# -*- coding: utf-8 -*-
# vim: tabstop=4 shiftwidth=4 softtabstop=4
#
# Copyright (C) 2010-2023 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/>.
"""
Logic tree parser, verifier and processor. See specs at
https://blueprints.launchpad.net/openquake-old/+spec/openquake-logic-tree-module
A logic tree object must be iterable and yielding realizations, i.e. objects
with attributes `value`, `weight`, `lt_path` and `ordinal`.
"""
import os
import re
import ast
import copy
import json
import time
import logging
import functools
import itertools
import collections
import operator
import numpy
from openquake.baselib import hdf5, node
from openquake.baselib.python3compat import decode
from openquake.baselib.node import node_from_elem, context, Node
from openquake.baselib.general import groupby, group_array, AccumDict, BASE183
from openquake.hazardlib import nrml, InvalidFile, pmf, valid
from openquake.hazardlib.sourceconverter import SourceGroup
from openquake.hazardlib.gsim_lt import (
GsimLogicTree, bsnodes, fix_bytes, keyno, abs_paths, ImtWeight)
from openquake.hazardlib.lt import (
Branch, BranchSet, count_paths, Realization, CompositeLogicTree,
dummy_branchset, LogicTreeError, parse_uncertainty, random)
U16 = numpy.uint16
U32 = numpy.uint32
I32 = numpy.int32
F32 = numpy.float32
TWO24 = 2 ** 24
rlz_dt = numpy.dtype([
('ordinal', U32),
('branch_path', hdf5.vstr),
('weight', F32),
])
source_dt = numpy.dtype([
('branch', hdf5.vstr),
('trt', hdf5.vstr),
('fname', hdf5.vstr), # useful to reduce the XML files
('source', hdf5.vstr),
])
source_model_dt = numpy.dtype([
('name', hdf5.vstr),
('weight', F32),
('path', hdf5.vstr),
('samples', U32),
])
src_group_dt = numpy.dtype(
[('trt_smr', U32),
('name', hdf5.vstr),
('trti', U16),
('effrup', I32),
('totrup', I32),
('sm_id', U32),
])
branch_dt = numpy.dtype([
('branchset', hdf5.vstr),
('branch', hdf5.vstr),
('utype', hdf5.vstr),
('uvalue', hdf5.vstr),
('weight', float),
])
TRT_REGEX = re.compile(r'tectonicRegion="([^"]+?)"')
ID_REGEX = re.compile(r'Source\s+id="([^"]+?)"')
# this is very fast
[docs]def get_trt_by_src(source_model_file, source_id=''):
"""
:returns: a dictionary source ID -> tectonic region type of the source
"""
xml = source_model_file.read()
trt_by_src = {}
if "http://openquake.org/xmlns/nrml/0.5" in xml:
# fast lane using regex, tectonicRegion is always before Source id
pieces = TRT_REGEX.split(xml.replace("'", '"')) # fix single quotes
for text, trt in zip(pieces[2::2], pieces[1::2]):
for src_id in ID_REGEX.findall(text):
# disagg/case_12
src_id = src_id.split(':')[0] # colon convention
if source_id:
if source_id == src_id:
trt_by_src[src_id] = trt
else:
trt_by_src[src_id] = trt
else: # parse the XML with ElementTree
for src in node.fromstring(xml)[0]:
src_id = src.attrib['id'].split(':')[0] # colon convention
if source_id:
if source_id == src_id:
trt_by_src[src_id] = src.attrib['tectonicRegion']
else:
trt_by_src[src_id] = src.attrib['tectonicRegion']
return trt_by_src
[docs]def prod(iterator):
"""
Replacement of math.prod for Python < 3.8
"""
res = 1
for el in iterator:
res *= el
return res
[docs]def unique(objects, key=None):
"""
Raise a ValueError if there is a duplicated object, otherwise
returns the objects as they are.
"""
dupl = []
for obj, group in itertools.groupby(sorted(objects), key):
if sum(1 for _ in group) > 1:
dupl.append(obj)
if dupl:
raise ValueError('Found duplicates %s' % dupl)
return objects
[docs]@functools.lru_cache()
def get_effective_rlzs(rlzs):
"""
Group together realizations with the same path
and yield the first representative of each group.
:param rlzs: a list of Realization instances with a .pid property
"""
effective = []
ordinal = 0
for group in groupby(rlzs, operator.attrgetter('pid')).values():
rlz = group[0]
effective.append(
Realization(rlz.value, sum(r.weight for r in group),
ordinal, rlz.lt_path, len(group)))
ordinal += 1
return effective
[docs]def get_eff_rlzs(sm_rlzs, gsim_rlzs):
"""
Group together realizations with the same path
and yield the first representative of each group
"""
triples = [] # pid, sm_rlz, gsim_rlz
for sm_rlz, gsim_rlz in zip(sm_rlzs, gsim_rlzs):
triples.append((sm_rlz.pid + '~' + gsim_rlz.pid, sm_rlz, gsim_rlz))
ordinal = 0
effective = []
for rows in groupby(triples, operator.itemgetter(0)).values():
pid, sm_rlz, gsim_rlz = rows[0]
weight = ImtWeight.new(len(rows) / len(triples))
effective.append(
LtRealization(ordinal, sm_rlz.lt_path, gsim_rlz, weight))
ordinal += 1
return effective
Info = collections.namedtuple('Info', 'smpaths h5paths applytosources')
[docs]def collect_info(smltpath, branchID=''):
"""
Given a path to a source model logic tree, collect all of the
path names to the source models it contains.
