINED_FOR_STANDARD_DEVIATION_TYPES) == 1:
out[valid.modified_gsim(gsim, add_between_within_stds=betw)] \
= uints
else:
out[gsim] = uints
return out
return gsimdict
[docs]class ContextMaker(object):
"""
A class to manage the creation of contexts and to compute mean/stddevs
and possibly PoEs.
:param trt: tectonic region type string
:param gsims: list of GSIMs or a dictionary gsim -> rlz indices
:param oq:
dictionary of parameters like the maximum_distance, the IMTLs,
the investigation time, etc, or an OqParam instance
:param extraparams:
additional site parameters to consider, used only in the tests
NB: the trt can be different from the tectonic region type for which
the underlying GSIMs are defined. This is intentional.
"""
REQUIRES = ['DISTANCES', 'SITES_PARAMETERS', 'RUPTURE_PARAMETERS']
scenario = False
deltagetter = None
fewsites = False
tom = None
def __init__(self, trt, gsims, oq, monitor=Monitor(), extraparams=()):
self.trt = trt
if isinstance(oq, dict):
# this happens when instantiating RuptureData in extract.py
param = oq
oq = Oq(**param)
self.mags = param.get('mags', ()) # list of strings %.2f
self.cross_correl = param.get('cross_correl') # cond_spectra_test
else: # OqParam
param = vars(oq)
param['reqv'] = oq.get_reqv()
param['af'] = getattr(oq, 'af', None)
self.cross_correl = oq.cross_correl
self.imtls = oq.imtls
try:
self.mags = oq.mags_by_trt[trt]
except AttributeError:
self.mags = ()
except KeyError: # missing TRT but there is only one
[(_, self.mags)] = oq.mags_by_trt.items()
if oq.with_betw_ratio:
betw_ratio = {'with_betw_ratio': oq.with_betw_ratio}
elif oq.aristotle:
betw_ratio = {'with_betw_ratio': 1.7} # same as in GEESE
else:
betw_ratio = {}
if isinstance(gsims, dict):
self.gsims = _fix(gsims, betw_ratio)
else:
self.gsims = _fix({gsim: U32([i]) for i, gsim in enumerate(gsims)},
betw_ratio)
# NB: the gid array can be overridden later on
self.gid = numpy.arange(len(gsims), dtype=numpy.uint16)
self.oq = oq
self.monitor = monitor
self._init1(param)
self._init2(param, extraparams)
self.set_imts_conv()
self.init_monitoring(self.monitor)
def _init1(self, param):
if 'poes' in param:
self.poes = param['poes']
if 'imtls' in param:
for imt in param['imtls']:
if not isinstance(imt, str):
raise TypeError('Expected string, got %s' % type(imt))
self.imtls = param['imtls']
elif 'hazard_imtls' in param:
self.imtls = imt_module.dictarray(param['hazard_imtls'])
elif not hasattr(self, 'imtls'):
raise KeyError('Missing imtls in ContextMaker!')
self.cache_distances = param.get('cache_distances', False)
self.max_sites_disagg = param.get('max_sites_disagg', 10)
self.time_per_task = param.get('time_per_task', 60)
self.collapse_level = int(param.get('collapse_level', -1))
self.disagg_by_src = param.get('disagg_by_src', False)
self.horiz_comp = param.get('horiz_comp_to_geom_mean', False)
self.maximum_distance = _interp(param, 'maximum_distance', self.trt)
if 'pointsource_distance' not in param:
self.pointsource_distance = float(
config.performance.pointsource_distance)
else:
self.pointsource_distance = getdefault(
param['pointsource_distance'], self.trt)
self.minimum_distance = param.get('minimum_distance', 0)
self.investigation_time = param.get('investigation_time')
self.ses_seed = param.get('ses_seed', 42)
self.ses_per_logic_tree_path = param.get('ses_per_logic_tree_path', 1)
self.truncation_level = param.get('truncation_level', 99.)
self.phi_b = ndtr(self.truncation_level)
self.num_epsilon_bins = param.get('num_epsilon_bins', 1)
self.disagg_bin_edges = param.get('disagg_bin_edges', {})
self.ps_grid_spacing = param.get('ps_grid_spacing')
self.split_sources = self.oq.split_sources
for gsim in self.gsims:
if hasattr(gsim, 'set_tables'):
if len(self.mags) == 0 and not is_modifiable(gsim):
raise ValueError(
'You must supply a list of magnitudes as 2-digit '
'strings, like mags=["6.00", "6.10", "6.20"]')
gsim.set_tables(self.mags, self.imtls)
def _init2(self, param, extraparams):
for req in self.REQUIRES:
reqset = set()
for gsim in self.gsims:
reqset.update(getattr(gsim, 'REQUIRES_' + req))
if getattr(self.oq, 'af', None) and req == 'SITES_PARAMETERS':
reqset.add('ampcode')
if is_modifiable(gsim) and req == 'SITES_PARAMETERS':
reqset.add('vs30') # required by the ModifiableGMPE
reqset.update(gsim.gmpe.REQUIRES_SITES_PARAMETERS)
if 'apply_swiss_amplification' in gsim.params:
reqset.add('amplfactor')
if ('apply_swiss_amplification_sa' in gsim.params):
reqset.add('ch_ampl03')
reqset.add('ch_ampl06')
reqset.add('ch_phis2s03')
reqset.add('ch_phis2s06')
reqset.add('ch_phiss03')
reqset.add('ch_phiss06')
setattr(self, 'REQUIRES_' + req, reqset)
self.min_iml = self.oq.min_iml
self.reqv = param.get('reqv')
if self.reqv is not None:
self.REQUIRES_DISTANCES.add('repi')
# NB: REQUIRES_DISTANCES is empty when gsims = [FromFile]
REQUIRES_DISTANCES = self.REQUIRES_DISTANCES | {'rrup'}
reqs = (sorted(self.REQUIRES_RUPTURE_PARAMETERS) +
sorted(self.REQUIRES_SITES_PARAMETERS | set(extraparams)) +
sorted(REQUIRES_DISTANCES))
dic = {}
for req in reqs:
if req in site_param_dt:
dt = site_param_dt[req]
if isinstance(dt, tuple): # (string_, size)
dic[req] = b'X' * dt[1]
else:
dic[req] = dt(0)
else:
dic[req] = 0.
