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