# -*- coding: utf-8 -*-
# vim: tabstop=4 shiftwidth=4 softtabstop=4
#
# Copyright (C) 2010-2017 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.
#
# 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 re
import copy
import math
import logging
import operator
import collections
import random
import numpy
from openquake.baselib import hdf5, node
from openquake.baselib.python3compat import decode
from openquake.baselib.general import groupby, group_array
from openquake.hazardlib import nrml, sourceconverter, InvalidFile
from openquake.commonlib import logictree
MAXWEIGHT = sourceconverter.MAXWEIGHT
MAX_INT = 2 ** 31 - 1
U16 = numpy.uint16
U32 = numpy.uint32
I32 = numpy.int32
F32 = numpy.float32
[docs]class LtRealization(object):
"""
Composite realization build on top of a source model realization and
a GSIM realization.
"""
def __init__(self, ordinal, sm_lt_path, gsim_rlz, weight, sampleid):
self.ordinal = ordinal
self.sm_lt_path = tuple(sm_lt_path)
self.gsim_rlz = gsim_rlz
self.weight = weight
self.sampleid = sampleid
def __repr__(self):
return '<%d,%s,w=%s>' % (self.ordinal, self.uid, self.weight)
@property
def gsim_lt_path(self):
return self.gsim_rlz.lt_path
@property
def uid(self):
"""An unique identifier for effective realizations"""
return '_'.join(self.sm_lt_path) + '~' + self.gsim_rlz.uid
def __lt__(self, other):
return self.ordinal < other.ordinal
def __eq__(self, other):
return repr(self) == repr(other)
def __ne__(self, other):
return repr(self) != repr(other)
def __hash__(self):
return hash(repr(self))
[docs]def capitalize(words):
"""
Capitalize words separated by spaces.
>>> capitalize('active shallow crust')
'Active Shallow Crust'
"""
return ' '.join(w.capitalize() for w in words.split(' '))
[docs]class RlzsAssoc(collections.Mapping):
"""
Realization association class. It should not be instantiated directly,
but only via the method :meth:
`openquake.commonlib.source.CompositeSourceModel.get_rlzs_assoc`.
:attr realizations: list of :class:`LtRealization` objects
:attr gsim_by_trt: list of dictionaries {trt: gsim}
:attr rlzs_assoc: dictionary {src_group_id, gsim: rlzs}
:attr rlzs_by_smodel: list of lists of realizations
For instance, for the non-trivial logic tree in
:mod:`openquake.qa_tests_data.classical.case_15`, which has 4 tectonic
region types and 4 + 2 + 2 realizations, there are the following
associations:
(0, 'BooreAtkinson2008()') ['#0-SM1-BA2008_C2003', '#1-SM1-BA2008_T2002']
(0, 'CampbellBozorgnia2008()') ['#2-SM1-CB2008_C2003', '#3-SM1-CB2008_T2002']
(1, 'Campbell2003()') ['#0-SM1-BA2008_C2003', '#2-SM1-CB2008_C2003']
(1, 'ToroEtAl2002()') ['#1-SM1-BA2008_T2002', '#3-SM1-CB2008_T2002']
(2, 'BooreAtkinson2008()') ['#4-SM2_a3pt2b0pt8-BA2008']
(2, 'CampbellBozorgnia2008()') ['#5-SM2_a3pt2b0pt8-CB2008']
(3, 'BooreAtkinson2008()') ['#6-SM2_a3b1-BA2008']
(3, 'CampbellBozorgnia2008()') ['#7-SM2_a3b1-CB2008']
"""
def __init__(self, csm_info):
self.seed = csm_info.seed
self.num_samples = csm_info.num_samples
self.rlzs_assoc = collections.defaultdict(list)
self.gsim_by_trt = [] # rlz.ordinal -> {trt: gsim}
self.rlzs_by_smodel = {sm.ordinal: [] for sm in csm_info.source_models}
self.gsims_by_grp_id = {}
self.sm_ids = {}
self.samples = {}
for sm in csm_info.source_models:
for sg in sm.src_groups:
self.sm_ids[sg.id] = sm.ordinal
self.samples[sg.id] = sm.samples
def _init(self):
"""
Finalize the initialization of the RlzsAssoc object by setting
the (reduced) weights of the realizations and the attribute
gsims_by_grp_id.