dic['src_id'] = I32(0)
dic['rup_id'] = U32(0)
dic['sids'] = U32(0)
dic['rrup'] = F64(0)
dic['occurrence_rate'] = F64(0)
self.defaultdict = dic
self.shift_hypo = param.get('shift_hypo')
[docs] def init_monitoring(self, monitor):
# instantiating child monitors, may be called in the workers
self.pla_mon = monitor('planar contexts', measuremem=False)
self.ctx_mon = monitor('nonplanar contexts', measuremem=False)
self.gmf_mon = monitor('computing mean_std', measuremem=False)
self.poe_mon = monitor('get_poes', measuremem=False)
self.ir_mon = monitor('iter_ruptures', measuremem=False)
self.sec_mon = monitor('building dparam', measuremem=True)
self.delta_mon = monitor('getting delta_rates', measuremem=False)
self.task_no = getattr(monitor, 'task_no', 0)
self.out_no = getattr(monitor, 'out_no', self.task_no)
self.cfactor = numpy.zeros(2)
[docs] def copy(self, **kw):
"""
:returns: a copy of the ContextMaker with modified attributes
"""
new = copy.copy(self)
for k, v in kw.items():
setattr(new, k, v)
if 'imtls' in kw:
new.set_imts_conv()
return new
[docs] def restrict(self, imts):
"""
:param imts: a list of IMT strings subset of the full list
:returns: a new ContextMaker involving less IMTs
"""
new = copy.copy(self)
new.imtls = DictArray({imt: self.imtls[imt] for imt in imts})
new.set_imts_conv()
return new
[docs] def set_imts_conv(self):
"""
Set the .imts list and .conv dictionary for the horizontal component
conversion (if any). Also set the .loglevels.
"""
self.loglevels = DictArray(self.imtls) if self.imtls else {}
with warnings.catch_warnings():
# avoid RuntimeWarning: divide by zero encountered in log
warnings.simplefilter("ignore")
for imt, imls in self.imtls.items():
if imt != 'MMI':
self.loglevels[imt] = numpy.log(imls)
self.imts = tuple(imt_module.from_string(im) for im in self.imtls)
self.conv = {} # gsim -> imt -> (conv_median, conv_sigma, rstd)
if not self.horiz_comp:
return # do not convert
for gsim in self.gsims:
self.conv[gsim] = {}
imc = gsim.DEFINED_FOR_INTENSITY_MEASURE_COMPONENT
if imc.name == 'GEOMETRIC_MEAN':
pass # nothing to do
elif imc.name in OK_COMPONENTS:
dic = {imt: imc.apply_conversion(imt) for imt in self.imts}
self.conv[gsim].update(dic)
else:
logging.info(f'Conversion from {imc.name} not applicable to'
f' {gsim.__class__.__name__}')
[docs] def split(self, blocksize):
"""
Split the ContextMaker by blocks of GSIMs
"""
for gid, wei, gsims in zip(block_splitter(self.gid, blocksize),
block_splitter(self.wei, blocksize),
block_splitter(self.gsims, blocksize)):
new = copy.copy(self)
new.gsims = gsims
new.gid = gid
new.wei = wei
yield new
[docs] def horiz_comp_to_geom_mean(self, mean_stds, gsim):
"""
This function converts ground-motion obtained for a given description
of horizontal component into ground-motion values for geometric_mean.
The conversion equations used are from:
- Beyer and Bommer (2006): for arithmetic mean, GMRot and random
- Boore and Kishida (2017): for RotD50
"""
if not self.conv[gsim]:
return
for m, imt in enumerate(self.imts):
me, si, _ta, _ph = mean_stds[:, m]
conv_median, conv_sigma, rstd = self.conv[gsim][imt]
me[:] = numpy.log(numpy.exp(me) / conv_median)
si[:] = ((si**2 - conv_sigma**2) / rstd**2)**0.5
@property
def Z(self):
"""
:returns: the number of realizations associated to self
"""
return sum(len(rlzs) for rlzs in self.gsims.values())
[docs] def new_ctx(self, size):
"""
:returns: a recarray of the given size full of zeros
"""
return RecordBuilder(**self.defaultdict).zeros(size)
[docs] def recarray(self, ctxs):
"""
:params ctxs: a non-empty list of homogeneous contexts
:returns: a recarray, possibly collapsed
"""
assert ctxs
dd = self.defaultdict.copy()
if not hasattr(ctxs[0], 'probs_occur'):
for ctx in ctxs:
ctx.probs_occur = numpy.zeros(0)
np = 0
else:
shps = [ctx.probs_occur.shape for ctx in ctxs]
np = max(i[1] if len(i) > 1 else i[0] for i in shps)
dd['probs_occur'] = numpy.zeros(np)
C = sum(len(ctx) for ctx in ctxs)
ra = RecordBuilder(**dd).zeros(C)
start = 0
for ctx in ctxs:
if self.minimum_distance:
for name in self.REQUIRES_DISTANCES:
array = ctx[name]
small_distances = array < self.minimum_distance
if small_distances.any():
array = numpy.array(array) # make a copy first
array[small_distances] = self.minimum_distance
ctx[name] = array
slc = slice(start, start + len(ctx))
for par in dd:
if par == 'rup_id':
val = getattr(ctx, par)
else:
val = getattr(ctx, par, numpy.nan)
if par == 'clon_clat':
ra['clon'][slc] = ctx.clon
ra['clat'][slc] = ctx.clat
else:
getattr(ra, par)[slc] = val
ra.sids[slc] = ctx.sids
start = slc.stop
return ra
[docs] def get_ctx_params(self):
"""
:returns: the interesting attributes of the context
"""
params = {'occurrence_rate', 'sids', 'src_id',
'probs_occur', 'clon', 'clat', 'rrup'}
params.update(self.REQUIRES_RUPTURE_PARAMETERS)
for dparam in self.REQUIRES_DISTANCES:
params.add(dparam + '_')
return params
[docs] def from_planar(self, rup, hdist, step, point='TC', toward_azimuth=90.,
direction='positive'):
"""
:param rup:
a BaseRupture instance with a PlanarSurface and site parameters
:returns: a context array for the sites around the rupture
"""
sitecol = SiteCollection.from_planar(
rup, point='TC', toward_azimuth=toward_azimuth,
direction=direction, hdist=hdist, step=step,
