Source code for

# -*- coding: utf-8 -*-
# vim: tabstop=4 shiftwidth=4 softtabstop=4
# Copyright (C) 2020, GEM Foundation
# OpenQuake is free software: you can redistribute it and/or modify it
# under the terms of the GNU Affero General Public License as published
# by the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
# OpenQuake is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# GNU Affero General Public License for more details.
# You should have received a copy of the GNU Affero General Public License
# along with OpenQuake.  If not, see <>.

import copy
import collections
import numpy

from openquake.baselib.general import CallableDict
from openquake.hazardlib import geo, source as ohs
from openquake.hazardlib.sourceconverter import (
    split_coords_2d, split_coords_3d)

[docs]class LogicTreeError(Exception): """ Logic tree file contains a logic error. :param node: XML node object that causes fail. Used to determine the affected line number. All other constructor parameters are passed to :class:`superclass' <LogicTreeError>` constructor. """ def __init__(self, node, filename, message): self.filename = filename self.message = message self.lineno = node if isinstance(node, int) else getattr( node, 'lineno', '?') def __str__(self): return "filename '%s', line %s: %s" % ( self.filename, self.lineno, self.message)
# parse_uncertainty #
[docs]def unknown(utype, node, filename): try: return float(node.text) except (TypeError, ValueError): raise LogicTreeError(node, filename, 'expected single float value')
parse_uncertainty = CallableDict(keymissing=unknown)
[docs]@parse_uncertainty.add('sourceModel', 'extendModel') def smodel(utype, node, filename): return node.text.strip()
[docs]@parse_uncertainty.add('abGRAbsolute') def abGR(utype, node, filename): try: [a, b] = node.text.split() return float(a), float(b) except ValueError: raise LogicTreeError( node, filename, 'expected a pair of floats separated by space')
[docs]@parse_uncertainty.add('incrementalMFDAbsolute') def incMFD(utype, node, filename): min_mag, bin_width = (node.incrementalMFD["minMag"], node.incrementalMFD["binWidth"]) return min_mag, bin_width, ~node.incrementalMFD.occurRates
[docs]@parse_uncertainty.add('truncatedGRFromSlipAbsolute') def trucMFDFromSlip_absolute(utype, node, filename): slip_rate, rigidity = (node.faultActivityData["slipRate"], node.faultActivityData["rigidity"]) return slip_rate, rigidity
[docs]@parse_uncertainty.add('simpleFaultGeometryAbsolute') def simpleGeom(utype, node, filename): if hasattr(node, 'simpleFaultGeometry'): node = node.simpleFaultGeometry _validate_simple_fault_geometry(utype, node, filename) spacing = node["spacing"] usd, lsd, dip = (~node.upperSeismoDepth, ~node.lowerSeismoDepth, ~node.dip) coords = split_coords_2d(~node.LineString.posList) trace = geo.Line([geo.Point(*p) for p in coords]) return trace, usd, lsd, dip, spacing
[docs]@parse_uncertainty.add('complexFaultGeometryAbsolute') def complexGeom(utype, node, filename): if hasattr(node, 'complexFaultGeometry'): node = node.complexFaultGeometry _validate_complex_fault_geometry(utype, node, filename) spacing = node["spacing"] edges = [] for edge_node in node.nodes: coords = split_coords_3d(~edge_node.LineString.posList) edges.append(geo.Line([geo.Point(*p) for p in coords])) return edges, spacing
[docs]@parse_uncertainty.add('characteristicFaultGeometryAbsolute') def charGeom(utype, node, filename): surfaces = [] for geom_node in node.surface: if "simpleFaultGeometry" in geom_node.tag: _validate_simple_fault_geometry(utype, geom_node, filename) trace, usd, lsd, dip, spacing = parse_uncertainty( 'simpleFaultGeometryAbsolute', geom_node, filename) surfaces.append(geo.SimpleFaultSurface.from_fault_data( trace, usd, lsd, dip, spacing)) elif "complexFaultGeometry" in geom_node.tag: _validate_complex_fault_geometry(utype, geom_node, filename) edges, spacing = parse_uncertainty( 'complexFaultGeometryAbsolute', geom_node, filename) surfaces.append(geo.ComplexFaultSurface.from_fault_data( edges, spacing)) elif "planarSurface" in geom_node.tag: _validate_planar_fault_geometry(utype, geom_node, filename) nodes = [] for key in ["topLeft", "topRight", "bottomRight", "bottomLeft"]: nodes.append(geo.Point(getattr(geom_node, key)["lon"], getattr(geom_node, key)["lat"], getattr(geom_node, key)["depth"])) top_left, top_right, bottom_right, bottom_left = tuple(nodes) surface = geo.PlanarSurface.from_corner_points( top_left, top_right, bottom_right, bottom_left) surfaces.append(surface) else: raise LogicTreeError( geom_node, filename, "Surface geometry type not recognised") if len(surfaces) > 1: return geo.MultiSurface(surfaces) else: return surfaces[0]
# validations def _validate_simple_fault_geometry(utype, node, filename): try: coords = split_coords_2d(~node.LineString.posList) trace = geo.Line([geo.Point(*p) for p in coords]) except ValueError: # If the geometry cannot be created then use the LogicTreeError # to point the user to the incorrect node. Hence, if trace is # compiled successfully then len(trace) is True, otherwise it is # False trace = [] if len(trace): return raise LogicTreeError( node, filename, "'simpleFaultGeometry' node is not valid") def _validate_complex_fault_geometry(utype, node, filename): # NB: if the geometry does not conform to the Aki & Richards convention # this will not be verified here, but will raise an error when the surface # is created valid_edges = [] for edge_node in node.nodes: try: coords = split_coords_3d(edge_node.LineString.posList.text) edge = geo.Line([geo.Point(*p) for p in coords]) except ValueError: # See use of validation error in simple geometry case # The node is valid if all of the edges compile correctly edge = [] if len(edge): valid_edges.append(True) else: valid_edges.append(False) if node["spacing"] and all(valid_edges): return raise LogicTreeError( node, filename, "'complexFaultGeometry' node is not valid") def _validate_planar_fault_geometry(utype, node, filename): valid_spacing = node["spacing"] for key in ["topLeft", "topRight", "bottomLeft", "bottomRight"]: lon = getattr(node, key)["lon"] lat = getattr(node, key)["lat"] depth = getattr(node, key)["depth"] valid_lon = (lon >= -180.0) and (lon <= 180.0) valid_lat = (lat >= -90.0) and (lat <= 90.0) valid_depth = (depth >= 0.0) is_valid = valid_lon and valid_lat and valid_depth if not is_valid or not valid_spacing: raise LogicTreeError( node, filename, "'planarFaultGeometry' node is not valid") # apply_uncertainty # apply_uncertainty = CallableDict() @apply_uncertainty.add('simpleFaultDipRelative') def _simple_fault_dip_relative(utype, source, value): source.modify('adjust_dip', dict(increment=value)) @apply_uncertainty.add('simpleFaultDipAbsolute') def _simple_fault_dip_absolute(bset, source, value): source.modify('set_dip', dict(dip=value)) @apply_uncertainty.add('simpleFaultGeometryAbsolute') def _simple_fault_geom_absolute(utype, source, value): trace, usd, lsd, dip, spacing = value source.modify( 'set_geometry', dict(fault_trace=trace, upper_seismogenic_depth=usd, lower_seismogenic_depth=lsd, dip=dip, spacing=spacing)) @apply_uncertainty.add('complexFaultGeometryAbsolute') def _complex_fault_geom_absolute(utype, source, value): edges, spacing = value source.modify('set_geometry', dict(edges=edges, spacing=spacing)) @apply_uncertainty.add('characteristicFaultGeometryAbsolute') def _char_fault_geom_absolute(utype, source, value): source.modify('set_geometry', dict(surface=value)) @apply_uncertainty.add('abGRAbsolute') def _abGR_absolute(utype, source, value): a, b = value source.mfd.modify('set_ab', dict(a_val=a, b_val=b)) @apply_uncertainty.add('bGRRelative') def _abGR_relative(utype, source, value): source.mfd.modify('increment_b', dict(value=value)) @apply_uncertainty.add('maxMagGRRelative') def _maxmagGR_relative(utype, source, value): source.mfd.modify('increment_max_mag', dict(value=value)) @apply_uncertainty.add('maxMagGRAbsolute') def _maxmagGR_absolute(utype, source, value): source.mfd.modify('set_max_mag', dict(value=value)) @apply_uncertainty.add('incrementalMFDAbsolute') def _incMFD_absolute(utype, source, value): min_mag, bin_width, occur_rates = value source.mfd.modify('set_mfd', dict(min_mag=min_mag, bin_width=bin_width, occurrence_rates=occur_rates)) @apply_uncertainty.add('truncatedGRFromSlipAbsolute') def _trucMFDFromSlip_absolute(utype, source, value): slip_rate, rigidity = value source.modify('adjust_mfd_from_slip', dict(slip_rate=slip_rate, rigidity=rigidity)) # ######################### apply_uncertainties ########################### #
[docs]def apply_uncertainties(bset_values, src_group): """ :param bset_value: a list of pairs (branchset, value) List of branch IDs :param src_group: SourceGroup instance :returns: A copy of the original group with possibly modified sources """ sg = copy.copy(src_group) sg.sources = [] sg.changes = 0 for source in src_group: oks = [bset.filter_source(source) for bset, value in bset_values] if sum(oks): # source not filtered out src = copy.deepcopy(source) srcs = [] for (bset, value), ok in zip(bset_values, oks): if ok and bset.collapsed: if src.code == b'N': raise NotImplementedError( 'Collapsing of the logic tree is not implemented ' 'for %s' % src) for br in bset.branches: newsrc = copy.deepcopy(src) newsrc.scaling_rate = br.weight apply_uncertainty( bset.uncertainty_type, newsrc, br.value) srcs.append(newsrc) sg.changes += len(srcs) elif ok: if not srcs: # only the first time srcs.append(src) apply_uncertainty(bset.uncertainty_type, src, value) sg.changes += 1 else: srcs = [copy.copy(source)] # this is ultra-fast sg.sources.extend(srcs) return sg
# ######################### sampling ######################## #
[docs]def random(size, seed, sampling_method='early_weights'): """ :param size: size of the returned array (integer or pair of integers) :param seed: random seed :param sampling_method: 'early_weights', 'early_latin', ... :returns: an array of floats in the range 0..1 You can compare montecarlo sampling with latin square sampling with the following code: import matplotlib.pyplot as plt samples, seed = 10, 42 x, y = random((samples, 2), seed, 'early_latin').T plt.xlim([0, 1]) plt.ylim([0, 1]) plt.scatter(x, y, color='green') # points on a latin square x, y = random((samples, 2), seed, 'early_weights').T plt.scatter(x, y, color='red') # points NOT on a latin square for x in numpy.arange(0, 1, 1/samples): for y in numpy.arange(0, 1, 1/samples): plt.axvline(x) plt.axhline(y) """ numpy.random.seed(seed) xs = numpy.random.uniform(size=size) if sampling_method.endswith('latin'): # try: s, d = size except TypeError: # cannot unpack non-iterable int object return (numpy.argsort(xs) + xs) / size for i in range(d): xs[:, i] = (numpy.argsort(xs[:, i]) + xs[:, i]) / s return xs
def _cdf(weighted_objects): weights = [] for obj in weighted_objects: w = obj.weight if isinstance(obj.weight, float): weights.append(w) else: weights.append(w['weight']) return numpy.cumsum(weights)
[docs]def sample(weighted_objects, probabilities, sampling_method): """ Take random samples of a sequence of weighted objects :param weighted_objects: A finite sequence of N objects with a `.weight` attribute. The weights must sum up to 1. :param probabilities: An array of S random numbers in the range 0..1 :return: A list of S objects extracted randomly """ if sampling_method.startswith('early'): # consider the weights idxs = numpy.searchsorted(_cdf(weighted_objects), probabilities) elif sampling_method.startswith('late'): n = len(weighted_objects) # consider all weights equal idxs = numpy.searchsorted(numpy.arange(1/n, 1, 1/n), probabilities) # NB: returning an array would break things return [weighted_objects[idx] for idx in idxs]
Weighted = collections.namedtuple('Weighted', 'object weight') # used in notebooks for teaching, not in the engine
