Source code for openquake.hazardlib.calc.stochastic

# -*- coding: utf-8 -*-
# vim: tabstop=4 shiftwidth=4 softtabstop=4
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#
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"""
:mod:`openquake.hazardlib.calc.stochastic` contains
:func:`stochastic_event_set`.
"""
import sys
import time
import operator
import itertools
import numpy
import pandas
from openquake.baselib import hdf5
from openquake.baselib.general import AccumDict
from openquake.baselib.performance import Monitor
from openquake.baselib.python3compat import raise_
from openquake.hazardlib.contexts import basename
from openquake.hazardlib.calc.filters import nofilter, SourceFilter
from openquake.hazardlib.source.rupture import (
    BaseRupture, EBRupture, rupture_dt)
from openquake.hazardlib.geo.mesh import surface_to_arrays

TWO16 = 2 ** 16  # 65,536
TWO32 = 2 ** 32  # 4,294,967,296
F64 = numpy.float64
U16 = numpy.uint16
U32 = numpy.uint32
U8 = numpy.uint8
I32 = numpy.int32
F32 = numpy.float32
MAX_RUPTURES = 2000
by_trt = operator.attrgetter('tectonic_region_type')


# this is used in acceptance/stochastic_test.py, not in the engine
[docs]def stochastic_event_set(sources, source_site_filter=nofilter, **kwargs): """ Generates a 'Stochastic Event Set' (that is a collection of earthquake ruptures) representing a possible *realization* of the seismicity as described by a source model. The calculator loops over sources. For each source, it loops over ruptures. For each rupture, the number of occurrence is randomly sampled by calling :meth:`openquake.hazardlib.source.rupture.BaseProbabilisticRupture.sample_number_of_occurrences` .. note:: This calculator is using random numbers. In order to reproduce the same results numpy random numbers generator needs to be seeded, see http://docs.scipy.org/doc/numpy/reference/generated/numpy.random.seed.html :param sources: An iterator of seismic sources objects (instances of subclasses of :class:`~openquake.hazardlib.source.base.BaseSeismicSource`). :param source_site_filter: The source filter to use (default noop filter) :returns: Generator of :class:`~openquake.hazardlib.source.rupture.Rupture` objects that are contained in an event set. Some ruptures can be missing from it, others can appear one or more times in a row. """ shift_hypo = kwargs['shift_hypo'] if 'shift_hypo' in kwargs else False for source, _ in source_site_filter.filter(sources): try: for rupture in source.iter_ruptures(shift_hypo=shift_hypo): [n_occ] = rupture.sample_number_of_occurrences() for _ in range(n_occ): yield rupture except Exception as err: etype, err, tb = sys.exc_info() msg = 'An error occurred with source id=%s. Error: %s' msg %= (source.source_id, str(err)) raise_(etype, msg, tb)
# ######################## rupture calculator ############################ # # this is really fast
[docs]def get_rup_array(ebruptures, srcfilter=nofilter): """ Convert a list of EBRuptures into a numpy composite array, by filtering out the ruptures far away from every site """ if not BaseRupture._code: BaseRupture.init() # initialize rupture codes rups = [] geoms = [] for ebrupture in ebruptures: rup = ebrupture.rupture arrays = surface_to_arrays(rup.surface) # one array per surface points = [] shapes = [] for array in arrays: s0, s1, s2 = array.shape assert s0 == 3, s0 assert s1 < TWO16, 'Too many lines' assert s2 < TWO16, 'The rupture mesh spacing is too small' shapes.append(s1) shapes.append(s2) points.extend(array.flat) # example of points: [25.0, 25.1, 25.1, 25.0, # -24.0, -24.0, -24.1, -24.1, # 5.0, 5.0, 5.0, 5.0] points = F32(points) shapes = U32(shapes) hypo = rup.hypocenter.x, rup.hypocenter.y, rup.hypocenter.z rec = numpy.zeros(1, rupture_dt)[0] rec['seed'] = rup.rup_id n = len(points) // 3 lons = points[0:n] lats = points[n:2*n] rec['minlon'] = minlon = numpy.nanmin(lons) # NaNs are in KiteSurfaces rec['minlat'] = minlat = numpy.nanmin(lats) rec['maxlon'] = maxlon = numpy.nanmax(lons) rec['maxlat'] = maxlat = numpy.nanmax(lats) rec['mag'] = rup.mag rec['hypo'] = hypo if srcfilter.sitecol is not None and len( srcfilter.close_sids(rec, rup.tectonic_region_type)) == 0: continue rate = getattr(rup, 'occurrence_rate', numpy.nan) tup = (0, ebrupture.rup_id, ebrupture.source_id, ebrupture.trt_smr, rup.code, ebrupture.n_occ, rup.mag, rup.rake, rate, minlon, minlat, maxlon, maxlat, hypo, 0, 0) rups.append(tup) # we are storing the geometries as arrays of 32 bit floating points; # the first element is the number of surfaces, then there are # 2 * num_surfaces integers describing the first and second # dimension of each surface, and then the lons, lats and deps of # the underlying meshes of points. geom = numpy.concatenate([[len(shapes) // 2], shapes, points]) geoms.append(geom) if not rups: return () dic = dict(geom=numpy.array(geoms, object)) # NB: PMFs for nonparametric ruptures are not saved since they # are useless for the GMF computation return hdf5.ArrayWrapper(numpy.array(rups, rupture_dt), dic)
[docs]def sample_cluster(sources, srcfilter, num_ses, param): """ Yields ruptures generated by a cluster of sources. :param sources: A sequence of sources of the same group :param num_ses: Number of stochastic event sets :param param: a dictionary of additional parameters including ses_per_logic_tree_path :yields: dictionaries with keys rup_array, source_data, eff_ruptures """ eb_ruptures = [] ses_seed = param['ses_seed'] numpy.random.seed(sources[0].serial(ses_seed)) [trt_smr] = set(src.trt_smr for src in sources) # AccumDict of arrays with 3 elements nsites, nruptures, calc_time source_data = AccumDict(accum=[]) # Set the parameters required to compute the number of occurrences # of the group of sources # assert param['oqparam'].number_of_logic_tree_samples > 0 samples = getattr(sources[0], 'samples', 1) tom = getattr(sources, 'temporal_occurrence_model') rate = tom.occurrence_rate time_span = tom.time_span # Note that using a single time interval corresponding to the product # of the investigation time and the number of realisations as we do # here is admitted only in the case of a time-independent model grp_num_occ = numpy.random.poisson(rate * time_span * samples * num_ses) # Now we process the sources included in the group. Possible cases: # * The group is a cluster. In this case we choose one rupture per each # source; uncertainty in the ruptures can be handled in this case # using mutually exclusive ruptures (note that this is admitted # only for nons-parametric sources). # * The group contains mutually exclusive sources. In this case we # choose one source and then one rupture from this source. rup_counter = {} rup_data = {} for rlz_num in range(grp_num_occ): if sources.cluster: for src, _ in srcfilter.filter(sources): # Track calculation time t0 = time.time() src_id = src.source_id rup = src.get_one_rupture(ses_seed) # The problem here is that we do not know a-priori the # number of occurrences of a given rupture. if src_id not in rup_counter: rup_counter[src_id] = {} rup_data[src_id] = {} if rup.idx not in rup_counter[src_id]: rup_counter[src_id][rup.idx] = 1 rup_data[src_id][rup.idx] = [rup, src_id, trt_smr] else: rup_counter[src_id][rup.idx] += 1 # Store info dt = time.time() - t0 source_data['src_id'].append(src.source_id) source_data['nsites'].append(src.nsites) source_data['nrups'].append(len(rup_data[src_id])) source_data['ctimes'].append(dt) source_data['weight'].append(src.weight) source_data['taskno'].append(param['task_no']) elif param['src_interdep'] == 'mutex': raise NotImplementedError('src_interdep == mutex') # Create event based ruptures for src_key in rup_data: for rup_key in rup_data[src_key]: rup, source_id, trt_smr = rup_data[src_key][rup_key] cnt = rup_counter[src_key][rup_key] ebr = EBRupture(rup, source_id, trt_smr, cnt) eb_ruptures.append(ebr) return eb_ruptures, source_data