req_site_params=self.REQUIRES_SITES_PARAMETERS)
ctxs = list(self.genctxs([rup], sitecol, src_id=0))
return self.recarray(ctxs)
[docs] def from_srcs(self, srcs, sitecol):
# used in disagg.disaggregation
"""
:param srcs: a list of Source objects
:param sitecol: a SiteCollection instance
:returns: a list of context arrays
"""
ctxs = []
srcfilter = SourceFilter(sitecol, self.maximum_distance)
for i, src in enumerate(srcs):
if src.id == -1: # not set yet
src.id = i
sites = srcfilter.get_close_sites(src)
if sites is not None:
ctxs.extend(self.get_ctx_iter(src, sites))
return concat(ctxs)
[docs] def get_rparams(self, rup):
"""
:returns: a dictionary with the rupture parameters
"""
dic = {}
if hasattr(self, 'dparam') and self.dparam:
msparam = rup.surface.msparam
else:
msparam = None
for param in self.REQUIRES_RUPTURE_PARAMETERS:
if param == 'mag':
value = numpy.round(rup.mag, 3)
elif param == 'strike':
if msparam:
value = msparam['strike']
else:
value = rup.surface.get_strike()
elif param == 'dip':
if msparam:
value = msparam['dip']
else:
value = rup.surface.get_dip()
elif param == 'rake':
value = rup.rake
elif param == 'ztor':
if msparam:
value = msparam['ztor']
else:
value = rup.surface.get_top_edge_depth()
elif param == 'hypo_lon':
value = rup.hypocenter.longitude
elif param == 'hypo_lat':
value = rup.hypocenter.latitude
elif param == 'hypo_depth':
value = rup.hypocenter.depth
elif param == 'width':
if msparam:
value = msparam['width']
else:
value = rup.surface.get_width()
elif param == 'in_cshm':
# used in McVerry and Bradley GMPEs
if rup.surface:
# this is really expensive
lons = rup.surface.mesh.lons.flatten()
lats = rup.surface.mesh.lats.flatten()
points_in_polygon = (
shapely.geometry.Point(lon, lat).within(cshm_polygon)
for lon, lat in zip(lons, lats))
value = any(points_in_polygon)
else:
value = False
elif param == 'zbot':
# needed for width estimation in CampbellBozorgnia2014
if msparam:
value = msparam['zbot']
elif rup.surface and hasattr(rup, 'surfaces'):
value = rup.surface.zbot
elif rup.surface:
value = rup.surface.mesh.depths.max()
else:
value = rup.hypocenter.depth
else:
raise ValueError('%s requires unknown rupture parameter %r' %
(type(self).__name__, param))
dic[param] = value
dic['occurrence_rate'] = getattr(rup, 'occurrence_rate', numpy.nan)
if hasattr(rup, 'temporal_occurrence_model'):
if isinstance(rup.temporal_occurrence_model, NegativeBinomialTOM):
dic['probs_occur'] = rup.temporal_occurrence_model.get_pmf(
rup.occurrence_rate)
elif hasattr(rup, 'probs_occur'):
dic['probs_occur'] = rup.probs_occur
return dic
[docs] def genctxs(self, same_mag_rups, sites, src_id):
"""
:params same_mag_rups: a list of ruptures
:param sites: a (filtered) site collection
:param src_id: source index
:yields: a context array for each rupture
"""
magdist = self.maximum_distance(same_mag_rups[0].mag)
dparam = getattr(self, 'dparam', None)
for rup in same_mag_rups:
if dparam:
rrups = _get(rup.surface.surfaces, 'rrup', dparam)
rrup = numpy.min(rrups, axis=0)
else:
rrup = get_distances(rup, sites, 'rrup')
mask = rrup <= magdist
if not mask.any():
continue
r_sites = sites.filter(mask)
# to debug you can insert here
# print(rup.surface.tor.get_tuw_df(r_sites))
# import pdb; pdb.set_trace()
''' # sanity check
true_rrup = rup.surface.get_min_distance(r_sites)
numpy.testing.assert_allclose(true_rrup, rrup[mask])
'''
rparams = self.get_rparams(rup)
dd = self.defaultdict.copy()
np = len(rparams.get('probs_occur', []))
dd['probs_occur'] = numpy.zeros(np)
ctx = RecordBuilder(**dd).zeros(len(r_sites))
for par, val in rparams.items():
ctx[par] = val
ctx.rrup = rrup[mask]
ctx.sids = r_sites.sids
params = self.REQUIRES_DISTANCES - {'rrup'}
if self.fewsites or 'clon' in params or 'clat' in params:
params.add('clon_clat')
# compute tu only once
if dparam and ('rx' in params or 'ry0' in params):
tu = _get_tu(rup, dparam, mask)
else:
tu = None
for param in params - {'clon', 'clat'}:
set_distances(ctx, rup, r_sites, param, dparam, mask, tu)
# Equivalent distances
reqv_obj = (self.reqv.get(self.trt) if self.reqv else None)
if reqv_obj and not rup.surface: # PointRuptures have no surface
reqv = reqv_obj.get(ctx.repi, rup.mag)
if 'rjb' in self.REQUIRES_DISTANCES:
ctx.rjb = reqv
if 'rrup' in self.REQUIRES_DISTANCES:
ctx.rrup = numpy.sqrt(reqv**2 + rup.hypocenter.depth**2)
for name in r_sites.array.dtype.names:
setattr(ctx, name, r_sites[name])
ctx.src_id = src_id
if src_id >= 0:
ctx.rup_id = rup.rup_id
yield ctx
# this is called for non-point sources (or point sources in preclassical)
[docs] def gen_contexts(self, rups_sites, src_id):
"""
:yields: the old-style RuptureContexts generated by the source
"""
for rups, sites in rups_sites: # ruptures with the same magnitude
yield from self.genctxs(rups, sites, src_id)
[docs] def get_ctx_iter(self, src, sitecol, src_id=0, step=1):
"""
:param src:
a source object (already split) or a list of ruptures
:param sitecol:
a (filtered) SiteCollection
:param src_id:
integer source ID used where src is actually a list
:param step:
> 1 only in preclassical
:returns:
iterator over recarrays
"""
self.fewsites = len(sitecol.complete) <= self.max_sites_disagg
if self.fewsites or 'clon' in self.REQUIRES_DISTANCES:
self.defaultdict['clon'] = F64(0.)
self.defaultdict['clat'] = F64(0.)