[docs]def random_sample(branchsets, num_samples, seed, sampling_method): """ >>> bsets = [[('X', .4), ('Y', .6)], [('A', .2), ('B', .3), ('C', .5)]] >>> paths = random_sample(bsets, 100, 42, 'early_weights') >>> collections.Counter(paths) Counter({'YC': 26, 'XC': 24, 'YB': 17, 'XA': 13, 'YA': 10, 'XB': 10}) >>> paths = random_sample(bsets, 100, 42, 'late_weights') >>> collections.Counter(paths) Counter({'XA': 20, 'YA': 18, 'XB': 17, 'XC': 15, 'YB': 15, 'YC': 15}) >>> paths = random_sample(bsets, 100, 42, 'early_latin') >>> collections.Counter(paths) Counter({'YC': 31, 'XC': 19, 'YB': 17, 'XB': 13, 'YA': 12, 'XA': 8}) >>> paths = random_sample(bsets, 100, 45, 'late_latin') >>> collections.Counter(paths) Counter({'YC': 18, 'XA': 18, 'XC': 16, 'YA': 16, 'XB': 16, 'YB': 16}) """ probs = random((num_samples, len(branchsets)), seed, sampling_method) arr = numpy.zeros((num_samples, len(branchsets)), object) for b, bset in enumerate(branchsets): arr[:, b] = sample([Weighted(*it) for it in bset], probs[:, b], sampling_method) return [''.join(w.object for w in row) for row in arr]
# ######################### branches and branchsets ######################## #
[docs]class Branch(object): """ Branch object, represents a ``<logicTreeBranch />`` element. :param bs_id: BranchSetID of the branchset to which the branch belongs :param branch_id: String identifier of the branch :param weight: float value of weight assigned to the branch. A text node contents of ``<uncertaintyWeight />`` child node. :param value: The actual uncertainty parameter value. A text node contents of ``<uncertaintyModel />`` child node. Type depends on the branchset's uncertainty type. """ def __init__(self, bs_id, branch_id, weight, value): self.bs_id = bs_id self.branch_id = branch_id self.weight = weight self.value = value self.bset = None def __repr__(self): if self.bset: return '%s%s' % (self.branch_id, self.bset) else: return self.branch_id
[docs]class BranchSet(object): """ Branchset object, represents a ``<logicTreeBranchSet />`` element. :param uncertainty_type: String value. According to the spec one of: gmpeModel Branches contain references to different GMPEs. Values are parsed as strings and are supposed to be one of supported GMPEs. See list at :class:`GMPELogicTree`. sourceModel Branches contain references to different PSHA source models. Values are treated as file names, relatively to base path. maxMagGRRelative Different values to add to Gutenberg-Richter ("GR") maximum magnitude. Value should be interpretable as float. bGRRelative Values to add to GR "b" value. Parsed as float. maxMagGRAbsolute Values to replace GR maximum magnitude. Values expected to be lists of floats separated by space, one float for each GR MFD in a target source in order of appearance. abGRAbsolute Values to replace "a" and "b" values of GR MFD. Lists of pairs of floats, one pair for one GR MFD in a target source. incrementalMFDAbsolute Replaces an evenly discretized MFD with the values provided simpleFaultDipRelative Increases or decreases the angle of fault dip from that given in the original source model simpleFaultDipAbsolute Replaces the fault dip in the specified source(s) simpleFaultGeometryAbsolute Replaces the simple fault geometry (trace, upper seismogenic depth lower seismogenic depth and dip) of a given source with the values provided complexFaultGeometryAbsolute Replaces the complex fault geometry edges of a given source with the values provided characteristicFaultGeometryAbsolute Replaces the complex fault geometry surface of a given source with the values provided truncatedGRFromSlipAbsolute Updates a TruncatedGR using a slip rate and a rigidity :param filters: Dictionary, a set of filters to specify which sources should the uncertainty be applied to. Represented as branchset element's attributes in xml: applyToSources The uncertainty should be applied only to specific sources. This filter is required for absolute uncertainties (also only one source can be used for those). Value should be the list of source ids. Can be used only in source model logic tree. applyToSourceType Can be used in the source model logic tree definition. Allows to specify to which source type (area, point, simple fault, complex fault) the uncertainty applies to. applyToTectonicRegionType Can be used in both the source model and GMPE logic trees. Allows to specify to which tectonic region type (Active Shallow Crust, Stable Shallow Crust, etc.) the uncertainty applies to. This filter is required for all branchsets in GMPE logic tree. """ def __init__(self, uncertainty_type, ordinal=0, filters=None, collapsed=False): self.uncertainty_type = uncertainty_type self.ordinal = ordinal self.filters = filters or {} self.collapsed = collapsed self.branches = []
