def __computeD(self, i):
"""Compute d for the i-th quad/segment.
Y = d/W, where d is the portion (in km) of the width of the fault which
ruptures up-dip from the hypocenter to the top of the fault.
:param i:
index of segment for which d is to be computed.
"""
hyp_ecef = self.phyp[i] # already in ECEF
hyp_col = np.array([[hyp_ecef.x], [hyp_ecef.y], [hyp_ecef.z]])
# First compute "updip" vector
P0, P1, P2 = self._rup.getQuadrilaterals()[i][0:3]
p1 = Vector.fromPoint(P1) # convert to ECEF
p2 = Vector.fromPoint(P2)
e21 = p1 - p2
e21norm = e21.norm()
hp1 = p1 - hyp_ecef
# convert to km (used as max later)
udip_len = Vector.dot(hp1, e21norm) / 1000.0
udip_col = np.array([[e21norm.x], [e21norm.y],
[e21norm.z]]) # ECEF coords
# Sites
slat = self._lat
slon = self._lon
# Convert sites 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 = ecef.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, ))
])
# Hypocenter-to-site matrix
h2s_mat = site_mat - hyp_col # in ECEF
# Dot hypocenter-to-site with updip vector
d_raw = np.abs(np.sum(h2s_mat * udip_col, axis=0)) / \
1000.0 # convert to km
d_raw = np.reshape(d_raw, self._lat.shape)
self.d = d_raw.clip(min=1.0, max=udip_len)
def __computeD(self, i):
"""Compute d for the i-th quad/segment.
Y = d/W, where d is the portion (in km) of the width of the fault which
ruptures up-dip from the hypocenter to the top of the fault.
:param i:
index of segment for which d is to be computed.
"""
hyp_ecef = self.phyp[i] # already in ECEF
hyp_col = np.array([[hyp_ecef.x], [hyp_ecef.y], [hyp_ecef.z]])
# First compute "updip" vector
P0, P1, P2, P3 = self._flt.getQuadrilaterals()[i]
p1 = Vector.fromPoint(P1) # convert to ECEF
p2 = Vector.fromPoint(P2)
e21 = p1 - p2
e21norm = e21.norm()
hp1 = p1 - hyp_ecef
# convert to km (used as max later)
udip_len = Vector.dot(hp1, e21norm) / 1000.0
udip_col = np.array([[e21norm.x], [e21norm.y],
[e21norm.z]]) # ECEF coords
# Sites
slat = self._lat
slon = self._lon
# Convert sites 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 = ecef.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, ))
])
# Hypocenter-to-site matrix
h2s_mat = site_mat - hyp_col # in ECEF
# Dot hypocenter-to-site with updip vector
d_raw = np.abs(np.sum(h2s_mat * udip_col, axis=0)) / \
1000.0 # convert to km
d_raw = np.reshape(d_raw, self._lat.shape)
self.d = d_raw.clip(min=1.0, max=udip_len)
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 rupture
quadrilateral.
:param P1:
Point object, representing the second top-edge vertex of a rupture
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 rupture
quadrilateral.
:param P1:
Point object, representing the second top-edge vertex of a rupture
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 __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 __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 __computeWrup(self):
"""
Compute the the portion (in km) of the width of the fault which ruptures
up-dip from the hypocenter to the top of the fault.
Wrup is the portion (in km) of the width of the fault which
ruptures up-dip from the hypocenter to the top of the fault.
* This is ambiguous for faults with varible top of rupture (not
allowed in NGA). For now, lets just compute this for the
quad where the hypocenter is located.
* Alternative is to compute max Wrup for the different quads.
"""
nquad = len(self._flt._quadrilaterals)
#-------------------------------------------
# First find which quad the hypocenter is on
#-------------------------------------------
x, y, z = latlon2ecef(
self._hyp.latitude, self._hyp.longitude, self._hyp.depth)
hyp_ecef = np.array([[x, y, z]])
qdist = np.zeros(nquad)
for i in range(0, nquad):
P0, P1, P2, P3 = self._flt.getQuadrilaterals()[i]
qdist[i] = _calc_rupture_distance(P0, P1, P2, P3, hyp_ecef)
ind = int(np.where(qdist == np.min(qdist))[0])
# *** check that this doesn't break with more than one quad
q = self._flt.getQuadrilaterals()[ind]
#--------------------------
# Compute Wrup on that quad
#--------------------------
pp0 = Vector.fromPoint(geo.point.Point(
q[0].longitude, q[0].latitude, q[0].depth))
hyp_ecef = Vector.fromPoint(geo.point.Point(
self._hyp.longitude, self._hyp.latitude, self._hyp.depth))
hp0 = hyp_ecef - pp0
ddv = fault.get_quad_down_dip_vector(q)
self._Wrup = Vector.dot(ddv, hp0) / 1000
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_rupture_distance(P0, P1, P2, P3, points):
"""
Calculate the shortest distance from a set of points to a rupture surface.
: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 P2:
Point object, representing the first bottom-edge vertex of a fault quadrilateral.
:param P3:
Point object, representing the second bottom-edge vertex of a fault quadrilateral.
:param points:
Numpy array Nx3 of points (ECEF) to calculate distance from.
:returns:
Array of size N of distances (in km) from input points to rupture surface.
"""
# Convert to ecef
p0 = Vector.fromPoint(P0)
p1 = Vector.fromPoint(P1)
p2 = Vector.fromPoint(P2)
p3 = Vector.fromPoint(P3)
# Make a unit vector normal to the plane
normalVector = (p1 - p0).cross(p2 - p0).norm()
dist = np.ones_like(points[:, 0]) * np.nan
p0d = p0.getArray() - points
p1d = p1.getArray() - points
p2d = p2.getArray() - points
p3d = p3.getArray() - points
# Create 4 planes with normals pointing outside rectangle
n0 = (p1 - p0).cross(normalVector).getArray()
n1 = (p2 - p1).cross(normalVector).getArray()
n2 = (p3 - p2).cross(normalVector).getArray()
n3 = (p0 - p3).cross(normalVector).getArray()
sgn0 = np.signbit(np.sum(n0 * p0d, axis=1))
sgn1 = np.signbit(np.sum(n1 * p1d, axis=1))
sgn2 = np.signbit(np.sum(n2 * p2d, axis=1))
sgn3 = np.signbit(np.sum(n3 * p3d, axis=1))
inside_idx = (sgn0 == sgn1) & (sgn1 == sgn2) & (sgn2 == sgn3)
dist[inside_idx] = np.power(
np.abs(np.sum(p0d[inside_idx, :] * normalVector.getArray(), axis=1)),
2)
outside_idx = np.logical_not(inside_idx)
s0 = _distance_sq_to_segment(p0d, p1d)
s1 = _distance_sq_to_segment(p1d, p2d)
s2 = _distance_sq_to_segment(p2d, p3d)
s3 = _distance_sq_to_segment(p3d, p0d)
smin = np.minimum(np.minimum(s0, s1), np.minimum(s2, s3))
dist[outside_idx] = smin[outside_idx]
dist = np.sqrt(dist) / 1000.0
shp = dist.shape
if len(shp) == 1:
dist.shape = (shp[0], 1)
if np.any(np.isnan(dist)):
raise ShakeMapException("Could not calculate some distances!")