if getattr(src, 'location', None) and step == 1:
return self.pla_mon.iter(genctxs_Pp(src, sitecol, self))
elif hasattr(src, 'source_id'): # other source
if src.code == b'F' and step == 1:
with self.sec_mon:
self.dparam = _build_dparam(src, sitecol, self)
else:
self.dparam = None
minmag = self.maximum_distance.x[0]
maxmag = self.maximum_distance.x[-1]
with self.ir_mon:
allrups = list(src.iter_ruptures(
shift_hypo=self.shift_hypo, step=step))
for i, rup in enumerate(allrups):
rup.rup_id = src.offset + i
allrups = sorted([rup for rup in allrups
if minmag < rup.mag < maxmag],
key=bymag)
if not allrups:
return iter([])
self.num_rups = len(allrups)
# sorted by mag by construction
u32mags = U32([rup.mag * 100 for rup in allrups])
rups_sites = [(rups, sitecol) for rups in split_array(
numpy.array(allrups), u32mags)]
src_id = src.id
else: # in event based we get a list with a single rupture
rups_sites = [(src, sitecol)]
self.dparam = None
src_id = -1
ctxs = self.gen_contexts(rups_sites, src_id)
blocks = block_splitter(ctxs, 10_000, weight=len)
# the weight of 10_000 ensure less than 1MB per block (recarray)
return self.ctx_mon.iter(map(self.recarray, blocks))
[docs] def max_intensity(self, sitecol1, mags, dists):
"""
:param sitecol1: a SiteCollection instance with a single site
:param mags: a sequence of magnitudes
:param dists: a sequence of distances
:returns: an array of GMVs of shape (#mags, #dists)
"""
assert len(sitecol1) == 1, sitecol1
nmags, ndists = len(mags), len(dists)
gmv = numpy.zeros((nmags, ndists))
for m, d in itertools.product(range(nmags), range(ndists)):
mag, dist = mags[m], dists[d]
ctx = RuptureContext()
for par in self.REQUIRES_RUPTURE_PARAMETERS:
setattr(ctx, par, 0)
for dst in self.REQUIRES_DISTANCES:
setattr(ctx, dst, numpy.array([dist]))
for par in self.REQUIRES_SITES_PARAMETERS:
setattr(ctx, par, getattr(sitecol1, par))
ctx.sids = sitecol1.sids
ctx.mag = mag
ctx.width = .01 # 10 meters to avoid warnings in abrahamson_2014
try:
maxmean = self.get_mean_stds([ctx])[0].max()
# shape NM
except ValueError: # magnitude outside of supported range
continue
else:
gmv[m, d] = numpy.exp(maxmean)
return gmv
[docs] def get_occ_rates(self, ctxt):
"""
:param ctxt: context array generated by this ContextMaker
:returns: occurrence rates, possibly from probs_occur[0]
"""
# thanks to split_by_tom we can assume ctx to be homogeneous
if numpy.isfinite(ctxt[0].occurrence_rate):
return ctxt.occurrence_rate
else:
probs = [rec.probs_occur[0] for rec in ctxt]
return -numpy.log(probs) / self.investigation_time
# not used by the engine, is is meant for notebooks
[docs] def get_poes(self, srcs, sitecol, tom=None, rup_mutex={},
collapse_level=-1):
"""
:param srcs: a list of sources with the same TRT
:param sitecol: a SiteCollection instance with N sites
:returns: an array of PoEs of shape (N, L, G)
"""
ctxs = self.from_srcs(srcs, sitecol)
return self.get_pmap(ctxs, tom, rup_mutex).array
def _gen_poes(self, ctx):
from openquake.hazardlib.site_amplification import get_poes_site
(M, L1), G = self.loglevels.array.shape, len(self.gsims)
# split large context arrays to avoid filling the CPU cache
with self.gmf_mon:
# split_by_mag=False because already contains a single mag
mean_stdt = self.get_mean_stds([ctx], split_by_mag=False)
# making plenty of slices so that the array `poes` is small
for slc in split_in_slices(len(ctx), 2*L1):
with self.poe_mon:
# this is allocating at most a few MB of RAM
poes = numpy.zeros((slc.stop-slc.start, M*L1, G), F32)
# NB: using .empty would break the MixtureModelGMPETestCase
for g, gsim in enumerate(self.gsims):
ms = mean_stdt[:2, g, :, slc]
# builds poes of shape (n, L, G)
if self.oq.af: # amplification method
poes[:, :, g] = get_poes_site(ms, self, ctx[slc])
else: # regular case
set_poes(gsim, ms, self, ctx, poes[:, :, g], slc)
yield poes, mean_stdt[0, :, :, slc], mean_stdt[1, :, :, slc], slc
#cs, ms, ps = ctx.nbytes/TWO20, mean_stdt.nbytes/TWO20, poes.nbytes/TWO20
#print('C=%.1fM, mean_stds=%.1fM, poes=%.1fM, G=%d' % (cs, ms, ps, G))
[docs] def gen_poes(self, ctx):
"""
:param ctx: a vectorized context (recarray) of size N
:param rup_indep: rupture flag (false for mutex ruptures)
:yields: poes, mea_sig, ctxt with poes of shape (N, L, G)
"""
ctx.mag = numpy.round(ctx.mag, 3)
for mag in numpy.unique(ctx.mag):
ctxt = ctx[ctx.mag == mag]
self.cfactor += [len(ctxt), 1]
for poes, mea, sig, slc in self._gen_poes(ctxt):
yield poes, mea, sig, ctxt[slc]
# documented but not used in the engine
[docs] def get_pmap(self, ctxs, tom=None, rup_mutex={}):
"""
:param ctxs: a list of context arrays (only one for poissonian ctxs)
:param tom: temporal occurrence model (default PoissonTom)
:param rup_mutex: dictionary of weights (default empty)
:returns: a MapArray
"""
rup_indep = not rup_mutex
sids = numpy.unique(ctxs[0].sids)
pmap = MapArray(sids, size(self.imtls), len(self.gsims)).fill(rup_indep)
ptom = PoissonTOM(self.investigation_time)
for ctx in ctxs:
self.update(pmap, ctx, tom or ptom, rup_mutex)
return ~pmap if rup_indep else pmap
[docs] def ratesNLG(self, srcgroup, sitecol):
"""
Used for debugging simple sources
:param srcgroup: a group of sources
:param sitecol: a SiteCollection instance
:returns: an array of annual rates of shape (N, L, G)
"""
pmap = self.get_pmap(self.from_srcs(srcgroup, sitecol))
return (~pmap).to_rates()
[docs] def update(self, pmap, ctx, rup_mutex=None):
"""
:param pmap: probability map to update
:param ctx: a context array
:param rup_mutex: dictionary (src_id, rup_id) -> weight
"""
for poes, mea, sig, ctxt in self.gen_poes(ctx):
if rup_mutex:
pmap.update_mutex(poes, ctxt, self.tom.time_span, rup_mutex)
elif self.cluster:
for poe, sidx in zip(poes, pmap.sidx[ctxt.sids]):
pmap.array[sidx] *= 1. - poe
else:
pmap.update_indep(poes, ctxt, self.tom.time_span)
# called by gen_poes and by the GmfComputer
[docs] def get_mean_stds(self, ctxs, split_by_mag=True):
"""
:param ctxs: a list of contexts with N=sum(len(ctx) for ctx in ctxs)
:param split_by_mag: where to split by magnitude
:returns: an array of shape (4, G, M, N) with mean and stddevs
