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gem/oq-engine
openquake/hazardlib/gsim/berge_thierry_2003.py
BergeThierryEtAl2003Ms._get_stddevs
def _get_stddevs(self, C, stddev_types, num_sites, mag_conversion_sigma): """ Return total standard deviation. """ assert all(stddev_type in self.DEFINED_FOR_STANDARD_DEVIATION_TYPES for stddev_type in stddev_types) sigma = np.zeros(num_sites) + C['sigma'] * np.log(10) sigma = np.sqrt(sigma ** 2 + (C['a'] ** 2) * (mag_conversion_sigma ** 2)) stddevs = [sigma for _ in stddev_types] return stddevs
python
def _get_stddevs(self, C, stddev_types, num_sites, mag_conversion_sigma): assert all(stddev_type in self.DEFINED_FOR_STANDARD_DEVIATION_TYPES for stddev_type in stddev_types) sigma = np.zeros(num_sites) + C['sigma'] * np.log(10) sigma = np.sqrt(sigma ** 2 + (C['a'] ** 2) * (mag_conversion_sigma ** 2)) stddevs = [sigma for _ in stddev_types] return stddevs
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Return total standard deviation.
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train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/gsim/berge_thierry_2003.py#L77-L88
gem/oq-engine
openquake/hazardlib/gsim/berge_thierry_2003.py
BergeThierryEtAl2003Ms._get_mean_and_stddevs
def _get_mean_and_stddevs(self, sites, rup, dists, imt, stddev_types, mag_conversion_sigma=0.0): """ See :meth:`superclass method <.base.GroundShakingIntensityModel.get_mean_and_stddevs>` for spec of input and result values. """ # extract dictionaries of coefficients specific to required # intensity measure type C = self.COEFFS[imt] # clip distance at 4 km, minimum distance for which the equation is # valid (see section 2.2.4, page 201). This also avoids singularity # in the equation rhypo = dists.rhypo rhypo[rhypo < 4.] = 4. mean = C['a'] * rup.mag + C['b'] * rhypo - np.log10(rhypo) mean[sites.vs30 >= 800] += C['c1'] mean[sites.vs30 < 800] += C['c2'] # convert from log10 to ln, and from cm/s2 to g mean = mean * np.log(10) - 2 * np.log(10) - np.log(g) stddevs = self._get_stddevs(C, stddev_types, rhypo.shape[0], mag_conversion_sigma=mag_conversion_sigma) return mean, stddevs
python
def _get_mean_and_stddevs(self, sites, rup, dists, imt, stddev_types, mag_conversion_sigma=0.0): C = self.COEFFS[imt] rhypo = dists.rhypo rhypo[rhypo < 4.] = 4. mean = C['a'] * rup.mag + C['b'] * rhypo - np.log10(rhypo) mean[sites.vs30 >= 800] += C['c1'] mean[sites.vs30 < 800] += C['c2'] mean = mean * np.log(10) - 2 * np.log(10) - np.log(g) stddevs = self._get_stddevs(C, stddev_types, rhypo.shape[0], mag_conversion_sigma=mag_conversion_sigma) return mean, stddevs
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See :meth:`superclass method <.base.GroundShakingIntensityModel.get_mean_and_stddevs>` for spec of input and result values.
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train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/gsim/berge_thierry_2003.py#L90-L118
gem/oq-engine
openquake/hazardlib/gsim/can15/sslab.py
SSlabCan15Mid.get_mean_and_stddevs
def get_mean_and_stddevs(self, sites, rup, dists, imt, stddev_types): """ See :meth:`superclass method <.base.GroundShakingIntensityModel.get_mean_and_stddevs>` for spec of input and result values. """ # get original values hslab = 50 # See info in GMPEt_Inslab_med.dat rjb, rrup = utils.get_equivalent_distance_inslab(rup.mag, dists.repi, hslab) dists.rjb = rjb dists.rrup = rrup mean, stddevs = super().get_mean_and_stddevs(sites, rup, dists, imt, stddev_types) cff = self.SITE_COEFFS[imt] mean_adj = np.log(np.exp(mean) * 10**cff['mf']) stddevs = [np.ones(len(dists.rrup))*get_sigma(imt)] return mean_adj, stddevs
python
def get_mean_and_stddevs(self, sites, rup, dists, imt, stddev_types): hslab = 50 rjb, rrup = utils.get_equivalent_distance_inslab(rup.mag, dists.repi, hslab) dists.rjb = rjb dists.rrup = rrup mean, stddevs = super().get_mean_and_stddevs(sites, rup, dists, imt, stddev_types) cff = self.SITE_COEFFS[imt] mean_adj = np.log(np.exp(mean) * 10**cff['mf']) stddevs = [np.ones(len(dists.rrup))*get_sigma(imt)] return mean_adj, stddevs
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train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/gsim/can15/sslab.py#L45-L63
gem/oq-engine
openquake/calculators/classical.py
get_src_ids
def get_src_ids(sources): """ :returns: a string with the source IDs of the given sources, stripping the extension after the colon, if any """ src_ids = [] for src in sources: long_src_id = src.source_id try: src_id, ext = long_src_id.rsplit(':', 1) except ValueError: src_id = long_src_id src_ids.append(src_id) return ' '.join(set(src_ids))
python
def get_src_ids(sources): src_ids = [] for src in sources: long_src_id = src.source_id try: src_id, ext = long_src_id.rsplit(':', 1) except ValueError: src_id = long_src_id src_ids.append(src_id) return ' '.join(set(src_ids))
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:returns: a string with the source IDs of the given sources, stripping the extension after the colon, if any
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train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/calculators/classical.py#L45-L59
gem/oq-engine
openquake/calculators/classical.py
get_extreme_poe
def get_extreme_poe(array, imtls): """ :param array: array of shape (L, G) with L=num_levels, G=num_gsims :param imtls: DictArray imt -> levels :returns: the maximum PoE corresponding to the maximum level for IMTs and GSIMs """ return max(array[imtls(imt).stop - 1].max() for imt in imtls)
python
def get_extreme_poe(array, imtls): return max(array[imtls(imt).stop - 1].max() for imt in imtls)
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:param array: array of shape (L, G) with L=num_levels, G=num_gsims :param imtls: DictArray imt -> levels :returns: the maximum PoE corresponding to the maximum level for IMTs and GSIMs
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train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/calculators/classical.py#L62-L69
gem/oq-engine
openquake/calculators/classical.py
classical_split_filter
def classical_split_filter(srcs, srcfilter, gsims, params, monitor): """ Split the given sources, filter the subsources and the compute the PoEs. Yield back subtasks if the split sources contain more than maxweight ruptures. """ # first check if we are sampling the sources ss = int(os.environ.get('OQ_SAMPLE_SOURCES', 0)) if ss: splits, stime = split_sources(srcs) srcs = readinput.random_filtered_sources(splits, srcfilter, ss) yield classical(srcs, srcfilter, gsims, params, monitor) return sources = [] with monitor("filtering/splitting sources"): for src, _sites in srcfilter(srcs): if src.num_ruptures >= params['maxweight']: splits, stime = split_sources([src]) sources.extend(srcfilter.filter(splits)) else: sources.append(src) blocks = list(block_splitter(sources, params['maxweight'], operator.attrgetter('num_ruptures'))) if blocks: # yield the first blocks (if any) and compute the last block in core # NB: the last block is usually the smallest one for block in blocks[:-1]: yield classical, block, srcfilter, gsims, params yield classical(blocks[-1], srcfilter, gsims, params, monitor)
python
def classical_split_filter(srcs, srcfilter, gsims, params, monitor): ss = int(os.environ.get('OQ_SAMPLE_SOURCES', 0)) if ss: splits, stime = split_sources(srcs) srcs = readinput.random_filtered_sources(splits, srcfilter, ss) yield classical(srcs, srcfilter, gsims, params, monitor) return sources = [] with monitor("filtering/splitting sources"): for src, _sites in srcfilter(srcs): if src.num_ruptures >= params['maxweight']: splits, stime = split_sources([src]) sources.extend(srcfilter.filter(splits)) else: sources.append(src) blocks = list(block_splitter(sources, params['maxweight'], operator.attrgetter('num_ruptures'))) if blocks: for block in blocks[:-1]: yield classical, block, srcfilter, gsims, params yield classical(blocks[-1], srcfilter, gsims, params, monitor)
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train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/calculators/classical.py#L72-L100
gem/oq-engine
openquake/calculators/classical.py
build_hazard_stats
def build_hazard_stats(pgetter, N, hstats, individual_curves, monitor): """ :param pgetter: an :class:`openquake.commonlib.getters.PmapGetter` :param N: the total number of sites :param hstats: a list of pairs (statname, statfunc) :param individual_curves: if True, also build the individual curves :param monitor: instance of Monitor :returns: a dictionary kind -> ProbabilityMap The "kind" is a string of the form 'rlz-XXX' or 'mean' of 'quantile-XXX' used to specify the kind of output. """ with monitor('combine pmaps'): pgetter.init() # if not already initialized try: pmaps = pgetter.get_pmaps() except IndexError: # no data return {} if sum(len(pmap) for pmap in pmaps) == 0: # no data return {} R = len(pmaps) imtls, poes, weights = pgetter.imtls, pgetter.poes, pgetter.weights pmap_by_kind = {} hmaps_stats = [] hcurves_stats = [] with monitor('compute stats'): for statname, stat in hstats.items(): pmap = compute_pmap_stats(pmaps, [stat], weights, imtls) hcurves_stats.append(pmap) if pgetter.poes: hmaps_stats.append( calc.make_hmap(pmap, pgetter.imtls, pgetter.poes)) if statname == 'mean' and R > 1 and N <= FEWSITES: pmap_by_kind['rlz_by_sid'] = rlz = {} for sid, pcurve in pmap.items(): rlz[sid] = util.closest_to_ref( [pm.setdefault(sid, 0).array for pm in pmaps], pcurve.array)['rlz'] if hcurves_stats: pmap_by_kind['hcurves-stats'] = hcurves_stats if hmaps_stats: pmap_by_kind['hmaps-stats'] = hmaps_stats if R > 1 and individual_curves or not hstats: pmap_by_kind['hcurves-rlzs'] = pmaps if pgetter.poes: with monitor('build individual hmaps'): pmap_by_kind['hmaps-rlzs'] = [ calc.make_hmap(pmap, imtls, poes) for pmap in pmaps] return pmap_by_kind
python
def build_hazard_stats(pgetter, N, hstats, individual_curves, monitor): with monitor('combine pmaps'): pgetter.init() try: pmaps = pgetter.get_pmaps() except IndexError: return {} if sum(len(pmap) for pmap in pmaps) == 0: return {} R = len(pmaps) imtls, poes, weights = pgetter.imtls, pgetter.poes, pgetter.weights pmap_by_kind = {} hmaps_stats = [] hcurves_stats = [] with monitor('compute stats'): for statname, stat in hstats.items(): pmap = compute_pmap_stats(pmaps, [stat], weights, imtls) hcurves_stats.append(pmap) if pgetter.poes: hmaps_stats.append( calc.make_hmap(pmap, pgetter.imtls, pgetter.poes)) if statname == 'mean' and R > 1 and N <= FEWSITES: pmap_by_kind['rlz_by_sid'] = rlz = {} for sid, pcurve in pmap.items(): rlz[sid] = util.closest_to_ref( [pm.setdefault(sid, 0).array for pm in pmaps], pcurve.array)['rlz'] if hcurves_stats: pmap_by_kind['hcurves-stats'] = hcurves_stats if hmaps_stats: pmap_by_kind['hmaps-stats'] = hmaps_stats if R > 1 and individual_curves or not hstats: pmap_by_kind['hcurves-rlzs'] = pmaps if pgetter.poes: with monitor('build individual hmaps'): pmap_by_kind['hmaps-rlzs'] = [ calc.make_hmap(pmap, imtls, poes) for pmap in pmaps] return pmap_by_kind
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train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/calculators/classical.py#L336-L384
gem/oq-engine
openquake/hazardlib/gsim/cauzzi_faccioli_2008_swiss.py
CauzziFaccioli2008SWISS01.get_mean_and_stddevs
def get_mean_and_stddevs(self, sites, rup, dists, imt, stddev_types): """ See :meth:`superclass method <.base.GroundShakingIntensityModel.get_mean_and_stddevs>` for spec of input and result values. """ sites.vs30 = 700 * np.ones(len(sites.vs30)) mean, stddevs = super().get_mean_and_stddevs( sites, rup, dists, imt, stddev_types) C = CauzziFaccioli2008SWISS01.COEFFS tau_ss = 'tau' log_phi_ss = np.log(10) mean, stddevs = _apply_adjustments( C, self.COEFFS_FS_ROCK[imt], tau_ss, mean, stddevs, sites, rup, dists.rhypo, imt, stddev_types, log_phi_ss) return mean, np.log(10 ** np.array(stddevs))
python
def get_mean_and_stddevs(self, sites, rup, dists, imt, stddev_types): sites.vs30 = 700 * np.ones(len(sites.vs30)) mean, stddevs = super().get_mean_and_stddevs( sites, rup, dists, imt, stddev_types) C = CauzziFaccioli2008SWISS01.COEFFS tau_ss = 'tau' log_phi_ss = np.log(10) mean, stddevs = _apply_adjustments( C, self.COEFFS_FS_ROCK[imt], tau_ss, mean, stddevs, sites, rup, dists.rhypo, imt, stddev_types, log_phi_ss) return mean, np.log(10 ** np.array(stddevs))
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See :meth:`superclass method <.base.GroundShakingIntensityModel.get_mean_and_stddevs>` for spec of input and result values.
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train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/gsim/cauzzi_faccioli_2008_swiss.py#L73-L94
gem/oq-engine
openquake/hazardlib/source/point.py
_get_rupture_dimensions
def _get_rupture_dimensions(src, mag, nodal_plane): """ Calculate and return the rupture length and width for given magnitude ``mag`` and nodal plane. :param src: a PointSource, AreaSource or MultiPointSource :param mag: a magnitude :param nodal_plane: Instance of :class:`openquake.hazardlib.geo.nodalplane.NodalPlane`. :returns: Tuple of two items: rupture length in width in km. The rupture area is calculated using method :meth:`~openquake.hazardlib.scalerel.base.BaseMSR.get_median_area` of source's magnitude-scaling relationship. In any case the returned dimensions multiplication is equal to that value. Than the area is decomposed to length and width with respect to source's rupture aspect ratio. If calculated rupture width being inclined by nodal plane's dip angle would not fit in between upper and lower seismogenic depth, the rupture width is shrunken to a maximum possible and rupture length is extended to preserve the same area. """ area = src.magnitude_scaling_relationship.get_median_area( mag, nodal_plane.rake) rup_length = math.sqrt(area * src.rupture_aspect_ratio) rup_width = area / rup_length seismogenic_layer_width = (src.lower_seismogenic_depth - src.upper_seismogenic_depth) max_width = (seismogenic_layer_width / math.sin(math.radians(nodal_plane.dip))) if rup_width > max_width: rup_width = max_width rup_length = area / rup_width return rup_length, rup_width
python
def _get_rupture_dimensions(src, mag, nodal_plane): area = src.magnitude_scaling_relationship.get_median_area( mag, nodal_plane.rake) rup_length = math.sqrt(area * src.rupture_aspect_ratio) rup_width = area / rup_length seismogenic_layer_width = (src.lower_seismogenic_depth - src.upper_seismogenic_depth) max_width = (seismogenic_layer_width / math.sin(math.radians(nodal_plane.dip))) if rup_width > max_width: rup_width = max_width rup_length = area / rup_width return rup_length, rup_width
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Calculate and return the rupture length and width for given magnitude ``mag`` and nodal plane. :param src: a PointSource, AreaSource or MultiPointSource :param mag: a magnitude :param nodal_plane: Instance of :class:`openquake.hazardlib.geo.nodalplane.NodalPlane`. :returns: Tuple of two items: rupture length in width in km. The rupture area is calculated using method :meth:`~openquake.hazardlib.scalerel.base.BaseMSR.get_median_area` of source's magnitude-scaling relationship. In any case the returned dimensions multiplication is equal to that value. Than the area is decomposed to length and width with respect to source's rupture aspect ratio. If calculated rupture width being inclined by nodal plane's dip angle would not fit in between upper and lower seismogenic depth, the rupture width is shrunken to a maximum possible and rupture length is extended to preserve the same area.
