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 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_gm_calculation_hard_rock(self):
""" Test mean and std calculation - on hard rock using AB06"""
# Modified gmpe
mgmpe = NRCan15SiteTermLinear(gmpe_name='AtkinsonBoore2006')
# Set parameters
sites = Dummy.get_site_collection(2, vs30=[760, 2010])
rup = Dummy.get_rupture(mag=7.0)
dists = DistancesContext()
dists.rrup = np.array([15., 15.])
stdt = [const.StdDev.TOTAL]
gmpe = AtkinsonBoore2006()
for imt in [PGA(), SA(1.0), SA(5.0)]:
#
# Computes results
mean, stds = mgmpe.get_mean_and_stddevs(sites, rup, dists, imt,
stdt)
# Compute the expected results
mean_expected, stds_expected = gmpe.get_mean_and_stddevs(
sites, rup, dists, imt, stdt)
# Test that for reference soil conditions the modified GMPE gives
# the same results of the original gmpe
np.testing.assert_allclose(np.exp(mean),
np.exp(mean_expected),
rtol=1.0e-1)
np.testing.assert_allclose(stds, stds_expected)
def test_gm_calculation_soil_reference(self):
""" Test mean and std calculation - CY14 on reference rock"""
# Modified gmpe
mgmpe = CY14SiteTerm(gmpe_name='ChiouYoungs2014')
# Set parameters
sites = Dummy.get_site_collection(4,
vs30=1130.,
vs30measured=True,
z1pt0=0.)
rup = Dummy.get_rupture(mag=6.0)
rup.dip = 90.
rup.ztor = 0.
dists = DistancesContext()
dists.rrup = np.array([1., 10., 30., 70.])
dists.rx = np.array([1., 10., 30., 70.])
dists.rjb = np.array([1., 10., 30., 70.])
imt = PGA()
stdt = [const.StdDev.TOTAL]
# Compute results
mean, stds = mgmpe.get_mean_and_stddevs(sites, rup, dists, imt, stdt)
# Compute the expected results
gmpe = ChiouYoungs2014()
mean_expected, stds_expected = gmpe.get_mean_and_stddevs(
sites, rup, dists, imt, stdt)
# Test that for reference soil conditions the modified GMPE gives the
# same results of the original gmpe
np.testing.assert_almost_equal(mean, mean_expected)
np.testing.assert_almost_equal(stds, stds_expected)
def test_gm_calculation_hard_rock(self):
""" Test mean and std calculation - on hard rock using AB06"""
# Modified gmpe
mgmpe = NRCan15SiteTermLinear(gmpe_name='AtkinsonBoore2006')
# Set parameters
sites = Dummy.get_site_collection(2, vs30=[760, 2010])
rup = Dummy.get_rupture(mag=7.0)
dists = DistancesContext()
dists.rrup = np.array([15., 15.])
stdt = [const.StdDev.TOTAL]
gmpe = AtkinsonBoore2006()
for imt in [PGA(), SA(1.0), SA(5.0)]:
#
# Computes results
mean, stds = mgmpe.get_mean_and_stddevs(sites, rup, dists, imt,
stdt)
# Compute the expected results
mean_expected, stds_expected = gmpe.get_mean_and_stddevs(sites,
rup, dists, imt, stdt)
# Test that for reference soil conditions the modified GMPE gives
# the same results of the original gmpe
np.testing.assert_allclose(np.exp(mean), np.exp(mean_expected),
rtol=1.0e-1)
np.testing.assert_allclose(stds, stds_expected)
def test01(self):
filename = os.path.join(DATA_PATH, 'kappa.txt')
gmm = AlAtikSigmaModel(gmpe_name='BindiEtAl2014Rjb',
kappa_file=filename,
kappa_val='high')
sites = Dummy.get_site_collection(4, vs30=760.)
rup = Dummy.get_rupture(mag=6.0)
dists = DistancesContext()
dists.rjb = np.array([1., 10., 30., 70.])
imt = SA(0.1)
stdt = [const.StdDev.TOTAL]
mean_expected, stds_expected = gmm.get_mean_and_stddevs(
sites, rup, dists, imt, stdt)
def setUp(self):
# Set parameters
self.sites = Dummy.get_site_collection(4, vs30=760.)
