def test_newmarkhall1982():
# Inputs
PGVin = np.log(10)
PSA10in = np.log(0.1)
sd = 0.6
# NewmarkHall1982
PGVout, PGVsdout = NewmarkHall1982.psa102pgv(PSA10in, sd)
mfact = NewmarkHall1982.getConversionFactor()
lnsig = NewmarkHall1982.getLnSigma()
assert abs(PGVout - np.log(9.46658)) < 0.001
assert abs(PGVsdout - 0.790489) < 0.001
assert abs(mfact - 94.6658) < 0.001
assert abs(lnsig - 0.5146578) < 0.001
def get_mean_and_stddevs(self, sites, rup, dists, imt, stddev_types):
"""
See superclass `method <http://docs.openquake.org/oq-hazardlib/master/gsim/index.html#openquake.hazardlib.gsim.base.GroundShakingIntensityModel.get_mean_and_stddevs>`__.
"""
# These are arrays to hold the weighted combination of the GMPEs
lnmu = np.zeros_like(sites.vs30)
stddev_types = self.DEFINED_FOR_STANDARD_DEVIATION_TYPES
lnsd2 = [np.zeros_like(sites.vs30) for a in stddev_types]
for i in range(len(self.GMPEs)):
#---------------------------------------------------------------
# Loop over GMPE list
#---------------------------------------------------------------
gmpe = self.GMPEs[i]
#---------------------------------------------------------------
# Need to select the appropriate z1pt0 value for different GMPEs.
# Note that these are required site parameters, so even though
# OQ has these equations built into the class, the arrays must
# be provided in the sites context. It might be worth sending
# a request to OQ to provide a subclass that that computes the
# depth parameters when not provided (as is done for BSSA14 but
# not the others).
#---------------------------------------------------------------
if gmpe == 'AbrahamsonEtAl2014()':
sites.z1pt0 = sites.z1pt0ask14
if gmpe == 'BooreEtAl2014()' or gmpe == 'ChiouYoungs2014()':
sites.z1pt0 = sites.z1pt0cy14
#---------------------------------------------------------------
# Evaluate GMPEs
#---------------------------------------------------------------
gmpe_imts = [imt.__name__ for imt in \
gmpe.DEFINED_FOR_INTENSITY_MEASURE_TYPES]
if (isinstance(imt, PGV)) and ("PGV" not in gmpe_imts):
# If IMT is PGV and not given by GMPE, convert from PSA10
psa10, psa10sd = gmpe.get_mean_and_stddevs(
sites, rup, dists, SA(1.0), stddev_types)
lmean, lsd = NewmarkHall1982.psa102pgv(psa10, psa10sd[0])
else:
lmean, lsd = gmpe.get_mean_and_stddevs(
sites, rup, dists, imt, stddev_types)
#---------------------------------------------------------------
# Convertions due to component definition
#---------------------------------------------------------------
imc_in = gmpe.DEFINED_FOR_INTENSITY_MEASURE_COMPONENT
imc_out = self.DEFINED_FOR_INTENSITY_MEASURE_COMPONENT
lmean = BeyerBommer2006.ampIMCtoIMC(lmean, imc_in, imc_out, imt)
for j in range(len(lnsd2)):
lsd[j] = BeyerBommer2006.sigmaIMCtoIMC(
lsd[j], imc_in, imc_out, imt)
#---------------------------------------------------------------
# Compute weighted mean and sd
#---------------------------------------------------------------
lnmu = lnmu + self.weights[i] * lmean
# Note: the lnsd2 calculation isn't complete until we drop out of
# this loop and substract lnmu**2
for j in range(len(lnsd2)):
lnsd2[j] = lnsd2[j] + self.weights[i] * (lmean**2 + lsd[j]**2)
for j in range(len(lnsd2)):
lnsd2[j] = lnsd2[j] - lnmu**2
lnsd = [np.sqrt(a) for a in lnsd2]
return lnmu, lnsd
def get_mean_and_stddevs(self, sites, rup, dists, imt, stddev_types):
"""
See superclass `method <http://docs.openquake.org/oq-hazardlib/master/gsim/index.html#openquake.hazardlib.gsim.base.GroundShakingIntensityModel.get_mean_and_stddevs>`__.