"""
N = sum(len(ctx) for ctx in ctxs)
M = len(self.imts)
G = len(self.gsims)
out = numpy.zeros((4, G, M, N))
if all(isinstance(ctx, numpy.recarray) for ctx in ctxs):
# contexts already vectorized
recarrays = ctxs
else: # vectorize the contexts
recarrays = [self.recarray(ctxs)]
if split_by_mag:
recarr = numpy.concatenate(
recarrays, dtype=recarrays[0].dtype).view(numpy.recarray)
recarrays = split_array(recarr, U32(numpy.round(recarr.mag*100)))
for g, gsim in enumerate(self.gsims):
out[:, g] = self.get_4MN(recarrays, gsim)
return out
[docs] def get_4MN(self, ctxs, gsim):
"""
Called by the GmfComputer
"""
N = sum(len(ctx) for ctx in ctxs)
M = len(self.imts)
out = numpy.zeros((4, M, N))
gsim.adj = [] # NSHM2014P adjustments
compute = gsim.__class__.compute
start = 0
for ctx in ctxs:
slc = slice(start, start + len(ctx))
adj = compute(gsim, ctx, self.imts, *out[:, :, slc])
if adj is not None:
gsim.adj.append(adj)
start = slc.stop
if self.truncation_level not in (0, 1E-9, 99.) and (out[1] == 0.).any():
raise ValueError('Total StdDev is zero for %s' % gsim)
if gsim.adj:
gsim.adj = numpy.concatenate(gsim.adj)
if self.conv: # apply horizontal component conversion
self.horiz_comp_to_geom_mean(out, gsim)
return out
# not used right now
[docs] def get_att_curves(self, site, msr, mag, aratio=1., strike=0.,
dip=45., rake=-90):
"""
:returns:
4 attenuation curves mea, sig, tau, phi
(up to 500 km from the site at steps of 5 km)
"""
from openquake.hazardlib.source import rupture
rup = rupture.get_planar(
site, msr, mag, aratio, strike, dip, rake, self.trt)
ctx = self.from_planar(rup, hdist=500, step=5)
mea, sig, tau, phi = self.get_mean_stds([ctx])
return (interp1d(ctx.rrup, mea),
interp1d(ctx.rrup, sig),
interp1d(ctx.rrup, tau),
interp1d(ctx.rrup, phi))
[docs] def estimate_sites(self, src, sites):
"""
:param src: a (Collapsed)PointSource
:param sites: a filtered SiteCollection
:returns: how many sites are impacted overall
"""
magdist = {mag: self.maximum_distance(mag)
for mag, rate in src.get_annual_occurrence_rates()}
nphc = src.count_nphc()
dists = sites.get_cdist(src.location)
planardict = src.get_planar(iruptures=True)
esites = 0
for m, (mag, [planar]) in enumerate(planardict.items()):
rrup = dists[dists < magdist[mag]]
nclose = (rrup < src.get_psdist(m, mag, self.pointsource_distance,
magdist)).sum()
nfar = len(rrup) - nclose
esites += nclose * nphc + nfar
return esites
# tested in test_collapse_small
[docs] def estimate_weight(self, src, srcfilter, multiplier=1):
"""
:param src: a source object
:param srcfilter: a SourceFilter instance
:returns: (weight, estimate_sites)
"""
sites = srcfilter.get_close_sites(src)
if sites is None:
# may happen for CollapsedPointSources
return 0, 0
src.nsites = len(sites)
N = len(srcfilter.sitecol.complete) # total sites
if (hasattr(src, 'location') and src.count_nphc() > 1 and
self.pointsource_distance < 1000):
# cps or pointsource with nontrivial nphc
esites = self.estimate_sites(src, sites) * multiplier
else:
step = 100 if src.code == b'F' else 10
ctxs = list(self.get_ctx_iter(src, sites, step=step)) # reduced
if not ctxs:
return src.num_ruptures if N == 1 else 0, 0
esites = (sum(len(ctx) for ctx in ctxs) * src.num_ruptures /
self.num_rups * multiplier) # num_rups from get_ctx_iter
weight = esites / N # the weight is the effective number of ruptures
return weight, int(esites)
[docs] def set_weight(self, sources, srcfilter, multiplier=1, mon=Monitor()):
"""
Set the weight attribute on each prefiltered source
"""
if hasattr(srcfilter, 'array'): # a SiteCollection was passed
srcfilter = SourceFilter(srcfilter, self.maximum_distance)
G = len(self.gsims)
for src in sources:
if src.nsites == 0: # was discarded by the prefiltering
src.esites = 0
src.weight = .01
else:
with mon:
src.weight, src.esites = self.estimate_weight(
src, srcfilter, multiplier)
if src.weight == 0:
src.weight = 0.001
src.weight *= G
if src.code == b'P':
src.weight += .1
elif src.code == b'C':
src.weight += 10.
elif src.code == b'F':
src.weight += .25 * src.num_ruptures
else:
src.weight += 1.
[docs]def by_dists(gsim):
return tuple(sorted(gsim.REQUIRES_DISTANCES))
# see contexts_tests.py for examples of collapse
# probs_occur = functools.reduce(combine_pmf, (r.probs_occur for r in rups))
[docs]def combine_pmf(o1, o2):
"""
Combine probabilities of occurrence; used to collapse nonparametric
ruptures.
:param o1: probability distribution of length n1
:param o2: probability distribution of length n2
:returns: probability distribution of length n1 + n2 - 1
>>> combine_pmf([.99, .01], [.98, .02])
array([9.702e-01, 2.960e-02, 2.000e-04])
"""
n1 = len(o1)
n2 = len(o2)
o = numpy.zeros(n1 + n2 - 1)
for i in range(n1):
for j in range(n2):
o[i + j] += o1[i] * o2[j]
return o
[docs]def print_finite_size(rups):
"""
Used to print the number of finite-size ruptures
"""
c = collections.Counter()
for rup in rups:
if rup.surface:
c['%.2f' % rup.mag] += 1
print(c)
print('total finite size ruptures = ', sum(c.values()))
def _get_poes(mean_std, loglevels, phi_b):
# returns a matrix of shape (N, L)
N = mean_std.shape[2] # shape (2, M, N)
out = numpy.zeros((loglevels.size, N), F32) # shape (L, N)
_set_poes(mean_std, loglevels, phi_b, out)
return out.T
[docs]def set_poes(gsim, mean_std, cmaker, ctx, out, slc):
"""
Calculate and return probabilities of exceedance (PoEs) of one or more
intensity measure levels (IMLs) of one intensity measure type (IMT)
for one or more pairs "site -- rupture".
:param gsim:
A GMPE instance
:param mean_std:
An array of shape (2, M, N) with mean and standard deviations
for the sites and intensity measure types
:param cmaker:
A ContextMaker instance, used only in nhsm_2014
:param ctx:
A context array used only in avg_poe_gmpe
:param out:
An array of PoEs of shape (N, L) to be filled
:param slc:
A slice object used only in avg_poe_gmpe
:raises ValueError:
If truncation level is not ``None`` and neither non-negative
float number, and if ``imts`` dictionary contain wrong or
unsupported IMTs (see :attr:`DEFINED_FOR_INTENSITY_MEASURE_TYPES`).
