def test():
print('Testing Vector class...')
a = Vector(1, 1, 1)
b = Vector(2, 2, 2)
c = Vector(1, 1, 1)
np.testing.assert_almost_equal(a.getArray(), np.array([1, 1, 1]))
assert a == c
alen = a.mag()
np.testing.assert_almost_equal(alen, 1.73205, decimal=5)
anorm = a.norm()
bnorm = b.norm()
assert anorm == bnorm
acrossb = a.cross(b)
assert acrossb == Vector(0, 0, 0)
adotb = a.dot(b)
assert adotb == 6
aplusb = a + b
print('Passed Vector class tests.')
def __calc_t_i(P0, P1, lat, lon, proj):
"""
Calculate t_i distance. See Spudich and Chiou OFR 2015-1028. This is the distance
measured normal to strike from the i-th segment. Values on the hanging-wall are
positive and those on the foot-wall are negative.
:param P0:
Point object, representing the first top-edge vertex of a fault quadrilateral.
:param P1:
Point object, representing the second top-edge vertex of a fault quadrilateral.
:param lat:
A numpy array of latitudes.
:param lon:
A numpy array of longitudes.
:param proj:
An orthographic projection from
openquake.hazardlib.geo.utils.get_orthographic_projection.
:returns:
Array of size N of distances (in km) from input points to rupture surface.
"""
# projected coordinates are in km
p0x, p0y = proj(P0.x, P0.y)
p1x, p1y = proj(P1.x, P1.y)
# Unit vector pointing normal to strike
t_i_hat = Vector(p1y - p0y, -(p1x - p0x), 0).norm()
# Convert sites to Cartesian
sx, sy = proj(lon, lat)
sx1d = np.reshape(sx, (-1, ))
sy1d = np.reshape(sy, (-1, ))
# Vectors from P0 to sites
r = np.zeros([len(sx1d), 2])
r[:, 0] = sx1d - p0x
r[:, 1] = sy1d - p0y
# Dot product gives t_i
t_i = np.sum(t_i_hat.getArray()[0:2] * r, axis=1)
shp = t_i.shape
if len(shp) == 1:
t_i.shape = (shp[0], 1)
t_i = np.fliplr(t_i)
return t_i
def __calc_u_i(P0, P1, lat, lon, proj):
"""
Calculate u_i distance. See Spudich and Chiou OFR 2015-1028. This is the distance
along strike from the first vertex (P0) of the i-th segment.
:param P0:
Point object, representing the first top-edge vertex of a fault quadrilateral.
:param P1:
Point object, representing the second top-edge vertex of a fault quadrilateral.
:param lat:
A numpy array of latitude.
:param lon:
A numpy array of longitude.
:param proj:
An orthographic projection from
openquake.hazardlib.geo.utils.get_orthographic_projection.
:returns:
Array of size lat.shape of distances (in km).
"""
# projected coordinates are in km
p0x, p0y = proj(P0.x, P0.y)
p1x, p1y = proj(P1.x, P1.y)
# Unit vector pointing along strike
u_i_hat = Vector(p1x - p0x, p1y - p0y, 0).norm()
# Convert sites to Cartesian
sx, sy = proj(lon, lat)
sx1d = np.reshape(sx, (-1, ))
sy1d = np.reshape(sy, (-1, ))
# Vectors from P0 to sites
r = np.zeros([len(sx1d), 2])
r[:, 0] = sx1d - p0x
r[:, 1] = sy1d - p0y
# Dot product gives t_i
u_i = np.sum(u_i_hat.getArray()[0:2] * r, axis=1)
shp = u_i.shape
if len(shp) == 1:
u_i.shape = (shp[0], 1)
u_i = np.fliplr(u_i)
return u_i
def __computeXiPrime(self):
"""
Computes the xi' value.
"""
hypo_ecef = Vector.fromPoint(geo.point.Point(
self._hyp.longitude, self._hyp.latitude, self._hyp.depth))
epi_ll = Vector(self._hyp.longitude, self._hyp.latitude, 0)
epi_ecef = Vector.fromPoint(geo.point.Point(
epi_ll.x, epi_ll.y, 0))
slat = self._lat
slon = self._lon
# Convert site to ECEF:
site_ecef_x = np.ones_like(slat)
site_ecef_y = np.ones_like(slat)
site_ecef_z = np.ones_like(slat)
# Make a 3x(#number of sites) matrix of site locations
# (rows are x, y, z) in ECEF
site_ecef_x, site_ecef_y, site_ecef_z = latlon2ecef(
slat, slon, np.zeros(slon.shape))
site_mat = np.array([np.reshape(site_ecef_x, (-1,)),
np.reshape(site_ecef_y, (-1,)),
np.reshape(site_ecef_z, (-1,))])
xi_prime_unscaled = np.zeros_like(slat)
# Normalize by total number of subfaults. For mtype == 1, the number
# of subfaults will vary with site and be different for xi_s and
# xi_p, so keep two variables and sum them for each quad.
nsubs = np.zeros(np.product(slat.shape))
nsubp = np.zeros(np.product(slat.shape))
xi_prime_s = np.zeros(np.product(slat.shape))
xi_prime_p = np.zeros(np.product(slat.shape))
for k in range(len(self._flt._quadrilaterals)):
# Select a quad
q = self._flt.getQuadrilaterals()[k]
# Quad mesh (ECEF coords)
mesh = fault.get_quad_mesh(q, self._dx)
# Rupture plane normal vector (ECEF coords)
rpnv = fault.get_quad_normal(q)
rpnvcol = np.array([[rpnv.x],
[rpnv.y],
[rpnv.z]])
# Strike vector (ECEF coords)
strike_vec = fault.get_quad_strike_vector(q)
strike_vec_col = np.array([[strike_vec.x], [strike_vec.y], [
strike_vec.z]]) # convert to column vector
# Make 3x(i*j) matrix of cp
ni, nj = mesh['llx'].shape
cp_mat = np.array([np.reshape(mesh['cpx'], (-1,)),
np.reshape(mesh['cpy'], (-1,)),
np.reshape(mesh['cpz'], (-1,))])
# Compute matrix of p vectors
hypcol = np.array([[hypo_ecef.x],
[hypo_ecef.y],
[hypo_ecef.z]])
pmat = cp_mat - hypcol
# Project pmat onto quad
ndotp = np.sum(pmat * rpnvcol, axis=0)
pmat = pmat - ndotp * rpnvcol
mag = np.sqrt(np.sum(pmat * pmat, axis=0))
pmatnorm = pmat / mag # like r1
# According to Rowshandel:
# "The choice of the +/- sign in the above equations
# depends on the (along-the-strike and across-the-dip)
# location of the rupturing sub-fault relative to the
# location of the hypocenter."
# and:
# "for the along the strike component of the slip unit
# vector, the choice of the sign should result in the
# slip unit vector (s) being exactly the same as the
# rupture unit vector (p) for a pure strike-slip case"
# Strike slip and dip slip components of unit slip vector
# (ECEF coords)
ds_mat, ss_mat = _get_quad_slip_ds_ss(
q, self._rake, cp_mat, pmatnorm)
slpmat = (ds_mat + ss_mat)
mag = np.sqrt(np.sum(slpmat * slpmat, axis=0))
slpmatnorm = slpmat / mag
# Loop over sites
for i in range(site_mat.shape[1]):
sitecol = np.array([[site_mat[0, i]],
[site_mat[1, i]],
[site_mat[2, i]]])
qmat = sitecol - cp_mat # 3x(ni*nj), like r2
mag = np.sqrt(np.sum(qmat * qmat, axis=0))
qmatnorm = qmat / mag
# Propagation dot product
pdotqraw = np.sum(pmatnorm * qmatnorm, axis=0)
# Slip vector dot product
sdotqraw = np.sum(slpmatnorm * qmatnorm, axis=0)
if self._mtype == 1:
# Only sum over (+) directivity effect subfaults
# xi_p_prime
pdotq = pdotqraw.clip(min=0)
nsubp[i] = nsubp[i] + np.sum(pdotq > 0)
# xi_s_prime
sdotq = sdotqraw.clip(min=0)
nsubs[i] = nsubs[i] + np.sum(sdotq > 0)
elif self._mtype == 2:
# Sum over contributing subfaults
# xi_p_prime
pdotq = pdotqraw
nsubp[i] = nsubp[i] + cp_mat.shape[1]
# xi_s_prime
sdotq = sdotqraw
nsubs[i] = nsubs[i] + cp_mat.shape[1]
# Normalize by n sub faults later
xi_prime_s[i] = xi_prime_s[i] + np.sum(sdotq)
xi_prime_p[i] = xi_prime_p[i] + np.sum(pdotq)
# Apply a water level to nsubp and nsubs to avoid division by
# zero. This should only occur when the numerator is also zero
# and so the resulting value should be zero.
nsubs = np.maximum(nsubs, 1)
nsubp = np.maximum(nsubp, 1)