:param smltpath: source model logic tree file
:param branchID: if given, consider only that branch
:returns: an Info namedtuple (smpaths, h5paths, applytosources)
"""
n = nrml.read(smltpath)
try:
blevels = n.logicTree
except Exception:
raise InvalidFile('%s is not a valid source_model_logic_tree_file'
% smltpath)
smpaths = set()
h5paths = set()
applytosources = collections.defaultdict(list) # branchID -> source IDs
for blevel in blevels:
for bset in bsnodes(smltpath, blevel):
if 'applyToSources' in bset.attrib:
applytosources[bset.get('applyToBranches')].extend(
bset['applyToSources'].split())
if bset['uncertaintyType'] in 'sourceModel extendModel':
for br in bset:
if branchID and branchID != br['branchID']:
continue
with context(smltpath, br):
fnames = abs_paths(
smltpath, unique(br.uncertaintyModel.text.split()))
smpaths.update(fnames)
for fname in fnames:
hdf5file = os.path.splitext(fname)[0] + '.hdf5'
if os.path.exists(hdf5file):
h5paths.add(hdf5file)
if os.environ.get('OQ_REDUCE'): # only take first branch
break
return Info(sorted(smpaths), sorted(h5paths), applytosources)
[docs]def reduce_fnames(fnames, source_id):
"""
If the source ID is ambiguous (i.e. there is "!") only returns
the filenames containing the source, otherwise return all the filenames
"""
try:
srcid, fname = source_id.split('!')
except ValueError:
return fnames
return [f for f in fnames if fname in f]
[docs]def read_source_groups(fname):
"""
:param fname: a path to a source model XML file
:return: a list of SourceGroup objects containing source nodes
"""
smodel = nrml.read(fname).sourceModel
src_groups = []
if smodel[0].tag.endswith('sourceGroup'): # NRML 0.5 format
for sg_node in smodel:
sg = SourceGroup(sg_node['tectonicRegion'])
sg.sources = sg_node.nodes
src_groups.append(sg)
else: # NRML 0.4 format: smodel is a list of source nodes
src_groups.extend(SourceGroup.collect(smodel))
return src_groups
[docs]def shorten(path_tuple, shortener):
"""
:path: sequence of strings
:shortener: dictionary longstring -> shortstring
:returns: shortened version of the path
"""
if len(shortener) == 1:
return 'A'
chars = []
for key in path_tuple:
if key[0] == '.': # dummy branch
chars.append('.')
else:
chars.append(shortener[key][0])
return ''.join(chars)
# useful to print reduced logic trees
[docs]def collect_paths(paths, b1=ord('['), b2=ord(']'), til=ord('~')):
"""
Collect branch paths belonging to the same cluster
>>> collect_paths([b'0~A0', b'0~A1'])
b'[0]~[A][01]'
"""
n = len(paths[0])
for path in paths[1:]:
assert len(path) == n, (len(path), n)
sets = [set() for _ in range(n)]
for c, s in enumerate(sets):
for path in paths:
s.add(path[c])
ints = []
for s in sets:
chars = sorted(s)
if chars != [til]:
ints.append(b1)
ints.extend(chars)
if chars != [til]:
ints.append(b2)
return bytes(ints)
[docs]def reducible(lt, cluster_paths):
"""
:param lt: a logic tree with B branches
:param cluster_paths: list of paths for a realization cluster
:returns: a list [filename, (branchSetID, branchIDs), ...]
"""
longener = {short: long for long, short in lt.shortener.items()}
tuplesets = [set() for _ in lt.bsetdict]
for path in cluster_paths:
for b, chars in enumerate(path.strip('][').split('][')):
tuplesets[b].add(tuple(c + str(i) for i, c in enumerate(chars)))
res = [lt.filename]
for bs, tupleset in zip(sorted(lt.bsetdict), tuplesets):
# a branch is reducible if there is the same combinations for all paths
try:
[br_ids] = tupleset
except ValueError:
continue
res.append((bs, [longener[brid] for brid in br_ids]))
return res
# this is not used right now, but tested
[docs]def reduce_full(full_lt, rlz_clusters):
"""
:param full_lt: a FullLogicTree instance
:param rlz_clusters: list of paths for a realization cluster
:returns: a dictionary with what can be reduced
"""
smrlz_clusters = []
gsrlz_clusters = []
for path in rlz_clusters:
smr, gsr = decode(path).split('~')
smrlz_clusters.append(smr)
gsrlz_clusters.append(gsr)
f1, *p1 = reducible(full_lt.source_model_lt, smrlz_clusters)
f2, *p2 = reducible(full_lt.gsim_lt, gsrlz_clusters)
before = (full_lt.source_model_lt.get_num_paths() *
full_lt.gsim_lt.get_num_paths())
after = before / prod(len(p[1]) for p in p1 + p2)
return {f1: dict(p1), f2: dict(p2), 'size_before_after': (before, after)}
[docs]class SourceModelLogicTree(object):
"""
Source model logic tree parser.
:param filename:
Full pathname of logic tree file
:raises LogicTreeError:
If logic tree file has a logic error, which can not be prevented
by xml schema rules (like referencing sources with missing id).