"""
if self.num_samples:
assert len(self.realizations) == self.num_samples, (
len(self.realizations), self.num_samples)
for rlz in self.realizations:
rlz.weight = 1. / self.num_samples
else:
tot_weight = sum(rlz.weight for rlz in self.realizations)
if tot_weight == 0:
raise ValueError('All realizations have zero weight??')
elif abs(tot_weight - 1) > 1E-8:
# this may happen for rounding errors or because of the
# logic tree reduction; we ensure the sum of the weights is 1
for rlz in self.realizations:
rlz.weight = rlz.weight / tot_weight
self.gsims_by_grp_id = groupby(
self.rlzs_assoc, operator.itemgetter(0),
lambda group: sorted(gsim for grp_id, gsim in group))
@property
def realizations(self):
"""Flat list with all the realizations"""
return sum(self.rlzs_by_smodel.values(), [])
[docs] def get_rlz(self, rlzstr):
"""
Get a Realization instance for a string of the form 'rlz-\d+'
"""
mo = re.match('rlz-(\d+)', rlzstr)
if not mo:
return
return self.realizations[int(mo.group(1))]
[docs] def get_rlzs_by_grp_id(self):
"""
Returns a dictionary grp_id > [sorted rlzs]
"""
rlzs_by_grp_id = collections.defaultdict(set)
for (grp_id, gsim), rlzs in self.rlzs_assoc.items():
rlzs_by_grp_id[grp_id].update(rlzs)
return {grp_id: sorted(rlzs)
for grp_id, rlzs in rlzs_by_grp_id.items()}
def _add_realizations(self, idx, lt_model, gsim_lt, gsim_rlzs):
trts = gsim_lt.tectonic_region_types
rlzs = []
for i, gsim_rlz in enumerate(gsim_rlzs):
weight = float(lt_model.weight) * float(gsim_rlz.weight)
rlz = LtRealization(idx[i], lt_model.path, gsim_rlz, weight, i)
self.gsim_by_trt.append(
dict(zip(gsim_lt.all_trts, gsim_rlz.value)))
for src_group in lt_model.src_groups:
if src_group.trt in trts:
# ignore the associations to discarded TRTs
gs = gsim_lt.get_gsim_by_trt(gsim_rlz, src_group.trt)
self.rlzs_assoc[src_group.id, gs].append(rlz)
rlzs.append(rlz)
self.rlzs_by_smodel[lt_model.ordinal] = rlzs
def __iter__(self):
return iter(self.rlzs_assoc)
def __getitem__(self, key):
return self.rlzs_assoc[key]
def __len__(self):
return len(self.rlzs_assoc)
def __repr__(self):
pairs = []
for key in sorted(self.rlzs_assoc):
rlzs = list(map(str, self.rlzs_assoc[key]))
if len(rlzs) > 10: # short representation
rlzs = ['%d realizations' % len(rlzs)]
pairs.append(('%s,%s' % key, rlzs))
return '<%s(size=%d, rlzs=%d)\n%s>' % (
self.__class__.__name__, len(self), len(self.realizations),
'\n'.join('%s: %s' % pair for pair in pairs))
LENGTH = 256
source_model_dt = numpy.dtype([
('name', hdf5.vstr),
('weight', F32),
('path', hdf5.vstr),
('num_rlzs', U32),
('samples', U32),
])
src_group_dt = numpy.dtype(
[('grp_id', U32),
('trti', U16),
('effrup', I32),
('sm_id', U32)])
[docs]class CompositionInfo(object):
"""
An object to collect information about the composition of
a composite source model.
:param source_model_lt: a SourceModelLogicTree object
:param source_models: a list of SourceModel instances
"""
@classmethod
[docs] def fake(cls, gsimlt=None):
"""
:returns:
a fake `CompositionInfo` instance with the given gsim logic tree
object; if None, builds automatically a fake gsim logic tree
"""
weight = 1
gsim_lt = gsimlt or logictree.GsimLogicTree.from_('FromFile')
fakeSM = sourceconverter.SourceModel(
'fake', weight, 'b1',
[sourceconverter.SourceGroup('*', eff_ruptures=1)],
gsim_lt.get_num_paths(), ordinal=0, samples=1)
return cls(gsim_lt, seed=0, num_samples=0, source_models=[fakeSM],
tot_weight=0)
def __init__(self, gsim_lt, seed, num_samples, source_models, tot_weight):
self.gsim_lt = gsim_lt
self.seed = seed
self.num_samples = num_samples
self.source_models = source_models
self.tot_weight = tot_weight
[docs] def get_info(self, sm_id):
"""
Extract a CompositionInfo instance containing the single
model of index `sm_id`.
