def test_equality(self):
sctx1 = SitesContext()
sctx1.vs30 = numpy.array([500., 600., 700.])
sctx1.vs30measured = True
sctx1.z1pt0 = numpy.array([40., 50., 60.])
sctx1.z2pt5 = numpy.array([1, 2, 3])
sctx2 = SitesContext()
sctx2.vs30 = numpy.array([500., 600., 700.])
sctx2.vs30measured = True
sctx2.z1pt0 = numpy.array([40., 50., 60.])
sctx2.z2pt5 = numpy.array([1, 2, 3])
self.assertTrue(sctx1 == sctx2)
sctx2 = SitesContext()
sctx2.vs30 = numpy.array([500., 600.])
sctx2.vs30measured = True
sctx2.z1pt0 = numpy.array([40., 50., 60.])
sctx2.z2pt5 = numpy.array([1, 2, 3])
self.assertTrue(sctx1 != sctx2)
sctx2 = SitesContext()
sctx2.vs30 = numpy.array([500., 600., 700.])
sctx2.vs30measured = False
sctx2.z1pt0 = numpy.array([40., 50., 60.])
sctx2.z2pt5 = numpy.array([1, 2, 3])
self.assertTrue(sctx1 != sctx2)
sctx2 = SitesContext()
sctx2.vs30 = numpy.array([500., 600., 700.])
sctx2.vs30measured = True
sctx2.z1pt0 = numpy.array([40., 50., 60.])
self.assertTrue(sctx1 != sctx2)
rctx = RuptureContext()
rctx.mag = 5.
self.assertTrue(sctx1 != rctx)
def test_equality(self):
sctx1 = SitesContext()
sctx1.vs30 = numpy.array([500., 600., 700.])
sctx1.vs30measured = True
sctx1.z1pt0 = numpy.array([40., 50., 60.])
sctx1.z2pt5 = numpy.array([1, 2, 3])
sctx2 = SitesContext()
sctx2.vs30 = numpy.array([500., 600., 700.])
sctx2.vs30measured = True
sctx2.z1pt0 = numpy.array([40., 50., 60.])
sctx2.z2pt5 = numpy.array([1, 2, 3])
self.assertTrue(sctx1 == sctx2)
sctx2 = SitesContext()
sctx2.vs30 = numpy.array([500., 600.])
sctx2.vs30measured = True
sctx2.z1pt0 = numpy.array([40., 50., 60.])
sctx2.z2pt5 = numpy.array([1, 2, 3])
self.assertTrue(sctx1 != sctx2)
sctx2 = SitesContext()
sctx2.vs30 = numpy.array([500., 600., 700.])
sctx2.vs30measured = False
sctx2.z1pt0 = numpy.array([40., 50., 60.])
sctx2.z2pt5 = numpy.array([1, 2, 3])
self.assertTrue(sctx1 != sctx2)
sctx2 = SitesContext()
sctx2.vs30 = numpy.array([500., 600., 700.])
sctx2.vs30measured = True
sctx2.z1pt0 = numpy.array([40., 50., 60.])
self.assertTrue(sctx1 != sctx2)
rctx = RuptureContext()
rctx.mag = 5.
self.assertTrue(sctx1 != rctx)
def getSitesContext(self):
"""
:returns:
SitesContext object.
"""
sctx = SitesContext()
sctx.vs30 = self._Vs30.getData().copy()
sctx.z1pt0 = self._Z1Pt0
sctx.z2pt5 = self._Z2Pt5
sctx.backarc = self._backarc # zoneconfig might have this info
if self._vs30measured_grid is None: # If we don't know, then use false
sctx.vs30measured = np.zeros_like(sctx.vs30, dtype=bool)
else:
sctx.vs30measured = self._vs30measured_grid
sctx.lons = self._lons
sctx.lats = self._lats
return sctx
def getSitesContext(self):
"""
:returns:
SitesContext object.
"""
sctx = SitesContext()
sctx.vs30 = self._Vs30.getData().copy()
sctx.z1pt0 = self._Z1Pt0
sctx.z2pt5 = self._Z2Pt5
sctx.backarc = self._backarc # zoneconfig might have this info
if self._vs30measured_grid is None: # If we don't know, then use false
sctx.vs30measured = np.zeros_like(
sctx.vs30, dtype=bool)
else:
sctx.vs30measured = self._vs30measured_grid
sctx.lons = self._lons
sctx.lats = self._lats
return sctx
def sampleFromSites(self, lats, lons, vs30measured_grid=None):
"""
Create a SitesContext object by sampling the current Sites object.
:param lats:
Sequence of latitudes.
:param lons:
Sequence of longitudes.
:param vs30measured_grid:
Sequence of booleans of the same shape as lats/lons indicating
whether the vs30 values are measured or inferred.
:returns:
SitesContext object where data are sampled from the current Sites
object.
:raises ShakeMapException:
When lat/lon input sequences do not share dimensionality.
