def manage_imts(imt_str):
if imt_str is None:
return None, None
elif imt_str == 'PGA':
imt_obj = PGA()
imt_label = 'PGA (%g)'
elif imt_str == 'PGV':
imt_obj = PGV()
imt_label = 'PGV (cm/s)'
elif 'SA' in imt_str:
prd = float(imt_str.split('(')[1].split(')')[0])
imt_obj = SA(prd)
imt_label = str(imt_obj) + ' (%g)'
elif 'FAS' in imt_str:
prd = float(imt_str.split('(')[1].split(')')[0])
imt_obj = None
imt_label = str(imt_obj) + ' (%g)'
elif imt_str == 'DURATION':
imt_obj = RSD595()
imt_label = 'Duration (s)'
elif imt_str == 'ARIAS':
imt_obj = IA()
imt_label = 'Arias intensity (cm/s)'
else:
raise ValueError('IMT %s is not supported' % imt_str)
return imt_obj, imt_label
def __init__(self):
self._pga = PGA()
self._pgv = PGV()
self._pgd = PGD()
self._ia = IA()
self._ih = IH()
self._sa03 = SA(0.3)
self._sa10 = SA(1.0)
self._sa30 = SA(3.0)
def __init__(self):
self._pga = PGA()
self._pgv = PGV()
self._pgd = PGD()
self._ia = IA()
self._ih = IH()
self._sa03 = SA(0.3)
self._sa10 = SA(1.0)
self._sa30 = SA(3.0)
self.DEFINED_FOR_INTENSITY_MEASURE_TYPES = set()
def _getParamsFromIMT(self, imt):
"""
Helper function to return (possibly interpolated) conversion
parameters for a given IMT.
Args:
imt (OpenQuake IMT): The intensity measure type of the input
ground motions. Valid IMTs are PGA, PGV, and SA.
Returns:
(float, float, float, float, float): Coeffients for conversion.
"""
if imt == PGA():
sigma = self.pars['sigma'][0]
c0 = self.pars['c0smooth'][0]
r1 = self.pars['r1smooth'][0]
m1 = self.pars['m1smooth'][0]
m2 = self.pars['m2smooth'][0]
elif imt == PGV():
sigma = self.pars['sigma'][1]
c0 = self.pars['c0smooth'][1]
r1 = self.pars['r1smooth'][1]
m1 = self.pars['m1smooth'][1]
m2 = self.pars['m2smooth'][1]
elif imt == IA():
sigma = self.pars['sigma'][3]
c0 = self.pars['c0smooth'][3]
r1 = self.pars['r1smooth'][3]
m1 = self.pars['m1smooth'][3]
m2 = self.pars['m2smooth'][3]
elif imt == PGD():
sigma = self.pars['sigma'][4]
c0 = self.pars['c0smooth'][4]
r1 = self.pars['r1smooth'][4]
m1 = self.pars['m1smooth'][4]
m2 = self.pars['m2smooth'][4]
elif imt == IH():
sigma = self.pars['sigma'][5]
c0 = self.pars['c0smooth'][5]
r1 = self.pars['r1smooth'][5]
m1 = self.pars['m1smooth'][5]
m2 = self.pars['m2smooth'][5]
elif 'SA' in imt:
imt_per = imt.period
pa = self.pars['per'][2:]
sigma = np.interp(imt_per, pa, self.pars['sigma'][2:])
c0 = np.interp(imt_per, pa, self.pars['c0smooth'][2:])
r1 = np.interp(imt_per, pa, self.pars['r1smooth'][2:])
m1 = np.interp(imt_per, pa, self.pars['m1smooth'][2:])
m2 = np.interp(imt_per, pa, self.pars['m2smooth'][2:])
else:
raise ValueError("Unknown IMT: %s" % str(imt))
return (sigma, c0, r1, m1, m2)
def filter_gmpe_list(gmpes, wts, imt):
"""
Method to remove GMPEs from the GMPE list that are not applicable
to a specific IMT. Rescales the weights to sum to one.
Args:
gmpes (list): List of GMPE instances.
wts (list): List of floats indicating the weight of the GMPEs.
imt (IMT): OQ IMT to filter GMPE list for.
Returns:
tuple: List of GMPE instances and list of weights.
"""
if wts is None:
n = len(gmpes)
wts = [1 / n] * n
per_max = [np.max(get_gmpe_sa_periods(g)) for g in gmpes]
per_min = [np.min(get_gmpe_sa_periods(g)) for g in gmpes]
if imt == PGA():
sgmpe = [g for g in gmpes if imt in
get_gmpe_coef_table(g).non_sa_coeffs]
swts = [w for g, w in zip(gmpes, wts) if imt in
get_gmpe_coef_table(g).non_sa_coeffs]
elif imt == PGV():
sgmpe = []
swts = []
for i in range(len(gmpes)):
if (imt in get_gmpe_coef_table(gmpes[i]).non_sa_coeffs) or\
(per_max[i] >= 1.0 and per_min[i] <= 1.0):
sgmpe.append(gmpes[i])
swts.append(wts[i])
elif imt == IA():
sgmpe = []
swts = []
for i in range(len(gmpes)):
if (imt in get_gmpe_coef_table(gmpes[i]).non_sa_coeffs) or\
(per_max[i] >= 1.0 and per_min[i] <= 1.0):
sgmpe.append(gmpes[i])
swts.append(wts[i])
elif imt == PGD():
sgmpe = []
swts = []
for i in range(len(gmpes)):
if (imt in get_gmpe_coef_table(gmpes[i]).non_sa_coeffs) or\
(per_max[i] >= 1.0 and per_min[i] <= 1.0):
sgmpe.append(gmpes[i])
swts.append(wts[i])
elif imt == IH():
sgmpe = []
swts = []
for i in range(len(gmpes)):
if (imt in get_gmpe_coef_table(gmpes[i]).non_sa_coeffs) or\
(per_max[i] >= 1.0 and per_min[i] <= 1.0):
sgmpe.append(gmpes[i])
swts.append(wts[i])
else:
per = imt.period
sgmpe = []
swts = []
for i in range(len(gmpes)):
if (per_max[i] >= per and per_min[i] <= per):
sgmpe.append(gmpes[i])
swts.append(wts[i])
if len(sgmpe) == 0:
raise Exception('No applicable GMPEs from GMPE list for %s' % imt)
# Scale weights to sum to one
swts = np.array(swts)
swts = swts / np.sum(swts)
return sgmpe, swts
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
from openquake.hazardlib.gsim.base import DistancesContext
from openquake.hazardlib.gsim.base import RuptureContext
from openquake.hazardlib import const
# Create an instance of the gmpe and input contexts.
trav2003 = TravasarouEtAl2003()
sx = SitesContext()
rx = RuptureContext()
dx = DistancesContext()
# Import data frame.
df = pd.read_csv(
'/Users/tnye/PROJECTS/Duration/data/dataframes/select_data.csv')
# Define IMTs and standard deviations.
imt = IA()
sd_types = [const.StdDev.INTER_EVENT, const.StdDev.INTRA_EVENT]
# List of event IDs.
eventids = [
'usp000a1b0', 'usp000d6vk', 'usp000fg9t', 'usp000g9h6', 'us2000gge9',
'us1000etmq', 'us2000dwh6', 'nc30228270', 'nc72282711', 'ci14383980',
'ci14607652', 'usp0009eq0'
]
# Calculate mean Arias intensity and the standard deviations.
Trav_Ia_mean = []
interSD = []
intraSD = []
for event in eventids: