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gem/oq-engine | openquake/hazardlib/gsim/utils_swiss_gmpe.py | _compute_C1_term | def _compute_C1_term(C, dists):
"""
Return C1 coeffs as function of Rrup as proposed by
Rodriguez-Marek et al (2013)
The C1 coeff are used to compute the single station sigma
"""
c1_dists = np.zeros_like(dists)
idx = dists < C['Rc11']
c1_dists[idx] = C['phi_11']
idx = (dists >= C['Rc11']) & (dists <= C['Rc21'])
c1_dists[idx] = C['phi_11'] + (C['phi_21'] - C['phi_11']) * \
((dists[idx] - C['Rc11']) / (C['Rc21'] - C['Rc11']))
idx = dists > C['Rc21']
c1_dists[idx] = C['phi_21']
return c1_dists | python | def _compute_C1_term(C, dists):
c1_dists = np.zeros_like(dists)
idx = dists < C['Rc11']
c1_dists[idx] = C['phi_11']
idx = (dists >= C['Rc11']) & (dists <= C['Rc21'])
c1_dists[idx] = C['phi_11'] + (C['phi_21'] - C['phi_11']) * \
((dists[idx] - C['Rc11']) / (C['Rc21'] - C['Rc11']))
idx = dists > C['Rc21']
c1_dists[idx] = C['phi_21']
return c1_dists | [
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gem/oq-engine | openquake/hazardlib/gsim/utils_swiss_gmpe.py | _compute_small_mag_correction_term | def _compute_small_mag_correction_term(C, mag, rhypo):
"""
small magnitude correction applied to the median values
"""
if mag >= 3.00 and mag < 5.5:
min_term = np.minimum(rhypo, C['Rm'])
max_term = np.maximum(min_term, 10)
term_ln = np.log(max_term / 20)
term_ratio = ((5.50 - mag) / C['a1'])
temp = (term_ratio) ** C['a2'] * (C['b1'] + C['b2'] * term_ln)
return 1 / np.exp(temp)
else:
return 1 | python | def _compute_small_mag_correction_term(C, mag, rhypo):
if mag >= 3.00 and mag < 5.5:
min_term = np.minimum(rhypo, C['Rm'])
max_term = np.maximum(min_term, 10)
term_ln = np.log(max_term / 20)
term_ratio = ((5.50 - mag) / C['a1'])
temp = (term_ratio) ** C['a2'] * (C['b1'] + C['b2'] * term_ln)
return 1 / np.exp(temp)
else:
return 1 | [
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gem/oq-engine | openquake/hazardlib/gsim/utils_swiss_gmpe.py | _compute_phi_ss | def _compute_phi_ss(C, mag, c1_dists, log_phi_ss, mean_phi_ss):
"""
Returns the embeded logic tree for single station sigma
as defined to be used in the Swiss Hazard Model 2014:
the single station sigma branching levels combines with equal
weights: the phi_ss reported as function of magnitude
as proposed by Rodriguez-Marek et al (2013) with the mean
(mean_phi_ss) single station value;
the resulted phi_ss is in natural logarithm units
"""
phi_ss = 0
if mag < C['Mc1']:
phi_ss = c1_dists
elif mag >= C['Mc1'] and mag <= C['Mc2']:
phi_ss = c1_dists + \
(C['C2'] - c1_dists) * \
((mag - C['Mc1']) / (C['Mc2'] - C['Mc1']))
elif mag > C['Mc2']:
phi_ss = C['C2']
return (phi_ss * 0.50 + mean_phi_ss * 0.50) / log_phi_ss | python | def _compute_phi_ss(C, mag, c1_dists, log_phi_ss, mean_phi_ss):
phi_ss = 0
if mag < C['Mc1']:
phi_ss = c1_dists
elif mag >= C['Mc1'] and mag <= C['Mc2']:
phi_ss = c1_dists + \
(C['C2'] - c1_dists) * \
((mag - C['Mc1']) / (C['Mc2'] - C['Mc1']))
elif mag > C['Mc2']:
phi_ss = C['C2']
return (phi_ss * 0.50 + mean_phi_ss * 0.50) / log_phi_ss | [
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gem/oq-engine | openquake/hazardlib/gsim/utils_swiss_gmpe.py | _get_corr_stddevs | def _get_corr_stddevs(C, tau_ss, stddev_types, num_sites, phi_ss, NL=None,
tau_value=None):
"""
Return standard deviations adjusted for single station sigma
as the total standard deviation - as proposed to be used in
the Swiss Hazard Model [2014].
"""
stddevs = []
temp_stddev = phi_ss * phi_ss
if tau_value is not None and NL is not None:
temp_stddev = temp_stddev + tau_value * tau_value * ((1 + NL) ** 2)
else:
temp_stddev = temp_stddev + C[tau_ss] * C[tau_ss]
for stddev_type in stddev_types:
if stddev_type == const.StdDev.TOTAL:
stddevs.append(np.sqrt(temp_stddev) + np.zeros(num_sites))
return stddevs | python | def _get_corr_stddevs(C, tau_ss, stddev_types, num_sites, phi_ss, NL=None,
tau_value=None):
stddevs = []
temp_stddev = phi_ss * phi_ss
if tau_value is not None and NL is not None:
temp_stddev = temp_stddev + tau_value * tau_value * ((1 + NL) ** 2)
else:
temp_stddev = temp_stddev + C[tau_ss] * C[tau_ss]
for stddev_type in stddev_types:
if stddev_type == const.StdDev.TOTAL:
stddevs.append(np.sqrt(temp_stddev) + np.zeros(num_sites))
return stddevs | [
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gem/oq-engine | openquake/hazardlib/gsim/utils_swiss_gmpe.py | _apply_adjustments | def _apply_adjustments(COEFFS, C_ADJ, tau_ss, mean, stddevs, sites, rup, dists,
imt, stddev_types, log_phi_ss, NL=None, tau_value=None):
"""
This method applies adjustments to the mean and standard deviation.
The small-magnitude adjustments are applied to mean, whereas the
embeded single station sigma logic tree is applied to the
total standard deviation.
"""
c1_dists = _compute_C1_term(C_ADJ, dists)
phi_ss = _compute_phi_ss(
C_ADJ, rup.mag, c1_dists, log_phi_ss, C_ADJ['mean_phi_ss']
)
mean_corr = np.exp(mean) * C_ADJ['k_adj'] * \
_compute_small_mag_correction_term(C_ADJ, rup.mag, dists)
mean_corr = np.log(mean_corr)
std_corr = _get_corr_stddevs(COEFFS[imt], tau_ss, stddev_types,
len(sites.vs30), phi_ss, NL, tau_value)
stddevs = np.array(std_corr)
return mean_corr, stddevs | python | def _apply_adjustments(COEFFS, C_ADJ, tau_ss, mean, stddevs, sites, rup, dists,
imt, stddev_types, log_phi_ss, NL=None, tau_value=None):
c1_dists = _compute_C1_term(C_ADJ, dists)
phi_ss = _compute_phi_ss(
C_ADJ, rup.mag, c1_dists, log_phi_ss, C_ADJ['mean_phi_ss']
)
mean_corr = np.exp(mean) * C_ADJ['k_adj'] * \
_compute_small_mag_correction_term(C_ADJ, rup.mag, dists)
mean_corr = np.log(mean_corr)
std_corr = _get_corr_stddevs(COEFFS[imt], tau_ss, stddev_types,
len(sites.vs30), phi_ss, NL, tau_value)
stddevs = np.array(std_corr)
return mean_corr, stddevs | [
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gem/oq-engine | openquake/commonlib/source.py | CompositionInfo.fake | def fake(cls, gsimlt=None):
"""
:returns:
a fake `CompositionInfo` instance with the given gsim logic tree
object; if None, builds automatically a fake gsim logic tree
"""
weight = 1
gsim_lt = gsimlt or logictree.GsimLogicTree.from_('[FromFile]')
fakeSM = logictree.LtSourceModel(
'scenario', weight, 'b1',
[sourceconverter.SourceGroup('*', eff_ruptures=1)],
gsim_lt.get_num_paths(), ordinal=0, samples=1)
return cls(gsim_lt, seed=0, num_samples=0, source_models=[fakeSM],
totweight=0) | python | def fake(cls, gsimlt=None):
weight = 1
gsim_lt = gsimlt or logictree.GsimLogicTree.from_('[FromFile]')
fakeSM = logictree.LtSourceModel(
'scenario', weight, 'b1',
[sourceconverter.SourceGroup('*', eff_ruptures=1)],
gsim_lt.get_num_paths(), ordinal=0, samples=1)
return cls(gsim_lt, seed=0, num_samples=0, source_models=[fakeSM],
totweight=0) | [
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gem/oq-engine | openquake/commonlib/source.py | CompositionInfo.get_info | def get_info(self, sm_id):
"""
Extract a CompositionInfo instance containing the single
model of index `sm_id`.
"""
sm = self.source_models[sm_id]
num_samples = sm.samples if self.num_samples else 0
return self.__class__(
self.gsim_lt, self.seed, num_samples, [sm], self.tot_weight) | python | def get_info(self, sm_id):
sm = self.source_models[sm_id]
num_samples = sm.samples if self.num_samples else 0
return self.__class__(
self.gsim_lt, self.seed, num_samples, [sm], self.tot_weight) | [
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gem/oq-engine | openquake/commonlib/source.py | CompositionInfo.classify_gsim_lt | def classify_gsim_lt(self, source_model):
"""
:returns: (kind, num_paths), where kind is trivial, simple, complex
"""
trts = set(sg.trt for sg in source_model.src_groups if sg.eff_ruptures)
gsim_lt = self.gsim_lt.reduce(trts)
num_branches = list(gsim_lt.get_num_branches().values())
num_paths = gsim_lt.get_num_paths()
num_gsims = '(%s)' % ','.join(map(str, num_branches))
multi_gsim_trts = sum(1 for num_gsim in num_branches if num_gsim > 1)
if multi_gsim_trts == 0:
return "trivial" + num_gsims, num_paths
elif multi_gsim_trts == 1:
return "simple" + num_gsims, num_paths
else:
return "complex" + num_gsims, num_paths | python | def classify_gsim_lt(self, source_model):
trts = set(sg.trt for sg in source_model.src_groups if sg.eff_ruptures)
gsim_lt = self.gsim_lt.reduce(trts)
num_branches = list(gsim_lt.get_num_branches().values())
num_paths = gsim_lt.get_num_paths()
num_gsims = '(%s)' % ','.join(map(str, num_branches))
multi_gsim_trts = sum(1 for num_gsim in num_branches if num_gsim > 1)
if multi_gsim_trts == 0:
return "trivial" + num_gsims, num_paths
elif multi_gsim_trts == 1:
return "simple" + num_gsims, num_paths
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return "complex" + num_gsims, num_paths | [
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gem/oq-engine | openquake/commonlib/source.py | CompositionInfo.get_samples_by_grp | def get_samples_by_grp(self):
"""
:returns: a dictionary src_group_id -> source_model.samples
"""
return {grp.id: sm.samples for sm in self.source_models
for grp in sm.src_groups} | python | def get_samples_by_grp(self):
return {grp.id: sm.samples for sm in self.source_models
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gem/oq-engine | openquake/commonlib/source.py | CompositionInfo.get_rlzs_by_gsim_grp | def get_rlzs_by_gsim_grp(self, sm_lt_path=None, trts=None):
"""
:returns: a dictionary src_group_id -> gsim -> rlzs
"""
self.rlzs_assoc = self.get_rlzs_assoc(sm_lt_path, trts)
dic = {grp.id: self.rlzs_assoc.get_rlzs_by_gsim(grp.id)
for sm in self.source_models for grp in sm.src_groups}
return dic | python | def get_rlzs_by_gsim_grp(self, sm_lt_path=None, trts=None):
self.rlzs_assoc = self.get_rlzs_assoc(sm_lt_path, trts)
dic = {grp.id: self.rlzs_assoc.get_rlzs_by_gsim(grp.id)
for sm in self.source_models for grp in sm.src_groups}
return dic | [
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gem/oq-engine | openquake/commonlib/source.py | CompositionInfo.trt2i | def trt2i(self):
"""
:returns: trt -> trti
"""
trts = sorted(set(src_group.trt for sm in self.source_models
for src_group in sm.src_groups))
return {trt: i for i, trt in enumerate(trts)} | python | def trt2i(self):
trts = sorted(set(src_group.trt for sm in self.source_models
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return {trt: i for i, trt in enumerate(trts)} | [
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gem/oq-engine | openquake/commonlib/source.py | CompositionInfo.get_num_rlzs | def get_num_rlzs(self, source_model=None):
"""
:param source_model: a LtSourceModel instance (or None)
:returns: the number of realizations per source model (or all)
"""
if source_model is None:
return sum(self.get_num_rlzs(sm) for sm in self.source_models)
if self.num_samples:
return source_model.samples
trts = set(sg.trt for sg in source_model.src_groups if sg.eff_ruptures)
if sum(sg.eff_ruptures for sg in source_model.src_groups) == 0:
return 0
return self.gsim_lt.reduce(trts).get_num_paths() | python | def get_num_rlzs(self, source_model=None):
if source_model is None:
return sum(self.get_num_rlzs(sm) for sm in self.source_models)
if self.num_samples:
return source_model.samples
trts = set(sg.trt for sg in source_model.src_groups if sg.eff_ruptures)
if sum(sg.eff_ruptures for sg in source_model.src_groups) == 0:
return 0
return self.gsim_lt.reduce(trts).get_num_paths() | [
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gem/oq-engine | openquake/commonlib/source.py | CompositionInfo.rlzs | def rlzs(self):
"""
:returns: an array of realizations
"""
tups = [(r.ordinal, r.uid, r.weight['weight'])
for r in self.get_rlzs_assoc().realizations]
return numpy.array(tups, rlz_dt) | python | def rlzs(self):
tups = [(r.ordinal, r.uid, r.weight['weight'])
for r in self.get_rlzs_assoc().realizations]
return numpy.array(tups, rlz_dt) | [
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gem/oq-engine | openquake/commonlib/source.py | CompositionInfo.update_eff_ruptures | def update_eff_ruptures(self, count_ruptures):
"""
:param count_ruptures: function or dict src_group_id -> num_ruptures
"""
for smodel in self.source_models:
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for smodel in self.source_models:
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sg.eff_ruptures = (count_ruptures(sg.id)
if callable(count_ruptures)
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gem/oq-engine | openquake/commonlib/source.py | CompositionInfo.get_source_model | def get_source_model(self, src_group_id):
"""
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"""
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return smodel | python | def get_source_model(self, src_group_id):
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gem/oq-engine | openquake/commonlib/source.py | CompositionInfo.get_grp_ids | def get_grp_ids(self, sm_id):
"""
:returns: a list of source group IDs for the given source model ID
"""
return [sg.id for sg in self.source_models[sm_id].src_groups] | python | def get_grp_ids(self, sm_id):
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gem/oq-engine | openquake/commonlib/source.py | CompositionInfo.get_sm_by_grp | def get_sm_by_grp(self):
"""
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"""
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gem/oq-engine | openquake/commonlib/source.py | CompositionInfo.grp_by | def grp_by(self, name):
"""
:returns: a dictionary grp_id -> TRT string
"""
dic = {}
for smodel in self.source_models:
for src_group in smodel.src_groups:
dic[src_group.id] = getattr(src_group, name)
return dic | python | def grp_by(self, name):
dic = {}
for smodel in self.source_models:
for src_group in smodel.src_groups:
dic[src_group.id] = getattr(src_group, name)
return dic | [
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gem/oq-engine | openquake/commonlib/source.py | CompositeSourceModel.grp_by_src | def grp_by_src(self):
"""
:returns: a new CompositeSourceModel with one group per source
"""
smodels = []
grp_id = 0
for sm in self.source_models:
src_groups = []
smodel = sm.__class__(sm.names, sm.weight, sm.path, src_groups,
sm.num_gsim_paths, sm.ordinal, sm.samples)
for sg in sm.src_groups:
for src in sg.sources:
src.src_group_id = grp_id
src_groups.append(
sourceconverter.SourceGroup(
sg.trt, [src], name=src.source_id, id=grp_id))
grp_id += 1
smodels.append(smodel)
return self.__class__(self.gsim_lt, self.source_model_lt, smodels,
self.optimize_same_id) | python | def grp_by_src(self):
smodels = []
grp_id = 0
for sm in self.source_models:
src_groups = []
smodel = sm.__class__(sm.names, sm.weight, sm.path, src_groups,
sm.num_gsim_paths, sm.ordinal, sm.samples)
for sg in sm.src_groups:
for src in sg.sources:
src.src_group_id = grp_id
src_groups.append(
sourceconverter.SourceGroup(
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grp_id += 1
smodels.append(smodel)
return self.__class__(self.gsim_lt, self.source_model_lt, smodels,
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gem/oq-engine | openquake/commonlib/source.py | CompositeSourceModel.get_model | def get_model(self, sm_id):
"""
Extract a CompositeSourceModel instance containing the single
model of index `sm_id`.
"""
sm = self.source_models[sm_id]
if self.source_model_lt.num_samples:
self.source_model_lt.num_samples = sm.samples
new = self.__class__(self.gsim_lt, self.source_model_lt, [sm],
self.optimize_same_id)
new.sm_id = sm_id
return new | python | def get_model(self, sm_id):
sm = self.source_models[sm_id]
if self.source_model_lt.num_samples:
self.source_model_lt.num_samples = sm.samples
new = self.__class__(self.gsim_lt, self.source_model_lt, [sm],
self.optimize_same_id)
new.sm_id = sm_id
return new | [
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gem/oq-engine | openquake/commonlib/source.py | CompositeSourceModel.new | def new(self, sources_by_grp):
"""
Generate a new CompositeSourceModel from the given dictionary.