self.rup = Dummy.get_rupture(mag=6.0)
self.dists = DistancesContext()
self.dists.rrup = np.array([1., 10., 30., 70.])
self.dists.rjb = np.array([1., 10., 30., 70.])
self.imt = PGA()
def setUp(self):
# Set parameters
self.sites = Dummy.get_site_collection(
4, amplfactor=[-1.0, 1.5, 0.00, -1.99])
self.rup = Dummy.get_rupture(mag=6.0, hypo_depth=10)
self.dists = DistancesContext()
self.dists.rhypo = np.array([1., 10., 30., 70.])
self.dists.repi = np.array([1., 10., 30., 70.])
self.imt = MMI()
def test02(self):
avg_periods = [0.05, 0.15, 1.0, 2.0, 4.0]
gmm = gsim.mgmpe.generic_gmpe_avgsa.GenericGmpeAvgSA(
gmpe_name='AkkarEtAlRepi2014',
avg_periods=avg_periods,
corr_func='akkar')
msg = 'The class name is incorrect'
self.assertTrue(gmm.__class__.__name__ == 'GenericGmpeAvgSA', msg=msg)
sites = Dummy.get_site_collection(4, vs30=760.)
rup = Dummy.get_rupture(mag=6.0)
dists = DistancesContext()
dists.repi = np.array([1., 10., 30., 70.])
imtype = PGA()
stdt = [const.StdDev.TOTAL]
# Computes results
mean, _ = gmm.get_mean_and_stddevs(sites, rup, dists, imtype, stdt)
expected = np.array([-2.0383581, -2.6548699, -3.767237, -4.7775653])
np.testing.assert_almost_equal(mean, expected)
def test02(self):
# checks if gmpe is always being evaluated at vs30=760
# see HID 2.6.2
filename = os.path.join(DATA_PATH, 'kappa.txt')
gmm = AlAtikSigmaModel(gmpe_name='YenierAtkinson2015ACME2019',
kappa_file=filename,
kappa_val='high')
sites = Dummy.get_site_collection(4, vs30=760.)
rup = Dummy.get_rupture(mag=6.0)
dists = DistancesContext()
dists.rjb = np.array([1., 10., 30., 70.])
dists.rrup = np.array([1., 10., 30., 70.])
imt = SA(0.1)
stdt = [const.StdDev.TOTAL]
mean_760, _ = gmm.get_mean_and_stddevs(sites, rup, dists, imt, stdt)
sites2 = Dummy.get_site_collection(4, vs30=1500.)
mean_1500, _ = gmm.get_mean_and_stddevs(sites2, rup, dists, imt, stdt)
self.assertAlmostEqual(mean_760[-1], mean_1500[-1], 4)
def create_distances_context(self):
'''Creates, initializes and returns a distances context by setting the
default values of the attributes defined in `self.distances_context_attrs`.
The returned context is intended to be used in `self.get_contexts`.
:return: a :class:`openquake.hazardlib.contexts.DistancesContext`
'''
ctx = DistancesContext()
for _ in self.distances_context_attrs:
setattr(ctx, _, [])
return ctx
def test01(self):
avg_periods = [0.05, 0.15, 1.0, 2.0, 4.0]
gmm = gsim.mgmpe.generic_gmpe_avgsa.GenericGmpeAvgSA(
gmpe_name='ZhaoEtAl2006Asc',
avg_periods=avg_periods,
corr_func='akkar')
msg = 'The class name is incorrect'
self.assertTrue(gmm.__class__.__name__ == 'GenericGmpeAvgSA', msg=msg)
sites = Dummy.get_site_collection(4, vs30=760.)
rup = Dummy.get_rupture(mag=6.0)
rup.hypo_depth = 10.
dists = DistancesContext()
dists.rrup = np.array([1., 10., 30., 70.])
imtype = PGA()
stdt = [const.StdDev.TOTAL]
# Computes results
mean, _ = gmm.get_mean_and_stddevs(sites, rup, dists, imtype, stdt)
expected = np.array(
[-1.33735637, -2.62649473, -3.64500654, -4.60067093])
np.testing.assert_almost_equal(mean, expected)
def test_gm_calculationAB06_hard_bedrock(self):
""" Test mean and std calculation - AB06 on vs30 hard bedrock"""
# Modified gmpe
mgmpe = NRCan15SiteTerm(gmpe_name='AtkinsonBoore2006')
# Set parameters
sites = Dummy.get_site_collection(4, vs30=1999.0)
rup = Dummy.get_rupture(mag=6.0)
dists = DistancesContext()
dists.rrup = np.array([10., 20., 30., 40.])