dist = np.fliplr(dist)
return dist
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
def test_so6():
event_name = 'so6'
magnitude = 7.2
dip = np.array([70])
rake = 135
width = np.array([15])
L = 80
rupx = np.array([0, 0])
rupy = np.array([0, L])
zp = np.array([0])
# Convert to lat/lon
proj = geo.utils.get_orthographic_projection(-122, -120, 39, 37)
tlon,tlat = proj(rupx, rupy, reverse = True)
# Dummy origin
origin = Origin({'lat':0,
'lon':0,
'depth':0,
'mag':0,
'id':'so6',
'rake':rake})
# Rupture
rup = rupture.QuadRupture.fromTrace(
np.array([tlon[0]]), np.array([tlat[0]]),
np.array([tlon[1]]), np.array([tlat[1]]),
zp, width, dip, origin, reference = 'rv4')
# Sites
x = np.linspace(-80, 80, 21)
y = np.linspace(-50, 130, 21)
site_x,site_y = np.meshgrid(x, y)
slon,slat = proj(site_x, site_y, reverse = True)
sdepth = np.zeros_like(slon)
# Fix origin
tmp = rup.getQuadrilaterals()[0]
pp0 = Vector.fromPoint(point.Point(tmp[0].longitude, tmp[0].latitude, tmp[0].depth))
pp1 = Vector.fromPoint(point.Point(tmp[1].longitude, tmp[1].latitude, tmp[1].depth))
pp2 = Vector.fromPoint(point.Point(tmp[2].longitude, tmp[2].latitude, tmp[2].depth))
pp3 = Vector.fromPoint(point.Point(tmp[3].longitude, tmp[3].latitude, tmp[3].depth))
dxp = 10/L
dyp = (width-5)/width
mp0 = pp0 + (pp1 - pp0)*dxp
mp1 = pp3 + (pp2 - pp3)*dxp
rp = mp0 + (mp1 - mp0)*dyp
epilat,epilon,epidepth = ecef2latlon(rp.x, rp.y, rp.z)
epix,epiy = proj(epilon, epilat, reverse = False)
origin = Origin({'lat':epilat,
'lon':epilon,
'depth':epidepth,
'mag':magnitude,
'id':'so6',
'rake':rake})
ruplat = [a.latitude for a in rup.getQuadrilaterals()[0]]
ruplon = [a.longitude for a in rup.getQuadrilaterals()[0]]
ruplat = np.append(ruplat, ruplat[0])
ruplon = np.append(ruplon, ruplon[0])
rupx,rupy = proj(ruplon, ruplat, reverse = False)
test1 = Bayless2013(origin, rup, slat, slon, sdepth, T = 5)
fd = test1.getFd()
fd_test = np.array(
[[ 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
-8.92879772e-03, -1.74526918e-02, -2.22981746e-02,
-2.34350450e-02, -2.13620062e-02, -1.72712346e-02,
-1.29509613e-02, -1.02545064e-02, -1.03010185e-02,
-1.28847597e-02, -1.66274727e-02, -1.96984070e-02,
-2.05377743e-02, -1.81831337e-02, -1.21881814e-02,
-2.64862879e-03, 0.00000000e+00, 0.00000000e+00],
[ 0.00000000e+00, 0.00000000e+00, -8.73221519e-03,
-2.21421374e-02, -3.18438939e-02, -3.71488270e-02,
-3.76239913e-02, -3.35015951e-02, -2.61748968e-02,
-1.83864728e-02, -1.34793002e-02, -1.36687799e-02,
-1.85727143e-02, -2.55527671e-02, -3.14227568e-02,
-3.38933995e-02, -3.19289607e-02, -2.53396980e-02,
-1.45943649e-02, -3.71405488e-04, 0.00000000e+00],
[ 0.00000000e+00, -2.54621422e-03, -2.11428566e-02,
-3.68609103e-02, -4.87464747e-02, -5.56539037e-02,
-5.64419387e-02, -5.05331157e-02, -3.52919381e-02,
-2.18782050e-02, -1.40858125e-02, -1.47354546e-02,
-2.35727189e-02, -3.74838465e-02, -4.75915414e-02,
-5.13000399e-02, -4.87882409e-02, -4.05716321e-02,
-2.77368254e-02, -1.13542729e-02, 0.00000000e+00],
[ 0.00000000e+00, -1.21642958e-02, -3.33747360e-02,
-5.21661817e-02, -6.74724509e-02, -7.77628842e-02,
-8.00243748e-02, -6.42496853e-02, -4.38124530e-02,
-1.97027426e-02, -1.45897731e-02, -1.07427056e-02,
-3.08235222e-02, -4.82656988e-02, -6.67692677e-02,
-7.35152908e-02, -6.85574283e-02, -5.71811573e-02,
-4.12138780e-02, -2.20396726e-02, -6.24121310e-04],
[ 0.00000000e+00, -2.00643401e-02, -4.39827328e-02,
-6.62722434e-02, -8.60268414e-02, -1.01730306e-01,
-9.86277741e-02, -9.82914922e-02, -5.22335876e-02,
-1.54622435e-02, -1.57487554e-02, -3.06190808e-03,
-4.81481586e-02, -8.92480491e-02, -8.63776477e-02,
-9.98130440e-02, -8.95491230e-02, -7.33553695e-02,
-5.34401725e-02, -3.11601812e-02, -7.33715103e-03],
[ 0.00000000e+00, -2.50053614e-02, -5.11695772e-02,
-7.65997026e-02, -1.00809054e-01, -1.22877573e-01,
-1.18738178e-01, -1.55236782e-01, -7.45388001e-02,
1.92779182e-03, -1.94380016e-02, 1.94922939e-02,
-7.66669920e-02, -1.53909722e-01, -1.10846875e-01,
-1.19746768e-01, -1.07680300e-01, -8.59905101e-02,
-6.22042294e-02, -3.71802472e-02, -1.13867485e-02],
[ 0.00000000e+00, -2.63645827e-02, -5.37984901e-02,
-8.11337022e-02, -1.08298371e-01, -1.35146441e-01,
-1.34825430e-01, -1.85836050e-01, -1.10730875e-01,
-3.18861095e-02, 4.14395701e-02, -1.52711946e-02,
-1.31840763e-01, -1.96794707e-01, -1.33453212e-01,
-1.34989129e-01, -1.17922385e-01, -9.21637323e-02,
-6.58369237e-02, -3.91646838e-02, -1.22685698e-02],
[ 0.00000000e+00, -2.64622244e-02, -5.40483999e-02,
-8.16190336e-02, -1.09162854e-01, -1.36656677e-01,
-1.37081504e-01, -1.89522811e-01, -1.17723634e-01,
-4.88765748e-02, -5.04529015e-03, -5.76414497e-02,
-1.45712183e-01, -2.03062804e-01, -1.36859828e-01,
-1.37107390e-01, -1.19124650e-01, -9.28263279e-02,
-6.61800709e-02, -3.93088682e-02, -1.22842049e-02],
[ 0.00000000e+00, -2.58466495e-02, -5.24858827e-02,
-7.86086164e-02, -1.03856343e-01, -1.27529509e-01,
-1.23794779e-01, -1.68810613e-01, -8.22602627e-02,
1.74236964e-02, 9.38708725e-02, 4.23208284e-02,
-8.46343723e-02, -1.70476759e-01, -1.17547884e-01,
-1.24569752e-01, -1.11518670e-01, -8.84736806e-02,
-6.38037151e-02, -3.81874381e-02, -1.19867610e-02],
[ 0.00000000e+00, -2.42186547e-02, -4.84175525e-02,
-7.09428614e-02, -9.07754575e-02, -1.06117824e-01,
-9.50228292e-02, -1.29781980e-01, -3.08573454e-02,
7.39058739e-02, 1.30478117e-01, 8.28181149e-02,
-2.70389535e-02, -1.20837502e-01, -8.02081725e-02,
-9.70274506e-02, -9.35853383e-02, -7.77422806e-02,
-5.77817530e-02, -3.53067886e-02, -1.12414659e-02],
[ 0.00000000e+00, -2.16818717e-02, -4.22363856e-02,
-5.96909893e-02, -7.24805224e-02, -7.81867829e-02,
-6.11838569e-02, -9.05679744e-02, 9.95934969e-03,
1.07503875e-01, 1.52073917e-01, 1.05894634e-01,
8.68652263e-03, -7.98571818e-02, -4.16548658e-02,
-6.40511838e-02, -6.99337160e-02, -6.26305633e-02,
-4.89098800e-02, -3.09284566e-02, -1.00919381e-02],
[ 0.00000000e+00, -1.84940182e-02, -3.47054606e-02,
-4.65278129e-02, -5.22037664e-02, -4.93977115e-02,
-2.95395230e-02, -5.82421092e-02, 3.91025654e-02,
1.29337956e-01, 1.67436703e-01, 1.21969296e-01,
3.20823547e-02, -5.00287386e-02, -9.22993907e-03,
-3.27186625e-02, -4.52706958e-02, -4.57409325e-02,
-3.84701291e-02, -2.55751405e-02, -8.64950254e-03],
[ 0.00000000e+00, -1.49431380e-02, -2.65887341e-02,
-3.29162158e-02, -3.22994323e-02, -2.29081781e-02,
-2.60259636e-03, -3.29856530e-02, 6.02631314e-02,
1.45003704e-01, 1.79361264e-01, 1.34292814e-01,
4.88007115e-02, -2.82328554e-02, 1.64212421e-02,
-5.72391847e-03, -2.23438861e-02, -2.90246794e-02,
-2.76054402e-02, -1.97779758e-02, -7.03945406e-03],
[ 0.00000000e+00, -1.12771143e-02, -1.84737590e-02,
