Source code for openquake.hazardlib.source.base

# The Hazard Library
# Copyright (C) 2012-2020 GEM Foundation
# This program 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
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# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
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Module :mod:`openquake.hazardlib.source.base` defines a base class for
seismic sources.
import abc
import numpy
from openquake.hazardlib.geo import Point
from openquake.hazardlib.source.rupture import ParametricProbabilisticRupture

[docs]class BaseSeismicSource(metaclass=abc.ABCMeta): """ Base class representing a seismic source, that is a structure generating earthquake ruptures. :param source_id: Some (numeric or literal) source identifier. Supposed to be unique within the source model. :param name: String, a human-readable name of the source. :param tectonic_region_type: Source's tectonic regime. See :class:`openquake.hazardlib.const.TRT`. """ ngsims = 1 min_mag = 0 # set in get_oqparams and CompositeSourceModel.filter splittable = True serial = 0 # set in init_serials checksum = 0 # set in source_reader @abc.abstractproperty def MODIFICATIONS(self): pass @property def weight(self): """ Determine the source weight from the number of ruptures """ if not self.num_ruptures: self.num_ruptures = self.count_ruptures() if hasattr(self, 'nodal_plane_distribution'): rescale = len( * len( return self.num_ruptures / rescale return self.num_ruptures @property def nsites(self): """ :returns: the number of sites affected by this source """ try: # the engine sets self.indices when filtering the sources return len(self.indices) except AttributeError: # this happens in several hazardlib tests, therefore we return # a fake number of affected sites to avoid changing all tests return 1 @property def grp_ids(self): """ :returns: a list of source group IDs (usually of 1 element) """ grp_id = self.grp_id return [grp_id] if isinstance(grp_id, int) else grp_id def __init__(self, source_id, name, tectonic_region_type): self.source_id = source_id = name self.tectonic_region_type = tectonic_region_type self.grp_id = -1 # set by the engine self.num_ruptures = 0 # set by the engine self.seed = None # set by the engine self.min_mag = 0 # set by the SourceConverter
[docs] def is_gridded(self): """ :returns: True if the source contains only gridded ruptures """ return False
[docs] @abc.abstractmethod def iter_ruptures(self, **kwargs): """ Get a generator object that yields probabilistic ruptures the source consists of. :returns: Generator of instances of sublclass of :class: `~openquake.hazardlib.source.rupture.BaseProbabilisticRupture`. """
[docs] def sample_ruptures(self, eff_num_ses): """ :param eff_num_ses: number of stochastic event sets * number of samples :yields: pairs (rupture, num_occurrences[num_samples]) """ rup_id = self.serial numpy.random.seed(self.serial) for grp_id in self.grp_ids: for rup, num_occ in self._sample_ruptures(eff_num_ses): rup.rup_id = rup_id rup_id += 1 yield rup, grp_id, num_occ
def _sample_ruptures(self, eff_num_ses): tom = getattr(self, 'temporal_occurrence_model', None) if tom: # time-independent source yield from self.sample_ruptures_poissonian(eff_num_ses) else: # time-dependent source (nonparametric) mutex_weight = getattr(self, 'mutex_weight', 1) for rup in self.iter_ruptures(): occurs = rup.sample_number_of_occurrences(eff_num_ses) if mutex_weight < 1: # consider only the occurrencies below the mutex_weight occurs *= (numpy.random.random(eff_num_ses) < mutex_weight) num_occ = occurs.sum() if num_occ: yield rup, num_occ
[docs] def get_mags(self): """ :returns: the magnitudes of the ruptures contained in the source """ mags = set() if hasattr(self, 'get_annual_occurrence_rates'): for mag, rate in self.get_annual_occurrence_rates(): mags.add(mag) elif hasattr(self, 'source_file'): # unbound UCERFSource mags.add(numpy.nan) else: # nonparametric for rup, pmf in if rup.mag >= self.min_mag: mags.add(rup.mag) return sorted(mags)
[docs] def sample_ruptures_poissonian(self, eff_num_ses): """ :param eff_num_ses: number of stochastic event sets * number of samples :yields: pairs (rupture, num_occurrences[num_samples]) """ tom = self.temporal_occurrence_model if not hasattr(self, 'nodal_plane_distribution'): # fault ruptures = list(self.iter_ruptures()) rates = numpy.array([rup.occurrence_rate for rup in ruptures]) occurs = numpy.random.poisson(rates * tom.time_span * eff_num_ses) for rup, num_occ in zip(ruptures, occurs): if num_occ: yield rup, num_occ return # else (multi)point sources and area sources rup_args = [] rates = [] for src in self: for mag, mag_occ_rate in src.get_annual_occurrence_rates(): if mag < self.min_mag: continue for np_prob, np in for hc_prob, hc_depth in args = (mag_occ_rate, np_prob, hc_prob, mag, np, hc_depth, src) rup_args.append(args) rates.append(mag_occ_rate * np_prob * hc_prob) eff_rates = numpy.array(rates) * tom.time_span * eff_num_ses occurs = numpy.random.poisson(eff_rates) for num_occ, args, rate in zip(occurs, rup_args, rates): if num_occ: mag_occ_rate, np_prob, hc_prob, mag, np, hc_depth, src = args hc = Point(latitude=src.location.latitude, longitude=src.location.longitude, depth=hc_depth) surface, _ = src._get_rupture_surface(mag, np, hc) rup = ParametricProbabilisticRupture( mag, np.rake, src.tectonic_region_type, hc, surface, rate, tom) yield rup, num_occ
