def test_calculation_addition_args(self):
avg_periods = [0.05, 0.15, 1.0, 2.0, 4.0]
gmm = GenericGmpeAvgSA(gmpe_name="KothaEtAl2020ESHM20",
avg_periods=avg_periods,
corr_func="akkar",
sigma_mu_epsilon=1.0)
rctx = RuptureContext()
rctx.mag = 6.
rctx.hypo_depth = 15.
dctx = DistancesContext()
dctx.rjb = np.array([1., 10., 30., 70.])
sctx = SitesContext()
sctx.vs30 = 500.0 * np.ones(4)
sctx.vs30measured = np.ones(4, dtype="bool")
sctx.region = np.zeros(4, dtype=int)
stdt = [const.StdDev.TOTAL]
expected_mean = np.array(
[-1.72305707, -2.2178751, -3.20100306, -4.19948242])
expected_stddev = np.array(
[0.5532021, 0.5532021, 0.5532021, 0.5532021])
imtype = imt.AvgSA()
mean, [stddev] = gmm.get_mean_and_stddevs(sctx, rctx, dctx, imtype,
stdt)
np.testing.assert_almost_equal(mean, expected_mean)
np.testing.assert_almost_equal(stddev, expected_stddev)
def test_param(self):
ctxs = [RuptureContext([('occurrence_rate', .001)]),
RuptureContext([('occurrence_rate', .002)])]
for poe in (.1, .5, .9):
c1, pnes1 = compose(ctxs, poe)
c2, pnes2 = compose(_collapse(ctxs), poe)
aac(c1, c2) # the same
def test_calculation_addition_args(self):
avg_periods = [0.05, 0.15, 1.0, 2.0, 4.0]
gmm = GenericGmpeAvgSA(gmpe_name="KothaEtAl2019SERA",
avg_periods=avg_periods,
corr_func="akkar",
sigma_mu_epsilon=1.0)
rctx = RuptureContext()
rctx.mag = 6.
rctx.hypo_depth = 15.
dctx = DistancesContext()
dctx.rjb = np.array([1., 10., 30., 70.])
sctx = SitesContext()
sctx.vs30 = 500.0 * np.ones(4)
sctx.vs30measured = np.ones(4, dtype="bool")
stdt = [const.StdDev.TOTAL]
expected_mean = np.array(
[-1.45586338, -1.94419233, -2.91884965, -3.91919928])
expected_stddev = np.array(
[0.58317566, 0.58317566, 0.58317566, 0.58317566])
imtype = imt.AvgSA()
mean, [stddev] = gmm.get_mean_and_stddevs(sctx, rctx, dctx, imtype,
stdt)
np.testing.assert_almost_equal(mean, expected_mean)
np.testing.assert_almost_equal(stddev, expected_stddev)
def ctx(self, nsites, vs30):
sites = Dummy.get_site_collection(nsites, vs30=vs30)
rup = Dummy.get_rupture(mag=6.0)
ctx = RuptureContext()
ctx.sid = np.arange(nsites)
vars(ctx).update(vars(rup))
for name in sites.array.dtype.names:
setattr(ctx, name, sites[name])
return ctx
def test_mixed(self):
ctxs = [RuptureContext([('occurrence_rate', .001)]),
RuptureContext([('occurrence_rate', .002)]),
RuptureContext([('occurrence_rate', numpy.nan),
('probs_occur', [.999, .001])]),
RuptureContext([('occurrence_rate', numpy.nan),
('probs_occur', [.998, .002])])]
for poe in (.1, .5, .9):
c1, pnes1 = compose(ctxs, poe)
c2, pnes2 = compose(_collapse(ctxs), poe)
aac(c1, c2) # the same
def _prepare_ctxs(rupdata, cmaker, sitecol):
ctxs = []
for u in range(len(rupdata['mag'])):
ctx = RuptureContext()
for par in rupdata:
if not par.endswith('_'):
setattr(ctx, par, rupdata[par][u])
else: # site-dependent parameter
setattr(ctx, par[:-1], rupdata[par][u])
for par in cmaker.REQUIRES_SITES_PARAMETERS:
setattr(ctx, par, sitecol[par])
ctx.sids = sitecol.sids
ctxs.append(ctx)
return ctxs
def test_calculation_Baker_Jayaram(self):
DATA_FILE = data / 'GENERIC_GMPE_AVGSA_MEAN_STD_TOTAL_BAKER_JAYARAM.csv'
# Initialise meta-GMPE
mgmpe = gsim.mgmpe.generic_gmpe_avgsa.GenericGmpeAvgSA(
gmpe_name='BooreAtkinson2008',
avg_periods=[0.05, 0.15, 1.0, 2.0, 4.0],
corr_func='baker_jayaram')
ctx = RuptureContext()
ctx.sids = [0]
P = imt.AvgSA
S = [const.StdDev.TOTAL]
with open(DATA_FILE, 'r') as f:
# Skip header
for i in [1, 2, 3]:
f.readline()
for line in f:
arr = np.float_(line.strip().split(','))
# Setting ground motion attributes
ctx.mag = arr[0]
ctx.rjb = np.array([arr[1]])
ctx.rake = arr[2]
ctx.hypo_depth = arr[3]
ctx.vs30 = np.array([arr[4]])
# Compute ground motion
mean, stdv = mgmpe.get_mean_and_stddevs(ctx, ctx, ctx, P, S)
np.testing.assert_almost_equal(mean, arr[6])
np.testing.assert_almost_equal(stdv, arr[7])
def create_context(self, evt_id, imts=None):
"""Create a new Context `dict`. Objects of this type will be yielded
by `get_context`.