-1.98228664e-02, -1.40092305e-02, 1.84580818e-04,
1.95817303e-02, -1.32608487e-02, 7.62783168e-02,
1.57076433e-01, 1.89083905e-01, 1.44259188e-01,
6.15722813e-02, -1.17505212e-02, 3.65938109e-02,
1.66937711e-02, -2.18970818e-03, -1.35507683e-02,
-1.70890527e-02, -1.39519424e-02, -5.37036892e-03],
[ 0.00000000e+00, -7.67615215e-03, -1.07348257e-02,
-7.75276739e-03, 2.22351695e-03, 1.98662250e-02,
3.77611177e-02, 2.42018661e-03, 8.89036172e-02,
1.66855206e-01, 1.97260700e-01, 1.52590263e-01,
7.17981256e-02, 1.18005972e-03, 5.26852303e-02,
3.51638855e-02, 1.51012176e-02, 2.69654076e-04,
-7.33815554e-03, -8.36639665e-03, -3.72176313e-03],
[ 0.00000000e+00, -4.50552324e-03, -4.32262850e-03,
1.73559158e-03, 1.42670366e-02, 3.35040699e-02,
4.97279358e-02, 1.85410528e-02, 9.39950666e-02,
1.46646579e-01, 9.13474746e-02, 1.37004651e-01,
7.74648339e-02, 1.59777072e-02, 6.25334939e-02,
4.74577418e-02, 2.72155518e-02, 1.06174952e-02,
3.94103899e-04, -3.68465400e-03, -2.19830733e-03],
[ 0.00000000e+00, -1.74629916e-03, 5.44471813e-04,
8.22933499e-03, 2.15699287e-02, 4.04232250e-02,
5.69678048e-02, 5.52408259e-02, 9.04381272e-02,
1.08204635e-01, 9.14439984e-02, 1.06884511e-01,
8.17241884e-02, 5.55282924e-02, 6.78528399e-02,
5.47188925e-02, 3.35251483e-02, 1.69615982e-02,
5.72048628e-03, -8.81437278e-05, -7.36518436e-04],
[ 0.00000000e+00, 4.07838765e-05, 3.63933766e-03,
1.20080876e-02, 2.51274691e-02, 4.25687176e-02,
6.25685606e-02, 7.33480475e-02, 8.37515545e-02,
9.52500287e-02, 9.15135660e-02, 9.66442834e-02,
8.66659913e-02, 8.10325633e-02, 7.18836713e-02,
5.45548434e-02, 3.55884875e-02, 2.00142359e-02,
8.71200201e-03, 2.04407846e-03, -6.53680674e-06],
[ 0.00000000e+00, 2.40054729e-04, 4.44975227e-03,
1.27572519e-02, 2.49362989e-02, 4.03831326e-02,
5.80039988e-02, 7.61280192e-02, 8.37404162e-02,
8.89634569e-02, 9.15651607e-02, 9.13586235e-02,
8.83589144e-02, 8.27804032e-02, 6.75666471e-02,
5.00483249e-02, 3.36733366e-02, 1.96758691e-02,
9.00603204e-03, 2.18370401e-03, 0.00000000e+00],
[ 0.00000000e+00, 0.00000000e+00, 2.78776980e-03,
1.05086036e-02, 2.13238822e-02, 3.45577738e-02,
4.91570145e-02, 6.36787133e-02, 7.63710088e-02,
8.54072310e-02, 8.92960200e-02, 8.75702197e-02,
8.07095447e-02, 6.97999389e-02, 5.63787286e-02,
4.20734776e-02, 2.83073312e-02, 1.61614525e-02,
6.56194125e-03, 1.00721924e-04, 0.00000000e+00],
[ 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
5.49667845e-03, 1.47563319e-02, 2.57955743e-02,
3.76689418e-02, 4.91861917e-02, 5.90108907e-02,
6.58478416e-02, 6.87018515e-02, 6.73174642e-02,
6.20270643e-02, 5.35456385e-02, 4.29400416e-02,
3.14129728e-02, 2.00795162e-02, 9.84001885e-03,
1.53992995e-03, 0.00000000e+00, 0.00000000e+00]]
)
np.testing.assert_allclose(fd, fd_test, rtol=1e-4)
def test_rv4():
magnitude = 7.0
rake = 90.0
width = np.array([28])
fltx = np.array([0, 0])
flty = np.array([0, 32])
zp = np.array([0])
dip = np.array([30])
# Convert to lat/lon
proj = geo.utils.get_orthographic_projection(-122, -120, 39, 37)
tlon, tlat = proj(fltx, flty, reverse=True)
flt = fault.Fault.fromTrace(np.array([tlon[0]]),
np.array([tlat[0]]),
np.array([tlon[1]]),
np.array([tlat[1]]),
zp,
width,
dip,
reference='')
L = flt.getFaultLength()
# Try to figure out epicenter
tmp = flt.getQuadrilaterals()[0]
pp0 = Vector.fromPoint(
point.Point(tmp[0].longitude, tmp[0].latitude, tmp[0].depth))
pp1 = Vector.fromPoint(
point.Point(tmp[1].longitude, tmp[1].latitude, tmp[1].depth))
pp2 = Vector.fromPoint(
point.Point(tmp[2].longitude, tmp[2].latitude, tmp[2].depth))
pp3 = Vector.fromPoint(
point.Point(tmp[3].longitude, tmp[3].latitude, tmp[3].depth))
dxp = 6 / L
dyp = (width - 8) / width
mp0 = pp0 + (pp1 - pp0) * dxp
mp1 = pp3 + (pp2 - pp3) * dxp
rp = mp0 + (mp1 - mp0) * dyp
epilat, epilon, epidepth = ecef2latlon(rp.x, rp.y, rp.z)
event = {
'lat': epilat,
'lon': epilon,
'depth': epidepth,
'mag': magnitude,
'id': 'test',
'locstring': 'rv4',
'type': 'DS',
'timezone': 'UTC'
}
event['time'] = ShakeDateTime.utcfromtimestamp(int(time.time()))
event['created'] = ShakeDateTime.utcfromtimestamp(int(time.time()))
x = np.linspace(-50, 50, 11)
y = np.linspace(-50, 50, 11)
site_x, site_y = np.meshgrid(x, y)
slon, slat = proj(site_x, site_y, reverse=True)
deps = np.zeros_like(slon)
source = Source(event, flt)
source.setEventParam('rake', rake)
test1 = Bayless2013(source, slat, slon, deps, T=2.0)
# Test fd
fd = test1.getFd()
fd_test = np.array(
[[
0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.72143257e-03,
1.34977260e-03, 4.33616224e-15, 1.24446253e-03, 1.16142357e-03,
2.25464716e-03, 7.05281751e-04, 0.00000000e+00
],
[
0.00000000e+00, 0.00000000e+00, 7.62610242e-03, 1.25133844e-02,
5.61896104e-03, 7.63126014e-15, 4.52266194e-03, 4.67970900e-03,
1.02820316e-02, 5.13160096e-03, -6.13926251e-03
],
[
0.00000000e+00, 4.00495234e-03, 2.37608386e-02, 2.37139333e-02,
9.55224050e-03, 5.66364910e-15, 7.70344813e-03, 7.36466362e-03,
1.48239704e-02, 8.40388145e-03, -1.58592485e-02
],
[
8.08385547e-19, 9.38150101e-03, 3.38610620e-02, 3.85351492e-02,
1.91044918e-02, 3.98697802e-15, 1.54321666e-02, 1.21913760e-02,
2.04435166e-02, 1.04931859e-02, -1.85935894e-02
],
[
2.12025421e-18, 1.37316085e-02, 4.40193799e-02, 6.16562477e-02,
4.77612496e-02, 2.60257085e-15, 3.86322888e-02, 1.97965887e-02,
2.64882038e-02, 1.23335908e-02, -2.07389932e-02
],
[
2.64338576e-18, 1.45898292e-02, 4.89104213e-02, 7.70703166e-02,
9.55225258e-02, 1.01875104e-01, 7.73459329e-02, 2.50275508e-02,
2.93537540e-02, 1.30949577e-02, -2.15685454e-02
],
[
2.64330042e-18, 1.45898262e-02, 4.89104186e-02, 7.70703146e-02,
9.55225248e-02, 1.01910945e-01, 7.74050835e-02, 2.52307946e-02,
2.92970736e-02, 1.30880504e-02, -2.15685424e-02
],
[
2.64318867e-18, 1.45898259e-02, 4.89104184e-02, 7.70703144e-02,
9.55225247e-02, 1.01933432e-01, 7.74421258e-02, 2.53572923e-02,
2.92615130e-02, 1.30837284e-02, -2.15685422e-02
],
[
2.64305117e-18, 1.45898284e-02, 4.89104206e-02, 7.70703161e-02,
9.55225256e-02, 1.01942593e-01, 7.74571359e-02, 2.54081640e-02,
2.92472117e-02, 1.30819985e-02, -2.15685446e-02
],
[
2.30141673e-18, 1.40210825e-02, 4.56205547e-02, 6.63109661e-02,
5.79266964e-02, 2.33044622e-15, 4.69672564e-02, 2.18401553e-02,
2.72864925e-02, 1.25728575e-02, -2.10227772e-02
],
[
1.10672535e-18, 1.04777076e-02, 3.59041065e-02, 4.24614318e-02,
2.24217216e-02, 3.66914762e-15, 1.81728517e-02, 1.39301504e-02,
2.14956836e-02, 1.08711460e-02, -1.90802849e-02
]])
np.testing.assert_allclose(fd, fd_test, rtol=2e-4)
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 __computeThetaAndS(self, i):
"""
:param i:
Compute d for the i-th quad/segment.