"""
lats = np.array(lats)
lons = np.array(lons)
latshape = lats.shape
lonshape = lons.shape
if latshape != lonshape:
msg = 'Input lat/lon arrays must have the same dimensions'
raise ShakeMapException(msg)
site = SitesContext()
# use default vs30 if outside grid
site.vs30 = self._Vs30.getValue(lats, lons, default=self._defaultVs30)
site.lats = lats
site.lons = lons
site.z1pt0 = _calculate_z1p0(site.vs30)
site.z2pt5 = _calculate_z2p5(site.z1pt0)
if vs30measured_grid is None: # If we don't know, then use false
site.vs30measured = np.zeros_like(lons, dtype=bool)
else:
site.vs30measured = vs30measured_grid
site.backarc = self._backarc
return site
def sampleFromSites(self, lats, lons, vs30measured_grid=None):
"""
Create a SitesContext object by sampling the current Sites object.
:param lats:
Sequence of latitudes.
:param lons:
Sequence of longitudes.
:param vs30measured_grid:
Sequence of booleans of the same shape as lats/lons indicating
whether the vs30 values are measured or inferred.
:returns:
SitesContext object where data are sampled from the current Sites
object.
:raises ShakeMapException:
When lat/lon input sequences do not share dimensionality.
"""
lats = np.array(lats)
lons = np.array(lons)
latshape = lats.shape
lonshape = lons.shape
if latshape != lonshape:
msg = 'Input lat/lon arrays must have the same dimensions'
raise ShakeMapException(msg)
site = SitesContext()
# use default vs30 if outside grid
site.vs30 = self._Vs30.getValue(lats, lons, default=self._defaultVs30)
site.lats = lats
site.lons = lons
site.z1pt0 = _calculate_z1p0(site.vs30)
site.z2pt5 = _calculate_z2p5(site.z1pt0)
if vs30measured_grid is None: # If we don't know, then use false
site.vs30measured = np.zeros_like(lons, dtype=bool)
else:
site.vs30measured = vs30measured_grid
site.backarc = self._backarc
return site
rctx.width = 10.0
rctx.hypo_depth = 8.0
#dctx.rrup = np.logspace(1,np.log10(200),100)
dctx.rrup = np.logspace(np.log10(10),np.log10(10.0),1)
# Assuming average ztor, get rjb:
dctx.rjb = np.sqrt(dctx.rrup**2 - rctx.ztor**2)
dctx.rhypo = dctx.rrup
dctx.rx = dctx.rjb
dctx.ry0 = dctx.rx
sctx.vs30 = np.ones_like(dctx.rrup) * 760.0
sctx.vs30measured = np.full_like(dctx.rrup, False, dtype='bool')
sctx.z1pt0 = np.ones_like(dctx.rrup) * 0.05
lmean_ask14, sd_ask14 = ASK14.get_mean_and_stddevs(
sctx, rctx, dctx, IMT, [const.StdDev.TOTAL])
###############################################################################
#
### Now try using mien which uses this... ##
#
## Read in annemarie's ask:
#ask_data_ab = np.genfromtxt('/Users/vsahakian/anza/data/coeffs/ASK_for_Valerie.txt',skip_header=1)
#
def signal_end(st, event_time, event_lon, event_lat, event_mag,
method=None, vmin=None, floor=None,
model=None, epsilon=2.0):
"""
Estimate end of signal by using a model of the 5-95% significant
duration, and adding this value to the "signal_split" time. This probably
only works well when the split is estimated with a p-wave picker since
the velocity method often ends up with split times that are well before
signal actually starts.
Args:
st (StationStream):
Stream of data.
event_time (UTCDateTime):
Event origin time.
event_mag (float):
Event magnitude.
event_lon (float):
Event longitude.
event_lat (float):
Event latitude.
method (str):
Method for estimating signal end time. Either 'velocity'
or 'model'.
vmin (float):
Velocity (km/s) for estimating end of signal. Only used if
method="velocity".
floor (float):
Minimum duration (sec) applied along with vmin.
model (str):
Short name of duration model to use. Must be defined in the
gmprocess/data/modules.yml file.
epsilon (float):
Number of standard deviations; if epsilon is 1.0, then the signal
window duration is the mean Ds + 1 standard deviation. Only used
for method="model".
Returns:
trace with stats dict updated to include a
stats['processing_parameters']['signal_end'] dictionary.