"""
# These are arrays to hold the weighted combination of the GMPEs
lnmu = np.zeros_like(sites.vs30)
stddev_types = self.DEFINED_FOR_STANDARD_DEVIATION_TYPES
lnsd2 = [np.zeros_like(sites.vs30) for a in stddev_types]
for i in range(len(self.GMPEs)):
#---------------------------------------------------------------
# Loop over GMPE list
#---------------------------------------------------------------
gmpe = self.GMPEs[i]
#---------------------------------------------------------------
# Need to select the appropriate z1pt0 value for different GMPEs.
# Note that these are required site parameters, so even though
# OQ has these equations built into the class, the arrays must
# be provided in the sites context. It might be worth sending
# a request to OQ to provide a subclass that that computes the
# depth parameters when not provided (as is done for BSSA14 but
# not the others).
#---------------------------------------------------------------
if gmpe == 'AbrahamsonEtAl2014()':
sites.z1pt0 = sites.z1pt0ask14
if gmpe == 'BooreEtAl2014()' or gmpe == 'ChiouYoungs2014()':
sites.z1pt0 = sites.z1pt0cy14
#---------------------------------------------------------------
# Evaluate GMPEs
#---------------------------------------------------------------
gmpe_imts = [imt.__name__ for imt in \
gmpe.DEFINED_FOR_INTENSITY_MEASURE_TYPES]
if (isinstance(imt, PGV)) and ("PGV" not in gmpe_imts):
# If IMT is PGV and not given by GMPE, convert from PSA10
psa10, psa10sd = gmpe.get_mean_and_stddevs(
sites, rup, dists, SA(1.0), stddev_types)
lmean, lsd = NewmarkHall1982.psa102pgv(psa10, psa10sd[0])
else:
lmean, lsd = gmpe.get_mean_and_stddevs(sites, rup, dists, imt,
stddev_types)
#---------------------------------------------------------------
# Convertions due to component definition
#---------------------------------------------------------------
imc_in = gmpe.DEFINED_FOR_INTENSITY_MEASURE_COMPONENT
imc_out = self.DEFINED_FOR_INTENSITY_MEASURE_COMPONENT
lmean = BeyerBommer2006.ampIMCtoIMC(lmean, imc_in, imc_out, imt)
for j in range(len(lnsd2)):
lsd[j] = BeyerBommer2006.sigmaIMCtoIMC(lsd[j], imc_in, imc_out,
imt)
#---------------------------------------------------------------
# Compute weighted mean and sd
#---------------------------------------------------------------
lnmu = lnmu + self.weights[i] * lmean
# Note: the lnsd2 calculation isn't complete until we drop out of
# this loop and substract lnmu**2
for j in range(len(lnsd2)):
lnsd2[j] = lnsd2[j] + self.weights[i] * (lmean**2 + lsd[j]**2)
for j in range(len(lnsd2)):
lnsd2[j] = lnsd2[j] - lnmu**2
lnsd = [np.sqrt(a) for a in lnsd2]
return lnmu, lnsd
def get_mean_and_stddevs(self, sites, rup, dists, imt, stddev_types):
"""
See superclass `method <http://docs.openquake.org/oq-hazardlib/master/gsim/index.html#openquake.hazardlib.gsim.base.GroundShakingIntensityModel.get_mean_and_stddevs>`__.
"""
#-----------------------------------------------------------------------
# Sort out shapes of sites and dists elements
#-----------------------------------------------------------------------
shapes = []
for k, v in sites.__dict__.items():
if (k is not 'lons') and (k is not 'lats'):
shapes.append(v.shape)
for k, v in dists.__dict__.items():
if (k is not 'lons') and (k is not 'lats'):
shapes.append(v.shape)
shapeset = set(shapes)
if len(shapeset) != 1:
raise Exception('All sites and dists elements must have same shape.')