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train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/source/point.py#L29-L67
gem/oq-engine
openquake/hmtk/seismicity/gcmt_utils.py
tensor_components_to_use
def tensor_components_to_use(mrr, mtt, mpp, mrt, mrp, mtp): ''' Converts components to Up, South, East definition:: USE = [[mrr, mrt, mrp], [mtt, mtt, mtp], [mrp, mtp, mpp]] ''' return np.array([[mrr, mrt, mrp], [mrt, mtt, mtp], [mrp, mtp, mpp]])
python
def tensor_components_to_use(mrr, mtt, mpp, mrt, mrp, mtp): return np.array([[mrr, mrt, mrp], [mrt, mtt, mtp], [mrp, mtp, mpp]])
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Converts components to Up, South, East definition:: USE = [[mrr, mrt, mrp], [mtt, mtt, mtp], [mrp, mtp, mpp]]
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train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hmtk/seismicity/gcmt_utils.py#L55-L63
gem/oq-engine
openquake/hmtk/seismicity/gcmt_utils.py
get_azimuth_plunge
def get_azimuth_plunge(vect, degrees=True): ''' For a given vector in USE format, retrieve the azimuth and plunge ''' if vect[0] > 0: vect = -1. * np.copy(vect) vect_hor = sqrt(vect[1] ** 2. + vect[2] ** 2.) plunge = atan2(-vect[0], vect_hor) azimuth = atan2(vect[2], -vect[1]) if degrees: icr = 180. / pi return icr * azimuth % 360., icr * plunge else: return azimuth % (2. * pi), plunge
python
def get_azimuth_plunge(vect, degrees=True): if vect[0] > 0: vect = -1. * np.copy(vect) vect_hor = sqrt(vect[1] ** 2. + vect[2] ** 2.) plunge = atan2(-vect[0], vect_hor) azimuth = atan2(vect[2], -vect[1]) if degrees: icr = 180. / pi return icr * azimuth % 360., icr * plunge else: return azimuth % (2. * pi), plunge
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train
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gem/oq-engine
openquake/hmtk/seismicity/gcmt_utils.py
use_to_ned
def use_to_ned(tensor): ''' Converts a tensor in USE coordinate sytem to NED ''' return np.array(ROT_NED_USE.T * np.matrix(tensor) * ROT_NED_USE)
python
def use_to_ned(tensor): return np.array(ROT_NED_USE.T * np.matrix(tensor) * ROT_NED_USE)
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train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hmtk/seismicity/gcmt_utils.py#L101-L105
gem/oq-engine
openquake/hmtk/seismicity/gcmt_utils.py
ned_to_use
def ned_to_use(tensor): ''' Converts a tensor in NED coordinate sytem to USE ''' return np.array(ROT_NED_USE * np.matrix(tensor) * ROT_NED_USE.T)
python
def ned_to_use(tensor): return np.array(ROT_NED_USE * np.matrix(tensor) * ROT_NED_USE.T)
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train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hmtk/seismicity/gcmt_utils.py#L108-L112
gem/oq-engine
openquake/hmtk/seismicity/gcmt_utils.py
tensor_to_6component
def tensor_to_6component(tensor, frame='USE'): ''' Returns a tensor to six component vector [Mrr, Mtt, Mpp, Mrt, Mrp, Mtp] ''' if 'NED' in frame: tensor = ned_to_use(tensor) return [tensor[0, 0], tensor[1, 1], tensor[2, 2], tensor[0, 1], tensor[0, 2], tensor[1, 2]]
python
def tensor_to_6component(tensor, frame='USE'): if 'NED' in frame: tensor = ned_to_use(tensor) return [tensor[0, 0], tensor[1, 1], tensor[2, 2], tensor[0, 1], tensor[0, 2], tensor[1, 2]]
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train
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gem/oq-engine
openquake/hmtk/seismicity/gcmt_utils.py
normalise_tensor
def normalise_tensor(tensor): ''' Normalise the tensor by dividing it by its norm, defined such that np.sqrt(X:X) ''' tensor_norm = np.linalg.norm(tensor) return tensor / tensor_norm, tensor_norm
python
def normalise_tensor(tensor): tensor_norm = np.linalg.norm(tensor) return tensor / tensor_norm, tensor_norm
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Normalise the tensor by dividing it by its norm, defined such that np.sqrt(X:X)
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train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hmtk/seismicity/gcmt_utils.py#L126-L132
gem/oq-engine
openquake/hmtk/seismicity/gcmt_utils.py
eigendecompose
def eigendecompose(tensor, normalise=False): ''' Performs and eigendecomposition of the tensor and orders into descending eigenvalues ''' if normalise: tensor, tensor_norm = normalise_tensor(tensor) else: tensor_norm = 1. eigvals, eigvects = np.linalg.eigh(tensor, UPLO='U') isrt = np.argsort(eigvals) eigenvalues = eigvals[isrt] * tensor_norm eigenvectors = eigvects[:, isrt] return eigenvalues, eigenvectors
python
def eigendecompose(tensor, normalise=False): if normalise: tensor, tensor_norm = normalise_tensor(tensor) else: tensor_norm = 1. eigvals, eigvects = np.linalg.eigh(tensor, UPLO='U') isrt = np.argsort(eigvals) eigenvalues = eigvals[isrt] * tensor_norm eigenvectors = eigvects[:, isrt] return eigenvalues, eigenvectors
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train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hmtk/seismicity/gcmt_utils.py#L135-L150
gem/oq-engine
openquake/hmtk/seismicity/gcmt_utils.py
matrix_to_euler
def matrix_to_euler(rotmat): '''Inverse of euler_to_matrix().''' if not isinstance(rotmat, np.matrixlib.defmatrix.matrix): # As this calculation relies on np.matrix algebra - convert array to # matrix rotmat = np.matrix(rotmat) def cvec(x, y, z): return np.matrix([[x, y, z]]).T ex = cvec(1., 0., 0.) ez = cvec(0., 0., 1.) exs = rotmat.T * ex ezs = rotmat.T * ez enodes = np.cross(ez.T, ezs.T).T if np.linalg.norm(enodes) < 1e-10: enodes = exs enodess = rotmat * enodes cos_alpha = float((ez.T * ezs)) if cos_alpha > 1.: cos_alpha = 1. if cos_alpha < -1.: cos_alpha = -1. alpha = acos(cos_alpha) beta = np.mod(atan2(enodes[1, 0], enodes[0, 0]), pi * 2.) gamma = np.mod(-atan2(enodess[1, 0], enodess[0, 0]), pi * 2.) return unique_euler(alpha, beta, gamma)
python
def matrix_to_euler(rotmat): if not isinstance(rotmat, np.matrixlib.defmatrix.matrix): rotmat = np.matrix(rotmat) def cvec(x, y, z): return np.matrix([[x, y, z]]).T ex = cvec(1., 0., 0.) ez = cvec(0., 0., 1.) exs = rotmat.T * ex ezs = rotmat.T * ez enodes = np.cross(ez.T, ezs.T).T if np.linalg.norm(enodes) < 1e-10: enodes = exs enodess = rotmat * enodes cos_alpha = float((ez.T * ezs)) if cos_alpha > 1.: cos_alpha = 1. if cos_alpha < -1.: cos_alpha = -1. alpha = acos(cos_alpha) beta = np.mod(atan2(enodes[1, 0], enodes[0, 0]), pi * 2.) gamma = np.mod(-atan2(enodess[1, 0], enodess[0, 0]), pi * 2.) return unique_euler(alpha, beta, gamma)
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train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hmtk/seismicity/gcmt_utils.py#L153-L178
gem/oq-engine
openquake/hmtk/seismicity/gcmt_utils.py
unique_euler
def unique_euler(alpha, beta, gamma): ''' Uniquify euler angle triplet. Put euler angles into ranges compatible with (dip,strike,-rake) in seismology: alpha (dip) : [0, pi/2] beta (strike) : [0, 2*pi) gamma (-rake) : [-pi, pi) If alpha is near to zero, beta is replaced by beta+gamma and gamma is set to zero, to prevent that additional ambiguity. If alpha is near to pi/2, beta is put into the range [0,pi). ''' alpha = np.mod(alpha, 2.0 * pi) if 0.5 * pi < alpha and alpha <= pi: alpha = pi - alpha beta = beta + pi gamma = 2.0 * pi - gamma elif pi < alpha and alpha <= 1.5 * pi: alpha = alpha - pi gamma = pi - gamma elif 1.5 * pi < alpha and alpha <= 2.0 * pi: alpha = 2.0 * pi - alpha beta = beta + pi gamma = pi + gamma alpha = np.mod(alpha, 2.0 * pi) beta = np.mod(beta, 2.0 * pi) gamma = np.mod(gamma + pi, 2.0 * pi) - pi # If dip is exactly 90 degrees, one is still # free to choose between looking at the plane from either side. # Choose to look at such that beta is in the range [0,180) # This should prevent some problems, when dip is close to 90 degrees: if fabs(alpha - 0.5 * pi) < 1e-10: alpha = 0.5 * pi if fabs(beta - pi) < 1e-10: beta = pi if fabs(beta - 2. * pi) < 1e-10: beta = 0. if fabs(beta) < 1e-10: beta = 0. if alpha == 0.5 * pi and beta >= pi: gamma = - gamma beta = np.mod(beta - pi, 2.0 * pi) gamma = np.mod(gamma + pi, 2.0 * pi) - pi assert 0. <= beta < pi assert -pi <= gamma < pi if alpha < 1e-7: beta = np.mod(beta + gamma, 2.0 * pi) gamma = 0. return (alpha, beta, gamma)
python
def unique_euler(alpha, beta, gamma): alpha = np.mod(alpha, 2.0 * pi) if 0.5 * pi < alpha and alpha <= pi: alpha = pi - alpha beta = beta + pi gamma = 2.0 * pi - gamma elif pi < alpha and alpha <= 1.5 * pi: alpha = alpha - pi gamma = pi - gamma elif 1.5 * pi < alpha and alpha <= 2.0 * pi: alpha = 2.0 * pi - alpha beta = beta + pi gamma = pi + gamma alpha = np.mod(alpha, 2.0 * pi) beta = np.mod(beta, 2.0 * pi) gamma = np.mod(gamma + pi, 2.0 * pi) - pi if fabs(alpha - 0.5 * pi) < 1e-10: alpha = 0.5 * pi if fabs(beta - pi) < 1e-10: beta = pi if fabs(beta - 2. * pi) < 1e-10: beta = 0. if fabs(beta) < 1e-10: beta = 0. if alpha == 0.5 * pi and beta >= pi: gamma = - gamma beta = np.mod(beta - pi, 2.0 * pi) gamma = np.mod(gamma + pi, 2.0 * pi) - pi assert 0. <= beta < pi assert -pi <= gamma < pi if alpha < 1e-7: beta = np.mod(beta + gamma, 2.0 * pi) gamma = 0. return (alpha, beta, gamma)
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Uniquify euler angle triplet. Put euler angles into ranges compatible with (dip,strike,-rake) in seismology: alpha (dip) : [0, pi/2] beta (strike) : [0, 2*pi) gamma (-rake) : [-pi, pi) If alpha is near to zero, beta is replaced by beta+gamma and gamma is set to zero, to prevent that additional ambiguity. If alpha is near to pi/2, beta is put into the range [0,pi).
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train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hmtk/seismicity/gcmt_utils.py#L181-L238
gem/oq-engine
openquake/hmtk/seismicity/gcmt_utils.py
moment_magnitude_scalar
def moment_magnitude_scalar(moment): ''' Uses Hanks & Kanamori formula for calculating moment magnitude from a scalar moment (Nm) ''' if isinstance(moment, np.ndarray): return (2. / 3.) * (np.log10(moment) - 9.05) else: return (2. / 3.) * (log10(moment) - 9.05)
python
def moment_magnitude_scalar(moment): if isinstance(moment, np.ndarray): return (2. / 3.) * (np.log10(moment) - 9.05) else: return (2. / 3.) * (log10(moment) - 9.05)
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Uses Hanks & Kanamori formula for calculating moment magnitude from a scalar moment (Nm)
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train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hmtk/seismicity/gcmt_utils.py#L241-L249
gem/oq-engine
openquake/hazardlib/gsim/boore_atkinson_2008.py
BooreAtkinson2008.get_mean_and_stddevs
def get_mean_and_stddevs(self, sites, rup, dists, imt, stddev_types): """ See :meth:`superclass method <.base.GroundShakingIntensityModel.get_mean_and_stddevs>` for spec of input and result values. """ # extracting dictionary of coefficients specific to required # intensity measure type. C = self.COEFFS[imt] C_SR = self.COEFFS_SOIL_RESPONSE[imt] # compute PGA on rock conditions - needed to compute non-linear # site amplification term pga4nl = self._get_pga_on_rock(rup, dists, C) # equation 1, pag 106, without sigma term, that is only the first 3 # terms. The third term (site amplification) is computed as given in # equation (6), that is the sum of a linear term - equation (7) - and # a non-linear one - equations (8a) to (8c). # Mref, Rref values are given in the caption to table 6, pag 119. if imt == PGA(): # avoid recomputing PGA on rock, just add site terms mean = np.log(pga4nl) + \ self._get_site_amplification_linear(sites.vs30, C_SR) + \ self._get_site_amplification_non_linear(sites.vs30, pga4nl, C_SR) else: mean = self._compute_magnitude_scaling(rup, C) + \ self._compute_distance_scaling(rup, dists, C) + \ self._get_site_amplification_linear(sites.vs30, C_SR) + \ self._get_site_amplification_non_linear(sites.vs30, pga4nl, C_SR) stddevs = self._get_stddevs(C, stddev_types, num_sites=len(sites.vs30)) return mean, stddevs
python
def get_mean_and_stddevs(self, sites, rup, dists, imt, stddev_types): C = self.COEFFS[imt] C_SR = self.COEFFS_SOIL_RESPONSE[imt] pga4nl = self._get_pga_on_rock(rup, dists, C) if imt == PGA(): mean = np.log(pga4nl) + \ self._get_site_amplification_linear(sites.vs30, C_SR) + \ self._get_site_amplification_non_linear(sites.vs30, pga4nl, C_SR) else: mean = self._compute_magnitude_scaling(rup, C) + \ self._compute_distance_scaling(rup, dists, C) + \ self._get_site_amplification_linear(sites.vs30, C_SR) + \ self._get_site_amplification_non_linear(sites.vs30, pga4nl, C_SR) stddevs = self._get_stddevs(C, stddev_types, num_sites=len(sites.vs30)) return mean, stddevs
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See :meth:`superclass method <.base.GroundShakingIntensityModel.get_mean_and_stddevs>` for spec of input and result values.
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train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/gsim/boore_atkinson_2008.py#L78-L113
gem/oq-engine
openquake/hazardlib/gsim/boore_atkinson_2008.py
BooreAtkinson2008._compute_distance_scaling
def _compute_distance_scaling(self, rup, dists, C): """ Compute distance-scaling term, equations (3) and (4), pag 107. """ Mref = 4.5 Rref = 1.0 R = np.sqrt(dists.rjb ** 2 + C['h'] ** 2) return (C['c1'] + C['c2'] * (rup.mag - Mref)) * np.log(R / Rref) + \ C['c3'] * (R - Rref)
python
def _compute_distance_scaling(self, rup, dists, C): Mref = 4.5 Rref = 1.0 R = np.sqrt(dists.rjb ** 2 + C['h'] ** 2) return (C['c1'] + C['c2'] * (rup.mag - Mref)) * np.log(R / Rref) + \ C['c3'] * (R - Rref)
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Compute distance-scaling term, equations (3) and (4), pag 107.
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train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/gsim/boore_atkinson_2008.py#L130-L138
gem/oq-engine
openquake/hazardlib/gsim/boore_atkinson_2008.py
BooreAtkinson2008._compute_magnitude_scaling
def _compute_magnitude_scaling(self, rup, C): """ Compute magnitude-scaling term, equations (5a) and (5b), pag 107. """ U, SS, NS, RS = self._get_fault_type_dummy_variables(rup) if rup.mag <= C['Mh']: return C['e1'] * U + C['e2'] * SS + C['e3'] * NS + C['e4'] * RS + \ C['e5'] * (rup.mag - C['Mh']) + \ C['e6'] * (rup.mag - C['Mh']) ** 2 else: return C['e1'] * U + C['e2'] * SS + C['e3'] * NS + C['e4'] * RS + \ C['e7'] * (rup.mag - C['Mh'])
python
def _compute_magnitude_scaling(self, rup, C): U, SS, NS, RS = self._get_fault_type_dummy_variables(rup) if rup.mag <= C['Mh']: return C['e1'] * U + C['e2'] * SS + C['e3'] * NS + C['e4'] * RS + \ C['e5'] * (rup.mag - C['Mh']) + \ C['e6'] * (rup.mag - C['Mh']) ** 2 else: return C['e1'] * U + C['e2'] * SS + C['e3'] * NS + C['e4'] * RS + \ C['e7'] * (rup.mag - C['Mh'])
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Compute magnitude-scaling term, equations (5a) and (5b), pag 107.
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train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/gsim/boore_atkinson_2008.py#L140-L151
gem/oq-engine
openquake/hazardlib/gsim/boore_atkinson_2008.py
BooreAtkinson2008._get_pga_on_rock
def _get_pga_on_rock(self, rup, dists, _C): """ Compute and return PGA on rock conditions (that is vs30 = 760.0 m/s). This is needed to compute non-linear site amplification term """ # Median PGA in g for Vref = 760.0, without site amplification, # that is equation (1) pag 106, without the third and fourth terms # Mref and Rref values are given in the caption to table 6, pag 119 # Note that in the original paper, the caption reads: # "Distance-scaling coefficients (Mref=4.5 and Rref=1.0 km for all # periods, except Rref=5.0 km for pga4nl)". However this is a mistake # as reported in http://www.daveboore.com/pubs_online.php: # ERRATUM: 27 August 2008. Tom Blake pointed out that the caption to # Table 6 should read "Distance-scaling coefficients (Mref=4.5 and # Rref=1.0 km for all periods)". C_pga = self.COEFFS[PGA()] pga4nl = np.exp(self._compute_magnitude_scaling(rup, C_pga) + self._compute_distance_scaling(rup, dists, C_pga)) return pga4nl
python
def _get_pga_on_rock(self, rup, dists, _C): C_pga = self.COEFFS[PGA()] pga4nl = np.exp(self._compute_magnitude_scaling(rup, C_pga) + self._compute_distance_scaling(rup, dists, C_pga)) return pga4nl
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Compute and return PGA on rock conditions (that is vs30 = 760.0 m/s). This is needed to compute non-linear site amplification term
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train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/gsim/boore_atkinson_2008.py#L187-L206
gem/oq-engine
openquake/hazardlib/gsim/boore_atkinson_2008.py
BooreAtkinson2008._get_site_amplification_non_linear
def _get_site_amplification_non_linear(self, vs30, pga4nl, C): """ Compute site amplification non-linear term, equations (8a) to (13d), pag 108-109. """ # non linear slope bnl = self._compute_non_linear_slope(vs30, C) # compute the actual non-linear term return self._compute_non_linear_term(pga4nl, bnl)
python
def _get_site_amplification_non_linear(self, vs30, pga4nl, C): bnl = self._compute_non_linear_slope(vs30, C) return self._compute_non_linear_term(pga4nl, bnl)
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Compute site amplification non-linear term, equations (8a) to (13d), pag 108-109.
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train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/gsim/boore_atkinson_2008.py#L208-L216
gem/oq-engine
openquake/hazardlib/gsim/boore_atkinson_2008.py
BooreAtkinson2008._compute_non_linear_slope
def _compute_non_linear_slope(self, vs30, C): """ Compute non-linear slope factor, equations (13a) to (13d), pag 108-109. """ V1 = 180.0 V2 = 300.0 Vref = 760.0 # equation (13d), values are zero for vs30 >= Vref = 760.0 bnl = np.zeros(vs30.shape) # equation (13a) idx = vs30 <= V1 bnl[idx] = C['b1'] # equation (13b) idx = np.where((vs30 > V1) & (vs30 <= V2)) bnl[idx] = (C['b1'] - C['b2']) * \ np.log(vs30[idx] / V2) / np.log(V1 / V2) + C['b2'] # equation (13c) idx = np.where((vs30 > V2) & (vs30 < Vref)) bnl[idx] = C['b2'] * np.log(vs30[idx] / Vref) / np.log(V2 / Vref) return bnl
python
def _compute_non_linear_slope(self, vs30, C): V1 = 180.0 V2 = 300.0 Vref = 760.0 bnl = np.zeros(vs30.shape) idx = vs30 <= V1 bnl[idx] = C['b1'] idx = np.where((vs30 > V1) & (vs30 <= V2)) bnl[idx] = (C['b1'] - C['b2']) * \ np.log(vs30[idx] / V2) / np.log(V1 / V2) + C['b2'] idx = np.where((vs30 > V2) & (vs30 < Vref)) bnl[idx] = C['b2'] * np.log(vs30[idx] / Vref) / np.log(V2 / Vref) return bnl
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Compute non-linear slope factor, equations (13a) to (13d), pag 108-109.
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train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/gsim/boore_atkinson_2008.py#L218-L242
gem/oq-engine
openquake/hazardlib/gsim/boore_atkinson_2008.py
BooreAtkinson2008._compute_non_linear_term
def _compute_non_linear_term(self, pga4nl, bnl): """ Compute non-linear term, equation (8a) to (8c), pag 108. """ fnl = np.zeros(pga4nl.shape) a1 = 0.03 a2 = 0.09 pga_low = 0.06 # equation (8a) idx = pga4nl <= a1 fnl[idx] = bnl[idx] * np.log(pga_low / 0.1) # equation (8b) idx = np.where((pga4nl > a1) & (pga4nl <= a2)) delta_x = np.log(a2 / a1) delta_y = bnl[idx] * np.log(a2 / pga_low) c = (3 * delta_y - bnl[idx] * delta_x) / delta_x ** 2 d = -(2 * delta_y - bnl[idx] * delta_x) / delta_x ** 3 fnl[idx] = bnl[idx] * np.log(pga_low / 0.1) +\ c * (np.log(pga4nl[idx] / a1) ** 2) + \ d * (np.log(pga4nl[idx] / a1) ** 3) # equation (8c) idx = pga4nl > a2 fnl[idx] = np.squeeze(bnl[idx]) * np.log(pga4nl[idx] / 0.1) return fnl
python
def _compute_non_linear_term(self, pga4nl, bnl): fnl = np.zeros(pga4nl.shape) a1 = 0.03 a2 = 0.09 pga_low = 0.06 idx = pga4nl <= a1 fnl[idx] = bnl[idx] * np.log(pga_low / 0.1) idx = np.where((pga4nl > a1) & (pga4nl <= a2)) delta_x = np.log(a2 / a1) delta_y = bnl[idx] * np.log(a2 / pga_low) c = (3 * delta_y - bnl[idx] * delta_x) / delta_x ** 2 d = -(2 * delta_y - bnl[idx] * delta_x) / delta_x ** 3 fnl[idx] = bnl[idx] * np.log(pga_low / 0.1) +\ c * (np.log(pga4nl[idx] / a1) ** 2) + \ d * (np.log(pga4nl[idx] / a1) ** 3) idx = pga4nl > a2 fnl[idx] = np.squeeze(bnl[idx]) * np.log(pga4nl[idx] / 0.1) return fnl
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Compute non-linear term, equation (8a) to (8c), pag 108.