"""
# self.phyp is in ECEF
tmp = ecef.ecef2latlon(self.phyp[i].x, self.phyp[i].y, self.phyp[i].z)
epi_ecef = Vector.fromPoint(geo.point.Point(tmp[1], tmp[0], 0.0))
epi_col = np.array([[epi_ecef.x], [epi_ecef.y], [epi_ecef.z]])
# First compute along strike vector
P0, P1, P2, P3 = self._flt.getQuadrilaterals()[i]
p0 = Vector.fromPoint(P0) # convert to ECEF
p1 = Vector.fromPoint(P1)
e01 = p1 - p0
e01norm = e01.norm()
hp0 = p0 - epi_ecef
hp1 = p1 - epi_ecef
strike_min = Vector.dot(hp0, e01norm) / 1000.0 # convert to km
strike_max = Vector.dot(hp1, e01norm) / 1000.0 # convert to km
strike_col = np.array([[e01norm.x], [e01norm.y],
[e01norm.z]]) # ECEF coords
# Sites
slat = self._lat
slon = self._lon
# Convert sites 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 = ecef.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, ))
])
# Epicenter-to-site matrix
e2s_mat = site_mat - epi_col # in ECEF
mag = np.sqrt(np.sum(e2s_mat * e2s_mat, axis=0))
# Avoid division by zero
mag[mag == 0] = 1e-12
e2s_norm = e2s_mat / mag
# Dot epicenter-to-site with along-strike vector
s_raw = np.sum(e2s_mat * strike_col, axis=0) / 1000.0 # conver to km
# Put back into a 2d array
s_raw = np.reshape(s_raw, self._lat.shape)
self.s = np.abs(s_raw.clip(min=strike_min,
max=strike_max)).clip(min=np.exp(1))
# Compute theta
sdots = np.sum(e2s_norm * strike_col, axis=0)
theta_raw = np.arccos(sdots)
# But theta is defined to be the reference angle
# (i.e., the equivalent angle between 0 and 90 deg)
sintheta = np.abs(np.sin(theta_raw))
costheta = np.abs(np.cos(theta_raw))
theta = np.arctan2(sintheta, costheta)
self.theta = np.reshape(theta, self._lat.shape)
def __setPseudoHypocenters(self):
""" Set a pseudo-hypocenter.
Adapted from ShakeMap 3.5 src/contour/directivity.c
From Bayless and Somerville:
"Define the pseudo-hypocenter for rupture of successive segments as
the point on the side edge of the fault segment that is closest to
the side edge of the previous segment, and that lies half way
between the top and bottom of the fault. We assume that the fault is
segmented along strike, not updip. All geometric parameters are
computed relative to the pseudo-hypocenter."
"""
hyp_ecef = Vector.fromPoint(
geo.point.Point(self._hyp.longitude, self._hyp.latitude,
self._hyp.depth))
# Loop over each quad
self.phyp = [None] * self._nq
for i in range(self._nq):
P0, P1, P2, P3 = self._flt.getQuadrilaterals()[i]
p0 = Vector.fromPoint(P0) # convert to ECEF
p1 = Vector.fromPoint(P1)
p2 = Vector.fromPoint(P2)
p3 = Vector.fromPoint(P3)
# Create 4 planes with normals pointing outside rectangle
hpnp = Vector.cross(p1 - p0, p2 - p0).norm()
hpp = -hpnp.x * p0.x - hpnp.y * p0.y - hpnp.z * p0.z
n0 = Vector.cross(p1 - p0, hpnp)
n1 = Vector.cross(p2 - p1, hpnp)
n2 = Vector.cross(p3 - p2, hpnp)
n3 = Vector.cross(p0 - p3, hpnp)
# Is the hypocenter inside the projected rectangle?
# Dot products show which side the origin is on.
# If origin is on same side of all the planes, then it is 'inside'
sgn0 = np.signbit(Vector.dot(n0, p0 - hyp_ecef))
sgn1 = np.signbit(Vector.dot(n1, p1 - hyp_ecef))
sgn2 = np.signbit(Vector.dot(n2, p2 - hyp_ecef))
sgn3 = np.signbit(Vector.dot(n3, p3 - hyp_ecef))
if (sgn0 == sgn1) and (sgn1 == sgn2) and (sgn2 == sgn3):
# Origin is inside. Use distance-to-plane formula.
d = Vector.dot(hpnp, hyp_ecef) + hpp
d = d * d
# Put the pseudo hypocenter on the plane
D = Vector.dot(hpnp, hyp_ecef) + hpp
self.phyp[i] = hyp_ecef - hpnp * D
else:
# Origin is outside. Find distance to edges
p0p = np.reshape(p0.getArray() - hyp_ecef.getArray(), [1, 3])
p1p = np.reshape(p1.getArray() - hyp_ecef.getArray(), [1, 3])
p2p = np.reshape(p2.getArray() - hyp_ecef.getArray(), [1, 3])
p3p = np.reshape(p3.getArray() - hyp_ecef.getArray(), [1, 3])
s1 = dist2_to_segment(p1p, p2p)
s2 = dist2_to_segment(p2p, p3p)
s3 = dist2_to_segment(p3p, p0p)
# Assuming that the fault is segmented along strike and not
# updip (as described by Bayless and somerville), we only
# need to consider s1 and s3:
if s1 > s3:
e30 = p0 - p3
e30norm = e30.norm()
mag = e30.mag()
self.phyp[i] = p3 + e30norm * (0.5 * mag)
else:
e21 = p1 - p2
e21norm = e21.norm()
mag = e21.mag()
self.phyp[i] = p2 + e21norm * (0.5 * mag)
def test_rv4():
magnitude = 7.0
rake = 90.0
width = np.array([28])
rupx = np.array([0, 0])
rupy = np.array([0, 32])
zp = np.array([0])
dip = np.array([30])
# Convert to lat/lon
proj = geo.utils.get_orthographic_projection(-122, -120, 39, 37)
tlon, tlat = proj(rupx, rupy, reverse=True)
# Dummy Origin
origin = Origin({'lat':0,
'lon':0,
'depth':0,
'mag':0,
'id':'rv4',
'rake':rake})
# Rupture
rup = rupture.QuadRupture.fromTrace(
np.array([tlon[0]]), np.array([tlat[0]]),
np.array([tlon[1]]), np.array([tlat[1]]),
zp, width, dip, origin, reference='')
L = rup.getLength()
# Figure out epicenter
tmp = rup.getQuadrilaterals()[0]
pp0 = Vector.fromPoint(point.Point(
tmp[0].longitude, tmp[0].latitude, tmp[0].depth))
pp1 = Vector.fromPoint(point.Point(
tmp[1].longitude, tmp[1].latitude, tmp[1].depth))
pp2 = Vector.fromPoint(point.Point(
tmp[2].longitude, tmp[2].latitude, tmp[2].depth))
pp3 = Vector.fromPoint(point.Point(
tmp[3].longitude, tmp[3].latitude, tmp[3].depth))
dxp = 6/L
dyp = (width-8)/width
mp0 = pp0 + (pp1 - pp0)*dxp
mp1 = pp3 + (pp2 - pp3)*dxp
rp = mp0 + (mp1 - mp0)*dyp
epilat,epilon,epidepth = ecef2latlon(rp.x, rp.y, rp.z)
# Fix Origin:
origin = Origin({'lat':epilat,
'lon':epilon,
'depth':epidepth,
'mag':magnitude,
'id':'rv4',
'rake':rake})
x = np.linspace(-50, 50, 11)
y = np.linspace(-50, 50, 11)
site_x, site_y = np.meshgrid(x, y)
slon, slat = proj(site_x, site_y, reverse=True)
deps = np.zeros_like(slon)
test1 = Bayless2013(origin, rup, slat, slon, deps, T=2.0)
# Test fd
fd = test1.getFd()
fd_test = np.array(