:param evt_id: the earthquake id (e.g. int, or str)
:param imts: a list of strings denoting the IMTs to be included in the
context. If missing or None, the returned dict **will NOT** have
the keys "Observations" and "Num. Sites"
:return: the dict with keys:
```
{
'EventID': evt_id,
'Ctx: a new :class:`openquake.hazardlib.contexts.RuptureContext`
'Observations": dict[str, list] # (each imt in imts mapped to `[]`)
'Num. Sites': 0
}
```
NOTE: Remember 'Observations' and 'Num. Sites' are missing if `imts`
is missing, None or an emtpy sequence.
"""
dic = {'EventID': evt_id, 'Ctx': RuptureContext()}
if imts is not None and len(imts):
dic["Observations"] = OrderedDict([(imt, []) for imt in imts])
dic["Num. Sites"] = 0
return dic
def stuff_context(sites, rup, dists):
"""
Function to fill a rupture context with the contents of all of the
other contexts.
Args:
sites (SiteCollection): A SiteCollection object.
rup (RuptureContext): A RuptureContext object.
dists (DistanceContext): A DistanceContext object.
Returns:
RuptureContext: A new RuptureContext whose attributes are all of
the elements of the three inputs.
"""
ctx = RuptureContext()
for name in [name for name in vars(sites) if not name.startswith("__")]:
setattr(ctx, name, getattr(sites, name))
for name in [name for name in vars(rup) if not name.startswith("__")]:
setattr(ctx, name, getattr(rup, name))
for name in [name for name in vars(dists) if not name.startswith("__")]:
setattr(ctx, name, getattr(dists, name))
return ctx
def setUp(self):
fname = gettemp(ampl_func)
df = read_csv(fname, {
'ampcode': ampcode_dt,
None: numpy.float64
},
index='ampcode')
self.df = AmplFunction(df)
# Set GMMs
gmmA = BooreAtkinson2008()
gmmB = BooreEtAl2014()
# Set parameters
dsts = [10., 15., 20., 30., 40.]
dsts = [10.]
imts = [PGA(), SA(1.0)]
sites = Dummy.get_site_collection(len(dsts), vs30=760.0)
self.mag = 5.5
rup = Dummy.get_rupture(mag=self.mag)
ctx = RuptureContext.full(rup, sites)
ctx.rjb = numpy.array(dsts)
ctx.rrup = numpy.array(dsts)
self.rrup = ctx.rrup
# Compute GM on rock
self.meastd = gmmA.get_mean_std([ctx], imts) # shape (2, N=1, M=2)
def setUp(self):
self.ctx = ctx = RuptureContext()
ctx.mag = 6.
ctx.rake = 0.
ctx.hypo_depth = 10.
sites = Dummy.get_site_collection(4, vs30=760.)
for name in sites.array.dtype.names:
setattr(ctx, name, sites[name])
ctx.rrup = np.array([1., 10., 30., 70.])
ctx.rjb = np.array([1., 10., 30., 70.])
self.imt = PGA()
def create_rupture_context(self, evt_id):
'''Creates, initializes and returns a rupture context by setting the
default values of the attributes defined in `self.rupture_context_attrs`.
The returned context is intended to be used in `self.get_contexts`.
