def read_gm(eqrm_output_path, site_tag, flag_soil=True):
from eqrm_code.RSA2MMI import rsa2mmi_array
from eqrm_code.worden_et_al import worden_et_al
''' read ground motion, an output of EQRM run'''
atten_periods = np.load(os.path.join(eqrm_output_path, site_tag + \
'_motion/atten_periods.npy'))
selected_periods = [0.0, 0.3, 1.0]
idx_period = [(np.abs(atten_periods - period)).argmin() \
for period in selected_periods]
if flag_soil:
gmotion = np.load(os.path.join(eqrm_output_path, site_tag +\
'_motion/soil_SA.npy')) # (1,1,1,sites,nsims,nperiods)
else:
gmotion = np.load(os.path.join(eqrm_output_path, site_tag +\
'_motion/bedrock_SA.npy'))
pga = gmotion[0, 0, 0, :, :, idx_period[0]]
sa03 = gmotion[0, 0, 0, :, :, idx_period[1]]
sa10 = gmotion[0, 0, 0, :, :, idx_period[2]]
mmi_AK = rsa2mmi_array(sa03)
mmi_W = worden_et_al(sa03*980, 0.3)
return(pga, sa03, sa10, mmi_AK, mmi_W)
def read_gm(eqrm_output_path, site_tag):
''' read ground motion, an output of EQRM run'''
from eqrm_code.RSA2MMI import rsa2mmi_array
atten_periods = np.load(os.path.join(eqrm_output_path, site_tag +
'_motion/atten_periods.npy'))
selected_periods = [0.0, 0.3, 1.0]
idx_period = [(np.abs(atten_periods - period)).argmin()
for period in selected_periods]
try:
print 'GM at soil is loaded'
gmotion = np.load(os.path.join(eqrm_output_path, site_tag +
'_motion/soil_SA.npy')) # (1,1,1,sites,nsims,nperiods)
except IOError:
print 'GM at bedrock is loaded'
gmotion = np.load(os.path.join(eqrm_output_path, site_tag +
'_motion/bedrock_SA.npy'))
pga = gmotion[0, 0, 0, :, :, idx_period[0]]
sa03 = gmotion[0, 0, 0, :, :, idx_period[1]]
sa10 = gmotion[0, 0, 0, :, :, idx_period[2]]
mmi = rsa2mmi_array(sa10, period=1.0)
return(pga, sa03, sa10, mmi)
def calc_loss(self, SA, atten_periods):
"""
Calculate the economic loss at a site for a user defined vulnerability
curve.
The curve data should be loaded in with the analysis function
load_data() and self.vulnerability_set defined. If it hasn't and this
function is called a runtime error is thrown.
SA array of Spectral Acceleration, in g, with axis;
sites, psudo_events, periods
the site axis usually has a size of 1
Returns economic_loss where:
economic_loss A dollar loss, with the the dimensions of;
(site, event)
"""
if self.vulnerability_set is None:
raise RuntimeError('Vulnerability set not found')
IMT = self.vulnerability_set.intensity_measure_type
# print "IMT", IMT
if IMT == "PGA":
period = 0
elif IMT == "MMI":
# Only send the SA at this period, when converting to MMI
period = 1.0
periods_indexs = find_period_indices(atten_periods,
wanted_periods=[period],
epsilon=1.0e-3)
# Calculate intensity measure level
# print "SA", SA
SA_period = SA[..., periods_indexs[period]]
if IMT == "PGA":
IML = array(SA_period)
elif IMT == "MMI":
IML = rsa2mmi_array(SA_period, period=period)
# Get mean and sigma for this measure level and site type
mean_loss = zeros(IML.shape)
sigma = zeros(IML.shape)
for i, func_id in enumerate(
self.attributes['STRUCTURE_CLASSIFICATION']):
(mean_loss[i, :], sigma[i, :]) = self.vulnerability_set.calc_mean(
func_id, IML[i, :])
# Get the loss_ratio by taking a random sample
loss_ratio = self.vulnerability_set.sample(func_id, mean_loss, sigma)
# loss_ratio dimensions (site, psudo_events)
# Note psudo_events probably has other dimensions colapsed into it,
# such as ground motion models, spawning and reccurance models.
# Perform a cutoff for the ratio limits
loss_ratio = self.vulnerability_set.ratio_cutoff(loss_ratio)
# Calculate economic loss
structure_cost = self.cost_breakdown()
economic_loss = structure_cost[:, newaxis, newaxis] * loss_ratio
# economic_loss shape (sites, pseudo_events)
return economic_loss
def calc_loss(self, SA, atten_periods):
"""
Calculate the economic loss at a site for a user defined vulnerability
curve.
The curve data should be loaded in with the analysis function
load_data() and self.vulnerability_set defined. If it hasn't and this
function is called a runtime error is thrown.
SA array of Spectral Acceleration, in g, with axis;
sites, psudo_events, periods
the site axis usually has a size of 1
Returns economic_loss where:
economic_loss A dollar loss, with the the dimensions of;
(site, event)
"""
if self.vulnerability_set is None:
raise RuntimeError('Vulnerability set not found')
IMT = self.vulnerability_set.intensity_measure_type
# print "IMT", IMT
if IMT == "PGA":
period = 0
elif IMT == "MMI":
# Only send the SA at this period, when converting to MMI
period = 1.0
periods_indexs = find_period_indices(atten_periods,
wanted_periods=[period],
epsilon=1.0e-3)
# Calculate intensity measure level
# print "SA", SA
SA_period = SA[..., periods_indexs[period]]
if IMT == "PGA":
IML = array(SA_period)
elif IMT == "MMI":
IML = rsa2mmi_array(SA_period,
period=period)
# Get mean and sigma for this measure level and site type
mean_loss = zeros(IML.shape)
sigma = zeros(IML.shape)
for i, func_id in enumerate(self.attributes['STRUCTURE_CLASSIFICATION']):
(mean_loss[i, :],
sigma[i, :]) = self.vulnerability_set.calc_mean(func_id, IML[i,:])
# Get the loss_ratio by taking a random sample
loss_ratio = self.vulnerability_set.sample(func_id, mean_loss, sigma)
# loss_ratio dimensions (site, psudo_events)
# Note psudo_events probably has other dimensions colapsed into it,
# such as ground motion models, spawning and reccurance models.
# Perform a cutoff for the ratio limits
loss_ratio = self.vulnerability_set.ratio_cutoff(loss_ratio)
# Calculate economic loss
structure_cost = self.cost_breakdown()
economic_loss = structure_cost[:, newaxis, newaxis] * loss_ratio
# economic_loss shape (sites, pseudo_events)
return economic_loss