# We are outside the 'k' loop over nquads.
# o Normalize xi_prime_s and xi_prime_p
# o Reshape them
# o Add them together with the 'a' weights
xi_prime_tmp = (self._a_weight) * (xi_prime_s / nsubs) + \
(1 - self._a_weight) * (xi_prime_p / nsubp)
xi_prime_unscaled = xi_prime_unscaled + \
np.reshape(xi_prime_tmp, slat.shape)
# Scale so that xi_prime has range (0, 1)
if self._mtype == 1:
xi_prime = xi_prime_unscaled
elif self._mtype == 2:
xi_prime = 0.5 * (xi_prime_unscaled + 1)
self._xi_prime = xi_prime
def get_distance(methods, lat, lon, dep, source, use_median_distance=True):
"""
Calculate distance using any one of a number of distance measures.
One of quadlist OR hypo must be specified. The following table gives
the allowed distance strings and a description of each.
+--------+----------------------------------------------------------+
| String | Description |
+========+==========================================================+
| repi | Distance to epicenter. |
+--------+----------------------------------------------------------+
| rhypo | Distance to hypocenter. |
+--------+----------------------------------------------------------+
| rjb | Joyner-Boore distance; this is closest distance to the |
| | surface projection of the rupture plane. |
+--------+----------------------------------------------------------+
| rrup | Rupture distance; closest distance to the rupture plane. |
+--------+----------------------------------------------------------+
| rx | Strike-normal distance; same as GC2 coordiante T. |
+--------+----------------------------------------------------------+
| ry | Strike-parallel distance; same as GC2 coordiante U, but |
| | with a shift in origin definition. See Spudich and Chiou |
| | (2015) http://dx.doi.org/10.3133/ofr20151028. |
+--------+----------------------------------------------------------+
| ry0 | Horizontal distance off the end of the rupture measured |
| | parallel to strike. Can only be zero or positive. We |
| | compute this as a function of GC2 coordinate U. |
+--------+----------------------------------------------------------+
| U | GC2 coordinate U. |
+--------+----------------------------------------------------------+
| T | GC2 coordinate T. |
+--------+----------------------------------------------------------+
:param methods:
List of strings (or just a string) of distances to compute.
:param lat:
A numpy array of latitudes.
:param lon:
A numpy array of longidues.
:param dep:
A numpy array of depths (km).
:param source:
source instance.
:param use_median_distance:
Boolean; only used if GMPE requests fault distances and not fault is
availalbe. Default is True, meaning that point-source distances are
adjusted based on magnitude to get the median fault distance.
:returns:
dictionary of numpy array of distances, size of lon.shape
"""
fault = source.getFault()
hypo = source.getHypo()
if fault is not None:
quadlist = fault.getQuadrilaterals()
else:
quadlist = None
# Dictionary for holding the distances
distdict = dict()
if not isinstance(methods, list):
methods = [methods]
methods_available = set(
['repi', 'rhypo', 'rjb', 'rrup', 'rx', 'ry', 'ry0', 'U', 'T'])
if not set(methods).issubset(methods_available):
raise NotImplementedError(
'One or more requested distance method is not '
'valid or is not implemented yet')
if (lat.shape == lon.shape) and (lat.shape == dep.shape):
pass
else:
raise ShakeMapException('lat, lon, and dep must have the same shape.')
