Source code for openquake.calculators.getters

# -*- coding: utf-8 -*-
# vim: tabstop=4 shiftwidth=4 softtabstop=4
#
# Copyright (C) 2018-2022 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 time
import operator
import numpy

from openquake.baselib import general, hdf5
from openquake.baselib.python3compat import decode
from openquake.hazardlib import probability_map, stats
from openquake.hazardlib.source.rupture import (
    BaseRupture, RuptureProxy, to_arrays)
from openquake.commonlib import datastore

U16 = numpy.uint16
U32 = numpy.uint32
F32 = numpy.float32
by_taxonomy = operator.attrgetter('taxonomy')
code2cls = BaseRupture.init()
weight = operator.itemgetter('n_occ')


[docs]class NotFound(Exception): pass
[docs]def build_stat_curve(pcurve, imtls, stat, weights): """ Build statistics by taking into account IMT-dependent weights """ poes = pcurve.array.T # shape R, L assert len(poes) == len(weights), (len(poes), len(weights)) L = imtls.size array = numpy.zeros((L, 1)) if isinstance(weights, list): # IMT-dependent weights # this is slower since the arrays are shorter for imt in imtls: slc = imtls(imt) ws = [w[imt] for w in weights] if sum(ws) == 0: # expect no data for this IMT continue array[slc, 0] = stat(poes[:, slc], ws) else: array[:, 0] = stat(poes, weights) return probability_map.ProbabilityCurve(array)
[docs]def sig_eps_dt(imts): """ :returns: a composite data type for the sig_eps output """ lst = [('eid', U32), ('rlz_id', U16)] for imt in imts: lst.append(('sig_inter_' + imt, F32)) for imt in imts: lst.append(('eps_inter_' + imt, F32)) return numpy.dtype(lst)
[docs]class HcurvesGetter(object): """ Read the contribution to the hazard curves coming from each source in a calculation with a source specific logic tree """ def __init__(self, dstore): self.dstore = dstore self.imtls = dstore['oqparam'].imtls self.full_lt = dstore['full_lt'] self.sslt = self.full_lt.source_model_lt.decompose() self.source_info = dstore['source_info'][:] self.disagg_by_grp = dstore['disagg_by_grp'][:] gsim_lt = self.full_lt.gsim_lt self.bysrc = {} # src_id -> (start, gsims, weights) for row in self.source_info: dis = self.disagg_by_grp[row['grp_id']] trt = decode(dis['grp_trt']) weights = gsim_lt.get_weights(trt) self.bysrc[decode(row['source_id'])] = ( dis['grp_start'], gsim_lt.values[trt], weights)
[docs] def get_hcurve(self, src_id, imt=None, site_id=0, gsim_idx=None): """ Return the curve associated to the given src_id, imt and gsim_idx as an array of length L """ assert ';' in src_id, src_id # must be a realization specific src_id imt_slc = self.imtls(imt) if imt else slice(None) start, gsims, weights = self.bysrc[src_id] dset = self.dstore['_poes'] if gsim_idx is None: curves = dset[start:start + len(gsims), site_id, imt_slc]
[docs] return weights @ curves return dset[start + gsim_idx, site_id, imt_slc]
def get_hcurves(self, src, imt=None, site_id=0, gsim_idx=None): """ Return the curves associated to the given src, imt and gsim_idx as an array of shape (R, L) """ assert ';' not in src, src # not a rlz specific source ID curves = [] for i in range(self.sslt[src].num_paths): src_id = '%s;%d' % (src, i) curves.append(self.get_hcurve(src_id, imt, site_id, gsim_idx)) return numpy.array(curves)
[docs] def get_mean_hcurve(self, src=None, imt=None, site_id=0, gsim_idx=None): """ Return the mean curve associated to the given src, imt and gsim_idx as an array of shape L """ if src is None: hcurves = [self.get_mean_hcurve(src) for src in self.sslt] return general.agg_probs(*hcurves) weights = [rlz.weight for rlz in self.sslt[src]] curves = self.get_hcurves(src, imt, site_id, gsim_idx)
