import mpl_toolkits.mplot3d as a3
import pylab as pl
p = int(sys.argv[1])
maxit = int(sys.argv[2])
theta = 0.35
errorType = [
'$|| u_I - u_h ||_{l_2}$', '$|| u - u_h ||_{S,0}$',
'$||\\nabla_S u - \\nabla_S u_h||_{S,0}$'
]
Ndof = np.zeros((maxit, ), dtype=np.int)
errorMatrix = np.zeros((len(errorType), maxit), dtype=np.float)
integrator = TriangleQuadrature(3)
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)
from fealpy.tools.show import showmultirate
pde = LShapeRSinData()
mesh = pde.init_mesh(n=2, meshtype='tet')
maxit = 20
theta = 0.2
k = maxit - 15
p = 1
q = 3
errorType = ['$|| u_I - u_h ||_{l_2}$',
'$|| u - u_h||_{0}$',
'$||\\nabla u - \\nabla u_h||_{0}$',
'$||\\nabla u - G(\\nabla u_h)||_{0}$']
ralg = FEMFunctionRecoveryAlg()
Ndof = np.zeros((maxit,), dtype=np.int)
errorMatrix = np.zeros((len(errorType), maxit), dtype=np.float)
for i in range(maxit):
print('step:', i)
fem = PoissonFEMModel(pde, mesh, 1, q=3)
fem.solve()
uh = fem.uh
Ndof[i] = fem.mesh.number_of_nodes()
errorMatrix[0, i] = fem.l2_error()
errorMatrix[1, i] = fem.L2_error()
errorMatrix[2, i] = fem.H1_semi_error()
# rguh = ralg.harmonic_average(uh)
# eta = fem.recover_estimate(rguh)
eta = fem.residual_estimate()
meshtype = int(sys.argv[2])
n = int(sys.argv[3])
maxit = 4
box = [0, 1, 0, 1]
if m == 1:
pde = CosCosData()
elif m == 2:
pde = ExpData()
elif m == 3:
pde = PolynomialData()
elif m == 4:
pde = SinSinData()
Mesh = Meshtype()
ralg = FEMFunctionRecoveryAlg()
errorType = [ #'$|| u_I - u_h ||_{l_2}$',
#'$|| u - u_h||_{0}$',
#'$||\\nabla u - \\nabla u_h||_{0}$',
'$||\\nabla u - G(\\nabla u_h)||_{0}sim$',
'$||\\nabla u - G(\\nabla u_h)||_{0}are$',
'$||\\nabla u - G(\\nabla u_h)||_{0}har$',
'$||\\nabla u - G(\\nabla u_h)||_{0}angle$',
'$||\\nabla u - G(\\nabla u_h)||_{0}dhar$',
'$||\\nabla u - G(\\nabla u_h)||_{0}scr$',
'$||\\nabla u - G(\\nabla u_h)||_{0}zz$',
'$||\\nabla u - G(\\nabla u_h)||_{0}ppr$'
]
Ndof = np.zeros((maxit, ), dtype=np.int)
errorMatrix = np.zeros((len(errorType), maxit), dtype=np.float)
# get the pde model
pde = LShapeRSinData()
mesh = pde.init_mesh(n=4, meshtype='tri')
node = mesh.entity('node')
cell = mesh.entity('cell')
tmesh = Tritree(node, cell)
errorType = ['$|| u_I - u_h ||_{l_2}$',
'$|| u - u_h||_{0}$',
'$||\\nabla u - \\nabla u_h||_{0}$',
'$||\\nabla u - G(\\nabla u_h)||_{0}sim$',]
maxit = 1
ralg = FEMFunctionRecoveryAlg()
Ndof = np.zeros((maxit,), dtype=np.int)
errorMatrix = np.zeros((len(errorType), maxit), dtype=np.float)
integrator = mesh.integrator(3)
for i in range(maxit):
print('step:', i)
fem = PoissonFEMModel(pde, mesh, 1, integrator)
fem.solve()
uh = fem.uh
Ndof[i] = fem.mesh.number_of_nodes()
errorMatrix[0, i] = fem.get_l2_error()
errorMatrix[1, i] = fem.get_L2_error()
errorMatrix[2, i] = fem.get_H1_error()
rguh = ralg.simple_average(uh)