:return: a :class:`openquake.hazardlib.contexts.RuptureContext`
'''
ctx = RuptureContext()
for _ in self.rupture_context_attrs:
setattr(ctx, _, np.nan)
return ctx
def setUp(self):
self.ctx = ctx = RuptureContext()
ctx.mag = 6.0
ctx.hypo_depth = 10.
sites = Dummy.get_site_collection(4,
amplfactor=[-1.0, 1.5, 0.00, -1.99])
for name in sites.array.dtype.names:
setattr(ctx, name, sites[name])
ctx.rhypo = np.array([1., 10., 30., 70.])
ctx.repi = np.array([1., 10., 30., 70.])
self.imt = MMI()
def test_make_pmap(self):
trunclevel = 3
imtls = DictArray({'PGA': [0.01]})
gsims = [valid.gsim('AkkarBommer2010')]
ctxs = []
for occ_rate in (.001, .002):
ctx = RuptureContext()
ctx.mag = 5.5
ctx.rake = 90
ctx.occurrence_rate = occ_rate
ctx.sids = numpy.array([0.])
ctx.vs30 = numpy.array([760.])
ctx.rrup = numpy.array([100.])
ctx.rjb = numpy.array([99.])
ctxs.append(ctx)
pmap = make_pmap(ctxs, gsims, imtls, trunclevel, 50.)
numpy.testing.assert_almost_equal(pmap[0].array, 0.066381)
def pt_src_are(self, pt_src, gsim, weight, lnSA, monitor):
"""
Returns the vector-valued Annual Rate of Exceedance for one single point-source
:param pt_src: single instance of class "openquake.hazardlib.source.area.PointSource"
:param gsim: tuple, containing (only one?) instance of Openquake GSIM class
:param: weight, weight to be multiplied to ARE estimate
:param lnSA: list, natural logarithm of acceleration values for each spectral period.
Note : Values should be ordered in the same order than self.periods
"""
annual_rate = 0
# Loop over ruptures:
# i.e. one rupture for each combination of (mag, nodal plane, hypocentral depth):
for r in pt_src.iter_ruptures():
# NOTE: IF ACCOUNTING FOR "pointsource_distance" IN THE INI FILE, ONE SHOULD USE THE
# "point_ruptures()" METHOD BELOW:
# Loop over ruptures, one rupture for each magnitude ( neglect floating and combination on
# nodal plane and hypocentral depth):
## for r in pt_src.point_ruptures():
# Note: Seismicity rate evenly distributed over all point sources
# Seismicity rate also accounts for FMD (i.e. decreasing for
# increasing magnitude value)
# Filter the site collection with respect to the rupture and prepare context objects:
context_maker = ContextMaker(r.tectonic_region_type, gsim)
site_ctx, dist_ctx = context_maker.make_contexts(self.sites, r)
rup_ctx = RuptureContext()
rup_ctx.mag = r.mag
rup_ctx.rake = r.rake
assert len(gsim)==1
annual_rate += r.occurrence_rate * weight * self.gm_poe(gsim[0],
dist_ctx,
rup_ctx,
site_ctx,
lnSA)
return annual_rate
def test_get_pmap(self):
truncation_level = 3
imtls = DictArray({'PGA': [0.01]})
gsims = [valid.gsim('AkkarBommer2010')]
ctxs = []
for occ_rate in (.001, .002):
ctx = RuptureContext()
ctx.mag = 5.5
ctx.rake = 90
ctx.occurrence_rate = occ_rate
ctx.sids = numpy.array([0.])
ctx.vs30 = numpy.array([760.])
ctx.rrup = numpy.array([100.])
ctx.rjb = numpy.array([99.])
ctxs.append(ctx)
cmaker = ContextMaker(
'TRT', gsims, dict(imtls=imtls, truncation_level=truncation_level))
cmaker.tom = PoissonTOM(time_span=50)
pmap = cmaker.get_pmap(ctxs)
numpy.testing.assert_almost_equal(pmap[0].array, 0.066381)
def _get_stds(self, within_absolute=None, between_absolute=None):
if within_absolute is not None:
gmm = SplitSigmaGMPE(gmpe_name='Campbell2003',
within_absolute=within_absolute)
elif between_absolute is not None:
gmm = SplitSigmaGMPE(gmpe_name='Campbell2003',
between_absolute=between_absolute)
else:
raise ValueError('Unknown option')
# Set parameters
ctx = RuptureContext()
ctx.mag = 6.0
ctx.sids = [0, 1, 2, 3]
ctx.vs30 = [760.] * 4
ctx.rrup = np.array([1., 10., 30., 70.])
ctx.occurrence_rate = .0001
imt = PGA()
stds_types = [const.StdDev.TOTAL, const.StdDev.INTER_EVENT,
const.StdDev.INTRA_EVENT]
# Compute results
mean, stds = gmm.get_mean_and_stddevs(ctx, ctx, ctx, imt, stds_types)
return stds, stds_types
def _parse_csv_line(headers, values, req_site_params):
"""
Parse a single line from data file.
