def solve(n_cells, degree=3, with_plot=False):
# Space
# element = HermiteElement(degree)
poly_set = leg.basis_functions(degree)
dof_set = equidistant_points(degree)
element = LagrangeElement(poly_set, dof_set)
a, b = -1., 1.
mesh = IntervalMesh(a=a, b=b, n_cells=n_cells)
V = FunctionSpace(mesh, element)
# Need mass matrix to intefrate the rhs
Mget_geom_tensor = lambda cell: 1./cell.Jac
M = assemble_matrix(V, 'mass', Mget_geom_tensor, timer=0)
# Stiffness matrix for Laplacian
Aget_geom_tensor = lambda cell: cell.Jac
A = assemble_matrix(V, 'stiff', Aget_geom_tensor, timer=0)
print 'M sym', is_symmetric(M)
print 'A sym', is_symmetric(A)
ew, ev = eigh(A.toarray(), M.toarray())
ew = np.abs(ew)
# Now add inner(dot(grad(u), n), v)*ds
G = get_G(V)
# A = A - G
# print 'A sym', is_symmetric(A)
# ew, ev = eig(A.toarray())#, M.toarray())
# ew = np.sort(np.abs(ew))[:3]
# print ew
# from sympy import symbols, sqrt, S
# x = symbols('x')
# nullspace = map(Expression, [S(1/2.), sqrt(3/2.)*x])
# Z = [V.interpolate(f).vector for f in nullspace]
# print [np.inner(pi_z, A.dot(pi_z)) for pi_z in Z]
print 'xxx'
print M.toarray()
print
print A.toarray()
print
print G.toarray()
def solve(n_cells, degree=3, with_plot=False):
# Problem
x = Symbol('x')
vvvv = -0.0625*x**3/pi**3 + 0.0625*x/pi**3 + sin(2*pi*x)/(16*pi**4)
u = -vvvv.diff(x, 2)
f = sin(2*pi*x)
# As Expr
u = Expression(u)
f = Expression(f)
vvvv = Expression(vvvv)
# Space
element = HermiteElement(degree)
mesh = IntervalMesh(a=-1, b=1, n_cells=n_cells)
V = FunctionSpace(mesh, element)
bc = DirichletBC(V, vvvv)
# Need mass matrix to intefrate the rhs
M = assemble_matrix(V, 'mass', get_geom_tensor=None, timer=0)
# NOTE We cannot you apply the alpha transform idea because the functions
# are mapped with this selective weight on 2nd, 3rd functions. So some rows
# of alpha would have to be multiplied by weights which are cell specific.
# And then on top of this there would be a dx = J*dy term. Better just to
# use the qudrature representations
# Mpoly_matrix = leg.mass_matrix(degree)
# M_ = assemble_matrix(V, Mpoly_matrix, Mget_geom_tensor, timer=0)
# Stiffness matrix for Laplacian
A = assemble_matrix(V, 'bending', get_geom_tensor=None, timer=0)
# NOTE the above
# Apoly_matrix = leg.stiffness_matrix(degree)
# A_ = assemble_matrix(V, Apoly_matrix, Aget_geom_tensor, timer=0)
# Interpolant of source
fV = V.interpolate(f)
# Integrate in L2 to get the vector
b = M.dot(fV.vector)
# Apply boundary conditions
bc.apply(A, b, True)
x = spsolve(A, b)
print '>>>>', np.linalg.norm(x - V.interpolate(vvvv).vector)
ALaplace = assemble_matrix(V, 'stiffness', get_geom_tensor=None, timer=0)
c = ALaplace.dot(x)
y = spsolve(M, c)
uh = Function(V, y)
# This is a (slow) way of plotting the high order
if with_plot:
fig = plt.figure()
ax = fig.gca()
uV = V.interpolate(u)
for cell in Cells(mesh):
a, b = cell.vertices[0, 0], cell.vertices[1, 0]
x = np.linspace(a, b, 100)
y = uh.eval_cell(x, cell)
ax.plot(x, y, color=random.choice(['b', 'g', 'm', 'c']))
y = uV.eval_cell(x, cell)
ax.plot(x, y, color='r')
y = u.eval_cell(x, cell)
ax.plot(x, y, color='k')
plt.show()
# Error norm
fine_degree = degree + 3
element = HermiteElement(fine_degree)
V_fine = FunctionSpace(mesh, element)
# Interpolate exact solution to fine
u_fine = V_fine.interpolate(u)
# Interpolate approx solution fine
uh_fine = V_fine.interpolate(uh)
# Difference vector
e = u_fine.vector - uh_fine.vector
# Integrate the error
A_fine = assemble_matrix(V_fine, 'mass', get_geom_tensor=None, timer=0)
e = sqrt(np.sum(e*A_fine.dot(e)))
# Mesh size
hmin = mesh.hmin()
# Add the cond number
kappa = np.linalg.cond(A.toarray())
return hmin, e, kappa, A.shape[0]
def _solve(mode, points, degree, n_cells, u, f):
'''
In mode == convergence:
Solve -u`` = f with dirichet bcs bdry of (-1, 1) given by exact solution.
The Vh space is CG_space of degree elements and n_cells. Return hmin, error
for convergence computation.
In mode == cond:
Just return h and the matrix A.
