Source code for openquake.commonlib.calc

# -*- coding: utf-8 -*-
# vim: tabstop=4 shiftwidth=4 softtabstop=4
#
# Copyright (C) 2014-2017 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.
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# You should have received a copy of the GNU Affero General Public License
# along with OpenQuake. If not, see <http://www.gnu.org/licenses/>.

from __future__ import division
import logging
import copy
import numpy

from openquake.baselib import hdf5
from openquake.baselib.python3compat import encode, decode
from openquake.baselib.general import (
    get_array, group_array, AccumDict)
from openquake.hazardlib.geo.mesh import RectangularMesh, build_array
from openquake.hazardlib.gsim.base import ContextMaker
from openquake.hazardlib.imt import from_string
from openquake.hazardlib import geo, tom
from openquake.hazardlib.geo.point import Point
from openquake.hazardlib.probability_map import ProbabilityMap
from openquake.commonlib import readinput, oqvalidation, util
from openquake.hazardlib import valid


MAX_INT = 2 ** 31 - 1  # this is used in the random number generator
# in this way even on 32 bit machines Python will not have to convert
# the generated seed into a long integer

U8 = numpy.uint8
U16 = numpy.uint16
U32 = numpy.uint32
F32 = numpy.float32
F64 = numpy.float64

event_dt = numpy.dtype([('eid', U32), ('ses', U32), ('occ', U32),
                        ('sample', U32)])
stored_event_dt = numpy.dtype([
    ('rupserial', U32), ('year', U32),
    ('ses', U32), ('occ', U32),
    ('sample', U32), ('grp_id', U16),
    ('source_id', 'S%d' % valid.MAX_ID_LENGTH)])

# ############## utilities for the classical calculator ############### #


# used in classical and event_based calculators
[docs]def combine_pmaps(rlzs_assoc, results): """ :param rlzs_assoc: a :class:`openquake.commonlib.source.RlzsAssoc` instance :param results: dictionary src_group_id -> probability map :returns: a dictionary rlz -> aggregate probability map """ acc = AccumDict() for grp_id in results: for i, gsim in enumerate(rlzs_assoc.gsims_by_grp_id[grp_id]): pmap = results[grp_id].extract(i) for rlz in rlzs_assoc.rlzs_assoc[grp_id, gsim]: if rlz in acc: acc[rlz] |= pmap else: acc[rlz] = copy.copy(pmap) return acc
# ######################### hazard maps ################################### # # cutoff value for the poe EPSILON = 1E-30
[docs]def compute_hazard_maps(curves, imls, poes): """ Given a set of hazard curve poes, interpolate a hazard map at the specified ``poe``. :param curves: 2D array of floats. Each row represents a curve, where the values in the row are the PoEs (Probabilities of Exceedance) corresponding to ``imls``. Each curve corresponds to a geographical location. :param imls: Intensity Measure Levels associated with these hazard ``curves``. Type should be an array-like of floats. :param poes: Value(s) on which to interpolate a hazard map from the input ``curves``. Can be an array-like or scalar value (for a single PoE). :returns: An array of shape N x P, where N is the number of curves and P the number of poes. """ poes = numpy.array(poes) if len(poes.shape) == 0: # `poes` was passed in as a scalar; # convert it to 1D array of 1 element poes = poes.reshape(1) if len(curves.shape) == 1: # `curves` was passed as 1 dimensional array, there is a single site curves = curves.reshape((1,) + curves.shape) # 1 x L L = curves.shape[1] # number of levels if L != len(imls): raise ValueError('The curves have %d levels, %d were passed' % (L, len(imls))) result = [] imls = numpy.log(numpy.array(imls[::-1])) for curve in curves: # the hazard curve, having replaced the too small poes with EPSILON curve_cutoff = [max(poe, EPSILON) for poe in curve[::-1]] hmap_val = [] for poe in poes: # special