oldshape = lon.shape
if len(oldshape) == 2:
newshape = (oldshape[0] * oldshape[1], 1)
else:
newshape = (oldshape[0], 1)
if ('rrup' in methods) or ('rjb' in methods):
x, y, z = latlon2ecef(lat, lon, dep)
x.shape = newshape
y.shape = newshape
z.shape = newshape
sites_ecef = np.hstack((x, y, z))
# Define a projection that spands sites and fault
if fault is None:
all_lat = lat
all_lon = lon
else:
all_lat = np.append(lat, fault.getLats())
all_lon = np.append(lon, fault.getLons())
west = np.nanmin(all_lon)
east = np.nanmax(all_lon)
south = np.nanmin(all_lat)
north = np.nanmax(all_lat)
proj = get_orthographic_projection(west, east, north, south)
# ---------------------------------------------
# Distances that do not require loop over quads
# ---------------------------------------------
if ('repi' in methods) or \
(('rjb' in methods) and (quadlist is None)) or \
(('rrup' in methods) and (quadlist is None)) or \
(('ry0' in methods) and (quadlist is None)) or \
(('rx' in methods) and (quadlist is None)) or \
(('T' in methods) and (quadlist is None)) or \
(('U' in methods) and (quadlist is None)):
if hypo is None:
raise ShakeMapException('Cannot calculate epicentral distance '
'without a point object')
repidist = geodetic.distance(hypo.longitude, hypo.latitude, 0.0, lon,
lat, dep)
repidist = repidist.reshape(oldshape)
distdict['repi'] = repidist
if ('rhypo' in methods) or \
(('rrup' in methods) and (quadlist is None)):
if hypo is None:
raise ShakeMapException('Cannot calculate epicentral distance '
'without a point object')
rhypodist = geodetic.distance(hypo.longitude, hypo.latitude,
hypo.depth, lon, lat, dep)
rhypodist = rhypodist.reshape(oldshape)
distdict['rhypo'] = rhypodist
# --------------------------------------------------------
# Loop over quadlist for those distances that require loop
# --------------------------------------------------------
if 'rrup' in methods:
minrrup = np.ones(newshape, dtype=lon.dtype) * 1e16
if 'rjb' in methods:
minrjb = np.ones(newshape, dtype=lon.dtype) * 1e16
if ('rx' in methods) or ('ry' in methods) or \
('ry0' in methods) or ('U' in methods) or ('T' in methods):
totweight = np.zeros(newshape, dtype=lon.dtype)
GC2T = np.zeros(newshape, dtype=lon.dtype)
GC2U = np.zeros(newshape, dtype=lon.dtype)
if quadlist is not None:
#-----------------------------------------------------------------
# For these distances, we need to sort out strike discordance and
# nominal strike prior to starting the loop if there is more than
# one segment.
#-----------------------------------------------------------------
segind = fault._getSegmentIndex()
segindnp = np.array(segind)
uind = np.unique(segind)
nseg = len(uind)
#-------------------------------------------------------------------
# The first thing we need to worry about is finding the coordinate
# shift. U's origin is " selected from the two endpoints most
# distant from each other."
#-------------------------------------------------------------------
if nseg > 1:
# Need to get index of first and last quad
# for each segment
iq0 = np.zeros(nseg, dtype='int16')
iq1 = np.zeros(nseg, dtype='int16')
for k in uind:
ii = [i for i, j in enumerate(segind) if j == uind[k]]
iq0[k] = int(np.min(ii))
iq1[k] = int(np.max(ii))
#---------------------------------------------------------------
# This is an iterator for each possible combination of segments
# including segment orientations (i.e., flipped).
#---------------------------------------------------------------
it_seg = it.product(it.combinations(uind, 2),
it.product([0, 1], [0, 1]))
# Placeholder for the segment pair/orientation that gives the
# largest distance.
dist_save = 0
for k in it_seg:
s0ind = k[0][0]
s1ind = k[0][1]
p0ind = k[1][0]
p1ind = k[1][1]
if p0ind == 0:
P0 = quadlist[iq0[s0ind]][0]
else:
P0 = quadlist[iq1[s0ind]][1]
if p1ind == 0:
P1 = quadlist[iq1[s1ind]][0]
else:
P1 = quadlist[iq0[s1ind]][1]
dist = geodetic.distance(P0.longitude, P0.latitude, 0.0,
P1.longitude, P1.latitude, 0.0)
if dist > dist_save:
dist_save = dist
A0 = P0
A1 = P1
#---------------------------------------------------------------
# A0 and A1 are the furthest two segment endpoints, but we still
# need to sort out which one is the "origin".
#---------------------------------------------------------------
# Primate fixes the trend of the trial a vector.
primate = -1
while primate < 0:
A0.depth = 0
A1.depth = 0
p_origin = Vector.fromPoint(A0)
a0 = Vector.fromPoint(A0)
a1 = Vector.fromPoint(A1)
ahat = (a1 - a0).norm()
# Loop over traces
e_j = np.zeros(nseg)
b_prime = [None] * nseg
for j in range(nseg):
P0 = quadlist[iq0[j]][0]
P1 = quadlist[iq1[j]][1]
P0.depth = 0
P1.depth = 0
p0 = Vector.fromPoint(P0)
p1 = Vector.fromPoint(P1)
b_prime[j] = p1 - p0
e_j[j] = ahat.dot(b_prime[j])
E = np.sum(e_j)
# List of discordancy
dc = [np.sign(a) * np.sign(E) for a in e_j]
b = Vector(0, 0, 0)
for j in range(nseg):
b.x = b.x + b_prime[j].x * dc[j]
b.y = b.y + b_prime[j].y * dc[j]
b.z = b.z + b_prime[j].z * dc[j]
bhat = b.norm()
primate = bhat.dot(ahat)
if primate < 0:
tmpA0 = copy.copy(A0)
tmpA1 = copy.copy(A1)
A0 = tmpA1
A1 = tmpA0
if quadlist is not None:
# Length of prior segments
s_i = 0.0
l_i = np.zeros(len(quadlist))
for i in range(len(quadlist)):
P0, P1, P2, P3 = quadlist[i]
if 'rrup' in methods:
rrupdist = _calc_rupture_distance(P0, P1, P2, P3, sites_ecef)
minrrup = np.minimum(minrrup, rrupdist)
if 'rjb' in methods:
S0 = copy.deepcopy(P0)
S1 = copy.deepcopy(P1)
S2 = copy.deepcopy(P2)
S3 = copy.deepcopy(P3)
S0.depth = 0.0
S1.depth = 0.0
S2.depth = 0.0
S3.depth = 0.0
rjbdist = _calc_rupture_distance(S0, S1, S2, S3, sites_ecef)
minrjb = np.minimum(minrjb, rjbdist)
if ('rx' in methods) or ('ry' in methods) or \
('ry0' in methods) or ('U' in methods) or ('T' in methods):
# Rx, Ry, and Ry0 are all computed if one is requested since
# they all require similar information for the weights. This
# isn't necessary for a single segment fault though.
# Note, we are basing these calculations on GC2 coordinates U
# and T as described in:
# Spudich and Chiou (2015)
# http://dx.doi.org/10.3133/ofr20151028.
# Compute u_i and t_i for this segment
t_i = __calc_t_i(P0, P1, lat, lon, proj)
u_i = __calc_u_i(P0, P1, lat, lon, proj)
# Quad length
l_i[i] = get_quad_length(quadlist[i])
# Weight of segment, three cases
# Case 3: t_i == 0 and 0 <= u_i <= l_i
w_i = np.zeros_like(t_i)
# Case 1:
ix = t_i != 0
w_i[ix] = (1.0 / t_i[ix]) * (np.arctan(
(l_i[i] - u_i[ix]) / t_i[ix]) -
np.arctan(-u_i[ix] / t_i[ix]))
# Case 2:
ix = (t_i == 0) & ((u_i < 0) | (u_i > l_i[i]))
w_i[ix] = 1 / (u_i[ix] - l_i[i]) - 1 / u_i[ix]
totweight = totweight + w_i
GC2T = GC2T + w_i * t_i
if nseg == 1:
GC2U = GC2U + w_i * (u_i + s_i)
else:
if i == 0:
qind = np.array(range(len(quadlist)))
l_kj = 0
s_ij_1 = 0
else:
l_kj = l_i[(segindnp == segindnp[i]) & (qind < i)]
s_ij_1 = np.sum(l_kj)
p1 = Vector.fromPoint(quadlist[iq0[segind[i]]][0])
s_ij_2 = (
(p1 - p_origin) * dc[segind[i]]).dot(ahat) / 1000.0
# This is implemented with GC2N, for GC2T use:
# s_ij_2 = (p1 - p_origin).dot(bhat) / 1000.0
s_ij = s_ij_1 + s_ij_2
GC2U = GC2U + w_i * (u_i + s_ij)
s_i = s_i + l_i[i]