:param sources_by_group: a dictionary grp_id -> sources
:returns: a new CompositeSourceModel instance
"""
source_models = []
for sm in self.source_models:
src_groups = []
for src_group in sm.src_groups:
sg = copy.copy(src_group)
sg.sources = sorted(sources_by_grp.get(sg.id, []),
key=operator.attrgetter('id'))
src_groups.append(sg)
newsm = logictree.LtSourceModel(
sm.names, sm.weight, sm.path, src_groups,
sm.num_gsim_paths, sm.ordinal, sm.samples)
source_models.append(newsm)
new = self.__class__(self.gsim_lt, self.source_model_lt, source_models,
self.optimize_same_id)
new.info.update_eff_ruptures(new.get_num_ruptures())
new.info.tot_weight = new.get_weight()
return new | python | def new(self, sources_by_grp):
source_models = []
for sm in self.source_models:
src_groups = []
for src_group in sm.src_groups:
sg = copy.copy(src_group)
sg.sources = sorted(sources_by_grp.get(sg.id, []),
key=operator.attrgetter('id'))
src_groups.append(sg)
newsm = logictree.LtSourceModel(
sm.names, sm.weight, sm.path, src_groups,
sm.num_gsim_paths, sm.ordinal, sm.samples)
source_models.append(newsm)
new = self.__class__(self.gsim_lt, self.source_model_lt, source_models,
self.optimize_same_id)
new.info.update_eff_ruptures(new.get_num_ruptures())
new.info.tot_weight = new.get_weight()
return new | [
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gem/oq-engine | openquake/commonlib/source.py | CompositeSourceModel.get_weight | def get_weight(self, weight=operator.attrgetter('weight')):
"""
:param weight: source weight function
:returns: total weight of the source model
"""
return sum(weight(src) for src in self.get_sources()) | python | def get_weight(self, weight=operator.attrgetter('weight')):
return sum(weight(src) for src in self.get_sources()) | [
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gem/oq-engine | openquake/commonlib/source.py | CompositeSourceModel.get_nonparametric_sources | def get_nonparametric_sources(self):
"""
:returns: list of non parametric sources in the composite source model
"""
return [src for sm in self.source_models
for src_group in sm.src_groups
for src in src_group if hasattr(src, 'data')] | python | def get_nonparametric_sources(self):
return [src for sm in self.source_models
for src_group in sm.src_groups
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gem/oq-engine | openquake/commonlib/source.py | CompositeSourceModel.check_dupl_sources | def check_dupl_sources(self): # used in print_csm_info
"""
Extracts duplicated sources, i.e. sources with the same source_id in
different source groups. Raise an exception if there are sources with
the same ID which are not duplicated.
:returns: a list of list of sources, ordered by source_id
"""
dd = collections.defaultdict(list)
for src_group in self.src_groups:
for src in src_group:
try:
srcid = src.source_id
except AttributeError: # src is a Node object
srcid = src['id']
dd[srcid].append(src)
dupl = []
for srcid, srcs in sorted(dd.items()):
if len(srcs) > 1:
_assert_equal_sources(srcs)
dupl.append(srcs)
return dupl | python | def check_dupl_sources(self):
dd = collections.defaultdict(list)
for src_group in self.src_groups:
for src in src_group:
try:
srcid = src.source_id
except AttributeError:
srcid = src['id']
dd[srcid].append(src)
dupl = []
for srcid, srcs in sorted(dd.items()):
if len(srcs) > 1:
_assert_equal_sources(srcs)
dupl.append(srcs)
return dupl | [
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gem/oq-engine | openquake/commonlib/source.py | CompositeSourceModel.get_sources | def get_sources(self, kind='all'):
"""
Extract the sources contained in the source models by optionally
filtering and splitting them, depending on the passed parameter.
"""
assert kind in ('all', 'indep', 'mutex'), kind
sources = []
for sm in self.source_models:
for src_group in sm.src_groups:
if kind in ('all', src_group.src_interdep):
for src in src_group:
if sm.samples > 1:
src.samples = sm.samples
sources.append(src)
return sources | python | def get_sources(self, kind='all'):
assert kind in ('all', 'indep', 'mutex'), kind
sources = []
for sm in self.source_models:
for src_group in sm.src_groups:
if kind in ('all', src_group.src_interdep):
for src in src_group:
if sm.samples > 1:
src.samples = sm.samples
sources.append(src)
return sources | [
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gem/oq-engine | openquake/commonlib/source.py | CompositeSourceModel.get_trt_sources | def get_trt_sources(self, optimize_same_id=None):
"""
:returns: a list of pairs [(trt, group of sources)]
"""
atomic = []
acc = AccumDict(accum=[])
for sm in self.source_models:
for grp in sm.src_groups:
if grp and grp.atomic:
atomic.append((grp.trt, grp))
elif grp:
acc[grp.trt].extend(grp)
if optimize_same_id is None:
optimize_same_id = self.optimize_same_id
if optimize_same_id is False:
return atomic + list(acc.items())
# extract a single source from multiple sources with the same ID
n = 0
tot = 0
dic = {}
for trt in acc:
dic[trt] = []
for grp in groupby(acc[trt], lambda x: x.source_id).values():
src = grp[0]
n += 1
tot += len(grp)
# src.src_group_id can be a list if get_sources_by_trt was
# called before
if len(grp) > 1 and not isinstance(src.src_group_id, list):
src.src_group_id = [s.src_group_id for s in grp]
dic[trt].append(src)
if n < tot:
logging.info('Reduced %d sources to %d sources with unique IDs',
tot, n)
return atomic + list(dic.items()) | python | def get_trt_sources(self, optimize_same_id=None):
atomic = []
acc = AccumDict(accum=[])
for sm in self.source_models:
for grp in sm.src_groups:
if grp and grp.atomic:
atomic.append((grp.trt, grp))
elif grp:
acc[grp.trt].extend(grp)
if optimize_same_id is None:
optimize_same_id = self.optimize_same_id
if optimize_same_id is False:
return atomic + list(acc.items())
n = 0
tot = 0
dic = {}
for trt in acc:
dic[trt] = []
for grp in groupby(acc[trt], lambda x: x.source_id).values():
src = grp[0]
n += 1
tot += len(grp)
if len(grp) > 1 and not isinstance(src.src_group_id, list):
src.src_group_id = [s.src_group_id for s in grp]
dic[trt].append(src)
if n < tot:
logging.info('Reduced %d sources to %d sources with unique IDs',
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return atomic + list(dic.items()) | [
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gem/oq-engine | openquake/commonlib/source.py | CompositeSourceModel.get_num_ruptures | def get_num_ruptures(self):
"""
:returns: the number of ruptures per source group ID
"""
return {grp.id: sum(src.num_ruptures for src in grp)
for grp in self.src_groups} | python | def get_num_ruptures(self):
return {grp.id: sum(src.num_ruptures for src in grp)
for grp in self.src_groups} | [
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gem/oq-engine | openquake/commonlib/source.py | CompositeSourceModel.init_serials | def init_serials(self, ses_seed):
"""
Generate unique seeds for each rupture with numpy.arange.
This should be called only in event based calculators
"""
sources = self.get_sources()
serial = ses_seed
for src in sources:
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src.serial = serial
serial += nr | python | def init_serials(self, ses_seed):
sources = self.get_sources()
serial = ses_seed
for src in sources:
nr = src.num_ruptures
src.serial = serial
serial += nr | [
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gem/oq-engine | openquake/commonlib/source.py | CompositeSourceModel.get_maxweight | def get_maxweight(self, weight, concurrent_tasks, minweight=MINWEIGHT):
"""
Return an appropriate maxweight for use in the block_splitter
"""
totweight = self.get_weight(weight)
ct = concurrent_tasks or 1
mw = math.ceil(totweight / ct)
return max(mw, minweight) | python | def get_maxweight(self, weight, concurrent_tasks, minweight=MINWEIGHT):
totweight = self.get_weight(weight)
ct = concurrent_tasks or 1
mw = math.ceil(totweight / ct)
return max(mw, minweight) | [
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gem/oq-engine | openquake/commonlib/source.py | CompositeSourceModel.get_floating_spinning_factors | def get_floating_spinning_factors(self):
"""
:returns: (floating rupture factor, spinning rupture factor)
"""
data = []
for src in self.get_sources():
if hasattr(src, 'hypocenter_distribution'):
data.append(
(len(src.hypocenter_distribution.data),
len(src.nodal_plane_distribution.data)))
if not data:
return numpy.array([1, 1])
return numpy.array(data).mean(axis=0) | python | def get_floating_spinning_factors(self):
data = []
for src in self.get_sources():
if hasattr(src, 'hypocenter_distribution'):
data.append(
(len(src.hypocenter_distribution.data),
len(src.nodal_plane_distribution.data)))
if not data:
return numpy.array([1, 1])
return numpy.array(data).mean(axis=0) | [
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gem/oq-engine | openquake/hmtk/parsers/faults/fault_yaml_parser.py | weight_list_to_tuple | def weight_list_to_tuple(data, attr_name):
'''
Converts a list of values and corresponding weights to a tuple of values
'''
if len(data['Value']) != len(data['Weight']):
raise ValueError('Number of weights do not correspond to number of '
'attributes in %s' % attr_name)
weight = np.array(data['Weight'])
if fabs(np.sum(weight) - 1.) > 1E-7:
raise ValueError('Weights do not sum to 1.0 in %s' % attr_name)
data_tuple = []
for iloc, value in enumerate(data['Value']):
data_tuple.append((value, weight[iloc]))
return data_tuple | python | def weight_list_to_tuple(data, attr_name):
if len(data['Value']) != len(data['Weight']):
raise ValueError('Number of weights do not correspond to number of '
'attributes in %s' % attr_name)
weight = np.array(data['Weight'])
if fabs(np.sum(weight) - 1.) > 1E-7:
raise ValueError('Weights do not sum to 1.0 in %s' % attr_name)
data_tuple = []
for iloc, value in enumerate(data['Value']):
data_tuple.append((value, weight[iloc]))
return data_tuple | [
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gem/oq-engine | openquake/hmtk/parsers/faults/fault_yaml_parser.py | parse_tect_region_dict_to_tuples | def parse_tect_region_dict_to_tuples(region_dict):
'''
Parses the tectonic regionalisation dictionary attributes to tuples
'''
output_region_dict = []
tuple_keys = ['Displacement_Length_Ratio', 'Shear_Modulus']
# Convert MSR string name to openquake.hazardlib.scalerel object
for region in region_dict:
for val_name in tuple_keys:
region[val_name] = weight_list_to_tuple(region[val_name],
val_name)
# MSR works differently - so call get_scaling_relation_tuple
region['Magnitude_Scaling_Relation'] = weight_list_to_tuple(
region['Magnitude_Scaling_Relation'],
'Magnitude Scaling Relation')
output_region_dict.append(region)
return output_region_dict | python | def parse_tect_region_dict_to_tuples(region_dict):
output_region_dict = []
tuple_keys = ['Displacement_Length_Ratio', 'Shear_Modulus']
for region in region_dict:
for val_name in tuple_keys:
region[val_name] = weight_list_to_tuple(region[val_name],
val_name)
region['Magnitude_Scaling_Relation'] = weight_list_to_tuple(
region['Magnitude_Scaling_Relation'],
'Magnitude Scaling Relation')
output_region_dict.append(region)
return output_region_dict | [
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gem/oq-engine | openquake/hmtk/parsers/faults/fault_yaml_parser.py | get_scaling_relation_tuple | def get_scaling_relation_tuple(msr_dict):
'''
For a dictionary of scaling relation values convert string list to
object list and then to tuple
'''
# Convert MSR string name to openquake.hazardlib.scalerel object
for iloc, value in enumerate(msr_dict['Value']):
if not value in SCALE_REL_MAP.keys():
raise ValueError('Scaling relation %s not supported!' % value)
msr_dict['Value'][iloc] = SCALE_REL_MAP[value]()
return weight_list_to_tuple(msr_dict,
'Magnitude Scaling Relation') | python | def get_scaling_relation_tuple(msr_dict):
for iloc, value in enumerate(msr_dict['Value']):
if not value in SCALE_REL_MAP.keys():
raise ValueError('Scaling relation %s not supported!' % value)
msr_dict['Value'][iloc] = SCALE_REL_MAP[value]()
return weight_list_to_tuple(msr_dict,
'Magnitude Scaling Relation') | [
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gem/oq-engine | openquake/hmtk/parsers/faults/fault_yaml_parser.py | FaultYmltoSource.read_file | def read_file(self, mesh_spacing=1.0):
'''
Reads the file and returns an instance of the FaultSource class.
:param float mesh_spacing:
Fault mesh spacing (km)
'''
# Process the tectonic regionalisation
tectonic_reg = self.process_tectonic_regionalisation()
model = mtkActiveFaultModel(self.data['Fault_Model_ID'],
self.data['Fault_Model_Name'])
for fault in self.data['Fault_Model']:
fault_geometry = self.read_fault_geometry(fault['Fault_Geometry'],
mesh_spacing)
if fault['Shear_Modulus']:
fault['Shear_Modulus'] = weight_list_to_tuple(
fault['Shear_Modulus'], '%s Shear Modulus' % fault['ID'])
if fault['Displacement_Length_Ratio']:
fault['Displacement_Length_Ratio'] = weight_list_to_tuple(
fault['Displacement_Length_Ratio'],
'%s Displacement to Length Ratio' % fault['ID'])
fault_source = mtkActiveFault(
fault['ID'],
fault['Fault_Name'],
fault_geometry,
weight_list_to_tuple(fault['Slip'], '%s - Slip' % fault['ID']),
float(fault['Rake']),
fault['Tectonic_Region'],
float(fault['Aseismic']),
weight_list_to_tuple(
fault['Scaling_Relation_Sigma'],
'%s Scaling_Relation_Sigma' % fault['ID']),
neotectonic_fault=None,
scale_rel=get_scaling_relation_tuple(
fault['Magnitude_Scaling_Relation']),
aspect_ratio=fault['Aspect_Ratio'],
shear_modulus=fault['Shear_Modulus'],
disp_length_ratio=fault['Displacement_Length_Ratio'])
if tectonic_reg:
fault_source.get_tectonic_regionalisation(
tectonic_reg,
fault['Tectonic_Region'])
assert isinstance(fault['MFD_Model'], list)
fault_source.generate_config_set(fault['MFD_Model'])
model.faults.append(fault_source)
return model, tectonic_reg | python | def read_file(self, mesh_spacing=1.0):
tectonic_reg = self.process_tectonic_regionalisation()
model = mtkActiveFaultModel(self.data['Fault_Model_ID'],
self.data['Fault_Model_Name'])
for fault in self.data['Fault_Model']:
fault_geometry = self.read_fault_geometry(fault['Fault_Geometry'],
mesh_spacing)
if fault['Shear_Modulus']:
fault['Shear_Modulus'] = weight_list_to_tuple(
fault['Shear_Modulus'], '%s Shear Modulus' % fault['ID'])
if fault['Displacement_Length_Ratio']:
fault['Displacement_Length_Ratio'] = weight_list_to_tuple(
fault['Displacement_Length_Ratio'],
'%s Displacement to Length Ratio' % fault['ID'])
fault_source = mtkActiveFault(
fault['ID'],
fault['Fault_Name'],
fault_geometry,
weight_list_to_tuple(fault['Slip'], '%s - Slip' % fault['ID']),
float(fault['Rake']),
fault['Tectonic_Region'],
float(fault['Aseismic']),
weight_list_to_tuple(
fault['Scaling_Relation_Sigma'],
'%s Scaling_Relation_Sigma' % fault['ID']),
neotectonic_fault=None,
scale_rel=get_scaling_relation_tuple(
fault['Magnitude_Scaling_Relation']),
aspect_ratio=fault['Aspect_Ratio'],
shear_modulus=fault['Shear_Modulus'],
disp_length_ratio=fault['Displacement_Length_Ratio'])
if tectonic_reg:
fault_source.get_tectonic_regionalisation(
tectonic_reg,
fault['Tectonic_Region'])
assert isinstance(fault['MFD_Model'], list)
fault_source.generate_config_set(fault['MFD_Model'])
model.faults.append(fault_source)
return model, tectonic_reg | [
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Fault mesh spacing (km) | [
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gem/oq-engine | openquake/hmtk/parsers/faults/fault_yaml_parser.py | FaultYmltoSource.process_tectonic_regionalisation | def process_tectonic_regionalisation(self):
'''
Processes the tectonic regionalisation from the yaml file
'''
if 'tectonic_regionalisation' in self.data.keys():
tectonic_reg = TectonicRegionalisation()
tectonic_reg.populate_regions(
parse_tect_region_dict_to_tuples(
self.data['tectonic_regionalisation']))
else:
tectonic_reg = None
return tectonic_reg | python | def process_tectonic_regionalisation(self):
if 'tectonic_regionalisation' in self.data.keys():
tectonic_reg = TectonicRegionalisation()
tectonic_reg.populate_regions(
parse_tect_region_dict_to_tuples(
self.data['tectonic_regionalisation']))
else:
tectonic_reg = None
return tectonic_reg | [
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gem/oq-engine | openquake/hmtk/parsers/faults/fault_yaml_parser.py | FaultYmltoSource.read_fault_geometry | def read_fault_geometry(self, geo_dict, mesh_spacing=1.0):
'''
Creates the fault geometry from the parameters specified in the
dictionary.