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train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/gsim/boore_atkinson_2008.py#L244-L273
gem/oq-engine
openquake/hazardlib/gsim/boore_atkinson_2008.py
Atkinson2010Hawaii.get_mean_and_stddevs
def get_mean_and_stddevs(self, sites, rup, dists, imt, stddev_types): """ Using a frequency dependent correction for the mean ground motion. Standard deviation is fixed. """ mean, stddevs = super().get_mean_and_stddevs(sites, rup, dists, imt, stddev_types) # Defining frequency if imt == PGA(): freq = 50.0 elif imt == PGV(): freq = 2.0 else: freq = 1./imt.period # Equation 3 of Atkinson (2010) x1 = np.min([-0.18+0.17*np.log10(freq), 0]) # Equation 4 a-b-c of Atkinson (2010) if rup.hypo_depth < 20.0: x0 = np.max([0.217-0.321*np.log10(freq), 0]) elif rup.hypo_depth > 35.0: x0 = np.min([0.263+0.0924*np.log10(freq), 0.35]) else: x0 = 0.2 # Limiting calculation distance to 1km # (as suggested by C. Bruce Worden) rjb = [d if d > 1 else 1 for d in dists.rjb] # Equation 2 and 5 of Atkinson (2010) mean += (x0 + x1*np.log10(rjb))/np.log10(np.e) return mean, stddevs
python
def get_mean_and_stddevs(self, sites, rup, dists, imt, stddev_types): mean, stddevs = super().get_mean_and_stddevs(sites, rup, dists, imt, stddev_types) if imt == PGA(): freq = 50.0 elif imt == PGV(): freq = 2.0 else: freq = 1./imt.period x1 = np.min([-0.18+0.17*np.log10(freq), 0]) if rup.hypo_depth < 20.0: x0 = np.max([0.217-0.321*np.log10(freq), 0]) elif rup.hypo_depth > 35.0: x0 = np.min([0.263+0.0924*np.log10(freq), 0.35]) else: x0 = 0.2 rjb = [d if d > 1 else 1 for d in dists.rjb] mean += (x0 + x1*np.log10(rjb))/np.log10(np.e) return mean, stddevs
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Using a frequency dependent correction for the mean ground motion. Standard deviation is fixed.
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train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/gsim/boore_atkinson_2008.py#L383-L416
gem/oq-engine
openquake/hazardlib/gsim/boore_atkinson_2008.py
Atkinson2010Hawaii._get_stddevs
def _get_stddevs(self, C, stddev_types, num_sites): """ Return total standard deviation. """ assert all(stddev_type in self.DEFINED_FOR_STANDARD_DEVIATION_TYPES for stddev_type in stddev_types) # Using a frequency independent value of sigma as recommended # in the caption of Table 2 of Atkinson (2010) stddevs = [0.26/np.log10(np.e) + np.zeros(num_sites)] return stddevs
python
def _get_stddevs(self, C, stddev_types, num_sites): assert all(stddev_type in self.DEFINED_FOR_STANDARD_DEVIATION_TYPES for stddev_type in stddev_types) stddevs = [0.26/np.log10(np.e) + np.zeros(num_sites)] return stddevs
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Return total standard deviation.
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train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/gsim/boore_atkinson_2008.py#L418-L429
gem/oq-engine
openquake/commonlib/rlzs_assoc.py
accept_path
def accept_path(path, ref_path): """ :param path: a logic tree path (list or tuple of strings) :param ref_path: reference logic tree path :returns: True if `path` is consistent with `ref_path`, False otherwise >>> accept_path(['SM2'], ('SM2', 'a3b1')) False >>> accept_path(['SM2', '@'], ('SM2', 'a3b1')) True >>> accept_path(['@', 'a3b1'], ('SM2', 'a3b1')) True >>> accept_path('@@', ('SM2', 'a3b1')) True """ if len(path) != len(ref_path): return False for a, b in zip(path, ref_path): if a != '@' and a != b: return False return True
python
def accept_path(path, ref_path): if len(path) != len(ref_path): return False for a, b in zip(path, ref_path): if a != '@' and a != b: return False return True
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train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/commonlib/rlzs_assoc.py#L226-L246
gem/oq-engine
openquake/commonlib/rlzs_assoc.py
get_rlzs_assoc
def get_rlzs_assoc(cinfo, sm_lt_path=None, trts=None): """ :param cinfo: a :class:`openquake.commonlib.source.CompositionInfo` :param sm_lt_path: logic tree path tuple used to select a source model :param trts: tectonic region types to accept """ assoc = RlzsAssoc(cinfo) offset = 0 trtset = set(cinfo.gsim_lt.values) for smodel in cinfo.source_models: # discard source models with non-acceptable lt_path if sm_lt_path and not accept_path(smodel.path, sm_lt_path): continue # collect the effective tectonic region types and ruptures trts_ = set() for sg in smodel.src_groups: if sg.eff_ruptures: if (trts and sg.trt in trts) or not trts: trts_.add(sg.trt) # recompute the GSIM logic tree if needed if trts_ != {'*'} and trtset != trts_: before = cinfo.gsim_lt.get_num_paths() gsim_lt = cinfo.gsim_lt.reduce(trts_) after = gsim_lt.get_num_paths() if sm_lt_path and before > after: # print the warning only when saving the logic tree, # i.e. when called with sm_lt_path in store_rlz_info logging.warning('Reducing the logic tree of %s from %d to %d ' 'realizations', smodel.name, before, after) gsim_rlzs = list(gsim_lt) all_trts = list(gsim_lt.values) else: gsim_rlzs = list(cinfo.gsim_lt) all_trts = list(cinfo.gsim_lt.values) rlzs = cinfo._get_rlzs(smodel, gsim_rlzs, cinfo.seed + offset) assoc._add_realizations(offset, smodel, all_trts, rlzs) offset += len(rlzs) if assoc.realizations: assoc._init() return assoc
python
def get_rlzs_assoc(cinfo, sm_lt_path=None, trts=None): assoc = RlzsAssoc(cinfo) offset = 0 trtset = set(cinfo.gsim_lt.values) for smodel in cinfo.source_models: if sm_lt_path and not accept_path(smodel.path, sm_lt_path): continue trts_ = set() for sg in smodel.src_groups: if sg.eff_ruptures: if (trts and sg.trt in trts) or not trts: trts_.add(sg.trt) if trts_ != {'*'} and trtset != trts_: before = cinfo.gsim_lt.get_num_paths() gsim_lt = cinfo.gsim_lt.reduce(trts_) after = gsim_lt.get_num_paths() if sm_lt_path and before > after: logging.warning('Reducing the logic tree of %s from %d to %d ' 'realizations', smodel.name, before, after) gsim_rlzs = list(gsim_lt) all_trts = list(gsim_lt.values) else: gsim_rlzs = list(cinfo.gsim_lt) all_trts = list(cinfo.gsim_lt.values) rlzs = cinfo._get_rlzs(smodel, gsim_rlzs, cinfo.seed + offset) assoc._add_realizations(offset, smodel, all_trts, rlzs) offset += len(rlzs) if assoc.realizations: assoc._init() return assoc
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:param cinfo: a :class:`openquake.commonlib.source.CompositionInfo` :param sm_lt_path: logic tree path tuple used to select a source model :param trts: tectonic region types to accept
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train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/commonlib/rlzs_assoc.py#L249-L292
gem/oq-engine
openquake/commonlib/rlzs_assoc.py
RlzsAssoc.get_rlzs_by_gsim
def get_rlzs_by_gsim(self, trt_or_grp_id, sm_id=None): """ :param trt_or_grp_id: a tectonic region type or a source group ID :param sm_id: source model ordinal (or None) :returns: a dictionary gsim -> rlzs """ if isinstance(trt_or_grp_id, (int, U16, U32)): # grp_id trt = self.csm_info.trt_by_grp[trt_or_grp_id] sm_id = self.csm_info.get_sm_by_grp()[trt_or_grp_id] else: # assume TRT string trt = trt_or_grp_id acc = collections.defaultdict(list) if sm_id is None: # full dictionary for rlz, gsim_by_trt in zip(self.realizations, self.gsim_by_trt): acc[gsim_by_trt[trt]].append(rlz.ordinal) else: # dictionary for the selected source model for rlz in self.rlzs_by_smodel[sm_id]: gsim_by_trt = self.gsim_by_trt[rlz.ordinal] try: # if there is a single TRT [gsim] = gsim_by_trt.values() except ValueError: # there is more than 1 TRT gsim = gsim_by_trt[trt] acc[gsim].append(rlz.ordinal) return {gsim: numpy.array(acc[gsim], dtype=U16) for gsim in sorted(acc)}
python
def get_rlzs_by_gsim(self, trt_or_grp_id, sm_id=None): if isinstance(trt_or_grp_id, (int, U16, U32)): trt = self.csm_info.trt_by_grp[trt_or_grp_id] sm_id = self.csm_info.get_sm_by_grp()[trt_or_grp_id] else: trt = trt_or_grp_id acc = collections.defaultdict(list) if sm_id is None: for rlz, gsim_by_trt in zip(self.realizations, self.gsim_by_trt): acc[gsim_by_trt[trt]].append(rlz.ordinal) else: for rlz in self.rlzs_by_smodel[sm_id]: gsim_by_trt = self.gsim_by_trt[rlz.ordinal] try: [gsim] = gsim_by_trt.values() except ValueError: gsim = gsim_by_trt[trt] acc[gsim].append(rlz.ordinal) return {gsim: numpy.array(acc[gsim], dtype=U16) for gsim in sorted(acc)}
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:param trt_or_grp_id: a tectonic region type or a source group ID :param sm_id: source model ordinal (or None) :returns: a dictionary gsim -> rlzs
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train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/commonlib/rlzs_assoc.py#L102-L126
gem/oq-engine
openquake/commonlib/rlzs_assoc.py
RlzsAssoc.by_grp
def by_grp(self): """ :returns: a dictionary grp -> rlzis """ dic = {} # grp -> [(gsim_idx, rlzis), ...] for sm in self.csm_info.source_models: for sg in sm.src_groups: if not sg.eff_ruptures: continue rlzs_by_gsim = self.get_rlzs_by_gsim(sg.trt, sm.ordinal) if not rlzs_by_gsim: continue dic['grp-%02d' % sg.id] = numpy.array( list(rlzs_by_gsim.values())) return dic
python
def by_grp(self): dic = {} for sm in self.csm_info.source_models: for sg in sm.src_groups: if not sg.eff_ruptures: continue rlzs_by_gsim = self.get_rlzs_by_gsim(sg.trt, sm.ordinal) if not rlzs_by_gsim: continue dic['grp-%02d' % sg.id] = numpy.array( list(rlzs_by_gsim.values())) return dic
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gem/oq-engine
openquake/commonlib/rlzs_assoc.py
RlzsAssoc._init
def _init(self): """ Finalize the initialization of the RlzsAssoc object by setting the (reduced) weights of the realizations. """ if self.num_samples: assert len(self.realizations) == self.num_samples, ( len(self.realizations), self.num_samples) for rlz in self.realizations: for k in rlz.weight.dic: rlz.weight.dic[k] = 1. / self.num_samples else: tot_weight = sum(rlz.weight for rlz in self.realizations) if not tot_weight.is_one(): # this may happen for rounding errors or because of the # logic tree reduction; we ensure the sum of the weights is 1 for rlz in self.realizations: rlz.weight = rlz.weight / tot_weight
python
def _init(self): if self.num_samples: assert len(self.realizations) == self.num_samples, ( len(self.realizations), self.num_samples) for rlz in self.realizations: for k in rlz.weight.dic: rlz.weight.dic[k] = 1. / self.num_samples else: tot_weight = sum(rlz.weight for rlz in self.realizations) if not tot_weight.is_one(): for rlz in self.realizations: rlz.weight = rlz.weight / tot_weight
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Finalize the initialization of the RlzsAssoc object by setting the (reduced) weights of the realizations.
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train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/commonlib/rlzs_assoc.py#L144-L161
gem/oq-engine
openquake/commonlib/rlzs_assoc.py
RlzsAssoc.combine_pmaps
def combine_pmaps(self, pmap_by_grp): """ :param pmap_by_grp: dictionary group string -> probability map :returns: a list of probability maps, one per realization """ grp = list(pmap_by_grp)[0] # pmap_by_grp must be non-empty num_levels = pmap_by_grp[grp].shape_y pmaps = [probability_map.ProbabilityMap(num_levels, 1) for _ in self.realizations] array = self.by_grp() for grp in pmap_by_grp: for gsim_idx, rlzis in enumerate(array[grp]): pmap = pmap_by_grp[grp].extract(gsim_idx) for rlzi in rlzis: pmaps[rlzi] |= pmap return pmaps
python
def combine_pmaps(self, pmap_by_grp): grp = list(pmap_by_grp)[0] num_levels = pmap_by_grp[grp].shape_y pmaps = [probability_map.ProbabilityMap(num_levels, 1) for _ in self.realizations] array = self.by_grp() for grp in pmap_by_grp: for gsim_idx, rlzis in enumerate(array[grp]): pmap = pmap_by_grp[grp].extract(gsim_idx) for rlzi in rlzis: pmaps[rlzi] |= pmap return pmaps
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train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/commonlib/rlzs_assoc.py#L173-L188
gem/oq-engine
openquake/commonlib/rlzs_assoc.py
RlzsAssoc.get_rlz
def get_rlz(self, rlzstr): r""" Get a Realization instance for a string of the form 'rlz-\d+' """ mo = re.match(r'rlz-(\d+)', rlzstr) if not mo: return return self.realizations[int(mo.group(1))]
python
def get_rlz(self, rlzstr): r mo = re.match(r'rlz-(\d+)', rlzstr) if not mo: return return self.realizations[int(mo.group(1))]
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r""" Get a Realization instance for a string of the form 'rlz-\d+'
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train
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gem/oq-engine
openquake/commands/export.py
export
def export(datastore_key, calc_id=-1, exports='csv', export_dir='.'): """ Export an output from the datastore. """ dstore = util.read(calc_id) parent_id = dstore['oqparam'].hazard_calculation_id if parent_id: dstore.parent = util.read(parent_id) dstore.export_dir = export_dir with performance.Monitor('export', measuremem=True) as mon: for fmt in exports.split(','): fnames = export_((datastore_key, fmt), dstore) nbytes = sum(os.path.getsize(f) for f in fnames) print('Exported %s in %s' % (general.humansize(nbytes), fnames)) if mon.duration > 1: print(mon) dstore.close()
python
def export(datastore_key, calc_id=-1, exports='csv', export_dir='.'): dstore = util.read(calc_id) parent_id = dstore['oqparam'].hazard_calculation_id if parent_id: dstore.parent = util.read(parent_id) dstore.export_dir = export_dir with performance.Monitor('export', measuremem=True) as mon: for fmt in exports.split(','): fnames = export_((datastore_key, fmt), dstore) nbytes = sum(os.path.getsize(f) for f in fnames) print('Exported %s in %s' % (general.humansize(nbytes), fnames)) if mon.duration > 1: print(mon) dstore.close()
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train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/commands/export.py#L27-L43
gem/oq-engine
openquake/calculators/ucerf_base.py
convert_UCERFSource
def convert_UCERFSource(self, node): """ Converts the Ucerf Source node into an SES Control object """ dirname = os.path.dirname(self.fname) # where the source_model_file is source_file = os.path.join(dirname, node["filename"]) if "startDate" in node.attrib and "investigationTime" in node.attrib: # Is a time-dependent model - even if rates were originally # poissonian # Verify that the source time span is the same as the TOM time span inv_time = float(node["investigationTime"]) if inv_time != self.investigation_time: raise ValueError("Source investigation time (%s) is not " "equal to configuration investigation time " "(%s)" % (inv_time, self.investigation_time)) start_date = datetime.strptime(node["startDate"], "%d/%m/%Y") else: start_date = None return UCERFSource( source_file, self.investigation_time, start_date, float(node["minMag"]), npd=self.convert_npdist(node), hdd=self.convert_hpdist(node), aspect=~node.ruptAspectRatio, upper_seismogenic_depth=~node.pointGeometry.upperSeismoDepth, lower_seismogenic_depth=~node.pointGeometry.lowerSeismoDepth, msr=valid.SCALEREL[~node.magScaleRel](), mesh_spacing=self.rupture_mesh_spacing, trt=node["tectonicRegion"])
python
def convert_UCERFSource(self, node): dirname = os.path.dirname(self.fname) source_file = os.path.join(dirname, node["filename"]) if "startDate" in node.attrib and "investigationTime" in node.attrib: inv_time = float(node["investigationTime"]) if inv_time != self.investigation_time: raise ValueError("Source investigation time (%s) is not " "equal to configuration investigation time " "(%s)" % (inv_time, self.investigation_time)) start_date = datetime.strptime(node["startDate"], "%d/%m/%Y") else: start_date = None return UCERFSource( source_file, self.investigation_time, start_date, float(node["minMag"]), npd=self.convert_npdist(node), hdd=self.convert_hpdist(node), aspect=~node.ruptAspectRatio, upper_seismogenic_depth=~node.pointGeometry.upperSeismoDepth, lower_seismogenic_depth=~node.pointGeometry.lowerSeismoDepth, msr=valid.SCALEREL[~node.magScaleRel](), mesh_spacing=self.rupture_mesh_spacing, trt=node["tectonicRegion"])
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train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/calculators/ucerf_base.py#L58-L88
gem/oq-engine
openquake/calculators/ucerf_base.py
build_idx_set
def build_idx_set(branch_id, start_date): """ Builds a dictionary of keys based on the branch code """ code_set = branch_id.split("/") code_set.insert(3, "Rates") idx_set = { "sec": "/".join([code_set[0], code_set[1], "Sections"]), "mag": "/".join([code_set[0], code_set[1], code_set[2], "Magnitude"])} idx_set["rate"] = "/".join(code_set) idx_set["rake"] = "/".join([code_set[0], code_set[1], "Rake"]) idx_set["msr"] = "-".join(code_set[:3]) idx_set["geol"] = code_set[0] if start_date: # time-dependent source idx_set["grid_key"] = "_".join( branch_id.replace("/", "_").split("_")[:-1]) else: # time-independent source idx_set["grid_key"] = branch_id.replace("/", "_") idx_set["total_key"] = branch_id.replace("/", "|") return idx_set
python
def build_idx_set(branch_id, start_date): code_set = branch_id.split("/") code_set.insert(3, "Rates") idx_set = { "sec": "/".join([code_set[0], code_set[1], "Sections"]), "mag": "/".join([code_set[0], code_set[1], code_set[2], "Magnitude"])} idx_set["rate"] = "/".join(code_set) idx_set["rake"] = "/".join([code_set[0], code_set[1], "Rake"]) idx_set["msr"] = "-".join(code_set[:3]) idx_set["geol"] = code_set[0] if start_date: idx_set["grid_key"] = "_".join( branch_id.replace("/", "_").split("_")[:-1]) else: idx_set["grid_key"] = branch_id.replace("/", "_") idx_set["total_key"] = branch_id.replace("/", "|") return idx_set
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train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/calculators/ucerf_base.py#L433-L452
gem/oq-engine
openquake/calculators/ucerf_base.py
get_rupture_dimensions
def get_rupture_dimensions(mag, nodal_plane, msr, rupture_aspect_ratio, upper_seismogenic_depth, lower_seismogenic_depth): """ Calculate and return the rupture length and width for given magnitude ``mag`` and nodal plane. :param nodal_plane: Instance of :class:`openquake.hazardlib.geo.nodalplane.NodalPlane`. :returns: Tuple of two items: rupture length in width in km. The rupture area is calculated using method :meth:`~openquake.hazardlib.scalerel.base.BaseMSR.get_median_area` of source's magnitude-scaling relationship. In any case the returned dimensions multiplication is equal to that value. Than the area is decomposed to length and width with respect to source's rupture aspect ratio. If calculated rupture width being inclined by nodal plane's dip angle would not fit in between upper and lower seismogenic depth, the rupture width is shrunken to a maximum possible and rupture length is extended to preserve the same area. """ area = msr.get_median_area(mag, nodal_plane.rake) rup_length = math.sqrt(area * rupture_aspect_ratio) rup_width = area / rup_length seismogenic_layer_width = (lower_seismogenic_depth - upper_seismogenic_depth) max_width = (seismogenic_layer_width / math.sin(math.radians(nodal_plane.dip))) if rup_width > max_width: rup_width = max_width rup_length = area / rup_width return rup_length, rup_width
python
def get_rupture_dimensions(mag, nodal_plane, msr, rupture_aspect_ratio, upper_seismogenic_depth, lower_seismogenic_depth): area = msr.get_median_area(mag, nodal_plane.rake) rup_length = math.sqrt(area * rupture_aspect_ratio) rup_width = area / rup_length seismogenic_layer_width = (lower_seismogenic_depth - upper_seismogenic_depth) max_width = (seismogenic_layer_width / math.sin(math.radians(nodal_plane.dip))) if rup_width > max_width: rup_width = max_width rup_length = area / rup_width return rup_length, rup_width
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Calculate and return the rupture length and width for given magnitude ``mag`` and nodal plane. :param nodal_plane: Instance of :class:`openquake.hazardlib.geo.nodalplane.NodalPlane`. :returns: Tuple of two items: rupture length in width in km. The rupture area is calculated using method :meth:`~openquake.hazardlib.scalerel.base.BaseMSR.get_median_area` of source's magnitude-scaling relationship. In any case the returned dimensions multiplication is equal to that value. Than the area is decomposed to length and width with respect to source's rupture aspect ratio. If calculated rupture width being inclined by nodal plane's dip angle would not fit in between upper and lower seismogenic depth, the rupture width is shrunken to a maximum possible and rupture length is extended to preserve the same area.