"""
_xmlschema = None
FILTERS = ('applyToTectonicRegionType',
'applyToSources',
'applyToBranches')
ABSOLUTE_UNCERTAINTIES = ('abGRAbsolute', 'bGRAbsolute',
'maxMagGRAbsolute',
'simpleFaultGeometryAbsolute',
'truncatedGRFromSlipAbsolute',
'complexFaultGeometryAbsolute',
'setMSRAbsolute')
[docs] @classmethod
def fake(cls):
"""
:returns: a fake SourceModelLogicTree with a single branch
"""
self = object.__new__(cls)
arr = numpy.array([('bs0', 'b0', 'sourceModel', 'fake.xml', 1)],
branch_dt)
dic = dict(filename='fake.xml', seed=0, num_samples=0,
sampling_method='early_weights', num_paths=1,
is_source_specific=0, source_data=[],
tectonic_region_types=set(), source_id='', branchID='',
bsetdict='{"bs0": {"uncertaintyType": "sourceModel"}}')
self.__fromh5__(arr, dic)
return self
def __init__(self, filename, seed=0, num_samples=0,
sampling_method='early_weights', test_mode=False,
branchID='', source_id=''):
self.filename = filename
self.basepath = os.path.dirname(filename)
# NB: converting the random_seed into an integer is needed on Windows
self.seed = int(seed)
self.num_samples = num_samples
self.sampling_method = sampling_method
self.test_mode = test_mode
self.branchID = branchID # used to read only one sourceModel branch
self.source_id = source_id
self.branches = {} # branch_id -> branch
self.bsetdict = {}
self.previous_branches = []
self.tectonic_region_types = set()
self.root_branchset = None
root = nrml.read(filename)
try:
tree = root.logicTree
except AttributeError:
raise LogicTreeError(
root, self.filename, "missing logicTree node")
self.shortener = {}
self.branchsets = []
self.parse_tree(tree)
self.set_num_paths()
[docs] def set_num_paths(self):
"""
Count the total number of paths in a smart way
"""
# determine if the logic tree is source specific
dicts = list(self.bsetdict.values())[1:]
if not dicts:
self.is_source_specific = False
self.num_paths = count_paths(self.root_branchset.branches)
return
src_ids = set()
for dic in dicts:
ats = dic.get('applyToSources')
if not ats:
self.is_source_specific = False
self.num_paths = count_paths(self.root_branchset.branches)
return
elif len(ats.split()) != 1:
self.is_source_specific = False
self.num_paths = count_paths(self.root_branchset.branches)
return
src_ids.add(ats)
# to be source-specific applyToBranches must be trivial
self.is_source_specific = all(
bset.applied is None for bset in self.branchsets)
if self.is_source_specific:
# fast algorithm, otherwise models like ZAF would hang
self.num_paths = prod(
sslt.num_paths for sslt in self.decompose().values())
else: # slow algorithm
self.num_paths = count_paths(self.root_branchset.branches)
[docs] def reduce(self, source_id, num_samples=None):
"""
:returns: a new logic tree reduced to a single source
"""
num_samples = self.num_samples if num_samples is None else num_samples
new = self.__class__(self.filename, self.seed, num_samples,
self.sampling_method, self.test_mode,
self.branchID, source_id)
return new
[docs] def parse_tree(self, tree_node):
"""
Parse the whole tree and point ``root_branchset`` attribute
to the tree's root.
"""
t0 = time.time()
self.info = collect_info(self.filename, self.branchID)
# the list is populated in collect_source_model_data
self.source_data = []
for bsno, bnode in enumerate(tree_node.nodes):
[bsnode] = bsnodes(self.filename, bnode)
self.parse_branchset(bsnode, bsno)
self.source_data = numpy.array(self.source_data, source_dt)
unique = numpy.unique(self.source_data['fname'])
dt = time.time() - t0
logging.debug('Validated source model logic tree with %d underlying '
'files in %.2f seconds', len(unique), dt)
[docs] def parse_branchset(self, branchset_node, bsno):
"""
:param branchset_ node:
``etree.Element`` object with tag "logicTreeBranchSet".
:param bsno:
The sequential number of the branchset, starting from 0.
Enumerates children branchsets and call :meth:`parse_branchset`,
:meth:`validate_branchset`, :meth:`parse_branches` and finally
:meth:`apply_branchset` for each.
Keeps track of "open ends" -- the set of branches that don't have
any child branchset on this step of execution. After processing
of every branchset only those branches that are listed in it
can have child branchsets (if there is one on the next level).
"""
attrs = branchset_node.attrib.copy()
uncertainty_type = branchset_node.attrib.get('uncertaintyType')
dic = dict((filtername, branchset_node.attrib.get(filtername))
for filtername in self.FILTERS
if filtername in branchset_node.attrib)
self.validate_filters(branchset_node, uncertainty_type, dic)
filters = self.parse_filters(branchset_node, uncertainty_type, dic)
if 'applyToSources' in filters and not filters['applyToSources']:
return # ignore the branchset
ordinal = len(self.bsetdict)
branchset = BranchSet(uncertainty_type, ordinal, filters)
branchset.id = bsid = attrs.pop('branchSetID')
if bsid in self.bsetdict:
raise nrml.DuplicatedID('%s in %s' % (bsid, self.filename))
self.bsetdict[bsid] = attrs
self.validate_branchset(branchset_node, bsno, branchset)
self.parse_branches(branchset_node, branchset)
dummies = [] # dummy branches in case of applyToBranches
if self.root_branchset is None: # not set yet
self.root_branchset = branchset
if not branchset.branches:
del self.bsetdict[bsid]
return
prev_ids = ' '.join(pb.branch_id for pb in self.previous_branches)
app2brs = branchset_node.attrib.get('applyToBranches') or prev_ids
missing = set(prev_ids.split()) - set(app2brs.split())
if missing:
# apply only to some branches
branchset.applied = app2brs
self.apply_branchset(
app2brs, branchset_node.lineno, branchset)
not_applied = set(prev_ids.split()) - set(app2brs.split())
for brid in not_applied:
if brid in self.branches:
self.branches[brid].bset = dummy = dummy_branchset()
[dummybranch] = dummy.branches
self.branches[dummybranch.branch_id] = dummybranch
dummies.append(dummybranch)
else:
# apply to all previous branches
for branch in self.previous_branches:
branch.bset = branchset
self.previous_branches = branchset.branches + dummies
self.branchsets.append(branchset)
[docs] def parse_branches(self, branchset_node, branchset):
"""
Create and attach branches at ``branchset_node`` to ``branchset``.
:param branchset_node:
Same as for :meth:`parse_branchset`.
:param branchset:
An instance of :class:`BranchSet`.
Checks that each branch has :meth:`valid <validate_uncertainty_value>`
value, unique id and that all branches have total weight of 1.0.
