dataRANS_aij[:,:,j,k] = tau_RANS[:,:,j,k]/(2.*dataRANS_k[j,k]) - np.diag([1/3.,1/3.,1/3.])
aneigVal_DNS = foam.calcEigenvalues(ReStress_DNS, dataDNS_i['k'])
baryMap_DNS = foam.barycentricMap(aneigVal_DNS)
aneigVal_RANS = foam.calcEigenvalues(tau_RANS, dataRANS_k)
baryMap_RANS = foam.barycentricMap(aneigVal_RANS)
eigVal_RANS, eigVec_RANS = foam.eigenDecomposition(dataRANS_aij)
eigVal_DNS, eigVec_DNS = foam.eigenDecomposition(aij_DNS)
phi_RANS = np.reshape(( np.reshape((foam.eigenvectorToEuler(eigVec_RANS)).swapaxes(1,2), (nx*ny, 3), "F")).swapaxes(1,0), (nx*ny, 3))
phi_DNS = np.reshape(( np.reshape((foam.eigenvectorToEuler(eigVec_DNS)).swapaxes(1,2), (nx*ny, 3), "F")).swapaxes(1,0), (nx*ny, 3))
baryMap_discr = np.reshape(( np.reshape((foam.baryMap_discr(baryMap_RANS, baryMap_DNS)).swapaxes(1,2), (nx*ny, 2), "F")).swapaxes(1,0), (nx*ny, 2))
phi_discr = phi_DNS - phi_RANS
k_discr = np.reshape(( np.reshape((dataDNS_i['k'] - k_RANS).swapaxes(1,2), (nx*ny, 1), "F")).swapaxes(1,0), (nx*ny, 1))
Y[i*nx*ny:(i+1)*nx*ny, 0:2] = baryMap_discr
Y[i*nx*ny:(i+1)*nx*ny, 2:5] = phi_discr
Y[i*nx*ny:(i+1)*nx*ny, 5] = k_discr[:,0]
print('return Y')
return Y
else:
print('train = false')
for i in range(len(Re)):
aneigVal_RANS = foam.calcEigenvalues(tau_RANS, dataRANS_k)
baryMap_RANS = foam.barycentricMap(aneigVal_RANS)
eigVal_RANS, eigVec_RANS = foam.eigenDecomposition(dataRANS_aij)
eigVal_DNS, eigVec_DNS = foam.eigenDecomposition(aij_DNS)
phi_RANS = np.reshape((np.reshape(
(foam.eigenvectorToEuler(eigVec_RANS)).swapaxes(1, 2),
(nx * ny, 3), "F")).swapaxes(1, 0), (nx * ny, 3))
phi_DNS = np.reshape((np.reshape(
(foam.eigenvectorToEuler(eigVec_DNS)).swapaxes(1, 2),
(nx * ny, 3), "F")).swapaxes(1, 0), (nx * ny, 3))
baryMap_discr = np.reshape((np.reshape(
(foam.baryMap_discr(baryMap_RANS, baryMap_DNS)).swapaxes(1, 2),
(nx * ny, 2), "F")).swapaxes(1, 0), (nx * ny, 2))
phi_discr = phi_DNS - phi_RANS
k_discr = np.reshape(
(np.reshape((dataDNS_i['k'] - k_RANS).swapaxes(1, 2),
(nx * ny, 1), "F")).swapaxes(1, 0), (nx * ny, 1))
Y[i * nx * ny:(i + 1) * nx * ny, 0:2] = baryMap_discr
Y[i * nx * ny:(i + 1) * nx * ny, 2:5] = phi_discr
Y[i * nx * ny:(i + 1) * nx * ny, 5] = k_discr[:, 0]
print('return Y')
return Y
else: