Source code for openquake.calculators.disaggregation

# -*- coding: utf-8 -*-
# vim: tabstop=4 shiftwidth=4 softtabstop=4
# Copyright (C) 2015-2018 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
# GNU Affero General Public License for more details.
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Disaggregation calculator core functionality
import logging
import operator
import numpy

from openquake.baselib import parallel
from openquake.baselib.general import AccumDict, groupby, block_splitter
from openquake.baselib.python3compat import encode
from openquake.hazardlib.stats import compute_stats
from openquake.hazardlib.calc import disagg
from openquake.hazardlib.calc.filters import SourceFilter
from openquake.hazardlib.gsim.base import ContextMaker
from openquake.calculators import getters
from openquake.calculators import base, classical

weight = operator.attrgetter('weight')
DISAGG_RES_FMT = '%(poe)s%(rlz)s-%(imt)s-sid-%(sid)s/'

def _to_matrix(matrices, num_trts):
    # convert a dict trti -> matrix into a single matrix of shape (T, ...)
    trti = next(iter(matrices))
    mat = numpy.zeros((num_trts,) + matrices[trti].shape)
    for trti in matrices:
        mat[trti] = matrices[trti]
    return mat

[docs]def compute_disagg(sitecol, sources, cmaker, iml4, trti, bin_edges, oqparam, monitor): # see for an explanation # of the algorithm used """ :param sitecol: a :class:`` instance :param sources: list of hazardlib source objects :param cmaker: a :class:`openquake.hazardlib.gsim.base.ContextMaker` instance :param iml4: an array of intensities of shape (N, R, M, P) :param dict trti: tectonic region type index :param bin_egdes: a dictionary site_id -> edges :param oqparam: the parameters in the job.ini file :param monitor: monitor of the currently running job :returns: a dictionary of probability arrays, with composite key (sid, rlzi, poe, imt, iml, trti). """ result = {'trti': trti, 'num_ruptures': 0} # all the time is spent in collect_bin_data ruptures = [] for src in sources: ruptures.extend(src.iter_ruptures()) bin_data = disagg.collect_bin_data( ruptures, sitecol, cmaker, iml4, oqparam.truncation_level, oqparam.num_epsilon_bins, monitor) if bin_data: # dictionary poe, imt, rlzi -> pne for sid in sitecol.sids: for (poe, imt, rlzi), matrix in disagg.build_disagg_matrix( bin_data, bin_edges, sid, monitor).items(): result[sid, rlzi, poe, imt] = matrix result['cache_info'] = monitor.cache_info result['num_ruptures'] = len(bin_data.mags) return result # sid, rlzi, poe, imt, iml -> array
[docs]def agg_probs(*probs): """ Aggregate probabilities withe the usual formula 1 - (1 - P1) ... (1 - Pn) """ acc = 1. - probs[0] for prob in probs[1:]: acc *= 1. - prob return 1. - acc
[docs]@base.calculators.add('disaggregation') class DisaggregationCalculator(base.HazardCalculator): """ Classical PSHA disaggregation calculator """ accept_precalc = ['psha'] POE_TOO_BIG = '''\ You are trying to disaggregate for poe=%s. However the source model #%d, '%s', produces at most probabilities of %.7f for rlz=#%d, IMT=%s. The disaggregation PoE is too big or your model is wrong, producing too small PoEs.'''
[docs] def pre_execute(self): oq = self.oqparam if oq.iml_disagg and not oq.disagg_by_src: base.HazardCalculator.pre_execute(self) else: # we need to run a ClassicalCalculator cl = classical.ClassicalCalculator(oq, self.datastore.calc_id) self.csm = cl.csm self.rlzs_assoc = cl.rlzs_assoc # often reduced logic tree self.sitecol = cl.sitecol
[docs] def execute(self): """Performs the disaggregation""" oq = self.oqparam if oq.iml_disagg: curves = [None] * len(self.sitecol) # no hazard curves are needed else: curves = [self.get_curves(sid) for sid in self.sitecol.sids] self.check_poes_disagg(curves) return self.full_disaggregation(curves)
[docs] def agg_result(self, acc, result): """ Collect the results coming from compute_disagg into self.results, a dictionary with key (sid, rlzi, poe, imt, trti) and values which are probability arrays. :param acc: dictionary k -> dic accumulating the results :param result: dictionary with the result coming from a task """ # this is fast trti = result.pop('trti') self.num_ruptures[trti] += result.pop('num_ruptures') self.cache_info += result.pop('cache_info', 0) for key, val in result.items(): acc[key][trti] = agg_probs(acc[key].get(trti, 0), val) return acc
[docs] def get_curves(self, sid): """ Get all the relevant hazard curves for the given site ordinal. Returns a dictionary rlz_id -> curve_by_imt. """ dic = {} imtls = self.oqparam.imtls pgetter = getters.PmapGetter( self.datastore, self.rlzs_assoc, numpy.array([sid])) for rlz in self.rlzs_assoc.realizations: try: pmap = pgetter.get(rlz.ordinal) except ValueError: # empty pmaps 'hazard curve contains all zero probabilities; ' 'skipping site %d, rlz=%d', sid, rlz.ordinal) continue if sid not in pmap: continue poes = pmap[sid].convert(imtls) for imt_str in imtls: if all(x == 0.0 for x in poes[imt_str]): 'hazard curve contains all zero probabilities; ' 'skipping site %d, rlz=%d, IMT=%s', sid, rlz.ordinal, imt_str) continue dic[rlz.ordinal] = poes return dic
[docs] def check_poes_disagg(self, curves): """ Raise an error if the given poes_disagg are too small compared to the hazard curves. """ oq = self.oqparam max_poe = numpy.zeros(len(self.rlzs_assoc.realizations), oq.imt_dt()) # check for too big poes_disagg for smodel in self.csm.source_models: sm_id = smodel.ordinal for sid, site in enumerate(self.sitecol): for rlzi, poes in curves[sid].items(): for imt in oq.imtls: max_poe[rlzi][imt] = max( max_poe[rlzi][imt], poes[imt].max()) for poe in oq.poes_disagg: for rlz in self.rlzs_assoc.rlzs_by_smodel[sm_id]: rlzi = rlz.ordinal for imt in oq.imtls: min_poe = max_poe[rlzi][imt] if poe > min_poe: raise ValueError(self.POE_TOO_BIG % ( poe, sm_id, smodel.names, min_poe, rlzi, imt))
[docs] def full_disaggregation(self, curves): """ Run the disaggregation phase. :param curves: a list of hazard curves, one per site The curves can be all None if iml_disagg is set in the job.ini """ oq = self.oqparam tl = oq.truncation_level src_filter = SourceFilter(self.sitecol, oq.maximum_distance) csm = self.csm for sg in csm.src_groups: if sg.atomic: raise NotImplemented('Atomic groups are not supported yet') if not csm.get_sources(): raise RuntimeError('All sources were filtered away!') R = len(self.rlzs_assoc.realizations) M = len(oq.imtls) P = len(oq.poes_disagg) or 1 if R * M * P > 10: logging.warning( 'You have %d realizations, %d IMTs and %d poes_disagg: the ' 'disaggregation will be heavy and memory consuming', R, M, P) iml4 = disagg.make_iml4( R, oq.iml_disagg, oq.imtls, oq.poes_disagg or (None,), curves) if oq.disagg_by_src: if R == 1: self.build_disagg_by_src(iml4) else: logging.warning('disagg_by_src works only with 1 realization, ' 'you have %d', R) eps_edges = numpy.linspace(-tl, tl, oq.num_epsilon_bins + 1) self.bin_edges = {} # build trt_edges trts = tuple(sorted(set(sg.trt for smodel in csm.source_models for sg in smodel.src_groups))) trt_num = {trt: i for i, trt in