:param headers:
A list of header names, the strings from the first line of csv file.
:param values:
A list of values of a single row to parse.
:returns:
A tuple of the following values (in specified order):
sctx
An instance of :class:`openquake.hazardlib.gsim.base.SitesContext`
with attributes populated by the information from in row in a form
of single-element numpy arrays.
rctx
An instance of
:class:`openquake.hazardlib.gsim.base.RuptureContext`.
dctx
An instance of
:class:`openquake.hazardlib.gsim.base.DistancesContext`.
stddev_types
An empty list, if the ``result_type`` column says "MEAN"
for that row, otherwise it is a list with one item --
a requested standard deviation type.
expected_results
A dictionary mapping IMT-objects to one-element arrays of expected
result values. Those results represent either standard deviation
or mean value of corresponding IMT depending on ``result_type``.
result_type
A string literal, one of ``'STDDEV'`` or ``'MEAN'``. Value
is taken from column ``result_type``.
"""
rctx = RuptureContext()
sctx = SitesContext(slots=req_site_params)
dctx = DistancesContext()
expected_results = {}
stddev_types = result_type = damping = None
for param, value in zip(headers, values):
if param == 'result_type':
value = value.upper()
if value.endswith('_STDDEV'):
# the row defines expected stddev results
result_type = 'STDDEV'
stddev_types = [getattr(const.StdDev, value[:-len('_STDDEV')])]
else:
# the row defines expected exponents of mean values
assert value == 'MEAN'
stddev_types = []
result_type = 'MEAN'
elif param == 'damping':
damping = float(value)
elif param.startswith('site_'):
# value is sites context object attribute
if param == 'site_vs30measured' or param == 'site_backarc':
value = float(value) != 0
elif param in ('site_siteclass', 'site_ec8', 'site_ec8_p18',
'site_geology'):
value = numpy.string_(value)
else:
value = float(value)
# site_lons, site_lats, site_depths -> lon, lat, depth
if param.endswith(('lons', 'lats', 'depths')):
attr = param[len('site_'):-1]
else: # vs30s etc
attr = param[len('site_'):]
setattr(sctx, attr, numpy.array([value]))
elif param.startswith('dist_'):
# value is a distance measure
value = float(value)
setattr(dctx, param[len('dist_'):], numpy.array([value]))
elif param.startswith('rup_'):
# value is a rupture context attribute
try:
value = float(value)
except ValueError:
if value != 'undefined':
raise
setattr(rctx, param[len('rup_'):], value)
elif param == 'component_type':
pass
else:
# value is the expected result (of result_type type)
value = float(value)
if param == 'arias': # ugly legacy corner case
param = 'ia'
if param == 'avgsa':
imt = from_string('AvgSA')
else:
try: # The title of the column should be IMT(args)
imt = from_string(param.upper())
except KeyError: # Then it is just a period for SA
imt = registry['SA'](float(param), damping)
expected_results[imt] = numpy.array([value])
assert result_type is not None
return sctx, rctx, dctx, stddev_types, expected_results, result_type
def compute_disagg(dstore, idxs, cmaker, iml3, trti, bin_edges, oq, monitor):
# see https://bugs.launchpad.net/oq-engine/+bug/1279247 for an explanation
# of the algorithm used
"""
:param dstore
a DataStore instance
:param idxs:
an array of indices to ruptures
:param cmaker:
a :class:`openquake.hazardlib.gsim.base.ContextMaker` instance
:param iml3:
an ArrayWrapper of shape (N, P, Z) with an attribute imt
:param trti:
tectonic region type index
:param bin_egdes:
a quintet (mag_edges, dist_edges, lon_edges, lat_edges, eps_edges)
:param monitor:
monitor of the currently running job
:returns:
a dictionary sid -> 8D-array
"""
with monitor('reading rupdata', measuremem=True):
dstore.open('r')
sitecol = dstore['sitecol']
rupdata = {k: dstore['rup/' + k][idxs] for k in dstore['rup']}
RuptureContext.temporal_occurrence_model = PoissonTOM(
oq.investigation_time)
pne_mon = monitor('disaggregate_pne', measuremem=False)
mat_mon = monitor('build_disagg_matrix', measuremem=True)