# Collect distances from loop into the distance dict
if 'rjb' in methods:
minrjb = minrjb.reshape(oldshape)
distdict['rjb'] = minrjb
if ('rx' in methods) or ('ry' in methods) or \
('ry0' in methods) or ('U' in methods) or ('T' in methods):
# Normalize by sum of quad weights
GC2T = GC2T / totweight
GC2U = GC2U / totweight
distdict['T'] = copy.deepcopy(GC2T).reshape(oldshape)
distdict['U'] = copy.deepcopy(GC2U).reshape(oldshape)
# Take care of Rx
Rx = copy.deepcopy(GC2T) # preserve sign (no absolute value)
Rx = Rx.reshape(oldshape)
distdict['rx'] = Rx
# Ry
Ry = GC2U - s_i / 2.0
Ry = Ry.reshape(oldshape)
distdict['ry'] = Ry
# Ry0
Ry0 = np.zeros_like(GC2U)
ix = GC2U < 0
Ry0[ix] = np.abs(GC2U[ix])
if nseg > 1:
s_i = s_ij + l_i[-1]
ix = GC2U > s_i
Ry0[ix] = GC2U[ix] - s_i
Ry0 = Ry0.reshape(oldshape)
distdict['ry0'] = Ry0
if 'rrup' in methods:
minrrup = minrrup.reshape(oldshape)
distdict['rrup'] = minrrup
else:
if 'rjb' in methods:
if use_median_distance:
warnings.warn(
'No fault; Replacing rjb with median rjb given M and repi.'
)
cdir, tmp = os.path.split(__file__)
# -------------------
# Sort out file names
# -------------------
mech = source.getEventParam('mech')
if not hasattr(source, '_tectonic_region'):
rf = os.path.join(
cdir, "data", "ps2ff",
"Rjb_WC94_mechA_ar1p0_seis0_20_Ratios.csv")
vf = os.path.join(cdir, "data", "ps2ff",
"Rjb_WC94_mechA_ar1p0_seis0_20_Var.csv")
elif source._tectonic_region == 'Active Shallow Crust':
if mech == 'ALL':
rf = os.path.join(
cdir, "data", "ps2ff",
"Rjb_WC94_mechA_ar1p7_seis0_20_Ratios.csv")
vf = os.path.join(
cdir, "data", "ps2ff",
"Rjb_WC94_mechA_ar1p7_seis0_20_Var.csv")
elif mech == 'RS':
rf = os.path.join(
cdir, "data", "ps2ff",
"Rjb_WC94_mechR_ar1p7_seis0_20_Ratios.csv")
vf = os.path.join(
cdir, "data", "ps2ff",
"Rjb_WC94_mechR_ar1p7_seis0_20_Var.csv")
elif mech == 'NM':
rf = os.path.join(
cdir, "data", "ps2ff",
"Rjb_WC94_mechN_ar1p7_seis0_20_Ratios.csv")
vf = os.path.join(
cdir, "data", "ps2ff",
"Rjb_WC94_mechN_ar1p7_seis0_20_Var.csv")
elif mech == 'SS':
rf = os.path.join(
cdir, "data", "ps2ff",
"Rjb_WC94_mechSS_ar1p7_seis0_20_Ratios.csv")
vf = os.path.join(
cdir, "data", "ps2ff",
"Rjb_WC94_mechSS_ar1p7_seis0_20_Var.csv")
elif source._tectonic_region == 'Stable Shallow Crust':
if mech == 'ALL':
rf = os.path.join(
cdir, "data", "ps2ff",
"Rjb_S14_mechA_ar1p0_seis0_15_Ratios.csv")
vf = os.path.join(
cdir, "data", "ps2ff",
"Rjb_S14_mechA_ar1p0_seis0_15_Var.csv")
elif mech == 'RS':
rf = os.path.join(
cdir, "data", "ps2ff",
"Rjb_S14_mechR_ar1p0_seis0_15_Ratios.csv")
vf = os.path.join(
cdir, "data", "ps2ff",
"Rjb_S14_mechR_ar1p0_seis0_15_Var.csv")
elif mech == 'NM':
rf = os.path.join(
cdir, "data", "ps2ff",
"Rjb_S14_mechN_ar1p0_seis0_15_Ratios.csv")
vf = os.path.join(
cdir, "data", "ps2ff",
"Rjb_S14_mechN_ar1p0_seis0_15_Var.csv")
elif mech == 'SS':
rf = os.path.join(
cdir, "data", "ps2ff",
"Rjb_S14_mechSS_ar1p0_seis0_15_Ratios.csv")
vf = os.path.join(
cdir, "data", "ps2ff",
"Rjb_S14_mechSS_ar1p0_seis0_15_Var.csv")
else:
warnings.warn(
'Unsupported tectonic region; using coefficients for unknown'
'tectonic region.')