"""
loglevels = cmaker.loglevels.array
phi_b = cmaker.phi_b
_M, L1 = loglevels.shape
if hasattr(gsim, 'weights_signs'): # for nshmp_2014, case_72
adj = gsim.adj[slc]
outs = []
weights, signs = zip(*gsim.weights_signs)
for s in signs:
ms = numpy.array(mean_std) # make a copy
for m in range(len(loglevels)):
ms[0, m] += s * adj
outs.append(_get_poes(ms, loglevels, phi_b))
out[:] = numpy.average(outs, weights=weights, axis=0)
elif hasattr(gsim, 'mixture_model'):
for f, w in zip(gsim.mixture_model["factors"],
gsim.mixture_model["weights"]):
mean_stdi = mean_std.copy()
mean_stdi[1] *= f # multiply stddev by factor
out[:] += w * _get_poes(mean_stdi, loglevels, phi_b)
elif hasattr(gsim, 'weights'): # avg_poe_gmpe
cm = copy.copy(cmaker)
cm.poe_mon = Monitor() # avoid double counts
cm.gsims = gsim.gsims
avgs = []
for poes, _mea, _sig, _ctx in cm.gen_poes(ctx[slc]):
# poes has shape N, L, G
avgs.append(poes @ gsim.weights)
out[:] = numpy.concatenate(avgs)
else: # regular case
_set_poes(mean_std, loglevels, phi_b, out.T)
imtweight = getattr(gsim, 'weight', None) # ImtWeight or None
for m, imt in enumerate(cmaker.imtls):
mL1 = m * L1
if imtweight and imtweight.dic.get(imt) == 0:
# set by the engine when parsing the gsim logictree
# when 0 ignore the contribution: see _build_branches
out[:, mL1:mL1 + L1] = 0
[docs]class PmapMaker(object):
"""
A class to compute the PoEs from a given source
"""
def __init__(self, cmaker, srcfilter, group):
vars(self).update(vars(cmaker))
self.cmaker = cmaker
self.srcfilter = srcfilter
self.N = len(self.srcfilter.sitecol.complete)
try:
self.sources = group.sources
except AttributeError: # already a list of sources
self.sources = group
self.src_mutex = getattr(group, 'src_interdep', None) == 'mutex'
self.rup_indep = getattr(group, 'rup_interdep', None) != 'mutex'
if self.rup_indep:
self.rup_mutex = {}
else:
self.rup_mutex = {} # src_id, rup_id -> rup_weight
for src in group:
for i, (rup, _) in enumerate(src.data):
self.rup_mutex[src.id, i] = rup.weight
self.fewsites = self.N <= cmaker.max_sites_disagg
self.grp_probability = getattr(group, 'grp_probability', 1.)
self.cluster = self.cmaker.cluster = getattr(group, 'cluster', 0)
if self.cluster:
tom = group.temporal_occurrence_model
else:
tom = getattr(self.sources[0], 'temporal_occurrence_model',
PoissonTOM(self.cmaker.investigation_time))
self.cmaker.tom = self.tom = tom
M, G = len(self.cmaker.imtls), len(self.cmaker.gsims)
self.maxsize = 8 * TWO20 // (M*G) # crucial for a fast get_mean_stds
[docs] def count_bytes(self, ctxs):
# # usuful for debugging memory issues
rparams = len(self.cmaker.REQUIRES_RUPTURE_PARAMETERS)
sparams = len(self.cmaker.REQUIRES_SITES_PARAMETERS) + 1
dparams = len(self.cmaker.REQUIRES_DISTANCES)
nbytes = 0
for ctx in ctxs:
nsites = len(ctx)
nbytes += 8 * rparams
nbytes += 8 * sparams * nsites
nbytes += 8 * dparams * nsites
return nbytes
[docs] def gen_ctxs(self, src):
sites = self.srcfilter.get_close_sites(src)
if sites is None:
return
for ctx in self.cmaker.get_ctx_iter(src, sites):
if self.cmaker.deltagetter:
# adjust occurrence rates in case of aftershocks
with self.cmaker.delta_mon:
delta = self.cmaker.deltagetter(src.id)
ctx.occurrence_rate += delta[ctx.rup_id]
if self.fewsites: # keep rupdata in memory (before collapse)
if self.src_mutex:
# needed for Disaggregator.init
ctx.src_id = valid.fragmentno(src)
self.rupdata.append(ctx)
yield ctx
def _make_src_indep(self):
# sources with the same ID
cm = self.cmaker
allctxs = []
ctxlen = 0
totlen = 0
t0 = time.time()
sids = self.srcfilter.sitecol.sids
# using most memory here; limited by pmap_max_gb
pnemap = MapArray(
sids, self.cmaker.imtls.size, len(self.cmaker.gsims),
not self.cluster).fill(self.cluster)
for src in self.sources:
src.nsites = 0
for ctx in self.gen_ctxs(src):
ctxlen += len(ctx)
src.nsites += len(ctx)
totlen += len(ctx)
allctxs.append(ctx)
if ctxlen > self.maxsize:
for ctx in concat(allctxs):
cm.update(pnemap, ctx)
allctxs.clear()
ctxlen = 0
if allctxs:
# all sources have the same tom by construction
for ctx in concat(allctxs):
cm.update(pnemap, ctx)
allctxs.clear()
dt = time.time() - t0
nsrcs = len(self.sources)
for src in self.sources:
self.source_data['src_id'].append(src.source_id)
self.source_data['grp_id'].append(src.grp_id)
self.source_data['nsites'].append(src.nsites)
self.source_data['esites'].append(src.esites)
self.source_data['nrupts'].append(src.num_ruptures)
self.source_data['weight'].append(src.weight)
self.source_data['ctimes'].append(
dt * src.nsites / totlen if totlen else dt / nsrcs)
self.source_data['taskno'].append(cm.task_no)
return pnemap
def _make_src_mutex(self):
# used in Japan (case_27) and in New Madrid (case_80)
cm = self.cmaker
t0 = time.time()
weight = 0.
nsites = 0
esites = 0
nctxs = 0
sids = self.srcfilter.sitecol.sids
pmap = MapArray(
sids, self.cmaker.imtls.size, len(self.cmaker.gsims)).fill(0)
for src in self.sources:
t0 = time.time()
pm = MapArray(pmap.sids, cm.imtls.size, len(cm.gsims)).fill(self.rup_indep)
ctxs = list(self.gen_ctxs(src))
n = sum(len(ctx) for ctx in ctxs)
if n == 0:
continue
nctxs += len(ctxs)
nsites += n
esites += src.esites
for ctx in ctxs:
if self.rup_mutex:
cm.update(pm, ctx, self.rup_mutex)
else:
cm.update(pm, ctx)
if hasattr(src, 'mutex_weight'):
arr = 1. - pm.array if self.rup_indep else pm.array
pmap.array += arr * src.mutex_weight
else:
pmap.array = 1. - (1-pmap.array) * (1-pm.array)
weight += src.weight
pmap.array *= self.grp_probability
dt = time.time() - t0
self.source_data['src_id'].append(valid.basename(src))
self.source_data['grp_id'].append(src.grp_id)
self.source_data['nsites'].append(nsites)
self.source_data['esites'].append(esites)
self.source_data['nrupts'].append(nctxs)
self.source_data['weight'].append(weight)
self.source_data['ctimes'].append(dt)
self.source_data['taskno'].append(cm.task_no)
return ~pmap
[docs] def make(self):
dic = {}
self.rupdata = []
self.source_data = AccumDict(accum=[])
if self.rup_indep and not self.src_mutex:
pnemap = self._make_src_indep()
else:
pnemap = self._make_src_mutex()
if self.cluster:
for nocc in range(0, 50):
prob_n_occ = self.tom.get_probability_n_occurrences(
self.tom.occurrence_rate, nocc)
if nocc == 0:
pmapclu = pnemap.new(numpy.full(pnemap.shape, prob_n_occ))
else:
pmapclu.array += pnemap.array**nocc * prob_n_occ
pnemap.array[:] = pmapclu.array
dic['rmap'] = pnemap.to_rates()
dic['rmap'].gid = self.cmaker.gid
dic['cfactor'] = self.cmaker.cfactor
dic['rup_data'] = concat(self.rupdata)
dic['source_data'] = self.source_data
dic['task_no'] = self.task_no
dic['grp_id'] = self.sources[0].grp_id
if self.disagg_by_src:
# all the sources in the group must have the same source_id because
# of the groupby(group, corename) in classical.py
coreids = set(map(valid.corename, self.sources))
if len(coreids) > 1:
raise NameError('Invalid source naming: %s' % coreids)
# in oq-risk-tests test_phl there are multiple srcids
# (mps-0!b1;0, mps-0!b1;1, ...); you can simply use the first,
# since in `store_mean_rates_by_src` we use corename
dic['basename'] = valid.basename(self.sources[0])
return dic
[docs]class BaseContext(metaclass=abc.ABCMeta):
"""
Base class for context object.