[[ 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
1.72143257e-03, 1.34977260e-03, 4.33616224e-15,
1.24446253e-03, 1.16142357e-03, 2.25464716e-03,
7.05281751e-04, 0.00000000e+00],
[ 0.00000000e+00, 0.00000000e+00, 7.62610242e-03,
1.25133844e-02, 5.61896104e-03, 7.63126014e-15,
4.52266194e-03, 4.67970900e-03, 1.02820316e-02,
5.13160096e-03, -6.13926251e-03],
[ 0.00000000e+00, 4.00495234e-03, 2.37608386e-02,
2.37139333e-02, 9.55224050e-03, 5.66364910e-15,
7.70344813e-03, 7.36466362e-03, 1.48239704e-02,
8.40388145e-03, -1.58592485e-02],
[ 8.08385547e-19, 9.38150101e-03, 3.38610620e-02,
3.85351492e-02, 1.91044918e-02, 3.98697802e-15,
1.54321666e-02, 1.21913760e-02, 2.04435166e-02,
1.04931859e-02, -1.85935894e-02],
[ 2.12025421e-18, 1.37316085e-02, 4.40193799e-02,
6.16562477e-02, 4.77612496e-02, 2.60257085e-15,
3.86322888e-02, 1.97965887e-02, 2.64882038e-02,
1.23335908e-02, -2.07389932e-02],
[ 2.64338576e-18, 1.45898292e-02, 4.89104213e-02,
7.70703166e-02, 9.55225258e-02, 1.01875104e-01,
7.73459329e-02, 2.50275508e-02, 2.93537540e-02,
1.30949577e-02, -2.15685454e-02],
[ 2.64330042e-18, 1.45898262e-02, 4.89104186e-02,
7.70703146e-02, 9.55225248e-02, 1.01910945e-01,
7.74050835e-02, 2.52307946e-02, 2.92970736e-02,
1.30880504e-02, -2.15685424e-02],
[ 2.64318867e-18, 1.45898259e-02, 4.89104184e-02,
7.70703144e-02, 9.55225247e-02, 1.01933432e-01,
7.74421258e-02, 2.53572923e-02, 2.92615130e-02,
1.30837284e-02, -2.15685422e-02],
[ 2.64305117e-18, 1.45898284e-02, 4.89104206e-02,
7.70703161e-02, 9.55225256e-02, 1.01942593e-01,
7.74571359e-02, 2.54081640e-02, 2.92472117e-02,
1.30819985e-02, -2.15685446e-02],
[ 2.30141673e-18, 1.40210825e-02, 4.56205547e-02,
6.63109661e-02, 5.79266964e-02, 2.33044622e-15,
4.69672564e-02, 2.18401553e-02, 2.72864925e-02,
1.25728575e-02, -2.10227772e-02],
[ 1.10672535e-18, 1.04777076e-02, 3.59041065e-02,
4.24614318e-02, 2.24217216e-02, 3.66914762e-15,
1.81728517e-02, 1.39301504e-02, 2.14956836e-02,
1.08711460e-02, -1.90802849e-02]]
)
np.testing.assert_allclose(fd, fd_test, rtol=2e-4)
def test_so6():
event_name = 'so6'
magnitude = 7.2
dip = np.array([70])
rake = 135
width = np.array([15])
L = 80
fltx = np.array([0, 0])
flty = np.array([0, L])
zp = np.array([0])
# Convert to lat/lon
proj = geo.utils.get_orthographic_projection(-122, -120, 39, 37)
tlon, tlat = proj(fltx, flty, reverse=True)
flt = fault.Fault.fromTrace(np.array([tlon[0]]),
np.array([tlat[0]]),
np.array([tlon[1]]),
np.array([tlat[1]]),
zp,
width,
dip,
reference='rv4')
x = np.linspace(-80, 80, 21)
y = np.linspace(-50, 130, 21)
site_x, site_y = np.meshgrid(x, y)
slon, slat = proj(site_x, site_y, reverse=True)
sdepth = np.zeros_like(slon)
tmp = flt.getQuadrilaterals()[0]
pp0 = Vector.fromPoint(
point.Point(tmp[0].longitude, tmp[0].latitude, tmp[0].depth))
pp1 = Vector.fromPoint(
point.Point(tmp[1].longitude, tmp[1].latitude, tmp[1].depth))
pp2 = Vector.fromPoint(
point.Point(tmp[2].longitude, tmp[2].latitude, tmp[2].depth))
pp3 = Vector.fromPoint(
point.Point(tmp[3].longitude, tmp[3].latitude, tmp[3].depth))
dxp = 10 / L
dyp = (width - 5) / width
mp0 = pp0 + (pp1 - pp0) * dxp
mp1 = pp3 + (pp2 - pp3) * dxp
rp = mp0 + (mp1 - mp0) * dyp
epilat, epilon, epidepth = ecef2latlon(rp.x, rp.y, rp.z)
epix, epiy = proj(epilon, epilat, reverse=False)
event = {
'lat': epilat,
'lon': epilon,
'depth': epidepth,
'mag': magnitude,
'id': 'so6',
'locstring': 'so6',
'type': 'RV',
'timezone': 'UTC'
}
event['time'] = ShakeDateTime.utcfromtimestamp(int(time.time()))
event['created'] = ShakeDateTime.utcfromtimestamp(int(time.time()))
fltlat = [a.latitude for a in flt.getQuadrilaterals()[0]]
fltlon = [a.longitude for a in flt.getQuadrilaterals()[0]]
fltlat = np.append(fltlat, fltlat[0])
fltlon = np.append(fltlon, fltlon[0])
fltx, flty = proj(fltlon, fltlat, reverse=False)
source = Source(event, flt)
source.setEventParam('rake', rake)
test1 = Bayless2013(source, slat, slon, sdepth, T=5)
fd = test1.getFd()
fd_test = np.array([
[
0.00000000e+00, 0.00000000e+00, 0.00000000e+00, -8.92879772e-03,
-1.74526918e-02, -2.22981746e-02, -2.34350450e-02, -2.13620062e-02,
-1.72712346e-02, -1.29509613e-02, -1.02545064e-02, -1.03010185e-02,
-1.28847597e-02, -1.66274727e-02, -1.96984070e-02, -2.05377743e-02,
-1.81831337e-02, -1.21881814e-02, -2.64862879e-03, 0.00000000e+00,
0.00000000e+00
],
[
0.00000000e+00, 0.00000000e+00, -8.73221519e-03, -2.21421374e-02,
-3.18438939e-02, -3.71488270e-02, -3.76239913e-02, -3.35015951e-02,
-2.61748968e-02, -1.83864728e-02, -1.34793002e-02, -1.36687799e-02,
-1.85727143e-02, -2.55527671e-02, -3.14227568e-02, -3.38933995e-02,
-3.19289607e-02, -2.53396980e-02, -1.45943649e-02, -3.71405488e-04,
0.00000000e+00
],
[
0.00000000e+00, -2.54621422e-03, -2.11428566e-02, -3.68609103e-02,
-4.87464747e-02, -5.56539037e-02, -5.64419387e-02, -5.05331157e-02,
-3.52919381e-02, -2.18782050e-02, -1.40858125e-02, -1.47354546e-02,
-2.35727189e-02, -3.74838465e-02, -4.75915414e-02, -5.13000399e-02,
-4.87882409e-02, -4.05716321e-02, -2.77368254e-02, -1.13542729e-02,
0.00000000e+00
],
[
0.00000000e+00, -1.21642958e-02, -3.33747360e-02, -5.21661817e-02,
-6.74724509e-02, -7.77628842e-02, -8.00243748e-02, -6.42496853e-02,
-4.38124530e-02, -1.97027426e-02, -1.45897731e-02, -1.07427056e-02,
-3.08235222e-02, -4.82656988e-02, -6.67692677e-02, -7.35152908e-02,
-6.85574283e-02, -5.71811573e-02, -4.12138780e-02, -2.20396726e-02,
-6.24121310e-04
],
[
0.00000000e+00, -2.00643401e-02, -4.39827328e-02, -6.62722434e-02,
-8.60268414e-02, -1.01730306e-01, -9.86277741e-02, -9.82914922e-02,
-5.22335876e-02, -1.54622435e-02, -1.57487554e-02, -3.06190808e-03,
-4.81481586e-02, -8.92480491e-02, -8.63776477e-02, -9.98130440e-02,
-8.95491230e-02, -7.33553695e-02, -5.34401725e-02, -3.11601812e-02,
-7.33715103e-03