"""
sm = self.source_models[sm_id]
num_samples = sm.samples if self.num_samples else 0
return self.__class__(
self.gsim_lt, self.seed, num_samples, [sm], self.tot_weight)
def __getnewargs__(self):
# with this CompositionInfo instances will be unpickled correctly
return self.seed, self.num_samples, self.source_models
def __toh5__(self):
trts = sorted(set(src_group.trt for sm in self.source_models
for src_group in sm.src_groups))
trti = {trt: i for i, trt in enumerate(trts)}
data = []
for sm in self.source_models:
for src_group in sm.src_groups:
# the number of effective realizations is set by get_rlzs_assoc
data.append((src_group.id, trti[src_group.trt],
src_group.eff_ruptures, sm.ordinal))
lst = [(sm.name, sm.weight, '_'.join(sm.path),
sm.num_gsim_paths, sm.samples)
for i, sm in enumerate(self.source_models)]
return (dict(
sg_data=numpy.array(data, src_group_dt),
sm_data=numpy.array(lst, source_model_dt)),
dict(seed=self.seed, num_samples=self.num_samples,
trts=hdf5.array_of_vstr(trts),
gsim_lt_xml=str(self.gsim_lt),
gsim_fname=self.gsim_lt.fname,
tot_weight=self.tot_weight))
def __fromh5__(self, dic, attrs):
sg_data = group_array(dic['sg_data'], 'sm_id')
sm_data = dic['sm_data']
vars(self).update(attrs)
self.gsim_fname = decode(self.gsim_fname)
if self.gsim_fname.endswith('.xml'):
self.gsim_lt = logictree.GsimLogicTree(
self.gsim_fname, sorted(self.trts))
else: # fake file with the name of the GSIM
self.gsim_lt = logictree.GsimLogicTree.from_(self.gsim_fname)
self.source_models = []
for sm_id, rec in enumerate(sm_data):
tdata = sg_data[sm_id]
srcgroups = [
sourceconverter.SourceGroup(
self.trts[trti], id=grp_id, eff_ruptures=effrup)
for grp_id, trti, effrup, sm_id in tdata if effrup]
path = tuple(str(decode(rec['path'])).split('_'))
trts = set(sg.trt for sg in srcgroups)
num_gsim_paths = self.gsim_lt.reduce(trts).get_num_paths()
sm = sourceconverter.SourceModel(
rec['name'], rec['weight'], path, srcgroups,
num_gsim_paths, sm_id, rec['samples'])
self.source_models.append(sm)
[docs] def get_num_rlzs(self, source_model=None):
"""
:param source_model: a SourceModel instance (or None)
:returns: the number of realizations per source model (or all)
"""
if source_model is None:
return sum(self.get_num_rlzs(sm) for sm in self.source_models)
if self.num_samples:
return source_model.samples
trts = set(sg.trt for sg in source_model.src_groups)
return self.gsim_lt.reduce(trts).get_num_paths()
[docs] def get_rlzs_assoc(self, count_ruptures=None):
"""
Return a RlzsAssoc with fields realizations, gsim_by_trt,
rlz_idx and trt_gsims.