"""
def __eq__(self, other):
"""
Return True if ``other`` has same attributes with same values.
"""
if isinstance(other, self.__class__):
if self._slots_ == other._slots_:
oks = []
for s in self._slots_:
a, b = getattr(self, s, None), getattr(other, s, None)
if a is None and b is None:
ok = True
elif a is None and b is not None:
ok = False
elif a is not None and b is None:
ok = False
elif hasattr(a, 'shape') and hasattr(b, 'shape'):
if a.shape == b.shape:
ok = numpy.allclose(a, b)
else:
ok = False
else:
ok = a == b
oks.append(ok)
return numpy.all(oks)
return False
# mock of a site collection used in the tests and in the SMT
[docs]class SitesContext(BaseContext):
"""
Sites calculation context for ground shaking intensity models.
Instances of this class are passed into
:meth:`GroundShakingIntensityModel.get_mean_and_stddevs`. They are
intended to represent relevant features of the sites collection.
Every GSIM class is required to declare what :attr:`sites parameters
<GroundShakingIntensityModel.REQUIRES_SITES_PARAMETERS>` does it need.
Only those required parameters are made available in a result context
object.
"""
# _slots_ is used in hazardlib check_gsim and in the SMT
def __init__(self, slots='vs30 vs30measured z1pt0 z2pt5'.split(),
sitecol=None):
self._slots_ = slots
if sitecol is not None:
self.sids = sitecol.sids
for slot in slots:
setattr(self, slot, getattr(sitecol, slot))
# used in the SMT
def __len__(self):
return len(self.sids)
[docs]class DistancesContext(BaseContext):
"""
Distances context for ground shaking intensity models.
Instances of this class are passed into
:meth:`GroundShakingIntensityModel.get_mean_and_stddevs`. They are
intended to represent relevant distances between sites from the collection
and the rupture. Every GSIM class is required to declare what
:attr:`distance measures <GroundShakingIntensityModel.REQUIRES_DISTANCES>`
does it need. Only those required values are calculated and made available
in a result context object.
"""
_slots_ = ('rrup', 'rx', 'rjb', 'rhypo', 'repi', 'ry0', 'rcdpp',
'azimuth', 'hanging_wall', 'rvolc')
def __init__(self, param_dist_pairs=()):
for param, dist in param_dist_pairs:
setattr(self, param, dist)
# used in boore_atkinson_2008
[docs]def get_dists(ctx):
"""
Extract the distance parameters from a context.
:returns: a dictionary dist_name -> distances
"""
return {par: dist for par, dist in vars(ctx).items()
if par in KNOWN_DISTANCES}
# used to produce a RuptureContext suitable for legacy code, i.e. for calls
# to .get_mean_and_stddevs, like for instance in the SMT
[docs]def full_context(sites, rup, dctx=None):
"""
:returns: a full RuptureContext with all the relevant attributes
"""
self = RuptureContext()
self.src_id = 0
for par, val in vars(rup).items():
setattr(self, par, val)
if not hasattr(self, 'occurrence_rate'):
self.occurrence_rate = numpy.nan
if hasattr(sites, 'array'): # is a SiteCollection
for par in sites.array.dtype.names:
setattr(self, par, sites[par])
else: # sites is a SitesContext
for par, val in vars(sites).items():
setattr(self, par, val)
if dctx:
for par, val in vars(dctx).items():
setattr(self, par, val)
return self
[docs]def get_mean_stds(gsim, ctx, imts, **kw):
"""
:param gsim: a single GSIM or a a list of GSIMs
:param ctx: a RuptureContext or a recarray of size N with same magnitude
:param imts: a list of M IMTs
:param kw: additional keyword arguments
:returns:
an array of shape (4, M, N) obtained by applying the
given GSIM, ctx amd imts, or an array of shape (G, 4, M, N)
"""
single = hasattr(gsim, 'compute')
kw['imtls'] = {imt.string: [0] for imt in imts}
cmaker = ContextMaker('*', [gsim] if single else gsim, kw)
out = cmaker.get_mean_stds([ctx], split_by_mag=False) # (4, G, M, N)
return out[:, 0] if single else out
# mock of a rupture used in the tests and in the SMT
[docs]class RuptureContext(BaseContext):
"""
Rupture calculation context for ground shaking intensity models.
Instances of this class are passed into
:meth:`GroundShakingIntensityModel.get_mean_and_stddevs`. They are
intended to represent relevant features of a single rupture. Every
GSIM class is required to declare what :attr:`rupture parameters
<GroundShakingIntensityModel.REQUIRES_RUPTURE_PARAMETERS>` does it need.
Only those required parameters are made available in a result context
object.
"""
src_id = 0
rup_id = 0
_slots_ = (
'mag', 'strike', 'dip', 'rake', 'ztor', 'hypo_lon', 'hypo_lat',
'hypo_depth', 'width', 'hypo_loc', 'src_id', 'rup_id')
def __init__(self, param_pairs=()):
for param, value in param_pairs:
setattr(self, param, value)
[docs] def size(self):
"""
If the context is a multi rupture context, i.e. it contains an array
of magnitudes and it refers to a single site, returns the size of
the array, otherwise returns 1.
"""
nsites = len(self.sids)
if nsites == 1 and isinstance(self.mag, numpy.ndarray):
return len(self.mag)
return nsites
# used in acme_2019
def __len__(self):
return len(self.sids)
[docs]class Effect(object):
"""
Compute the effect of a rupture of a given magnitude and distance.
