def lambda_mu_inverse(dummy, keys, vals):
input = Struct(zip(keys, vals))
output = Struct()
lame1 = input.lame1
lame2 = input.lame2
rho = input.rho
output.vp = ((lame1 + 2. * lame2) / rho)**0.5
output.vs = (lame2 / rho)**0.5
output.rho = rho
return output
def kappa_mu_inverse(dummy, keys, vals):
input = Struct(zip(keys, vals))
output = Struct()
kappa = input.kappa
mu = input.mu
rho = input.rho
output.vp = ((kappa + (4. / 3.) * mu) / rho)**0.5
output.vs = (mu / rho)**0.5
output.rho = rho
return output
def lambda_mu_forward(dummy, keys, vals):
input = Struct(zip(keys, vals))
output = Struct()
vp = input.vp
vs = input.vs
rho = input.rho
output.lame1 = rho * (vp**2. - 2. * vs**2.)
output.lame2 = rho * vs**2.
output.rho = rho
return output
def lambda_mu_inverse(dummy, keys, vals):
input = Struct(zip(keys, vals))
output = Struct()
lame1 = input.lame1
lame2 = input.lame2
rho = input.rho
output.vp = ((lame1 + 2.*lame2)/rho)**0.5
output.vs = (lame2/rho)**0.5
output.rho = rho
return output
def kappa_mu_forward(dummy, keys, vals):
input = Struct(zip(keys, vals))
output = Struct()
vp = input.vp
vs = input.vs
rho = input.rho
output.kappa = rho * (vp**2. - (4. / 3.) * vs**2.)
output.mu = rho * vs**2.
output.rho = rho
return output
def lambda_mu_forward(dummy, keys, vals):
input = Struct(zip(keys, vals))
output = Struct()
vp = input.vp
vs = input.vs
rho = input.rho
output.lame1 = rho*(vp**2. - 2.*vs**2.)
output.lame2 = rho*vs**2.
output.rho = rho
return output
def kappa_mu_inverse(dummy, keys, vals):
input = Struct(zip(keys, vals))
output = Struct()
kappa = input.kappa
mu = input.mu
rho = input.rho
output.vp = ((kappa+(4./3.)*mu)/rho)**0.5
output.vs = (mu/rho)**0.5
output.rho = rho
return output
def kappa_mu_forward(dummy, keys, vals):
input = Struct(zip(keys, vals))
output = Struct()
vp = input.vp
vs = input.vs
rho = input.rho
output.kappa = rho*(vp**2.-(4./3.)*vs**2.)
output.mu = rho*vs**2.
output.rho = rho
return output
def phi_beta_forward(dummy, keys, vals):
input = Struct(zip(keys, vals))
output = Struct()
vp = input.vp
vs = input.vs
rho = input.rho
kappa = rho * (vp**2. - (4. / 3.) * vs**2.)
output.bulk_c = (kappa / rho)**0.5
output.bulk_beta = vs
output.rho = rho
return output
def phi_beta_forward(dummy, keys, vals):
input = Struct(zip(keys, vals))
output = Struct()
vp = input.vp
vs = input.vs
rho = input.rho
kappa = rho*(vp**2.-(4./3.)*vs**2.)
output.bulk_c = (kappa/rho)**0.5
output.bulk_beta = vs
output.rho = rho
return output
def phi_beta_inverse(dummy, keys, vals):
input = Struct(zip(keys, vals))
output = Struct()
phi = input.bulk_c
vs = input.bulk_beta
rho = input.rho
kappa = rho * phi**2.
mu = rho * vs**2.
output.vp = ((kappa + (4. / 3.) * mu) / rho)**0.5
output.vs = vs
output.rho = rho
return output
def phi_beta_inverse(dummy, keys, vals):
input = Struct(zip(keys, vals))
output = Struct()
phi = input.bulk_c
vs = input.bulk_beta
rho = input.rho
kappa = rho*phi**2.
mu = rho*vs**2.
output.vp = ((kappa+(4./3.)*mu)/rho)**0.5
output.vs = vs
output.rho = rho
return output