[docs] return weights @ curves
class PmapGetter(object): """ Read hazard curves from the datastore for all realizations or for a specific realization. :param dstore: a DataStore instance or file system path to it :param sids: the subset of sites to consider (if None, all sites) """ def __init__(self, dstore, weights, slices, imtls=(), poes=()): self.filename = dstore if isinstance(dstore, str) else dstore.filename if len(weights[0].dic) == 1: # no weights by IMT self.weights = numpy.array([w['weight'] for w in weights]) else: self.weights = weights self.imtls = imtls self.poes = poes self.num_rlzs = len(weights) self.eids = None self.rlzs_by_g = dstore['rlzs_by_g'][()] self.slices = slices self._pmap = {} @property def sids(self): self.init() return list(self._pmap) @property def imts(self): return list(self.imtls) @property def L(self): return self.imtls.size @property def N(self): self.init() return len(self._pmap) @property def M(self): return len(self.imtls) @property def R(self): return len(self.weights)
[docs] def init(self): """ Build the probability curves from the underlying dataframes """ if self._pmap: return self._pmap G = len(self.rlzs_by_g) with hdf5.File(self.filename) as dstore: for start, stop in self.slices: poes_df = dstore.read_df('_poes', slc=slice(start, stop)) for sid, df in poes_df.groupby('sid'): try: array = self._pmap[sid].array except KeyError: array = numpy.zeros((self.L, G)) self._pmap[sid] = probability_map.ProbabilityCurve( array) array[df.lid, df.gid] = df.poe return self._pmap
# used in risk calculations where there is a single site per getter
[docs] def get_hazard(self, gsim=None): """ :param gsim: ignored :returns: a probability curve of shape (L, R) for the given site """ self.init() if not self.sids: # this happens when the poes are all zeros, as in # classical_risk/case_3 for the first site return probability_map.ProbabilityCurve( numpy.zeros((self.L, self.num_rlzs))) return self.get_pcurve(self.sids[0])
[docs] def get_pcurve(self, sid): # used in classical """ :returns: a ProbabilityCurve of shape L, R """ pmap = self.init() pc0 = probability_map.ProbabilityCurve( numpy.zeros((self.L, self.num_rlzs))) try: pc0.combine(pmap[sid], self.rlzs_by_g) except KeyError: # no hazard for sid pass return pc0
[docs] def get_mean(self): """ Compute the mean curve as a ProbabilityMap :param grp: if not None must be a string of the form "grp-XX"; in that case returns the mean considering only the contribution for group XX """ self.init() if len(self.weights) == 1: # one realization # the standard deviation is zero pmap = self.get(0) for sid, pcurve in pmap.items(): array = numpy.zeros(pcurve.array.shape) array[:, 0] = pcurve.array[:, 0] pcurve.array = array return pmap L = self.imtls.size pmap = probability_map.ProbabilityMap.build(L, 1, self.sids) for sid in self.sids: pmap[sid] = build_stat_curve( self.get_pcurve(sid), self.imtls, stats.mean_curve, self.weights) return pmap
time_dt = numpy.dtype( [('rup_id', U32), ('nsites', U16), ('time', F32), ('task_no', U16)])
[docs]def get_rupture_getters(dstore, ct=0, slc=slice(None), srcfilter=None): """ :param dstore: a :class:`openquake.commonlib.datastore.DataStore` :param ct: number of concurrent tasks :returns: a list of RuptureGetters """ full_lt = dstore['full_lt'] rlzs_by_gsim = full_lt.get_rlzs_by_gsim() rup_array = dstore['ruptures'][slc] if len(rup_array) == 0: raise NotFound('There are no ruptures in %s' % dstore) rup_array.sort(order=['trt_smr', 'n_occ']) scenario = 'scenario' in dstore['oqparam'].calculation_mode proxies = [RuptureProxy(rec, scenario) for rec in rup_array] maxweight = rup_array['n_occ'].sum() / (ct / 2 or 1) rgetters = [] for block in general.block_splitter( proxies, maxweight, operator.itemgetter('n_occ'), key=operator.itemgetter('trt_smr')): trt_smr = block[0]['trt_smr'] rbg = rlzs_by_gsim[trt_smr] rg = RuptureGetter(block, dstore.filename, trt_smr, full_lt.trt_by(trt_smr), rbg) rgetters.append(rg) return rgetters
[docs]def get_ebruptures(dstore): """ Extract EBRuptures from the datastore """ ebrs = [] for rgetter in get_rupture_getters(dstore): for proxy in rgetter.get_proxies(): ebrs.append(proxy.to_ebr(rgetter.trt)) return ebrs
[docs]def line(points): return '(%s)' % ', '.join('%.5f %.5f %.5f' % tuple(p) for p in points)
[docs]def multiline(array3RC): """ :param array3RC: array of shape (3, R, C) :returns: a MULTILINESTRING """ D, R, C = array3RC.shape assert D == 3, D lines = 'MULTILINESTRING(%s)' % ', '.join( line(array3RC[:, r, :].T) for r in range(R)) return lines
# this is never called directly; get_rupture_getters is used instead
[docs]class RuptureGetter(object): """ :param proxies: a list of RuptureProxies :param filename: path to the HDF5 file containing a 'rupgeoms' dataset :param trt_smr: source group index :param trt: tectonic region type string :param rlzs_by_gsim: dictionary gsim -> rlzs for the group """ def __init__(self, proxies, filename, trt_smr, trt, rlzs_by_gsim): self.proxies = proxies self.weight = sum(proxy['n_occ'] for proxy in proxies) self.filename = filename self.trt_smr = trt_smr self.trt = trt self.rlzs_by_gsim = rlzs_by_gsim self.num_events = sum(int(proxy['n_occ']) for proxy in proxies) @property def num_ruptures(self): return len(self.proxies)
[docs] def get_rupdict(self): # used in extract_event_info and show rupture """ :returns: a dictionary with the parameters of the rupture """ assert len(self.proxies) == 1, 'Please specify a slice of length 1' dic = {'trt': self.trt} with datastore.read(self.filename) as dstore: rupgeoms = dstore['rupgeoms'] rec = self.proxies[0].rec geom = rupgeoms[rec['id']] arrays = to_arrays(geom) # one array per surface for a, array in enumerate(arrays): dic['surface_%d' % a] = multiline(array) rupclass, surclass = code2cls[rec['code']] dic['rupture_class'] = rupclass.__name__ dic['surface_class'] = surclass.__name__ dic['hypo'] = rec['hypo'] dic['occurrence_rate'] = rec['occurrence_rate'] dic['trt_smr'] = rec['trt_smr'] dic['n_occ'] = rec['n_occ'] dic['seed'] = rec['seed'] dic['mag'] = rec['mag'] dic['srcid'] = rec['source_id'] return dic
[docs] def get_proxies(self, min_mag=0): """ :returns: a list of RuptureProxies """ proxies = [] with datastore.read(self.filename) as dstore: rupgeoms = dstore['rupgeoms'] for proxy in self.proxies: if proxy['mag'] < min_mag: continue proxy.geom = rupgeoms[proxy['geom_id']] proxies.append(proxy) return proxies
# called in ebrisk calculations
[docs] def split(self, srcfilter, maxw): """ :returns: RuptureProxies with weight < maxw """ proxies = [] for proxy in self.proxies: sids = srcfilter.close_sids(proxy.rec, self.trt) if len(sids): proxies.append(proxy) rgetters = [] for block in general.block_splitter(proxies, maxw, weight): rg = RuptureGetter(block, self.filename, self.trt_smr, self.trt, self.rlzs_by_gsim) rgetters.append(rg) return rgetters
def __len__(self): return len(self.proxies) def __repr__(self): wei = ' [w=%d]' % self.weight if hasattr(self, 'weight') else '' return '<%s trt_smr=%d, %d rupture(s)%s>' % ( self.__class__.__name__, self.trt_smr, len(self), wei)