Source code for openquake.commonlib.logictree

# -*- coding: utf-8 -*-
# vim: tabstop=4 shiftwidth=4 softtabstop=4
#
# Copyright (C) 2010-2022 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
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"""
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
from openquake.baselib.python3compat import decode
from openquake.baselib.node import node_from_elem, context
from openquake.baselib.general import groupby, AccumDict
from openquake.hazardlib import nrml, InvalidFile, pmf
from openquake.hazardlib.sourceconverter import SourceGroup
from openquake.hazardlib.gsim_lt import (
    GsimLogicTree, bsnodes, fix_bytes, keyno, abs_paths)
from openquake.hazardlib.lt import (
    Branch, BranchSet, Realization, CompositeLogicTree, dummy_branchset,
    LogicTreeError, parse_uncertainty, random, attach_to_branches)

TRT_REGEX = re.compile(r'tectonicRegion="([^"]+?)"')
ID_REGEX = re.compile(r'id="([^"]+?)"')
SOURCE_TYPE_REGEX = re.compile(r'<(\w+Source)\b')

U16 = numpy.uint16
U32 = numpy.uint32
I32 = numpy.int32
F32 = numpy.float32

rlz_dt = numpy.dtype([
    ('ordinal', U32),
    ('branch_path', hdf5.vstr),
    ('weight', F32)
])

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 = [('branchset', hdf5.vstr), ('branch', hdf5.vstr),
             ('utype', hdf5.vstr), ('uvalue', hdf5.vstr), ('weight', float)]