:param count_ruptures: a function src_group -> num_ruptures
"""
assoc = RlzsAssoc(self)
random_seed = self.seed
idx = 0
trtset = set(self.gsim_lt.tectonic_region_types)
for i, smodel in enumerate(self.source_models):
# collect the effective tectonic region types and ruptures
trts = set()
for sg in smodel.src_groups:
if count_ruptures:
sg.eff_ruptures = count_ruptures(sg)
if sg.eff_ruptures:
trts.add(sg.trt)
# recompute the GSIM logic tree if needed
if trtset != trts:
before = self.gsim_lt.get_num_paths()
gsim_lt = self.gsim_lt.reduce(trts)
after = gsim_lt.get_num_paths()
if count_ruptures and before > after:
logging.warn('Reducing the logic tree of %s from %d to %d '
'realizations', smodel.name, before, after)
else:
gsim_lt = self.gsim_lt
if self.num_samples: # sampling
# the int is needed on Windows to convert numpy.uint32 objects
rnd = random.Random(int(random_seed + idx))
rlzs = logictree.sample(gsim_lt, smodel.samples, rnd)
else: # full enumeration
rlzs = logictree.get_effective_rlzs(gsim_lt)
if rlzs:
indices = numpy.arange(idx, idx + len(rlzs))
idx += len(indices)
assoc._add_realizations(indices, smodel, gsim_lt, rlzs)
elif trts:
logging.warn('No realizations for %s, %s',
'_'.join(smodel.path), smodel.name)
# NB: realizations could be filtered away by logic tree reduction
if assoc.realizations:
assoc._init()
return assoc
[docs] def get_source_model(self, src_group_id):
"""
Return the source model for the given src_group_id
"""
for smodel in self.source_models:
for src_group in smodel.src_groups:
if src_group.id == src_group_id:
return smodel
[docs] def get_grp_ids(self, sm_id):
"""
:returns: a list of source group IDs for the given source model ID
"""
return [sg.id for sg in self.source_models[sm_id].src_groups]
[docs] def get_sm_by_rlz(self, realizations):
"""
:returns: a dictionary rlz -> source model name
"""
dic = {}
for sm in self.source_models:
for rlz in realizations:
if rlz.sm_lt_path == sm.path:
dic[rlz] = sm.name
return dic
[docs] def get_sm_by_grp(self):
"""
:returns: a dictionary grp_id -> sm_id
"""
return {grp.id: sm.ordinal for sm in self.source_models
for grp in sm.src_groups}
[docs] def grp_trt(self):
"""
:returns: a dictionary grp_id -> TRT string
"""
dic = {}
for smodel in self.source_models:
for src_group in smodel.src_groups:
dic[src_group.id] = src_group.trt
return dic
def __repr__(self):
info_by_model = collections.OrderedDict()
for sm in self.source_models:
info_by_model[sm.path] = (
'_'.join(map(decode, sm.path)),
decode(sm.name),
[sg.id for sg in sm.src_groups],
sm.weight,
self.get_num_rlzs(sm))
summary = ['%s, %s, grp=%s, weight=%s: %d realization(s)' % ibm
for ibm in info_by_model.values()]
return '<%s\n%s>' % (
self.__class__.__name__, '\n'.join(summary))
[docs]class CompositeSourceModel(collections.Sequence):
"""
:param source_model_lt:
a :class:`openquake.commonlib.logictree.SourceModelLogicTree` instance
:param source_models:
a list of :class:`openquake.hazardlib.sourceconverter.SourceModel`
tuples
"""
def __init__(self, gsim_lt, source_model_lt, source_models,
set_weight=False):
self.gsim_lt = gsim_lt
self.source_model_lt = source_model_lt
self.source_models = source_models
self.source_info = ()
self.split_map = {}
if set_weight:
self.set_weights()
else:
self.weight = 0
# must go after set_weights to have the correct .num_ruptures
self.info = CompositionInfo(
gsim_lt, self.source_model_lt.seed,
self.source_model_lt.num_samples,
[sm.get_skeleton() for sm in self.source_models],
self.weight)
[docs] def get_model(self, sm_id):
"""
Extract a CompositeSourceModel instance containing the single
model of index `sm_id`.
"""
sm = self.source_models[sm_id]
if self.source_model_lt.num_samples:
self.source_model_lt.num_samples = sm.samples
new = self.__class__(
self.gsim_lt, self.source_model_lt, [sm], set_weight=False)
new.sm_id = sm_id
new.weight = sum(src.weight for sg in sm.src_groups
for src in sg.sources)
return new
[docs] def filter(self, src_filter):
"""
Generate a new CompositeSourceModel by filtering the sources on
the given site collection.