],
[
0.00000000e+00, -2.50053614e-02, -5.11695772e-02, -7.65997026e-02,
-1.00809054e-01, -1.22877573e-01, -1.18738178e-01, -1.55236782e-01,
-7.45388001e-02, 1.92779182e-03, -1.94380016e-02, 1.94922939e-02,
-7.66669920e-02, -1.53909722e-01, -1.10846875e-01, -1.19746768e-01,
-1.07680300e-01, -8.59905101e-02, -6.22042294e-02, -3.71802472e-02,
-1.13867485e-02
],
[
0.00000000e+00, -2.63645827e-02, -5.37984901e-02, -8.11337022e-02,
-1.08298371e-01, -1.35146441e-01, -1.34825430e-01, -1.85836050e-01,
-1.10730875e-01, -3.18861095e-02, 4.14395701e-02, -1.52711946e-02,
-1.31840763e-01, -1.96794707e-01, -1.33453212e-01, -1.34989129e-01,
-1.17922385e-01, -9.21637323e-02, -6.58369237e-02, -3.91646838e-02,
-1.22685698e-02
],
[
0.00000000e+00, -2.64622244e-02, -5.40483999e-02, -8.16190336e-02,
-1.09162854e-01, -1.36656677e-01, -1.37081504e-01, -1.89522811e-01,
-1.17723634e-01, -4.88765748e-02, -5.04529015e-03, -5.76414497e-02,
-1.45712183e-01, -2.03062804e-01, -1.36859828e-01, -1.37107390e-01,
-1.19124650e-01, -9.28263279e-02, -6.61800709e-02, -3.93088682e-02,
-1.22842049e-02
],
[
0.00000000e+00, -2.58466495e-02, -5.24858827e-02, -7.86086164e-02,
-1.03856343e-01, -1.27529509e-01, -1.23794779e-01, -1.68810613e-01,
-8.22602627e-02, 1.74236964e-02, 9.38708725e-02, 4.23208284e-02,
-8.46343723e-02, -1.70476759e-01, -1.17547884e-01, -1.24569752e-01,
-1.11518670e-01, -8.84736806e-02, -6.38037151e-02, -3.81874381e-02,
-1.19867610e-02
],
[
0.00000000e+00, -2.42186547e-02, -4.84175525e-02, -7.09428614e-02,
-9.07754575e-02, -1.06117824e-01, -9.50228292e-02, -1.29781980e-01,
-3.08573454e-02, 7.39058739e-02, 1.30478117e-01, 8.28181149e-02,
-2.70389535e-02, -1.20837502e-01, -8.02081725e-02, -9.70274506e-02,
-9.35853383e-02, -7.77422806e-02, -5.77817530e-02, -3.53067886e-02,
-1.12414659e-02
],
[
0.00000000e+00, -2.16818717e-02, -4.22363856e-02, -5.96909893e-02,
-7.24805224e-02, -7.81867829e-02, -6.11838569e-02, -9.05679744e-02,
9.95934969e-03, 1.07503875e-01, 1.52073917e-01, 1.05894634e-01,
8.68652263e-03, -7.98571818e-02, -4.16548658e-02, -6.40511838e-02,
-6.99337160e-02, -6.26305633e-02, -4.89098800e-02, -3.09284566e-02,
-1.00919381e-02
],
[
0.00000000e+00, -1.84940182e-02, -3.47054606e-02, -4.65278129e-02,
-5.22037664e-02, -4.93977115e-02, -2.95395230e-02, -5.82421092e-02,
3.91025654e-02, 1.29337956e-01, 1.67436703e-01, 1.21969296e-01,
3.20823547e-02, -5.00287386e-02, -9.22993907e-03, -3.27186625e-02,
-4.52706958e-02, -4.57409325e-02, -3.84701291e-02, -2.55751405e-02,
-8.64950254e-03
],
[
0.00000000e+00, -1.49431380e-02, -2.65887341e-02, -3.29162158e-02,
-3.22994323e-02, -2.29081781e-02, -2.60259636e-03, -3.29856530e-02,
6.02631314e-02, 1.45003704e-01, 1.79361264e-01, 1.34292814e-01,
4.88007115e-02, -2.82328554e-02, 1.64212421e-02, -5.72391847e-03,
-2.23438861e-02, -2.90246794e-02, -2.76054402e-02, -1.97779758e-02,
-7.03945406e-03
],
[
0.00000000e+00, -1.12771143e-02, -1.84737590e-02, -1.98228664e-02,
-1.40092305e-02, 1.84580818e-04, 1.95817303e-02, -1.32608487e-02,
7.62783168e-02, 1.57076433e-01, 1.89083905e-01, 1.44259188e-01,
6.15722813e-02, -1.17505212e-02, 3.65938109e-02, 1.66937711e-02,
-2.18970818e-03, -1.35507683e-02, -1.70890527e-02, -1.39519424e-02,
-5.37036892e-03
],
[
0.00000000e+00, -7.67615215e-03, -1.07348257e-02, -7.75276739e-03,
2.22351695e-03, 1.98662250e-02, 3.77611177e-02, 2.42018661e-03,
8.89036172e-02, 1.66855206e-01, 1.97260700e-01, 1.52590263e-01,
7.17981256e-02, 1.18005972e-03, 5.26852303e-02, 3.51638855e-02,
1.51012176e-02, 2.69654076e-04, -7.33815554e-03, -8.36639665e-03,
-3.72176313e-03
],
[
0.00000000e+00, -4.50552324e-03, -4.32262850e-03, 1.73559158e-03,
1.42670366e-02, 3.35040699e-02, 4.97279358e-02, 1.85410528e-02,
9.39950666e-02, 1.46646579e-01, 9.13474746e-02, 1.37004651e-01,
7.74648339e-02, 1.59777072e-02, 6.25334939e-02, 4.74577418e-02,
2.72155518e-02, 1.06174952e-02, 3.94103899e-04, -3.68465400e-03,
-2.19830733e-03
],
[
0.00000000e+00, -1.74629916e-03, 5.44471813e-04, 8.22933499e-03,
2.15699287e-02, 4.04232250e-02, 5.69678048e-02, 5.52408259e-02,
9.04381272e-02, 1.08204635e-01, 9.14439984e-02, 1.06884511e-01,
8.17241884e-02, 5.55282924e-02, 6.78528399e-02, 5.47188925e-02,
3.35251483e-02, 1.69615982e-02, 5.72048628e-03, -8.81437278e-05,
-7.36518436e-04
],
[
0.00000000e+00, 4.07838765e-05, 3.63933766e-03, 1.20080876e-02,
2.51274691e-02, 4.25687176e-02, 6.25685606e-02, 7.33480475e-02,
8.37515545e-02, 9.52500287e-02, 9.15135660e-02, 9.66442834e-02,
8.66659913e-02, 8.10325633e-02, 7.18836713e-02, 5.45548434e-02,
3.55884875e-02, 2.00142359e-02, 8.71200201e-03, 2.04407846e-03,
-6.53680674e-06
],
[
0.00000000e+00, 2.40054729e-04, 4.44975227e-03, 1.27572519e-02,
2.49362989e-02, 4.03831326e-02, 5.80039988e-02, 7.61280192e-02,
8.37404162e-02, 8.89634569e-02, 9.15651607e-02, 9.13586235e-02,
8.83589144e-02, 8.27804032e-02, 6.75666471e-02, 5.00483249e-02,
3.36733366e-02, 1.96758691e-02, 9.00603204e-03, 2.18370401e-03,
0.00000000e+00
],
[
0.00000000e+00, 0.00000000e+00, 2.78776980e-03, 1.05086036e-02,
2.13238822e-02, 3.45577738e-02, 4.91570145e-02, 6.36787133e-02,
7.63710088e-02, 8.54072310e-02, 8.92960200e-02, 8.75702197e-02,
8.07095447e-02, 6.97999389e-02, 5.63787286e-02, 4.20734776e-02,
2.83073312e-02, 1.61614525e-02, 6.56194125e-03, 1.00721924e-04,
0.00000000e+00
],
[
0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 5.49667845e-03,
1.47563319e-02, 2.57955743e-02, 3.76689418e-02, 4.91861917e-02,
5.90108907e-02, 6.58478416e-02, 6.87018515e-02, 6.73174642e-02,
6.20270643e-02, 5.35456385e-02, 4.29400416e-02, 3.14129728e-02,
2.00795162e-02, 9.84001885e-03, 1.53992995e-03, 0.00000000e+00,
0.00000000e+00
]
])
np.testing.assert_allclose(fd, fd_test, rtol=1e-4)
def __setPseudoHypocenters(self):
""" Set a pseudo-hypocenter.