:param dict geo_dict:
Sub-dictionary of main fault dictionary containing only
the geometry attributes
:param float mesh_spacing:
Fault mesh spacing (km)
:returns:
Instance of SimpleFaultGeometry or ComplexFaultGeometry, depending
on typology
'''
if geo_dict['Fault_Typology'] == 'Simple':
# Simple fault geometry
raw_trace = geo_dict['Fault_Trace']
trace = Line([Point(raw_trace[ival], raw_trace[ival + 1])
for ival in range(0, len(raw_trace), 2)])
geometry = SimpleFaultGeometry(trace,
geo_dict['Dip'],
geo_dict['Upper_Depth'],
geo_dict['Lower_Depth'],
mesh_spacing)
elif geo_dict['Fault_Typology'] == 'Complex':
# Complex Fault Typology
trace = []
for raw_trace in geo_dict['Fault_Trace']:
fault_edge = Line(
[Point(raw_trace[ival], raw_trace[ival + 1],
raw_trace[ival + 2]) for ival in range(0, len(raw_trace),
3)])
trace.append(fault_edge)
geometry = ComplexFaultGeometry(trace, mesh_spacing)
else:
raise ValueError('Unrecognised or unsupported fault geometry!')
return geometry | python | def read_fault_geometry(self, geo_dict, mesh_spacing=1.0):
if geo_dict['Fault_Typology'] == 'Simple':
raw_trace = geo_dict['Fault_Trace']
trace = Line([Point(raw_trace[ival], raw_trace[ival + 1])
for ival in range(0, len(raw_trace), 2)])
geometry = SimpleFaultGeometry(trace,
geo_dict['Dip'],
geo_dict['Upper_Depth'],
geo_dict['Lower_Depth'],
mesh_spacing)
elif geo_dict['Fault_Typology'] == 'Complex':
trace = []
for raw_trace in geo_dict['Fault_Trace']:
fault_edge = Line(
[Point(raw_trace[ival], raw_trace[ival + 1],
raw_trace[ival + 2]) for ival in range(0, len(raw_trace),
3)])
trace.append(fault_edge)
geometry = ComplexFaultGeometry(trace, mesh_spacing)
else:
raise ValueError('Unrecognised or unsupported fault geometry!')
return geometry | [
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gem/oq-engine | openquake/hazardlib/gsim/atkinson_2015.py | Atkinson2015.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.
"""
C = self.COEFFS[imt]
imean = (self._get_magnitude_term(C, rup.mag) +
self._get_distance_term(C, dists.rhypo, rup.mag))
# Convert mean from cm/s and cm/s/s
if imt.name in "SA PGA":
mean = np.log((10.0 ** (imean - 2.0)) / g)
else:
mean = np.log(10.0 ** imean)
stddevs = self._get_stddevs(C, len(dists.rhypo), stddev_types)
return mean, stddevs | python | def get_mean_and_stddevs(self, sites, rup, dists, imt, stddev_types):
C = self.COEFFS[imt]
imean = (self._get_magnitude_term(C, rup.mag) +
self._get_distance_term(C, dists.rhypo, rup.mag))
if imt.name in "SA PGA":
mean = np.log((10.0 ** (imean - 2.0)) / g)
else:
mean = np.log(10.0 ** imean)
stddevs = self._get_stddevs(C, len(dists.rhypo), stddev_types)
return mean, stddevs | [
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gem/oq-engine | openquake/hazardlib/gsim/atkinson_2015.py | Atkinson2015._get_distance_term | def _get_distance_term(self, C, rhypo, mag):
"""
Returns the distance scaling term
"""
h_eff = self._get_effective_distance(mag)
r_val = np.sqrt(rhypo ** 2.0 + h_eff ** 2.0)
return C["c3"] * np.log10(r_val) | python | def _get_distance_term(self, C, rhypo, mag):
h_eff = self._get_effective_distance(mag)
r_val = np.sqrt(rhypo ** 2.0 + h_eff ** 2.0)
return C["c3"] * np.log10(r_val) | [
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gem/oq-engine | openquake/hazardlib/gsim/douglas_stochastic_2013.py | DouglasEtAl2013StochasticSD001Q200K005.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.
"""
C = self.COEFFS[imt]
C_SIG = self.SIGMA_COEFFS[imt]
mean = (self.get_magnitude_scaling_term(C, rup.mag) +
self.get_distance_scaling_term(C, dists.rhypo))
std_devs = self.get_stddevs(C_SIG, stddev_types, len(dists.rhypo))
#: Mean ground motions initially returned in cm/s/s (for PGA, SA)
#: and cm/s for PGV
if not imt.name == "PGV":
# Convert mean from log(cm/s/s) to g
mean = np.log(np.exp(mean) / (100. * g))
return mean, std_devs | python | def get_mean_and_stddevs(self, sites, rup, dists, imt, stddev_types):
C = self.COEFFS[imt]
C_SIG = self.SIGMA_COEFFS[imt]
mean = (self.get_magnitude_scaling_term(C, rup.mag) +
self.get_distance_scaling_term(C, dists.rhypo))
std_devs = self.get_stddevs(C_SIG, stddev_types, len(dists.rhypo))
if not imt.name == "PGV":
mean = np.log(np.exp(mean) / (100. * g))
return mean, std_devs | [
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gem/oq-engine | openquake/hazardlib/gsim/douglas_stochastic_2013.py | DouglasEtAl2013StochasticSD001Q200K005.get_magnitude_scaling_term | def get_magnitude_scaling_term(self, C, mag):
"""
Returns the magnitude scaling term (equation 1)
"""
mval = mag - 3.0
return C['b1'] + C['b2'] * mval + C['b3'] * (mval ** 2.0) +\
C['b4'] * (mval ** 3.0) | python | def get_magnitude_scaling_term(self, C, mag):
mval = mag - 3.0
return C['b1'] + C['b2'] * mval + C['b3'] * (mval ** 2.0) +\
C['b4'] * (mval ** 3.0) | [
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gem/oq-engine | openquake/hazardlib/gsim/douglas_stochastic_2013.py | DouglasEtAl2013StochasticSD001Q200K005.get_distance_scaling_term | def get_distance_scaling_term(self, C, rhyp):
"""
Returns the distance scaling term (equation 1)
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rval = rhyp + C['bh']
return C['b5'] * np.log(rval) + C['b6'] * rval | [
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gem/oq-engine | openquake/hazardlib/gsim/douglas_stochastic_2013.py | DouglasEtAl2013StochasticSD001Q200K005.get_stddevs | def get_stddevs(self, C_SIG, stddev_types, num_sites):
"""
Returns the standard deviations
N.B. In the paper, and with confirmation from the author, the
aleatory variability terms from the empirical model are used in
conjunction with the median coefficients from the stochastic model.
In the empirical model, coefficients for a single-station intra-event
sigma are derived. These are labeled as "phi". Inter-event coefficients
corresponding to two observed geothermal sequences (Soultz-Sous-Forets
and Basel) are also derived. The inter-event standard deviation is
therefore taken as the ordinary mean of the two inter-event
sigma terms
"""
stddevs = []
intra = C_SIG['phi']
inter = (C_SIG['tau_s'] + C_SIG['tau_b']) / 2.0
total = sqrt(intra ** 2.0 + inter ** 2.0)
for stddev_type in stddev_types:
assert stddev_type in self.DEFINED_FOR_STANDARD_DEVIATION_TYPES
if stddev_type == const.StdDev.TOTAL:
stddevs.append(total + np.zeros(num_sites))
elif stddev_type == const.StdDev.INTER_EVENT:
stddevs.append(inter + np.zeros(num_sites))
elif stddev_type == const.StdDev.INTRA_EVENT:
stddevs.append(intra + np.zeros(num_sites))
return stddevs | python | def get_stddevs(self, C_SIG, stddev_types, num_sites):
stddevs = []
intra = C_SIG['phi']
inter = (C_SIG['tau_s'] + C_SIG['tau_b']) / 2.0
total = sqrt(intra ** 2.0 + inter ** 2.0)
for stddev_type in stddev_types:
assert stddev_type in self.DEFINED_FOR_STANDARD_DEVIATION_TYPES
if stddev_type == const.StdDev.TOTAL:
stddevs.append(total + np.zeros(num_sites))
elif stddev_type == const.StdDev.INTER_EVENT:
stddevs.append(inter + np.zeros(num_sites))
elif stddev_type == const.StdDev.INTRA_EVENT:
stddevs.append(intra + np.zeros(num_sites))
return stddevs | [
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and Basel) are also derived. The inter-event standard deviation is
therefore taken as the ordinary mean of the two inter-event
sigma terms | [
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"standard",
"deviations"
] | train | https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/gsim/douglas_stochastic_2013.py#L181-L207 |
gem/oq-engine | openquake/hazardlib/gsim/cauzzi_2014.py | CauzziEtAl2014.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.
"""
# extract dictionaries of coefficients specific to required
# intensity measure type
C = self.COEFFS[imt]
mean = self._compute_mean(C, rup, dists, sites, imt)
stddevs = self._get_stddevs(C, stddev_types, sites.vs30.shape[0])
return mean, stddevs | python | def get_mean_and_stddevs(self, sites, rup, dists, imt, stddev_types):
C = self.COEFFS[imt]
mean = self._compute_mean(C, rup, dists, sites, imt)
stddevs = self._get_stddevs(C, stddev_types, sites.vs30.shape[0])
return mean, stddevs | [
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gem/oq-engine | openquake/hazardlib/gsim/cauzzi_2014.py | CauzziEtAl2014._get_distance_scaling_term | def _get_distance_scaling_term(self, C, mag, rrup):
"""
Returns the distance scaling parameter
"""
return (C["r1"] + C["r2"] * mag) * np.log10(rrup + C["r3"]) | python | def _get_distance_scaling_term(self, C, mag, rrup):
return (C["r1"] + C["r2"] * mag) * np.log10(rrup + C["r3"]) | [
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gem/oq-engine | openquake/hazardlib/gsim/cauzzi_2014.py | CauzziEtAl2014._get_style_of_faulting_term | def _get_style_of_faulting_term(self, C, rake):
"""
Returns the style of faulting term. Cauzzi et al. determind SOF from
the plunge of the B-, T- and P-axes. For consistency with existing
GMPEs the Wells & Coppersmith model is preferred
"""
if rake > -150.0 and rake <= -30.0:
return C['fN']
elif rake > 30.0 and rake <= 150.0:
return C['fR']
else:
return C['fSS'] | python | def _get_style_of_faulting_term(self, C, rake):
if rake > -150.0 and rake <= -30.0:
return C['fN']
elif rake > 30.0 and rake <= 150.0:
return C['fR']
else:
return C['fSS'] | [
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gem/oq-engine | openquake/hazardlib/gsim/cauzzi_2014.py | CauzziEtAl2014NoSOF._compute_mean | def _compute_mean(self, C, rup, dists, sites, imt):
"""
Returns the mean ground motion acceleration and velocity
"""
mean = (self._get_magnitude_scaling_term(C, rup.mag) +
self._get_distance_scaling_term(C, rup.mag, dists.rrup) +
self._get_site_amplification_term(C, sites.vs30))
# convert from cm/s**2 to g for SA and from m/s**2 to g for PGA (PGV
# is already in cm/s) and also convert from base 10 to base e.
if imt.name == "PGA":
mean = np.log((10 ** mean) * ((2 * np.pi / 0.01) ** 2) *
1e-2 / g)
elif imt.name == "SA":
mean = np.log((10 ** mean) * ((2 * np.pi / imt.period) ** 2) *
1e-2 / g)
else:
mean = np.log(10 ** mean)
return mean | python | def _compute_mean(self, C, rup, dists, sites, imt):
mean = (self._get_magnitude_scaling_term(C, rup.mag) +
self._get_distance_scaling_term(C, rup.mag, dists.rrup) +
self._get_site_amplification_term(C, sites.vs30))
if imt.name == "PGA":
mean = np.log((10 ** mean) * ((2 * np.pi / 0.01) ** 2) *
1e-2 / g)
elif imt.name == "SA":
mean = np.log((10 ** mean) * ((2 * np.pi / imt.period) ** 2) *
1e-2 / g)
else:
mean = np.log(10 ** mean)
return mean | [
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gem/oq-engine | openquake/hazardlib/gsim/cauzzi_2014.py | CauzziEtAl2014Eurocode8._get_site_amplification_term | def _get_site_amplification_term(self, C, vs30):
"""
Returns the site amplification term on the basis of Eurocode 8
site class
"""
s_b, s_c, s_d = self._get_site_dummy_variables(vs30)
return (C["sB"] * s_b) + (C["sC"] * s_c) + (C["sD"] * s_d) | python | def _get_site_amplification_term(self, C, vs30):
s_b, s_c, s_d = self._get_site_dummy_variables(vs30)
return (C["sB"] * s_b) + (C["sC"] * s_c) + (C["sD"] * s_d) | [
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gem/oq-engine | openquake/hazardlib/gsim/cauzzi_2014.py | CauzziEtAl2014Eurocode8._get_site_dummy_variables | def _get_site_dummy_variables(self, vs30):
"""
Returns the Eurocode 8 site class dummy variable
"""
s_b = np.zeros_like(vs30)
s_c = np.zeros_like(vs30)
s_d = np.zeros_like(vs30)
s_b[np.logical_and(vs30 >= 360., vs30 < 800.)] = 1.0
s_c[np.logical_and(vs30 >= 180., vs30 < 360.)] = 1.0
s_d[vs30 < 180] = 1.0
return s_b, s_c, s_d | python | def _get_site_dummy_variables(self, vs30):
s_b = np.zeros_like(vs30)
s_c = np.zeros_like(vs30)
s_d = np.zeros_like(vs30)
s_b[np.logical_and(vs30 >= 360., vs30 < 800.)] = 1.0
s_c[np.logical_and(vs30 >= 180., vs30 < 360.)] = 1.0
s_d[vs30 < 180] = 1.0
return s_b, s_c, s_d | [
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gem/oq-engine | openquake/hmtk/faults/fault_models.py | RecurrenceBranch.get_recurrence | def get_recurrence(self, config):
'''
Calculates the recurrence model for the given settings as
an instance of the openquake.hmtk.models.IncrementalMFD
:param dict config:
Configuration settings of the magnitude frequency distribution.
'''
model = MFD_MAP[config['Model_Name']]()
model.setUp(config)
model.get_mmax(config, self.msr, self.rake, self.area)
model.mmax = model.mmax + (self.msr_sigma * model.mmax_sigma)
# As the Anderson & Luco arbitrary model requires the input of the
# displacement to length ratio
if 'AndersonLucoAreaMmax' in config['Model_Name']:
if not self.disp_length_ratio:
# If not defined then default to 1.25E-5
self.disp_length_ratio = 1.25E-5
min_mag, bin_width, occur_rates = model.get_mfd(
self.slip,
self.area, self.shear_modulus, self.disp_length_ratio)
else:
min_mag, bin_width, occur_rates = model.get_mfd(self.slip,
self.area,
self.shear_modulus)
self.recurrence = IncrementalMFD(min_mag, bin_width, occur_rates)
self.magnitudes = min_mag + np.cumsum(
bin_width *
np.ones(len(occur_rates), dtype=float)) - bin_width
self.max_mag = np.max(self.magnitudes) | python | def get_recurrence(self, config):
model = MFD_MAP[config['Model_Name']]()
model.setUp(config)
model.get_mmax(config, self.msr, self.rake, self.area)
model.mmax = model.mmax + (self.msr_sigma * model.mmax_sigma)
if 'AndersonLucoAreaMmax' in config['Model_Name']:
if not self.disp_length_ratio:
self.disp_length_ratio = 1.25E-5
min_mag, bin_width, occur_rates = model.get_mfd(
self.slip,
self.area, self.shear_modulus, self.disp_length_ratio)
else:
min_mag, bin_width, occur_rates = model.get_mfd(self.slip,
self.area,
self.shear_modulus)
self.recurrence = IncrementalMFD(min_mag, bin_width, occur_rates)
self.magnitudes = min_mag + np.cumsum(
bin_width *
np.ones(len(occur_rates), dtype=float)) - bin_width
self.max_mag = np.max(self.magnitudes) | [
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gem/oq-engine | openquake/hmtk/faults/fault_models.py | mtkActiveFault.get_tectonic_regionalisation | def get_tectonic_regionalisation(self, regionalisation, region_type=None):
'''
Defines the tectonic region and updates the shear modulus,
magnitude scaling relation and displacement to length ratio using
the regional values, if not previously defined for the fault
:param regionalistion:
Instance of the :class:
openquake.hmtk.faults.tectonic_regionalisaion.TectonicRegionalisation
:param str region_type:
Name of the region type - if not in regionalisation an error will
be raised
'''
if region_type:
self.trt = region_type
if not self.trt in regionalisation.key_list:
raise ValueError('Tectonic region classification missing or '
'not defined in regionalisation')
for iloc, key_val in enumerate(regionalisation.key_list):
if self.trt in key_val:
self.regionalisation = regionalisation.regionalisation[iloc]
# Update undefined shear modulus from tectonic regionalisation
if not self.shear_modulus:
self.shear_modulus = self.regionalisation.shear_modulus
# Update undefined scaling relation from tectonic
# regionalisation
if not self.msr:
self.msr = self.regionalisation.scaling_rel
# Update undefined displacement to length ratio from tectonic
# regionalisation
if not self.disp_length_ratio:
self.disp_length_ratio = \
self.regionalisation.disp_length_ratio
break
return | python | def get_tectonic_regionalisation(self, regionalisation, region_type=None):
if region_type:
self.trt = region_type
if not self.trt in regionalisation.key_list:
raise ValueError('Tectonic region classification missing or '
'not defined in regionalisation')
for iloc, key_val in enumerate(regionalisation.key_list):
if self.trt in key_val:
self.regionalisation = regionalisation.regionalisation[iloc]
if not self.shear_modulus:
self.shear_modulus = self.regionalisation.shear_modulus
if not self.msr:
self.msr = self.regionalisation.scaling_rel
if not self.disp_length_ratio:
self.disp_length_ratio = \
self.regionalisation.disp_length_ratio
break
return | [
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the regional values, if not previously defined for the fault
:param regionalistion:
Instance of the :class:
openquake.hmtk.faults.tectonic_regionalisaion.TectonicRegionalisation
:param str region_type:
Name of the region type - if not in regionalisation an error will
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gem/oq-engine | openquake/hmtk/faults/fault_models.py | mtkActiveFault.select_catalogue | def select_catalogue(self, selector, distance, distance_metric="rupture",
upper_eq_depth=None, lower_eq_depth=None):
"""
Select earthquakes within a specied distance of the fault
"""
if selector.catalogue.get_number_events() < 1:
raise ValueError('No events found in catalogue!')