enumerate(trts)} self.trts = trts # build mag_edges mmm = numpy.array([src.get_min_max_mag() for src in csm.get_sources()]) min_mag = mmm[:, 0].min() max_mag = mmm[:, 1].max() mag_edges = oq.mag_bin_width * numpy.arange( int(numpy.floor(min_mag / oq.mag_bin_width)), int(numpy.ceil(max_mag / oq.mag_bin_width) + 1)) # build dist_edges maxdist = max(oq.maximum_distance(trt, max_mag) for trt in trts) dist_edges = oq.distance_bin_width * numpy.arange( 0, int(numpy.ceil(maxdist / oq.distance_bin_width) + 1)) # build eps_edges eps_edges = numpy.linspace(-tl, tl, oq.num_epsilon_bins + 1) # build lon_edges, lat_edges per sid bbs = src_filter.get_bounding_boxes(mag=max_mag) lon_edges, lat_edges = {}, {} # by sid for sid, bb in zip(self.sitecol.sids, bbs): lon_edges[sid], lat_edges[sid] = disagg.lon_lat_bins( bb, oq.coordinate_bin_width) self.bin_edges = mag_edges, dist_edges, lon_edges, lat_edges, eps_edges self.save_bin_edges() # build all_args all_args = [] maxweight = csm.get_maxweight(weight, oq.concurrent_tasks) R = iml4.shape[1] self.imldict = {} # sid, rlzi, poe, imt -> iml for s in self.sitecol.sids: for r in range(R): for p, poe in enumerate(oq.poes_disagg or [None]): for m, imt in enumerate(oq.imtls): self.imldict[s, r, poe, imt] = iml4[s, r, m, p] for smodel in csm.source_models: sm_id = smodel.ordinal for trt, groups in groupby( smodel.src_groups, operator.attrgetter('trt')).items(): trti = trt_num[trt] sources = sum([grp.sources for grp in groups], []) rlzs_by_gsim = self.rlzs_assoc.get_rlzs_by_gsim(trt, sm_id) cmaker = ContextMaker( trt, rlzs_by_gsim, src_filter.integration_distance, {'filter_distance': oq.filter_distance}) for block in block_splitter(sources, maxweight, weight): all_args.append( (src_filter.sitecol, block, cmaker, iml4, trti, self.bin_edges, oq)) self.num_ruptures = [0] * len(self.trts) self.cache_info = numpy.zeros(3) # operations, cache_hits, num_zeros results = parallel.Starmap( compute_disagg, all_args, self.monitor() ).reduce(self.agg_result, AccumDict(accum={})) # set eff_ruptures trti = for smodel in for sg in smodel.src_groups: sg.eff_ruptures = self.num_ruptures[trti[sg.trt]] self.datastore['csm_info'] = ops, hits, num_zeros = self.cache_info'Cache speedup %s', ops / (ops - hits))'Discarded zero matrices: %d', num_zeros) return results
[docs] def save_bin_edges(self): """ Save disagg-bins """ b = self.bin_edges for sid in self.sitecol.sids: 'disagg_matrix_shape=%s, site=#%d', str(disagg.get_shape(b, sid) + (len(self.trts),)), sid) self.datastore['disagg-bins/mags'] = b[0] self.datastore['disagg-bins/dists'] = b[1] for sid in self.sitecol.sids: self.datastore['disagg-bins/lons/sid-%d' % sid] = b[2][sid] self.datastore['disagg-bins/lats/sid-%d' % sid] = b[3][sid] self.datastore['disagg-bins/eps'] = b[4]
[docs] def build_stats(self, results, hstats): """ :param results: dict key -> 6D disagg_matrix :param hstats: (statname, statfunc) pairs """ weights = [rlz.weight for rlz in self.rlzs_assoc.realizations] R = len(weights) T = len(self.trts) dic = {} # sid, poe, imt -> disagg_matrix for sid in self.sitecol.sids: shape = disagg.get_shape(self.bin_edges, sid) for poe in self.oqparam.poes_disagg or (None,): for imt in self.oqparam.imtls: dic[sid, poe, imt] = numpy.zeros((R, T) + shape) for (sid, rlzi, poe, imt), matrix in results.items(): dic[sid, poe, imt][rlzi] = matrix res = {} # sid, stat, poe, imt -> disagg_matrix for (sid, poe, imt), array in dic.items(): wei_imt = [weight[imt] for weight in weights] for stat, func in hstats: [matrix] = compute_stats(array, [func], wei_imt) res[sid, stat, poe, imt] = matrix return res
[docs] def get_NRPM(self): """ :returns: (num_sites, num_rlzs, num_poes, num_imts) """ N = len(self.sitecol) R = len(self.rlzs_assoc.realizations) P = len(self.oqparam.poes_disagg or (None,)) M = len(self.oqparam.imtls) return (N, R, P, M)