gmf_mon = monitor('disagg mean_std', measuremem=False)
for sid, iml2 in zip(sitecol.sids, iml3):
singlesite = sitecol.filtered([sid])
bins = disagg.get_bins(bin_edges, sid)
gsim_by_z = {}
for z in range(iml3.shape[-1]):
try:
gsim = cmaker.gsim_by_rlzi[iml3.rlzs[sid, z]]
except KeyError:
pass
else:
gsim_by_z[z] = gsim
ctxs = []
ok, = numpy.where(
rupdata['rrup_'][:, sid] <= cmaker.maximum_distance(cmaker.trt))
for ridx in ok: # consider only the ruptures close to the site
ctx = RuptureContext((par, rupdata[par][ridx])
for par in rupdata if not par.endswith('_'))
for par in rupdata:
if par.endswith('_'):
setattr(ctx, par[:-1], rupdata[par][ridx, [sid]])
ctxs.append(ctx)
if not ctxs:
continue
eps3 = disagg._eps3(cmaker.trunclevel, oq.num_epsilon_bins)
matrix = numpy.zeros([len(b) - 1 for b in bins] + list(iml2.shape))
for z, gsim in gsim_by_z.items():
with gmf_mon:
ms = disagg.get_mean_stdv(singlesite, ctxs, iml3.imt, gsim)
bdata = disagg.disaggregate(
ms, ctxs, iml3.imt, iml2[:, z], eps3, pne_mon)
if bdata.pnes.sum():
with mat_mon:
matrix[..., z] = disagg.build_disagg_matrix(bdata, bins)
if matrix.any():
yield {'trti': trti, 'imti': iml3.imti, sid: matrix}
def _parse_csv_line(headers, values):
"""
Parse a single line from data file.
:param headers:
A list of header names, the strings from the first line of csv file.
:param values:
A list of values of a single row to parse.
:returns:
A tuple of the following values (in specified order):
sctx
An instance of :class:`openquake.hazardlib.gsim.base.SitesContext`
with attributes populated by the information from in row in a form
of single-element numpy arrays.
rctx
An instance of
:class:`openquake.hazardlib.gsim.base.RuptureContext`.
dctx
An instance of
:class:`openquake.hazardlib.gsim.base.DistancesContext`.
stddev_types
An empty list, if the ``result_type`` column says "MEAN"
for that row, otherwise it is a list with one item --
a requested standard deviation type.
expected_results
A dictionary mapping IMT-objects to one-element arrays of expected
result values. Those results represent either standard deviation
or mean value of corresponding IMT depending on ``result_type``.
result_type
A string literal, one of ``'STDDEV'`` or ``'MEAN'``. Value
is taken from column ``result_type``.
"""
rctx = RuptureContext()
sctx = SitesContext()
dctx = DistancesContext()
expected_results = {}
stddev_types = result_type = damping = None
for param, value in zip(headers, values):
if param == 'result_type':
value = value.upper()
if value.endswith('_STDDEV'):
# the row defines expected stddev results
result_type = 'STDDEV'
stddev_types = [getattr(const.StdDev, value[:-len('_STDDEV')])]
else:
# the row defines expected exponents of mean values
assert value == 'MEAN'
stddev_types = []
result_type = 'MEAN'
elif param == 'damping':
damping = float(value)
elif param.startswith('site_'):
# value is sites context object attribute
if (param == 'site_vs30measured') or (param == 'site_backarc'):
value = float(value) != 0
else:
value = float(value)
setattr(sctx, param[len('site_'):], numpy.array([value]))
elif param.startswith('dist_'):
# value is a distance measure
value = float(value)
setattr(dctx, param[len('dist_'):], numpy.array([value]))
elif param.startswith('rup_'):
# value is a rupture context attribute
value = float(value)
setattr(rctx, param[len('rup_'):], value)
elif param == 'component_type':
pass
else:
# value is the expected result (of result_type type)
value = float(value)
if param == 'pga':
imt = PGA()
elif param == 'pgv':
imt = PGV()
elif param == 'pgd':
imt = PGD()
elif param == 'cav':
imt = CAV()
elif param == 'mmi':
imt = MMI()
elif param == "arias":
imt = IA()
elif param == "rsd595":
imt = RSD595()
elif param == "rsd575":
imt = RSD575()
elif param == "rsd2080":
imt = RSD2080()
else:
period = float(param)
assert damping is not None
imt = SA(period, damping)
expected_results[imt] = numpy.array([value])
assert result_type is not None
return sctx, rctx, dctx, stddev_types, expected_results, result_type