'''
# Element. The polynomial space is spanned by Legendre basis
poly_set = leg.basis_functions(degree)
dof_set = points(degree)
element = LagrangeElement(poly_set, dof_set)
# Mesh
mesh = IntervalMesh(a=-1, b=1, n_cells=n_cells)
# Space
V = FunctionSpace(mesh, element)
bc = DirichletBC(V, u)
# Need mass matrix to intefrate the rhs
Mpoly_matrix = leg.mass_matrix(degree)
Mget_geom_tensor = lambda cell: 1./cell.Jac
M = assemble_matrix(V, Mpoly_matrix, Mget_geom_tensor, timer=0)
# Stiffness matrix for Laplacian
Apoly_matrix = leg.stiffness_matrix(degree)
Aget_geom_tensor = lambda cell: cell.Jac
A = assemble_matrix(V, Apoly_matrix, Aget_geom_tensor, timer=0)
# Interpolant of source
fV = V.interpolate(f)
# Integrate in L2 to get the vector
b = M.dot(fV.vector)
# Apply boundary conditions
bc.apply(A, b, True)
x = spsolve(A, b)
if mode == 'condition':
return mesh.hmin(), A
# As function
uh = Function(V, x)
# Error norm
# Higher order DG element
fine_degree = degree + 3
poly_set = leg.basis_functions(fine_degree)
dof_set = chebyshev_points(fine_degree)
element = LagrangeElement(poly_set, dof_set)
# THe space
V_fine = FunctionSpace(mesh, element, 'L2')
# Interpolate exact solution to fine
u_fine = V_fine.interpolate(u)
# Interpolate approx solution fine
uh_fine = V_fine.interpolate(uh)
# Difference vector
e = u_fine.vector - uh_fine.vector
# Need matrix for integration of H10 norm
Apoly_matrix = leg.stiffness_matrix(fine_degree)
A_fine = assemble_matrix(V_fine, Apoly_matrix, Aget_geom_tensor, timer=1)
# Integrate the error
e = sqrt(np.sum(e*A_fine.dot(e)))
# Mesh size
hmin = mesh.hmin()
return hmin, e
def solve(n_cells, degree=3, with_plot=False):
# Problem
w = 3 * np.pi
x = Symbol("x")
u = sin(w * x)
f = -u.diff(x, 2)
# As Expr
u = Expression(u)
f = Expression(f)
# Space
# element = HermiteElement(degree)
poly_set = leg.basis_functions(degree)
dof_set = chebyshev_points(degree)
element = LagrangeElement(poly_set, dof_set)
mesh = IntervalMesh(a=-1, b=1, n_cells=n_cells)
V = FunctionSpace(mesh, element)
bc = DirichletBC(V, u)
# Need mass matrix to intefrate the rhs
M = assemble_matrix(V, "mass", get_geom_tensor=None, timer=0)
# NOTE We cannot you apply the alpha transform idea because the functions
# are mapped with this selective weight on 2nd, 3rd functions. So some rows
# of alpha would have to be multiplied by weights which are cell specific.
# And then on top of this there would be a dx = J*dy term. Better just to
# use the qudrature representations
# Mpoly_matrix = leg.mass_matrix(degree)
# M_ = assemble_matrix(V, Mpoly_matrix, Mget_geom_tensor, timer=0)
# Stiffness matrix for Laplacian
A = assemble_matrix(V, "stiffness", get_geom_tensor=None, timer=0)
# NOTE the above
# Apoly_matrix = leg.stiffness_matrix(degree)
# A_ = assemble_matrix(V, Apoly_matrix, Aget_geom_tensor, timer=0)
# Interpolant of source
fV = V.interpolate(f)
# Integrate in L2 to get the vector
b = M.dot(fV.vector)
# Apply boundary conditions
bc.apply(A, b, True)
x = spsolve(A, b)
# As function
uh = Function(V, x)
# This is a (slow) way of plotting the high order
if with_plot:
fig = plt.figure()
ax = fig.gca()
uV = V.interpolate(u)
for cell in Cells(mesh):
a, b = cell.vertices[0, 0], cell.vertices[1, 0]
x = np.linspace(a, b, 100)
y = uh.eval_cell(x, cell)
ax.plot(x, y, color=random.choice(["b", "g", "m", "c"]))
y = uV.eval_cell(x, cell)
ax.plot(x, y, color="r")
y = u.eval_cell(x, cell)
ax.plot(x, y, color="k")
plt.show()
# Error norm in CG high order
fine_degree = degree + 3
poly_set = leg.basis_functions(fine_degree)
dof_set = chebyshev_points(fine_degree)
element = LagrangeElement(poly_set, dof_set)
V_fine = FunctionSpace(mesh, element)
# Interpolate exact solution to fine
u_fine = V_fine.interpolate(u)
# Interpolate approx solution fine
uh_fine = V_fine.interpolate(uh)
# Difference vector
e = u_fine.vector - uh_fine.vector
# L2
if False:
Apoly_matrix = leg.mass_matrix(fine_degree)
get_geom_tensor = lambda cell: 1.0 / cell.Jac
# Need matrix for integration of H10 norm
else:
Apoly_matrix = leg.stiffness_matrix(fine_degree)
get_geom_tensor = lambda cell: cell.Jac
A_fine = assemble_matrix(V_fine, Apoly_matrix, get_geom_tensor, timer=0)
# Integrate the error
e = sqrt(np.sum(e * A_fine.dot(e)))
# Mesh size
hmin = mesh.hmin()
# Add the cond number
kappa = np.linalg.cond(A.toarray())
return hmin, e, kappa, A.shape[0]