:return:
``None``, all branches are attached to provided branchset.
"""
bs_id = branchset_node['branchSetID']
weight_sum = 0
branches = branchset_node.nodes
if os.environ.get('OQ_REDUCE'): # only take first branch
branches = [branches[0]]
branches[0].uncertaintyWeight.text = 1.
values = []
bsno = len(self.branchsets)
zeros = []
if len(branches) > len(BASE183):
msg = ('%s: the branchset %s has too many branches (%d > %d)\n'
'you should split it, see https://docs.openquake.org/'
'oq-engine/advanced/latest/logic_trees.html')
raise InvalidFile(
msg % (self.filename, bs_id, len(branches), len(BASE183)))
for brno, branchnode in enumerate(branches):
weight = ~branchnode.uncertaintyWeight
value_node = node_from_elem(branchnode.uncertaintyModel)
if value_node.text is not None:
values.append(value_node.text.strip())
value = parse_uncertainty(branchset.uncertainty_type,
value_node, self.filename)
if branchset.uncertainty_type in ('sourceModel', 'extendModel'):
vals = [] # filenames with sources in it
try:
for fname in value_node.text.split():
if (fname.endswith(('.xml', '.nrml'))
and not self.test_mode):
ok = self.collect_source_model_data(
branchnode['branchID'], fname)
if ok:
vals.append(fname)
except Exception as exc:
raise LogicTreeError(
value_node, self.filename, str(exc)) from exc
if self.branchID and branchnode['branchID'] != self.branchID:
value = '' # reduce all branches except branchID
elif self.source_id: # only the files containing source_id
value = ' '.join(reduce_fnames(vals, self.source_id))
branch_id = branchnode.attrib.get('branchID')
if branch_id in self.branches:
raise LogicTreeError(
branchnode, self.filename,
"branchID '%s' is not unique" % branch_id)
if value == '':
# with logic tree reduction a branch can be empty
# see case_68_bis
zero_id = branch_id
zeros.append(weight)
else:
branch = Branch(bs_id, branch_id, weight, value)
self.branches[branch_id] = branch
branchset.branches.append(branch)
self.shortener[branch_id] = keyno(branch_id, bsno, brno)
weight_sum += weight
if zeros:
branch = Branch(bs_id, zero_id, sum(zeros), '')
self.branches[branch_id] = branch
branchset.branches.append(branch)
if abs(weight_sum - 1.0) > pmf.PRECISION:
raise LogicTreeError(
branchset_node, self.filename,
"branchset weights don't sum up to 1.0")
if ''.join(values) and len(set(values)) < len(values):
raise LogicTreeError(
branchset_node, self.filename,
"there are duplicate values in uncertaintyModel: " +
' '.join(values))
[docs] def get_num_paths(self):
"""
:returns: the number of paths in the logic tree
"""
return self.num_samples if self.num_samples else self.num_paths
def __iter__(self):
"""
Yield Realization tuples. Notice that the weight is homogeneous when
sampling is enabled, since it is accounted for in the sampling
procedure.
"""
if self.num_samples:
# random sampling of the logic tree
probs = random((self.num_samples, len(self.bsetdict)),
self.seed, self.sampling_method)
ordinal = 0
for branches in self.root_branchset.sample(
probs, self.sampling_method):
value = [br.value for br in branches]
smlt_path_ids = [br.branch_id for br in branches]
if self.sampling_method.startswith('early_'):
weight = 1. / self.num_samples # already accounted
elif self.sampling_method.startswith('late_'):
weight = numpy.prod([br.weight for br in branches])
else:
raise NotImplementedError(self.sampling_method)
yield Realization(value, weight, ordinal, tuple(smlt_path_ids))
ordinal += 1
else: # full enumeration
ordinal = 0
for weight, branches in self.root_branchset.enumerate_paths():
value = [br.value for br in branches]
branch_ids = [branch.branch_id for branch in branches]
yield Realization(value, weight, ordinal, tuple(branch_ids))
ordinal += 1
[docs] def parse_filters(self, branchset_node, uncertainty_type, filters):
"""
Converts "applyToSources" and "applyToBranches" filters by
splitting into lists.
"""
if 'applyToSources' in filters:
srcs = filters['applyToSources'].split()
if self.source_id:
srcs = [src for src in srcs if src == self.source_id]
filters['applyToSources'] = srcs
if 'applyToBranches' in filters:
filters['applyToBranches'] = filters['applyToBranches'].split()
return filters
[docs] def validate_filters(self, branchset_node, uncertainty_type, filters):
"""
See superclass' method for description and signature specification.
Checks that the following conditions are met:
* "sourceModel" uncertainties can not have filters.
* Absolute uncertainties must have only one filter --
"applyToSources", with only one source id.
* All other uncertainty types can have either no or one filter.
* Filter "applyToSources" must mention only source ids that
exist in source models.
* Filter "applyToTectonicRegionType" must mention only tectonic
region types that exist in source models.