case when the interpolation poe is bigger than the # maximum, i.e the iml must be smaller than the minumum if poe > curve_cutoff[-1]: # the greatest poes in the curve # extrapolate the iml to zero as per # https://bugs.launchpad.net/oq-engine/+bug/1292093 # a consequence is that if all poes are zero any poe > 0 # is big and the hmap goes automatically to zero hmap_val.append(0) else: # exp-log interpolation, to reduce numerical errors # see https://bugs.launchpad.net/oq-engine/+bug/1252770 val = numpy.exp( numpy.interp( numpy.log(poe), numpy.log(curve_cutoff), imls)) hmap_val.append(val) result.append(hmap_val) return numpy.array(result)
# ######################### GMF->curves #################################### # # NB (MS): the approach used here will not work for non-poissonian models def _gmvs_to_haz_curve(gmvs, imls, invest_time, duration): """ Given a set of ground motion values (``gmvs``) and intensity measure levels (``imls``), compute hazard curve probabilities of exceedance. :param gmvs: A list of ground motion values, as floats. :param imls: A list of intensity measure levels, as floats. :param float invest_time: Investigation time, in years. It is with this time span that we compute probabilities of exceedance. Another way to put it is the following. When computing a hazard curve, we want to answer the question: What is the probability of ground motion meeting or exceeding the specified levels (``imls``) in a given time span (``invest_time``). :param float duration: Time window during which GMFs occur. Another was to say it is, the period of time over which we simulate ground motion occurrences. NOTE: Duration is computed as the calculation investigation time multiplied by the number of stochastic event sets. :returns: Numpy array of PoEs (probabilities of exceedance). """ # convert to numpy array and redimension so that it can be broadcast with # the gmvs for computing PoE values; there is a gmv for each rupture # here is an example: imls = [0.03, 0.04, 0.05], gmvs=[0.04750576] # => num_exceeding = [1, 1, 0] coming from 0.04750576 > [0.03, 0.04, 0.05] imls = numpy.array(imls).reshape((len(imls), 1)) num_exceeding = numpy.sum(numpy.array(gmvs) >= imls, axis=1) poes = 1 - numpy.exp(- (invest_time / duration) * num_exceeding) return poes # ################## utilities for classical calculators ################ #
[docs]def get_imts_periods(imtls): """ Returns a list of IMT strings and a list of periods. There is an element for each IMT of type Spectral Acceleration, including PGA which is considered an alias for SA(0.0). The lists are sorted by period. :param imtls: a set of intensity measure type strings :returns: a list of IMT strings and a list of periods """ def getperiod(imt): return imt[1] or 0 imts = sorted((from_string(imt) for imt in imtls if imt.startswith('SA') or imt == 'PGA'), key=getperiod) return [str(imt) for imt in imts], [imt[1] or 0.0 for imt in imts]
[docs]def make_hmap(pmap, imtls, poes): """ Compute the hazard maps associated to the passed probability map. :param pmap: hazard curves in the form of a ProbabilityMap :param imtls: DictArray of intensity measure types and levels :param poes: P PoEs where to compute the maps :returns: a ProbabilityMap with size (N, I * P, 1) """ I, P = len(imtls), len(poes) hmap = ProbabilityMap.build(I * P, 1, pmap) for i, imt in enumerate(imtls): curves = numpy.array([pmap[sid].array[imtls.slicedic[imt], 0] for sid in pmap.sids]) data = compute_hazard_maps(curves, imtls[imt], poes) # array N x P for sid, value in zip(pmap.sids, data): array = hmap[sid].array for j, val in enumerate(value): array[i * P + j] = val return hmap