# rupture metric is selected
if ('rupture' in distance_metric):
# Use rupture distance
self.catalogue = selector.within_rupture_distance(
self.geometry.surface,
distance,
upper_depth=upper_eq_depth,
lower_depth=lower_eq_depth)
else:
# Use Joyner-Boore distance
self.catalogue = selector.within_joyner_boore_distance(
self.geometry.surface,
distance,
upper_depth=upper_eq_depth,
lower_depth=lower_eq_depth) | python | def select_catalogue(self, selector, distance, distance_metric="rupture",
upper_eq_depth=None, lower_eq_depth=None):
if selector.catalogue.get_number_events() < 1:
raise ValueError('No events found in catalogue!')
if ('rupture' in distance_metric):
self.catalogue = selector.within_rupture_distance(
self.geometry.surface,
distance,
upper_depth=upper_eq_depth,
lower_depth=lower_eq_depth)
else:
self.catalogue = selector.within_joyner_boore_distance(
self.geometry.surface,
distance,
upper_depth=upper_eq_depth,
lower_depth=lower_eq_depth) | [
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gem/oq-engine | openquake/hmtk/faults/fault_models.py | mtkActiveFault._generate_branching_index | def _generate_branching_index(self):
'''
Generates a branching index (i.e. a list indicating the number of
branches in each branching level. Current branching levels are:
1) Slip
2) MSR
3) Shear Modulus
4) DLR
5) MSR_Sigma
6) Config
:returns:
* branch_index - A 2-D numpy.ndarray where each row is a pointer
to a particular combination of values
* number_branches - Total number of branches (int)
'''
branch_count = np.array([len(self.slip),
len(self.msr),
len(self.shear_modulus),
len(self.disp_length_ratio),
len(self.msr_sigma),
len(self.config)])
n_levels = len(branch_count)
number_branches = np.prod(branch_count)
branch_index = np.zeros([number_branches, n_levels], dtype=int)
cumval = 1
dstep = 1E-9
for iloc in range(0, n_levels):
idx = np.linspace(0.,
float(branch_count[iloc]) - dstep,
number_branches // cumval)
branch_index[:, iloc] = np.reshape(np.tile(idx, [cumval, 1]),
number_branches)
cumval *= branch_count[iloc]
return branch_index.tolist(), number_branches | python | def _generate_branching_index(self):
branch_count = np.array([len(self.slip),
len(self.msr),
len(self.shear_modulus),
len(self.disp_length_ratio),
len(self.msr_sigma),
len(self.config)])
n_levels = len(branch_count)
number_branches = np.prod(branch_count)
branch_index = np.zeros([number_branches, n_levels], dtype=int)
cumval = 1
dstep = 1E-9
for iloc in range(0, n_levels):
idx = np.linspace(0.,
float(branch_count[iloc]) - dstep,
number_branches // cumval)
branch_index[:, iloc] = np.reshape(np.tile(idx, [cumval, 1]),
number_branches)
cumval *= branch_count[iloc]
return branch_index.tolist(), number_branches | [
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2) MSR
3) Shear Modulus
4) DLR
5) MSR_Sigma
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* number_branches - Total number of branches (int) | [
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gem/oq-engine | openquake/hmtk/faults/fault_models.py | mtkActiveFault.generate_config_set | def generate_config_set(self, config):
'''
Generates a list of magnitude frequency distributions and renders as
a tuple
:param dict/list config:
Configuration paramters of magnitude frequency distribution
'''
if isinstance(config, dict):
# Configuration list contains only one element
self.config = [(config, 1.0)]
elif isinstance(config, list):
# Multiple configurations with correscponding weights
total_weight = 0.
self.config = []
for params in config:
weight = params['Model_Weight']
total_weight += params['Model_Weight']
self.config.append((params, weight))
if fabs(total_weight - 1.0) > 1E-7:
raise ValueError('MFD config weights do not sum to 1.0 for '
'fault %s' % self.id)
else:
raise ValueError('MFD config must be input as dictionary or list!') | python | def generate_config_set(self, config):
if isinstance(config, dict):
self.config = [(config, 1.0)]
elif isinstance(config, list):
total_weight = 0.
self.config = []
for params in config:
weight = params['Model_Weight']
total_weight += params['Model_Weight']
self.config.append((params, weight))
if fabs(total_weight - 1.0) > 1E-7:
raise ValueError('MFD config weights do not sum to 1.0 for '
'fault %s' % self.id)
else:
raise ValueError('MFD config must be input as dictionary or list!') | [
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gem/oq-engine | openquake/hmtk/faults/fault_models.py | mtkActiveFault.generate_recurrence_models | def generate_recurrence_models(
self, collapse=False, bin_width=0.1,
config=None, rendered_msr=None):
'''
Iterates over the lists of values defining epistemic uncertainty
in the parameters and calculates the corresponding recurrence model
At present epistemic uncertainty is supported for: 1) slip rate,
2) magnitude scaling relation, 3) shear modulus, 4) displacement
to length ratio) and 5) recurrence model.
:param list config:
List of MFD model configurations
:param bool collapse:
Boolean flag indicating whether to collapse the logic tree branches
:param float bin_width:
If collapsing the logic tree branches the reference mfd must be
defined. The minimum and maximum magnitudes are updated from the
model, but the bin width must be specified here
:param list/dict config:
Configuration (or sets of configurations) of the recurrence
calculations
:param rendered_msr:
If collapsing the logic tree branches a resulting magnitude
scaling relation must be defined as instance of
:class: openquake.hazardlib.scalerel.base.BaseASR
'''
if collapse and not rendered_msr:
raise ValueError('Collapsing logic tree branches requires input '
'of a single msr for rendering sources')
# Generate a set of tuples with corresponding weights
if config is not None:
self.generate_config_set(config)
if not isinstance(self.config, list):
raise ValueError('MFD configuration missing or incorrectly '
'formatted')
# Generate the branching index
branch_index, _number_branches = self._generate_branching_index()
mmin = np.inf
mmax = -np.inf
for idx in branch_index:
tuple_list = []
# Get slip
tuple_list.append(self.slip[idx[0]])
# Get msr
tuple_list.append(self.msr[idx[1]])
# Get shear modulus
tuple_list.append(self.shear_modulus[idx[2]])
# Get displacement length ratio
tuple_list.append(self.disp_length_ratio[idx[3]])
# Get msr sigma
tuple_list.append(self.msr_sigma[idx[4]])
# Get config
tuple_list.append(self.config[idx[5]])
# Calculate branch weight as product of tuple weights
branch_weight = np.prod(np.array([val[1] for val in tuple_list]))
# Instantiate recurrence model
model = RecurrenceBranch(self.area,
tuple_list[0][0],
tuple_list[1][0],
self.rake,
tuple_list[2][0],
tuple_list[3][0],
tuple_list[4][0],
weight=branch_weight)
model.get_recurrence(tuple_list[5][0])
self.mfd_models.append(model)
# Update the total minimum and maximum magnitudes for the fault
if model.recurrence.min_mag < mmin:
mmin = model.recurrence.min_mag
if np.max(model.magnitudes) > mmax:
mmax = np.max(model.magnitudes)
if collapse:
self.mfd = ([self.collapse_branches(mmin, bin_width, mmax)],
[1.0],
[rendered_msr])
else:
mfd_mods = []
mfd_wgts = []
mfd_msr = []
for model in self.mfd_models:
mfd_mods.append(IncrementalMFD(model.recurrence.min_mag,
model.recurrence.bin_width,
model.recurrence.occur_rates))
mfd_wgts.append(model.weight)
mfd_msr.append(model.msr)
self.mfd = (mfd_mods, mfd_wgts, mfd_msr) | python | def generate_recurrence_models(
self, collapse=False, bin_width=0.1,
config=None, rendered_msr=None):
if collapse and not rendered_msr:
raise ValueError('Collapsing logic tree branches requires input '
'of a single msr for rendering sources')
if config is not None:
self.generate_config_set(config)
if not isinstance(self.config, list):
raise ValueError('MFD configuration missing or incorrectly '
'formatted')
branch_index, _number_branches = self._generate_branching_index()
mmin = np.inf
mmax = -np.inf
for idx in branch_index:
tuple_list = []
tuple_list.append(self.slip[idx[0]])
tuple_list.append(self.msr[idx[1]])
tuple_list.append(self.shear_modulus[idx[2]])
tuple_list.append(self.disp_length_ratio[idx[3]])
tuple_list.append(self.msr_sigma[idx[4]])
tuple_list.append(self.config[idx[5]])
branch_weight = np.prod(np.array([val[1] for val in tuple_list]))
model = RecurrenceBranch(self.area,
tuple_list[0][0],
tuple_list[1][0],
self.rake,
tuple_list[2][0],
tuple_list[3][0],
tuple_list[4][0],
weight=branch_weight)
model.get_recurrence(tuple_list[5][0])
self.mfd_models.append(model)
if model.recurrence.min_mag < mmin:
mmin = model.recurrence.min_mag
if np.max(model.magnitudes) > mmax:
mmax = np.max(model.magnitudes)
if collapse:
self.mfd = ([self.collapse_branches(mmin, bin_width, mmax)],
[1.0],
[rendered_msr])
else:
mfd_mods = []
mfd_wgts = []
mfd_msr = []
for model in self.mfd_models:
mfd_mods.append(IncrementalMFD(model.recurrence.min_mag,
model.recurrence.bin_width,
model.recurrence.occur_rates))
mfd_wgts.append(model.weight)
mfd_msr.append(model.msr)
self.mfd = (mfd_mods, mfd_wgts, mfd_msr) | [
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At present epistemic uncertainty is supported for: 1) slip rate,
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to length ratio) and 5) recurrence model.
:param list config:
List of MFD model configurations
:param bool collapse:
Boolean flag indicating whether to collapse the logic tree branches
:param float bin_width:
If collapsing the logic tree branches the reference mfd must be
defined. The minimum and maximum magnitudes are updated from the
model, but the bin width must be specified here
:param list/dict config:
Configuration (or sets of configurations) of the recurrence
calculations
:param rendered_msr:
If collapsing the logic tree branches a resulting magnitude
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gem/oq-engine | openquake/hmtk/faults/fault_models.py | mtkActiveFault.collapse_branches | def collapse_branches(self, mmin, bin_width, mmax):
'''
Collapse the logic tree branches into a single IncrementalMFD
:param float mmin:
Minimum magnitude of reference mfd
:param float bin_width:
Bin width of reference mfd
:param float mmax:
Maximum magnitude of reference mfd
:returns:
:class: openquake.hmtk.models.IncrementalMFD
'''
master_mags = np.arange(mmin, mmax + (bin_width / 2.), bin_width)
master_rates = np.zeros(len(master_mags), dtype=float)
for model in self.mfd_models:
id0 = np.logical_and(
master_mags >= np.min(model.magnitudes) - 1E-9,
master_mags <= np.max(model.magnitudes) + 1E-9)
# Use interpolation in log10-y values
yvals = np.log10(model.recurrence.occur_rates)
interp_y = np.interp(master_mags[id0],
model.magnitudes,
yvals)
master_rates[id0] = master_rates[id0] + (model.weight *
10. ** interp_y)
return IncrementalMFD(mmin, bin_width, master_rates) | python | def collapse_branches(self, mmin, bin_width, mmax):
master_mags = np.arange(mmin, mmax + (bin_width / 2.), bin_width)
master_rates = np.zeros(len(master_mags), dtype=float)
for model in self.mfd_models:
id0 = np.logical_and(
master_mags >= np.min(model.magnitudes) - 1E-9,
master_mags <= np.max(model.magnitudes) + 1E-9)
yvals = np.log10(model.recurrence.occur_rates)
interp_y = np.interp(master_mags[id0],
model.magnitudes,
yvals)
master_rates[id0] = master_rates[id0] + (model.weight *
10. ** interp_y)
return IncrementalMFD(mmin, bin_width, master_rates) | [
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:param float bin_width:
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:param float mmax:
Maximum magnitude of reference mfd
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gem/oq-engine | openquake/hmtk/faults/fault_models.py | mtkActiveFault.generate_fault_source_model | def generate_fault_source_model(self):
'''
Creates a resulting `openquake.hmtk` fault source set.
:returns:
source_model - list of instances of either the :class:
`openquake.hmtk.sources.simple_fault_source.mtkSimpleFaultSource`
or :class:
`openquake.hmtk.sources.complex_fault_source.mtkComplexFaultSource`
model_weight - Corresponding weights for each source model
'''
source_model = []
model_weight = []
for iloc in range(0, self.get_number_mfd_models()):
model_mfd = EvenlyDiscretizedMFD(
self.mfd[0][iloc].min_mag,
self.mfd[0][iloc].bin_width,
self.mfd[0][iloc].occur_rates.tolist())
if isinstance(self.geometry, ComplexFaultGeometry):
# Complex fault class
source = mtkComplexFaultSource(
self.id,
self.name,
self.trt,
self.geometry.surface,
self.mfd[2][iloc],
self.rupt_aspect_ratio,
model_mfd,
self.rake)
source.fault_edges = self.geometry.trace
else:
# Simple Fault source
source = mtkSimpleFaultSource(
self.id,
self.name,
self.trt,
self.geometry.surface,
self.geometry.dip,
self.geometry.upper_depth,
self.geometry.lower_depth,
self.mfd[2][iloc],
self.rupt_aspect_ratio,
model_mfd,
self.rake)
source.fault_trace = self.geometry.trace
source_model.append(source)
model_weight.append(self.mfd[1][iloc])
return source_model, model_weight | python | def generate_fault_source_model(self):
source_model = []
model_weight = []
for iloc in range(0, self.get_number_mfd_models()):
model_mfd = EvenlyDiscretizedMFD(
self.mfd[0][iloc].min_mag,
self.mfd[0][iloc].bin_width,
self.mfd[0][iloc].occur_rates.tolist())
if isinstance(self.geometry, ComplexFaultGeometry):
source = mtkComplexFaultSource(
self.id,
self.name,
self.trt,
self.geometry.surface,
self.mfd[2][iloc],
self.rupt_aspect_ratio,
model_mfd,
self.rake)
source.fault_edges = self.geometry.trace
else:
source = mtkSimpleFaultSource(
self.id,
self.name,
self.trt,
self.geometry.surface,
self.geometry.dip,
self.geometry.upper_depth,
self.geometry.lower_depth,
self.mfd[2][iloc],
self.rupt_aspect_ratio,
model_mfd,
self.rake)
source.fault_trace = self.geometry.trace
source_model.append(source)
model_weight.append(self.mfd[1][iloc])
return source_model, model_weight | [
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source_model - list of instances of either the :class:
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or :class:
`openquake.hmtk.sources.complex_fault_source.mtkComplexFaultSource`
model_weight - Corresponding weights for each source model | [
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gem/oq-engine | openquake/hmtk/models.py | SeismicSource.attrib | def attrib(self):
"""
General XML element attributes for a seismic source, as a dict.
"""
return dict([
('id', str(self.id)),
('name', str(self.name)),
('tectonicRegion', str(self.trt)),
]) | python | def attrib(self):
return dict([
('id', str(self.id)),
('name', str(self.name)),
('tectonicRegion', str(self.trt)),
]) | [
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gem/oq-engine | openquake/hmtk/models.py | TGRMFD.attrib | def attrib(self):
"""
An dict of XML element attributes for this MFD.
"""
return dict([
('aValue', str(self.a_val)),
('bValue', str(self.b_val)),
('minMag', str(self.min_mag)),
('maxMag', str(self.max_mag)),
]) | python | def attrib(self):
return dict([
('aValue', str(self.a_val)),
('bValue', str(self.b_val)),
('minMag', str(self.min_mag)),
('maxMag', str(self.max_mag)),
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gem/oq-engine | openquake/hmtk/models.py | NodalPlane.attrib | def attrib(self):
"""
A dict of XML element attributes for this NodalPlane.
"""
return dict([
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return dict([
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gem/oq-engine | openquake/hazardlib/correlation.py | jbcorrelation | def jbcorrelation(sites_or_distances, imt, vs30_clustering=False):
"""
Returns the Jayaram-Baker correlation model.
