top_level)
mid_jacobi_smoother = SplitSmoother(l_plus / mid_level.h**2,
l_minus / mid_level.h**2,
mid_level)
low_jacobi_smoother = SplitSmoother(l_plus / low_level.h**2,
l_minus / low_level.h**2,
low_level)
low_direct_smoother = DirectSolverSmoother(laplace_stencil, low_level)
# time to test the relaxation methods
print("===== DirectSolverSmoother Test =====")
low_level.rhs[:] = 0.0
low_level.pad()
print("arr:", low_level.arr)
laplace_stencil.modify_rhs(low_level)
print("rhs:", low_level.rhs)
low_direct_smoother.relax()
print(low_level.arr)
low_level.pad()
# Lets test the SplitSmoother by using the jacobi smoother
# but for this case we need an initial guess
print("===== JacobiSmoother Test =====")
# define the 3 different JacobiSmoother Implementations
jacobi_loop = WeightedJacobiSmoother(laplace_stencil,
low_level, 0.5, "loop")
jacobi_matrix = WeightedJacobiSmoother(laplace_stencil,
low_level, 0.5, "matrix")
jacobi_convolve = WeightedJacobiSmoother(laplace_stencil,
low_level, 0.5, "convolve")
low_level.mid[:] = 105.0
low_level.pad()
mid_jacobi_smoother.relax(n_pre)
print("\t\t --After %d Jacobi iterations --" % n_pre)
print_all(mid_level)
mid_level.compute_residual(mid_stencil)
print("\t\t --After the computation of the residual--")
print_all(mid_level)
rst_mid_to_low.restrict()
print("\t\t --After restriction to low level")
print_all(low_level)
low_direct_smoother.relax()
print("\t\t --After direct solve --")
print_all(low_level)
ipl_low_to_mid.eval()
print("\t\t --After interpolation to mid level")
print_all(mid_level)
mid_jacobi_smoother.relax(n_post)
print("\t\t --After %d Jacobi iterations --" % n_post)
print_all(mid_level)
ipl_mid_to_top.eval()