[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. """ effective = [] ordinal = 0 for group in groupby(rlzs, operator.attrgetter('pid')).values(): rlz = group[0] if all(path == '@' for path in rlz.lt_path): # empty realization continue effective.append( Realization(rlz.value, sum(r.weight for r in group), ordinal, rlz.lt_path, len(group))) ordinal += 1 return effective
Info = collections.namedtuple('Info', 'smpaths h5paths applytosources')
[docs]def collect_info(smltpath, branchID=None): """ 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) paths = 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 = unique(br.uncertaintyModel.text.split()) paths.update(abs_paths(smltpath, fnames)) for fname in fnames: hdf5file = os.path.splitext(fname)[0] + '.hdf5' if os.path.exists(hdf5file): h5paths.add(hdf5file) return Info(sorted(paths), sorted(h5paths), applytosources)
[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 / numpy.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', 'applyToSourceType') 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, source_ids="{}", is_source_specific=0, 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=None): 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.branches = {} # branch_id -> branch self.bsetdict = {} self.previous_branches = [] self.tectonic_region_types = set() self.source_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) # determine if the logic tree is source specific dicts = list(self.bsetdict.values())[1:] if not dicts: self.is_source_specific = False return src_ids = set() for dic in dicts: ats = dic.get('applyToSources') if not ats: self.is_source_specific = False return elif len(ats.split()) != 1: self.is_source_specific = False 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)
[docs] def parse_tree(self, tree_node): """ Parse the whole tree and point ``root_branchset`` attribute to the tree's root. """ self.info = collect_info(self.filename, self.branchID) self.source_ids = collections.defaultdict(list) # src_id->branchIDs t0 = time.time() for depth, blnode in enumerate(tree_node.nodes): [bsnode] = bsnodes(self.filename, blnode) self.parse_branchset(bsnode, depth) dt = time.time() - t0 bname = os.path.basename(self.filename) logging.info('Validated %s in %.2f seconds', bname, dt)
[docs] def parse_branchset(self, branchset_node, depth): """ :param branchset_ node: ``etree.Element`` object with tag "logicTreeBranchSet". :param depth: The sequential number of this branching level, based on 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) 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, depth, branchset) self.parse_branches(branchset_node, branchset) dummies = [] # dummy branches in case of applyToBranches if self.root_branchset is None: # not set yet self.num_paths = 1 self.root_branchset = branchset else: prev_ids = ' '.join(pb.branch_id for pb in self.previous_branches) app2brs = branchset_node.attrib.get('applyToBranches') or prev_ids if app2brs != prev_ids: branchset.applied = app2brs self.apply_branchset( app2brs, branchset_node.lineno, branchset) for brid in set(prev_ids.split()) - set(app2brs.split()): 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.num_paths *= len(branchset) self.branchsets.append(branchset)
[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
[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 values = [] bsno = len(self.branchsets) for brno, branchnode in enumerate(branches): weight = ~branchnode.uncertaintyWeight weight_sum += weight value_node = node_from_elem(branchnode.uncertaintyModel) if value_node.text is not None: values.append(value_node.text.strip()) if branchset.uncertainty_type in ('sourceModel', 'extendModel'): if self.branchID and branchnode['branchID'] != self.branchID: continue try: for fname in value_node.text.strip().split(): if (fname.endswith(('.xml', '.nrml')) # except UCERF and not self.test_mode): self.collect_source_model_data( branchnode['branchID'], fname) except Exception as exc: raise LogicTreeError( value_node, self.filename, str(exc)) from exc value = parse_uncertainty(branchset.uncertainty_type, value_node, self.filename) branch_id = branchnode.attrib.get('branchID') branch = Branch(bs_id, branch_id, weight, value) if branch_id in self.branches: raise LogicTreeError( branchnode, self.filename, "branchID '%s' is not unique" % branch_id) self.branches[branch_id] = branch self.shortener[branch_id] = keyno( branch_id, bsno, brno, self.filename) 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 len(set(values)) < len(values): raise LogicTreeError( branchset_node, self.filename, "there are duplicate values in uncertaintyModel: " + ' '.join(values))
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: filters['applyToSources'] = filters['applyToSources'].split() 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. * Filter "applyToSourceType" must mention only source 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 not f['applyToTectonicRegionType'] \ 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 (not ('applyToSources' in f) and 'applyToSourceType' not in f): raise LogicTreeError( branchset_node, self.filename, "uncertainty of type '%s' must define either" "'applyToSources' or 'applyToSourceType'" % uncertainty_type) if 'applyToSourceType' in f: if not f['applyToSourceType'] in self.source_types: raise LogicTreeError( branchset_node, self.filename, "source models don't define sources of type '%s'" % f['applyToSourceType']) if 'applyToSources' in f: for source_id in f['applyToSources'].split(): branchIDs = self.source_ids[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, depth, 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 depth == 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, source_model): """ 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`. """ # using regular expressions is a lot faster than parsing with self._get_source_model(source_model) as sm: xml = sm.read() self.tectonic_region_types.update(TRT_REGEX.findall(xml)) for src_id in ID_REGEX.findall(xml): self.source_ids[src_id].append(branch_id) self.source_types.update(SOURCE_TYPE_REGEX.findall(xml))
[docs] def collapse(self, branchset_ids): """ Set the attribute .collapsed on the given branchsets """ for bsid, bset in self.bsetdict.items(): if bsid in branchset_ids: bset.collapsed = True
[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
# 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 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) 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, attrs['filename']) bset.branches.append(br) bsets.append(bset) attach_to_branches(bsets) 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))
[docs]class FullLogicTree(object): """ The full logic tree as composition of :param source_model_lt: :class:`SourceModelLogicTree` object :param gsim_lt: :class:`GsimLogicTree` object """
[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): self.source_model_lt = source_model_lt self.gsim_lt = gsim_lt self.init() # set .sm_rlzs and .trts
[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.trti = {trt: i for i, trt in enumerate(self.gsim_lt.values)} self.trts = list(self.gsim_lt.values)
[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 // len(self.sm_rlzs)]
@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 """ 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_trti_smr(self, trt_smr): """ :returns: (trti, smr) """ return divmod(trt_smr, len(self.sm_rlzs))
[docs] def get_trt_smr(self, trt, smr): """ :returns: trt_smr """ if self.trti == {'*': 0}: # passed gsim=XXX in the job.ini return int(smr) return self.trti[trt] * len(self.sm_rlzs) + int(smr)
[docs] def get_trt_smrs(self, smr): """ :param smr: effective realization index :returns: array of T group IDs, being T the number of TRTs """ nt = len(self.gsim_lt.values) ns = len(self.sm_rlzs) return smr + numpy.arange(nt) * ns
[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 = [] self._gsims_by_trt = AccumDict(accum=set()) # trt -> gsims if self.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(self.num_samples, self.seed + 1, self.sampling_method) for t, trt in enumerate(self.gsim_lt.values): self._gsims_by_trt[trt].update(g.value[t] for g in gsim_rlzs) 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) else: # full enumeration gsim_rlzs = list(self.gsim_lt) self._gsims_by_trt = self.gsim_lt.values 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 assert rlzs, 'No realizations found??' if self.num_samples and self.sampling_method.startswith('early_'): assert len(rlzs) == self.num_samples, (len(rlzs), self.num_samples) for rlz in rlzs: for k in rlz.weight.dic: rlz.weight.dic[k] = 1. / self.num_samples else: # keep the weights tot_weight = sum(rlz.weight for rlz in rlzs) if not tot_weight.is_one(): # this may happen for rounding errors; we ensure the sum of # the weights is 1 for rlz in rlzs: rlz.weight = rlz.weight / tot_weight return rlzs
[docs] def get_rlzs_by_smr(self): """ :returns: a dict smr -> rlzs """ smltpath = operator.attrgetter('sm_lt_path') smr_by_ltp = self.get_smr_by_ltp() rlzs = self.get_realizations() dic = {smr_by_ltp['~'.join(ltp)]: rlzs for ltp, rlzs in groupby( rlzs, smltpath).items()} return dic
def _rlzs_by_gsim(self, grp_id): """ :returns: a dictionary gsim -> array of rlz indices """ if not hasattr(self, '_rlzs_by_grp'): 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: for gid in self.get_trt_smrs(sm.ordinal): trti, smr = divmod(gid, len(self.sm_rlzs)) for rlz in rlzs: idx = smr_by_ltp['~'.join(rlz.sm_lt_path)] if idx == smr: acc[gid][rlz.gsim_rlz.value[trti]].append( rlz.ordinal) self._rlzs_by_grp = {} for gid, dic in acc.items(): self._rlzs_by_grp[gid] = { gsim: U32(rlzs) for gsim, rlzs in sorted(dic.items())} return self._rlzs_by_grp[grp_id]
[docs] def get_rlzs_by_gsim(self): """ :returns: a dictionary trt_smr -> gsim -> rlzs """ dic = {} for sm in self.sm_rlzs: for trt_smr in self.get_trt_smrs(sm.ordinal): dic[trt_smr] = self._rlzs_by_gsim(trt_smr) return dic
[docs] def get_rlzs_by_grp(self): """ :returns: a dictionary grp_id -> [rlzis, ...] """ dic = {} for sm in self.sm_rlzs: for trt_smr in self.get_trt_smrs(sm.ordinal): grp = 'grp-%02d' % trt_smr dic[grp] = list(self._rlzs_by_gsim(trt_smr).values()) return {grp_id: dic[grp_id] for grp_id in sorted(dic)}
[docs] def get_rlzs_by_gsim_list(self, list_of_trt_smrs): """ :returns: a list of dictionaries rlzs_by_gsim, one for each grp_id """ out = [] for grp_id, trt_smrs in enumerate(list_of_trt_smrs): dic = AccumDict(accum=[]) for trt_smr in trt_smrs: for gsim, rlzs in self._rlzs_by_gsim(trt_smr).items(): dic[gsim].extend(rlzs) out.append(dic) return out
# 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, 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))) # FullLogicTree def __fromh5__(self, dic, attrs): # TODO: this is called more times than needed, maybe we should cache it sm_data = dic['sm_data'] vars(self).update(attrs) self.source_model_lt = dic['source_model_lt'] 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_rlzs(self, sm_rlz=None): """ :param sm_rlz: a Realization instance (or None) :returns: the number of realizations per source model (or all) """ if sm_rlz is None: return sum(self.get_num_rlzs(sm) for sm in self.sm_rlzs) if self.num_samples: return sm_rlz.samples return self.gsim_lt.get_num_paths()
[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)
[docs] def get_gsims_by_trt(self): """ :returns: a dictionary trt -> sorted gsims """ if not hasattr(self, '_gsims_by_trt'): self.get_realizations() return {trt: sorted(gs) for trt, gs in self._gsims_by_trt.items()}
[docs] def get_sm_by_grp(self): """ :returns: a dictionary trt_smr -> sm_id """ return {trt_smr: sm.ordinal for sm in self.sm_rlzs for trt_smr in self.get_trt_smrs(sm.ordinal)}
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, self.get_num_rlzs(sm)) summary = ['%s, %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 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