rf = os.path.join(
cdir, "data", "ps2ff",
"Rjb_WC94_mechA_ar1p0_seis0_20_Ratios.csv")
vf = os.path.join(cdir, "data", "ps2ff",
"Rjb_WC94_mechA_ar1p0_seis0_20_Var.csv")
# -----------------
# Start with ratios
# -----------------
repi2rjb_ratios_tbl = pd.read_csv(rf, comment='#')
r2rrt_cols = repi2rjb_ratios_tbl.columns[1:]
mag_list = []
for column in (r2rrt_cols):
if re.search('R\d+\.*\d*', column):
magnitude = float(
re.findall('R(\d+\.*\d*)', column)[0])
mag_list.append(magnitude)
mag_list = np.array(mag_list)
dist_list = np.log(np.array(repi2rjb_ratios_tbl['Repi_km']))
repi2rjb_grid = repi2rjb_ratios_tbl.values[:, 1:]
repi2rjb_obj = spint.RectBivariateSpline(dist_list,
mag_list,
repi2rjb_grid,
kx=1,
ky=1)
def repi2rjb_tbl(repi, M):
ratio = repi2rjb_obj.ev(np.log(repi), M)
rjb = repi * ratio
return rjb
repis = distdict['repi']
mags = np.ones_like(repis) * source.getEventParam('mag')
rjb_hat = repi2rjb_tbl(repis, mags)
distdict['rjb'] = rjb_hat
# -------------------
# Additional Variance
# -------------------
repi2rjbvar_ratios_tbl = pd.read_csv(vf, comment='#')
repi2rjbvar_grid = repi2rjbvar_ratios_tbl.values[:, 1:]
repi2rjbvar_obj = spint.RectBivariateSpline(dist_list,
mag_list,
repi2rjbvar_grid,
kx=1,
ky=1)
rjbvar = repi2rjbvar_obj.ev(np.log(repis), mags)
distdict['rjbvar'] = rjbvar
else:
warnings.warn('No fault; Replacing rjb with repi')
distdict['rjb'] = distdict['repi']
if 'rrup' in methods:
if use_median_distance:
warnings.warn(
'No fault; Replacing rrup with median rrup given M and repi.'
)
cdir, tmp = os.path.split(__file__)
# -------------------
# Sort out file names
# -------------------
rake = source._event_dict.get('rake')
mech = rake_to_mech(rake)
if not hasattr(source, '_tectonic_region'):
rf = os.path.join(
cdir, "data", "ps2ff",
"Rrup_WC94_mechA_ar1p0_seis0-20_Ratios.csv")
vf = os.path.join(
cdir, "data", "ps2ff",
"Rrup_WC94_mechA_ar1p0_seis0-20_Var.csv")
elif source._tectonic_region == 'Active Shallow Crust':
if mech == 'ALL':
rf = os.path.join(
cdir, "data", "ps2ff",
"Rrup_WC94_mechA_ar1p7_seis0-20_Ratios.csv")
vf = os.path.join(
cdir, "data", "ps2ff",
"Rrup_WC94_mechA_ar1p7_seis0-20_Var.csv")
elif mech == 'RS':
rf = os.path.join(
cdir, "data", "ps2ff",
"Rrup_WC94_mechR_ar1p7_seis0-20_Ratios.csv")
vf = os.path.join(
cdir, "data", "ps2ff",
"Rrup_WC94_mechR_ar1p7_seis0-20_Var.csv")
elif mech == 'NM':
rf = os.path.join(
cdir, "data", "ps2ff",
"Rrup_WC94_mechN_ar1p7_seis0-20_Ratios.csv")
vf = os.path.join(
cdir, "data", "ps2ff",
"Rrup_WC94_mechN_ar1p7_seis0-20_Var.csv")
elif mech == 'SS':
rf = os.path.join(
cdir, "data", "ps2ff",
"Rrup_WC94_mechSS_ar1p7_seis0-20_Ratios.csv")
vf = os.path.join(
cdir, "data", "ps2ff",
"Rrup_WC94_mechSS_ar1p7_seis0-20_Var.csv")