[docs]def make_uhs(pmap, imtls, poes, nsites): """ Make Uniform Hazard Spectra curves for each location. It is assumed that the `lons` and `lats` for each of the `maps` are uniform. :param pmap: a probability map of hazard curves :param imtls: a dictionary of intensity measure types and levels :param poes: a sequence of PoEs for the underlying hazard maps :returns: an composite array containing nsites uniform hazard maps """ P = len(poes) imts, _ = get_imts_periods(imtls) array = make_hmap(pmap, imtls, poes).array # size (N, I x P, 1) imts_dt = numpy.dtype([(str(imt), F64) for imt in imts]) uhs_dt = numpy.dtype([(str(poe), imts_dt) for poe in poes]) uhs = numpy.zeros(nsites, uhs_dt) for j, poe in enumerate(map(str, poes)): for i, imt in enumerate(imtls): if imt in imts: uhs[poe][imt] = array[:, i * P + j, 0] return uhs
[docs]def get_gmfs(dstore, precalc=None): """ :param dstore: a datastore :param precalc: a scenario calculator with attribute .gmfa :returns: a dictionary grp_id, gsid -> gmfa """ oq = dstore['oqparam'] if 'gmfs' in oq.inputs: # from file logging.info('Reading gmfs from file') sitecol, etags, gmfs_by_imt = readinput.get_gmfs(oq) # reduce the gmfs matrices to the filtered sites for imt in oq.imtls: gmfs_by_imt[imt] = gmfs_by_imt[imt][sitecol.indices] logging.info('Preparing the risk input') return etags, [gmfs_by_imt] rlzs_assoc = dstore['csm_info'].get_rlzs_assoc() rlzs = rlzs_assoc.realizations sitecol = dstore['sitecol'] # NB: if the hazard site collection has N sites, the hazard # filtered site collection for the nonzero GMFs has N' <= N sites # whereas the risk site collection associated to the assets # has N'' <= N' sites if dstore.parent: haz_sitecol = dstore.parent['sitecol'] # N' values else: haz_sitecol = sitecol risk_indices = set(sitecol.indices) # N'' values N = len(haz_sitecol.complete) imt_dt = numpy.dtype([(str(imt), F32) for imt in oq.imtls]) E = oq.number_of_ground_motion_fields etags = numpy.array(sorted(b'scenario-%010d~ses=1' % i for i in range(E))) gmfs = numpy.zeros((len(rlzs_assoc), N, E), imt_dt) if precalc: for i, gsim in enumerate(precalc.gsims): for imti, imt in enumerate(oq.imtls): gmfs[imt][i, sitecol.sids] = precalc.gmfa[gsim][imti] return etags, gmfs # else read from the datastore for i, rlz in enumerate(rlzs): data = group_array(dstore['gmf_data/sm-0000/%04d' % i], 'sid') for sid, array in data.items(): if sid in risk_indices: for imti, imt in enumerate(oq.imtls): a = get_array(array, imti=imti) gmfs[imt][i, sid, a['eid']] = a['gmv'] return etags, gmfs
[docs]def fix_minimum_intensity(min_iml, imts): """ :param min_iml: a dictionary, possibly with a 'default' key :param imts: an ordered list of IMTs :returns: a numpy array of intensities, one per IMT Make sure the dictionary minimum_intensity (provided by the user in the job.ini file) is filled for all intensity measure types and has no key named 'default'. Here is how it works: >>> min_iml = {'PGA': 0.1, 'default': 0.05} >>> fix_minimum_intensity(min_iml, ['PGA', 'PGV']) array([ 0.1 , 0.05], dtype=float32) >>> sorted(min_iml.items()) [('PGA', 0.1), ('PGV', 0.05)] """ if min_iml: for imt in imts: try: min_iml[imt] = oqvalidation.getdefault(min_iml, imt) except KeyError: raise ValueError( 'The parameter `minimum_intensity` in the job.ini ' 'file is missing the IMT %r' % imt) if 'default' in min_iml: del min_iml['default'] return F32([min_iml.get(imt, 0) for imt in imts])
gmv_dt = numpy.dtype([('sid', U16), ('eid', U32), ('imti', U8), ('gmv', F32)])
[docs]def check_overflow(calc): """ :param calc: an event based calculator Raise a ValueError if the number of sites is larger than 65,536 or the number of IMTs is larger than 256 or the number of ruptures is larger than 4,294,967,296. The limits are due to the numpy dtype used to store the GMFs (gmv_dt). They could be relaxed in the future. """ max_ = dict(sites=2**16, events=2**32, imts=2**8) num_ = dict(sites=len(calc.sitecol), events=len(calc.datastore['events']), imts=len(calc.oqparam.imtls)) for var in max_: if num_[var] > max_[var]: raise ValueError( 'The event based calculator is restricted to ' '%d %s, got %d' % (max_[var], var, num_[var]))