imt = PGA()
stdt = [const.StdDev.TOTAL]
# Computes results
mean, stds = mgmpe.get_mean_and_stddevs(sites, rup, dists, imt, stdt)
# Compute the expected results
gmpe = AtkinsonBoore2006()
mean_expected, stds_expected = gmpe.get_mean_and_stddevs(
sites, rup, dists, imt, stdt)
# Test that for reference soil conditions the modified GMPE gives the
# same results of the original gmpe
np.testing.assert_almost_equal(mean, mean_expected)
np.testing.assert_almost_equal(stds, stds_expected)
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
sites = Dummy.get_site_collection(4, vs30=760.)
rup = Dummy.get_rupture(mag=6.0)
dists = DistancesContext()
dists.rrup = np.array([1., 10., 30., 70.])
imt = PGA()
stds_types = [const.StdDev.TOTAL, const.StdDev.INTER_EVENT,
const.StdDev.INTRA_EVENT]
# Compute results
mean, stds = gmm.get_mean_and_stddevs(sites, rup, dists, imt,
stds_types)
return stds, stds_types
def test_gm_calculationBA08_vs30variable(self):
""" Test mean and std calculation - BA08 - Vs30 variable"""
# Modified gmpe
mgmpe = NRCan15SiteTerm(gmpe_name='BooreAtkinson2008')
# Set parameters
sites = Dummy.get_site_collection(3, vs30=[400., 600, 1000])
rup = Dummy.get_rupture(mag=6.0)
dists = DistancesContext()
dists.rjb = np.array([10., 10., 10.])
imt = PGA()
stdt = [const.StdDev.TOTAL]
# Computes results
mean, stds = mgmpe.get_mean_and_stddevs(sites, rup, dists, imt, stdt)
# Compute the expected results
gmpe = BooreAtkinson2008()
mean_expected, stds_expected = gmpe.get_mean_and_stddevs(
sites, rup, dists, imt, stdt)
# Test that for reference soil conditions the modified GMPE gives the
# same results of the original gmpe
np.testing.assert_almost_equal(mean[:-1], mean_expected[:-1])
np.testing.assert_almost_equal(stds[:-1], stds_expected[:-1])
def setUp(self):
# Set parameters - Setting z1pt0 does not make sense but here we
# want to make sure that the modified gmm provided GM amplified
# by the site term exactly as the original model.
sites = Dummy.get_site_collection(4,
vs30=400.,
vs30measured=True,
z1pt0=0.)
rup = Dummy.get_rupture(mag=6.0)
rup.dip = 90.
rup.ztor = 0.
dists = DistancesContext()
dists.rrup = np.array([1., 10., 30., 70.])
dists.rx = np.array([1., 10., 30., 70.])
dists.rjb = np.array([1., 10., 30., 70.])
stdt = [const.StdDev.TOTAL]
self.rup = rup
self.dists = dists
self.stdt = stdt
self.sites = sites
def test_gm_calculationBA08_vs30variable(self):
""" Test mean and std calculation - BA08 - Vs30 variable"""
# Modified gmpe
mgmpe = NRCan15SiteTerm(gmpe_name='BooreAtkinson2008')
# Set parameters
sites = Dummy.get_site_collection(3, vs30=[400., 600, 1000])
rup = Dummy.get_rupture(mag=6.0)
dists = DistancesContext()
dists.rjb = np.array([10., 10., 10.])
imt = PGA()
stdt = [const.StdDev.TOTAL]
# Computes results
mean, stds = mgmpe.get_mean_and_stddevs(sites, rup, dists, imt, stdt)
# Compute the expected results
gmpe = BooreAtkinson2008()
mean_expected, stds_expected = gmpe.get_mean_and_stddevs(sites, rup,
dists, imt,
stdt)
# Test that for reference soil conditions the modified GMPE gives the
# same results of the original gmpe
np.testing.assert_almost_equal(mean[:-1], mean_expected[:-1])
np.testing.assert_almost_equal(stds[:-1], stds_expected[:-1])
def test_gm_calculationAB06_hard_bedrock(self):
""" Test mean and std calculation - AB06 on vs30 hard bedrock"""
# Modified gmpe
mgmpe = NRCan15SiteTerm(gmpe_name='AtkinsonBoore2006')
# Set parameters
sites = Dummy.get_site_collection(4, vs30=1999.0)
rup = Dummy.get_rupture(mag=6.0)
dists = DistancesContext()
dists.rrup = np.array([10., 20., 30., 40.])
