ralg = FEMFunctionRecoveryAlg()
pde = SphereSinSinSinData()
mesh = pde.init_mesh(2)
tmesh = Tritree(mesh.node, mesh.ds.cell, irule=1)
pmesh = tmesh.to_conformmesh()
fig = pl.figure()
axes = a3.Axes3D(fig)
pmesh.add_plot(axes)
for i in range(maxit):
print('step:', i)
fem = SurfacePoissonFEMModel(pmesh, pde, p, integrator)
fem.solve()
uh = fem.uh
rguh = ralg.harmonic_average(uh)
eta = fem.recover_estimate(rguh)
Ndof[i] = len(fem.uh)
errorMatrix[0, i] = fem.get_l2_error()
errorMatrix[1, i] = fem.get_L2_error()
errorMatrix[2, i] = fem.get_H1_semi_error()
if i < maxit - 1:
tmesh.refine(marker=AdaptiveMarker(eta, theta=theta))
pmesh = tmesh.to_conformmesh()
fig = pl.figure()
axes = a3.Axes3D(fig)
pmesh.add_plot(axes)
pl.show()
print('Ndof:', Ndof)
print('error:', errorMatrix)
ralg = FEMFunctionRecoveryAlg()
errorType = ['$\| u - u_h\|_0$', '$\|\\nabla u - \\nabla u_h\|$', '$\eta$']
Ndof = np.zeros((maxit, ), dtype=np.int)
errorMatrix = np.zeros((len(errorType), maxit), dtype=np.float)
mesh = tritree.to_conformmesh()
for i in range(maxit):
print('step:', i)
fem = PoissonFEMModel(pde, mesh, p=p, q=q)
fem.solve()
Ndof[i] = fem.space.number_of_global_dofs()
errorMatrix[0, i] = fem.L2_error()
errorMatrix[1, i] = fem.H1_semi_error()
rguh = ralg.harmonic_average(fem.uh)
eta = fem.recover_estimate(rguh)
errorMatrix[2, i] = np.sqrt(np.sum(eta**2))
if i < maxit - 1:
options = tritree.adaptive_options()
tritree.adaptive(eta, options)
mesh = tritree.to_conformmesh()
mesh.add_plot(plt, showaxis=True)
fig = plt.figure()
axes = fig.gca()
mesh.add_plot(axes)
showmultirate(plt, k, Ndof, errorMatrix, errorType)
plt.show()