:param sites_or_distances:
SiteCollection instance o ristance matrix
:param imt:
Intensity Measure Type (PGA or SA)
:param vs30_clustering:
flag, defalt false
"""
if hasattr(sites_or_distances, 'mesh'):
distances = sites_or_distances.mesh.get_distance_matrix()
else:
distances = sites_or_distances
# formulae are from page 1700
if imt.period < 1:
if not vs30_clustering:
# case 1, eq. (17)
b = 8.5 + 17.2 * imt.period
else:
# case 2, eq. (18)
b = 40.7 - 15.0 * imt.period
else:
# both cases, eq. (19)
b = 22.0 + 3.7 * imt.period
# eq. (20)
return numpy.exp((- 3.0 / b) * distances) | python | def jbcorrelation(sites_or_distances, imt, vs30_clustering=False):
if hasattr(sites_or_distances, 'mesh'):
distances = sites_or_distances.mesh.get_distance_matrix()
else:
distances = sites_or_distances
if imt.period < 1:
if not vs30_clustering:
b = 8.5 + 17.2 * imt.period
else:
b = 40.7 - 15.0 * imt.period
else:
b = 22.0 + 3.7 * imt.period
return numpy.exp((- 3.0 / b) * distances) | [
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gem/oq-engine | openquake/hazardlib/correlation.py | hmcorrelation | def hmcorrelation(sites_or_distances, imt, uncertainty_multiplier=0):
"""
Returns the Heresi-Miranda correlation model.
:param sites_or_distances:
SiteCollection instance o distance matrix
:param imt:
Intensity Measure Type (PGA or SA)
:param uncertainty_multiplier:
Value to be multiplied by the uncertainty in the correlation parameter
beta. If uncertainty_multiplier = 0 (default), the median value is
used as a constant value.
"""
if hasattr(sites_or_distances, 'mesh'):
distances = sites_or_distances.mesh.get_distance_matrix()
else:
distances = sites_or_distances
period = imt.period
# Eq. (9)
if period < 1.37:
Med_b = 4.231 * period * period - 5.180 * period + 13.392
else:
Med_b = 0.140 * period * period - 2.249 * period + 17.050
# Eq. (10)
Std_b = (4.63e-3 * period*period + 0.028 * period + 0.713)
# Obtain realization of b
if uncertainty_multiplier == 0:
beta = Med_b
else:
beta = numpy.random.lognormal(
numpy.log(Med_b), Std_b * uncertainty_multiplier)
# Eq. (8)
return numpy.exp(-numpy.power((distances / beta), 0.55)) | python | def hmcorrelation(sites_or_distances, imt, uncertainty_multiplier=0):
if hasattr(sites_or_distances, 'mesh'):
distances = sites_or_distances.mesh.get_distance_matrix()
else:
distances = sites_or_distances
period = imt.period
if period < 1.37:
Med_b = 4.231 * period * period - 5.180 * period + 13.392
else:
Med_b = 0.140 * period * period - 2.249 * period + 17.050
Std_b = (4.63e-3 * period*period + 0.028 * period + 0.713)
if uncertainty_multiplier == 0:
beta = Med_b
else:
beta = numpy.random.lognormal(
numpy.log(Med_b), Std_b * uncertainty_multiplier)
return numpy.exp(-numpy.power((distances / beta), 0.55)) | [
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:param sites_or_distances:
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:param imt:
Intensity Measure Type (PGA or SA)
:param uncertainty_multiplier:
Value to be multiplied by the uncertainty in the correlation parameter
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gem/oq-engine | openquake/hazardlib/correlation.py | BaseCorrelationModel.apply_correlation | def apply_correlation(self, sites, imt, residuals, stddev_intra=0):
"""
Apply correlation to randomly sampled residuals.
:param sites:
:class:`~openquake.hazardlib.site.SiteCollection` residuals were
sampled for.
:param imt:
Intensity measure type object, see :mod:`openquake.hazardlib.imt`.
:param residuals:
2d numpy array of sampled residuals, where first dimension
represents sites (the length as ``sites`` parameter) and
second one represents different realizations (samples).
:param stddev_intra:
Intra-event standard deviation array. Note that different sites do
not necessarily have the same intra-event standard deviation.
:returns:
Array of the same structure and semantics as ``residuals``
but with correlations applied.
NB: the correlation matrix is cached. It is computed only once
per IMT for the complete site collection and then the portion
corresponding to the sites is multiplied by the residuals.
"""
# intra-event residual for a single relization is a product
# of lower-triangle decomposed correlation matrix and vector
# of N random numbers (where N is equal to number of sites).
# we need to do that multiplication once per realization
# with the same matrix and different vectors.
try:
corma = self.cache[imt]
except KeyError:
corma = self.get_lower_triangle_correlation_matrix(
sites.complete, imt)
self.cache[imt] = corma
if len(sites.complete) == len(sites):
return numpy.dot(corma, residuals)
# it is important to allocate little memory, this is why I am
# accumulating below; if S is the length of the complete sites
# the correlation matrix has shape (S, S) and the residuals (N, s),
# where s is the number of samples
return numpy.sum(corma[sites.sids, sid] * res
for sid, res in zip(sites.sids, residuals)) | python | def apply_correlation(self, sites, imt, residuals, stddev_intra=0):
try:
corma = self.cache[imt]
except KeyError:
corma = self.get_lower_triangle_correlation_matrix(
sites.complete, imt)
self.cache[imt] = corma
if len(sites.complete) == len(sites):
return numpy.dot(corma, residuals)
return numpy.sum(corma[sites.sids, sid] * res
for sid, res in zip(sites.sids, residuals)) | [
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Intra-event standard deviation array. Note that different sites do
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gem/oq-engine | openquake/hazardlib/correlation.py | JB2009CorrelationModel.get_lower_triangle_correlation_matrix | def get_lower_triangle_correlation_matrix(self, sites, imt):
"""
Get lower-triangle matrix as a result of Cholesky-decomposition
of correlation matrix.
The resulting matrix should have zeros on values above
the main diagonal.
The actual implementations of :class:`BaseCorrelationModel` interface
might calculate the matrix considering site collection and IMT (like
:class:`JB2009CorrelationModel` does) or might have it pre-constructed
for a specific site collection and IMT, in which case they will need
to make sure that parameters to this function match parameters that
were used to pre-calculate decomposed correlation matrix.
:param sites:
:class:`~openquake.hazardlib.site.SiteCollection` to create
correlation matrix for.
:param imt:
Intensity measure type object, see :mod:`openquake.hazardlib.imt`.
"""
return numpy.linalg.cholesky(self._get_correlation_matrix(sites, imt)) | python | def get_lower_triangle_correlation_matrix(self, sites, imt):
return numpy.linalg.cholesky(self._get_correlation_matrix(sites, imt)) | [
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gem/oq-engine | openquake/hazardlib/correlation.py | HM2018CorrelationModel.apply_correlation | def apply_correlation(self, sites, imt, residuals, stddev_intra):
"""
Apply correlation to randomly sampled residuals.
See Parent function
"""
# stddev_intra is repeated if it is only 1 value for all the residuals
if stddev_intra.shape[0] == 1:
stddev_intra = numpy.matlib.repmat(
stddev_intra, len(sites.complete), 1)
# Reshape 'stddev_intra' if needed
stddev_intra = stddev_intra.squeeze()
if not stddev_intra.shape:
stddev_intra = stddev_intra[None]
if self.uncertainty_multiplier == 0: # No uncertainty
# residuals were sampled from a normal distribution with
# stddev_intra standard deviation. 'residuals_norm' are residuals
# normalized, sampled from a standard normal distribution.
# For this, every row of 'residuals' (every site) is divided by its
# corresponding standard deviation element.
residuals_norm = residuals / stddev_intra[sites.sids, None]
# Lower diagonal of the Cholesky decomposition from/to cache
try:
cormaLow = self.cache[imt]
except KeyError:
# Note that instead of computing the whole correlation matrix
# corresponding to sites.complete, here we compute only the
# correlation matrix corresponding to sites.
cormaLow = numpy.linalg.cholesky(
numpy.diag(stddev_intra[sites.sids]) *
self._get_correlation_matrix(sites, imt) *
numpy.diag(stddev_intra[sites.sids]))
self.cache[imt] = cormaLow
# Apply correlation
return numpy.dot(cormaLow, residuals_norm)
else: # Variability (uncertainty) is included
nsim = len(residuals[1])
nsites = len(residuals)
# Re-sample all the residuals
residuals_correlated = residuals * 0
for isim in range(0, nsim):
corma = self._get_correlation_matrix(sites, imt)
cov = (numpy.diag(stddev_intra[sites.sids]) * corma *
numpy.diag(stddev_intra[sites.sids]))
residuals_correlated[0:, isim] = (
numpy.random.multivariate_normal(
numpy.zeros(nsites), cov, 1))
return residuals_correlated | python | def apply_correlation(self, sites, imt, residuals, stddev_intra):
if stddev_intra.shape[0] == 1:
stddev_intra = numpy.matlib.repmat(
stddev_intra, len(sites.complete), 1)
stddev_intra = stddev_intra.squeeze()
if not stddev_intra.shape:
stddev_intra = stddev_intra[None]
if self.uncertainty_multiplier == 0:
residuals_norm = residuals / stddev_intra[sites.sids, None]
try:
cormaLow = self.cache[imt]
except KeyError:
cormaLow = numpy.linalg.cholesky(
numpy.diag(stddev_intra[sites.sids]) *
self._get_correlation_matrix(sites, imt) *
numpy.diag(stddev_intra[sites.sids]))
self.cache[imt] = cormaLow
return numpy.dot(cormaLow, residuals_norm)
else:
nsim = len(residuals[1])
nsites = len(residuals)
residuals_correlated = residuals * 0
for isim in range(0, nsim):
corma = self._get_correlation_matrix(sites, imt)
cov = (numpy.diag(stddev_intra[sites.sids]) * corma *
numpy.diag(stddev_intra[sites.sids]))
residuals_correlated[0:, isim] = (
numpy.random.multivariate_normal(
numpy.zeros(nsites), cov, 1))
return residuals_correlated | [
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gem/oq-engine | openquake/calculators/ebrisk.py | start_ebrisk | def start_ebrisk(rupgetter, srcfilter, param, monitor):
"""
Launcher for ebrisk tasks
"""
with monitor('weighting ruptures'):
rupgetter.set_weights(srcfilter, param['num_taxonomies'])
if rupgetter.weights.sum() <= param['maxweight']:
yield ebrisk(rupgetter, srcfilter, param, monitor)
else:
for rgetter in rupgetter.split(param['maxweight']):
yield ebrisk, rgetter, srcfilter, param | python | def start_ebrisk(rupgetter, srcfilter, param, monitor):
with monitor('weighting ruptures'):
rupgetter.set_weights(srcfilter, param['num_taxonomies'])
if rupgetter.weights.sum() <= param['maxweight']:
yield ebrisk(rupgetter, srcfilter, param, monitor)
else:
for rgetter in rupgetter.split(param['maxweight']):
yield ebrisk, rgetter, srcfilter, param | [
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gem/oq-engine | openquake/calculators/ebrisk.py | ebrisk | def ebrisk(rupgetter, srcfilter, param, monitor):
"""
:param rupgetter:
a RuptureGetter instance
:param srcfilter:
a SourceFilter instance
:param param:
a dictionary of parameters
:param monitor:
:class:`openquake.baselib.performance.Monitor` instance
:returns:
an ArrayWrapper with shape (E, L, T, ...)
"""
riskmodel = param['riskmodel']
E = rupgetter.num_events
L = len(riskmodel.lti)
N = len(srcfilter.sitecol.complete)
e1 = rupgetter.first_event
with monitor('getting assets', measuremem=False):
with datastore.read(srcfilter.filename) as dstore:
assetcol = dstore['assetcol']
assets_by_site = assetcol.assets_by_site()
A = len(assetcol)
getter = getters.GmfGetter(rupgetter, srcfilter, param['oqparam'])
with monitor('getting hazard'):
getter.init() # instantiate the computers
hazard = getter.get_hazard() # sid -> (rlzi, sid, eid, gmv)
mon_risk = monitor('computing risk', measuremem=False)
mon_agg = monitor('aggregating losses', measuremem=False)
events = rupgetter.get_eid_rlz()
# numpy.testing.assert_equal(events['eid'], sorted(events['eid']))
eid2idx = dict(zip(events['eid'], range(e1, e1 + E)))
tagnames = param['aggregate_by']
shape = assetcol.tagcol.agg_shape((E, L), tagnames)
elt_dt = [('eid', U64), ('rlzi', U16), ('loss', (F32, shape[1:]))]
if param['asset_loss_table']:
alt = numpy.zeros((A, E, L), F32)
acc = numpy.zeros(shape, F32) # shape (E, L, T...)
if param['avg_losses']:
losses_by_A = numpy.zeros((A, L), F32)
else:
losses_by_A = 0
# NB: IMT-dependent weights are not supported in ebrisk
times = numpy.zeros(N) # risk time per site_id
num_events_per_sid = 0
epspath = param['epspath']
for sid, haz in hazard.items():
t0 = time.time()
assets_on_sid = assets_by_site[sid]
if len(assets_on_sid) == 0:
continue
num_events_per_sid += len(haz)
weights = getter.weights[haz['rlzi'], 0]
assets_by_taxo = get_assets_by_taxo(assets_on_sid, epspath)
eidx = numpy.array([eid2idx[eid] for eid in haz['eid']]) - e1
haz['eid'] = eidx + e1
with mon_risk:
out = riskmodel.get_output(assets_by_taxo, haz)
with mon_agg:
for a, asset in enumerate(assets_on_sid):
aid = asset['ordinal']
tagi = asset[tagnames] if tagnames else ()
tagidxs = tuple(idx - 1 for idx in tagi)
for lti, lt in enumerate(riskmodel.loss_types):
lratios = out[lt][a]
if lt == 'occupants':
losses = lratios * asset['occupants_None']
else:
losses = lratios * asset['value-' + lt]
if param['asset_loss_table']:
alt[aid, eidx, lti] = losses
acc[(eidx, lti) + tagidxs] += losses
if param['avg_losses']:
losses_by_A[aid, lti] += losses @ weights
times[sid] = time.time() - t0
if hazard:
num_events_per_sid /= len(hazard)
with monitor('building event loss table'):
elt = numpy.fromiter(
((event['eid'], event['rlz'], losses)
for event, losses in zip(events, acc) if losses.sum()), elt_dt)
agg = general.AccumDict(accum=numpy.zeros(shape[1:], F32)) # rlz->agg
for rec in elt:
agg[rec['rlzi']] += rec['loss'] * param['ses_ratio']
res = {'elt': elt, 'agg_losses': agg, 'times': times,
'events_per_sid': num_events_per_sid}
if param['avg_losses']:
res['losses_by_A'] = losses_by_A * param['ses_ratio']
if param['asset_loss_table']:
res['alt_eids'] = alt, events['eid']
return res | python | def ebrisk(rupgetter, srcfilter, param, monitor):
riskmodel = param['riskmodel']
E = rupgetter.num_events
L = len(riskmodel.lti)
N = len(srcfilter.sitecol.complete)
e1 = rupgetter.first_event
with monitor('getting assets', measuremem=False):
with datastore.read(srcfilter.filename) as dstore:
assetcol = dstore['assetcol']
assets_by_site = assetcol.assets_by_site()
A = len(assetcol)
getter = getters.GmfGetter(rupgetter, srcfilter, param['oqparam'])
with monitor('getting hazard'):
getter.init()
hazard = getter.get_hazard()
mon_risk = monitor('computing risk', measuremem=False)
mon_agg = monitor('aggregating losses', measuremem=False)
events = rupgetter.get_eid_rlz()
eid2idx = dict(zip(events['eid'], range(e1, e1 + E)))
tagnames = param['aggregate_by']
shape = assetcol.tagcol.agg_shape((E, L), tagnames)
elt_dt = [('eid', U64), ('rlzi', U16), ('loss', (F32, shape[1:]))]
if param['asset_loss_table']:
alt = numpy.zeros((A, E, L), F32)
acc = numpy.zeros(shape, F32)
if param['avg_losses']:
losses_by_A = numpy.zeros((A, L), F32)
else:
losses_by_A = 0
times = numpy.zeros(N)
num_events_per_sid = 0
epspath = param['epspath']
for sid, haz in hazard.items():
t0 = time.time()
assets_on_sid = assets_by_site[sid]
if len(assets_on_sid) == 0:
continue
num_events_per_sid += len(haz)
weights = getter.weights[haz['rlzi'], 0]
assets_by_taxo = get_assets_by_taxo(assets_on_sid, epspath)
eidx = numpy.array([eid2idx[eid] for eid in haz['eid']]) - e1
haz['eid'] = eidx + e1
with mon_risk:
out = riskmodel.get_output(assets_by_taxo, haz)
with mon_agg:
for a, asset in enumerate(assets_on_sid):
aid = asset['ordinal']
tagi = asset[tagnames] if tagnames else ()
tagidxs = tuple(idx - 1 for idx in tagi)
for lti, lt in enumerate(riskmodel.loss_types):
lratios = out[lt][a]
if lt == 'occupants':
losses = lratios * asset['occupants_None']
else:
losses = lratios * asset['value-' + lt]
if param['asset_loss_table']:
alt[aid, eidx, lti] = losses
acc[(eidx, lti) + tagidxs] += losses
if param['avg_losses']:
losses_by_A[aid, lti] += losses @ weights
times[sid] = time.time() - t0
if hazard:
num_events_per_sid /= len(hazard)
with monitor('building event loss table'):
elt = numpy.fromiter(
((event['eid'], event['rlz'], losses)
for event, losses in zip(events, acc) if losses.sum()), elt_dt)
agg = general.AccumDict(accum=numpy.zeros(shape[1:], F32))
for rec in elt:
agg[rec['rlzi']] += rec['loss'] * param['ses_ratio']
res = {'elt': elt, 'agg_losses': agg, 'times': times,
'events_per_sid': num_events_per_sid}
if param['avg_losses']:
res['losses_by_A'] = losses_by_A * param['ses_ratio']
if param['asset_loss_table']:
res['alt_eids'] = alt, events['eid']
return res | [
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a RuptureGetter instance
:param srcfilter:
a SourceFilter instance
:param param:
a dictionary of parameters
:param monitor:
:class:`openquake.baselib.performance.Monitor` instance
:returns:
an ArrayWrapper with shape (E, L, T, ...) | [
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gem/oq-engine | openquake/calculators/ebrisk.py | compute_loss_curves_maps | def compute_loss_curves_maps(filename, builder, rlzi, monitor):
"""
:param filename: path to the datastore
:param builder: LossCurvesMapsBuilder instance
:param rlzi: realization index
:param monitor: Monitor instance
:returns: rlzi, (curves, maps)
"""
with datastore.read(filename) as dstore:
rlzs = dstore['losses_by_event']['rlzi']
losses = dstore['losses_by_event'][rlzs == rlzi]['loss']
return rlzi, builder.build_curves_maps(losses, rlzi) | python | def compute_loss_curves_maps(filename, builder, rlzi, monitor):
with datastore.read(filename) as dstore:
rlzs = dstore['losses_by_event']['rlzi']
losses = dstore['losses_by_event'][rlzs == rlzi]['loss']
return rlzi, builder.build_curves_maps(losses, rlzi) | [
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:param builder: LossCurvesMapsBuilder instance
:param rlzi: realization index
:param monitor: Monitor instance
:returns: rlzi, (curves, maps) | [
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gem/oq-engine | openquake/hazardlib/mfd/youngs_coppersmith_1985.py | YoungsCoppersmith1985MFD.get_min_max_mag | def get_min_max_mag(self):
"Return the minimum and maximum magnitudes"
mag, num_bins = self._get_min_mag_and_num_bins()
return mag, mag + self. bin_width * (num_bins - 1) | python | def get_min_max_mag(self):
"Return the minimum and maximum magnitudes"
mag, num_bins = self._get_min_mag_and_num_bins()
return mag, mag + self. bin_width * (num_bins - 1) | [
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gem/oq-engine | openquake/hazardlib/mfd/youngs_coppersmith_1985.py | YoungsCoppersmith1985MFD.check_constraints | def check_constraints(self):
"""
Checks the following constraints:
* minimum magnitude is positive.