[docs]class RuptureData(object): """ Container for information about the ruptures of a given tectonic region type. """ def __init__(self, trt, gsims): self.trt = trt self.cmaker = ContextMaker(gsims) self.params = sorted(self.cmaker.REQUIRES_RUPTURE_PARAMETERS - set('mag strike dip rake hypo_depth'.split())) self.dt = numpy.dtype([ ('rupserial', U32), ('multiplicity', U16), ('numsites', U32), ('occurrence_rate', F64), ('mag', F64), ('lon', F32), ('lat', F32), ('depth', F32), ('strike', F64), ('dip', F64), ('rake', F64), ('boundary', hdf5.vstr)] + [(param, F64) for param in self.params])
[docs] def to_array(self, ebruptures): data = [] for ebr in ebruptures: rup = ebr.rupture rc = self.cmaker.make_rupture_context(rup) ruptparams = tuple(getattr(rc, param) for param in self.params) point = rup.surface.get_middle_point() multi_lons, multi_lats = rup.surface.get_surface_boundaries() boundary = ','.join('((%s))' % ','.join( '%.5f %.5f' % (lon, lat) for lon, lat in zip(lons, lats)) for lons, lats in zip(multi_lons, multi_lats)) try: rate = ebr.rupture.occurrence_rate except AttributeError: # for nonparametric sources rate = numpy.nan data.append((ebr.serial, ebr.multiplicity, len(ebr.sids), rate, rup.mag, point.x, point.y, point.z, rup.surface.get_strike(), rup.surface.get_dip(), rup.rake, decode(boundary)) + ruptparams) return numpy.array(data, self.dt)
[docs]def get_geom(surface, is_from_fault_source, is_multi_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 rupture: an instance of :class:`openquake.hazardlib.source.rupture.BaseProbabilisticRupture` :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.get_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]) 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 EBRupture(object): """ An event based rupture. It is a wrapper over a hazardlib rupture object, containing an array of site indices affected by the rupture, as well as the tags of the corresponding seismic events. """ params = ['mag', 'rake', 'tectonic_region_type', 'hypo', 'source_class', 'pmf', 'occurrence_rate', 'time_span', 'rupture_slip_direction'] def __init__(self, rupture, sids, events, source_id, grp_id, serial): self.rupture = rupture self.sids = sids self.events = events self.source_id = source_id self.grp_id = grp_id self.serial = serial @property def weight(self): """ Weight of the EBRupture """ return len(self.sids) * len(self.events) @property def etags(self): """ An array of tags for the underlying seismic events """ tags = [] for (eid, ses, occ, sampleid) in self.events: tag = 'grp=%02d~ses=%04d~src=%s~rup=%d-%02d' % ( self.grp_id, ses, self.source_id, self.serial, occ) if sampleid > 0: tag += '~sample=%d' % sampleid tags.append(encode(tag)) return numpy.array(tags) @property def eids(self): """ An array with the underlying event IDs """ return self.events['eid'] @property def multiplicity(self): """ How many times the underlying rupture occurs. """ return len(self.events)