[docs] @abc.abstractmethod def get_one_rupture(self, rupture_mutex=False): """ Yields one random rupture from a source """
def __iter__(self): """ Override to implement source splitting """ yield self
[docs] @abc.abstractmethod def count_ruptures(self): """ Return the number of ruptures that will be generated by the source. """
[docs] @abc.abstractmethod def get_min_max_mag(self): """ Return minimum and maximum magnitudes of the ruptures generated by the source. """
[docs] def modify(self, modification, parameters): """ Apply a single modificaton to the source parameters Reflects the modification method and calls it passing ``parameters`` as keyword arguments. Modifications can be applied one on top of another. The logic of stacking modifications is up to a specific source implementation. :param modification: String name representing the type of modification. :param parameters: Dictionary of parameters needed for modification. :raises ValueError: If ``modification`` is missing from the attribute `MODIFICATIONS`. """ if modification not in self.MODIFICATIONS: raise ValueError('Modification %s is not supported by %s' % (modification, type(self).__name__)) meth = getattr(self, 'modify_%s' % modification) meth(**parameters)
[docs]class ParametricSeismicSource(BaseSeismicSource, metaclass=abc.ABCMeta): """ Parametric Seismic Source generates earthquake ruptures from source parameters, and associated probabilities of occurrence are defined through a magnitude frequency distribution and a temporal occurrence model. :param mfd: Magnitude-Frequency distribution for the source. See :mod:`openquake.hazardlib.mfd`. :param rupture_mesh_spacing: The desired distance between two adjacent points in source's ruptures' mesh, in km. Mainly this parameter allows to balance the trade-off between time needed to compute the :meth:`distance <openquake.hazardlib.geo.surface.base.BaseSurface.get_min_distance>` between the rupture surface and a site and the precision of that computation. :param magnitude_scaling_relationship: Instance of subclass of :class:`openquake.hazardlib.scalerel.base.BaseMSR` to describe how does the area of the rupture depend on magnitude and rake. :param rupture_aspect_ratio: Float number representing how much source's ruptures are more wide than tall. Aspect ratio of 1 means ruptures have square shape, value below 1 means ruptures stretch vertically more than horizontally and vice versa. :param temporal_occurrence_model: Instance of :class:`openquake.hazardlib.tom.PoissonTOM` defining temporal occurrence model for calculating rupture occurrence probabilities :raises ValueError: If either rupture aspect ratio or rupture mesh spacing is not positive (if not None). """ def __init__(self, source_id, name, tectonic_region_type, mfd, rupture_mesh_spacing, magnitude_scaling_relationship, rupture_aspect_ratio, temporal_occurrence_model): super().__init__(source_id, name, tectonic_region_type) if rupture_mesh_spacing is not None and not rupture_mesh_spacing > 0: raise ValueError('rupture mesh spacing must be positive') if rupture_aspect_ratio is not None and not rupture_aspect_ratio > 0: raise ValueError('rupture aspect ratio must be positive') self.mfd = mfd self.rupture_mesh_spacing = rupture_mesh_spacing self.magnitude_scaling_relationship = magnitude_scaling_relationship self.rupture_aspect_ratio = rupture_aspect_ratio self.temporal_occurrence_model = temporal_occurrence_model
[docs] def get_annual_occurrence_rates(self, min_rate=0): """ Get a list of pairs "magnitude -- annual occurrence rate". The list is taken from assigned MFD object (see :meth:`openquake.hazardlib.mfd.base.BaseMFD.get_annual_occurrence_rates`) with simple filtering by rate applied. :param min_rate: A non-negative value to filter magnitudes by minimum annual occurrence rate. Only magnitudes with rates greater than that are included in the result list. :returns: A list of two-item tuples -- magnitudes and occurrence rates. """ scaling_rate = getattr(self, 'scaling_rate', 1) return [(mag, occ_rate * scaling_rate) for (mag, occ_rate) in self.mfd.get_annual_occurrence_rates() if (min_rate is None or occ_rate > min_rate) and mag >= self.min_mag]
[docs] def get_min_max_mag(self): """ Get the minimum and maximum magnitudes of the ruptures generated by the source from the underlying MFD. """ min_mag, max_mag = self.mfd.get_min_max_mag() return max(self.min_mag, min_mag), max_mag
def __repr__(self): """ String representation of a source, displaying the source class name and the source id. """ return '<%s %s>' % (self.__class__.__name__, self.source_id)
[docs] def get_one_rupture(self, rupture_mutex=False): """ Yields one random rupture from a source """ # The Mutex case is admitted only for non-parametric ruptures msg = 'Mutually exclusive ruptures are admitted only in case of' msg += ' non-parametric sources' assert (not rupture_mutex), msg # Set random seed and get the number of ruptures num_ruptures = self.count_ruptures() numpy.random.seed(self.seed) idx = numpy.random.choice(num_ruptures) # NOTE Would be nice to have a method generating a rupture given two # indexes, one for magnitude and one setting the position for i, rup in enumerate(self.iter_ruptures()): if i == idx: if hasattr(self, 'rup_id'): rup.rup_id = self.rup_id rup.idx = idx return rup