imt = PGA()
stdt = [const.StdDev.TOTAL]
# Computes results
mean, stds = mgmpe.get_mean_and_stddevs(sites, rup, dists, imt, stdt)
# Compute the expected results
gmpe = AtkinsonBoore2006()
mean_expected, stds_expected = gmpe.get_mean_and_stddevs(sites, rup,
dists, imt,
stdt)
# Test that for reference soil conditions the modified GMPE gives the
# same results of the original gmpe
np.testing.assert_almost_equal(mean, mean_expected)
np.testing.assert_almost_equal(stds, stds_expected)
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
sites = Dummy.get_site_collection(4, vs30=760.)
rup = Dummy.get_rupture(mag=6.0)
dists = DistancesContext()
dists.rrup = np.array([1., 10., 30., 70.])
imt = PGA()
stds_types = [
const.StdDev.TOTAL, const.StdDev.INTER_EVENT,
const.StdDev.INTRA_EVENT
]
# Compute results
mean, stds = gmm.get_mean_and_stddevs(sites, rup, dists, imt,
stds_types)
return stds, stds_types
def extrapolate_in_PSA(self, sites, rup, dists, imt_high, set_imt,
stds_types, imt):
extrap_mean = []
t_log10 = np.log10([im for im in set_imt])
for d in np.arange(0, len(dists.rjb)):
dist = DistancesContext()
if hasattr(dists, 'rjb'):
dist.rjb = np.array([dists.rjb[d]])
if hasattr(dists, 'rrup'):
dist.rrup = np.array([dists.rrup[d]])
means_log10 = []
for im in set_imt:
mean_ln, _ = self.gmpe.get_mean_and_stddevs(
sites, rup, dist, SA(im), stds_types)
mean = np.exp(mean_ln[0])
means_log10.append(np.log10(mean))
mb = np.polyfit(t_log10, means_log10, 1)
mean_imt_log10 = mb[0] * np.log10(imt) + mb[1]
extrap_mean.append(np.log(10**mean_imt_log10))
return extrap_mean
def build_sites_distances_contexts(self,
event,
sites,
vs30measured=False,
backarc=False):
"""
Builds the contexts from the event and sites
"""
# Get distances
mesh = Mesh(sites["lon"], sites["lat"])
mshape = mesh.lons.shape
dctx = DistancesContext()
for param in self.context.REQUIRES_DISTANCES:
setattr(dctx, param, get_distances(event.get_rupture(), mesh,
param))
# Get sites context
sctx = SitesContext()
for key in self.site_attribs:
if key.startswith("z1pt0") and not "z1pt0" in sites:
setattr(sctx, "z1pt0",
vs30_to_z1pt0_cy14(sites["vs30"], japan=False))
elif key.startswith("z2pt5") and not "z5pt5" in sites:
setattr(sctx, "z2pt5",
vs30_to_z2pt5_cb14(sites["vs30"], japan=False))
elif key.startswith(
"vs30measured") and not "vs30measured" in sites:
if vs30measured:
setattr(sctx, "vs30measured", np.ones(mshape, dtype=bool))
else:
setattr(sctx, "vs30measured", np.zeros(mshape, dtype=bool))
elif key.startswith(
"vs30measured") and not "vs30measured" in sites:
if vs30measured:
setattr(sctx, "vs30measured", np.ones(mshape, dtype=bool))
else:
setattr(sctx, "vs30measured", np.zeros(mshape, dtype=bool))
elif key.startswith("backarc") and not "backarc" in sites:
if vs30measured:
setattr(sctx, "backarc", np.ones(mshape, dtype=bool))
else:
setattr(sctx, "backarc", np.zeros(mshape, dtype=bool))
else:
setattr(sctx, key, sites[key])
return sctx, dctx
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
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