[docs] def post_execute(self, results): """ Save all the results of the disaggregation. NB: the number of results to save is #sites * #rlzs * #disagg_poes * #IMTs. :param results: a dictionary (sid, rlzi, poe, imt) -> trti -> disagg matrix """ T = len(self.trts) # build a dictionary (sid, rlzi, poe, imt) -> 6D matrix results = {k: _to_matrix(v, T) for k, v in results.items()} # get the number of outputs shp = self.get_NRPM()'Extracting and saving the PMFs for %d outputs ' '(N=%s, R=%d, P=%d, M=%d)',, *shp) self.save_disagg_result('disagg', results) hstats = self.oqparam.hazard_stats() if len(self.rlzs_assoc.realizations) > 1 and hstats: with self.monitor('computing and saving stats', measuremem=True): res = self.build_stats(results, hstats) self.save_disagg_result('disagg-stats', res) self.datastore.set_attrs( 'disagg', trts=encode(self.trts), num_ruptures=self.num_ruptures)
[docs] def save_disagg_result(self, dskey, results): """ Save the computed PMFs in the datastore :param dskey: dataset key; can be 'disagg' or 'disagg-stats' :param results: a dictionary sid, rlz, poe, imt -> 6D disagg_matrix """ for (sid, rlz, poe, imt), matrix in sorted(results.items()): self._save_result(dskey, sid, rlz, poe, imt, matrix)
def _save_result(self, dskey, site_id, rlz_id, poe, imt_str, matrix): disagg_outputs = self.oqparam.disagg_outputs lon = self.sitecol.lons[site_id] lat = self.sitecol.lats[site_id] disp_name = dskey + '/' + DISAGG_RES_FMT % dict( poe='' if poe is None else 'poe-%s-' % poe, rlz='rlz-%d' if isinstance(rlz_id, int) else rlz_id, imt=imt_str, sid=site_id) mag, dist, lonsd, latsd, eps = self.bin_edges lons, lats = lonsd[site_id], latsd[site_id] with self.monitor('extracting PMFs'): poe_agg = [] aggmatrix = agg_probs(*matrix) for key, fn in disagg.pmf_map.items(): if not disagg_outputs or key in disagg_outputs: pmf = fn(matrix if key.endswith('TRT') else aggmatrix) self.datastore[disp_name + key] = pmf poe_agg.append(1. - - pmf)) attrs = self.datastore.hdf5[disp_name].attrs attrs['rlzi'] = rlz_id attrs['imt'] = imt_str try: attrs['iml'] = self.imldict[site_id, rlz_id, poe, imt_str] except KeyError: # when saving the stats attrs['iml'] = self.oqparam.iml_disagg.get(imt_str, -1) attrs['mag_bin_edges'] = mag attrs['dist_bin_edges'] = dist attrs['lon_bin_edges'] = lons attrs['lat_bin_edges'] = lats attrs['eps_bin_edges'] = eps attrs['location'] = (lon, lat) # sanity check: all poe_agg should be the same attrs['poe_agg'] = poe_agg if poe: attrs['poe'] = poe poe_agg = numpy.mean(attrs['poe_agg']) if abs(1 - poe_agg / poe) > .1: logging.warning( 'poe_agg=%s is quite different from the expected' ' poe=%s; perhaps the number of intensity measure' ' levels is too small?', poe_agg, poe)
[docs] def build_disagg_by_src(self, iml4): """ :param dstore: a datastore :param iml4: 4D array of IMLs with shape (N, 1, M, P) """ logging.warning('Disaggregation by source is experimental') oq = self.oqparam poes_disagg = oq.poes_disagg or (None,) pmap_by_grp = getters.PmapGetter( self.datastore, self.rlzs_assoc, self.sitecol.sids).pmap_by_grp grp_ids = numpy.array(sorted(int(grp[4:]) for grp in pmap_by_grp)) G = len(pmap_by_grp) P = len(poes_disagg) for rec in self.sitecol.array: sid = rec['sids'] for imti, imt in enumerate(oq.imtls): xs = oq.imtls[imt] poes = numpy.zeros((G, P)) for g, grp_id in enumerate(grp_ids): pmap = pmap_by_grp['grp-%02d' % grp_id] if sid in pmap: ys = pmap[sid].array[oq.imtls(imt), 0] poes[g] = numpy.interp(iml4[sid, 0, imti, :], xs, ys) for p, poe in enumerate(poes_disagg): prefix = ('iml-%s' % oq.iml_disagg[imt] if poe is None else 'poe-%s' % poe) name = 'disagg_by_src/%s-%s-%s-%s' % ( prefix, imt, rec['lon'], rec['lat']) if poes[:, p].sum(): # nonzero contribution poe_agg = 1 - - poes[:, p]) if poe and abs(1 - poe_agg / poe) > .1: logging.warning('poe_agg=%s is quite different from ' 'the expected poe=%s', poe_agg, poe) self.datastore[name] = poes[:, p] self.datastore.set_attrs(name, poe_agg=poe_agg)