[docs] def sample(self, probabilities, sampling_method): """ :param num_samples: the number of samples :param probabilities: (Ns, Nb) random numbers in the range 0..1 :param sampling_method: the sampling method used :returns: a list of num_samples lists of branches """ out = [] for probs in probabilities: # probs has a value for each branchset branchset = self branches = [] while branchset is not None: if branchset.collapsed: branch = branchset.branches[0] else: x = probs[branchset.ordinal] [branch] = sample(branchset.branches, [x], sampling_method) branches.append(branch) branchset = branch.bset out.append(branches) return out
[docs] def enumerate_paths(self): """ Generate all possible paths starting from this branch set. :returns: Generator of two-item tuples. Each tuple contains weight of the path (calculated as a product of the weights of all path's branches) and list of path's :class:`Branch` objects. Total sum of all paths' weights is 1.0 """ for path in self._enumerate_paths([]): flat_path = [] weight = 1.0 while path: path, branch = path weight *= branch.weight flat_path.append(branch) yield weight, flat_path[::-1]
def _enumerate_paths(self, prefix_path): """ Recursive (private) part of :func:`enumerate_paths`. Returns generator of recursive lists of two items, where second item is the branch object and first one is itself list of two items. """ if self.collapsed: b0 = copy.copy(self.branches[0]) b0.weight = 1.0 branches = [b0] else: branches = self.branches for branch in branches: path = [prefix_path, branch] if branch.bset is not None: yield from branch.bset._enumerate_paths(path) else: yield path def __getitem__(self, branch_id): """ Return :class:`Branch` object belonging to this branch set with id equal to ``branch_id``. """ for branch in self.branches: if branch.branch_id == branch_id: return branch raise KeyError(branch_id)
[docs] def filter_source(self, source): # pylint: disable=R0911,R0912 """ Apply filters to ``source`` and return ``True`` if uncertainty should be applied to it. """ for key, value in self.filters.items(): if key == 'applyToTectonicRegionType': if value != source.tectonic_region_type: return False elif key == 'applyToSourceType': if value == 'area': if not isinstance(source, ohs.AreaSource): return False elif value == 'point': # area source extends point source if (not isinstance(source, ohs.PointSource) or isinstance(source, ohs.AreaSource)): return False elif value == 'simpleFault': if not isinstance(source, ohs.SimpleFaultSource): return False elif value == 'complexFault': if not isinstance(source, ohs.ComplexFaultSource): return False elif value == 'characteristicFault': if not isinstance(source, ohs.CharacteristicFaultSource): return False else: raise AssertionError("unknown source type '%s'" % value) elif key == 'applyToSources': if source and source.source_id not in value: return False else: raise AssertionError("unknown filter '%s'" % key) # All filters pass, return True. return True
[docs] def get_bset_values(self, ltpath): """ :param ltpath: List of branch IDs :returns: A list of pairs [(bset, value), ...] """ pairs = [] bset = self while ltpath: brid, ltpath = ltpath[0], ltpath[1:] pairs.append((bset, bset[brid].value)) bset = bset[brid].bset if bset is None: break return pairs
def __str__(self): return repr(self.branches) def __repr__(self): return '<%s>' % ' '.join(br.branch_id for br in self.branches)