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train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/calculators/ucerf_base.py#L455-L489
gem/oq-engine
openquake/calculators/ucerf_base.py
get_rupture_surface
def get_rupture_surface(mag, nodal_plane, hypocenter, msr, rupture_aspect_ratio, upper_seismogenic_depth, lower_seismogenic_depth, mesh_spacing=1.0): """ Create and return rupture surface object with given properties. :param mag: Magnitude value, used to calculate rupture dimensions, see :meth:`_get_rupture_dimensions`. :param nodal_plane: Instance of :class:`openquake.hazardlib.geo.nodalplane.NodalPlane` describing the rupture orientation. :param hypocenter: Point representing rupture's hypocenter. :returns: Instance of :class:`~openquake.hazardlib.geo.surface.planar.PlanarSurface`. """ assert (upper_seismogenic_depth <= hypocenter.depth and lower_seismogenic_depth >= hypocenter.depth) rdip = math.radians(nodal_plane.dip) # precalculated azimuth values for horizontal-only and vertical-only # moves from one point to another on the plane defined by strike # and dip: azimuth_right = nodal_plane.strike azimuth_down = (azimuth_right + 90) % 360 azimuth_left = (azimuth_down + 90) % 360 azimuth_up = (azimuth_left + 90) % 360 rup_length, rup_width = get_rupture_dimensions( mag, nodal_plane, msr, rupture_aspect_ratio, upper_seismogenic_depth, lower_seismogenic_depth) # calculate the height of the rupture being projected # on the vertical plane: rup_proj_height = rup_width * math.sin(rdip) # and it's width being projected on the horizontal one: rup_proj_width = rup_width * math.cos(rdip) # half height of the vertical component of rupture width # is the vertical distance between the rupture geometrical # center and it's upper and lower borders: hheight = rup_proj_height / 2 # calculate how much shallower the upper border of the rupture # is than the upper seismogenic depth: vshift = upper_seismogenic_depth - hypocenter.depth + hheight # if it is shallower (vshift > 0) than we need to move the rupture # by that value vertically. if vshift < 0: # the top edge is below upper seismogenic depth. now we need # to check that we do not cross the lower border. vshift = lower_seismogenic_depth - hypocenter.depth - hheight if vshift > 0: # the bottom edge of the rupture is above the lower sesmogenic # depth. that means that we don't need to move the rupture # as it fits inside seismogenic layer. vshift = 0 # if vshift < 0 than we need to move the rupture up by that value. # now we need to find the position of rupture's geometrical center. # in any case the hypocenter point must lie on the surface, however # the rupture center might be off (below or above) along the dip. rupture_center = hypocenter if vshift != 0: # we need to move the rupture center to make the rupture fit # inside the seismogenic layer. hshift = abs(vshift / math.tan(rdip)) rupture_center = rupture_center.point_at( horizontal_distance=hshift, vertical_increment=vshift, azimuth=(azimuth_up if vshift < 0 else azimuth_down)) # from the rupture center we can now compute the coordinates of the # four coorners by moving along the diagonals of the plane. This seems # to be better then moving along the perimeter, because in this case # errors are accumulated that induce distorsions in the shape with # consequent raise of exceptions when creating PlanarSurface objects # theta is the angle between the diagonal of the surface projection # and the line passing through the rupture center and parallel to the # top and bottom edges. Theta is zero for vertical ruptures (because # rup_proj_width is zero) theta = math.degrees( math.atan((rup_proj_width / 2.) / (rup_length / 2.))) hor_dist = math.sqrt( (rup_length / 2.) ** 2 + (rup_proj_width / 2.) ** 2) left_top = rupture_center.point_at( horizontal_distance=hor_dist, vertical_increment=-rup_proj_height / 2, azimuth=(nodal_plane.strike + 180 + theta) % 360) right_top = rupture_center.point_at( horizontal_distance=hor_dist, vertical_increment=-rup_proj_height / 2, azimuth=(nodal_plane.strike - theta) % 360) left_bottom = rupture_center.point_at( horizontal_distance=hor_dist, vertical_increment=rup_proj_height / 2, azimuth=(nodal_plane.strike + 180 - theta) % 360) right_bottom = rupture_center.point_at( horizontal_distance=hor_dist, vertical_increment=rup_proj_height / 2, azimuth=(nodal_plane.strike + theta) % 360) return PlanarSurface(nodal_plane.strike, nodal_plane.dip, left_top, right_top, right_bottom, left_bottom)
python
def get_rupture_surface(mag, nodal_plane, hypocenter, msr, rupture_aspect_ratio, upper_seismogenic_depth, lower_seismogenic_depth, mesh_spacing=1.0): assert (upper_seismogenic_depth <= hypocenter.depth and lower_seismogenic_depth >= hypocenter.depth) rdip = math.radians(nodal_plane.dip) azimuth_right = nodal_plane.strike azimuth_down = (azimuth_right + 90) % 360 azimuth_left = (azimuth_down + 90) % 360 azimuth_up = (azimuth_left + 90) % 360 rup_length, rup_width = get_rupture_dimensions( mag, nodal_plane, msr, rupture_aspect_ratio, upper_seismogenic_depth, lower_seismogenic_depth) rup_proj_height = rup_width * math.sin(rdip) rup_proj_width = rup_width * math.cos(rdip) hheight = rup_proj_height / 2 vshift = upper_seismogenic_depth - hypocenter.depth + hheight if vshift < 0: vshift = lower_seismogenic_depth - hypocenter.depth - hheight if vshift > 0: vshift = 0 rupture_center = hypocenter if vshift != 0: hshift = abs(vshift / math.tan(rdip)) rupture_center = rupture_center.point_at( horizontal_distance=hshift, vertical_increment=vshift, azimuth=(azimuth_up if vshift < 0 else azimuth_down)) theta = math.degrees( math.atan((rup_proj_width / 2.) / (rup_length / 2.))) hor_dist = math.sqrt( (rup_length / 2.) ** 2 + (rup_proj_width / 2.) ** 2) left_top = rupture_center.point_at( horizontal_distance=hor_dist, vertical_increment=-rup_proj_height / 2, azimuth=(nodal_plane.strike + 180 + theta) % 360) right_top = rupture_center.point_at( horizontal_distance=hor_dist, vertical_increment=-rup_proj_height / 2, azimuth=(nodal_plane.strike - theta) % 360) left_bottom = rupture_center.point_at( horizontal_distance=hor_dist, vertical_increment=rup_proj_height / 2, azimuth=(nodal_plane.strike + 180 - theta) % 360) right_bottom = rupture_center.point_at( horizontal_distance=hor_dist, vertical_increment=rup_proj_height / 2, azimuth=(nodal_plane.strike + theta) % 360) return PlanarSurface(nodal_plane.strike, nodal_plane.dip, left_top, right_top, right_bottom, left_bottom)
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train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/calculators/ucerf_base.py#L492-L593
gem/oq-engine
openquake/calculators/ucerf_base.py
generate_background_ruptures
def generate_background_ruptures(tom, locations, occurrence, mag, npd, hdd, upper_seismogenic_depth, lower_seismogenic_depth, msr=WC1994(), aspect=1.5, trt=DEFAULT_TRT): """ :param tom: Temporal occurrence model as instance of :class: openquake.hazardlib.tom.TOM :param numpy.ndarray locations: Array of locations [Longitude, Latitude] of the point sources :param numpy.ndarray occurrence: Annual rates of occurrence :param float mag: Magnitude :param npd: Nodal plane distribution as instance of :class: openquake.hazardlib.pmf.PMF :param hdd: Hypocentral depth distribution as instance of :class: openquake.hazardlib.pmf.PMF :param float upper_seismogenic_depth: Upper seismogenic depth (km) :param float lower_seismogenic_depth: Lower seismogenic depth (km) :param msr: Magnitude scaling relation :param float aspect: Aspect ratio :param str trt: Tectonic region type :returns: List of ruptures """ ruptures = [] n_vals = len(locations) depths = hdd.sample_pairs(n_vals) nodal_planes = npd.sample_pairs(n_vals) for i, (x, y) in enumerate(locations): hypocentre = Point(x, y, depths[i][1]) surface = get_rupture_surface(mag, nodal_planes[i][1], hypocentre, msr, aspect, upper_seismogenic_depth, lower_seismogenic_depth) rupture_probability = (occurrence[i] * nodal_planes[i][0] * depths[i][0]) ruptures.append(ParametricProbabilisticRupture( mag, nodal_planes[i][1].rake, trt, hypocentre, surface, rupture_probability, tom)) return ruptures
python
def generate_background_ruptures(tom, locations, occurrence, mag, npd, hdd, upper_seismogenic_depth, lower_seismogenic_depth, msr=WC1994(), aspect=1.5, trt=DEFAULT_TRT): ruptures = [] n_vals = len(locations) depths = hdd.sample_pairs(n_vals) nodal_planes = npd.sample_pairs(n_vals) for i, (x, y) in enumerate(locations): hypocentre = Point(x, y, depths[i][1]) surface = get_rupture_surface(mag, nodal_planes[i][1], hypocentre, msr, aspect, upper_seismogenic_depth, lower_seismogenic_depth) rupture_probability = (occurrence[i] * nodal_planes[i][0] * depths[i][0]) ruptures.append(ParametricProbabilisticRupture( mag, nodal_planes[i][1].rake, trt, hypocentre, surface, rupture_probability, tom)) return ruptures
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train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/calculators/ucerf_base.py#L596-L644
gem/oq-engine
openquake/calculators/ucerf_base.py
UcerfFilter.get_indices
def get_indices(self, src, ridx, mag): """ :param src: an UCERF source :param ridx: a set of rupture indices :param mag: magnitude to use to compute the integration distance :returns: array with the IDs of the sites close to the ruptures """ centroids = src.get_centroids(ridx) mindistance = min_geodetic_distance( (centroids[:, 0], centroids[:, 1]), self.sitecol.xyz) idist = self.integration_distance(DEFAULT_TRT, mag) indices, = (mindistance <= idist).nonzero() return indices
python
def get_indices(self, src, ridx, mag): centroids = src.get_centroids(ridx) mindistance = min_geodetic_distance( (centroids[:, 0], centroids[:, 1]), self.sitecol.xyz) idist = self.integration_distance(DEFAULT_TRT, mag) indices, = (mindistance <= idist).nonzero() return indices
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:param src: an UCERF source :param ridx: a set of rupture indices :param mag: magnitude to use to compute the integration distance :returns: array with the IDs of the sites close to the ruptures
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train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/calculators/ucerf_base.py#L126-L138
gem/oq-engine
openquake/calculators/ucerf_base.py
UCERFSource.new
def new(self, grp_id, branch_id): """ :param grp_id: ordinal of the source group :param branch_name: name of the UCERF branch :param branch_id: string associated to the branch :returns: a new UCERFSource associated to the branch_id """ new = copy.copy(self) new.orig = new new.src_group_id = grp_id new.source_id = branch_id new.idx_set = build_idx_set(branch_id, self.start_date) with h5py.File(self.source_file, "r") as hdf5: new.start = 0 new.stop = len(hdf5[new.idx_set["mag"]]) return new
python
def new(self, grp_id, branch_id): new = copy.copy(self) new.orig = new new.src_group_id = grp_id new.source_id = branch_id new.idx_set = build_idx_set(branch_id, self.start_date) with h5py.File(self.source_file, "r") as hdf5: new.start = 0 new.stop = len(hdf5[new.idx_set["mag"]]) return new
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train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/calculators/ucerf_base.py#L242-L257
gem/oq-engine
openquake/calculators/ucerf_base.py
UCERFSource.get_ridx
def get_ridx(self, iloc): """List of rupture indices for the given iloc""" with h5py.File(self.source_file, "r") as hdf5: return hdf5[self.idx_set["geol"] + "/RuptureIndex"][iloc]
python
def get_ridx(self, iloc): with h5py.File(self.source_file, "r") as hdf5: return hdf5[self.idx_set["geol"] + "/RuptureIndex"][iloc]
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List of rupture indices for the given iloc
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train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/calculators/ucerf_base.py#L271-L274
gem/oq-engine
openquake/calculators/ucerf_base.py
UCERFSource.get_centroids
def get_centroids(self, ridx): """ :returns: array of centroids for the given rupture index """ centroids = [] with h5py.File(self.source_file, "r") as hdf5: for idx in ridx: trace = "{:s}/{:s}".format(self.idx_set["sec"], str(idx)) centroids.append(hdf5[trace + "/Centroids"].value) return numpy.concatenate(centroids)
python
def get_centroids(self, ridx): centroids = [] with h5py.File(self.source_file, "r") as hdf5: for idx in ridx: trace = "{:s}/{:s}".format(self.idx_set["sec"], str(idx)) centroids.append(hdf5[trace + "/Centroids"].value) return numpy.concatenate(centroids)
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:returns: array of centroids for the given rupture index
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train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/calculators/ucerf_base.py#L276-L285
gem/oq-engine
openquake/calculators/ucerf_base.py
UCERFSource.gen_trace_planes
def gen_trace_planes(self, ridx): """ :yields: trace and rupture planes for the given rupture index """ with h5py.File(self.source_file, "r") as hdf5: for idx in ridx: trace = "{:s}/{:s}".format(self.idx_set["sec"], str(idx)) plane = hdf5[trace + "/RupturePlanes"][:].astype("float64") yield trace, plane
python
def gen_trace_planes(self, ridx): with h5py.File(self.source_file, "r") as hdf5: for idx in ridx: trace = "{:s}/{:s}".format(self.idx_set["sec"], str(idx)) plane = hdf5[trace + "/RupturePlanes"][:].astype("float64") yield trace, plane
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:yields: trace and rupture planes for the given rupture index
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train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/calculators/ucerf_base.py#L287-L295
gem/oq-engine
openquake/calculators/ucerf_base.py
UCERFSource.get_background_sids
def get_background_sids(self, src_filter): """ We can apply the filtering of the background sites as a pre-processing step - this is done here rather than in the sampling of the ruptures themselves """ branch_key = self.idx_set["grid_key"] idist = src_filter.integration_distance(DEFAULT_TRT) with h5py.File(self.source_file, 'r') as hdf5: bg_locations = hdf5["Grid/Locations"].value distances = min_geodetic_distance( src_filter.sitecol.xyz, (bg_locations[:, 0], bg_locations[:, 1])) # Add buffer equal to half of length of median area from Mmax mmax_areas = self.msr.get_median_area( hdf5["/".join(["Grid", branch_key, "MMax"])].value, 0.0) # for instance hdf5['Grid/FM0_0_MEANFS_MEANMSR/MMax'] mmax_lengths = numpy.sqrt(mmax_areas / self.aspect) ok = distances <= (0.5 * mmax_lengths + idist) # get list of indices from array of booleans return numpy.where(ok)[0].tolist()
python
def get_background_sids(self, src_filter): branch_key = self.idx_set["grid_key"] idist = src_filter.integration_distance(DEFAULT_TRT) with h5py.File(self.source_file, 'r') as hdf5: bg_locations = hdf5["Grid/Locations"].value distances = min_geodetic_distance( src_filter.sitecol.xyz, (bg_locations[:, 0], bg_locations[:, 1])) mmax_areas = self.msr.get_median_area( hdf5["/".join(["Grid", branch_key, "MMax"])].value, 0.0) mmax_lengths = numpy.sqrt(mmax_areas / self.aspect) ok = distances <= (0.5 * mmax_lengths + idist) return numpy.where(ok)[0].tolist()
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We can apply the filtering of the background sites as a pre-processing step - this is done here rather than in the sampling of the ruptures themselves
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train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/calculators/ucerf_base.py#L297-L317
gem/oq-engine
openquake/calculators/ucerf_base.py
UCERFSource.get_ucerf_rupture
def get_ucerf_rupture(self, iloc, src_filter): """ :param iloc: Location of the rupture plane in the hdf5 file :param src_filter: Sites for consideration and maximum distance """ trt = self.tectonic_region_type ridx = self.get_ridx(iloc) mag = self.orig.mags[iloc] surface_set = [] indices = src_filter.get_indices(self, ridx, mag) if len(indices) == 0: return None for trace, plane in self.gen_trace_planes(ridx): # build simple fault surface for jloc in range(0, plane.shape[2]): top_left = Point( plane[0, 0, jloc], plane[0, 1, jloc], plane[0, 2, jloc]) top_right = Point( plane[1, 0, jloc], plane[1, 1, jloc], plane[1, 2, jloc]) bottom_right = Point( plane[2, 0, jloc], plane[2, 1, jloc], plane[2, 2, jloc]) bottom_left = Point( plane[3, 0, jloc], plane[3, 1, jloc], plane[3, 2, jloc]) try: surface_set.append( ImperfectPlanarSurface.from_corner_points( top_left, top_right, bottom_right, bottom_left)) except ValueError as err: raise ValueError(err, trace, top_left, top_right, bottom_right, bottom_left) rupture = ParametricProbabilisticRupture( mag, self.orig.rake[iloc], trt, surface_set[len(surface_set) // 2].get_middle_point(), MultiSurface(surface_set), self.orig.rate[iloc], self.tom) return rupture