:param effect_by_mag: a dictionary magstring -> intensities
:param dists: array of distances, one per each intensity
:param cdist: collapse distance
"""
def __init__(self, effect_by_mag, dists, collapse_dist=None):
self.effect_by_mag = effect_by_mag
self.dists = dists
self.nbins = len(dists)
[docs] def collapse_value(self, collapse_dist):
"""
:returns: intensity at collapse distance
"""
# get the maximum magnitude with a cutoff at 7
for mag in self.effect_by_mag:
if mag > '7.00':
break
effect = self.effect_by_mag[mag]
idx = numpy.searchsorted(self.dists, collapse_dist)
return effect[idx-1 if idx == self.nbins else idx]
def __call__(self, mag, dist):
di = numpy.searchsorted(self.dists, dist)
if di == self.nbins:
di = self.nbins
eff = self.effect_by_mag['%.2f' % mag][di]
return eff
# this is used to compute the magnitude-dependent pointsource_distance
[docs] def dist_by_mag(self, intensity):
"""
:returns: a dict magstring -> distance
"""
dst = {} # magnitude -> distance
for mag, intensities in self.effect_by_mag.items():
if intensity < intensities.min():
dst[mag] = self.dists[-1] # largest distance
elif intensity > intensities.max():
dst[mag] = self.dists[0] # smallest distance
else:
dst[mag] = interp1d(intensities, self.dists)(intensity)
return dst
[docs]def get_effect_by_mag(mags, sitecol1, gsims_by_trt, maximum_distance, imtls):
"""
:param mags: an ordered list of magnitude strings with format %.2f
:param sitecol1: a SiteCollection with a single site
:param gsims_by_trt: a dictionary trt -> gsims
:param maximum_distance: an IntegrationDistance object
:param imtls: a DictArray with intensity measure types and levels
:returns: a dict magnitude-string -> array(#dists, #trts)
"""
trts = list(gsims_by_trt)
ndists = 51
gmv = numpy.zeros((len(mags), ndists, len(trts)))
param = dict(maximum_distance=maximum_distance, imtls=imtls)
for t, trt in enumerate(trts):
dist_bins = maximum_distance.get_dist_bins(trt, ndists)
cmaker = ContextMaker(trt, gsims_by_trt[trt], param)
gmv[:, :, t] = cmaker.max_intensity(sitecol1, F64(mags), dist_bins)
return dict(zip(mags, gmv))
[docs]def get_cmakers(src_groups, full_lt, oq):
"""
:params src_groups: a list of SourceGroups
:param full_lt: a FullLogicTree instance
:param oq: object containing the calculation parameters
:returns: list of ContextMakers associated to the given src_groups
"""
all_trt_smrs = []
for sg in src_groups:
src = sg.sources[0]
all_trt_smrs.append(src.trt_smrs)
trts = list(full_lt.gsim_lt.values)
gweights = full_lt.g_weights(all_trt_smrs)[:, -1] # shape Gt
cmakers = []
for grp_id, trt_smrs in enumerate(all_trt_smrs):
rlzs_by_gsim = full_lt.get_rlzs_by_gsim(trt_smrs)
if not rlzs_by_gsim: # happens for gsim_lt.reduce() on empty TRTs
continue
trti = trt_smrs[0] // TWO24
cm = ContextMaker(trts[trti], rlzs_by_gsim, oq)
cm.trti = trti
cm.trt_smrs = trt_smrs
cm.grp_id = grp_id
cmakers.append(cm)
gids = full_lt.get_gids(cm.trt_smrs for cm in cmakers)
for cm in cmakers:
cm.gid = gids[cm.grp_id]
cm.wei = gweights[cm.gid]
return cmakers
[docs]def read_cmakers(dstore, csm=None):
"""
:param dstore: a DataStore-like object
:param csm: a CompositeSourceModel instance, if given
:returns: an array of ContextMaker instances, one per source group
"""
from openquake.hazardlib.site_amplification import AmplFunction
oq = dstore['oqparam']
oq.mags_by_trt = {
k: decode(v[:]) for k, v in dstore['source_mags'].items()}
if 'amplification' in oq.inputs and oq.amplification_method == 'kernel':
df = AmplFunction.read_df(oq.inputs['amplification'])
oq.af = AmplFunction.from_dframe(df)
else:
oq.af = None
if csm is None:
csm = dstore['_csm']
csm.full_lt = dstore['full_lt'].init()
cmakers = get_cmakers(csm.src_groups, csm.full_lt, oq)
if 'delta_rates' in dstore: # aftershock
for cmaker in cmakers:
cmaker.deltagetter = DeltaRatesGetter(dstore)
return numpy.array(cmakers)
# used in event_based
[docs]def read_cmaker(dstore, trt_smr):
"""
:param dstore: a DataStore-like object
:returns: a ContextMaker instance
"""
oq = dstore['oqparam']
full_lt = dstore['full_lt']
trts = list(full_lt.gsim_lt.values)
trt = trts[trt_smr // len(full_lt.sm_rlzs)]
rlzs_by_gsim = full_lt._rlzs_by_gsim(trt_smr)
return ContextMaker(trt, rlzs_by_gsim, oq)
[docs]def get_src_mutex(srcs):
"""
:param srcs: a list of sources with weights and the same grp_id
:returns: a dictionary grp_id -> {'src_id': [...], 'weight': [...]}
"""
grp_ids = [src.grp_id for src in srcs]
[grp_id] = set(grp_ids)
ok = all(hasattr(src, 'mutex_weight') for src in srcs)
if not ok:
return {grp_id: {}}
dic = dict(src_ids=U32([src.id for src in srcs]),
weights=F64([src.mutex_weight for src in srcs]))
return {grp_id: dic}
[docs]def read_ctx_by_grp(dstore):
"""
:param dstore: DataStore instance
:returns: dictionary grp_id -> ctx
"""
sitecol = dstore['sitecol'].complete.array
params = {n: dstore['rup/' + n][:] for n in dstore['rup']}
dtlist = []
for par, val in params.items():
if len(val) == 0:
return {}
elif par == 'probs_occur':
item = (par, object)
elif par == 'occurrence_rate':
item = (par, F64)
else:
item = (par, val[0].dtype)
dtlist.append(item)
for par in sitecol.dtype.names:
if par != 'sids':
dtlist.append((par, sitecol.dtype[par]))
ctx = numpy.zeros(len(params['grp_id']), dtlist).view(numpy.recarray)
for par, val in params.items():
ctx[par] = val
for par in sitecol.dtype.names:
if par != 'sids':
ctx[par] = sitecol[par][ctx.sids]
grp_ids = numpy.unique(ctx.grp_id)
ctx = ctx[numpy.argsort(ctx.mag)] # NB: crucial for performance
return {grp_id: ctx[ctx.grp_id == grp_id] for grp_id in grp_ids}