Adapted from ShakeMap 3.5 src/contour/directivity.c
From Bayless and Somerville:
"Define the pseudo-hypocenter for rupture of successive segments as
the point on the side edge of the rupture segment that is closest to
the side edge of the previous segment, and that lies half way
between the top and bottom of the rupture. We assume that the rupture
is segmented along strike, not updip. All geometric parameters are
computed relative to the pseudo-hypocenter."
"""
hyp_ecef = Vector.fromPoint(
geo.point.Point(self._hyp.longitude, self._hyp.latitude,
self._hyp.depth))
# Loop over each quad
self.phyp = [None] * self._nq
for i in range(self._nq):
P0, P1, P2, P3 = self._rup.getQuadrilaterals()[i]
p0 = Vector.fromPoint(P0) # convert to ECEF
p1 = Vector.fromPoint(P1)
p2 = Vector.fromPoint(P2)
p3 = Vector.fromPoint(P3)
# Create 4 planes with normals pointing outside rectangle
hpnp = Vector.cross(p1 - p0, p2 - p0).norm()
hpp = -hpnp.x * p0.x - hpnp.y * p0.y - hpnp.z * p0.z
n0 = Vector.cross(p1 - p0, hpnp)
n1 = Vector.cross(p2 - p1, hpnp)
n2 = Vector.cross(p3 - p2, hpnp)
n3 = Vector.cross(p0 - p3, hpnp)
#-------------------------------------------------------------------
# Is the hypocenter inside the projected rectangle?
# Dot products show which side the origin is on.
# If origin is on same side of all the planes, then it is 'inside'
#-------------------------------------------------------------------
sgn0 = np.signbit(Vector.dot(n0, p0 - hyp_ecef))
sgn1 = np.signbit(Vector.dot(n1, p1 - hyp_ecef))
sgn2 = np.signbit(Vector.dot(n2, p2 - hyp_ecef))
sgn3 = np.signbit(Vector.dot(n3, p3 - hyp_ecef))
if (sgn0 == sgn1) and (sgn1 == sgn2) and (sgn2 == sgn3):
# Origin is inside. Use distance-to-plane formula.
d = Vector.dot(hpnp, hyp_ecef) + hpp
d = d * d
# Put the pseudo hypocenter on the plane
D = Vector.dot(hpnp, hyp_ecef) + hpp
self.phyp[i] = hyp_ecef - hpnp * D
else:
# Origin is outside. Find distance to edges
p0p = np.reshape(p0.getArray() - hyp_ecef.getArray(), [1, 3])
p1p = np.reshape(p1.getArray() - hyp_ecef.getArray(), [1, 3])
p2p = np.reshape(p2.getArray() - hyp_ecef.getArray(), [1, 3])
p3p = np.reshape(p3.getArray() - hyp_ecef.getArray(), [1, 3])
s1 = _distance_sq_to_segment(p1p, p2p)
s3 = _distance_sq_to_segment(p3p, p0p)
# Assuming that the rupture is segmented along strike and not
# updip (as described by Bayless and somerville), we only
# need to consider s1 and s3:
if s1 > s3:
e30 = p0 - p3
e30norm = e30.norm()
mag = e30.mag()
self.phyp[i] = p3 + e30norm * (0.5 * mag)
else:
e21 = p1 - p2
e21norm = e21.norm()
mag = e21.mag()
self.phyp[i] = p2 + e21norm * (0.5 * mag)
def test_rv4():
magnitude = 7.0
rake = 90.0
width = np.array([28])
fltx = np.array([0, 0])
flty = np.array([0, 32])
zp = np.array([0])
dip = np.array([30])
# Convert to lat/lon
proj = geo.utils.get_orthographic_projection(-122, -120, 39, 37)
tlon, tlat = proj(fltx, flty, reverse=True)
flt = fault.Fault.fromTrace(np.array([tlon[0]]), np.array([tlat[0]]),
np.array([tlon[1]]), np.array([tlat[1]]),
zp, width, dip, reference='')
L = flt.getFaultLength()
# Try to figure out epicenter
tmp = flt.getQuadrilaterals()[0]
pp0 = Vector.fromPoint(point.Point(tmp[0].longitude, tmp[0].latitude,
tmp[0].depth))
pp1 = Vector.fromPoint(point.Point(tmp[1].longitude, tmp[1].latitude,
tmp[1].depth))
pp2 = Vector.fromPoint(point.Point(tmp[2].longitude, tmp[2].latitude,
tmp[2].depth))
pp3 = Vector.fromPoint(point.Point(tmp[3].longitude, tmp[3].latitude,
tmp[3].depth))
dxp = 6/L
dyp = (width-8)/width
mp0 = pp0 + (pp1 - pp0)*dxp
mp1 = pp3 + (pp2 - pp3)*dxp
rp = mp0 + (mp1 - mp0)*dyp
epilat,epilon,epidepth = ecef2latlon(rp.x, rp.y, rp.z)
event = {'lat': epilat,
'lon': epilon,
'depth': epidepth,
'mag': magnitude,
'id': 'test',
'locstring': 'rv4',
'type': 'DS',
'timezone': 'UTC'}
event['time'] = ShakeDateTime.utcfromtimestamp(int(time.time()))
event['created'] = ShakeDateTime.utcfromtimestamp(int(time.time()))
x = np.linspace(-50, 50, 11)
y = np.linspace(-50, 50, 11)
site_x, site_y = np.meshgrid(x, y)
slon, slat = proj(site_x, site_y, reverse=True)
deps = np.zeros_like(slon)
source = Source(event, flt)
source.setEventParam('rake', rake)
test1 = Bayless2013(source, slat, slon, deps, T=2.0)
# Test fd
fd = test1.getFd()
fd_test = np.array(
[[ 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
1.72147747e-03, 1.34981119e-03, 8.95673480e-29,
1.24449087e-03, 1.16145147e-03, 2.25470229e-03,
7.05301515e-04, 0.00000000e+00],
[ 0.00000000e+00, 0.00000000e+00, 7.62625126e-03,
1.25136528e-02, 5.61909403e-03, 3.18694606e-28,
4.52275052e-03, 4.67980272e-03, 1.02822365e-02,
5.13171639e-03, -6.13935060e-03],
[ 0.00000000e+00, 4.00499692e-03, 2.37611880e-02,
2.37143264e-02, 9.55241972e-03, 5.65693221e-28,
7.70357238e-03, 7.36477919e-03, 1.48241947e-02,
8.40402367e-03, -1.58594139e-02],
[ 8.08392720e-19, 9.38156493e-03, 3.38613859e-02,
3.85355818e-02, 1.91047521e-02, 1.27066310e-27,
1.54323543e-02, 1.21915074e-02, 2.04437211e-02,
1.04933053e-02, -1.85937074e-02],
[ 2.12026318e-18, 1.37316424e-02, 4.40195705e-02,
6.16565712e-02, 4.77616016e-02, 5.07336347e-27,
3.86325509e-02, 1.97966900e-02, 2.64883302e-02,
1.23336661e-02, -2.07390404e-02],
[ 2.64338576e-18, 1.45898071e-02, 4.89104103e-02,
7.70703129e-02, 9.55225254e-02, 1.01875104e-01,
7.73459333e-02, 2.50275520e-02, 2.93537605e-02,
1.30949772e-02, -2.15685118e-02],
[ 2.64330042e-18, 1.45898071e-02, 4.89104103e-02,
7.70703129e-02, 9.55225254e-02, 1.01910945e-01,
7.74050830e-02, 2.52307951e-02, 2.92970785e-02,
1.30880672e-02, -2.15685118e-02],
[ 2.64318867e-18, 1.45898071e-02, 4.89104103e-02,
7.70703129e-02, 9.55225254e-02, 1.01933432e-01,
7.74421253e-02, 2.53572928e-02, 2.92615177e-02,
1.30837449e-02, -2.15685118e-02],
[ 2.64305117e-18, 1.45898071e-02, 4.89104103e-02,
7.70703129e-02, 9.55225254e-02, 1.01942593e-01,
7.74571361e-02, 2.54081650e-02, 2.92472178e-02,
1.30820173e-02, -2.15685118e-02],
[ 2.30140686e-18, 1.40209885e-02, 4.56202616e-02,
6.63103459e-02, 5.79255225e-02, 7.72925496e-27,
4.69663059e-02, 2.18399567e-02, 2.72863359e-02,
1.25728195e-02, -2.10226512e-02],
[ 1.10671369e-18, 1.04775558e-02, 3.59035524e-02,
4.24605614e-02, 2.24210618e-02, 1.53459722e-27,
1.81723013e-02, 1.39298662e-02, 2.14953705e-02,
1.08710398e-02, -1.90800441e-02]]
)
np.testing.assert_allclose(fd, fd_test, rtol=1e-5)
def __computeThetaAndS(self, i):
"""
:param i:
Compute d for the i-th quad/segment.