elif source._tectonic_region == 'Stable Shallow Crust':
if mech == 'ALL':
rf = os.path.join(
cdir, "data", "ps2ff",
"Rrup_S14_mechA_ar1p0_seis0-15_Ratios.csv")
vf = os.path.join(
cdir, "data", "ps2ff",
"Rrup_S14_mechA_ar1p0_seis0-15_Var.csv")
elif mech == 'RS':
rf = os.path.join(
cdir, "data", "ps2ff",
"Rrup_S14_mechR_ar1p0_seis0-15_Ratios.csv")
vf = os.path.join(
cdir, "data", "ps2ff",
"Rrup_S14_mechR_ar1p0_seis0-15_Var.csv")
elif mech == 'NM':
rf = os.path.join(
cdir, "data", "ps2ff",
"Rrup_S14_mechN_ar1p0_seis0-15_Ratios.csv")
vf = os.path.join(
cdir, "data", "ps2ff",
"Rrup_S14_mechN_ar1p0_seis0-15_Var.csv")
elif mech == 'SS':
rf = os.path.join(
cdir, "data", "ps2ff",
"Rrup_S14_mechSS_ar1p0_seis0-15_Ratios.csv")
vf = os.path.join(
cdir, "data", "ps2ff",
"Rrup_S14_mechSS_ar1p0_seis0-15_Var.csv")
else:
warnings.warn(
'Unsupported tectonic region; using coefficients for unknown'
'tectonic region.')
rf = os.path.join(
cdir, "data", "ps2ff",
"Rrup_WC94_mechA_ar1p0_seis0-20_Ratios.csv")
vf = os.path.join(
cdir, "data", "ps2ff",
"Rrup_WC94_mechA_ar1p0_seis0-20_Var.csv")
# -----------------
# Start with ratios
# -----------------
repi2rrup_ratios_tbl = pd.read_csv(rf, comment='#')
r2rrt_cols = repi2rrup_ratios_tbl.columns[1:]
mag_list = []
for column in (r2rrt_cols):
if re.search('R\d+\.*\d*', column):
magnitude = float(
re.findall('R(\d+\.*\d*)', column)[0])
mag_list.append(magnitude)
mag_list = np.array(mag_list)
dist_list = np.log(np.array(repi2rrup_ratios_tbl['Repi_km']))
repi2rrup_grid = repi2rrup_ratios_tbl.values[:, 1:]
repi2rrup_obj = spint.RectBivariateSpline(dist_list,
mag_list,
repi2rrup_grid,
kx=1,
ky=1)
def repi2rrup_tbl(repi, M):
ratio = repi2rrup_obj.ev(np.log(repi), M)
rrup = repi * ratio
return rrup
repis = distdict['repi']
mags = np.ones_like(repis) * source.getEventParam('mag')
rrup_hat = repi2rrup_tbl(repis, mags)
distdict['rrup'] = rrup_hat
# -------------------
# Additional Variance
# -------------------
repi2rrupvar_ratios_tbl = pd.read_csv(vf, comment='#')
repi2rrupvar_grid = repi2rrupvar_ratios_tbl.values[:, 1:]
repi2rrupvar_obj = spint.RectBivariateSpline(dist_list,
mag_list,
repi2rrupvar_grid,
kx=1,
ky=1)
rrupvar = repi2rrupvar_obj.ev(np.log(repis), mags)
distdict['rrupvar'] = rrupvar
else:
warnings.warn('No fault; Replacing rrup with rhypo')
distdict['rrup'] = distdict['rhypo']
if 'rx' in methods:
warnings.warn('No fault; Setting Rx to zero.')
distdict['rx'] = np.zeros_like(distdict['repi'])
if 'ry0' in methods:
warnings.warn('No fault; Replacing ry0 with repi')
distdict['ry0'] = distdict['repi']
if 'ry' in methods:
warnings.warn('No fault; Replacing ry with repi')
distdict['ry'] = distdict['repi']
return distdict