python
def get_ucerf_rupture(self, iloc, src_filter): trt = self.tectonic_region_type ridx = self.get_ridx(iloc) mag = self.orig.mags[iloc] surface_set = [] indices = src_filter.get_indices(self, ridx, mag) if len(indices) == 0: return None for trace, plane in self.gen_trace_planes(ridx): for jloc in range(0, plane.shape[2]): top_left = Point( plane[0, 0, jloc], plane[0, 1, jloc], plane[0, 2, jloc]) top_right = Point( plane[1, 0, jloc], plane[1, 1, jloc], plane[1, 2, jloc]) bottom_right = Point( plane[2, 0, jloc], plane[2, 1, jloc], plane[2, 2, jloc]) bottom_left = Point( plane[3, 0, jloc], plane[3, 1, jloc], plane[3, 2, jloc]) try: surface_set.append( ImperfectPlanarSurface.from_corner_points( top_left, top_right, bottom_right, bottom_left)) except ValueError as err: raise ValueError(err, trace, top_left, top_right, bottom_right, bottom_left) rupture = ParametricProbabilisticRupture( mag, self.orig.rake[iloc], trt, surface_set[len(surface_set) // 2].get_middle_point(), MultiSurface(surface_set), self.orig.rate[iloc], self.tom) return rupture
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:param iloc: Location of the rupture plane in the hdf5 file :param src_filter: Sites for consideration and maximum distance
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train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/calculators/ucerf_base.py#L319-L357
gem/oq-engine
openquake/calculators/ucerf_base.py
UCERFSource.iter_ruptures
def iter_ruptures(self): """ Yield ruptures for the current set of indices """ assert self.orig, '%s is not fully initialized' % self for ridx in range(self.start, self.stop): if self.orig.rate[ridx]: # ruptures may have have zero rate rup = self.get_ucerf_rupture(ridx, self.src_filter) if rup: yield rup
python
def iter_ruptures(self): assert self.orig, '%s is not fully initialized' % self for ridx in range(self.start, self.stop): if self.orig.rate[ridx]: rup = self.get_ucerf_rupture(ridx, self.src_filter) if rup: yield rup
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Yield ruptures for the current set of indices
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train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/calculators/ucerf_base.py#L359-L368
gem/oq-engine
openquake/calculators/ucerf_base.py
UCERFSource.get_background_sources
def get_background_sources(self, src_filter, sample_factor=None): """ Turn the background model of a given branch into a set of point sources :param src_filter: SourceFilter instance :param sample_factor: Used to reduce the sources if OQ_SAMPLE_SOURCES is set """ background_sids = self.get_background_sids(src_filter) if sample_factor is not None: # hack for use in the mosaic background_sids = random_filter( background_sids, sample_factor, seed=42) with h5py.File(self.source_file, "r") as hdf5: grid_loc = "/".join(["Grid", self.idx_set["grid_key"]]) # for instance Grid/FM0_0_MEANFS_MEANMSR_MeanRates mags = hdf5[grid_loc + "/Magnitude"].value mmax = hdf5[grid_loc + "/MMax"][background_sids] rates = hdf5[grid_loc + "/RateArray"][background_sids, :] locations = hdf5["Grid/Locations"][background_sids, :] sources = [] for i, bg_idx in enumerate(background_sids): src_id = "_".join([self.idx_set["grid_key"], str(bg_idx)]) src_name = "|".join([self.idx_set["total_key"], str(bg_idx)]) mag_idx = (self.min_mag <= mags) & (mags < mmax[i]) src_mags = mags[mag_idx] src_mfd = EvenlyDiscretizedMFD( src_mags[0], src_mags[1] - src_mags[0], rates[i, mag_idx].tolist()) ps = PointSource( src_id, src_name, self.tectonic_region_type, src_mfd, self.mesh_spacing, self.msr, self.aspect, self.tom, self.usd, self.lsd, Point(locations[i, 0], locations[i, 1]), self.npd, self.hdd) ps.id = self.id ps.src_group_id = self.src_group_id ps.num_ruptures = ps.count_ruptures() sources.append(ps) return sources
python
def get_background_sources(self, src_filter, sample_factor=None): background_sids = self.get_background_sids(src_filter) if sample_factor is not None: background_sids = random_filter( background_sids, sample_factor, seed=42) with h5py.File(self.source_file, "r") as hdf5: grid_loc = "/".join(["Grid", self.idx_set["grid_key"]]) mags = hdf5[grid_loc + "/Magnitude"].value mmax = hdf5[grid_loc + "/MMax"][background_sids] rates = hdf5[grid_loc + "/RateArray"][background_sids, :] locations = hdf5["Grid/Locations"][background_sids, :] sources = [] for i, bg_idx in enumerate(background_sids): src_id = "_".join([self.idx_set["grid_key"], str(bg_idx)]) src_name = "|".join([self.idx_set["total_key"], str(bg_idx)]) mag_idx = (self.min_mag <= mags) & (mags < mmax[i]) src_mags = mags[mag_idx] src_mfd = EvenlyDiscretizedMFD( src_mags[0], src_mags[1] - src_mags[0], rates[i, mag_idx].tolist()) ps = PointSource( src_id, src_name, self.tectonic_region_type, src_mfd, self.mesh_spacing, self.msr, self.aspect, self.tom, self.usd, self.lsd, Point(locations[i, 0], locations[i, 1]), self.npd, self.hdd) ps.id = self.id ps.src_group_id = self.src_group_id ps.num_ruptures = ps.count_ruptures() sources.append(ps) return sources
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Turn the background model of a given branch into a set of point sources :param src_filter: SourceFilter instance :param sample_factor: Used to reduce the sources if OQ_SAMPLE_SOURCES is set
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train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/calculators/ucerf_base.py#L387-L427
gem/oq-engine
openquake/hazardlib/source/rupture_collection.py
split
def split(src, chunksize=MINWEIGHT): """ Split a complex fault source in chunks """ for i, block in enumerate(block_splitter(src.iter_ruptures(), chunksize, key=operator.attrgetter('mag'))): rup = block[0] source_id = '%s:%d' % (src.source_id, i) amfd = mfd.ArbitraryMFD([rup.mag], [rup.mag_occ_rate]) rcs = RuptureCollectionSource( source_id, src.name, src.tectonic_region_type, amfd, block) yield rcs
python
def split(src, chunksize=MINWEIGHT): for i, block in enumerate(block_splitter(src.iter_ruptures(), chunksize, key=operator.attrgetter('mag'))): rup = block[0] source_id = '%s:%d' % (src.source_id, i) amfd = mfd.ArbitraryMFD([rup.mag], [rup.mag_occ_rate]) rcs = RuptureCollectionSource( source_id, src.name, src.tectonic_region_type, amfd, block) yield rcs
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train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/source/rupture_collection.py#L61-L72
gem/oq-engine
openquake/hazardlib/source/rupture_collection.py
RuptureCollectionSource.get_bounding_box
def get_bounding_box(self, maxdist): """ Bounding box containing all the hypocenters, enlarged by the maximum distance """ locations = [rup.hypocenter for rup in self.ruptures] return get_bounding_box(locations, maxdist)
python
def get_bounding_box(self, maxdist): locations = [rup.hypocenter for rup in self.ruptures] return get_bounding_box(locations, maxdist)
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Bounding box containing all the hypocenters, enlarged by the maximum distance
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train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/source/rupture_collection.py#L52-L58
gem/oq-engine
openquake/commands/show_attrs.py
show_attrs
def show_attrs(key, calc_id=-1): """ Show the attributes of a HDF5 dataset in the datastore. """ ds = util.read(calc_id) try: attrs = h5py.File.__getitem__(ds.hdf5, key).attrs except KeyError: print('%r is not in %s' % (key, ds)) else: if len(attrs) == 0: print('%s has no attributes' % key) for name, value in attrs.items(): print(name, value) finally: ds.close()
python
def show_attrs(key, calc_id=-1): ds = util.read(calc_id) try: attrs = h5py.File.__getitem__(ds.hdf5, key).attrs except KeyError: print('%r is not in %s' % (key, ds)) else: if len(attrs) == 0: print('%s has no attributes' % key) for name, value in attrs.items(): print(name, value) finally: ds.close()
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Show the attributes of a HDF5 dataset in the datastore.
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train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/commands/show_attrs.py#L24-L39
gem/oq-engine
utils/compare_mean_curves.py
compare_mean_curves
def compare_mean_curves(calc_ref, calc, nsigma=3): """ Compare the hazard curves coming from two different calculations. """ dstore_ref = datastore.read(calc_ref) dstore = datastore.read(calc) imtls = dstore_ref['oqparam'].imtls if dstore['oqparam'].imtls != imtls: raise RuntimeError('The IMTs and levels are different between ' 'calculation %d and %d' % (calc_ref, calc)) sitecol_ref = dstore_ref['sitecol'] sitecol = dstore['sitecol'] site_id_ref = {(lon, lat): sid for sid, lon, lat in zip( sitecol_ref.sids, sitecol_ref.lons, sitecol_ref.lats)} site_id = {(lon, lat): sid for sid, lon, lat in zip( sitecol.sids, sitecol.lons, sitecol.lats)} common = set(site_id_ref) & set(site_id) if not common: raise RuntimeError('There are no common sites between calculation ' '%d and %d' % (calc_ref, calc)) pmap_ref = PmapGetter(dstore_ref, sids=[site_id_ref[lonlat] for lonlat in common]).get_mean() pmap = PmapGetter(dstore, sids=[site_id[lonlat] for lonlat in common]).get_mean() for lonlat in common: mean, std = pmap[site_id[lonlat]].array.T # shape (2, N) mean_ref, std_ref = pmap_ref[site_id_ref[lonlat]].array.T err = numpy.sqrt(std**2 + std_ref**2) for imt in imtls: sl = imtls(imt) ok = (numpy.abs(mean[sl] - mean_ref[sl]) < nsigma * err[sl]).all() if not ok: md = (numpy.abs(mean[sl] - mean_ref[sl])).max() plt.title('point=%s, imt=%s, maxdiff=%.2e' % (lonlat, imt, md)) plt.loglog(imtls[imt], mean_ref[sl] + std_ref[sl], label=str(calc_ref), color='black') plt.loglog(imtls[imt], mean_ref[sl] - std_ref[sl], color='black') plt.loglog(imtls[imt], mean[sl] + std[sl], label=str(calc), color='red') plt.loglog(imtls[imt], mean[sl] - std[sl], color='red') plt.legend() plt.show()
python
def compare_mean_curves(calc_ref, calc, nsigma=3): dstore_ref = datastore.read(calc_ref) dstore = datastore.read(calc) imtls = dstore_ref['oqparam'].imtls if dstore['oqparam'].imtls != imtls: raise RuntimeError('The IMTs and levels are different between ' 'calculation %d and %d' % (calc_ref, calc)) sitecol_ref = dstore_ref['sitecol'] sitecol = dstore['sitecol'] site_id_ref = {(lon, lat): sid for sid, lon, lat in zip( sitecol_ref.sids, sitecol_ref.lons, sitecol_ref.lats)} site_id = {(lon, lat): sid for sid, lon, lat in zip( sitecol.sids, sitecol.lons, sitecol.lats)} common = set(site_id_ref) & set(site_id) if not common: raise RuntimeError('There are no common sites between calculation ' '%d and %d' % (calc_ref, calc)) pmap_ref = PmapGetter(dstore_ref, sids=[site_id_ref[lonlat] for lonlat in common]).get_mean() pmap = PmapGetter(dstore, sids=[site_id[lonlat] for lonlat in common]).get_mean() for lonlat in common: mean, std = pmap[site_id[lonlat]].array.T mean_ref, std_ref = pmap_ref[site_id_ref[lonlat]].array.T err = numpy.sqrt(std**2 + std_ref**2) for imt in imtls: sl = imtls(imt) ok = (numpy.abs(mean[sl] - mean_ref[sl]) < nsigma * err[sl]).all() if not ok: md = (numpy.abs(mean[sl] - mean_ref[sl])).max() plt.title('point=%s, imt=%s, maxdiff=%.2e' % (lonlat, imt, md)) plt.loglog(imtls[imt], mean_ref[sl] + std_ref[sl], label=str(calc_ref), color='black') plt.loglog(imtls[imt], mean_ref[sl] - std_ref[sl], color='black') plt.loglog(imtls[imt], mean[sl] + std[sl], label=str(calc), color='red') plt.loglog(imtls[imt], mean[sl] - std[sl], color='red') plt.legend() plt.show()
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Compare the hazard curves coming from two different calculations.
[ "Compare", "the", "hazard", "curves", "coming", "from", "two", "different", "calculations", "." ]
train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/utils/compare_mean_curves.py#L27-L69
gem/oq-engine
openquake/hazardlib/gsim/chiou_youngs_2014.py
ChiouYoungs2014._get_mean
def _get_mean(self, sites, C, ln_y_ref, exp1, exp2): """ Add site effects to an intensity. Implements eq. 13b. """ # we do not support estimating of basin depth and instead # rely on it being available (since we require it). # centered_z1pt0 centered_z1pt0 = self._get_centered_z1pt0(sites) # we consider random variables being zero since we want # to find the exact mean value. eta = epsilon = 0. ln_y = ( # first line of eq. 12 ln_y_ref + eta # second line + C['phi1'] * np.log(sites.vs30 / 1130).clip(-np.inf, 0) # third line + C['phi2'] * (exp1 - exp2) * np.log((np.exp(ln_y_ref) * np.exp(eta) + C['phi4']) / C['phi4']) # fourth line + C['phi5'] * (1.0 - np.exp(-1. * centered_z1pt0 / C['phi6'])) # fifth line + epsilon ) return ln_y
python
def _get_mean(self, sites, C, ln_y_ref, exp1, exp2): centered_z1pt0 = self._get_centered_z1pt0(sites) eta = epsilon = 0. ln_y = ( ln_y_ref + eta + C['phi1'] * np.log(sites.vs30 / 1130).clip(-np.inf, 0) + C['phi2'] * (exp1 - exp2) * np.log((np.exp(ln_y_ref) * np.exp(eta) + C['phi4']) / C['phi4']) + C['phi5'] * (1.0 - np.exp(-1. * centered_z1pt0 / C['phi6'])) + epsilon ) return ln_y
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Add site effects to an intensity. Implements eq. 13b.
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train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/gsim/chiou_youngs_2014.py#L93-L122
gem/oq-engine
openquake/hazardlib/gsim/chiou_youngs_2014.py
ChiouYoungs2014._get_ln_y_ref
def _get_ln_y_ref(self, rup, dists, C): """ Get an intensity on a reference soil. Implements eq. 13a. """ # reverse faulting flag Frv = 1. if 30 <= rup.rake <= 150 else 0. # normal faulting flag Fnm = 1. if -120 <= rup.rake <= -60 else 0. # hanging wall flag Fhw = np.zeros_like(dists.rx) idx = np.nonzero(dists.rx >= 0.) Fhw[idx] = 1. # a part in eq. 11 mag_test1 = np.cosh(2. * max(rup.mag - 4.5, 0)) # centered DPP centered_dpp = self._get_centered_cdpp(dists) # centered_ztor centered_ztor = self._get_centered_ztor(rup, Frv) # dist_taper = np.fmax(1 - (np.fmax(dists.rrup - 40, np.zeros_like(dists)) / 30.), np.zeros_like(dists)) dist_taper = dist_taper.astype(np.float64) ln_y_ref = ( # first part of eq. 11 C['c1'] + (C['c1a'] + C['c1c'] / mag_test1) * Frv + (C['c1b'] + C['c1d'] / mag_test1) * Fnm + (C['c7'] + C['c7b'] / mag_test1) * centered_ztor + (C['c11'] + C['c11b'] / mag_test1) * np.cos(math.radians(rup.dip)) ** 2 # second part + C['c2'] * (rup.mag - 6) + ((C['c2'] - C['c3']) / C['cn']) * np.log(1 + np.exp(C['cn'] * (C['cm'] - rup.mag))) # third part + C['c4'] * np.log(dists.rrup + C['c5'] * np.cosh(C['c6'] * max(rup.mag - C['chm'], 0))) + (C['c4a'] - C['c4']) * np.log(np.sqrt(dists.rrup ** 2 + C['crb'] ** 2)) # forth part + (C['cg1'] + C['cg2'] / (np.cosh(max(rup.mag - C['cg3'], 0)))) * dists.rrup # fifth part + C['c8'] * dist_taper * min(max(rup.mag - 5.5, 0) / 0.8, 1.0) * np.exp(-1 * C['c8a'] * (rup.mag - C['c8b']) ** 2) * centered_dpp # sixth part + C['c9'] * Fhw * np.cos(math.radians(rup.dip)) * (C['c9a'] + (1 - C['c9a']) * np.tanh(dists.rx / C['c9b'])) * (1 - np.sqrt(dists.rjb ** 2 + rup.ztor ** 2) / (dists.rrup + 1.0)) ) return ln_y_ref
python
def _get_ln_y_ref(self, rup, dists, C): Frv = 1. if 30 <= rup.rake <= 150 else 0. Fnm = 1. if -120 <= rup.rake <= -60 else 0. Fhw = np.zeros_like(dists.rx) idx = np.nonzero(dists.rx >= 0.) Fhw[idx] = 1. mag_test1 = np.cosh(2. * max(rup.mag - 4.5, 0)) centered_dpp = self._get_centered_cdpp(dists) centered_ztor = self._get_centered_ztor(rup, Frv) dist_taper = np.fmax(1 - (np.fmax(dists.rrup - 40, np.zeros_like(dists)) / 30.), np.zeros_like(dists)) dist_taper = dist_taper.astype(np.float64) ln_y_ref = ( C['c1'] + (C['c1a'] + C['c1c'] / mag_test1) * Frv + (C['c1b'] + C['c1d'] / mag_test1) * Fnm + (C['c7'] + C['c7b'] / mag_test1) * centered_ztor + (C['c11'] + C['c11b'] / mag_test1) * np.cos(math.radians(rup.dip)) ** 2 + C['c2'] * (rup.mag - 6) + ((C['c2'] - C['c3']) / C['cn']) * np.log(1 + np.exp(C['cn'] * (C['cm'] - rup.mag))) + C['c4'] * np.log(dists.rrup + C['c5'] * np.cosh(C['c6'] * max(rup.mag - C['chm'], 0))) + (C['c4a'] - C['c4']) * np.log(np.sqrt(dists.rrup ** 2 + C['crb'] ** 2)) + (C['cg1'] + C['cg2'] / (np.cosh(max(rup.mag - C['cg3'], 0)))) * dists.rrup + C['c8'] * dist_taper * min(max(rup.mag - 5.5, 0) / 0.8, 1.0) * np.exp(-1 * C['c8a'] * (rup.mag - C['c8b']) ** 2) * centered_dpp + C['c9'] * Fhw * np.cos(math.radians(rup.dip)) * (C['c9a'] + (1 - C['c9a']) * np.tanh(dists.rx / C['c9b'])) * (1 - np.sqrt(dists.rjb ** 2 + rup.ztor ** 2) / (dists.rrup + 1.0)) ) return ln_y_ref
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Get an intensity on a reference soil. Implements eq. 13a.