"""
# Load openquake stuff if method="model"
if method == "model":
dmodel = load_model(model)
# Set some "conservative" inputs (in that they will tend to give
# larger durations).
sctx = SitesContext()
sctx.vs30 = np.array([180.0])
sctx.z1pt0 = np.array([0.51])
rctx = RuptureContext()
rctx.mag = event_mag
rctx.rake = -90.0
dur_imt = imt.from_string('RSD595')
stddev_types = [const.StdDev.INTRA_EVENT]
for tr in st:
if not tr.hasParameter('signal_split'):
continue
if method == "velocity":
if vmin is None:
raise ValueError('Must specify vmin if method is "velocity".')
if floor is None:
raise ValueError('Must specify floor if method is "velocity".')
epi_dist = gps2dist_azimuth(
lat1=event_lat,
lon1=event_lon,
lat2=tr.stats['coordinates']['latitude'],
lon2=tr.stats['coordinates']['longitude'])[0] / 1000.0
end_time = event_time + max(floor, epi_dist / vmin)
elif method == "model":
if model is None:
raise ValueError('Must specify model if method is "model".')
epi_dist = gps2dist_azimuth(
lat1=event_lat,
lon1=event_lon,
lat2=tr.stats['coordinates']['latitude'],
lon2=tr.stats['coordinates']['longitude'])[0] / 1000.0
dctx = DistancesContext()
# Repi >= Rrup, so substitution here should be conservative
# (leading to larger durations).
dctx.rrup = np.array([epi_dist])
lnmu, lnstd = dmodel.get_mean_and_stddevs(
sctx, rctx, dctx, dur_imt, stddev_types)
duration = np.exp(lnmu + epsilon * lnstd[0])
# Get split time
split_time = tr.getParameter('signal_split')['split_time']
end_time = split_time + float(duration)
else:
raise ValueError('method must be either "velocity" or "model".')
# Update trace params
end_params = {
'end_time': end_time,
'method': method,
'vsplit': vmin,
'floor': floor,
'model': model,
'epsilon': epsilon
}
tr.setParameter('signal_end', end_params)
return st
def signal_end(st, event_time, event_lon, event_lat, event_mag,
method=None, vmin=None, floor=None,
model=None, epsilon=2.0):
"""
Estimate end of signal by using a model of the 5-95% significant
duration, and adding this value to the "signal_split" time. This probably
only works well when the split is estimated with a p-wave picker since
the velocity method often ends up with split times that are well before
signal actually starts.
Args:
st (StationStream):
Stream of data.
event_time (UTCDateTime):
Event origin time.
event_mag (float):
Event magnitude.
event_lon (float):
Event longitude.
event_lat (float):
Event latitude.
method (str):
Method for estimating signal end time. Either 'velocity'
or 'model'.
vmin (float):
Velocity (km/s) for estimating end of signal. Only used if
method="velocity".
floor (float):
Minimum duration (sec) applied along with vmin.
model (str):
Short name of duration model to use. Must be defined in the
gmprocess/data/modules.yml file.
epsilon (float):
Number of standard deviations; if epsilon is 1.0, then the signal
window duration is the mean Ds + 1 standard deviation. Only used
for method="model".
Returns:
trace with stats dict updated to include a
stats['processing_parameters']['signal_end'] dictionary.
"""
# Load openquake stuff if method="model"
if method == "model":
mod_file = pkg_resources.resource_filename(
'gmprocess', os.path.join('data', 'modules.yml'))
with open(mod_file, 'r') as f:
mods = yaml.load(f)
# Import module
cname, mpath = mods['modules'][model]
dmodel = getattr(import_module(mpath), cname)()
# Set some "conservative" inputs (in that they will tend to give
# larger durations).
sctx = SitesContext()
sctx.vs30 = np.array([180.0])
sctx.z1pt0 = np.array([0.51])
rctx = RuptureContext()
rctx.mag = event_mag
rctx.rake = -90.0
dur_imt = imt.from_string('RSD595')
stddev_types = [const.StdDev.INTRA_EVENT]
for tr in st:
if not tr.hasParameter('signal_split'):
continue
if method == "velocity":
if vmin is None:
raise ValueError('Must specify vmin if method is "velocity".')
if floor is None:
raise ValueError('Must specify floor if method is "velocity".')
epi_dist = gps2dist_azimuth(
lat1=event_lat,
lon1=event_lon,
lat2=tr.stats['coordinates']['latitude'],
lon2=tr.stats['coordinates']['longitude'])[0] / 1000.0
end_time = event_time + max(floor, epi_dist / vmin)
elif method == "model":
if model is None:
raise ValueError('Must specify model if method is "model".')
epi_dist = gps2dist_azimuth(
lat1=event_lat,
lon1=event_lon,
lat2=tr.stats['coordinates']['latitude'],
lon2=tr.stats['coordinates']['longitude'])[0] / 1000.0
dctx = DistancesContext()
# Repi >= Rrup, so substitution here should be conservative
# (leading to larger durations).
dctx.rrup = np.array([epi_dist])
lnmu, lnstd = dmodel.get_mean_and_stddevs(
sctx, rctx, dctx, dur_imt, stddev_types)
duration = np.exp(lnmu + epsilon * lnstd[0])
# Get split time
split_time = tr.getParameter('signal_split')['split_time']
end_time = split_time + float(duration)
else:
raise ValueError('method must be either "velocity" or "model".')
# Update trace params
end_params = {
'end_time': end_time,
'method': method,
'vsplit': vmin,
'floor': floor,
'model': model,
'epsilon': epsilon
}
tr.setParameter('signal_end', end_params)
return st