else:
orig_shape = list(shapeset)[0]
# Need to turn all 2D arrays into 1D arrays because of
# inconsistencies in how arrays are handled in OpenQuake.
for k, v in dists.__dict__.items():
if (k is not 'lons') and (k is not 'lats'):
dists.__dict__[k] = np.reshape(dists.__dict__[k], (-1,))
for k, v in sites.__dict__.items():
if (k is not 'lons') and (k is not 'lats'):
sites.__dict__[k] = np.reshape(sites.__dict__[k], (-1,))
#-----------------------------------------------------------------------
# These are arrays to hold the weighted combination of the GMPEs
#-----------------------------------------------------------------------
lnmu = np.zeros_like(sites.vs30)
sd_avail = self.DEFINED_FOR_STANDARD_DEVIATION_TYPES
if not sd_avail.issuperset(set(stddev_types)):
raise Exception("Requested an unavailable stddev_type.")
lnsd2 = [np.zeros_like(sites.vs30) for a in stddev_types]
for i in range(len(self.GMPES)):
#-------------------------------------------------------------------
# Loop over GMPE list
#-------------------------------------------------------------------
gmpe = self.GMPES[i]
sites = MultiGMPE.set_sites_depth_parameters(sites, gmpe)
#-------------------------------------------------------------------
# Evaluate GMPEs
#-------------------------------------------------------------------
gmpe_imts = [imt.__name__ for imt in \
gmpe.DEFINED_FOR_INTENSITY_MEASURE_TYPES]
if (isinstance(imt, PGV)) and ("PGV" not in gmpe_imts):
#---------------------------------------------------------------
# If IMT is PGV and PGV is not given by the GMPE, then
# convert from PSA10.
#---------------------------------------------------------------
if self.HAS_SITE[i] is True:
psa10, psa10sd = gmpe.get_mean_and_stddevs(
sites, rup, dists, SA(1.0), stddev_types)
else:
lamps = self.get_site_factors(
sites, rup, dists, SA(1.0), default = True)
psa10, psa10sd = gmpe.get_mean_and_stddevs(
sites, rup, dists, SA(1.0), stddev_types)
psa10 = psa10 + lamps
lmean, lsd = NewmarkHall1982.psa102pgv(psa10, psa10sd[0])
else:
if self.HAS_SITE[i] is True:
lmean, lsd = gmpe.get_mean_and_stddevs(
sites, rup, dists, imt, stddev_types)
else:
lamps = self.get_site_factors(
sites, rup, dists, imt, default = True)
lmean, lsd = gmpe.get_mean_and_stddevs(
sites, rup, dists, imt, stddev_types)
lmean = lmean + lamps
#-------------------------------------------------------------------
# Convertions due to component definition
#-------------------------------------------------------------------
imc_in = gmpe.DEFINED_FOR_INTENSITY_MEASURE_COMPONENT
imc_out = self.DEFINED_FOR_INTENSITY_MEASURE_COMPONENT
lmean = BeyerBommer2006.ampIMCtoIMC(lmean, imc_in, imc_out, imt)
for j in range(len(lnsd2)):
lsd[j] = BeyerBommer2006.sigmaIMCtoIMC(
lsd[j], imc_in, imc_out, imt)
#-------------------------------------------------------------------
# Compute weighted mean and sd
#-------------------------------------------------------------------
lnmu = lnmu + self.WEIGHTS[i] * lmean
# Note: the lnsd2 calculation isn't complete until we drop out of
# this loop and substract lnmu**2
for j in range(len(lnsd2)):
lnsd2[j] = lnsd2[j] + self.WEIGHTS[i] * (lmean**2 + lsd[j]**2)
for j in range(len(lnsd2)):
lnsd2[j] = lnsd2[j] - lnmu**2
lnsd = [np.sqrt(a) for a in lnsd2]
# Undo reshapes of inputs
for k, v in dists.__dict__.items():
if (k is not 'lons') and (k is not 'lats'):
dists.__dict__[k] = np.reshape(dists.__dict__[k], orig_shape)
for k, v in sites.__dict__.items():
if (k is not 'lons') and (k is not 'lats'):
sites.__dict__[k] = np.reshape(sites.__dict__[k], orig_shape)
# Reshape output
lnmu = np.reshape(lnmu, orig_shape)
for i in range(len(lnsd)):
lnsd[i] = np.reshape(lnsd[i], orig_shape)
return lnmu, lnsd