[ "Get", "an", "intensity", "on", "a", "reference", "soil", "." ]
train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/gsim/chiou_youngs_2014.py#L162-L222
gem/oq-engine
openquake/hazardlib/gsim/chiou_youngs_2014.py
ChiouYoungs2014._get_centered_z1pt0
def _get_centered_z1pt0(self, sites): """ Get z1pt0 centered on the Vs30- dependent avarage z1pt0(m) California and non-Japan regions """ #: California and non-Japan regions mean_z1pt0 = (-7.15 / 4.) * np.log(((sites.vs30) ** 4. + 570.94 ** 4.) / (1360 ** 4. + 570.94 ** 4.)) centered_z1pt0 = sites.z1pt0 - np.exp(mean_z1pt0) return centered_z1pt0
python
def _get_centered_z1pt0(self, sites): mean_z1pt0 = (-7.15 / 4.) * np.log(((sites.vs30) ** 4. + 570.94 ** 4.) / (1360 ** 4. + 570.94 ** 4.)) centered_z1pt0 = sites.z1pt0 - np.exp(mean_z1pt0) return centered_z1pt0
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Get z1pt0 centered on the Vs30- dependent avarage z1pt0(m) California and non-Japan regions
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train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/gsim/chiou_youngs_2014.py#L224-L236
gem/oq-engine
openquake/hazardlib/gsim/chiou_youngs_2014.py
ChiouYoungs2014._get_centered_ztor
def _get_centered_ztor(self, rup, Frv): """ Get ztor centered on the M- dependent avarage ztor(km) by different fault types. """ if Frv == 1: mean_ztor = max(2.704 - 1.226 * max(rup.mag - 5.849, 0.0), 0.) ** 2 centered_ztor = rup.ztor - mean_ztor else: mean_ztor = max(2.673 - 1.136 * max(rup.mag - 4.970, 0.0), 0.) ** 2 centered_ztor = rup.ztor - mean_ztor return centered_ztor
python
def _get_centered_ztor(self, rup, Frv): if Frv == 1: mean_ztor = max(2.704 - 1.226 * max(rup.mag - 5.849, 0.0), 0.) ** 2 centered_ztor = rup.ztor - mean_ztor else: mean_ztor = max(2.673 - 1.136 * max(rup.mag - 4.970, 0.0), 0.) ** 2 centered_ztor = rup.ztor - mean_ztor return centered_ztor
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train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/gsim/chiou_youngs_2014.py#L238-L252
gem/oq-engine
openquake/hazardlib/gsim/chiou_youngs_2014.py
ChiouYoungs2014PEER._get_stddevs
def _get_stddevs(self, sites, rup, C, stddev_types, ln_y_ref, exp1, exp2): """ Returns the standard deviation, which is fixed at 0.65 for every site """ ret = [] for stddev_type in stddev_types: assert stddev_type in self.DEFINED_FOR_STANDARD_DEVIATION_TYPES if stddev_type == const.StdDev.TOTAL: # eq. 13 ret.append(0.65 * np.ones_like(sites.vs30)) return ret
python
def _get_stddevs(self, sites, rup, C, stddev_types, ln_y_ref, exp1, exp2): ret = [] for stddev_type in stddev_types: assert stddev_type in self.DEFINED_FOR_STANDARD_DEVIATION_TYPES if stddev_type == const.StdDev.TOTAL: ret.append(0.65 * np.ones_like(sites.vs30)) return ret
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train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/gsim/chiou_youngs_2014.py#L313-L323
gem/oq-engine
openquake/risklib/scientific.py
fine_graining
def fine_graining(points, steps): """ :param points: a list of floats :param int steps: expansion steps (>= 2) >>> fine_graining([0, 1], steps=0) [0, 1] >>> fine_graining([0, 1], steps=1) [0, 1] >>> fine_graining([0, 1], steps=2) array([0. , 0.5, 1. ]) >>> fine_graining([0, 1], steps=3) array([0. , 0.33333333, 0.66666667, 1. ]) >>> fine_graining([0, 0.5, 0.7, 1], steps=2) array([0. , 0.25, 0.5 , 0.6 , 0.7 , 0.85, 1. ]) N points become S * (N - 1) + 1 points with S > 0 """ if steps < 2: return points ls = numpy.concatenate([numpy.linspace(x, y, num=steps + 1)[:-1] for x, y in pairwise(points)]) return numpy.concatenate([ls, [points[-1]]])
python
def fine_graining(points, steps): if steps < 2: return points ls = numpy.concatenate([numpy.linspace(x, y, num=steps + 1)[:-1] for x, y in pairwise(points)]) return numpy.concatenate([ls, [points[-1]]])
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:param points: a list of floats :param int steps: expansion steps (>= 2) >>> fine_graining([0, 1], steps=0) [0, 1] >>> fine_graining([0, 1], steps=1) [0, 1] >>> fine_graining([0, 1], steps=2) array([0. , 0.5, 1. ]) >>> fine_graining([0, 1], steps=3) array([0. , 0.33333333, 0.66666667, 1. ]) >>> fine_graining([0, 0.5, 0.7, 1], steps=2) array([0. , 0.25, 0.5 , 0.6 , 0.7 , 0.85, 1. ]) N points become S * (N - 1) + 1 points with S > 0
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train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/risklib/scientific.py#L49-L71
gem/oq-engine
openquake/risklib/scientific.py
build_imls
def build_imls(ff, continuous_fragility_discretization, steps_per_interval=0): """ Build intensity measure levels from a fragility function. If the function is continuous, they are produced simply as a linear space between minIML and maxIML. If the function is discrete, they are generated with a complex logic depending on the noDamageLimit and the parameter steps per interval. :param ff: a fragility function object :param continuous_fragility_discretization: .ini file parameter :param steps_per_interval: .ini file parameter :returns: generated imls """ if ff.format == 'discrete': imls = ff.imls if ff.nodamage and ff.nodamage < imls[0]: imls = [ff.nodamage] + imls if steps_per_interval > 1: gen_imls = fine_graining(imls, steps_per_interval) else: gen_imls = imls else: # continuous gen_imls = numpy.linspace(ff.minIML, ff.maxIML, continuous_fragility_discretization) return gen_imls
python
def build_imls(ff, continuous_fragility_discretization, steps_per_interval=0): if ff.format == 'discrete': imls = ff.imls if ff.nodamage and ff.nodamage < imls[0]: imls = [ff.nodamage] + imls if steps_per_interval > 1: gen_imls = fine_graining(imls, steps_per_interval) else: gen_imls = imls else: gen_imls = numpy.linspace(ff.minIML, ff.maxIML, continuous_fragility_discretization) return gen_imls
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train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/risklib/scientific.py#L665-L690
gem/oq-engine
openquake/risklib/scientific.py
make_epsilons
def make_epsilons(matrix, seed, correlation): """ Given a matrix N * R returns a matrix of the same shape N * R obtained by applying the multivariate_normal distribution to N points and R samples, by starting from the given seed and correlation. """ if seed is not None: numpy.random.seed(seed) asset_count = len(matrix) samples = len(matrix[0]) if not correlation: # avoid building the covariance matrix return numpy.random.normal(size=(samples, asset_count)).transpose() means_vector = numpy.zeros(asset_count) covariance_matrix = ( numpy.ones((asset_count, asset_count)) * correlation + numpy.diag(numpy.ones(asset_count)) * (1 - correlation)) return numpy.random.multivariate_normal( means_vector, covariance_matrix, samples).transpose()
python
def make_epsilons(matrix, seed, correlation): if seed is not None: numpy.random.seed(seed) asset_count = len(matrix) samples = len(matrix[0]) if not correlation: return numpy.random.normal(size=(samples, asset_count)).transpose() means_vector = numpy.zeros(asset_count) covariance_matrix = ( numpy.ones((asset_count, asset_count)) * correlation + numpy.diag(numpy.ones(asset_count)) * (1 - correlation)) return numpy.random.multivariate_normal( means_vector, covariance_matrix, samples).transpose()
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Given a matrix N * R returns a matrix of the same shape N * R obtained by applying the multivariate_normal distribution to N points and R samples, by starting from the given seed and correlation.
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train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/risklib/scientific.py#L784-L802
gem/oq-engine
openquake/risklib/scientific.py
scenario_damage
def scenario_damage(fragility_functions, gmvs): """ :param fragility_functions: a list of D - 1 fragility functions :param gmvs: an array of E ground motion values :returns: an array of (D, E) damage fractions """ lst = [numpy.ones_like(gmvs)] for f, ff in enumerate(fragility_functions): # D - 1 functions lst.append(ff(gmvs)) lst.append(numpy.zeros_like(gmvs)) # convert a (D + 1, E) array into a (D, E) array arr = pairwise_diff(numpy.array(lst)) arr[arr < 1E-7] = 0 # sanity check return arr
python
def scenario_damage(fragility_functions, gmvs): lst = [numpy.ones_like(gmvs)] for f, ff in enumerate(fragility_functions): lst.append(ff(gmvs)) lst.append(numpy.zeros_like(gmvs)) arr = pairwise_diff(numpy.array(lst)) arr[arr < 1E-7] = 0 return arr
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:param fragility_functions: a list of D - 1 fragility functions :param gmvs: an array of E ground motion values :returns: an array of (D, E) damage fractions
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train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/risklib/scientific.py#L903-L916
gem/oq-engine
openquake/risklib/scientific.py
annual_frequency_of_exceedence
def annual_frequency_of_exceedence(poe, t_haz): """ :param poe: array of probabilities of exceedence :param t_haz: hazard investigation time :returns: array of frequencies (with +inf values where poe=1) """ with warnings.catch_warnings(): warnings.simplefilter("ignore") # avoid RuntimeWarning: divide by zero encountered in log return - numpy.log(1. - poe) / t_haz
python
def annual_frequency_of_exceedence(poe, t_haz): with warnings.catch_warnings(): warnings.simplefilter("ignore") return - numpy.log(1. - poe) / t_haz
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:param poe: array of probabilities of exceedence :param t_haz: hazard investigation time :returns: array of frequencies (with +inf values where poe=1)
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train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/risklib/scientific.py#L923-L932
gem/oq-engine
openquake/risklib/scientific.py
classical_damage
def classical_damage( fragility_functions, hazard_imls, hazard_poes, investigation_time, risk_investigation_time): """ :param fragility_functions: a list of fragility functions for each damage state :param hazard_imls: Intensity Measure Levels :param hazard_poes: hazard curve :param investigation_time: hazard investigation time :param risk_investigation_time: risk investigation time :returns: an array of M probabilities of occurrence where M is the numbers of damage states. """ spi = fragility_functions.steps_per_interval if spi and spi > 1: # interpolate imls = numpy.array(fragility_functions.interp_imls) min_val, max_val = hazard_imls[0], hazard_imls[-1] assert min_val > 0, hazard_imls # sanity check numpy.putmask(imls, imls < min_val, min_val) numpy.putmask(imls, imls > max_val, max_val) poes = interpolate.interp1d(hazard_imls, hazard_poes)(imls) else: imls = (hazard_imls if fragility_functions.format == 'continuous' else fragility_functions.imls) poes = numpy.array(hazard_poes) afe = annual_frequency_of_exceedence(poes, investigation_time) annual_frequency_of_occurrence = pairwise_diff( pairwise_mean([afe[0]] + list(afe) + [afe[-1]])) poes_per_damage_state = [] for ff in fragility_functions: frequency_of_exceedence_per_damage_state = numpy.dot( annual_frequency_of_occurrence, list(map(ff, imls))) poe_per_damage_state = 1. - numpy.exp( - frequency_of_exceedence_per_damage_state * risk_investigation_time) poes_per_damage_state.append(poe_per_damage_state) poos = pairwise_diff([1] + poes_per_damage_state + [0]) return poos
python
def classical_damage( fragility_functions, hazard_imls, hazard_poes, investigation_time, risk_investigation_time): spi = fragility_functions.steps_per_interval if spi and spi > 1: imls = numpy.array(fragility_functions.interp_imls) min_val, max_val = hazard_imls[0], hazard_imls[-1] assert min_val > 0, hazard_imls numpy.putmask(imls, imls < min_val, min_val) numpy.putmask(imls, imls > max_val, max_val) poes = interpolate.interp1d(hazard_imls, hazard_poes)(imls) else: imls = (hazard_imls if fragility_functions.format == 'continuous' else fragility_functions.imls) poes = numpy.array(hazard_poes) afe = annual_frequency_of_exceedence(poes, investigation_time) annual_frequency_of_occurrence = pairwise_diff( pairwise_mean([afe[0]] + list(afe) + [afe[-1]])) poes_per_damage_state = [] for ff in fragility_functions: frequency_of_exceedence_per_damage_state = numpy.dot( annual_frequency_of_occurrence, list(map(ff, imls))) poe_per_damage_state = 1. - numpy.exp( - frequency_of_exceedence_per_damage_state * risk_investigation_time) poes_per_damage_state.append(poe_per_damage_state) poos = pairwise_diff([1] + poes_per_damage_state + [0]) return poos
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train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/risklib/scientific.py#L935-L977
gem/oq-engine
openquake/risklib/scientific.py
classical
def classical(vulnerability_function, hazard_imls, hazard_poes, loss_ratios): """ :param vulnerability_function: an instance of :py:class:`openquake.risklib.scientific.VulnerabilityFunction` representing the vulnerability function used to compute the curve. :param hazard_imls: the hazard intensity measure type and levels :type hazard_poes: the hazard curve :param loss_ratios: a tuple of C loss ratios :returns: an array of shape (2, C) """ assert len(hazard_imls) == len(hazard_poes), ( len(hazard_imls), len(hazard_poes)) vf = vulnerability_function imls = vf.mean_imls() lrem = vf.loss_ratio_exceedance_matrix(loss_ratios) # saturate imls to hazard imls min_val, max_val = hazard_imls[0], hazard_imls[-1] numpy.putmask(imls, imls < min_val, min_val) numpy.putmask(imls, imls > max_val, max_val) # interpolate the hazard curve poes = interpolate.interp1d(hazard_imls, hazard_poes)(imls) # compute the poos pos = pairwise_diff(poes) lrem_po = numpy.empty(lrem.shape) for idx, po in enumerate(pos): lrem_po[:, idx] = lrem[:, idx] * po # column * po return numpy.array([loss_ratios, lrem_po.sum(axis=1)])
python
def classical(vulnerability_function, hazard_imls, hazard_poes, loss_ratios): assert len(hazard_imls) == len(hazard_poes), ( len(hazard_imls), len(hazard_poes)) vf = vulnerability_function imls = vf.mean_imls() lrem = vf.loss_ratio_exceedance_matrix(loss_ratios) min_val, max_val = hazard_imls[0], hazard_imls[-1] numpy.putmask(imls, imls < min_val, min_val) numpy.putmask(imls, imls > max_val, max_val) poes = interpolate.interp1d(hazard_imls, hazard_poes)(imls) pos = pairwise_diff(poes) lrem_po = numpy.empty(lrem.shape) for idx, po in enumerate(pos): lrem_po[:, idx] = lrem[:, idx] * po return numpy.array([loss_ratios, lrem_po.sum(axis=1)])
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gem/oq-engine
openquake/risklib/scientific.py
conditional_loss_ratio
def conditional_loss_ratio(loss_ratios, poes, probability): """ Return the loss ratio corresponding to the given PoE (Probability of Exceendance). We can have four cases: 1. If `probability` is in `poes` it takes the bigger corresponding loss_ratios. 2. If it is in `(poe1, poe2)` where both `poe1` and `poe2` are in `poes`, then we perform a linear interpolation on the corresponding losses 3. if the given probability is smaller than the lowest PoE defined, it returns the max loss ratio . 4. if the given probability is greater than the highest PoE defined it returns zero. :param loss_ratios: an iterable over non-decreasing loss ratio values (float) :param poes: an iterable over non-increasing probability of exceedance values (float) :param float probability: the probability value used to interpolate the loss curve """ assert len(loss_ratios) >= 3, loss_ratios rpoes = poes[::-1] if probability > poes[0]: # max poes return 0.0 elif probability < poes[-1]: # min PoE return loss_ratios[-1] if probability in poes: return max([loss for i, loss in enumerate(loss_ratios) if probability == poes[i]]) else: interval_index = bisect.bisect_right(rpoes, probability) if interval_index == len(poes): # poes are all nan return float('nan') elif interval_index == 1: # boundary case x1, x2 = poes[-2:] y1, y2 = loss_ratios[-2:] else: x1, x2 = poes[-interval_index-1:-interval_index + 1] y1, y2 = loss_ratios[-interval_index-1:-interval_index + 1] return (y2 - y1) / (x2 - x1) * (probability - x1) + y1
python
def conditional_loss_ratio(loss_ratios, poes, probability): assert len(loss_ratios) >= 3, loss_ratios rpoes = poes[::-1] if probability > poes[0]: return 0.0 elif probability < poes[-1]: return loss_ratios[-1] if probability in poes: return max([loss for i, loss in enumerate(loss_ratios) if probability == poes[i]]) else: interval_index = bisect.bisect_right(rpoes, probability) if interval_index == len(poes): return float('nan') elif interval_index == 1: x1, x2 = poes[-2:] y1, y2 = loss_ratios[-2:] else: x1, x2 = poes[-interval_index-1:-interval_index + 1] y1, y2 = loss_ratios[-interval_index-1:-interval_index + 1] return (y2 - y1) / (x2 - x1) * (probability - x1) + y1
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Return the loss ratio corresponding to the given PoE (Probability of Exceendance). We can have four cases: 1. If `probability` is in `poes` it takes the bigger corresponding loss_ratios. 2. If it is in `(poe1, poe2)` where both `poe1` and `poe2` are in `poes`, then we perform a linear interpolation on the corresponding losses 3. if the given probability is smaller than the lowest PoE defined, it returns the max loss ratio . 4. if the given probability is greater than the highest PoE defined it returns zero. :param loss_ratios: an iterable over non-decreasing loss ratio values (float) :param poes: an iterable over non-increasing probability of exceedance values (float) :param float probability: the probability value used to interpolate the loss curve
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gem/oq-engine
openquake/risklib/scientific.py
insured_losses
def insured_losses(losses, deductible, insured_limit): """ :param losses: an array of ground-up loss ratios :param float deductible: the deductible limit in fraction form :param float insured_limit: the insured limit in fraction form Compute insured losses for the given asset and losses, from the point of view of the insurance company. For instance: >>> insured_losses(numpy.array([3, 20, 101]), 5, 100) array([ 0, 15, 95]) - if the loss is 3 (< 5) the company does not pay anything - if the loss is 20 the company pays 20 - 5 = 15 - if the loss is 101 the company pays 100 - 5 = 95 """ return numpy.piecewise( losses, [losses < deductible, losses > insured_limit], [0, insured_limit - deductible, lambda x: x - deductible])
python
def insured_losses(losses, deductible, insured_limit): return numpy.piecewise( losses, [losses < deductible, losses > insured_limit], [0, insured_limit - deductible, lambda x: x - deductible])
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:param losses: an array of ground-up loss ratios :param float deductible: the deductible limit in fraction form :param float insured_limit: the insured limit in fraction form Compute insured losses for the given asset and losses, from the point of view of the insurance company. For instance: >>> insured_losses(numpy.array([3, 20, 101]), 5, 100) array([ 0, 15, 95]) - if the loss is 3 (< 5) the company does not pay anything - if the loss is 20 the company pays 20 - 5 = 15 - if the loss is 101 the company pays 100 - 5 = 95
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train
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gem/oq-engine
openquake/risklib/scientific.py
insured_loss_curve
def insured_loss_curve(curve, deductible, insured_limit): """ Compute an insured loss ratio curve given a loss ratio curve :param curve: an array 2 x R (where R is the curve resolution) :param float deductible: the deductible limit in fraction form :param float insured_limit: the insured limit in fraction form >>> losses = numpy.array([3, 20, 101]) >>> poes = numpy.array([0.9, 0.5, 0.1]) >>> insured_loss_curve(numpy.array([losses, poes]), 5, 100) array([[ 3. , 20. ], [ 0.85294118, 0.5 ]]) """ losses, poes = curve[:, curve[0] <= insured_limit] limit_poe = interpolate.interp1d( *curve, bounds_error=False, fill_value=1)(deductible) return numpy.array([ losses, numpy.piecewise(poes, [poes > limit_poe], [limit_poe, lambda x: x])])
python
def insured_loss_curve(curve, deductible, insured_limit): losses, poes = curve[:, curve[0] <= insured_limit] limit_poe = interpolate.interp1d( *curve, bounds_error=False, fill_value=1)(deductible) return numpy.array([ losses, numpy.piecewise(poes, [poes > limit_poe], [limit_poe, lambda x: x])])
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Compute an insured loss ratio curve given a loss ratio curve :param curve: an array 2 x R (where R is the curve resolution) :param float deductible: the deductible limit in fraction form :param float insured_limit: the insured limit in fraction form >>> losses = numpy.array([3, 20, 101]) >>> poes = numpy.array([0.9, 0.5, 0.1]) >>> insured_loss_curve(numpy.array([losses, poes]), 5, 100) array([[ 3. , 20. ], [ 0.85294118, 0.5 ]])
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train
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gem/oq-engine
openquake/risklib/scientific.py
bcr
def bcr(eal_original, eal_retrofitted, interest_rate, asset_life_expectancy, asset_value, retrofitting_cost): """ Compute the Benefit-Cost Ratio. BCR = (EALo - EALr)(1-exp(-r*t))/(r*C) Where: * BCR -- Benefit cost ratio * EALo -- Expected annual loss for original asset * EALr -- Expected annual loss for retrofitted asset * r -- Interest rate * t -- Life expectancy of the asset * C -- Retrofitting cost """ return ((eal_original - eal_retrofitted) * asset_value * (1 - numpy.exp(- interest_rate * asset_life_expectancy)) / (interest_rate * retrofitting_cost))
python
def bcr(eal_original, eal_retrofitted, interest_rate, asset_life_expectancy, asset_value, retrofitting_cost): return ((eal_original - eal_retrofitted) * asset_value * (1 - numpy.exp(- interest_rate * asset_life_expectancy)) / (interest_rate * retrofitting_cost))
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Compute the Benefit-Cost Ratio. BCR = (EALo - EALr)(1-exp(-r*t))/(r*C) Where: * BCR -- Benefit cost ratio * EALo -- Expected annual loss for original asset * EALr -- Expected annual loss for retrofitted asset * r -- Interest rate * t -- Life expectancy of the asset * C -- Retrofitting cost
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train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/risklib/scientific.py#L1124-L1142
gem/oq-engine
openquake/risklib/scientific.py
pairwise_mean
def pairwise_mean(values): "Averages between a value and the next value in a sequence" return numpy.array([numpy.mean(pair) for pair in pairwise(values)])
python
def pairwise_mean(values): "Averages between a value and the next value in a sequence" return numpy.array([numpy.mean(pair) for pair in pairwise(values)])
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train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/risklib/scientific.py#L1147-L1149
gem/oq-engine
openquake/risklib/scientific.py
pairwise_diff
def pairwise_diff(values): "Differences between a value and the next value in a sequence" return numpy.array([x - y for x, y in pairwise(values)])
python
def pairwise_diff(values): "Differences between a value and the next value in a sequence" return numpy.array([x - y for x, y in pairwise(values)])
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train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/risklib/scientific.py#L1152-L1154
gem/oq-engine
openquake/risklib/scientific.py
mean_std
def mean_std(fractions): """ Given an N x M matrix, returns mean and std computed on the rows, i.e. two M-dimensional vectors. """ n = fractions.shape[0] if n == 1: # avoid warnings when computing the stddev return fractions[0], numpy.ones_like(fractions[0]) * numpy.nan return numpy.mean(fractions, axis=0), numpy.std(fractions, axis=0, ddof=1)
python
def mean_std(fractions): n = fractions.shape[0] if n == 1: return fractions[0], numpy.ones_like(fractions[0]) * numpy.nan return numpy.mean(fractions, axis=0), numpy.std(fractions, axis=0, ddof=1)
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Given an N x M matrix, returns mean and std computed on the rows, i.e. two M-dimensional vectors.
