def spectra(M, Rrup, Rhyp, eventType, Z, Faba, Vs30, periods):
periods = np.array(periods)
idx = np.nonzero(periods <= 0.01)[0]
periods[idx] = 0.01 # Periods less than eq to 0.01 are essentially pga. Setting this allows us to avoid errors while interpolating in log-log scale.
augmentedPeriods = augmentPeriods(periods)
# compute R based on event type
R = [rrup if ev == 0 else rhyp for rrup, rhyp, ev in itertools.izip(Rrup, Rhyp, eventType)]
nRow = len(M)
nCol = len(augmentedPeriods)
M = np.array([M] * nCol).transpose()
R = np.array([R] * nCol).transpose()
eventType = np.array([eventType] * nCol).transpose()
Z = np.array([Z] * nCol).transpose()
Faba = np.array([Faba] * nCol).transpose()
Vs30 = np.array([Vs30] * nCol).transpose()
augmentedPeriods = np.array([augmentedPeriods] * nRow)
augmentedSpectra = np.array([[model.computeSpectra(mag, r, evt, z, faba, vs, per) for mag, r, evt, z, faba, vs, per in itertools.izip(mags, rs, evts, zs, fabas, vss, pers)] for mags, rs, evts, zs, fabas, vss, pers in itertools.izip(M, R, eventType, Z, Faba, Vs30, augmentedPeriods)])
fixedSpectra = fixPeriods(augmentedSpectra, periods, augmentedPeriods[0])
intraEventSigma = np.ones(fixedSpectra.shape) * model.intraEventSigma()
interEventSigma = np.ones(fixedSpectra.shape) * model.interEventSigma()
return (np.exp(fixedSpectra)).tolist(), intraEventSigma.tolist(), interEventSigma.tolist()
def spectra(M, Rrup, Faba, Vs30, cascadia, epistemic, periods):
periods = np.array(periods)
idx = np.nonzero(periods <= 0.01)[0]
periods[
idx
] = (
0.01
) # Periods less than eq to 0.01 are essentially pga. Setting this allows us to avoid errors while interpolating in log-log scale.
augmentedPeriods = augmentPeriods(periods)
R = Rrup
nRow = len(M)
nCol = len(augmentedPeriods)
M = np.array([M] * nCol).transpose()
R = np.array([R] * nCol).transpose()
Faba = np.array([Faba] * nCol).transpose()
Vs30 = np.array([Vs30] * nCol).transpose()
cascadia = np.array([cascadia] * nCol).transpose()
augmentedPeriods = np.array([augmentedPeriods] * nRow)
augmentedSpectra = np.array(
[
[
model.computeSpectra(mag, r, faba, vs, cas, epistemic, per)
for mag, r, faba, vs, cas, per in itertools.izip(mags, rs, fabas, vss, cass, pers)
]
for mags, rs, fabas, vss, cass, pers in itertools.izip(M, R, Faba, Vs30, cascadia, augmentedPeriods)
]
)
fixedSpectra = fixPeriods(augmentedSpectra, periods, augmentedPeriods[0])
# convert to cm/s/s
fixedSpectra = 10 ** fixedSpectra
intraEventSigma = model.intraEventSigma(periods) * nRow
interEventSigma = model.interEventSigma(periods) * nRow
return fixedSpectra.tolist(), intraEventSigma, interEventSigma
def spectra(M, Rrup, Rhyp, eventType, Z, Faba, Vs30, periods):
periods = np.array(periods)
idx = np.nonzero(periods <= 0.01)[0]
periods[
idx] = 0.01 # Periods less than eq to 0.01 are essentially pga. Setting this allows us to avoid errors while interpolating in log-log scale.
augmentedPeriods = augmentPeriods(periods)
# compute R based on event type
R = [
rrup if ev == 0 else rhyp
for rrup, rhyp, ev in itertools.izip(Rrup, Rhyp, eventType)
]
nRow = len(M)
nCol = len(augmentedPeriods)
M = np.array([M] * nCol).transpose()
R = np.array([R] * nCol).transpose()
eventType = np.array([eventType] * nCol).transpose()
Z = np.array([Z] * nCol).transpose()
Faba = np.array([Faba] * nCol).transpose()
Vs30 = np.array([Vs30] * nCol).transpose()
augmentedPeriods = np.array([augmentedPeriods] * nRow)
augmentedSpectra = np.array([[
model.computeSpectra(mag, r, evt, z, faba, vs, per)
for mag, r, evt, z, faba, vs, per in itertools.izip(
mags, rs, evts, zs, fabas, vss, pers)
] for mags, rs, evts, zs, fabas, vss, pers in itertools.izip(
M, R, eventType, Z, Faba, Vs30, augmentedPeriods)])
fixedSpectra = fixPeriods(augmentedSpectra, periods, augmentedPeriods[0])
intraEventSigma = np.ones(fixedSpectra.shape) * model.intraEventSigma()
interEventSigma = np.ones(fixedSpectra.shape) * model.interEventSigma()
return (np.exp(fixedSpectra)
).tolist(), intraEventSigma.tolist(), interEventSigma.tolist()