"""
f = filters.copy()
if 'applyToBranches' in f:
del f['applyToBranches']
if uncertainty_type == 'sourceModel' and f:
raise LogicTreeError(
branchset_node, self.filename,
'filters are not allowed on source model uncertainty')
if len(f) > 1:
raise LogicTreeError(
branchset_node, self.filename,
"only one filter is allowed per branchset")
if 'applyToTectonicRegionType' in f:
if f['applyToTectonicRegionType'] \
not in self.tectonic_region_types:
raise LogicTreeError(
branchset_node, self.filename,
"source models don't define sources of tectonic region "
"type '%s'" % f['applyToTectonicRegionType'])
if uncertainty_type in self.ABSOLUTE_UNCERTAINTIES:
if not f or not list(f) == ['applyToSources'] \
or not len(f['applyToSources'].split()) == 1:
raise LogicTreeError(
branchset_node, self.filename,
"uncertainty of type '%s' must define 'applyToSources' "
"with only one source id" % uncertainty_type)
if uncertainty_type in ('simpleFaultDipRelative',
'simpleFaultDipAbsolute'):
if not f or 'applyToSources' not in f:
raise LogicTreeError(
branchset_node, self.filename,
"uncertainty of type '%s' must define 'applyToSources'"
% uncertainty_type)
if 'applyToSources' in f:
if self.source_id:
srcids = [s for s in f['applyToSources'].split()
if s == self.source_id]
else:
srcids = f['applyToSources'].split()
for source_id in srcids:
branchIDs = {
brid for (brid, trt, fname, srcid) in self.source_data
if srcid == source_id}
if not branchIDs:
raise LogicTreeError(
branchset_node, self.filename,
"source with id '%s' is not defined in source "
"models" % source_id)
elif (len(branchIDs) > 1 and 'applyToBranches' not in
branchset_node.attrib):
raise LogicTreeError(
branchset_node, self.filename,
f"{source_id} belongs to multiple branches {branchIDs}"
": applyToBranches"" must be specified together with"
" applyToSources")
[docs] def validate_branchset(self, branchset_node, bsno, branchset):
"""
See superclass' method for description and signature specification.
Checks that the following conditions are met:
* First branching level must contain exactly one branchset, which
must be of type "sourceModel".
* All other branchsets must not be of type "sourceModel"
or "gmpeModel".
"""
if bsno == 0:
if branchset.uncertainty_type != 'sourceModel':
raise LogicTreeError(
branchset_node, self.filename,
'first branchset must define an uncertainty '
'of type "sourceModel"')
else:
if branchset.uncertainty_type == 'sourceModel':
raise LogicTreeError(
branchset_node, self.filename,
'uncertainty of type "sourceModel" can be defined '
'on first branchset only')
elif branchset.uncertainty_type == 'gmpeModel':
raise LogicTreeError(
branchset_node, self.filename,
'uncertainty of type "gmpeModel" is not allowed '
'in source model logic tree')
[docs] def apply_branchset(self, apply_to_branches, lineno, branchset):
"""
See superclass' method for description and signature specification.
Parses branchset node's attribute ``@applyToBranches`` to apply
following branchests to preceding branches selectively. Branching
level can have more than one branchset exactly for this: different
branchsets can apply to different open ends.
Checks that branchset tries to be applied only to branches on previous
branching level which do not have a child branchset yet.
"""
for branch_id in apply_to_branches.split():
if branch_id not in self.branches:
raise LogicTreeError(
lineno, self.filename,
"branch '%s' is not yet defined" % branch_id)
branch = self.branches[branch_id]
if not branch.is_leaf():
raise LogicTreeError(
lineno, self.filename,
"branch '%s' already has child branchset" % branch_id)
branch.bset = branchset
def _get_source_model(self, source_model_file):
# NB: do not remove this, it is meant to be overridden in the tests
return open(os.path.join(self.basepath, source_model_file),
encoding='utf-8')
[docs] def collect_source_model_data(self, branch_id, fname):
"""
Parse source model file and collect information about source ids,
source types and tectonic region types available in it. That
information is used then for :meth:`validate_filters` and
:meth:`validate_uncertainty_value`.
:param branch_id: source model logic tree branch ID
:param fname: relative filename for the current source model portion
:returns: the number of sources in the source model portion
"""
with self._get_source_model(fname) as sm:
trt_by_src = get_trt_by_src(sm, self.source_id.split('!')[0])
if self.basepath:
path = sm.name[len(self.basepath) + 1:]
else:
path = sm.name
for src_id, trt in trt_by_src.items():
try:
valid.source_id(src_id)
except ValueError:
raise InvalidFile(
'%s: contain invalid ID %s' % (sm.name, src_id))
self.source_data.append((branch_id, trt, path, src_id))
self.tectonic_region_types.add(trt)
return len(trt_by_src)
[docs] def bset_values(self, lt_path):
"""
:param sm_rlz: an effective realization
:returns: a list of B - 1 pairs (branchset, value)
"""
return self.root_branchset.get_bset_values(lt_path)[1:]
# used in the sslt page of the advanced manual
[docs] def decompose(self):
"""
If the logic tree is source specific, returns a dictionary
source ID -> SourceLogicTree instance
"""
assert self.is_source_specific
bsets = collections.defaultdict(list)
bsetdict = collections.defaultdict(dict)
for bset in self.branchsets[1:]:
if bset.filters['applyToSources']:
[src_id] = bset.filters['applyToSources']
bsets[src_id].append(bset)
bsetdict[src_id][bset.id] = self.bsetdict[bset.id]
root = self.branchsets[0]
if len(root) > 1:
out = {None: SourceLogicTree(None, [root], self.bsetdict[root.id])}
else:
out = {}
# src_id -> SourceLogicTree
for src_id in bsets:
out[src_id] = SourceLogicTree(
src_id, bsets[src_id], bsetdict[src_id])
return out
[docs] def to_node(self):
"""
:returns: a logicTree Node convertible into NRML format
"""
bsnodes = []
for bset in self.branchsets:
dic = dict(branchSetID='bs%02d' % bset.ordinal,
uncertaintyType=bset.uncertainty_type)
brnodes = []
for br in bset.branches:
um = Node('uncertaintyModel', {}, br.value)
uw = Node('uncertaintyWeight', {}, br.weight)
brnode = Node('logicTreeBranch', {'branchID': br.branch_id},
nodes=[um, uw])
brnodes.append(brnode)
bsnodes.append(Node('logicTreeBranchSet', dic, nodes=brnodes))
return Node('logicTree', {'logicTreeID': 'lt'}, nodes=bsnodes)
[docs] def get_duplicated_sources(self):
"""
:returns: {src_id: affected branches}
"""
sd = group_array(self.source_data, 'source')
u, c = numpy.unique(self.source_data['source'], return_counts=1)
# AUS event based was hanging with a slower implementation
return {src: sd[src]['branch'] for src in u[c > 1]}
# SourceModelLogicTree
def __toh5__(self):
tbl = []
for brid, br in self.branches.items():
if br.bs_id.startswith('dummy'):
continue # don't store dummy branches
dic = self.bsetdict[br.bs_id].copy()
utype = dic['uncertaintyType']
tbl.append((br.bs_id, brid, utype, repr(br.value), br.weight))
attrs = dict(bsetdict=json.dumps(self.bsetdict))
attrs['seed'] = self.seed
attrs['num_samples'] = self.num_samples
attrs['sampling_method'] = self.sampling_method
attrs['filename'] = self.filename
attrs['num_paths'] = self.num_paths
attrs['is_source_specific'] = self.is_source_specific
attrs['source_id'] = self.source_id
attrs['branchID'] = self.branchID
return numpy.array(tbl, branch_dt), attrs
# SourceModelLogicTree
def __fromh5__(self, array, attrs):
# this is rather tricky; to understand it, run the test
# SerializeSmltTestCase which has a logic tree with 3 branchsets
# with the form b11[b21[b31, b32], b22[b31, b32]] and 1 x 2 x 2 rlzs
vars(self).update(attrs)
self.test_mode = False
bsets = []
self.branches = {}
self.bsetdict = json.loads(attrs['bsetdict'])
self.shortener = {}
acc = AccumDict(accum=[]) # bsid -> rows
for rec in array:
rec = fix_bytes(rec)
# NB: it is important to keep the order of the branchsets
acc[rec['branchset']].append(rec)
for ordinal, (bsid, rows) in enumerate(acc.items()):
utype = rows[0]['utype']
filters = {}
ats = self.bsetdict[bsid].get('applyToSources')
atb = self.bsetdict[bsid].get('applyToBranches')
if ats:
filters['applyToSources'] = ats.split()
if atb:
filters['applyToBranches'] = atb.split()
bset = BranchSet(utype, ordinal, filters)
bset.id = bsid
for no, row in enumerate(rows):
try:
uvalue = ast.literal_eval(row['uvalue'])
except (SyntaxError, ValueError):
uvalue = row['uvalue'] # not really deserializable :-(
br = Branch(bsid, row['branch'], row['weight'], uvalue)
self.branches[br.branch_id] = br
self.shortener[br.branch_id] = keyno(br.branch_id, ordinal, no)
bset.branches.append(br)
bsets.append(bset)
CompositeLogicTree(bsets) # perform attach_to_branches
self.branchsets = bsets
# bsets [<b11>, <b21 b22>, <b31 b32>]
self.root_branchset = bsets[0]
def __str__(self):
return '<%s%s>' % (self.__class__.__name__, repr(self.root_branchset))
[docs]def capitalize(words):
"""
Capitalize words separated by spaces.
"""
return ' '.join(w.capitalize() for w in decode(words).split(' '))
[docs]def get_field(data, field, default):
"""
:param data: a record with a field `field`, possibily missing
"""
try:
return data[field]
except ValueError: # field missing in old engines
return default
[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):
self.ordinal = ordinal
self.sm_lt_path = tuple(sm_lt_path)
self.gsim_rlz = gsim_rlz
self.weight = weight
def __repr__(self):
return '<%d,w=%s>' % (self.ordinal, self.weight)
@property
def gsim_lt_path(self):
return self.gsim_rlz.lt_path
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))
def _get_smr(source_id):
# 'src1;0.0' => 0
suffix = source_id.split(';')[1]
smr = suffix.split('.')[0]
return int(smr)
[docs]class FullLogicTree(object):
"""
The full logic tree as composition of
:param source_model_lt: :class:`SourceModelLogicTree` object
:param gsim_lt: :class:`GsimLogicTree` object
"""
oversampling = 'tolerate'
[docs] @classmethod
def fake(cls, gsimlt=None):
"""
:returns:
a fake `FullLogicTree` instance with the given gsim logic tree
object; if None, builds automatically a fake gsim logic tree
"""
gsim_lt = gsimlt or GsimLogicTree.from_('[FromFile]')
fakeSM = Realization(
'scenario', weight=1, ordinal=0, lt_path='b1', samples=1)
self = object.__new__(cls)
self.source_model_lt = SourceModelLogicTree.fake()
self.gsim_lt = gsim_lt
self.sm_rlzs = [fakeSM]
return self
def __init__(self, source_model_lt, gsim_lt, oversampling='tolerate'):
self.source_model_lt = source_model_lt
self.gsim_lt = gsim_lt
self.oversampling = oversampling
self.init() # set .sm_rlzs and .trts
def __getstate__(self):
return {'source_model_lt': self.source_model_lt,
'gsim_lt': self.gsim_lt,
'oversampling': self.oversampling}
[docs] def init(self):
if self.source_model_lt.num_samples:
# NB: the number of effective rlzs can be less than the number
# of realizations in case of sampling
self.sm_rlzs = get_effective_rlzs(self.source_model_lt)
else: # full enumeration
samples = self.gsim_lt.get_num_paths()
self.sm_rlzs = []
for sm_rlz in self.source_model_lt:
sm_rlz.samples = samples
self.sm_rlzs.append(sm_rlz)
self.Re = len(self.sm_rlzs)
assert self.Re <= TWO24, len(self.sm_rlzs)
self.trti = {trt: i for i, trt in enumerate(self.gsim_lt.values)}
self.trts = list(self.gsim_lt.values)
self.weights = [rlz.weight for rlz in self.get_realizations()]
return self
[docs] def get_gids(self, all_trt_smrs):
"""
:returns: list of of arrays of gids, one for each source group
"""
gids = []
g = 0
for trt_smrs in all_trt_smrs:
rbg = self.get_rlzs_by_gsim(trt_smrs)
gids.append(numpy.arange(g, g + len(rbg)))
g += len(rbg)
return gids
[docs] def get_trt_rlzs(self, all_trt_smrs):
"""
:returns: a list with Gt arrays of dtype uint32
"""
data = []
for trt_smrs in all_trt_smrs:
for rlzs in self.get_rlzs_by_gsim(trt_smrs).values():
data.append(U32(rlzs) + TWO24 * (trt_smrs[0] // TWO24))
return data
[docs] def g_weights(self, trt_rlzs):
"""
:returns: a list of Gt weights
"""
out = [sum(self.weights[r] for r in trs % TWO24) for trs in trt_rlzs]
return out
[docs] def get_smr_by_ltp(self):
"""
:returns: a dictionary sm_lt_path -> effective realization index
"""
return {'~'.join(sm_rlz.lt_path): i
for i, sm_rlz in enumerate(self.sm_rlzs)}
[docs] def trt_by(self, trt_smr):
"""
:returns: the TRT associated to trt_smr
"""
if len(self.trts) == 1:
return self.trts[0]
return self.trts[trt_smr // TWO24]
@property
def seed(self):
"""
:returns: the source_model_lt seed
"""
return self.source_model_lt.seed
@property
def num_samples(self):
"""
:returns: the source_model_lt ``num_samples`` parameter
"""
if self.oversampling == 'reduce-rlzs':
return len(self.get_realizations())
return self.source_model_lt.num_samples
@property
def sampling_method(self):
"""
:returns: the source_model_lt ``sampling_method`` parameter
"""
return self.source_model_lt.sampling_method
[docs] def get_trt_smrs(self, src_id=None):
"""
:returns: a tuple of indices trt_smr for the given source
"""
try:
sd = self.source_model_lt.source_data
except AttributeError: # fake logic tree
return 0,
if src_id is None:
return tuple(trti * TWO24 + sm_rlz.ordinal
for sm_rlz in self.sm_rlzs
for trti in self.trti)
if not hasattr(self, 'sd'): # cache source_data by source
self.sd = group_array(
self.source_model_lt.source_data, 'source')
sd = self.sd[src_id]
trt = sd['trt'][0] # all same trt
trti = 0 if trt == '*' else self.trti[trt]
brids = set(sd['branch'])
return tuple(trti * TWO24 + sm_rlz.ordinal
for sm_rlz in self.sm_rlzs
if set(sm_rlz.lt_path) & brids)
# NB: called by the source_reader with smr and by
# .reduce_groups with source_id
[docs] def set_trt_smr(self, srcs, source_id=None, smr=None):
"""
:param srcs: source objects
:param source_id: base source ID
:param srm: source model realization index
:returns: list of sources with the same base source ID
"""
if not self.trti: # empty gsim_lt
return srcs
sd = group_array(self.source_model_lt.source_data, 'source')
out = []
for src in srcs:
srcid = valid.corename(src)
if source_id and srcid != source_id:
continue # filter
if self.trti == {'*': 0}: # passed gsim=XXX in the job.ini
trti = 0
else:
trti = self.trti[src.tectonic_region_type]
if smr is None and ';' in src.source_id:
# assume <base_id>;<smr>
smr = _get_smr(src.source_id)
if smr is None: # called by .reduce_groups
try:
# check if ambiguous source ID
srcid, fname = srcid.rsplit('!')
except ValueError:
# non-ambiguous source ID
fname = ''
ok = slice(None)
else:
ok = [fname in string for string in sd[srcid]['fname']]
brids = set(sd[srcid]['branch'][ok])
tup = tuple(trti * TWO24 + sm_rlz.ordinal
for sm_rlz in self.sm_rlzs
if set(sm_rlz.lt_path) & brids)
else:
tup = trti * TWO24 + smr
# print('Setting %s on %s' % (tup, src))
src.trt_smr = tup # realizations impacted by the source
out.append(src)
return out
[docs] def reduce_groups(self, src_groups):
"""
Filter the sources and set the tuple .trt_smr
"""
groups = []
source_id = self.source_model_lt.source_id
for sg in src_groups:
ok = self.set_trt_smr(sg, source_id)
if ok:
grp = copy.copy(sg)
grp.sources = ok
groups.append(grp)
return groups
[docs] def gsim_by_trt(self, rlz):
"""
:returns: a dictionary trt->gsim for the given realization
"""
return dict(zip(self.gsim_lt.values, rlz.gsim_rlz.value))
[docs] def get_num_paths(self):
"""
:returns: number of the paths in the full logic tree
"""
if self.num_samples:
return self.num_samples
return len(self.sm_rlzs) * self.gsim_lt.get_num_paths()
[docs] def get_realizations(self):
"""
:returns: the complete list of LtRealizations
"""
rlzs = []
num_samples = self.source_model_lt.num_samples
if num_samples: # sampling
sm_rlzs = []
for sm_rlz in self.sm_rlzs:
sm_rlzs.extend([sm_rlz] * sm_rlz.samples)
gsim_rlzs = self.gsim_lt.sample(
num_samples, self.seed + 1, self.sampling_method)
if self.oversampling == 'reduce-rlzs':
eff_rlzs = get_eff_rlzs(sm_rlzs, gsim_rlzs)
rlzs.extend(eff_rlzs)
else:
for i, gsim_rlz in enumerate(gsim_rlzs):
rlz = LtRealization(i, sm_rlzs[i].lt_path, gsim_rlz,
sm_rlzs[i].weight * gsim_rlz.weight)
rlzs.append(rlz)
if self.sampling_method.startswith('early_'):
for rlz in rlzs:
for k in rlz.weight.dic:
rlz.weight.dic[k] = 1. / num_samples
else: # full enumeration
gsim_rlzs = list(self.gsim_lt)
i = 0
for sm_rlz in self.sm_rlzs:
for gsim_rlz in gsim_rlzs:
rlz = LtRealization(i, sm_rlz.lt_path, gsim_rlz,
sm_rlz.weight * gsim_rlz.weight)
rlzs.append(rlz)
i += 1
# rescale the weights if not one, see case_52
tot_weight = sum(rlz.weight for rlz in rlzs)
if not tot_weight.is_one():
for rlz in rlzs:
rlz.weight = rlz.weight / tot_weight
assert rlzs, 'No realizations found??'