# NB: there is postfiltering of the ruptures, which is more efficient
[docs]def sample_ruptures(sources, cmaker, sitecol=None, monitor=Monitor()): """ :param sources: a sequence of sources of the same group :param cmaker: a ContextMaker instance with ses_per_logic_tree_path, ses_seed :param sitecol: SiteCollection instance used for filtering (None for no filtering) :param monitor: monitor instance :yields: dictionaries with keys rup_array, source_data """ srcfilter = SourceFilter(sitecol, cmaker.maximum_distance) source_data = AccumDict(accum=[]) # Compute and save stochastic event sets num_ses = cmaker.ses_per_logic_tree_path cmaker.task_no = monitor.task_no grp_id = sources[0].grp_id # Compute the number of occurrences of the source group. This is used # for cluster groups or groups with mutually exclusive sources. if (getattr(sources, 'atomic', False) and getattr(sources, 'cluster', False)): eb_ruptures, source_data = sample_cluster( sources, srcfilter, num_ses, vars(cmaker)) # Yield ruptures er = sum(src.num_ruptures for src, _ in srcfilter.filter(sources)) yield AccumDict(dict(rup_array=get_rup_array(eb_ruptures, srcfilter), source_data=source_data, eff_ruptures={grp_id: er})) else: eb_ruptures = [] eff_ruptures = 0 source_data = AccumDict(accum=[]) for src, _ in srcfilter.filter(sources): nr = src.num_ruptures eff_ruptures += nr t0 = time.time() if len(eb_ruptures) > MAX_RUPTURES: # yield partial result to avoid running out of memory yield AccumDict(dict(rup_array=get_rup_array(eb_ruptures, srcfilter), source_data={}, eff_ruptures={})) eb_ruptures.clear() samples = getattr(src, 'samples', 1) for rup, trt_smr, n_occ in src.sample_ruptures( samples * num_ses, cmaker.ses_seed): ebr = EBRupture(rup, src.source_id, trt_smr, n_occ) eb_ruptures.append(ebr) dt = time.time() - t0 source_data['src_id'].append(src.source_id) source_data['nsites'].append(src.nsites) source_data['nrups'].append(nr) source_data['ctimes'].append(dt) source_data['weight'].append(src.weight) source_data['taskno'].append(monitor.task_no) rup_array = get_rup_array(eb_ruptures, srcfilter) yield AccumDict(dict(rup_array=rup_array, source_data=source_data, eff_ruptures={grp_id: eff_ruptures}))
[docs]def sample_ebruptures(src_groups, cmakerdict): """ Sample independent sources without filtering. :param src_groups: a list of source groups :param cmakerdict: a dictionary TRT -> cmaker :returns: a list of EBRuptures """ ebrs = [] e0 = 0 ordinal = 0 for sg in src_groups: cmaker = cmakerdict[sg.trt] for src in sg: samples = getattr(src, 'samples', 1) for rup, trt_smr, n_occ in src.sample_ruptures( samples * cmaker.ses_per_logic_tree_path, cmaker.ses_seed): ebr = EBRupture(rup, src.source_id, trt_smr, n_occ, e0=e0) ebr.ordinal = ordinal ebrs.append(ebr) e0 += n_occ ordinal += 1 return ebrs
[docs]def get_ebr_df(ebruptures, cmakerdict): """ :param ebruptures: the output of sample_ebruptures :param rlzs_by_gsim_trt: a double dictionary trt -> gsim -> rlzs :returns: a DataFrame with fields eid, rlz indexed by rupture ordinal """ eids, rups, rlzs = [], [], [] for trt, ebrs in itertools.groupby(ebruptures, by_trt): rlzs_by_gsim = cmakerdict[trt].gsims for ebr in ebrs: for rlz_id, eids_ in ebr.get_eids_by_rlz(rlzs_by_gsim).items(): for eid in eids_: eids.append(eid) rups.append(ebr.ordinal) rlzs.append(rlz_id) return pandas.DataFrame(dict(eid=eids, rlz=rlzs), rups)