:param sitecol: a SiteCollection instance
:para src_filter: a SourceFilter instance
"""
source_models = []
weight = 0
for sm in self.source_models:
src_groups = [copy.copy(src) for src in sm.src_groups]
for src_group in src_groups:
sources = []
for src, sites in src_filter(src_group.sources):
sources.append(src)
weight += src.weight
src_group.sources = sources
newsm = sourceconverter.SourceModel(
sm.name, sm.weight, sm.path, src_groups,
sm.num_gsim_paths, sm.ordinal, sm.samples)
source_models.append(newsm)
new = self.__class__(self.gsim_lt, self.source_model_lt, source_models,
set_weight=True)
return new
@property
def src_groups(self):
"""
Yields the SourceGroups inside each source model.
"""
for sm in self.source_models:
for src_group in sm.src_groups:
yield src_group
[docs] def get_sources(self, kind='all', maxweight=None):
"""
Extract the sources contained in the source models by optionally
filtering and splitting them, depending on the passed parameters.
"""
if kind != 'all':
assert kind in ('light', 'heavy') and maxweight is not None, (
kind, maxweight)
sources = []
for src_group in self.src_groups:
for src in src_group:
if kind == 'all':
sources.append(src)
elif kind == 'light' and src.weight <= maxweight:
sources.append(src)
elif kind == 'heavy' and src.weight > maxweight:
sources.append(src)
return sources
[docs] def get_num_sources(self):
"""
:returns: the total number of sources in the model
"""
return sum(len(src_group) for src_group in self.src_groups)
[docs] def set_weights(self):
"""
Update the attributes .weight and src.num_ruptures for each TRT model
.weight of the CompositeSourceModel.
"""
self.weight = 0
for src_group in self.src_groups:
weight = 0
num_ruptures = 0
for src in src_group:
weight += src.weight
num_ruptures += src.num_ruptures
src_group.weight = weight
src_group.sources = sorted(
src_group, key=operator.attrgetter('source_id'))
self.weight += weight
[docs] def init_serials(self):
"""
Generate unique seeds for each rupture with numpy.arange.
This should be called only in event based calculators
"""
n = sum(sg.tot_ruptures() for sg in self.src_groups)
rup_serial = numpy.arange(n, dtype=numpy.uint32)
start = 0
for src in self.get_sources():
nr = src.num_ruptures
src.serial = rup_serial[start:start + nr]
start += nr
[docs] def get_maxweight(self, concurrent_tasks):
"""
Return an appropriate maxweight for use in the block_splitter
"""
ct = concurrent_tasks or 1
return max(math.ceil(self.weight / ct), MAXWEIGHT)
def __repr__(self):
"""
Return a string representation of the composite model
"""
models = ['%d-%s-%s,w=%s [%d src_group(s)]' % (
sm.ordinal, sm.name, '_'.join(sm.path), sm.weight,
len(sm.src_groups)) for sm in self.source_models]
return '<%s\n%s>' % (self.__class__.__name__, '\n'.join(models))
def __getitem__(self, i):
"""Return the i-th source model"""
return self.source_models[i]
def __iter__(self):
"""Return an iterator over the underlying source models"""
return iter(self.source_models)
def __len__(self):
"""Return the number of underlying source models"""
return len(self.source_models)
[docs]def collect_source_model_paths(smlt):
"""
Given a path to a source model logic tree or a file-like, collect all of
the soft-linked path names to the source models it contains and return them
as a uniquified list (no duplicates).
:param smlt: source model logic tree file
"""
n = nrml.read(smlt)
try:
blevels = n.logicTree
except:
raise InvalidFile('%s is not a valid source_model_logic_tree_file'
% smlt)
for blevel in blevels:
with node.context(smlt, blevel):
for bset in blevel:
for br in bset:
smfname = br.uncertaintyModel.text.strip()
if smfname:
yield smfname
# ########################## SourceManager ########################### #
[docs]class SourceInfo(object):
dt = numpy.dtype([
('grp_id', numpy.uint32), # 0
('source_id', (bytes, 100)), # 1
('source_class', (bytes, 30)), # 2
('num_ruptures', numpy.uint32), # 3
('calc_time', numpy.float32), # 4
('num_sites', numpy.uint32), # 5
('num_split', numpy.uint32), # 6
])
def __init__(self, src, calc_time=0, num_split=0):
self.grp_id = src.src_group_id
self.source_id = src.source_id
self.source_class = src.__class__.__name__
self.num_ruptures = src.num_ruptures
self.num_sites = src.nsites
self.calc_time = calc_time
self.num_split = num_split