"""
# self.phyp is in ECEF
tmp = ecef.ecef2latlon(self.phyp[i].x, self.phyp[i].y, self.phyp[i].z)
epi_ecef = Vector.fromPoint(geo.point.Point(tmp[1], tmp[0], 0.0))
epi_col = np.array([[epi_ecef.x], [epi_ecef.y], [epi_ecef.z]])
# First compute along strike vector
P0, P1, P2, P3 = self._rup.getQuadrilaterals()[i]
p0 = Vector.fromPoint(P0) # convert to ECEF
p1 = Vector.fromPoint(P1)
e01 = p1 - p0
e01norm = e01.norm()
hp0 = p0 - epi_ecef
hp1 = p1 - epi_ecef
strike_min = Vector.dot(hp0, e01norm) / 1000.0 # convert to km
strike_max = Vector.dot(hp1, e01norm) / 1000.0 # convert to km
strike_col = np.array([[e01norm.x], [e01norm.y],
[e01norm.z]]) # ECEF coords
# Sites
slat = self._lat
slon = self._lon
# Convert sites 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 = ecef.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, ))
])
# Epicenter-to-site matrix
e2s_mat = site_mat - epi_col # in ECEF
mag = np.sqrt(np.sum(e2s_mat * e2s_mat, axis=0))
# Avoid division by zero
mag[mag == 0] = 1e-12
e2s_norm = e2s_mat / mag
# Dot epicenter-to-site with along-strike vector
s_raw = np.sum(e2s_mat * strike_col, axis=0) / 1000.0 # conver to km
# Put back into a 2d array
s_raw = np.reshape(s_raw, self._lat.shape)
self.s = np.abs(s_raw.clip(min=strike_min,
max=strike_max)).clip(min=np.exp(1))
# Compute theta
sdots = np.sum(e2s_norm * strike_col, axis=0)
theta_raw = np.arccos(sdots)
# But theta is defined to be the reference angle
# (i.e., the equivalent angle between 0 and 90 deg)
sintheta = np.abs(np.sin(theta_raw))
costheta = np.abs(np.cos(theta_raw))
theta = np.arctan2(sintheta, costheta)
self.theta = np.reshape(theta, self._lat.shape)
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 arrays of distances, size of lon.shape
IMPORTANT: If a finite fault is not supplied, and the distance
measures requested include rx, ry, ry0, U, or T, then zeros will
be returned; if rjb is requested, repi will be returned; if rrup
is requested, rhypo will be returned.
"""
fault = source.getFault()
hypo = source.getHypo()
if fault is not None:
quadlist = fault.getQuadrilaterals()
# Need a copy for GC2 since order of verticies/quads needs to be modivied.
quadgc2 = copy.deepcopy(quadlist)
else:
quadlist = None
# Dictionary for holding the distances
distdict = dict()
if not isinstance(methods, list):
methods = [methods]
methods_available = set(get_distance_measures())
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)):
# I don't think this error check makes sense any more because hypo
# is assigned above with source.getHypo() that constructs it from
# source._event_dict entries.
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".
#---------------------------------------------------------------
# Goofy while-loop is to adjust the side of the fault where the
# origin is located
dummy = -1
while dummy < 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()
dummy = bhat.dot(ahat)
if dummy < 0:
tmpA0 = copy.copy(A0)
tmpA1 = copy.copy(A1)
A0 = tmpA1
A1 = tmpA0
# To fix discordancy, need to flip quads and rearrange
# the order of quadgc2
# 1) flip quads
for i in range(len(quadgc2)):
if dc[segind[i]] < 0: #***************U*UUUUUUUSDFUSDfkjjhakjsdhfljkhn
quadgc2[i] = reverse_quad(quadgc2[i])
# 2) rearrange quadlist to remove discordancy
qind = np.arange(len(quadgc2))
segnp = np.array(segind)
for i in range(nseg):
qsel = qind[segnp == uind[i]]
if dc[i] < 0:
qrev = qsel[::-1]
qind[segnp == uind[i]] = qrev
quadgc2old = copy.deepcopy(quadgc2)
for i in range(len(qind)):
quadgc2[i] = quadgc2old[qind[i]]
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]
G0, G1, G2, G3 = quadgc2[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 quad
t_i = __calc_t_i(G0, G1, lat, lon, proj)
u_i = __calc_u_i(G0, G1, 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(quadgc2[iq0[segind[i]]][0])
s_ij_2 = (p1 - p_origin).dot(np.sign(E)*ahat) / 1000.0
# Above is 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'].copy()
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'].copy()
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; Setting ry0 to zero')
distdict['ry0'] = np.zeros_like(distdict['repi'])
if 'ry' in methods:
warnings.warn('No fault; Setting ry to zero')
distdict['ry'] = np.zeros_like(distdict['repi'])
if 'U' in methods:
warnings.warn('No fault; Setting U to zero')
distdict['U'] = np.zeros_like(distdict['repi'])
if 'T' in methods:
warnings.warn('No fault; Setting T to zero')
distdict['T'] = np.zeros_like(distdict['repi'])
return distdict
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 _calc_rupture_distance(P0, P1, P2, P3, points):
"""
Calculate the shortest distance from a set of points to a rupture surface.
: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 P2:
Point object, representing the first bottom-edge vertex of a fault quadrilateral.
:param P3:
Point object, representing the second bottom-edge vertex of a fault quadrilateral.
:param points:
Numpy array Nx3 of points (ECEF) to calculate distance from.
:returns:
Array of size N of distances (in km) from input points to rupture surface.
"""
# Convert to ecef
p0 = Vector.fromPoint(P0)
p1 = Vector.fromPoint(P1)
p2 = Vector.fromPoint(P2)
p3 = Vector.fromPoint(P3)
# Make a unit vector normal to the plane
normalVector = (p1 - p0).cross(p2 - p0).norm()
dist = np.ones_like(points[:, 0]) * np.nan
p0d = p0.getArray() - points
p1d = p1.getArray() - points
p2d = p2.getArray() - points
p3d = p3.getArray() - points
# Create 4 planes with normals pointing outside rectangle
n0 = (p1 - p0).cross(normalVector).getArray()
n1 = (p2 - p1).cross(normalVector).getArray()
n2 = (p3 - p2).cross(normalVector).getArray()
n3 = (p0 - p3).cross(normalVector).getArray()
sgn0 = np.signbit(np.sum(n0 * p0d, axis=1))
sgn1 = np.signbit(np.sum(n1 * p1d, axis=1))
sgn2 = np.signbit(np.sum(n2 * p2d, axis=1))
sgn3 = np.signbit(np.sum(n3 * p3d, axis=1))
inside_idx = (sgn0 == sgn1) & (sgn1 == sgn2) & (sgn2 == sgn3)
dist[inside_idx] = np.power(np.abs(
np.sum(p0d[inside_idx, :] * normalVector.getArray(), axis=1)), 2)
outside_idx = np.logical_not(inside_idx)
s0 = _distance_sq_to_segment(p0d, p1d)
s1 = _distance_sq_to_segment(p1d, p2d)
s2 = _distance_sq_to_segment(p2d, p3d)
s3 = _distance_sq_to_segment(p3d, p0d)
smin = np.minimum(np.minimum(s0, s1), np.minimum(s2, s3))
dist[outside_idx] = smin[outside_idx]
dist = np.sqrt(dist) / 1000.0
shp = dist.shape
if len(shp) == 1:
dist.shape = (shp[0], 1)
if np.any(np.isnan(dist)):
raise ShakeMapException("Could not calculate some distances!")
dist = np.fliplr(dist)
return dist