return rlzs
def _rlzs_by_gsim(self, trt_smr):
# return dictionary gsim->rlzs
if not hasattr(self, '_rlzs_by'):
smr_by_ltp = self.get_smr_by_ltp()
rlzs = self.get_realizations()
acc = AccumDict(accum=AccumDict(accum=[])) # trt_smr->gsim->rlzs
for sm in self.sm_rlzs:
trtsmrs = sm.ordinal + numpy.arange(
len(self.gsim_lt.values)) * TWO24
for trtsmr in trtsmrs:
trti, smr = divmod(trtsmr, TWO24)
for rlz in rlzs:
idx = smr_by_ltp['~'.join(rlz.sm_lt_path)]
if idx == smr:
acc[trtsmr][rlz.gsim_rlz.value[trti]].append(
rlz.ordinal)
self._rlzs_by = {}
for trtsmr, dic in acc.items():
self._rlzs_by[trtsmr] = {
gsim: U32(rlzs) for gsim, rlzs in sorted(dic.items())}
if not self._rlzs_by:
return {}
return self._rlzs_by[trt_smr]
[docs] def get_rlzs_by_gsim(self, trt_smr):
"""
:param trt_smr: index or array of indices
:returns: a dictionary gsim -> array of rlz indices
"""
if isinstance(trt_smr, (numpy.ndarray, list, tuple)):
dic = AccumDict(accum=[])
for t in trt_smr:
for gsim, rlzs in self._rlzs_by_gsim(t).items():
dic[gsim].extend(rlzs)
return dic
return self._rlzs_by_gsim(trt_smr)
# FullLogicTree
def __toh5__(self):
sm_data = []
for sm in self.sm_rlzs:
sm_data.append((str(sm.value), sm.weight,
'~'.join(sm.lt_path), sm.samples))
return (dict(
source_model_lt=self.source_model_lt,
gsim_lt=self.gsim_lt,
source_data=self.source_model_lt.source_data,
sm_data=numpy.array(sm_data, source_model_dt)),
dict(seed=self.seed, num_samples=self.num_samples,
trts=hdf5.array_of_vstr(self.gsim_lt.values),
oversampling=self.oversampling))
# FullLogicTree
def __fromh5__(self, dic, attrs):
# TODO: this is called more times than needed, maybe we should cache it
sm_data = dic['sm_data']
sd = dic.pop('source_data', numpy.zeros(0)) # empty for engine <= 3.16
vars(self).update(attrs)
self.source_model_lt = dic['source_model_lt']
self.source_model_lt.source_data = sd[:]
self.gsim_lt = dic['gsim_lt']
self.sm_rlzs = []
for sm_id, rec in enumerate(sm_data):
path = tuple(str(decode(rec['path'])).split('~'))
sm = Realization(
rec['name'], rec['weight'], sm_id, path, rec['samples'])
self.sm_rlzs.append(sm)
[docs] def get_num_potential_paths(self):
"""
:returns: the number of potential realizations
"""
return self.gsim_lt.get_num_paths() * self.source_model_lt.num_paths
@property
def rlzs(self):
"""
:returns: an array of realizations
"""
sh1 = self.source_model_lt.shortener
sh2 = self.gsim_lt.shortener
tups = []
for r in self.get_realizations():
path = '%s~%s' % (shorten(r.sm_lt_path, sh1),
shorten(r.gsim_rlz.lt_path, sh2))
tups.append((r.ordinal, path, r.weight['weight']))
return numpy.array(tups, rlz_dt)
def __repr__(self):
info_by_model = {}
for sm in self.sm_rlzs:
info_by_model[sm.lt_path] = (
'~'.join(map(decode, sm.lt_path)), decode(sm.value), sm.weight)
summary = ['%s, %s, weight=%s' % ibm for ibm in info_by_model.values()]
return '<%s\n%s>' % (self.__class__.__name__, '\n'.join(summary))
[docs]class SourceLogicTree(object):
"""
Source specific logic tree (full enumeration)
"""
def __init__(self, source_id, branchsets, bsetdict):
self.source_id = source_id
self.bsetdict = bsetdict
branchsets = [copy.copy(bset) for bset in branchsets]
self.root_branchset = branchsets[0]
self.num_paths = 1
for child, parent in zip(branchsets[1:] + [None], branchsets):
branches = [copy.copy(br) for br in parent.branches]
for br in branches:
br.bset = child
parent.branches = branches
self.num_paths *= len(branches)
self.branchsets = branchsets
self.num_samples = 0
__iter__ = SourceModelLogicTree.__iter__
[docs] def get_num_paths(self):
return self.num_paths
def __repr__(self):
return '<SSLT:%s %s>' % (self.source_id, self.branchsets)
[docs]def compose(source_model_lt, gsim_lt):
"""
:returns: a CompositeLogicTree instance
"""
bsets = []
dic = groupby(gsim_lt.branches, operator.attrgetter('trt'))
bsno = len(source_model_lt.branchsets)
for trt, btuples in dic.items():
bsid = gsim_lt.bsetdict[trt]
bset = BranchSet('gmpeModel', bsno)
bset.branches = [Branch(bsid, bt.id, bt.weight['weight'], bt.gsim)
for bt in btuples] # branch ID fixed later
bsets.append(bset)
bsno += 1
clt = CompositeLogicTree(source_model_lt.branchsets + bsets)
return clt