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train
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gem/oq-engine
openquake/risklib/scientific.py
loss_maps
def loss_maps(curves, conditional_loss_poes): """ :param curves: an array of loss curves :param conditional_loss_poes: a list of conditional loss poes :returns: a composite array of loss maps with the same shape """ loss_maps_dt = numpy.dtype([('poe-%s' % poe, F32) for poe in conditional_loss_poes]) loss_maps = numpy.zeros(curves.shape, loss_maps_dt) for idx, curve in numpy.ndenumerate(curves): for poe in conditional_loss_poes: loss_maps['poe-%s' % poe][idx] = conditional_loss_ratio( curve['losses'], curve['poes'], poe) return loss_maps
python
def loss_maps(curves, conditional_loss_poes): loss_maps_dt = numpy.dtype([('poe-%s' % poe, F32) for poe in conditional_loss_poes]) loss_maps = numpy.zeros(curves.shape, loss_maps_dt) for idx, curve in numpy.ndenumerate(curves): for poe in conditional_loss_poes: loss_maps['poe-%s' % poe][idx] = conditional_loss_ratio( curve['losses'], curve['poes'], poe) return loss_maps
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:param curves: an array of loss curves :param conditional_loss_poes: a list of conditional loss poes :returns: a composite array of loss maps with the same shape
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train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/risklib/scientific.py#L1168-L1181
gem/oq-engine
openquake/risklib/scientific.py
broadcast
def broadcast(func, composite_array, *args): """ Broadcast an array function over a composite array """ dic = {} dtypes = [] for name in composite_array.dtype.names: dic[name] = func(composite_array[name], *args) dtypes.append((name, dic[name].dtype)) res = numpy.zeros(dic[name].shape, numpy.dtype(dtypes)) for name in dic: res[name] = dic[name] return res
python
def broadcast(func, composite_array, *args): dic = {} dtypes = [] for name in composite_array.dtype.names: dic[name] = func(composite_array[name], *args) dtypes.append((name, dic[name].dtype)) res = numpy.zeros(dic[name].shape, numpy.dtype(dtypes)) for name in dic: res[name] = dic[name] return res
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train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/risklib/scientific.py#L1184-L1196
gem/oq-engine
openquake/risklib/scientific.py
average_loss
def average_loss(lc): """ Given a loss curve array with `poe` and `loss` fields, computes the average loss on a period of time. :note: As the loss curve is supposed to be piecewise linear as it is a result of a linear interpolation, we compute an exact integral by using the trapeizodal rule with the width given by the loss bin width. """ losses, poes = (lc['loss'], lc['poe']) if lc.dtype.names else lc return -pairwise_diff(losses) @ pairwise_mean(poes)
python
def average_loss(lc): losses, poes = (lc['loss'], lc['poe']) if lc.dtype.names else lc return -pairwise_diff(losses) @ pairwise_mean(poes)
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Given a loss curve array with `poe` and `loss` fields, computes the average loss on a period of time. :note: As the loss curve is supposed to be piecewise linear as it is a result of a linear interpolation, we compute an exact integral by using the trapeizodal rule with the width given by the loss bin width.
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train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/risklib/scientific.py#L1200-L1211
gem/oq-engine
openquake/risklib/scientific.py
normalize_curves_eb
def normalize_curves_eb(curves): """ A more sophisticated version of normalize_curves, used in the event based calculator. :param curves: a list of pairs (losses, poes) :returns: first losses, all_poes """ # we assume non-decreasing losses, so losses[-1] is the maximum loss non_zero_curves = [(losses, poes) for losses, poes in curves if losses[-1] > 0] if not non_zero_curves: # no damage. all zero curves return curves[0][0], numpy.array([poes for _losses, poes in curves]) else: # standard case max_losses = [losses[-1] for losses, _poes in non_zero_curves] reference_curve = non_zero_curves[numpy.argmax(max_losses)] loss_ratios = reference_curve[0] curves_poes = [interpolate.interp1d( losses, poes, bounds_error=False, fill_value=0)(loss_ratios) for losses, poes in curves] # fix degenerated case with flat curve for cp in curves_poes: if numpy.isnan(cp[0]): cp[0] = 0 return loss_ratios, numpy.array(curves_poes)
python
def normalize_curves_eb(curves): non_zero_curves = [(losses, poes) for losses, poes in curves if losses[-1] > 0] if not non_zero_curves: return curves[0][0], numpy.array([poes for _losses, poes in curves]) else: max_losses = [losses[-1] for losses, _poes in non_zero_curves] reference_curve = non_zero_curves[numpy.argmax(max_losses)] loss_ratios = reference_curve[0] curves_poes = [interpolate.interp1d( losses, poes, bounds_error=False, fill_value=0)(loss_ratios) for losses, poes in curves] for cp in curves_poes: if numpy.isnan(cp[0]): cp[0] = 0 return loss_ratios, numpy.array(curves_poes)
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A more sophisticated version of normalize_curves, used in the event based calculator. :param curves: a list of pairs (losses, poes) :returns: first losses, all_poes
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train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/risklib/scientific.py#L1214-L1238
gem/oq-engine
openquake/risklib/scientific.py
build_loss_curve_dt
def build_loss_curve_dt(curve_resolution, insured_losses=False): """ :param curve_resolution: dictionary loss_type -> curve_resolution :param insured_losses: configuration parameter :returns: loss_curve_dt """ lc_list = [] for lt in sorted(curve_resolution): C = curve_resolution[lt] pairs = [('losses', (F32, C)), ('poes', (F32, C))] lc_dt = numpy.dtype(pairs) lc_list.append((str(lt), lc_dt)) if insured_losses: for lt in sorted(curve_resolution): C = curve_resolution[lt] pairs = [('losses', (F32, C)), ('poes', (F32, C))] lc_dt = numpy.dtype(pairs) lc_list.append((str(lt) + '_ins', lc_dt)) loss_curve_dt = numpy.dtype(lc_list) if lc_list else None return loss_curve_dt
python
def build_loss_curve_dt(curve_resolution, insured_losses=False): lc_list = [] for lt in sorted(curve_resolution): C = curve_resolution[lt] pairs = [('losses', (F32, C)), ('poes', (F32, C))] lc_dt = numpy.dtype(pairs) lc_list.append((str(lt), lc_dt)) if insured_losses: for lt in sorted(curve_resolution): C = curve_resolution[lt] pairs = [('losses', (F32, C)), ('poes', (F32, C))] lc_dt = numpy.dtype(pairs) lc_list.append((str(lt) + '_ins', lc_dt)) loss_curve_dt = numpy.dtype(lc_list) if lc_list else None return loss_curve_dt
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:param curve_resolution: dictionary loss_type -> curve_resolution :param insured_losses: configuration parameter :returns: loss_curve_dt
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train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/risklib/scientific.py#L1241-L1263
gem/oq-engine
openquake/risklib/scientific.py
return_periods
def return_periods(eff_time, num_losses): """ :param eff_time: ses_per_logic_tree_path * investigation_time :param num_losses: used to determine the minimum period :returns: an array of 32 bit periods Here are a few examples: >>> return_periods(1, 1) Traceback (most recent call last): ... AssertionError: eff_time too small: 1 >>> return_periods(2, 2) array([1, 2], dtype=uint32) >>> return_periods(2, 10) array([1, 2], dtype=uint32) >>> return_periods(100, 2) array([ 50, 100], dtype=uint32) >>> return_periods(1000, 1000) array([ 1, 2, 5, 10, 20, 50, 100, 200, 500, 1000], dtype=uint32) """ assert eff_time >= 2, 'eff_time too small: %s' % eff_time assert num_losses >= 2, 'num_losses too small: %s' % num_losses min_time = eff_time / num_losses period = 1 periods = [] loop = True while loop: for val in [1, 2, 5]: time = period * val if time >= min_time: if time > eff_time: loop = False break periods.append(time) period *= 10 return U32(periods)
python
def return_periods(eff_time, num_losses): assert eff_time >= 2, 'eff_time too small: %s' % eff_time assert num_losses >= 2, 'num_losses too small: %s' % num_losses min_time = eff_time / num_losses period = 1 periods = [] loop = True while loop: for val in [1, 2, 5]: time = period * val if time >= min_time: if time > eff_time: loop = False break periods.append(time) period *= 10 return U32(periods)
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:param eff_time: ses_per_logic_tree_path * investigation_time :param num_losses: used to determine the minimum period :returns: an array of 32 bit periods Here are a few examples: >>> return_periods(1, 1) Traceback (most recent call last): ... AssertionError: eff_time too small: 1 >>> return_periods(2, 2) array([1, 2], dtype=uint32) >>> return_periods(2, 10) array([1, 2], dtype=uint32) >>> return_periods(100, 2) array([ 50, 100], dtype=uint32) >>> return_periods(1000, 1000) array([ 1, 2, 5, 10, 20, 50, 100, 200, 500, 1000], dtype=uint32)
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train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/risklib/scientific.py#L1266-L1303
gem/oq-engine
openquake/risklib/scientific.py
losses_by_period
def losses_by_period(losses, return_periods, num_events=None, eff_time=None): """ :param losses: array of simulated losses :param return_periods: return periods of interest :param num_events: the number of events (>= to the number of losses) :param eff_time: investigation_time * ses_per_logic_tree_path :returns: interpolated losses for the return periods, possibly with NaN NB: the return periods must be ordered integers >= 1. The interpolated losses are defined inside the interval min_time < time < eff_time where min_time = eff_time /num_events. Outside the interval they have NaN values. Here is an example: >>> losses = [3, 2, 3.5, 4, 3, 23, 11, 2, 1, 4, 5, 7, 8, 9, 13] >>> losses_by_period(losses, [1, 2, 5, 10, 20, 50, 100], 20) array([ nan, nan, 0. , 3.5, 8. , 13. , 23. ]) If num_events is not passed, it is inferred from the number of losses; if eff_time is not passed, it is inferred from the longest return period. """ if len(losses) == 0: # zero-curve return numpy.zeros(len(return_periods)) if num_events is None: num_events = len(losses) elif num_events < len(losses): raise ValueError( 'There are not enough events (%d) to compute the loss curve ' 'from %d losses' % (num_events, len(losses))) if eff_time is None: eff_time = return_periods[-1] losses = numpy.sort(losses) num_zeros = num_events - len(losses) if num_zeros: losses = numpy.concatenate( [numpy.zeros(num_zeros, losses.dtype), losses]) periods = eff_time / numpy.arange(num_events, 0., -1) rperiods = [rp if periods[0] <= rp <= periods[-1] else numpy.nan for rp in return_periods] curve = numpy.interp(numpy.log(rperiods), numpy.log(periods), losses) return curve
python
def losses_by_period(losses, return_periods, num_events=None, eff_time=None): if len(losses) == 0: return numpy.zeros(len(return_periods)) if num_events is None: num_events = len(losses) elif num_events < len(losses): raise ValueError( 'There are not enough events (%d) to compute the loss curve ' 'from %d losses' % (num_events, len(losses))) if eff_time is None: eff_time = return_periods[-1] losses = numpy.sort(losses) num_zeros = num_events - len(losses) if num_zeros: losses = numpy.concatenate( [numpy.zeros(num_zeros, losses.dtype), losses]) periods = eff_time / numpy.arange(num_events, 0., -1) rperiods = [rp if periods[0] <= rp <= periods[-1] else numpy.nan for rp in return_periods] curve = numpy.interp(numpy.log(rperiods), numpy.log(periods), losses) return curve
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:param losses: array of simulated losses :param return_periods: return periods of interest :param num_events: the number of events (>= to the number of losses) :param eff_time: investigation_time * ses_per_logic_tree_path :returns: interpolated losses for the return periods, possibly with NaN NB: the return periods must be ordered integers >= 1. The interpolated losses are defined inside the interval min_time < time < eff_time where min_time = eff_time /num_events. Outside the interval they have NaN values. Here is an example: >>> losses = [3, 2, 3.5, 4, 3, 23, 11, 2, 1, 4, 5, 7, 8, 9, 13] >>> losses_by_period(losses, [1, 2, 5, 10, 20, 50, 100], 20) array([ nan, nan, 0. , 3.5, 8. , 13. , 23. ]) If num_events is not passed, it is inferred from the number of losses; if eff_time is not passed, it is inferred from the longest return period.
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train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/risklib/scientific.py#L1306-L1345
gem/oq-engine
openquake/risklib/scientific.py
VulnerabilityFunction.interpolate
def interpolate(self, gmvs): """ :param gmvs: array of intensity measure levels :returns: (interpolated loss ratios, interpolated covs, indices > min) """ # gmvs are clipped to max(iml) gmvs_curve = numpy.piecewise( gmvs, [gmvs > self.imls[-1]], [self.imls[-1], lambda x: x]) idxs = gmvs_curve >= self.imls[0] # indices over the minimum gmvs_curve = gmvs_curve[idxs] return self._mlr_i1d(gmvs_curve), self._cov_for(gmvs_curve), idxs
python
def interpolate(self, gmvs): gmvs_curve = numpy.piecewise( gmvs, [gmvs > self.imls[-1]], [self.imls[-1], lambda x: x]) idxs = gmvs_curve >= self.imls[0] gmvs_curve = gmvs_curve[idxs] return self._mlr_i1d(gmvs_curve), self._cov_for(gmvs_curve), idxs
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:param gmvs: array of intensity measure levels :returns: (interpolated loss ratios, interpolated covs, indices > min)
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train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/risklib/scientific.py#L147-L159
gem/oq-engine
openquake/risklib/scientific.py
VulnerabilityFunction.sample
def sample(self, means, covs, idxs, epsilons=None): """ Sample the epsilons and apply the corrections to the means. This method is called only if there are nonzero covs. :param means: array of E' loss ratios :param covs: array of E' floats :param idxs: array of E booleans with E >= E' :param epsilons: array of E floats (or None) :returns: array of E' loss ratios """ if epsilons is None: return means self.set_distribution(epsilons) res = self.distribution.sample(means, covs, means * covs, idxs) return res
python
def sample(self, means, covs, idxs, epsilons=None): if epsilons is None: return means self.set_distribution(epsilons) res = self.distribution.sample(means, covs, means * covs, idxs) return res
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Sample the epsilons and apply the corrections to the means. This method is called only if there are nonzero covs. :param means: array of E' loss ratios :param covs: array of E' floats :param idxs: array of E booleans with E >= E' :param epsilons: array of E floats (or None) :returns: array of E' loss ratios
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train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/risklib/scientific.py#L161-L181
gem/oq-engine
openquake/risklib/scientific.py
VulnerabilityFunction.strictly_increasing
def strictly_increasing(self): """ :returns: a new vulnerability function that is strictly increasing. It is built by removing piece of the function where the mean loss ratio is constant. """ imls, mlrs, covs = [], [], [] previous_mlr = None for i, mlr in enumerate(self.mean_loss_ratios): if previous_mlr == mlr: continue else: mlrs.append(mlr) imls.append(self.imls[i]) covs.append(self.covs[i]) previous_mlr = mlr return self.__class__( self.id, self.imt, imls, mlrs, covs, self.distribution_name)
python
def strictly_increasing(self): imls, mlrs, covs = [], [], [] previous_mlr = None for i, mlr in enumerate(self.mean_loss_ratios): if previous_mlr == mlr: continue else: mlrs.append(mlr) imls.append(self.imls[i]) covs.append(self.covs[i]) previous_mlr = mlr return self.__class__( self.id, self.imt, imls, mlrs, covs, self.distribution_name)
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:returns: a new vulnerability function that is strictly increasing. It is built by removing piece of the function where the mean loss ratio is constant.
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train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/risklib/scientific.py#L194-L214
gem/oq-engine
openquake/risklib/scientific.py
VulnerabilityFunction.mean_loss_ratios_with_steps
def mean_loss_ratios_with_steps(self, steps): """ Split the mean loss ratios, producing a new set of loss ratios. The new set of loss ratios always includes 0.0 and 1.0 :param int steps: the number of steps we make to go from one loss ratio to the next. For example, if we have [0.5, 0.7]:: steps = 1 produces [0.0, 0.5, 0.7, 1] steps = 2 produces [0.0, 0.25, 0.5, 0.6, 0.7, 0.85, 1] steps = 3 produces [0.0, 0.17, 0.33, 0.5, 0.57, 0.63, 0.7, 0.8, 0.9, 1] """ loss_ratios = self.mean_loss_ratios if min(loss_ratios) > 0.0: # prepend with a zero loss_ratios = numpy.concatenate([[0.0], loss_ratios]) if max(loss_ratios) < 1.0: # append a 1.0 loss_ratios = numpy.concatenate([loss_ratios, [1.0]]) return fine_graining(loss_ratios, steps)
python
def mean_loss_ratios_with_steps(self, steps): loss_ratios = self.mean_loss_ratios if min(loss_ratios) > 0.0: loss_ratios = numpy.concatenate([[0.0], loss_ratios]) if max(loss_ratios) < 1.0: loss_ratios = numpy.concatenate([loss_ratios, [1.0]]) return fine_graining(loss_ratios, steps)
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train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/risklib/scientific.py#L216-L240
gem/oq-engine
openquake/risklib/scientific.py
VulnerabilityFunction._cov_for
def _cov_for(self, imls): """ Clip `imls` to the range associated with the support of the vulnerability function and returns the corresponding covariance values by linear interpolation. For instance if the range is [0.005, 0.0269] and the imls are [0.0049, 0.006, 0.027], the clipped imls are [0.005, 0.006, 0.0269]. """ return self._covs_i1d( numpy.piecewise( imls, [imls > self.imls[-1], imls < self.imls[0]], [self.imls[-1], self.imls[0], lambda x: x]))
python
def _cov_for(self, imls): return self._covs_i1d( numpy.piecewise( imls, [imls > self.imls[-1], imls < self.imls[0]], [self.imls[-1], self.imls[0], lambda x: x]))
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Clip `imls` to the range associated with the support of the vulnerability function and returns the corresponding covariance values by linear interpolation. For instance if the range is [0.005, 0.0269] and the imls are [0.0049, 0.006, 0.027], the clipped imls are [0.005, 0.006, 0.0269].
