def slip_vec(x,coeff,strike,dip,seg): s1 = basis.slip(x,coeff[:,0],segment=seg) s2 = basis.slip(x,coeff[:,1],segment=seg) vec = np.array([s1,s2,0*s2]).transpose() argz = np.pi/2.0 - np.pi*strike/180 argx = np.pi/180.0*dip T = trans.point_rotation_x(argx) T += trans.point_rotation_z(argz) vec = T(vec) return vec
def slip_mag(x,coeff,seg): rightlateral = basis.slip(x,coeff[:,0],segment=seg) thrust = basis.slip(x,coeff[:,1],segment=seg) return np.sqrt(rightlateral**2 + thrust**2)
def _create_formatted_regularization(params, p, slip_model="stochastic"):
f = {}
order = params["slip_regularization_order"]
if order == 0:
N = p["Ns"]
slip_reg = params["slip_regularization"] * np.eye(N)
elif order == 2:
import basis
M = basis.FAULT_SEGMENTS
N = basis.FAULT_N
slip_reg = np.zeros((0, N))
for m in range(M):
col_x = np.unique(basis.FAULT_KNOTS[m][0])
col_x = reg_points(col_x)
# col_x = midspace(0,1,basis.FAULT_NLENGTH[m])
col_y = np.unique(basis.FAULT_KNOTS[m][1])
col_y = reg_points(col_y)
# col_y = midspace(-1,0,basis.FAULT_NWIDTH[m])
xgrid, ygrid = np.meshgrid(col_x, col_y)
xflat = xgrid.flatten()
yflat = ygrid.flatten()
zflat = 0 * yflat
col_m = np.array([xflat, yflat, zflat]).transpose()
col_m = basis.FAULT_TRANSFORMS[m](col_m)
slip_reg_m = np.zeros((len(col_m), N))
for ni, n in enumerate(Perturb(np.zeros(N), 1.0)):
slip_reg_m[:, ni] = basis.slip(col_m, n, segment=m, diff=(2, 0)) + basis.slip(
col_m, n, segment=m, diff=(0, 2)
)
# slip_reg_m2 = np.zeros((len(col_m),N))
# for ni,n in enumerate(Perturb(np.zeros(N),1.0)):
# slip_reg_m2[:,ni] = basis.slip(col_m,n,segment=m,diff=(0,2))
# slip_reg_m = np.vstack((slip_reg_m1,slip_reg_m2))
slip_reg = np.vstack((slip_reg, slip_reg_m))
slip_reg = np.concatenate((slip_reg[..., None], slip_reg[..., None]), axis=-1)
slip_reg /= np.max(np.max(np.abs(slip_reg)))
slip_reg *= params["slip_regularization"]
else:
print("invalid slip order")
if slip_model == "parameterized":
slip_reg = np.repeat(slip_reg[None, ...], p["Nt"], axis=0)
slip_reg = np.repeat(slip_reg[..., None], p["Ds"], axis=-1)
if slip_model == "stochastic":
slip_reg = np.repeat(slip_reg[..., None], p["Ds"], axis=-1)
f["slip"] = slip_reg
order = params["fluidity_regularization_order"]
if order == 0:
N = p["Nv"]
fluidity_reg = params["fluidity_regularization"] * np.eye(N)
elif order == 2:
import basis
N = basis.FLUIDITY_N
fluidity_reg = np.zeros((0, basis.FLUIDITY_N))
col_x = np.unique(basis.FLUIDITY_KNOTS[0])
col_x = reg_points(col_x)
col_y = np.unique(basis.FLUIDITY_KNOTS[1])
col_y = reg_points(col_y)
col_z = np.unique(basis.FLUIDITY_KNOTS[2])
col_z = reg_points(col_z)
xgrid, ygrid, zgrid = np.meshgrid(col_x, col_y, col_z)
xflat = xgrid.flatten()
yflat = ygrid.flatten()
zflat = zgrid.flatten()
col_m = np.array([xflat, yflat, zflat]).transpose()
col_m = basis.FLUIDITY_TRANSFORM(col_m)
fluidity_reg = np.zeros((len(col_m), N))
for ni, n in enumerate(Perturb(np.zeros(N), 1.0)):
fluidity_reg[:, ni] = basis.fluidity(col_m, n, diff=(0, 0, 2))
fluidity_reg /= np.max(np.max(np.abs(fluidity_reg)))
fluidity_reg *= params["fluidity_regularization"]
else:
print("invalid fluidity order")
f["fluidity"] = fluidity_reg
return f