[docs] def export(self, mesh, sm_by_grp): """ Yield :class:`openquake.commonlib.util.Rupture` objects, with all the attributes set, suitable for export in XML format. """ rupture = self.rupture for eid, etag in zip(self.eids, self.etags): new = util.Rupture(sm_by_grp[self.grp_id], eid, etag, self.sids) new.mesh = mesh[self.sids] new.etag = etag new.rupture = new new.is_from_fault_source = iffs = isinstance( rupture.surface, (geo.ComplexFaultSurface, geo.SimpleFaultSurface)) new.is_multi_surface = ims = isinstance( rupture.surface, geo.MultiSurface) new.lons, new.lats, new.depths = get_geom( rupture.surface, iffs, ims) new.surface = rupture.surface new.strike = rupture.surface.get_strike() new.dip = rupture.surface.get_dip() new.rake = rupture.rake new.hypocenter = rupture.hypocenter new.tectonic_region_type = rupture.tectonic_region_type new.magnitude = new.mag = rupture.mag new.top_left_corner = None if iffs or ims else ( new.lons[0], new.lats[0], new.depths[0]) new.top_right_corner = None if iffs or ims else ( new.lons[1], new.lats[1], new.depths[1]) new.bottom_left_corner = None if iffs or ims else ( new.lons[2], new.lats[2], new.depths[2]) new.bottom_right_corner = None if iffs or ims else ( new.lons[3], new.lats[3], new.depths[3]) yield new
def __toh5__(self): rup = self.rupture attrs = dict(source_id=self.source_id, grp_id=self.grp_id, serial=self.serial) for par in self.params: val = getattr(self.rupture, par, None) if val is not None: attrs[par] = val if hasattr(rup, 'temporal_occurrence_model'): attrs['time_span'] = rup.temporal_occurrence_model.time_span if hasattr(rup, 'pmf'): attrs['pmf'] = rup.pmf_array() attrs['seed'] = rup.seed attrs['hypo'] = rup.hypocenter.x, rup.hypocenter.y, rup.hypocenter.z attrs['source_class'] = hdf5.cls2dotname(rup.source_typology) attrs['rupture_class'] = hdf5.cls2dotname(rup.__class__) attrs['surface_class'] = hdf5.cls2dotname(rup.surface.__class__) surface = self.rupture.surface if isinstance(surface, geo.MultiSurface): # multiplanar surfaces n = len(surface.surfaces) arr = build_array([[s.corner_lons, s.corner_lats, s.corner_depths] for s in surface.surfaces]).reshape(n, 2, 2) attrs['mesh_spacing'] = surface.surfaces[0].mesh_spacing else: mesh = surface.mesh if mesh is None: # planar surface arr = build_array([[surface.corner_lons, surface.corner_lats, surface.corner_depths]]).reshape(1, 2, 2) attrs['mesh_spacing'] = surface.mesh_spacing else: # general surface shp = (1,) + mesh.lons.shape arr = build_array( [[mesh.lons, mesh.lats, mesh.depths]]).reshape(shp) attrs['nbytes'] = self.sids.nbytes + self.events.nbytes + arr.nbytes return dict(sids=self.sids, events=self.events, mesh=arr), attrs def __fromh5__(self, dic, attrs): attrs = dict(attrs) self.sids = dic['sids'].value self.events = dic['events'].value surface_class = attrs['surface_class'] surface_cls = hdf5.dotname2cls(surface_class) self.rupture = object.__new__(hdf5.dotname2cls(attrs['rupture_class'])) self.rupture.surface = surface = object.__new__(surface_cls) m = dic['mesh'].value if surface_class.endswith('PlanarSurface'): mesh_spacing = attrs.pop('mesh_spacing') self.rupture.surface = geo.PlanarSurface.from_array( mesh_spacing, m.flatten()) elif surface_class.endswith('MultiSurface'): mesh_spacing = attrs.pop('mesh_spacing') self.rupture.surface.surfaces = [ geo.PlanarSurface.from_array(mesh_spacing, m1.flatten()) for m1 in m] else: # fault surface surface.strike = surface.dip = None # they will be computed surface.mesh = RectangularMesh( m['lon'][0], m['lat'][0], m['depth'][0]) time_span = attrs.pop('time_span', None) if time_span: self.rupture.temporal_occurrence_model = tom.PoissonTOM(time_span) self.rupture.surface_nodes = () if 'rupture_slip_direction' in attrs: logging.error('rupture_slip_direction not implemented yet') self.rupture.rupture_slip_direction = None self.rupture.hypocenter = Point(*attrs.pop('hypo')) self.rupture.source_typology = hdf5.dotname2cls( attrs.pop('source_class')) self.source_id = attrs.pop('source_id') self.grp_id = attrs.pop('grp_id') self.serial = attrs.pop('serial') del attrs['rupture_class'] del attrs['surface_class'] vars(self.rupture).update(attrs) def __lt__(self, other): return self.serial < other.serial def __repr__(self): return '<%s #%d, grp_id=%d>' % (self.__class__.__name__, self.serial, self.grp_id)