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train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/risklib/scientific.py#L242-L255
gem/oq-engine
openquake/risklib/scientific.py
VulnerabilityFunction.loss_ratio_exceedance_matrix
def loss_ratio_exceedance_matrix(self, loss_ratios): """ Compute the LREM (Loss Ratio Exceedance Matrix). """ # LREM has number of rows equal to the number of loss ratios # and number of columns equal to the number of imls lrem = numpy.empty((len(loss_ratios), len(self.imls))) for row, loss_ratio in enumerate(loss_ratios): for col, (mean_loss_ratio, stddev) in enumerate( zip(self.mean_loss_ratios, self.stddevs)): lrem[row, col] = self.distribution.survival( loss_ratio, mean_loss_ratio, stddev) return lrem
python
def loss_ratio_exceedance_matrix(self, loss_ratios): lrem = numpy.empty((len(loss_ratios), len(self.imls))) for row, loss_ratio in enumerate(loss_ratios): for col, (mean_loss_ratio, stddev) in enumerate( zip(self.mean_loss_ratios, self.stddevs)): lrem[row, col] = self.distribution.survival( loss_ratio, mean_loss_ratio, stddev) return lrem
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Compute the LREM (Loss Ratio Exceedance Matrix).
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train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/risklib/scientific.py#L280-L292
gem/oq-engine
openquake/risklib/scientific.py
VulnerabilityFunction.mean_imls
def mean_imls(self): """ Compute the mean IMLs (Intensity Measure Level) for the given vulnerability function. :param vulnerability_function: the vulnerability function where the IMLs (Intensity Measure Level) are taken from. :type vuln_function: :py:class:`openquake.risklib.vulnerability_function.\ VulnerabilityFunction` """ return numpy.array( [max(0, self.imls[0] - (self.imls[1] - self.imls[0]) / 2.)] + [numpy.mean(pair) for pair in pairwise(self.imls)] + [self.imls[-1] + (self.imls[-1] - self.imls[-2]) / 2.])
python
def mean_imls(self): return numpy.array( [max(0, self.imls[0] - (self.imls[1] - self.imls[0]) / 2.)] + [numpy.mean(pair) for pair in pairwise(self.imls)] + [self.imls[-1] + (self.imls[-1] - self.imls[-2]) / 2.])
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Compute the mean IMLs (Intensity Measure Level) for the given vulnerability function. :param vulnerability_function: the vulnerability function where the IMLs (Intensity Measure Level) are taken from. :type vuln_function: :py:class:`openquake.risklib.vulnerability_function.\ VulnerabilityFunction`
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train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/risklib/scientific.py#L295-L309
gem/oq-engine
openquake/risklib/scientific.py
VulnerabilityFunctionWithPMF.interpolate
def interpolate(self, gmvs): """ :param gmvs: array of intensity measure levels :returns: (interpolated probabilities, zeros, indices > min) """ # gmvs are clipped to max(iml) gmvs_curve = numpy.piecewise( gmvs, [gmvs > self.imls[-1]], [self.imls[-1], lambda x: x]) idxs = gmvs_curve >= self.imls[0] # indices over the minimum gmvs_curve = gmvs_curve[idxs] return self._probs_i1d(gmvs_curve), numpy.zeros_like(gmvs_curve), idxs
python
def interpolate(self, gmvs): gmvs_curve = numpy.piecewise( gmvs, [gmvs > self.imls[-1]], [self.imls[-1], lambda x: x]) idxs = gmvs_curve >= self.imls[0] gmvs_curve = gmvs_curve[idxs] return self._probs_i1d(gmvs_curve), numpy.zeros_like(gmvs_curve), idxs
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:param gmvs: array of intensity measure levels :returns: (interpolated probabilities, zeros, indices > min)
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train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/risklib/scientific.py#L392-L404
gem/oq-engine
openquake/risklib/scientific.py
VulnerabilityFunctionWithPMF.sample
def sample(self, probs, _covs, idxs, epsilons): """ Sample the .loss_ratios with the given probabilities. :param probs: array of E' floats :param _covs: ignored, it is there only for API consistency :param idxs: array of E booleans with E >= E' :param epsilons: array of E floats :returns: array of E' probabilities """ self.set_distribution(epsilons) return self.distribution.sample(self.loss_ratios, probs)
python
def sample(self, probs, _covs, idxs, epsilons): self.set_distribution(epsilons) return self.distribution.sample(self.loss_ratios, probs)
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Sample the .loss_ratios with the given probabilities. :param probs: array of E' floats :param _covs: ignored, it is there only for API consistency :param idxs: array of E booleans with E >= E' :param epsilons: array of E floats :returns: array of E' probabilities
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train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/risklib/scientific.py#L406-L422
gem/oq-engine
openquake/risklib/scientific.py
FragilityFunctionList.build
def build(self, limit_states, discretization, steps_per_interval): """ :param limit_states: a sequence of limit states :param discretization: continouos fragility discretization parameter :param steps_per_interval: steps_per_interval parameter :returns: a populated FragilityFunctionList instance """ new = copy.copy(self) add_zero = (self.format == 'discrete' and self.nodamage and self.nodamage <= self.imls[0]) new.imls = build_imls(new, discretization) if steps_per_interval > 1: new.interp_imls = build_imls( # passed to classical_damage new, discretization, steps_per_interval) for i, ls in enumerate(limit_states): data = self.array[i] if self.format == 'discrete': if add_zero: new.append(FragilityFunctionDiscrete( ls, [self.nodamage] + self.imls, numpy.concatenate([[0.], data]), self.nodamage)) else: new.append(FragilityFunctionDiscrete( ls, self.imls, data, self.nodamage)) else: # continuous new.append(FragilityFunctionContinuous( ls, data['mean'], data['stddev'])) return new
python
def build(self, limit_states, discretization, steps_per_interval): new = copy.copy(self) add_zero = (self.format == 'discrete' and self.nodamage and self.nodamage <= self.imls[0]) new.imls = build_imls(new, discretization) if steps_per_interval > 1: new.interp_imls = build_imls( new, discretization, steps_per_interval) for i, ls in enumerate(limit_states): data = self.array[i] if self.format == 'discrete': if add_zero: new.append(FragilityFunctionDiscrete( ls, [self.nodamage] + self.imls, numpy.concatenate([[0.], data]), self.nodamage)) else: new.append(FragilityFunctionDiscrete( ls, self.imls, data, self.nodamage)) else: new.append(FragilityFunctionContinuous( ls, data['mean'], data['stddev'])) return new
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train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/risklib/scientific.py#L582-L610
gem/oq-engine
openquake/risklib/scientific.py
FragilityModel.build
def build(self, continuous_fragility_discretization, steps_per_interval): """ Return a new FragilityModel instance, in which the values have been replaced with FragilityFunctionList instances. :param continuous_fragility_discretization: configuration parameter :param steps_per_interval: configuration parameter """ newfm = copy.copy(self) for key, ffl in self.items(): newfm[key] = ffl.build(self.limitStates, continuous_fragility_discretization, steps_per_interval) return newfm
python
def build(self, continuous_fragility_discretization, steps_per_interval): newfm = copy.copy(self) for key, ffl in self.items(): newfm[key] = ffl.build(self.limitStates, continuous_fragility_discretization, steps_per_interval) return newfm
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Return a new FragilityModel instance, in which the values have been replaced with FragilityFunctionList instances. :param continuous_fragility_discretization: configuration parameter :param steps_per_interval: configuration parameter
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train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/risklib/scientific.py#L719-L734
gem/oq-engine
openquake/risklib/scientific.py
LossCurvesMapsBuilder.pair
def pair(self, array, stats): """ :return (array, array_stats) if stats, else (array, None) """ if len(self.weights) > 1 and stats: statnames, statfuncs = zip(*stats) array_stats = compute_stats2(array, statfuncs, self.weights) else: array_stats = None return array, array_stats
python
def pair(self, array, stats): if len(self.weights) > 1 and stats: statnames, statfuncs = zip(*stats) array_stats = compute_stats2(array, statfuncs, self.weights) else: array_stats = None return array, array_stats
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:return (array, array_stats) if stats, else (array, None)
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train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/risklib/scientific.py#L1369-L1378
gem/oq-engine
openquake/risklib/scientific.py
LossCurvesMapsBuilder.build
def build(self, losses_by_event, stats=()): """ :param losses_by_event: the aggregate loss table with shape R -> (E, L) :param stats: list of pairs [(statname, statfunc), ...] :returns: two arrays with shape (P, R, L) and (P, S, L) """ P, R = len(self.return_periods), len(self.weights) L = len(self.loss_dt.names) array = numpy.zeros((P, R, L), F32) for r in losses_by_event: num_events = self.num_events[r] losses = losses_by_event[r] for l, lt in enumerate(self.loss_dt.names): ls = losses[:, l].flatten() # flatten only in ucerf # NB: do not use squeeze or the gmf_ebrisk tests will break lbp = losses_by_period( ls, self.return_periods, num_events, self.eff_time) array[:, r, l] = lbp return self.pair(array, stats)
python
def build(self, losses_by_event, stats=()): P, R = len(self.return_periods), len(self.weights) L = len(self.loss_dt.names) array = numpy.zeros((P, R, L), F32) for r in losses_by_event: num_events = self.num_events[r] losses = losses_by_event[r] for l, lt in enumerate(self.loss_dt.names): ls = losses[:, l].flatten() lbp = losses_by_period( ls, self.return_periods, num_events, self.eff_time) array[:, r, l] = lbp return self.pair(array, stats)
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:param losses_by_event: the aggregate loss table with shape R -> (E, L) :param stats: list of pairs [(statname, statfunc), ...] :returns: two arrays with shape (P, R, L) and (P, S, L)
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train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/risklib/scientific.py#L1381-L1402
gem/oq-engine
openquake/risklib/scientific.py
LossCurvesMapsBuilder.build_pair
def build_pair(self, losses, stats): """ :param losses: a list of lists with R elements :returns: two arrays of shape (P, R) and (P, S) respectively """ P, R = len(self.return_periods), len(self.weights) assert len(losses) == R, len(losses) array = numpy.zeros((P, R), F32) for r, ls in enumerate(losses): ne = self.num_events.get(r, 0) if ne: array[:, r] = losses_by_period( ls, self.return_periods, ne, self.eff_time) return self.pair(array, stats)
python
def build_pair(self, losses, stats): P, R = len(self.return_periods), len(self.weights) assert len(losses) == R, len(losses) array = numpy.zeros((P, R), F32) for r, ls in enumerate(losses): ne = self.num_events.get(r, 0) if ne: array[:, r] = losses_by_period( ls, self.return_periods, ne, self.eff_time) return self.pair(array, stats)
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:param losses: a list of lists with R elements :returns: two arrays of shape (P, R) and (P, S) respectively
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train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/risklib/scientific.py#L1404-L1417
gem/oq-engine
openquake/risklib/scientific.py
LossCurvesMapsBuilder.build_maps
def build_maps(self, losses, clp, stats=()): """ :param losses: an array of shape (A, R, P) :param clp: a list of C conditional loss poes :param stats: list of pairs [(statname, statfunc), ...] :returns: an array of loss_maps of shape (A, R, C, LI) """ shp = losses.shape[:2] + (len(clp), len(losses.dtype)) # (A, R, C, LI) array = numpy.zeros(shp, F32) for lti, lt in enumerate(losses.dtype.names): for a, losses_ in enumerate(losses[lt]): for r, ls in enumerate(losses_): for c, poe in enumerate(clp): clratio = conditional_loss_ratio(ls, self.poes, poe) array[a, r, c, lti] = clratio return self.pair(array, stats)
python
def build_maps(self, losses, clp, stats=()): shp = losses.shape[:2] + (len(clp), len(losses.dtype)) array = numpy.zeros(shp, F32) for lti, lt in enumerate(losses.dtype.names): for a, losses_ in enumerate(losses[lt]): for r, ls in enumerate(losses_): for c, poe in enumerate(clp): clratio = conditional_loss_ratio(ls, self.poes, poe) array[a, r, c, lti] = clratio return self.pair(array, stats)
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:param losses: an array of shape (A, R, P) :param clp: a list of C conditional loss poes :param stats: list of pairs [(statname, statfunc), ...] :returns: an array of loss_maps of shape (A, R, C, LI)
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train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/risklib/scientific.py#L1426-L1441
gem/oq-engine
openquake/risklib/scientific.py
LossCurvesMapsBuilder.build_loss_maps
def build_loss_maps(self, losses, clp, stats=()): """ :param losses: an array of shape R, E :param clp: a list of C conditional loss poes :param stats: list of pairs [(statname, statfunc), ...] :returns: two arrays of shape (C, R) and (C, S) """ array = numpy.zeros((len(clp), len(losses)), F32) for r, ls in enumerate(losses): if len(ls) < 2: continue for c, poe in enumerate(clp): array[c, r] = conditional_loss_ratio(ls, self.poes, poe) return self.pair(array, stats)
python
def build_loss_maps(self, losses, clp, stats=()): array = numpy.zeros((len(clp), len(losses)), F32) for r, ls in enumerate(losses): if len(ls) < 2: continue for c, poe in enumerate(clp): array[c, r] = conditional_loss_ratio(ls, self.poes, poe) return self.pair(array, stats)
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:param losses: an array of shape R, E :param clp: a list of C conditional loss poes :param stats: list of pairs [(statname, statfunc), ...] :returns: two arrays of shape (C, R) and (C, S)
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train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/risklib/scientific.py#L1444-L1457
gem/oq-engine
openquake/calculators/event_based.py
store_rlzs_by_grp
def store_rlzs_by_grp(dstore): """ Save in the datastore a composite array with fields (grp_id, gsim_id, rlzs) """ lst = [] assoc = dstore['csm_info'].get_rlzs_assoc() for grp, arr in assoc.by_grp().items(): for gsim_id, rlzs in enumerate(arr): lst.append((int(grp[4:]), gsim_id, rlzs)) dstore['csm_info/rlzs_by_grp'] = numpy.array(lst, rlzs_by_grp_dt)
python
def store_rlzs_by_grp(dstore): lst = [] assoc = dstore['csm_info'].get_rlzs_assoc() for grp, arr in assoc.by_grp().items(): for gsim_id, rlzs in enumerate(arr): lst.append((int(grp[4:]), gsim_id, rlzs)) dstore['csm_info/rlzs_by_grp'] = numpy.array(lst, rlzs_by_grp_dt)
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Save in the datastore a composite array with fields (grp_id, gsim_id, rlzs)
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train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/calculators/event_based.py#L53-L62
gem/oq-engine
openquake/calculators/event_based.py
compute_gmfs
def compute_gmfs(rupgetter, srcfilter, param, monitor): """ Compute GMFs and optionally hazard curves """ getter = GmfGetter(rupgetter, srcfilter, param['oqparam']) with monitor('getting ruptures'): getter.init() return getter.compute_gmfs_curves(monitor)
python
def compute_gmfs(rupgetter, srcfilter, param, monitor): getter = GmfGetter(rupgetter, srcfilter, param['oqparam']) with monitor('getting ruptures'): getter.init() return getter.compute_gmfs_curves(monitor)
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Compute GMFs and optionally hazard curves
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train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/calculators/event_based.py#L82-L89
gem/oq-engine
utils/combine_mean_curves.py
combine_mean_curves
def combine_mean_curves(calc_big, calc_small): """ Combine the hazard curves coming from two different calculations. The result will be the hazard curves of calc_big, updated on the sites in common with calc_small with the PoEs of calc_small. For instance: calc_big = USA, calc_small = California """ dstore_big = datastore.read(calc_big) dstore_small = datastore.read(calc_small) sitecol_big = dstore_big['sitecol'] sitecol_small = dstore_small['sitecol'] site_id_big = {(lon, lat): sid for sid, lon, lat in zip( sitecol_big.sids, sitecol_big.lons, sitecol_big.lats)} site_id_small = {(lon, lat): sid for sid, lon, lat in zip( sitecol_small.sids, sitecol_small.lons, sitecol_small.lats)} common = set(site_id_big) & set(site_id_small) if not common: raise RuntimeError('There are no common sites between calculation ' '%d and %d' % (calc_big, calc_small)) sids_small = [site_id_small[lonlat] for lonlat in common] pmap_big = PmapGetter(dstore_big).get_mean() # USA pmap_small = PmapGetter(dstore_big, sids=sids_small).get_mean() # Cal for lonlat in common: pmap_big[site_id_big[lonlat]] |= pmap_small.get( site_id_small[lonlat], 0) out = 'combine_%d_%d.hdf5' % (calc_big, calc_small) with hdf5.File(out, 'w') as h5: h5['hcurves/mean'] = pmap_big h5['oqparam'] = dstore_big['oqparam'] h5['sitecol'] = dstore_big['sitecol'] print('Generated %s' % out)
python
def combine_mean_curves(calc_big, calc_small): dstore_big = datastore.read(calc_big) dstore_small = datastore.read(calc_small) sitecol_big = dstore_big['sitecol'] sitecol_small = dstore_small['sitecol'] site_id_big = {(lon, lat): sid for sid, lon, lat in zip( sitecol_big.sids, sitecol_big.lons, sitecol_big.lats)} site_id_small = {(lon, lat): sid for sid, lon, lat in zip( sitecol_small.sids, sitecol_small.lons, sitecol_small.lats)} common = set(site_id_big) & set(site_id_small) if not common: raise RuntimeError('There are no common sites between calculation ' '%d and %d' % (calc_big, calc_small)) sids_small = [site_id_small[lonlat] for lonlat in common] pmap_big = PmapGetter(dstore_big).get_mean() pmap_small = PmapGetter(dstore_big, sids=sids_small).get_mean() for lonlat in common: pmap_big[site_id_big[lonlat]] |= pmap_small.get( site_id_small[lonlat], 0) out = 'combine_%d_%d.hdf5' % (calc_big, calc_small) with hdf5.File(out, 'w') as h5: h5['hcurves/mean'] = pmap_big h5['oqparam'] = dstore_big['oqparam'] h5['sitecol'] = dstore_big['sitecol'] print('Generated %s' % out)
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Combine the hazard curves coming from two different calculations. The result will be the hazard curves of calc_big, updated on the sites in common with calc_small with the PoEs of calc_small. For instance: calc_big = USA, calc_small = California
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train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/utils/combine_mean_curves.py#L25-L55
gem/oq-engine
openquake/hmtk/sources/complex_fault_source.py
mtkComplexFaultSource.create_geometry
def create_geometry(self, input_geometry, mesh_spacing=1.0): ''' If geometry is defined as a numpy array then create instance of nhlib.geo.line.Line class, otherwise if already instance of class accept class :param input_geometry: List of at least two fault edges of the fault source from shallowest to deepest. Each edge can be represented as as either i) instance of nhlib.geo.polygon.Polygon class ii) numpy.ndarray [Longitude, Latitude, Depth] :param float mesh_spacing: Spacing of the fault mesh (km) {default = 1.0} ''' if not isinstance(input_geometry, list) or len(input_geometry) < 2: raise ValueError('Complex fault geometry incorrectly defined') self.fault_edges = [] for edge in input_geometry: if not isinstance(edge, Line): if not isinstance(edge, np.ndarray): raise ValueError('Unrecognised or unsupported geometry ' 'definition') else: self.fault_edges.append(Line([Point(row[0], row[1], row[2]) for row in edge])) else: self.fault_edges.append(edge) # Updates the upper and lower sesmogenic depths to reflect geometry self._get_minmax_edges(edge) # Build fault surface self.geometry = ComplexFaultSurface.from_fault_data(self.fault_edges, mesh_spacing) # Get a mean dip self.dip = self.geometry.get_dip()
python
def create_geometry(self, input_geometry, mesh_spacing=1.0): if not isinstance(input_geometry, list) or len(input_geometry) < 2: raise ValueError('Complex fault geometry incorrectly defined') self.fault_edges = [] for edge in input_geometry: if not isinstance(edge, Line): if not isinstance(edge, np.ndarray): raise ValueError('Unrecognised or unsupported geometry ' 'definition') else: self.fault_edges.append(Line([Point(row[0], row[1], row[2]) for row in edge])) else: self.fault_edges.append(edge) self._get_minmax_edges(edge) self.geometry = ComplexFaultSurface.from_fault_data(self.fault_edges, mesh_spacing) self.dip = self.geometry.get_dip()
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train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hmtk/sources/complex_fault_source.py#L115-L151