* ``b`` value is positive.
* characteristic magnitude is positive
* characteristic rate is positive
* bin width is in the range (0, 0.5] to allow for at least one bin
representing the characteristic distribution
* characteristic magnitude minus 0.25 (that is the maximum magnitude
of the G-R distribution) is greater than the minimum magnitude by at
least one magnitude bin.
* rate of events at the characteristic magnitude is equal to the
rate of events for magnitude equal to m_prime - 1. This is done
by asserting the equality (up to 7 digit precision) ::
10 ** (a_incr - b * (m' - 1)) == char_rate / 0.5
where ``a_incr`` is the incremental a value obtained from the
cumulative a value using the following formula ::
a_incr = a_val + log10(b_val * ln(10))
and ``m' - 1 = char_mag - 1.25``
"""
if not self.min_mag > 0:
raise ValueError('minimum magnitude must be positive')
if not self.b_val > 0:
raise ValueError('b value must be positive')
if not self.char_mag > 0:
raise ValueError('characteristic magnitude must be positive')
if not self.char_rate > 0:
raise ValueError('characteristic rate must be positive')
if not 0 < self.bin_width <= DELTA_CHAR:
err_msg = 'bin width must be in the range (0, %s] to allow for ' \
'at least one magnitude bin representing the ' \
'characteristic distribution' % DELTA_CHAR
raise ValueError(err_msg)
if not self.char_mag - DELTA_CHAR / 2 >= self.min_mag + self.bin_width:
err_msg = 'Maximum magnitude of the G-R distribution (char_mag ' \
'- 0.25) must be greater than the minimum magnitude ' \
'by at least one magnitude bin.'
raise ValueError(err_msg)
a_incr = self.a_val + numpy.log10(self.b_val * numpy.log(10))
actual = 10 ** (a_incr - self.b_val * (self.char_mag - 1.25))
desired = self.char_rate / DELTA_CHAR
if not numpy.allclose(actual, desired, rtol=0.0, atol=1e-07):
err_msg = 'Rate of events at the characteristic magnitude is ' \
'not equal to the rate of events for magnitude equal ' \
'to char_mag - 1.25'
raise ValueError(err_msg) | python | def check_constraints(self):
if not self.min_mag > 0:
raise ValueError('minimum magnitude must be positive')
if not self.b_val > 0:
raise ValueError('b value must be positive')
if not self.char_mag > 0:
raise ValueError('characteristic magnitude must be positive')
if not self.char_rate > 0:
raise ValueError('characteristic rate must be positive')
if not 0 < self.bin_width <= DELTA_CHAR:
err_msg = 'bin width must be in the range (0, %s] to allow for ' \
'at least one magnitude bin representing the ' \
'characteristic distribution' % DELTA_CHAR
raise ValueError(err_msg)
if not self.char_mag - DELTA_CHAR / 2 >= self.min_mag + self.bin_width:
err_msg = 'Maximum magnitude of the G-R distribution (char_mag ' \
'- 0.25) must be greater than the minimum magnitude ' \
'by at least one magnitude bin.'
raise ValueError(err_msg)
a_incr = self.a_val + numpy.log10(self.b_val * numpy.log(10))
actual = 10 ** (a_incr - self.b_val * (self.char_mag - 1.25))
desired = self.char_rate / DELTA_CHAR
if not numpy.allclose(actual, desired, rtol=0.0, atol=1e-07):
err_msg = 'Rate of events at the characteristic magnitude is ' \
'not equal to the rate of events for magnitude equal ' \
'to char_mag - 1.25'
raise ValueError(err_msg) | [
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* minimum magnitude is positive.
* ``b`` value is positive.
* characteristic magnitude is positive
* characteristic rate is positive
* bin width is in the range (0, 0.5] to allow for at least one bin
representing the characteristic distribution
* characteristic magnitude minus 0.25 (that is the maximum magnitude
of the G-R distribution) is greater than the minimum magnitude by at
least one magnitude bin.
* rate of events at the characteristic magnitude is equal to the
rate of events for magnitude equal to m_prime - 1. This is done
by asserting the equality (up to 7 digit precision) ::
10 ** (a_incr - b * (m' - 1)) == char_rate / 0.5
where ``a_incr`` is the incremental a value obtained from the
cumulative a value using the following formula ::
a_incr = a_val + log10(b_val * ln(10))
and ``m' - 1 = char_mag - 1.25`` | [
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gem/oq-engine | openquake/hazardlib/mfd/youngs_coppersmith_1985.py | YoungsCoppersmith1985MFD.from_total_moment_rate | def from_total_moment_rate(cls, min_mag, b_val, char_mag,
total_moment_rate, bin_width):
"""
Define Youngs and Coppersmith 1985 MFD by constraing cumulative a
value and characteristic rate from total moment rate.
The cumulative a value and characteristic rate are obtained by
solving equations (16) and (17), page 954, for the cumulative rate of
events with magnitude greater than the minimum magnitude - N(min_mag)
- and the cumulative rate of characteristic earthquakes - N(char_mag).
The difference ``N(min_mag) - N(char_mag)`` represents the rate of
noncharacteristic, exponentially distributed earthquakes and is used
to derive the cumulative a value by solving the following equation ::
10 ** (a_val - b_val * min_mag) -
10 ** (a_val - b_val * (char_mag - 0.25))
= N(min_mag) - N(char_mag)
which can be written as ::
a_val =
log10(N(min_mag) - N(char_mag)) /
(10 ** (- b_val * min_mag) - 10 ** (- b_val * (char_mag - 0.25))
In the calculation of N(min_mag) and N(char_mag), the Hanks and
Kanamori (1979) formula ::
M0 = 10 ** (1.5 * Mw + 9.05)
is used to convert moment magnitude (Mw) to seismic moment (M0,
Newton × m)
:param min_mag:
The lowest magnitude for the MFD. The first bin in the
:meth:`result histogram <get_annual_occurrence_rates>` is aligned
to make its left border match this value.
:param b_val:
The Gutenberg-Richter ``b`` value -- the gradient of the loglinear
G-R relationship.
:param char_mag:
The characteristic magnitude defining the middle point of
characteristic distribution. That is the boxcar function
representing the characteristic distribution is defined in the
range [char_mag - 0.25, char_mag + 0.25].
:param total_moment_rate:
Total moment rate in N * m / year.
:param bin_width:
A positive float value -- the width of a single histogram bin.
:returns:
An instance of :class:`YoungsCoppersmith1985MFD`.
Values for ``min_mag`` and the maximum magnitude (char_mag + 0.25)
don't have to be aligned with respect to ``bin_width``. They get
rounded accordingly anyway so that both are divisible by ``bin_width``
just before converting a function to a histogram.
See :meth:`_get_min_mag_and_num_bins`.
"""
beta = b_val * numpy.log(10)
mu = char_mag + DELTA_CHAR / 2
m0 = min_mag
# seismic moment (in Nm) for the maximum magnitude
c = 1.5
d = 9.05
mo_u = 10 ** (c * mu + d)
# equations (16) and (17) solved for N(min_mag) and N(char_mag)
c1 = numpy.exp(-beta * (mu - m0 - 0.5))
c2 = numpy.exp(-beta * (mu - m0 - 1.5))
c3 = beta * c2 / (2 * (1 - c1) + beta * c2)
c4 = (b_val * (10 ** (-c / 2)) / (c - b_val)) + \
(b_val * numpy.exp(beta) * (1 - (10 ** (-c / 2))) / c)
n_min_mag = (1 - c1) * total_moment_rate / ((1 - c3) * c1 * mo_u * c4)
n_char_mag = c3 * n_min_mag
a_val = numpy.log10(
(n_min_mag - n_char_mag) /
(10 ** (- b_val * min_mag) - 10 ** (- b_val * (char_mag - 0.25)))
)
return cls(min_mag, a_val, b_val, char_mag, n_char_mag, bin_width) | python | def from_total_moment_rate(cls, min_mag, b_val, char_mag,
total_moment_rate, bin_width):
beta = b_val * numpy.log(10)
mu = char_mag + DELTA_CHAR / 2
m0 = min_mag
c = 1.5
d = 9.05
mo_u = 10 ** (c * mu + d)
c1 = numpy.exp(-beta * (mu - m0 - 0.5))
c2 = numpy.exp(-beta * (mu - m0 - 1.5))
c3 = beta * c2 / (2 * (1 - c1) + beta * c2)
c4 = (b_val * (10 ** (-c / 2)) / (c - b_val)) + \
(b_val * numpy.exp(beta) * (1 - (10 ** (-c / 2))) / c)
n_min_mag = (1 - c1) * total_moment_rate / ((1 - c3) * c1 * mo_u * c4)
n_char_mag = c3 * n_min_mag
a_val = numpy.log10(
(n_min_mag - n_char_mag) /
(10 ** (- b_val * min_mag) - 10 ** (- b_val * (char_mag - 0.25)))
)
return cls(min_mag, a_val, b_val, char_mag, n_char_mag, bin_width) | [
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The cumulative a value and characteristic rate are obtained by
solving equations (16) and (17), page 954, for the cumulative rate of
events with magnitude greater than the minimum magnitude - N(min_mag)
- and the cumulative rate of characteristic earthquakes - N(char_mag).
The difference ``N(min_mag) - N(char_mag)`` represents the rate of
noncharacteristic, exponentially distributed earthquakes and is used
to derive the cumulative a value by solving the following equation ::
10 ** (a_val - b_val * min_mag) -
10 ** (a_val - b_val * (char_mag - 0.25))
= N(min_mag) - N(char_mag)
which can be written as ::
a_val =
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In the calculation of N(min_mag) and N(char_mag), the Hanks and
Kanamori (1979) formula ::
M0 = 10 ** (1.5 * Mw + 9.05)
is used to convert moment magnitude (Mw) to seismic moment (M0,
Newton × m)
:param min_mag:
The lowest magnitude for the MFD. The first bin in the
:meth:`result histogram <get_annual_occurrence_rates>` is aligned
to make its left border match this value.
:param b_val:
The Gutenberg-Richter ``b`` value -- the gradient of the loglinear
G-R relationship.
:param char_mag:
The characteristic magnitude defining the middle point of
characteristic distribution. That is the boxcar function
representing the characteristic distribution is defined in the
range [char_mag - 0.25, char_mag + 0.25].
:param total_moment_rate:
Total moment rate in N * m / year.
:param bin_width:
A positive float value -- the width of a single histogram bin.
:returns:
An instance of :class:`YoungsCoppersmith1985MFD`.
Values for ``min_mag`` and the maximum magnitude (char_mag + 0.25)
don't have to be aligned with respect to ``bin_width``. They get
rounded accordingly anyway so that both are divisible by ``bin_width``
just before converting a function to a histogram.
See :meth:`_get_min_mag_and_num_bins`. | [
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gem/oq-engine | openquake/hazardlib/mfd/youngs_coppersmith_1985.py | YoungsCoppersmith1985MFD.from_characteristic_rate | def from_characteristic_rate(cls, min_mag, b_val, char_mag, char_rate,
bin_width):
"""
Define Youngs and Coppersmith 1985 MFD by constraing cumulative a
value from characteristic rate.
The cumulative a value is obtained by making use of the property that
the rate of events at m' - 1 must be equal to the rate at the
characteristic magnitude, and therefore by first computing the
incremental a value, using the following equation::
10 ** (a_incr - b_val * (m_prime - 1)) == char_rate / 0.5
where ``m' - 1 = char_mag - 1.25``.
The cumulative a value is then obtained as ::
a_val = a_incr - log10(b_val * ln(10))
:param min_mag:
The lowest magnitude for the MFD. The first bin in the
:meth:`result histogram <get_annual_occurrence_rates>` is aligned
to make its left border match this value.
:param b_val:
The Gutenberg-Richter ``b`` value -- the gradient of the loglinear
G-R relationship.
:param char_mag:
The characteristic magnitude defining the middle point of
characteristic distribution. That is the boxcar function
representing the characteristic distribution is defined in the
range [char_mag - 0.25, char_mag + 0.25].
:param char_rate:
The characteristic rate associated to the characteristic magnitude,
to be distributed over the domain of the boxcar function
representing the characteristic distribution (that is λ_char =
char_rate / 0.5)
:param bin_width:
A positive float value -- the width of a single histogram bin.
:returns:
An instance of :class:`YoungsCoppersmith1985MFD`.
Values for ``min_mag`` and the maximum magnitude (char_mag + 0.25)
don't have to be aligned with respect to ``bin_width``. They get
rounded accordingly anyway so that both are divisible by ``bin_width``
just before converting a function to a histogram.
See :meth:`_get_min_mag_and_num_bins`.
"""
a_incr = b_val * (char_mag - 1.25) + numpy.log10(char_rate /
DELTA_CHAR)
a_val = a_incr - numpy.log10(b_val * numpy.log(10))
return cls(min_mag, a_val, b_val, char_mag, char_rate, bin_width) | python | def from_characteristic_rate(cls, min_mag, b_val, char_mag, char_rate,
bin_width):
a_incr = b_val * (char_mag - 1.25) + numpy.log10(char_rate /
DELTA_CHAR)
a_val = a_incr - numpy.log10(b_val * numpy.log(10))
return cls(min_mag, a_val, b_val, char_mag, char_rate, bin_width) | [
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characteristic magnitude, and therefore by first computing the
incremental a value, using the following equation::
10 ** (a_incr - b_val * (m_prime - 1)) == char_rate / 0.5
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The lowest magnitude for the MFD. The first bin in the
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:param b_val:
The Gutenberg-Richter ``b`` value -- the gradient of the loglinear
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:param char_mag:
The characteristic magnitude defining the middle point of
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:param char_rate:
The characteristic rate associated to the characteristic magnitude,
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:param bin_width:
A positive float value -- the width of a single histogram bin.
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An instance of :class:`YoungsCoppersmith1985MFD`.
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gem/oq-engine | openquake/hazardlib/mfd/youngs_coppersmith_1985.py | YoungsCoppersmith1985MFD._get_rate | def _get_rate(self, mag):
"""
Calculate and return the annual occurrence rate for a specific bin.
:param mag:
Magnitude value corresponding to the center of the bin of interest.
:returns:
Float number, the annual occurrence rate for the :param mag value.
"""
mag_lo = mag - self.bin_width / 2.0
mag_hi = mag + self.bin_width / 2.0
if mag >= self.min_mag and mag < self.char_mag - DELTA_CHAR / 2:
# return rate according to exponential distribution
return (10 ** (self.a_val - self.b_val * mag_lo)
- 10 ** (self.a_val - self.b_val * mag_hi))
else:
# return characteristic rate (distributed over the characteristic
# range) for the given bin width
return (self.char_rate / DELTA_CHAR) * self.bin_width | python | def _get_rate(self, mag):
mag_lo = mag - self.bin_width / 2.0
mag_hi = mag + self.bin_width / 2.0
if mag >= self.min_mag and mag < self.char_mag - DELTA_CHAR / 2:
return (10 ** (self.a_val - self.b_val * mag_lo)
- 10 ** (self.a_val - self.b_val * mag_hi))
else:
return (self.char_rate / DELTA_CHAR) * self.bin_width | [
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gem/oq-engine | openquake/hazardlib/mfd/youngs_coppersmith_1985.py | YoungsCoppersmith1985MFD._get_min_mag_and_num_bins | def _get_min_mag_and_num_bins(self):
"""
Estimate the number of bins in the histogram and return it along with
the first bin center value.
