print("Now we do a jacobi step using sparse matrix algorithms:")
print("But just before one needs to fill the rhs")
print("rhs at the beginning: \n", low_level.rhs)
mg_problem.fill_rhs(low_level)
print("rhs after filling it: \n", low_level.rhs)
# laplace_stencil.modify_rhs(low_level)
print("rhs after modification: \n", low_level.rhs)
jacobi_matrix.relax()
print(low_level.arr)
print("Now we check if the more general SplitSmootherClass :")
mg_problem.fill_rhs(low_level)
low_level.mid[:] = 105.0
low_level.pad()
low_jacobi_smoother.relax()
print(low_level.arr)
print("===== Restriction and Interpolation =====")
# generate restriction stencil
rst_inject = RestrictionStencilPure(np.asarray([1.0]), 2)
rst_fw = RestrictionStencilPure(np.asarray([0.25, 0.5, 0.25]), 2)
# try it with just some simple arrays
x_in = np.arange(9) ** 2
x_out = np.zeros(5)
print("test injection,\n x_in :", x_in)
print(" x_out :", x_out)
rst_inject.eval(x_in, x_out)
print("inject,\n x_out :", x_out)
# full weighting restriction needs another interpretation because
# the stencil needs also the values on the boundary, this men
print("level.arr after direct solution\n", level.mid)
print("test of the solution Ax=b by convolve\n ", laplace_stencil.eval_convolve(level.mid, "same"))
rhs_test = np.zeros(level.rhs.shape)
laplace_stencil.eval_sparse(level.mid, rhs_test)
print("test of the solution Ax=b by sparse matrix application \n", rhs_test)
print("==== SplitSmoother ====")
omega = 2.0/3.0
l_plus = np.asarray([[0, 0, 0],
[0, -4.0/omega, 0],
[0, 0, 0]])
l_minus = np.asarray([[0, 1.0, 0], [1.0, -4.0*(1.0 - 1.0/omega), 1.0], [0., 1., 0.]])
jacobi_smoother = SplitSmoother(l_plus, l_minus, level)
level.mid[:] = 0
jacobi_smoother.relax()
print("level.arr after one jacobi step with modified rhs\n", level.arr)
level.mid[:] = 0.
level.modified_rhs = False
level.rhs[:] = 0.
jacobi_smoother = SplitSmoother(l_plus, l_minus, level)
jacobi_smoother.relax()
print("level.arr after one jacobi step with unmodified rhs\n", level.arr)
# For the test of the level transitioning we define 3 levels
# with different roles but the same borders
n_jacobi_pre = 5
n_jacobi_post = 5
borders = np.ones((2, 2))
top_level = MultigridLevel2D((259, 259), mg_problem=mg_problem,