Rounds ``min_mag`` and ``max_mag`` with respect to ``bin_width`` to
make the distance between them include integer number of bins.
:returns:
A tuple of 2 items: first bin center, and total number of bins.
"""
min_mag = round(self.min_mag / self.bin_width) * self.bin_width
max_mag = (round((self.char_mag + DELTA_CHAR / 2) /
self.bin_width) * self.bin_width)
min_mag += self.bin_width / 2.0
max_mag -= self.bin_width / 2.0
# here we use math round on the result of division and not just
# cast it to integer because for some magnitude values that can't
# be represented as an IEEE 754 double precisely the result can
# look like 7.999999999999 which would become 7 instead of 8
# being naively casted to int so we would lose the last bin.
num_bins = int(round((max_mag - min_mag) / self.bin_width)) + 1
return min_mag, num_bins | python | def _get_min_mag_and_num_bins(self):
min_mag = round(self.min_mag / self.bin_width) * self.bin_width
max_mag = (round((self.char_mag + DELTA_CHAR / 2) /
self.bin_width) * self.bin_width)
min_mag += self.bin_width / 2.0
max_mag -= self.bin_width / 2.0
num_bins = int(round((max_mag - min_mag) / self.bin_width)) + 1
return min_mag, num_bins | [
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gem/oq-engine | openquake/hazardlib/mfd/youngs_coppersmith_1985.py | YoungsCoppersmith1985MFD.get_annual_occurrence_rates | def get_annual_occurrence_rates(self):
"""
Calculate and return the annual occurrence rates histogram.
:returns:
See :meth:
`openquake.hazardlib.mfd.base.BaseMFD.get_annual_occurrence_rates`.
"""
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rates = []
for i in range(num_bins):
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rates.append((mag, rate))
mag += self.bin_width
return rates | python | def get_annual_occurrence_rates(self):
mag, num_bins = self._get_min_mag_and_num_bins()
rates = []
for i in range(num_bins):
rate = self._get_rate(mag)
rates.append((mag, rate))
mag += self.bin_width
return rates | [
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gem/oq-engine | openquake/hmtk/sources/area_source.py | mtkAreaSource.create_geometry | def create_geometry(self, input_geometry, upper_depth, lower_depth):
'''
If geometry is defined as a numpy array then create instance of
nhlib.geo.polygon.Polygon class, otherwise if already instance of class
accept class
:param input_geometry:
Input geometry (polygon) as either
i) instance of nhlib.geo.polygon.Polygon class
ii) numpy.ndarray [Longitude, Latitude]
:param float upper_depth:
Upper seismogenic depth (km)
:param float lower_depth:
Lower seismogenic depth (km)
'''
self._check_seismogenic_depths(upper_depth, lower_depth)
# Check/create the geometry class
if not isinstance(input_geometry, Polygon):
if not isinstance(input_geometry, np.ndarray):
raise ValueError('Unrecognised or unsupported geometry '
'definition')
if np.shape(input_geometry)[0] < 3:
raise ValueError('Incorrectly formatted polygon geometry -'
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geometry = []
for row in input_geometry:
geometry.append(Point(row[0], row[1], self.upper_depth))
self.geometry = Polygon(geometry)
else:
self.geometry = input_geometry | python | def create_geometry(self, input_geometry, upper_depth, lower_depth):
self._check_seismogenic_depths(upper_depth, lower_depth)
if not isinstance(input_geometry, Polygon):
if not isinstance(input_geometry, np.ndarray):
raise ValueError('Unrecognised or unsupported geometry '
'definition')
if np.shape(input_geometry)[0] < 3:
raise ValueError('Incorrectly formatted polygon geometry -'
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geometry = []
for row in input_geometry:
geometry.append(Point(row[0], row[1], self.upper_depth))
self.geometry = Polygon(geometry)
else:
self.geometry = input_geometry | [
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gem/oq-engine | openquake/hmtk/sources/area_source.py | mtkAreaSource.select_catalogue | def select_catalogue(self, selector, distance=None):
'''
Selects the catalogue of earthquakes attributable to the source
:param selector:
Populated instance of openquake.hmtk.seismicity.selector.CatalogueSelector
class
:param float distance:
Distance (in km) to extend or contract (if negative) the zone for
selecting events
'''
if selector.catalogue.get_number_events() < 1:
raise ValueError('No events found in catalogue!')
self.catalogue = selector.within_polygon(self.geometry,
distance,
upper_depth=self.upper_depth,
lower_depth=self.lower_depth)
if self.catalogue.get_number_events() < 5:
# Throw a warning regarding the small number of earthquakes in
# the source!
warnings.warn('Source %s (%s) has fewer than 5 events'
% (self.id, self.name)) | python | def select_catalogue(self, selector, distance=None):
if selector.catalogue.get_number_events() < 1:
raise ValueError('No events found in catalogue!')
self.catalogue = selector.within_polygon(self.geometry,
distance,
upper_depth=self.upper_depth,
lower_depth=self.lower_depth)
if self.catalogue.get_number_events() < 5:
warnings.warn('Source %s (%s) has fewer than 5 events'
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gem/oq-engine | openquake/hmtk/sources/area_source.py | mtkAreaSource.create_oqhazardlib_source | def create_oqhazardlib_source(self, tom, mesh_spacing, area_discretisation,
use_defaults=False):
"""
Converts the source model into an instance of the :class:
openquake.hazardlib.source.area.AreaSource
:param tom:
Temporal Occurrence model as instance of :class:
openquake.hazardlib.tom.TOM
:param float mesh_spacing:
Mesh spacing
"""
if not self.mfd:
raise ValueError("Cannot write to hazardlib without MFD")
return AreaSource(
self.id,
self.name,
self.trt,
self.mfd,
mesh_spacing,
conv.mag_scale_rel_to_hazardlib(self.mag_scale_rel, use_defaults),
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conv.npd_to_pmf(self.nodal_plane_dist, use_defaults),
conv.hdd_to_pmf(self.hypo_depth_dist, use_defaults),
self.geometry,
area_discretisation) | python | def create_oqhazardlib_source(self, tom, mesh_spacing, area_discretisation,
use_defaults=False):
if not self.mfd:
raise ValueError("Cannot write to hazardlib without MFD")
return AreaSource(
self.id,
self.name,
self.trt,
self.mfd,
mesh_spacing,
conv.mag_scale_rel_to_hazardlib(self.mag_scale_rel, use_defaults),
conv.render_aspect_ratio(self.rupt_aspect_ratio, use_defaults),
tom,
self.upper_depth,
self.lower_depth,
conv.npd_to_pmf(self.nodal_plane_dist, use_defaults),
conv.hdd_to_pmf(self.hypo_depth_dist, use_defaults),
self.geometry,
area_discretisation) | [
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gem/oq-engine | openquake/baselib/performance.py | Monitor.get_data | def get_data(self):
"""
:returns:
an array of dtype perf_dt, with the information
of the monitor (operation, time_sec, memory_mb, counts);
the lenght of the array can be 0 (for counts=0) or 1 (otherwise).
"""
data = []
if self.counts:
time_sec = self.duration
memory_mb = self.mem / 1024. / 1024. if self.measuremem else 0
data.append((self.operation, time_sec, memory_mb, self.counts))
return numpy.array(data, perf_dt) | python | def get_data(self):
data = []
if self.counts:
time_sec = self.duration
memory_mb = self.mem / 1024. / 1024. if self.measuremem else 0
data.append((self.operation, time_sec, memory_mb, self.counts))
return numpy.array(data, perf_dt) | [
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gem/oq-engine | openquake/baselib/performance.py | Monitor.flush | def flush(self):
"""
Save the measurements on the performance file (or on stdout)
"""
if not self._flush:
raise RuntimeError(
'Monitor(%r).flush() must not be called in a worker' %
self.operation)
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child.hdf5 = self.hdf5
child.flush()
data = self.get_data()
if len(data) == 0: # no information
return []
elif self.hdf5:
hdf5.extend(self.hdf5['performance_data'], data)
# reset monitor
self.duration = 0
self.mem = 0
self.counts = 0
return data | python | def flush(self):
if not self._flush:
raise RuntimeError(
'Monitor(%r).flush() must not be called in a worker' %
self.operation)
for child in self.children:
child.hdf5 = self.hdf5
child.flush()
data = self.get_data()
if len(data) == 0:
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self.duration = 0
self.mem = 0
self.counts = 0
return data | [
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gem/oq-engine | openquake/baselib/performance.py | Monitor.new | def new(self, operation='no operation', **kw):
"""
Return a copy of the monitor usable for a different operation.
"""
self_vars = vars(self).copy()
del self_vars['operation']
del self_vars['children']
del self_vars['counts']
del self_vars['_flush']
new = self.__class__(operation)
vars(new).update(self_vars)
vars(new).update(kw)
return new | python | def new(self, operation='no operation', **kw):
self_vars = vars(self).copy()
del self_vars['operation']
del self_vars['children']
del self_vars['counts']
del self_vars['_flush']
new = self.__class__(operation)
vars(new).update(self_vars)
vars(new).update(kw)
return new | [
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gem/oq-engine | openquake/hazardlib/site.py | SiteCollection.from_shakemap | def from_shakemap(cls, shakemap_array):
"""
Build a site collection from a shakemap array
"""
self = object.__new__(cls)
self.complete = self
n = len(shakemap_array)
dtype = numpy.dtype([(p, site_param_dt[p])
for p in 'sids lon lat depth vs30'.split()])
self.array = arr = numpy.zeros(n, dtype)
arr['sids'] = numpy.arange(n, dtype=numpy.uint32)
arr['lon'] = shakemap_array['lon']
arr['lat'] = shakemap_array['lat']
arr['depth'] = numpy.zeros(n)
arr['vs30'] = shakemap_array['vs30']
arr.flags.writeable = False
return self | python | def from_shakemap(cls, shakemap_array):
self = object.__new__(cls)
self.complete = self
n = len(shakemap_array)
dtype = numpy.dtype([(p, site_param_dt[p])
for p in 'sids lon lat depth vs30'.split()])
self.array = arr = numpy.zeros(n, dtype)
arr['sids'] = numpy.arange(n, dtype=numpy.uint32)
arr['lon'] = shakemap_array['lon']
arr['lat'] = shakemap_array['lat']
arr['depth'] = numpy.zeros(n)
arr['vs30'] = shakemap_array['vs30']
arr.flags.writeable = False
return self | [
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gem/oq-engine | openquake/hazardlib/site.py | SiteCollection.from_points | def from_points(cls, lons, lats, depths=None, sitemodel=None,
req_site_params=()):
"""
Build the site collection from
:param lons:
a sequence of longitudes
:param lats:
a sequence of latitudes
:param depths:
a sequence of depths (or None)
:param sitemodel:
None or an object containing site parameters as attributes
:param req_site_params:
a sequence of required site parameters, possibly empty
"""
assert len(lons) < U32LIMIT, len(lons)
if depths is None:
depths = numpy.zeros(len(lons))
assert len(lons) == len(lats) == len(depths), (len(lons), len(lats),
len(depths))
self = object.__new__(cls)
self.complete = self
req = ['sids', 'lon', 'lat', 'depth'] + sorted(
par for par in req_site_params if par not in ('lon', 'lat'))
if 'vs30' in req and 'vs30measured' not in req:
req.append('vs30measured')
self.dtype = numpy.dtype([(p, site_param_dt[p]) for p in req])
self.array = arr = numpy.zeros(len(lons), self.dtype)
arr['sids'] = numpy.arange(len(lons), dtype=numpy.uint32)
arr['lon'] = fix_lon(numpy.array(lons))
arr['lat'] = numpy.array(lats)
arr['depth'] = numpy.array(depths)
if sitemodel is None:
pass
elif hasattr(sitemodel, 'reference_vs30_value'):
# sitemodel is actually an OqParam instance
self._set('vs30', sitemodel.reference_vs30_value)
self._set('vs30measured',
sitemodel.reference_vs30_type == 'measured')
self._set('z1pt0', sitemodel.reference_depth_to_1pt0km_per_sec)
self._set('z2pt5', sitemodel.reference_depth_to_2pt5km_per_sec)
self._set('siteclass', sitemodel.reference_siteclass)
else:
for name in sitemodel.dtype.names:
if name not in ('lon', 'lat'):
self._set(name, sitemodel[name])
return self | python | def from_points(cls, lons, lats, depths=None, sitemodel=None,
req_site_params=()):
assert len(lons) < U32LIMIT, len(lons)
if depths is None:
depths = numpy.zeros(len(lons))
assert len(lons) == len(lats) == len(depths), (len(lons), len(lats),
len(depths))
self = object.__new__(cls)
self.complete = self
req = ['sids', 'lon', 'lat', 'depth'] + sorted(
par for par in req_site_params if par not in ('lon', 'lat'))
if 'vs30' in req and 'vs30measured' not in req:
req.append('vs30measured')
self.dtype = numpy.dtype([(p, site_param_dt[p]) for p in req])
self.array = arr = numpy.zeros(len(lons), self.dtype)
arr['sids'] = numpy.arange(len(lons), dtype=numpy.uint32)
arr['lon'] = fix_lon(numpy.array(lons))
arr['lat'] = numpy.array(lats)
arr['depth'] = numpy.array(depths)
if sitemodel is None:
pass
elif hasattr(sitemodel, 'reference_vs30_value'):
self._set('vs30', sitemodel.reference_vs30_value)
self._set('vs30measured',
sitemodel.reference_vs30_type == 'measured')
self._set('z1pt0', sitemodel.reference_depth_to_1pt0km_per_sec)
self._set('z2pt5', sitemodel.reference_depth_to_2pt5km_per_sec)
self._set('siteclass', sitemodel.reference_siteclass)
else:
for name in sitemodel.dtype.names:
if name not in ('lon', 'lat'):
self._set(name, sitemodel[name])
return self | [
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:param lats:
a sequence of latitudes
:param depths:
a sequence of depths (or None)
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None or an object containing site parameters as attributes
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gem/oq-engine | openquake/hazardlib/site.py | SiteCollection.filtered | def filtered(self, indices):
"""
:param indices:
a subset of indices in the range [0 .. tot_sites - 1]
:returns:
a filtered SiteCollection instance if `indices` is a proper subset
of the available indices, otherwise returns the full SiteCollection
"""
if indices is None or len(indices) == len(self):
return self
new = object.__new__(self.__class__)
indices = numpy.uint32(sorted(indices))
new.array = self.array[indices]
new.complete = self.complete
return new | python | def filtered(self, indices):
if indices is None or len(indices) == len(self):
return self
new = object.__new__(self.__class__)
indices = numpy.uint32(sorted(indices))
new.array = self.array[indices]
new.complete = self.complete
return new | [
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gem/oq-engine | openquake/hazardlib/site.py | SiteCollection.make_complete | def make_complete(self):
"""
Turns the site collection into a complete one, if needed
"""
# reset the site indices from 0 to N-1 and set self.complete to self
self.array['sids'] = numpy.arange(len(self), dtype=numpy.uint32)
self.complete = self | python | def make_complete(self):
self.array['sids'] = numpy.arange(len(self), dtype=numpy.uint32)
self.complete = self | [
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gem/oq-engine | openquake/hazardlib/site.py | SiteCollection.split_in_tiles | def split_in_tiles(self, hint):
"""
Split a SiteCollection into a set of tiles (SiteCollection instances).
:param hint: hint for how many tiles to generate
"""
tiles = []
for seq in split_in_blocks(range(len(self)), hint or 1):
sc = SiteCollection.__new__(SiteCollection)
sc.array = self.array[numpy.array(seq, int)]
tiles.append(sc)
return tiles | python | def split_in_tiles(self, hint):
tiles = []
for seq in split_in_blocks(range(len(self)), hint or 1):
sc = SiteCollection.__new__(SiteCollection)
sc.array = self.array[numpy.array(seq, int)]
tiles.append(sc)
return tiles | [
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gem/oq-engine | openquake/hazardlib/site.py | SiteCollection.split | def split(self, location, distance):
"""
:returns: (close_sites, far_sites)
"""
if distance is None: # all close
return self, None
close = location.distance_to_mesh(self) < distance
return self.filter(close), self.filter(~close) | python | def split(self, location, distance):
if distance is None:
return self, None
close = location.distance_to_mesh(self) < distance
return self.filter(close), self.filter(~close) | [
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gem/oq-engine | openquake/hazardlib/site.py | SiteCollection.filter | def filter(self, mask):
"""
Create a SiteCollection with only a subset of sites.
:param mask:
Numpy array of boolean values of the same length as the site
collection. ``True`` values should indicate that site with that
index should be included into the filtered collection.
:returns:
A new :class:`SiteCollection` instance, unless all the
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is returned, or if all the values in ``mask`` are ``False``,
in which case method returns ``None``. New collection has data
of only those sites that were marked for inclusion in the mask.
"""
assert len(mask) == len(self), (len(mask), len(self))
if mask.all():
# all sites satisfy the filter, return
# this collection unchanged
return self
if not mask.any():
# no sites pass the filter, return None
return None
# extract indices of Trues from the mask
indices, = mask.nonzero()
return self.filtered(indices) | python | def filter(self, mask):
assert len(mask) == len(self), (len(mask), len(self))
if mask.all():
return self
if not mask.any():
return None
indices, = mask.nonzero()
return self.filtered(indices) | [
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Numpy array of boolean values of the same length as the site
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index should be included into the filtered collection.
:returns:
A new :class:`SiteCollection` instance, unless all the
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gem/oq-engine | openquake/hazardlib/site.py | SiteCollection.within | def within(self, region):
"""
:param region: a shapely polygon
:returns: a filtered SiteCollection of sites within the region
"""
mask = numpy.array([
geometry.Point(rec['lon'], rec['lat']).within(region)
for rec in self.array])
return self.filter(mask) | python | def within(self, region):
mask = numpy.array([
geometry.Point(rec['lon'], rec['lat']).within(region)
for rec in self.array])
return self.filter(mask) | [
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gem/oq-engine | openquake/hazardlib/site.py | SiteCollection.within_bbox | def within_bbox(self, bbox):
"""
:param bbox:
a quartet (min_lon, min_lat, max_lon, max_lat)
:returns:
site IDs within the bounding box
"""
min_lon, min_lat, max_lon, max_lat = bbox
lons, lats = self.array['lon'], self.array['lat']
if cross_idl(lons.min(), lons.max()) or cross_idl(min_lon, max_lon):
lons = lons % 360
min_lon, max_lon = min_lon % 360, max_lon % 360
mask = (min_lon < lons) * (lons < max_lon) * \
(min_lat < lats) * (lats < max_lat)
return mask.nonzero()[0] | python | def within_bbox(self, bbox):
min_lon, min_lat, max_lon, max_lat = bbox
lons, lats = self.array['lon'], self.array['lat']
if cross_idl(lons.min(), lons.max()) or cross_idl(min_lon, max_lon):
lons = lons % 360
min_lon, max_lon = min_lon % 360, max_lon % 360
mask = (min_lon < lons) * (lons < max_lon) * \
(min_lat < lats) * (lats < max_lat)
return mask.nonzero()[0] | [
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gem/oq-engine | openquake/hazardlib/geo/point.py | Point.point_at | def point_at(self, horizontal_distance, vertical_increment, azimuth):
"""
Compute the point with given horizontal, vertical distances
and azimuth from this point.
:param horizontal_distance:
Horizontal distance, in km.
:type horizontal_distance:
float
:param vertical_increment:
Vertical increment, in km. When positive, the new point
has a greater depth. When negative, the new point
has a smaller depth.
:type vertical_increment:
float
:type azimuth:
Azimuth, in decimal degrees.
:type azimuth:
float
:returns:
The point at the given distances.
:rtype:
Instance of :class:`Point`
"""
lon, lat = geodetic.point_at(self.longitude, self.latitude,
azimuth, horizontal_distance)
return Point(lon, lat, self.depth + vertical_increment) | python | def point_at(self, horizontal_distance, vertical_increment, azimuth):
lon, lat = geodetic.point_at(self.longitude, self.latitude,
azimuth, horizontal_distance)
return Point(lon, lat, self.depth + vertical_increment) | [
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gem/oq-engine | openquake/hazardlib/geo/point.py | Point.azimuth | def azimuth(self, point):
"""
Compute the azimuth (in decimal degrees) between this point
and the given point.
:param point:
Destination point.
:type point:
Instance of :class:`Point`
:returns:
The azimuth, value in a range ``[0, 360)``.
:rtype:
float
"""
return geodetic.azimuth(self.longitude, self.latitude,
point.longitude, point.latitude) | python | def azimuth(self, point):
return geodetic.azimuth(self.longitude, self.latitude,
point.longitude, point.latitude) | [
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gem/oq-engine | openquake/hazardlib/geo/point.py | Point.distance | def distance(self, point):
"""
Compute the distance (in km) between this point and the given point.
Distance is calculated using pythagoras theorem, where the
hypotenuse is the distance and the other two sides are the
horizontal distance (great circle distance) and vertical
distance (depth difference between the two locations).
:param point:
Destination point.
:type point:
Instance of :class:`Point`
:returns:
The distance.
:rtype:
float
"""
return geodetic.distance(self.longitude, self.latitude, self.depth,
point.longitude, point.latitude, point.depth) | python | def distance(self, point):
return geodetic.distance(self.longitude, self.latitude, self.depth,
point.longitude, point.latitude, point.depth) | [
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gem/oq-engine | openquake/hazardlib/geo/point.py | Point.distance_to_mesh | def distance_to_mesh(self, mesh, with_depths=True):
"""
Compute distance (in km) between this point and each point of ``mesh``.
:param mesh:
:class:`~openquake.hazardlib.geo.mesh.Mesh` of points to calculate
distance to.
:param with_depths:
If ``True`` (by default), distance is calculated between actual
point and the mesh, geodetic distance of projections is combined
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to ``False``, only geodetic distance between projections
is calculated.
:returns:
Numpy array of floats of the same shape as ``mesh`` with distance
values in km in respective indices.
"""
if with_depths:
if mesh.depths is None:
mesh_depths = numpy.zeros_like(mesh.lons)
else:
mesh_depths = mesh.depths
return geodetic.distance(self.longitude, self.latitude, self.depth,
mesh.lons, mesh.lats, mesh_depths)
else:
return geodetic.geodetic_distance(self.longitude, self.latitude,
mesh.lons, mesh.lats) | python | def distance_to_mesh(self, mesh, with_depths=True):
if with_depths:
if mesh.depths is None:
mesh_depths = numpy.zeros_like(mesh.lons)
else:
mesh_depths = mesh.depths
return geodetic.distance(self.longitude, self.latitude, self.depth,
mesh.lons, mesh.lats, mesh_depths)
else:
return geodetic.geodetic_distance(self.longitude, self.latitude,
mesh.lons, mesh.lats) | [
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gem/oq-engine | openquake/hazardlib/geo/point.py | Point.equally_spaced_points | def equally_spaced_points(self, point, distance):
"""
Compute the set of points equally spaced between this point
and the given point.
:param point:
Destination point.
:type point:
Instance of :class:`Point`
:param distance:
Distance between points (in km).
:type distance:
float
:returns:
The list of equally spaced points.
:rtype:
list of :class:`Point` instances
"""
lons, lats, depths = geodetic.intervals_between(
self.longitude, self.latitude, self.depth,
point.longitude, point.latitude, point.depth,
distance)
return [Point(lons[i], lats[i], depths[i]) for i in range(len(lons))] | python | def equally_spaced_points(self, point, distance):
lons, lats, depths = geodetic.intervals_between(
self.longitude, self.latitude, self.depth,
point.longitude, point.latitude, point.depth,
distance)
return [Point(lons[i], lats[i], depths[i]) for i in range(len(lons))] | [
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gem/oq-engine | openquake/hazardlib/geo/point.py | Point.to_polygon | def to_polygon(self, radius):
"""
Create a circular polygon with specified radius centered in the point.
:param radius:
Required radius of a new polygon, in km.
:returns:
Instance of :class:`~openquake.hazardlib.geo.polygon.Polygon` that
approximates a circle around the point with specified radius.
"""
assert radius > 0
# avoid circular imports
from openquake.hazardlib.geo.polygon import Polygon
# get a projection that is centered in the point
proj = geo_utils.OrthographicProjection(
self.longitude, self.longitude, self.latitude, self.latitude)
# create a shapely object from a projected point coordinates,
# which are supposedly (0, 0)
point = shapely.geometry.Point(*proj(self.longitude, self.latitude))
# extend the point to a shapely polygon using buffer()
# and create openquake.hazardlib.geo.polygon.Polygon object from it
return Polygon._from_2d(point.buffer(radius), proj) | python | def to_polygon(self, radius):
assert radius > 0
from openquake.hazardlib.geo.polygon import Polygon
proj = geo_utils.OrthographicProjection(
self.longitude, self.longitude, self.latitude, self.latitude)
point = shapely.geometry.Point(*proj(self.longitude, self.latitude))
return Polygon._from_2d(point.buffer(radius), proj) | [
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gem/oq-engine | openquake/hazardlib/geo/point.py | Point.closer_than | def closer_than(self, mesh, radius):
"""
Check for proximity of points in the ``mesh``.
:param mesh:
:class:`openquake.hazardlib.geo.mesh.Mesh` instance.
:param radius:
Proximity measure in km.
:returns:
Numpy array of boolean values in the same shape as the mesh
coordinate arrays with ``True`` on indexes of points that
are not further than ``radius`` km from this point. Function
:func:`~openquake.hazardlib.geo.geodetic.distance` is used to
calculate distances to points of the mesh. Points of the mesh that
lie exactly ``radius`` km away from this point also have
``True`` in their indices.
"""
dists = geodetic.distance(self.longitude, self.latitude, self.depth,
mesh.lons, mesh.lats,
0 if mesh.depths is None else mesh.depths)
return dists <= radius | python | def closer_than(self, mesh, radius):
dists = geodetic.distance(self.longitude, self.latitude, self.depth,
mesh.lons, mesh.lats,
0 if mesh.depths is None else mesh.depths)
return dists <= radius | [
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:param mesh:
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:param radius:
Proximity measure in km.
:returns:
Numpy array of boolean values in the same shape as the mesh
coordinate arrays with ``True`` on indexes of points that
are not further than ``radius`` km from this point. Function
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gem/oq-engine | openquake/commands/info.py | source_model_info | def source_model_info(nodes):
"""
Extract information about NRML/0.5 source models. Returns a table
with TRTs as rows and source classes as columns.
"""
c = collections.Counter()
for node in nodes:
for src_group in node:
trt = src_group['tectonicRegion']
for src in src_group:
src_class = src.tag.split('}')[1]
c[trt, src_class] += 1
trts, classes = zip(*c)
trts = sorted(set(trts))
classes = sorted(set(classes))
dtlist = [('TRT', (bytes, 30))] + [(name, int) for name in classes]
out = numpy.zeros(len(trts) + 1, dtlist) # +1 for the totals
for i, trt in enumerate(trts):
out[i]['TRT'] = trt
for src_class in classes:
out[i][src_class] = c[trt, src_class]
out[-1]['TRT'] = 'Total'
for name in out.dtype.names[1:]:
out[-1][name] = out[name][:-1].sum()
return rst_table(out) | python | def source_model_info(nodes):
c = collections.Counter()
for node in nodes:
for src_group in node:
trt = src_group['tectonicRegion']
for src in src_group:
src_class = src.tag.split('}')[1]
c[trt, src_class] += 1
trts, classes = zip(*c)
trts = sorted(set(trts))
classes = sorted(set(classes))
dtlist = [('TRT', (bytes, 30))] + [(name, int) for name in classes]
out = numpy.zeros(len(trts) + 1, dtlist)
for i, trt in enumerate(trts):
out[i]['TRT'] = trt
for src_class in classes:
out[i][src_class] = c[trt, src_class]
out[-1]['TRT'] = 'Total'
for name in out.dtype.names[1:]:
out[-1][name] = out[name][:-1].sum()
return rst_table(out) | [
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gem/oq-engine | openquake/commands/info.py | print_csm_info | def print_csm_info(fname):
"""
Parse the composite source model without instantiating the sources and
prints information about its composition and the full logic tree
"""
oqparam = readinput.get_oqparam(fname)
csm = readinput.get_composite_source_model(oqparam, in_memory=False)
print(csm.info)
print('See http://docs.openquake.org/oq-engine/stable/'
'effective-realizations.html for an explanation')
rlzs_assoc = csm.info.get_rlzs_assoc()
print(rlzs_assoc)
dupl = [(srcs[0]['id'], len(srcs)) for srcs in csm.check_dupl_sources()]
if dupl:
print(rst_table(dupl, ['source_id', 'multiplicity']))
tot, pairs = get_pickled_sizes(rlzs_assoc)
print(rst_table(pairs, ['attribute', 'nbytes'])) | python | def print_csm_info(fname):
oqparam = readinput.get_oqparam(fname)
csm = readinput.get_composite_source_model(oqparam, in_memory=False)
print(csm.info)
print('See http://docs.openquake.org/oq-engine/stable/'
'effective-realizations.html for an explanation')
rlzs_assoc = csm.info.get_rlzs_assoc()
print(rlzs_assoc)
dupl = [(srcs[0]['id'], len(srcs)) for srcs in csm.check_dupl_sources()]
if dupl:
print(rst_table(dupl, ['source_id', 'multiplicity']))
tot, pairs = get_pickled_sizes(rlzs_assoc)
print(rst_table(pairs, ['attribute', 'nbytes'])) | [
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gem/oq-engine | openquake/commands/info.py | do_build_reports | def do_build_reports(directory):
"""
Walk the directory and builds pre-calculation reports for all the
job.ini files found.
"""
for cwd, dirs, files in os.walk(directory):
for f in sorted(files):
if f in ('job.ini', 'job_h.ini', 'job_haz.ini', 'job_hazard.ini'):
job_ini = os.path.join(cwd, f)
logging.info(job_ini)
try:
reportwriter.build_report(job_ini, cwd)
except Exception as e:
logging.error(str(e)) | python | def do_build_reports(directory):
for cwd, dirs, files in os.walk(directory):
for f in sorted(files):
if f in ('job.ini', 'job_h.ini', 'job_haz.ini', 'job_hazard.ini'):
job_ini = os.path.join(cwd, f)
logging.info(job_ini)
try:
reportwriter.build_report(job_ini, cwd)
except Exception as e:
logging.error(str(e)) | [
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gem/oq-engine | openquake/commands/info.py | info | def info(calculators, gsims, views, exports, extracts, parameters,
report, input_file=''):
"""
Give information. You can pass the name of an available calculator,
a job.ini file, or a zip archive with the input files.
"""
if calculators:
for calc in sorted(base.calculators):
print(calc)
if gsims:
for gs in gsim.get_available_gsims():
print(gs)
if views:
for name in sorted(view):
print(name)
if exports:
dic = groupby(export, operator.itemgetter(0),
lambda group: [r[1] for r in group])
n = 0
for exporter, formats in dic.items():
print(exporter, formats)
n += len(formats)
print('There are %d exporters defined.' % n)
if extracts:
for key in extract:
func = extract[key]
if hasattr(func, '__wrapped__'):
fm = FunctionMaker(func.__wrapped__)
else:
fm = FunctionMaker(func)
print('%s(%s)%s' % (fm.name, fm.signature, fm.doc))
if parameters:
params = []
for val in vars(OqParam).values():
if hasattr(val, 'name'):
params.append(val)
params.sort(key=lambda x: x.name)
for param in params:
print(param.name)
if os.path.isdir(input_file) and report:
with Monitor('info', measuremem=True) as mon:
with mock.patch.object(logging.root, 'info'): # reduce logging
do_build_reports(input_file)
print(mon)
elif input_file.endswith('.xml'):
node = nrml.read(input_file)
if node[0].tag.endswith('sourceModel'):
if node['xmlns'].endswith('nrml/0.4'):
raise InvalidFile(
'%s is in NRML 0.4 format, please run the following '
'command:\noq upgrade_nrml %s' % (
input_file, os.path.dirname(input_file) or '.'))
print(source_model_info([node[0]]))
elif node[0].tag.endswith('logicTree'):
nodes = [nrml.read(sm_path)[0]
for sm_path in logictree.collect_info(input_file).smpaths]
print(source_model_info(nodes))
else:
print(node.to_str())
elif input_file.endswith(('.ini', '.zip')):
with Monitor('info', measuremem=True) as mon:
if report:
print('Generated', reportwriter.build_report(input_file))
else:
print_csm_info(input_file)
if mon.duration > 1:
print(mon)
elif input_file:
print("No info for '%s'" % input_file) | python | def info(calculators, gsims, views, exports, extracts, parameters,
report, input_file=''):
if calculators:
for calc in sorted(base.calculators):
print(calc)
if gsims:
for gs in gsim.get_available_gsims():
print(gs)
if views:
for name in sorted(view):
print(name)
if exports:
dic = groupby(export, operator.itemgetter(0),
lambda group: [r[1] for r in group])
n = 0
for exporter, formats in dic.items():
print(exporter, formats)
n += len(formats)
print('There are %d exporters defined.' % n)
if extracts:
for key in extract:
func = extract[key]
if hasattr(func, '__wrapped__'):
fm = FunctionMaker(func.__wrapped__)
else:
fm = FunctionMaker(func)
print('%s(%s)%s' % (fm.name, fm.signature, fm.doc))
if parameters:
params = []
for val in vars(OqParam).values():
if hasattr(val, 'name'):
params.append(val)
params.sort(key=lambda x: x.name)
for param in params:
print(param.name)
if os.path.isdir(input_file) and report:
with Monitor('info', measuremem=True) as mon:
with mock.patch.object(logging.root, 'info'):
do_build_reports(input_file)
print(mon)
elif input_file.endswith('.xml'):
node = nrml.read(input_file)
if node[0].tag.endswith('sourceModel'):
if node['xmlns'].endswith('nrml/0.4'):
raise InvalidFile(
'%s is in NRML 0.4 format, please run the following '
'command:\noq upgrade_nrml %s' % (
input_file, os.path.dirname(input_file) or '.'))
print(source_model_info([node[0]]))
elif node[0].tag.endswith('logicTree'):
nodes = [nrml.read(sm_path)[0]
for sm_path in logictree.collect_info(input_file).smpaths]
print(source_model_info(nodes))
else:
print(node.to_str())
elif input_file.endswith(('.ini', '.zip')):
with Monitor('info', measuremem=True) as mon:
if report:
print('Generated', reportwriter.build_report(input_file))
else:
print_csm_info(input_file)
if mon.duration > 1:
print(mon)
elif input_file:
print("No info for '%s'" % input_file) | [
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a job.ini file, or a zip archive with the input files. | [
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